Radio Technologies and Concepts for IMT-Advanced

RADIO TECHNOLOGIES AND CONCEPTS FOR IMT-ADVANCED RADIO TECHNOLOGIES AND CONCEPTS FOR IMT-ADVANCED Edited by Martin D...

Author: Martin Dottling | Werner Mohr | Afif Osseiran

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RADIO TECHNOLOGIES AND CONCEPTS FOR IMT-ADVANCED

RADIO TECHNOLOGIES AND CONCEPTS FOR IMT-ADVANCED Edited by

Martin D¨ottling, Nokia Siemens Networks, Germany Werner Mohr, Nokia Siemens Networks, Germany Afif Osseiran, Ericsson Research, Sweden

A John Wiley and Sons, Ltd., Publication

C 2009 Copyright

Martin D¨ottling, Werner Mohr, Afif Osseiran

Email (for orders and customer service enquiries): [email protected] Visit our Home Page on www.wileyeurope.com or www.wiley.com All Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except under the terms of the Copyright, Designs and Patents Act 1988 or under the terms of a licence issued by the Copyright Licensing Agency Ltd, 90 Tottenham Court Road, London W1T 4LP, UK, without the permission in writing of the Publisher. Requests to the Publisher should be addressed to the Permissions Department, John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England, or emailed to [email protected], or faxed to (+44) 1243 770620. Designations used by companies to distinguish their products are often claimed as trademarks. All brand names and product names used in this book are trade names, service marks, trade-marks or registered trademarks of their respective owners. The publisher is not associated with any product or vendor mentioned in this book. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold on the under-standing that the publisher is not engaged in rendering professional services. If professional ad-vice or other expert assistance is required, the services of a competent professional should be sought. Other Wiley Editorial Offices John Wiley & Sons Inc., 111 River Street, Hoboken, NJ 07030, USA Jossey-Bass, 989 Market Street, San Francisco, CA 94103-1741, USA Wiley-VCH Verlag GmbH, Boschstr. 12, D-69469 Weinheim, Germany John Wiley & Sons Australia Ltd, 33 Park Road, Milton, Queensland 4064, Australia John Wiley & Sons (Asia) Pte Ltd, 2 Clementi Loop #02-01, Jin Xing Distripark, Singapore 129809 John Wiley & Sons Canada Ltd, 22 Worcester Road, Etobicoke, Ontario, Canada M9W 1L1 Wiley also publishes its books in a variety of electronic formats. Some of the content that appears in print may not be available in electronic books. Library of Congress Cataloging-in-Publication Data D¨ottling, Martin. Radio technologies and concepts for IMT-Advanced / Martin D¨ottling, Werner Mohr, and Afif Osseiran. p. cm. Includes bibliographical references and index. ISBN 978-0-470-74763-6 (H/B) 1. Radio—Transmitter-receivers—Standards. 2. Cellular telephones—Standards. 3. Radio—Receivers and reception—Technological innovations. 4. Global system for mobile communications— Equipment and supplies— Standards. I. Mohr, Werner, 1955– II. Osseiran, Afif. III. Title. TK6562.P32D68 2009 621.384—dc22 2009007658 British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library. Typeset in 10/12pt Times by Aptara Inc., New Delhi, India Printed in Singapore by Markono

To my family and parents Martin D¨ottling To my parents Werner Mohr In memory of my parents, Hayat and Hani Afif Osseiran

Contents About the Editors

xxi

Preface

xxv

Acknowledgements

xxix

Abbreviations

xxxi

List of Contributors

xliii

1 1.1 1.2 1.3 1.4 1.5 1.6

Introduction Development and Status of Mobile and Wireless Communications Expectations of Data Traffic Growth Development Towards IMT-Advanced Global Research Activities WINNER Project Future Work References

2 Usage Scenarios and Technical Requirements 2.1 Introduction 2.2 Key Scenario Elements 2.2.1 Environment Type and Coverage Range 2.2.2 Terminal Type 2.2.3 User Density and Traffic Parameters 2.2.4 User Mobility 2.2.5 Deployment Scenarios 2.2.5.1 Wide Area 2.2.5.2 Metropolitan Area 2.2.5.3 Local Area 2.3 Service Classes and Service Requirements 2.3.1 Overview of Beyond-3G Applications 2.3.2 Requirements for Service Provisioning 2.3.3 Mapping of Service Requirements to RAN Requirements 2.3.4 Traffic Models 2.3.4.1 Internet Applications 2.3.4.2 Voice over IP

1 1 3 4 6 8 9 10 13 13 13 15 15 16 16 18 18 19 19 20 20 20 20 20 22 23

viii

2.4

2.5 2.6

2.7

2.8 2.9

Contents

2.3.4.3 Video Telephony 2.3.4.4 Streaming 2.3.4.5 File Transfer 2.3.4.6 Interactive Applications Requirements for System Capabilities 2.4.1 Generalised Mobility Support within WINNER 2.4.2 Generalised Mobility Support between WINNER and Legacy Networks 2.4.3 Measurement Requirements for the WINNER System 2.4.4 Support for QoS Mechanisms and Prioritisation of Flows Terminal Requirements Performance Requirements 2.6.1 Coverage 2.6.2 Data Rate 2.6.2.1 Definition of User Throughput 2.6.2.2 Peak Data Rate 2.6.2.3 Sustainable Data Rate 2.6.3 Allowable Error Rate 2.6.4 Delay 2.6.4.1 Definition of User-Plane Packet Delay 2.6.4.2 Achievable User-Plane Packet Delay 2.6.5 Spectral Efficiency 2.6.6 Maximum Terminal Speed Spectrum Requirements 2.7.1 WINNER Spectrum Range 2.7.2 Utilisation of Current Mobile Service Bands 2.7.3 Spectrum Fragmentation 2.7.4 Coexistence with Other Systems 2.7.5 Sharing Spectrum between WINNER RANs 2.7.6 Sharing Spectrum between Cell Layers of a WINNER System 2.7.7 System Bandwidth Dependency of Requirements Conclusion Acknowledgements References

3 WINNER II Channel Models 3.1 Introduction 3.2 Modelling Considerations 3.2.1 Propagation Scenarios 3.2.1.1 A1: Indoor Office 3.2.1.2 B1: Urban Microcell 3.2.1.3 B4: Outdoor to Indoor 3.2.1.4 C1: Suburban Macrocell 3.2.1.5 C2: Urban Macrocell 3.2.1.6 D1: Rural Macrocell

23 23 24 24 24 25 25 26 28 28 29 30 30 30 31 31 31 31 31 32 32 34 34 34 34 34 35 35 35 36 36 36 37 38 39 39 40 40 41 42 43 43 43 43

Contents

3.2.1.7 B2 and C3: Bad Urban Conditions Evolution of Channel Models from 2G to 4G Selection of Channel-modelling Approach Modelling Process Network Layout Measurements 3.2.6.1 Measurement Tools 3.2.6.2 Channel Measurements Channel-Modelling Approach 3.3.1 WINNER Generic Channel Model 3.3.1.1 Modelled Parameters 3.3.1.2 Correlations Between Large-Scale Parameters 3.3.2 Channel Segments, Drops and Time Evolution 3.3.3 Nomadic Channel Condition Channel Models and Parameters 3.4.1 Applicability 3.4.1.1 Environment Dependence 3.4.1.2 Frequency Dependence 3.4.2 Generation of Channel Coefficients 3.4.3 WINNER Path-loss Models 3.4.3.1 Frequency Dependencies of WINNER Path-loss Models 3.4.3.2 Transitions Between LOS and NLOS Conditions 3.4.4 Values for Generic Channel Models Channel Model Usage 3.5.1 System-level Description 3.5.1.1 Coordinate System 3.5.1.2 Single User (Handover) Multicell Simulation 3.5.1.3 Multi-user Multicell Simulation 3.5.2 SPACE–TIME Concept in Simulations 3.5.3 Bandwidth and Frequency Dependence 3.5.3.1 Frequency Sampling 3.5.3.2 Bandwidth Downscaling in the Delay Domain 3.5.3.3 Bandwidth Downscaling in the Frequency Domain 3.5.3.4 FDD Modelling 3.5.4 Approximation of Channel Models 3.5.4.1 Reduced Complexity Models 3.5.4.2 Comparison of Complexity of Modelling Methods Conclusion Acknowledgements References

43 44 46 47 48 50 50 55 59 63 63 64 68 70 70 71 71 71 71 75 75 77 77 81 81 81 81 84 84 85 85 85 85 86 86 86 87 89 90 90

System Concept and Architecture Introduction Design Principles and Main Characteristics Logical Node Architecture

93 93 94 96

3.2.2 3.2.3 3.2.4 3.2.5 3.2.6

3.3

3.4

3.5

3.6

4 4.1 4.2 4.3

ix

x

Contents

4.3.1 4.3.2 4.3.3 4.3.4 4.3.5 4.3.6

Overview Pool Concept and Micro Mobility Equipment Sharing Multicast and Broadcast Service Support Multiband Transmission from Different BSs Logical Nodes 4.3.6.1 Gateway Nodes: GW IPA L N and GW C L N 4.3.6.2 Base Station Node: BS L N 4.3.6.3 Relay Node: RN L N 4.3.6.4 User Terminal: UT L N 4.3.6.5 RRMserver L N 4.3.6.6 SpectrumServer L N 4.4 Protocol and Service Architecture 4.4.1 Overview 4.4.2 Layer 3: Radio Resource Control 4.4.3 Layer 2 4.4.3.1 IP Convergence Layer 4.4.3.2 Radio Link Control Layer 4.4.3.3 Medium Access Control Layer 4.4.4 Layer 1: Physical 4.4.4.1 Control Signalling 4.4.4.2 Physical Channels and Mappings to Transport Channels 4.4.4.3 Synchronisation Pilots 4.5 Conclusion Acknowledgements References

96 98 101 102 103 104 104 106 107 108 108 108 109 109 110 112 114 114 115 125 126 128 131 132 132 132

5 5.1 5.2 5.3

135 135 136 137 137 139 141 144 145 146 149 151 152 154 154 154 157 158 160

Modulation and Coding Techniques Introduction Basic Modulation and Coding Scheme Coding Schemes 5.3.1 Low-density Parity-check Codes 5.3.1.1 Encoding of BLDPC Codes 5.3.1.2 Decoding Methods 5.3.1.3 Scheduling Algorithms 5.3.1.4 Lifting Process of LDPC Codes 5.3.1.5 Rate-Compatible Puncturing Codes 5.3.1.6 SNR Mismatch Impact on LDPC Codes 5.3.2 Duo-Binary Turbo Codes 5.3.3 Low-Rate Convolutional Codes for Control Channel 5.3.4 Comparison of Coding Schemes 5.3.4.1 Performance Comparison 5.3.4.2 Performance–Complexity Trade-Off 5.3.4.3 Domain of Suitability 5.3.4.4 Implementation Issues: Flexibility, Parallelization and Throughput 5.4 Link Adaptation

Contents

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5.5 Link Level Aspects of H-ARQ 5.5.1 Incremental Redundancy Scheme 5.5.2 Throughput and Delay Analysis 5.6 Conclusions References

162 162 163 165 166

6 Link Level Procedures 6.1 Introduction 6.2 Pilot Design 6.2.1 Types of Pilot 6.2.2 Reference Pilot Design 6.2.2.1 In-band Pilot Patterns 6.2.2.2 Uplink Super-Frame Pilot Preamble 6.2.2.3 Case Study for the Reference Pilot Design 6.2.3 Capacity-Achieving Pilot Design 6.3 Channel Estimation 6.3.1 Channel Estimation Reference Design 6.3.2 Pilot-Aided Channel Estimation 6.3.3 Iterative Channel Estimation 6.3.3.1 Channel Estimation for Single-Input, Single-Output Scenarios 6.3.3.2 Channel Estimation for Multiple-Input, Multiple-Output Scenarios 6.3.4 Channel Prediction 6.4 Radio Frequency Impairments 6.4.1 HPA Non-Linearities 6.4.2 Phase Noise 6.4.2.1 Phase Noise Model 6.4.2.2 Phase Noise Suppression in OFDM with Spatial Multiplexing 6.4.2.3 Phase Noise Suppression for DFT-Precoded OFDM (Serial Modulation) 6.5 Measurements and Signalling 6.6 Link Level Synchronisation 6.6.1 Synchronisation Preamble Design 6.6.2 Synchronisation in a Licensed Band 6.6.2.1 Coarse Symbol Timing Synchronisation 6.6.2.2 Frequency Offset Estimation 6.6.3 Synchronisation in Shared Spectrum 6.7 Network Synchronisation 6.7.1 Firefly Synchronisation 6.7.1.1 Mathematical Model 6.7.1.2 Synchronisation of Coupled Oscillators 6.7.1.3 Refractory Period 6.7.2 Synchronisation Rules 6.7.3 Compensating for Propagation Delays: Timing Advance 6.7.4 Imposing a Global Time Reference on Firefly Synchronisation 6.8 Conclusion 6.8.1 Pilot Design 6.8.2 Channel Estimation

169 169 169 171 172 172 177 177 179 179 180 181 182 182 185 190 192 192 195 196 196 198 200 201 201 202 202 203 204 205 205 206 206 207 207 209 210 211 211 211

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Contents

6.8.3 RF Imperfections 6.8.4 Link Layer Synchronisation 6.8.5 Self-Organised Network Synchronisation Acknowledgements References 7 Advanced Antennas Concept for 4G 7.1 Introduction 7.2 Multiple Antennas Concept 7.2.1 Generic Transmitter 7.2.1.1 Per Stream Rate Control 7.2.1.2 Space–Time Block Code 7.2.1.3 SDMA 7.2.2 Control Signalling 7.3 Spatial Adaptation 7.3.1 Single Stream Per User 7.3.2 Multiple Streams Per User 7.4 Spatial Schemes 7.4.1 Receive Diversity 7.4.2 Beamforming 7.4.2.1 Signal Model 7.4.2.2 Results 7.4.3 Diversity and Spatial Multiplexing 7.4.4 Beamforming and Spatial Multiplexing 7.4.4.1 Clustered Array Structure 7.4.4.2 Results 7.4.5 Linear MU-MIMO: SMMSE and RBD 7.4.5.1 System Models 7.4.5.2 Results 7.5 Interference Mitigation 7.6 Pilots, Feedback and Measurements 7.6.1 Pilots 7.6.2 Feedback 7.6.3 Measurements 7.7 MIMO Aspects in Relaying 7.7.1 Cooperative Relaying 7.7.1.1 Cooperative Diversity Relaying 7.7.1.2 Two-Dimensional Cyclic Prefix 7.7.2 Distributed Antenna Systems 7.7.2.1 Distributed MIMO Configuration 7.7.2.2 Performance of Linear MU-MIMO Precoding 7.8 Conclusion 7.8.1 Beamforming 7.8.2 Diversity and Linear Dispersion Codes 7.8.3 Multi-User MIMO Precoding

212 212 212 213 213 219 219 221 221 226 227 228 228 229 230 231 231 231 232 233 235 237 241 243 243 247 249 250 250 253 253 255 257 258 260 261 262 264 265 266 269 269 270 271

Contents

7.8.4 Distributed Antenna Systems and Cooperative Relaying Acknowledgements References 8 Layer-2 Relays for IMT-Advanced Cellular Networks 8.1 Introduction 8.1.1 Rationale for Relays in Cellular Networks 8.1.2 Organization of this Chapter 8.2 Motivation for Layer-2 Relays and Prior Work 8.3 Relay-based Deployments 8.3.1 RN Deployment Concepts 8.3.1.1 Relaying for Coverage Improvement 8.3.1.2 Relaying for Capacity Optimization at Outer Cell Regions 8.3.1.3 Relaying to Cover Shadowed Areas 8.3.2 Sub-cell Capacity of a Relay-enhanced Cell 8.3.2.1 Multi-hop Throughput in Cellular Deployment 8.3.2.2 Sub-cell Capacity Served by an RN 8.3.2.3 Capacity of a Multi-hop Connection under Delay Constraint 8.3.3 WINNER Test Scenarios 8.3.3.1 Base Urban Coverage Test Scenario 8.3.3.2 Metropolitan Area Test Scenario 8.3.4 Cost Efficiency of RNs 8.4 Design Choices for Relay-based Cellular Networks 8.4.1 Half-duplex Saves Costs and Improves Deployment Flexibility 8.4.2 Decode-and-Forward Relaying Exploits Adaptive Modulation and Coding 8.4.3 Fixed Relays in MCN Assist Fast and Cheap Network Roll-out 8.4.4 Flexible Radio Resource Management Adapts to the Environment 8.4.4.1 Static Load-based Resource Partitioning 8.4.4.2 Dynamic-resource Sharing in Wide Area Deployment with Beamforming 8.4.4.3 Soft Frequency Re-use and Static Load-based Resource Partitioning 8.4.5 MIMO Techniques Boost Capacity 8.4.6 Cooperative Relaying Boosts Performance 8.5 System and Network Aspects 8.5.1 Relaying by the WINNER MAC Protocol 8.5.2 Cell Broadcast and Resource Allocation 8.5.3 Radio Resource Partitioning 8.5.4 Relay ARQ 8.6 System-level Performance Evaluation 8.6.1 Scenario and Traffic Modelling 8.6.2 System Model 8.6.3 Resource Partitioning 8.6.4 Uplink Power Control and Resource Allocation 8.6.5 Simulation Results 8.6.5.1 Baseline Resource Partitioning 8.6.5.2 Downlink Performance of Infinite Buffer and Optimum Resource Partitioning

xiii

271 271 271 277 277 277 280 280 282 283 284 285 285 286 287 287 289 291 291 292 293 295 296 296 296 297 299 300 302 302 304 306 308 308 310 311 312 312 313 315 316 317 318 319

xiv

Contents

8.7 Conclusion Acknowledgements References

319 321 321

9 9.1 9.2

325 325 326 326 326 327 328 328 328 329 330 332

9.3

9.4

Multiple Access Schemes and Inter-cell Interference Mitigation Techniques Introduction Multiple Access Schemes 9.2.1 Classic Multiple Access Schemes 9.2.1.1 Frequency Division Multiple Access 9.2.1.2 Time Division Multiple Access 9.2.1.3 Code Division Multiple Access 9.2.2 Multi-carrier Multiple Access Schemes 9.2.2.1 Orthogonal Frequency Division Multiple Access 9.2.2.2 Multi-Carrier Code Division Multiple Access 9.2.3 WINNER Multiple Access and Medium Access Control Concept 9.2.3.1 Chunk-wise Adaptive TDMA/OFDMA 9.2.3.2 Block Interleaved and Block Equidistant Frequency Division Multiple Access 9.2.3.3 Configuration of Non-Frequency-Adaptive Multiple Access Schemes 9.2.3.4 Co-existence and Switching 9.2.4 MAC Transmission Control 9.2.4.1 Transmission Control Sequences for Downlinks 9.2.4.2 Transmission Control Sequences for Uplinks 9.2.4.3 Transmission and Retransmission Delays Inter-cell Interference Mitigation Schemes 9.3.1 Modelling Inter-cell Interference 9.3.1.1 Link-Level Model 9.3.1.2 System-Level Model 9.3.2 Inter-cell Interference Averaging Techniques 9.3.2.1 Inter-cell Interference Cancellation 9.3.2.2 Dynamic Channel Allocation and Scheduling 9.3.3 Inter-cell Interference Avoidance Techniques 9.3.3.1 Resource Management by Restriction of Transmit Power 9.3.3.2 Self-adaptive Re-use Partitioning 9.3.3.3 Cost-function-based Scheduling 9.3.3.4 Simulation Results 9.3.4 Inter-cell Interference Mitigation Techniques Based on Smart Antennas 9.3.4.1 Beamforming Techniques 9.3.4.2 Transmit Diversity Techniques 9.3.4.3 Receive Diversity and Interference Suppression Techniques 9.3.4.4 Simulation Results Conclusion Acknowledgements References

336 340 343 346 346 347 348 349 350 350 351 351 352 357 360 360 362 363 364 365 365 368 370 370 372 373 373

Contents

10 Radio Resource Control and System Level Functions 10.1 Introduction 10.2 IPCL Layer 10.2.1 Transfer of User Data Between IPCL Entities 10.2.1.1 IPCL Header Compression 10.2.1.2 IPCL Data Ciphering and Ciphering Keys 10.2.2 IPCL and Handover 10.2.2.1 In-Sequence Delivery of Upper Layer PDUs 10.2.2.2 Duplicate Detection of Lower Layer SDUs 10.3 Radio Resource Control 10.3.1 RRC States 10.3.1.1 UT Detached State 10.3.1.2 UT Idle State 10.3.1.3 UT Active State 10.3.2 Mobility Management in Idle Mode 10.3.2.1 Paging 10.3.2.2 Tracking Area 10.3.3 Mobility Management in Active Mode 10.3.3.1 Micro Mobility 10.3.3.2 Macro Mobility 10.3.3.3 Intramode Handover 10.3.3.4 Intermode Handover 10.3.3.5 Intersystem Handover 10.3.3.6 Inter GW Handover and Load Balancing 10.3.4 Flow Admission Control 10.3.5 Congestion Avoidance Control 10.3.5.1 Admission Control: Two-Stage Approach 10.3.5.2 Flow Control 10.3.6 Load and Congestion Control 10.4 Centralised, Distributed and Hybrid RRM Architecture 10.4.1 Distributed RRM 10.4.2 Centralised RRM 10.4.3 Hybrid RRM 10.5 System-Level Performance Results 10.5.1 Intersystem Handover 10.5.2 Intermode Handover 10.5.2.1 Simulation Setup 10.5.2.2 Intramode and Intermode Handover Algorithms 10.5.3 Intermode Handover Results 10.5.3.1 Intermode Handover Triggered by Residual Throughput 10.5.3.2 Intermode Handover Triggered by UT Velocity 10.6 Conclusion Acknowledgements References

xv

377 377 378 378 379 380 381 382 382 383 383 383 384 384 385 385 385 386 386 388 389 390 392 393 394 396 396 401 404 406 406 406 407 407 407 409 409 410 412 412 414 414 415 416

xvi

11 Sharing and Flexible Spectrum Use Capabilities 11.1 Introduction 11.2 Spectrum Technologies Framework 11.2.1 Sharing and Co-existence Functions 11.2.1.1 Vertical Sharing 1: WINNER Is the Primary System 11.2.1.2 Vertical Sharing 2: WINNER Is the Secondary System 11.2.1.3 Horizontal Sharing with Coordination 11.2.1.4 Horizontal Sharing Without Coordination 11.2.2 Spectrum Assignment Functions 11.2.2.1 Long-term Assignment 11.2.2.2 Short-term Assignment 11.2.3 Generic Spectrum Functions 11.2.3.1 WINNER Spectrum Manager 11.2.3.2 Spectrum Register 11.3 Detailed Design of a Spectrum Assignment Negotiation Mechanism 11.3.1 Long-term Spectrum Assignment 11.3.2 Short-term Spectrum Assignment 11.3.3 Interactions between Long-term and Short-term Spectrum Assignment 11.3.4 Registration of Nodes with Spectrum Manager 11.3.5 Specific Short-term Spectrum Assignment Algorithms 11.3.5.1 Negotiated Amount of Resources Exchanged 11.3.5.2 Matching Amount of Resources Exchanged 11.3.5.3 Surplus of Resources Exchanged 11.4 Spectrum Assignment Enabling Mechanisms 11.4.1 Multi-band Scheduler 11.4.1.1 Hybrid ARQ Context Transfer 11.4.1.2 MBS and Spectrum Sharing 11.4.2 Communication Between Base Stations 11.4.2.1 Trends in BS-to-BS Communication and Site Sharing 11.4.2.2 Requirements for BS-to-BS Communication 11.4.2.3 Possibilities for Inter-BS Communication 11.4.2.4 Summary of BS-to-BS Communication Technologies 11.5 WINNER Sharing with FSS 11.5.1 Dimensioning of Hard Exclusion Zones 11.5.1.1 Typical FSS Parameters Considered for the Simulation 11.5.1.2 Results 11.5.2 Mitigated Exclusion Zone Calculation 11.5.3 Advanced Mitigation Techniques 11.5.3.1 Utilisation of Information Describing the FSS Usage in a Database 11.5.3.2 Utilisation of Spectrum Beacon Channel 11.5.3.3 Multi-antenna Technologies 11.6 Performance Evaluation of Spectrum Assignment Mechanisms 11.6.1 Performance Assessment of Long-term Spectrum Assignment 11.6.1.1 Considered Scenarios 11.6.1.2 Gradual Spectral Deployment of WINNER RANs 11.6.2 Performance Assessment of Short-term Spectrum Assignment

Contents

419 419 420 421 421 421 422 422 423 423 424 424 424 424 425 425 427 429 430 430 431 431 431 431 431 434 435 435 435 436 437 440 440 442 442 442 444 445 446 446 446 447 447 447 449 451

Contents

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11.6.2.1 Evaluation of Inter-cell Interference Issues for ST Spectrum Assignment 11.6.2.2 Cell-Pair Selection Algorithms for ST Assignment 11.6.2.3 Impact of Cell-Selection Algorithms on ST Performance Assignment 11.7 Conclusion Acknowledgements References

451 453 453 455 456 456

12 ITU-R Spectrum Demand Calculation for IMT-Advanced 12.1 Introduction 12.2 ITU-R Work on Spectrum Requirements of IMT-Advanced 12.2.1 Background and Role of ITU-R 12.2.2 ITU-R Preparations for WRC-07 12.2.3 WINNER Contributions to ITU-R 12.3 ITU-R Spectrum Calculation Methodology 12.3.1 Methodology Flow and Definitions 12.3.1.1 Services 12.3.1.2 Environments 12.3.1.3 Radio Access Technique Groups 12.3.2 Traffic Calculation and Distribution 12.3.3 Capacity Requirement Calculation 12.3.4 Spectrum Requirement Calculation 12.3.5 Summary of Methodology 12.4 Software Implementation of Methodology 12.4.1 Description and Use of Software Tool 12.4.2 Inputs to Software Tool 12.4.3 Intermediate Calculations and Outputs from Software Tool 12.5 Estimated Spectrum Requirements of IMT-Advanced 12.6 Conclusion Acknowledgements References

459 459 460 460 461 463 464 464 464 467 467 468 469 470 471 472 472 474 475 477 478 479 479

13 13.1 13.2 13.3 13.4

481 481 482 483 484 485 485 489 489 489 489 492 492 493 495

System Model, Test Scenarios, and Performance Evaluation Introduction Performance Assessment of Wireless Networks Interface between Link and System Simulations Test Scenarios 13.4.1 Test Environments 13.4.2 Deployment Assumptions 13.4.2.1 Base Station 13.4.2.2 User Terminal 13.4.2.3 Relay Node 13.4.2.4 Network Layout 13.4.2.5 Channel Modelling 13.4.3 Basic OFDM Parameters and Frame Dimensions 13.5 Spectral Efficiency and Number of Satisfied Users under QoS Constraints 13.6 End-to-End Performance Evaluation

xviii

Contents

13.6.1 Base Coverage Urban Scenario 13.6.1.1 Frequency-domain Link Adaptation Gains 13.6.1.2 Spectral Efficiency and Maximum Number of Satisfied Users 13.6.1.3 Improving Cell Edge Performance and Indoor Coverage by Relaying 13.6.1.4 Dynamic Resource Allocation in Relay-Enhanced Cells 13.6.1.5 Cooperative Relaying 13.6.1.6 Multicast/Broadcast Services in Relay-Enhanced Cells 13.6.1.7 Impact of Traffic and Packet Modelling on Spectral Efficiency 13.6.2 Microcellular Scenario 13.6.2.1 Indoor Coverage Improvement by Relay Deployments 13.6.2.2 Soft Frequency Re-use 13.6.2.3 Soft and Fractional Frequency Re-use and Re-use One 13.6.3 Local Area Scenarios 13.7 Conclusion Acknowledgements References

495 495 498 501 503 504 504 508 513 513 514 516 517 521 521 522

14 Cost Assessment and Optimisation for WINNER Deployments 14.1 Introduction 14.2 Cost Assessment Framework and Assumptions 14.2.1 General Cost Assessment Procedure 14.2.2 Types of Cost Assessment 14.2.3 Challenges in Cost Assessment 14.2.3.1 Spectrum Sharing 14.2.3.2 Roaming Agreements 14.2.3.3 Infrastructure Sharing 14.2.3.4 Third-party Network Ownership, Operation and Maintenance 14.2.3.5 New Business Entities 14.2.3.6 Summary 14.2.4 WINNER: Assumptions and Technology Options 14.3 Cost Components 14.3.1 Classification of Cost Components 14.3.2 RAN CAPEX Costs 14.3.2.1 Base Station Equipment 14.3.2.2 Relay Equipment 14.3.2.3 Base Station Deployment 14.3.2.4 Relay Deployment 14.3.2.5 Base Station Site Acquisition 14.3.2.6 Relay Site Acquisition 14.3.2.7 Gateways 14.3.2.8 Centralised RRM Servers 14.3.2.9 RAN Connectivity 14.3.2.10 Initial Radio Planning and Network Optimisation 14.3.3 RAN OPEX Costs 14.3.3.1 Base Station Site Rent and Maintenance

525 525 526 526 527 528 528 529 529 529 529 530 530 530 531 532 532 533 533 534 534 534 534 535 535 535 535 536

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14.3.3.2 Relay Site Rent and Maintenance 14.3.3.3 Rent for RAN Connectivity 14.3.3.4 Power 14.3.3.5 Network Operation and Maintenance 14.3.3.6 Software and Firmware Updates 14.3.4 Example Cost Figures 14.3.4.1 Breakdown of Macro BS Equipment Costs 14.3.4.2 Transformation of OPEX into CAPEX Costs 14.4 Cost Assessment Models 14.4.1 Previous Work 14.4.2 Background and Principles 14.4.3 Network Deployment 14.4.3.1 Traffic Modelling 14.4.3.2 RAP Deployment Strategies 14.4.3.3 Radio Propagation Models 14.4.3.4 Radio and Resource Assignment Model 14.4.4 Cost Calculation 14.4.4.1 Relay-Specific Cost Evaluation Issues 14.4.4.2 Deployment Representation by Indifference Maps 14.5 Reference Deployment Scenarios and Cost Assessments 14.5.1 Deployment Simulations and Assumptions 14.5.2 Case Studies 1 and 2: WA Urban, Relay Nodes vs Micro BS 14.5.2.1 Simulation Scenario Description 14.5.2.2 Cost-optimal Deployment and Total Deployment Cost of RNs 14.5.2.3 Cost-optimal Deployment and Total Deployment Cost of Micro BSs 14.5.2.4 Incumbent vs Greenfield Deployment Costs 14.5.3 Case Studies 3 and 4: WA Rural, Relay Nodes vs Micro BS 14.5.3.1 Simulation Scenario Description 14.5.3.2 Cost-optimal Deployment and Total Deployment Cost 14.5.4 Case Studies 5 and 6: WA Urban, Relay Nodes vs Micro BS, Intelligent BS Deployment 14.5.5 Case Studies 7 and 8: MIMO Assessment 14.5.5.1 Deployment Cost Comparison between SISO and MIMO Systems 14.5.5.2 Performance Improvement from the Use of Multiple Antennas 14.5.5.3 Deployment Evaluation 14.5.5.4 Cost Assessment of the Multi-antenna Configurations 14.6 Conclusion Acknowledgements References

536 536 537 537 537 537 538 539 540 540 541 541 542 543 544 546 549 549 550 555 555 555 555 557 559 560 560 560 561 562 564 564 565 565 566 566 567 567

Index

569

About the Editors Martin D¨ottling Dr Martin D¨ottling was born in Oppenau, Germany, on May 17, 1969. He received Dipl.-Ing. (MSEE) and Dr.-Ing. (PhDEE) degrees from Universit¨at Karlsruhe in 1995 and 2000, respectively. From 1995 to 2000, he was a research assistant at the Institut f¨ur H¨ochstfrequenztechnik und Elektronik (IHE), Universit¨at Karlsruhe. His research activities included ray optical propagation modelling, mobile communications and land mobile satellite systems. At the Universit¨at Karlsruhe and the Carl-Cranz Academy for scientific education, he was a lecturer in radio-wave propagation and radio-network planning. He participated as an expert in the European research programs COST 231, COST 255, and COST 273. From February 2001 until September 2006, he worked for Siemens AG, Mobile Communications in Munich, Germany. In October 2006, he joined Siemens Networks GmbH & Co. KG and, from April 2007 to August 2009, he has been with Nokia Siemens Networks GmbH & Co. KG (NSN). Since September 2009 he is with the European Patent Office, Munich, Germany. From 2001 to 2004, Martin D¨ottling worked on UMTS standardisation in the 3rd Generation Partnership Project (3GPP), focusing on the physical layer of wireless high-speed packet data transmission (HSDPA, HSUPA) covering both link- and system-level aspects. In 2005, he was responsible for the MIMO spatial-processing concept of the mobile communication system studied within the first phase of the European research project, WINNER. During 2006 and 2007, he was work package leader for the WINNER II system concept and expert for IMTAdvanced mobile communication systems research. From 2008 to 2009, he has been leading research and standardisation projects in the area of self-organising networks and contributes to the Long-Term Evolution (LTE) project of 3GPP. In 2009, he was acting as a Chief Architect for LTE. His publications include more than 90 contributions in books, journals, conferences and standardization documents. In 2004, he received the IEEE VTC 2004 Fall Best Paper Award and in 2008 he was appointed NSN Inventor of the year. During his career, he has served as a technical programme committee member, session chair, and reviewer for various international conferences, as a reviewer for international journals and has filed over 50 patent applications.

Werner Mohr Dr Werner Mohr was born in Hann. M¨unden, Germany, on June 2, 1955. He received a Masters degree and a PhD, both in electrical engineering, from the University of Hanover, Germany, in 1981 and 1987, respectively.

xxii

About the Editors

He worked for BEB Betriebsf¨uhrungsgesellschaft – an oil and gas company – from 1981 to 1982, where he was responsible for the investigation of a measurement system. In 1982, he returned to the University of Hanover as a member of the research staff of the Institute of High-Frequency Technology. From 1987 to 1990, he was senior engineer at the same institute. From 1989 to 1990, he was a lecturer at the Fachhochschule Hanover, Germany for telecommunication systems. Werner Mohr joined Siemens AG, Mobile Network Division in Munich, Germany in 1991. He was responsible for the development of a wideband propagation measurement system, propagation measurements and channel modelling and he was involved in the European RACE-II Project ATDMA in first investigations for the third-generation mobile radio interface. Wideband propagation channel models, which were developed based on extensive measurement campaigns in the ATDMA project, were internationally standardised by ETSI SMG and ITU-R TG 8/1 for the evaluation of third-generation mobile radio interface proposals. From 1995 to 1996, Werner Mohr was active in ETSI SMG5 for standardisation of UMTS. During that time, he was also responsible for the evaluation of several mobile radio standards. He was project manager of the ACTS FRAMES Project from December 1996 until the project finished in August 1999. This project developed the basic concepts of the UMTS radio interface and the ETSI SMG decision on the UMTS radio interface (UTRA concept) was based in large part on the contributions of the ACTS FRAMES project. He has held several positions in Siemens AG, Communications Business Unit, in the research domain and the CTO Office. He was involved in the 5th Framework Programme of the EU in different projects and as project coordinator. These projects dealt with preparatory research activities towards mobile communication systems beyond the third generation or IMT-Advanced. From 2001 to 2003, he was active in ITU-R WP8F, working on the development of ITU-R Recommendation M.1645 (Framework and overall objectives of the future development of IMT-2000 and systems beyond IMT-2000). From 2004 to 2007, he was the coordinator of the WINNER Project in Framework Programme 6 of the European Commission and chairman of Wireless World Initiative (WWI) – a group of cooperating projects towards systems beyond 3G in Framework Programme 6. The WINNER project developed concepts and algorithms that were the basis for the IMT-Advanced radio interface. Since 2008, he has been the coordinator of the Eureka Celtic project WINNER+, which is further developing such concepts based on the outcome of the World Radiocommunication Conference (WRC) 2007. Since April 2007, he has worked for Nokia Siemens Networks GmbH & Co. KG in Munich, Germany. He is Head of Research Alliances. Werner Mohr was chair of the Wireless World Research Forum (WWRF) from its launch in August 2001 to December 2003. He is vice-chair of the eMobility European Technology Platform from 2008 to 2009. He is a member of Verband der Elektrotechnik, Elektronik und Informationstechnik (VDE) and a Senior Member of IEEE. In 1990, he received the Award of the Information Technology Society (ITG) of VDE. He is a board member of ITG in VDE from 2006 to 2011. He is listed in the US Who’s Who in the World, Who’s Who in Science and Engineering and other publications. He has published over 100 technical papers in international journals and conferences, including invited papers. He has presented tutorials and organised and participated in panel discussions on several topics such as third-generation mobile radio systems and beyond. He is co-author of Third Generation Mobile Communications Systems and has contributed to other

About the Editors

xxiii

published books. He has served as session chair in several international conferences. He has been a member of several technical programme committees of international conferences, e.g., IEEE Globecom ’99, ’01, ’02, ’06, IEEE ICC ’04 to ’06, IEEE PIMRC ’00 to ’07, IEEE VTC Fall ’99, ’02, ’03, ’05, ’06, IEEE VTC Spring ’02, IEEE WCNC ’02, ’03, ’07, WPMC ’98 to ’08, European Wireless ’99, ’00, ’02, ’05, ’06, ’09 and was Executive Chair of IEEE WCNC 2005. In addition, he was the guest editor of a special issue of IEEE Network Magazine on 4G wireless in 2006.

Afif Osseiran Afif Osseiran was born in Saida, Lebanon. He received a BSc in Electrical Engineering and Electronics from Universit´e de Rennes 1, France, in 1995, and a DEA (postgraduate) degree in Electrical Engineering from Universit´e de Rennes 1 and INSA Rennes in 1997, and an ´ MASc degree in Electrical and Communication Engineering from Ecole Polytechnique de Montr´eal, Canada, in 1999. In May 2006, he successfully defended his PhD thesis at Radio Communication Group at the Royal Institute of Technology (KTH) in Stockholm. Since 1999, he has worked for Ericsson, Sweden. In 2004, as one of Ericsson’s representatives, he joined the European project WINNER funded under the 6th Framework Programme. During 2006 and 2007, he led the spatial temporal processing (i.e. MIMO) task, which mainly deals with multiple antenna techniques for future generations. Since April 2008, he has been the technical manager of the Eureka Celtic project WINNER+. He is also the leader of the System Concept Design Work Package in WINNER+. His research interests include many aspects of wireless communications with a special emphasis on advanced antenna systems for the third generation (WCDMA, HSPA, LTE) and future generations (IMT-Advanced), on radio resource management, network coding and cooperative communications. Afif Osseiran is listed in Who’s Who in the World and Who’s Who in Science and Engineering. He has published more than 40 technical papers in international journals and conferences; and has hitherto more than 15 approved or pending patents. During his career, he has served as technical programme committee member, session chair, and reviewer in various international conferences, as well as a reviewer for international journals. Since 2006, he has been teaching, at Master’s level, a few lectures every year on Advanced Antennas at the Royal Institute of Technology (KTH) in Stockholm.

Preface This book summarises the results of collaborative research by partners from the manufacturing, network operator, research centre and university domains that was carried out in the Wireless World Initiative New Radio (WINNER) Project in Framework Programme 6 of the European Commission. The main objective of the WINNER project was the development of a radio interface proposal that fulfils the requirements of Systems Beyond 3G and IMT-Advanced as preparation for the forthcoming international standardisation process. This was the major project in Framework Programme 6 dealing with the radio interface development for IMTAdvanced in an international consortium. Different regions of the world use different names for this research area. In Europe ‘Systems beyond 3G’ was preferred; in Asia, and in particular in Japan, the name ‘fourth generation (4G)’ was introduced. The International Telecommunication Union – Radio Sector (ITU-R) finally agreed the name ‘IMT-Advanced’ globally to show the relation of further developments towards future systems based on third-generation mobile communication systems (IMT-2000). The focus of this book is the physical and medium access control layers of the WINNER radio interface proposal and all its elements. It provides the basic concepts and the architecture of a radio interface proposal, which can be used as input for the development of the IMT-Advanced radio interface for the forthcoming international standardisation. The system development was based on generic requirements from ITU-R and has taken into account global research activities in this domain. Chapter 1 provides an introduction to the subject. Chapter 2 introduces usage scenarios and technical requirements. Channel models are presented in Chapter 3. Chapter 4 explains the system concept and architecture. Modulation and coding techniques are presented in Chapter 5. Chapter 6 discusses link level procedures. The advanced antenna concepts for IMTAdvanced are described in Chapter 7. Chapter 8 describes the Layer-2 relays, newly introduced by the WINNER project to IMT-Advanced cellular networks. Multiple access schemes and inter-cell interference mitigation techniques are presented in Chapter 9. Chapter 10 summarises the radio resource control and system level functions. Chapter 11 discusses sharing and flexible spectrum use capabilities. The ITU-R spectrum requirements calculation methodology for IMT-Advanced is presented in Chapter 12. Chapter 13 describes the system model, test scenarios, and performance evaluation results. Finally, Chapter 14 presents a cost assessment and optimisation method for WINNER deployments. The WINNER project started in a first phase from January 2004 to December 2005 (WINNER I) and continued in a second phase, WINNER II, from January 2006 to December 2007. At project start, Recommendation M.1645 [ITU03] had just been approved by

xxvi

Preface

ITU-R and was taken as the baseline for the project. In the first project phase, a number of alternative concepts were investigated in order to identify the most promising building blocks for the overall system concept. These building blocks and their combination were investigated, further developed, optimised and validated in the second phase. This phased approach was a key to building consensus in an international consortium with partners from the countries and organisations listed in Tables 1 to 4. The WINNER project participated actively in international conferences and workshops to disseminate its research results and concepts as well as in international workshops. Public workshops were organised, in cooperation with other EU Framework Programme 6 projects from the Wireless World Initiative (WWI), in December 2004 in Brussels, Belgium, in October 2005 in Shanghai, China (in conjunction with the International Conference on Systems beyond 3G), in December 2005 in Paris, France, in November 2006 in Helsinki, Finland and in November 2007 in Brussels. The final project workshop in Munich, Germany in December 2007 presented the project results to representatives from international standardisation bodies, taking into account the outcome of the World Radiocommunication Conference (WRC) 2007. In addition, the project organised public demonstrations of a trial system at the Wireless World Research Forum (WWRF) meeting in Espoo, Finland in June 2007, the IST Mobile Summit in Budapest, Hungary in July 2007, the WRC 2007 in Geneva, Switzerland in October and November 2007, the public WWI Innovation Day in Brussels in November 2007 and the final project workshop in Munich in December 2007. The international composition of the WINNER consortium with major partners from the different stakeholders in mobile communications, namely manufacturers, network operators, research centres and academia, enabled consensus building on the basic system approach and contributions to the global regulatory process in ITU-R towards the preparation of WRC 2007.

Table 1

Countries involved in WINNER phases 1 and 2.

Country Austria Canada China Denmark Finland France Germany Greece Italy Poland Portugal Slovakia Spain Sweden Switzerland United Kingdom United States of America

WINNER I (2004 to 2005)

WINNER II (2006 to 2007)

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Yes Yes Yes Yes Yes

Yes

Preface

xxvii

Table 2 Manufacturers involved in WINNER phases 1 and 2. Manufacturers

WINNER I (2004 to 2005)

Alcatel SEL AG, Germany Alcatel-Lucent Deutschland AG, Germany Alcatel-Lucent Telecom Limited, UK BenQ Mobile GmbH & Co. OHG, Germany Elektrobit Corporation, Finland Elektrobit Ltd, Finland Elektrobit Testing Ltd, Finland Ericsson AB, Sweden Ericsson GmbH, Germany Fujitsu Laboratories of Europe Ltd, UK International Business Machines Corporation, Research, USA IBM Research GmbH, Zurich, Switzerland Lucent Technologies Network Systems UK Ltd, UK Motorola S.A.S., France Nokia Corporation, Finland Nokia (China) Investment Co., Ltd, Beijing, China Nokia Siemens Networks GmbH & Co. KG, Germany Nokia Siemens Networks S.p.A., Italy Nokia Siemens Networks Technology (Beijing) Co. Ltd, China Nortel Networks UK Ltd, United Kingdom Philips Electronics UK Ltd, UK QUALCOMM CDMA Technologies GmbH, Germany Samsung Electronics UK Ltd, UK Siemens AG, Germany ¨ Siemens AG Osterreich, Austria Siemens Ltd China, China Siemens Program and System Engineering SRO, Slovakia Siemens S.p.A., Italy

Table 3

WINNER II (2006 to 2007)

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Yes Yes

Yes Yes Yes Yes Yes Yes Yes Yes Yes

Yes Yes Yes Yes Yes

Yes Yes Yes

Yes

Network operators involved in WINNER phases 1 and 2.

Operators DoCoMo Communications Laboratories Europe GmbH, Germany European Institute for Research and Strategic Studies in Telecommunications GmbH, Germany France T´el´ecom S.A., France Portugal Telecom Inovac¸a˜ o S.A., Portugal Telefónica Investigaci´on y Desarrollo Sociedad Anónima Unipersonal, Spain Vodafone Group Services Ltd, UK

WINNER I (2004 to 2005)

WINNER II (2006 to 2007)

Yes Yes

Yes Yes

Yes Yes Yes

Yes Yes Yes

Yes

Yes

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Table 4

Preface

Research centres and academic institutions involved in WINNER phases 1 and 2.

Research centres and academic institutions Aalborg University, Denmark Carleton University, Canada Commissariat à l’Énergie Atomique (CEA-Léti), France Centre Technològic de Telecomunicacions de Catalunya, Spain Chalmers University of Technology, Sweden China Academy of Telecommunication Research, China Deutsches Zentrum für Luft- und Raumfahrt e.V., Germany Helsinki University of Technology, Finland Kungliga Tekniska Högskolan (KTH), Sweden National Technical University of Athens, Greece Poznan University of Technology, Poland Rheinisch-Westfälische Technische Hochschule Aachen (ComNets), Germany Swiss Federal Institute of Technology Zurich, Switzerland Technical Research Centre of Finland VTT, Finland Technische Universität Dresden, Germany Technische Universität Ilmenau, Germany Centre for Wireless Communications (CWC), University of Oulu, Finland The University of Surrey, UK

WINNER I (2004 to 2005)

WINNER II (2006 to 2007)

Yes Yes

Yes Yes Yes Yes Yes Yes Yes

Yes Yes Yes Yes Yes Yes Yes Yes Yes

Yes Yes Yes Yes

Yes Yes Yes Yes Yes

Yes Yes Yes Yes

Yes

Yes

In particular, the project contributed significantly to the ITU-R spectrum requirements calculation methodology for IMT-Advanced (Chapter 12) and to channel models (Chapter 3) for the evaluation of systems in the ITU-R, 3GPP and IEEE standardisation bodies. The WINNER project influenced indirectly, via member organisations of 3GPP, the Long Term Evolution (LTE) standardisation process starting with the 3GPP LTE workshop in November 2004 in Toronto, Canada. With respect to these activities, the WINNER project was able to impact the ongoing and forthcoming regulatory and standardisation process for IMT-Advanced. This successful work and achievements were possible due to the close cooperation of project partners and respect for the interests of the different organisations. It was a very good experience to cooperate in such an environment, where issues were discussed in an atmosphere of trust, to develop consensus and to resolve complex technical issues. This book aims to support the forthcoming detailed standardisation process for IMTAdvanced and to inform researchers and developers working towards specifying IMTAdvanced about the results gained in the WINNER project. Werner Mohr

Reference [ITU03] ITU-R, Framework and overall objectives of the future development of IMT-2000 and systems beyond IMT-2000, Recommendation M.1645, 2003.

Acknowledgements The material in this book originates from the EU Framework Programme 6 WINNER project and contributions to the international regulatory and standardisation process. Therefore, we would like to thank all colleagues involved in the project for their support and the good cooperation that made success possible. In addition to the successful completion of the project, this book was supported as an additional item which was not originally planned in the consortium. We hope that our personal relationships remain; a major part of the consortium is continuing its cooperation in a follow-up project. The WINNER project was partly funded by the European Commission in a Phase I from 2004 to 2005 and a Phase II from 2006 to 2007. We thank especially Dr Joao Schwarz Dasilva, Dr Rainer Zimmermann, Dr Francisco Medeiros and Dr Peter Stuckmann from the EU Commission for their continuous support. We would like to acknowledge the contributions of our colleagues from Aalborg University, Alcatel-Lucent Deutschland AG (formerly Alcatel SEL AG), Alcatel-Lucent Telecom Ltd (formerly Lucent Technologies Network Systems UK Ltd), BenQ Mobile GmbH & Co. OHG, DoCoMo Communications Laboratories Europe GmbH, Carleton University, Centre Technol`ogic de Telecomunicacions de Catalunya, Chalmers University of Technology, China Academy of Telecommunication Research, Commissariat a` ´ l’Energie Atomique (CEA-L´eti), Deutsches Zentrum f¨ur Luft- und Raumfahrt e.V., Elektrobit Corporation (formerly Elektrobit Ltd and Elektrobit Testing Ltd), Ericsson AB, Ericsson GmbH, European Institute for Research and Strategic Studies in Telecommunications GmbH, France T´el´ecom S.A., Fujitsu Laboratories of Europe Ltd, Helsinki University of Technology, International Business Machines Corporation (formerly IBM Research GmbH Zurich), Kungliga Tekniska H¨ogskolan (Royal Institute of Technology, KTH), Motorola S.A.S., National Technical University of Athens, Nokia Corporation, Nokia Siemens Networks GmbH & Co. KG (formerly Siemens AG), Nokia Siemens Networks S.p.A. (formerly Siemens S.p.A.), Nokia Siemens Networks Technology (Beijing) Co., Ltd (formerly Nokia (China) Investment Co., Ltd, Beijing), Nortel Networks UK Ltd, Philips Electronics UK Ltd, Portugal Telecom Inovac¸a˜ o S.A., Poznan University of Technology, QUALCOMM CDMA Technologies GmbH, Rheinisch-Westf¨alische Technische Hochschule Aachen, Samsung Electronics ¨ UK Ltd, Siemens AG Osterreich, Siemens Ltd China, Siemens Program and System Engineering SRO, Swiss Federal Institute of Technology Zurich, Technical Research Centre of Finland VTT, Technische Universit¨at Dresden, Technische Universit¨at Ilmenau, Telef´onica Investigaci´on y Desarrollo Sociedad An´onima Unipersonal, CWC University of Oulu, The University of Surrey, and Vodafone Group Services Ltd for continuous support of the project and for finalising this book.

xxx

Acknowledgements

We also would like to thank Mark Hammond, Sarah Tilley and Katharine Unwin from John Wiley & Sons for their help to finalise this book. Martin D¨ottling Werner Mohr Afif Osseiran

Abbreviations 1D 2D 2D-CP 2G 3G 3GPP 3GPP2 AAA ABR AC ACK ACS ACM ADC ADSL AES AF AGC AM AMC AMPS AN AoA AoD AP APP ARIB ARQ AS AS AT AuC AWGN B3G

One-Dimensional Two-Dimensional Two-Dimensional Cyclic Prefix Second Generation Third Generation Third Generation Partnership Project Third Generation Partnership Project No. 2 Authentication Authorisation Accounting Available Bit Rate Admission Control Acknowledge Admission Control Server Adaptive Coding and Modulation Analog-Digital Converter Asymmetric Digital Subscriber Line Advanced Encryption Standard (Ciphering Algorithm) Amplify and Forward Automatic Gain Control Acknowledged Mode Adaptive Modulation and Coding Advanced Mobile Telephone System Ambient Networks (Integrated Project) Angle Of Arrival Angle of Departure Access Point A Posteriori Association of Radio Industry and Business Automatic Repeat Request Angular Spread Access Stratum Allocation Table Authentication Center Additive White Gaussian Noise Beyond Third Generation

xxxii

BCCH BCH BCQI B-EFDMA BER BICM B-IFDMA BLA BLER BP BS BSFDD BSTDD BWA C/I CAPEX CBR CbS CC C-CDD CCDF CDD CDF CDL CDMA CE CEPT CF CG CFBS CJK CM CMR CNU COST CP CPA CPB CPC CPE CPG CPM CQI CR CRC CSI

Abbreviations

Broadcast Control Channel Broadcast Channel Best Channel Quality Indicator Block Equidistant Frequency Division Multiple Access Bit Error Rate Bit Interleaved Coded Modulation Block Interleaved Frequency Division Multiple Access Basic Link Adaptation Block Error Rate Belief Propagation Base Station Base Station using FDD mode Base Station using TDD mode Broadband Wireless Access Carrier-to-Interference Ratio Capital Expenditure Constant Bit Rate Connection-based Scheduling Convolutional Codes Cellular Cyclic Delay Diversity Complementary Cumulative Distribution Function Cyclic Delay Diversity Cumulative Distribution Function Clustered Delay Line Code Division Multiple Access Channel Estimation Conference of European Post and Telecommunications Cost Function Cost Group Cost Function Based Scheduling China, Japan, Korea (Initiative) Coded Modulation Coding and Modulation Rate Check Node Unit European Cooperation in the Field of Scientific and Technical Research Cyclic Prefix Common Pilots per Antenna Common Pilots per Beam Common Pilots per Cell Common Phase Error Conference Preparatory Group Conference Preparatory Meeting Channel Quality Indicator Cooperative Relaying Cyclic Redundancy Check Channel State Information

Abbreviations

CSIT CT CTF CWER DARPA DAS DBTC DCA DCT DET DF DFICE DFT DL DoA DPA DPA-FB DPB DPB-FB DPC DPIIC DRS DRX DS DSL E2E E2R EC ECC ECC PT1 ECSI EDGE EFDMA EIRP EKF EMF ES ES ETRI EU EVM EXIT FB FCA FCC FCFS

xxxiii

Channel State Information at Transmitter Configuration Table Channel Transfer Function Code Word Error Rate Defence Advanced Research Project Agency Distributed Antenna System Duo-Binary Turbo Codes Dynamic Channel Allocation Discrete Cosine Transform Dominant Eigenmode Transmission Decode and Forward Decision feedback Iterative Channel Estimation Discrete Fourier Transform Downlink Direction of Arrival Dedicated Pilots per Antenna Dedicated Pilots per Antenna over the Full Band Dedicated Pilots per Beam Dedicated Pilots per Beam over the Full Band Dirty Paper Coding Direct Parallel Inter-Cell Interference Cancellation Dynamic Resource Sharing Discontinuous Reception Delay Spread Digital Subscriber Line End-to-End End-to-End Reconfigurability European Commission Electronic Communication Committee Electronic Communication Committee Project Team 1 Equivalent Channel State Information Enhanced Data Rates for GSM Evolution Equidistant Frequency Division Multiple Access Equivalent Isotropically Radiated Power Extended Kalman Filter Electro-Magnetic Field Elevation Spread Earth Station Electronics and Telecommunications Research Institute European Union Error Vector Magnitude Extrinsic Information Transfer (Chart) Fixed Beam Fixed Channel Allocation Frame Control Channel First-Come First-Served

xxxiv

FCS FD FD-CTF FDD FDE FDMA FEC FER FFR FFT FH-LFDMA FI FIR FL FMIP FRN FSS FSU FTP FTTx GA GF GGSN GMC GoB GoS GPRS GPS GSM GW HARQ HIS HLR HMIP HO HOS HPA HRRM HSDPA HSPA HSS HSUPA HT HTTP HW HwC

Abbreviations

Far Cluster Scatterers Frequency Domain Frequency Domain Channel Transfer Function Frequency Division Duplex Frequency Domain Equalisation Frequency Division Multiple Access Forward Error Correction Frame Error Rate Fractional Frequency Re-use Fast Fourier Transform Frequency Hopping Localised Frequency Division Multiple Access Full-power Isolation Finite Impulse Response Floor Loss Fast Mobile IP Fixed Relay Node Fixed Satellite Service Flexible Spectrum Use File Transfer Protocol Fibre to the (Building/Curb/Home) Genetic Algorithm Galois Field Gateway GPRS Support Node Generalised Multi-Carrier Grid-of-Beams Grade of Service General Packet Radio Service Global Positioning System Global System for Mobile Communication Gateway Hybrid Automatic Repeat Request Hybrid Information System Home Location Register Hierarchy Mobile IP Handover Higher Order Sectorisation High-Power Amplifier Hybrid RRM High Speed Downlink Packet Access High Speed Packet Access Home Subscriber Server High Speed Uplink Packet Access Hilly Terrain Hypertext Transfer Protocol Hardware Horizontal Sharing with Coordination

Abbreviations

HwoC Hz IBDFE IBI IBSC IC ICE ICI ICNIRP ICU ID IdPIIC IEEE IF IFDMA IFFT IIC IMT IP IPA IPCL IQ IR IRC ISARP ISD ISHO ISI IT ITS ITU ITU-R ITU-R SG 5

xxxv

Horizontal Sharing without Coordination Hertz Iterated Block Decision Feedback Equalisation Inter-Block Interference Inter-Base-Station Communication Interference Cancellation Iterative Channel Estimation Inter-Cell Interference International Commission on Non-Ionising Radio Protection In-Cell user Identifier Indirect Parallel Inter-Cell Interference Cancellation Institute of Electrical and Electronics Engineers Intermediate Frequency Interleaved Frequency Division Multiple Access Inverse Fast Fourier Transform Inter-Cell Interference Cancellation International Mobile Telecommunications Internet Protocol IP Anchor Internet Protocol convergence layer In-phase/Quadrature Incremental Redundancy Interference Rejection Combining Interference-Based Self-Adaptive Re-use Partitioning Inter-Site Distance Inter-System Handover Inter-Symbol Interference Information Technology Intelligent Traffic System International Telecommunication Union International Telecommunication Union Radiocommunication Sector International Telecommunication Union Radiocommunication Sector Study Group 5 ITU-R SG 8 International Telecommunication Union Radiocommunication Sector Study Group 8 ITU-R WP 5D International Telecommunication Union Radiocommunication Sector Working Party 5D ITU-R WP 8F International Telecommunication Union Radiocommunication Sector Working Party 8F Iu-Flex 3GPP Interface KPI Key Parameters Indicators L2 Layer 2 LA Local Area LA Link Adaptation LAN Local Area Network

xxxvi

LB LBCCH LCCCH LDC LDCCH LDPC LDPCC LDTCH LFDMA LFSR LI LLR LMCCH LMMSE LMS LMTCH LN LOS LPCH LPIF LS LS LSP LT LTE MA MAC MADM MAI MAP MBMS MBS MCBC MC-CDMA MCN MCS MCSSS MI-ACM MIDCA MIESM MIMO MIP MISO mITF MM MMR

Abbreviations

Logical Beam Logical Broadcast Control Channel Logical Common Control Channel Linear Dispersion Code Logical Dedicated Control Channel Low Density Parity Check Low Density Parity Check Code Logical Dedicated Traffic Channel Localised Frequency Division Multiple Access Linear Feedback Shift Register Length Indicator Log-Likelihood Ratio Logical Multicast Control Channel Linear Minimum Mean Square Error Least Mean Square Logical Multicast Traffic Channel Logical Node Line Of Sight Logical Paging Control Channel Lowpass Interpolation Filter Large Scale Least Squares Large-Scale Parameter Long-Term Long Term Evolution Metropolitan Area Medium Access Control Multiple Attribute Decision Making Multiple Access Interference Maximum A Posteriori Multicast Broadcast Messaging Service Multi-Band Scheduler Multicast/Broadcast Multi-Carrier Code Division Multiple Access Multi-hop Cellular Network Modulation and Coding Scheme Multi-Carrier Spread Spectrum Signal Mutual Information based Adaptive Coding and Modulation Minimum Interference Dynamic Channel Allocation Mutual Information Effective SINR Metric Multiple Input Multiple Output Mobile IP Multiple Input Single Output Mobile IT Forum Mobility Module Mobile Multi-hop Relaying

Abbreviations

MMS MMSE MPC MPEG MPIIC m-PSK m-QAM MRC MS MSA MSE MU MUD MU-MIMO MUX MVNO NACK NAS NGMC NGMN NIP NLOS NSF O&M OCI ODMA ODS OF OFDM OFDMA OPEX OS OSI OTA OVSF P2P PACE PADC PAN PAPC PAPR PARC PAS PBCH PDA PDC

xxxvii

Multimedia Messaging Service Minimum Mean Square Error Multipath Component Moving Picture Experts Group Mean Parallel Inter-Cell Interference Cancellation Phase-Shift Keying with m constellation points Quadrature Amplitude Modulation with m constellation points Maximum Ratio Combining Mobile Station Min-Sum Algorithm Mean Square Error Multi-User Multi-User Detector Multi-User Multiple Input Multiple Output Multiplexing Mobile Virtual Network Operator Negative Acknowledgement Non-Access Stratum Next Generation Mobile Committee Next Generation Mobile Networks Noise-plus-Interference Power Non Line Of Sight National Science Foundation Operation and Maintenance Out-of-Cell Interferer/Interference Opportunity Driven Multiple Access Optimum Distance Spectrum Objective Function Orthogonal Frequency Division Multiplexing Orthogonal Frequency Division Multiple Access Operational Expenditure Objective Score Open System Interconnection Over-the-Air Orthogonal Variable Spreading Factor Peer-to-Peer Pilot Assisted Channel Estimation Physical Frequency-Adaptive Data Channel Personal Area Network Per Antenna Power Constraint Peak-to-Average Power Ratio Per Antenna Rate Control Power Azimuth Spectrum Physical Broadcast Channel Personal Digital Assistant Personal Digital Cellular System

xxxviii

PDCFC PDCP PDF PDFCC PDP PDU PER PF PHY PI PI PIC PID PIIC PLM PLO PM PMBC PN PNDC PPP PRACH PSK PSRC PSU PUCH QAM QC-BLDPC QoS QPSK R3SARP RA RACH RAN RAP RAT RAU RBD RCDD REC RF RLAN RLC RMS RN RNC

Abbreviations

Physical Downlink Control Format Indicator Channel Packet Data Convergence Protocol Probability Density Function Physical Downlink Frame Control Channel Power Delay Profile Protocol Data Unit Packet Error Rate Proportional Fair Physical Layer Paging Indication Partial Power Isolation Parallel Interference Cancellation Packet Identifier Parallel Inter-Cell Interference Cancellation Physical Layer Mode Phase Locked Oscillator Paging Message Physical Multicast Broadcast Channel Pseudo-Noise Physical Non-Frequency-Adaptive Data Channel Point-to-Point Protocol Physical Random Access Channel Phase-Shift Keying Per Stream Rate Control Percentage of Satisfied Users Physical Uplink Control Channel Quadrature Amplitude Modulation Quasi-Cyclic Block Low Density Parity Check (Code) Quality of Service Quaternary Phase-Shift Keying (4-QAM) Re-use 3 Self-Adaptive Re-use Partitioning Rural Area Random Access Channel Radio Access Network Radio Access Point Radio Access Technology Resource Allocation Unit Regularised Block Diagonalisation Relay Cyclic Delay Diversity Relay-Enhanced Cell Radio Frequency Radio Local Area Network Radio Link Control Root Mean Square Relay Node Radio Network Controller

Abbreviations

RP RPC RR RRC RRM RS RSARP RT RTP RTT RTTM RTU RU RVQ RX S1-Flex SAE SAP SARP SC SCM(E) SCN SDH SDMA SDO SDU SE SF SF SFN SFR SGSN SI SIC SIMO SINR SIR SISO SLA SMMSE SMS SMUX SN SNDR SNR S-PARC

xxxix

Resource Partitioning Radio Paging Controller Round Robin Radio Resource Control Radio Resource Management Resource Scheduler Random Self-Adaptive Re-use Partitioning Real Time Real Time Protocol Round Trip Time Real Time Traffic Measurement Retransmission Unit Resource Unit Random Vector Quantisation Receiver 3GPP interface System Architecture Evolution Service Access Point Self-Adaptive Re-use Partitioning Service Category; Study Case (Ch. 14) Spatial Channel Model (Wideband Extension) Single-hop Cellular Network Synchronous Digital Hierarchy Space Division Multiple Access Standards Development Organisation Service Data Unit Service Environment Shadow Fading Super-Frame Single Frequency Network Soft Frequency Re-use Serving GPRS Support Node Self Interference Successive Interference Cancellation Single Input Multiple Output Signal-to-Interference-plus-Noise Ratio Signal-to-Interference Ratio Single Input Single Output Service Level Agreement Successive Minimum Mean Square Error Short Message Service Spatial Multiplexing Sequence Number Signal-to-Nonlinear-Distortion Ratio Signal-to-Noise Ratio Selective Per Antenna Rate Control

xl

SPICE SR-ARQ SRC SSC SS-MC-MA ST STBC STC SU SUC SVD TA TB TBCH TCM TCP TDD TDM TDMA TFT TLSP TM TMCH TMSI TP TPCH TRAC TSCH TTI TU TX U(P) UBR UDP UE UHF UIA2 UL ULA UM UMTS UN UT UTRAN VBR VNU

Abbreviations

Service Platform for Innovative Communication Environment Selective Repeat-ARQ Spectrum Resource Change Spectrum Sharing and Coexistence Spread Spectrum Multi-Carrier Multiple Access Short-Term Space Time Block Code Space Time Coding Single User Satisfied User Criterion Singular Value Decomposition Tracking Area Transmission Block Transport Broadcast Channel Trellis Coded Modulation Transmission Control Protocol Time Division Duplex Time Division Multiplex Time Division Multiple Access Transport Format Table Transformed LSP Transparent Mode Transport Multicast Channel Temporal Mobile Subscriber Identity Throughput Transport Paging Channel Transport Random Access Channel Shared Transport Channel Transmission Time Interval Typical Urban Transmitter User (Plane) Unspecified Bit rate User Datagram Protocol User Equipment Ultra High Frequency Ciphering algorithm Uplink Uniform Linear Array Unacknowledged Mode Universal Mobile Telecommunications System United Nations User Terminal Universal Terrestrial Radio Access Network Variable Bit Rate Variable Node Unit

Abbreviations

VoIP VS VSF-OFCDM WA WARC WG SERV WG SPEC WG TECH WIF WiMAX WINNER WLAN WPAN WRAN WRC WSI WWI WWL WWRF

xli

Voice Over Internet Protocol Vertical Sharing Variable Spreading Factor Orthogonal Frequency and Code Division Multiplexing Wide Area World Administrative Radio Conference Working Group Future Services and Market Aspects Working Group Spectrum Working Group Technology Wiener Interpolation Filter Worldwide Interoperability for Microwave Access Wireless World Initiative New Radio Wireless Local Area Network Wireless Personal Area Networks WINNER Radio Access Network World Radiocommunication Conference Wireless Strategic Initiative Wireless World Initiative Wireless Local Loop Wireless World Research Forum

List of Contributors Dr. Saied Abedi, Wireless Technology, Networks Systems Research Division, Fujitsu Laboratories of Europe LTD. (FLE), Hayes Park Central, Hayes End Road, Hayes, Middlesex, UB4 8FE UK Dr. Gunther Auer, DOCOMO Communications Laboratories Europe GmbH, Landsberger Str. 312, D-80687 Munich, Germany Mr. Mehdi Bennis, University of Oulu, Centre for Wireless Communications (CWC), PO Box 4500, Oulu, Finland Dr. Ivan Cosovic, formerly with DOCOMO Communications Laboratories Europe GmbH, Landsberger Str. 312, D-80687 Munich, Germany Mr. Klaus Doppler, Nokia Research Center, P.O. Box 100, 00045 NOKIA GROUP, Finland Dr. Martin D¨ottling, Nokia Siemens Networks GmbH & Co. KG, St.-Martin-Str. 76, D-81541 Munich, Germany, now with European Patent Office, Germany Prof. David Falconer, Carleton University, Ottawa, Canada Dr. Roberta Fracchia, formerly with Motorola Labs - Paris, Parc Les Algorithmes - Saint Aubin, 91193 Gif sur Yvette - France Mr. Lassi Hentil¨a, Elektrobit Corporation, Tutkijantie 7, FI-90570 Oulu, Finland Mr. Joerg Huschke, Ericsson Research, Corporate Unit, Ericsson GmbH, Eurolab R&D, Ericsson Allee 1, D-52134 Herzogenrath, Germany Dr. Ralf Irmer, Principal Engineer, Vodafone Group Research & Development, Newbury, UK Dr. Tim Irnich, Ericsson GmbH, Kackertstrasse 7-9, D-52072 Aachen, Germany, formerly with Communication Networks (ComNets), RWTH Aachen University, Kopernikusstrasse 1, D-52062 Aachen, Germany Mr. Jean-Philippe Javaudin, Access Networks, Orange Labs, France Telecom, 4 rue du Clos Courtel, 35512 Cesson-S´evign´e, France Mr. Paulo Jesus, Portugal Telecom Inovac¸a˜ o, S.A., R. Eng. Jos´e Ferreira Pinto Basto, 3810 106 Aveiro, Portugal

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List of Contributors

Mr. Niklas Johanson, Ericsson AB, Ericsson Research, Isafjordsgatan 14E, SE-164 80 Stockholm, Sweden Mr. Tommi J¨ams¨a, Elektrobit Corporation, Tutkijantie 7, FI-90570 Oulu, Finland Mr. Kari Kalliojarvi, Nokia Oyj, Visiokatu 1, FI-33720 Tampere, Finland Mr. J¨orn von H¨afen, R&S BICK Mobilfunk GmbH, Fritz-Hahne-Str. 7, D-31848 Bad M¨under, Dept. E, Germany, formerly with Nokia Siemens Networks GmbH & Co. KG, St. Martin Strasse 76, D-81541 Munich, Germany Mr. Pekka Ky¨osti, Elektrobit Corporation, Tutkijantie 7, FI-90570 Oulu, Finland Dr. Thierry Lestable, formerly with Samsung Electronics UK Ltd, UK, no with Sagem Communications, France Dr. Jijun Luo, Nokia Siemens Networks GmbH & Co. KG, St.-Martin-Strasse 76, D-81541 Munich, Germany Ms. Genevi`eve Mange, Alcatel-Lucent Germany, Bell Labs, Lorenzstr. 10, D-70435 Stuttgart Ms. Marja Matinmikko, VTT Technical Research Centre of Finland, Kaitov¨ayl¨a 1, FI-90571 Oulu, Finland Mr. Juha Meinil¨a, Elektrobit Corporation, Tutkijantie 7, FI-90570 Oulu, Finland Ms. Albena Mihovska, Aalborg University, Niels Jernes Vej, 12, 9220 Aalborg, Denmark Mr. Emilio Mino Diaz, Telefonica Investigaci´on y Desarrollo, Emilio Vargas, 6, 28043 Madrid, Spain Mr. Peter Moberg, Ericsson AB, Ericsson Research, Isafjordsgatan 14E, SE-164 80 Stockholm, Sweden Dr. Werner Mohr, Nokia Siemens Networks GmbH & Co. KG, St. Martin Strasse 76, D-81541 Munich, Germany Dr. Johan Nystr¨om, Ericsson AB, Ericsson Research, Isafjordsgatan 14E, SE-164 80 Stockholm, Sweden Mr. Pekka Ojanen, Nokia Corporation, P.O. Box 100, 00045 NOKIA Group, Finland Mr. Jussi Ojala, Nokia Corporation, P.O. Box 407, 00045 NOKIA Group, Finland Dr. Afif Osseiran, Ericsson AB, Ericsson Research, Isafjordsgatan 14E, SE-164 80 Stockholm, Sweden Mr. Ralf Pabst, Communication Networks (ComNets), RWTH Aachen University, Kopernikusstrasse 1, D-52062 Aachen, Germany Dr. Stephan Pfletschinger, Centre Tecnol`ogic de Telecomunicacions de Catalunya (CTTC), Parc Mediterrani de la Tecnologia, Av. del Canal Ol´ımpic s/n, 08860 Castelldefels, Spain Dr. Simone Redana, Nokia Siemens Networks GmbH & Co. KG, St. Martin Strasse 76, D-81541 Munich, Germany

List of Contributors

xlv

Dr. St´ephanie Rouquette-L´eveil, Motorola S.A.S., Seamless Radio Access Lab, Parc les Algorithmes, Saint-Aubin, 91193 Gif sur Yvette Cedex, France Mr. Daniel Chr. Schultz, formerly with Communication Networks (ComNets), RWTH Aachen University, Kopernikusstrasse 1, D-52062 Aachen, Germany, now with Detecon, Germany Prof. Mikael Sternad, Signals and Systems, Uppsala University, P O Box 534, SE-751 21 Uppsala, Sweden Dr. Tommy Svensson, Communication Systems Group, Department of Signals and Systems, Chalmers University of Technology, SE-412 96 G¨oteborg, Sweden Dr. Shyamalie Thilakawardana, CCSR, The University of Surrey, Guildford, Surrey, GU2 7XH, UK Mr. Elias Tragos, Athens University, Heroon Polytechniou 9, 15780 Zografou, Athens, Greece Prof. Dr. Bernhard H. Walke, Communication Networks (ComNets), RWTH Aachen University, Kopernikusstrasse 1, D-52062 Aachen, Germany Dr. Marc Werner, QUALCOMM CDMA Technologies GmbH, Nordostpark 89, D-90411 Nuremberg, Germany Dr. Carl Wijting, Nokia Research Center/Wireless Systems and Services, P.O. Box 407, FI-00045 NOKIA Group, (It¨amerenkatu 11-13, 00180 Helsinki, Finland) Ms. Roufia Yahi, Orange Labs, Spectrum Management Engineer, 38-40 rue du G´en´eral Leclerc, 92794 Issy-Moulineaux cedex 9, France

1 Introduction Werner Mohr Nokia Siemens Networks

This chapter describes the global development and status of mobile and wireless communications from the deployment of analogue first-generation systems towards the current global research activities on systems beyond 3G/IMT-Advanced. Development towards digital mobile communication systems in the second generation enabled successful, global mass-market penetration. Third-generation (3G) systems are providing improved user experience for data applications. In parallel, the wireless IT sector is developing systems for short-range and wide-area applications. Based on these developments and expected future traffic growth, requirements and basic system concepts for IMT-Advanced have been developed by ITU-R and major network operators. Research activities on such systems started about 1999 in all regions of the world. The European WINNER project is one major effort for the development of an IMT-Advanced radio interface concept under Framework Programme 6 of the European Commission. All these developments supported the identification of additional frequency spectrum for mobile and wireless communications in the World Radiocommunication Conference (WRC) 2007. After WRC 2007, the system concept has to be adapted to its outcome. In parallel with the forthcoming standardisation process, the necessary research activities for adapting and optimising the system concept are being continued in the European Eureka Celtic project WINNER+ as a follow-up to the WINNER project.

1.1 Development and Status of Mobile and Wireless Communications Mobile and wireless communication systems have been successfully deployed in different regions of the world since the 1980s. In the first generation, differing analogue systems were deployed mainly in the developed regions of the world to support telephony services for mobile subscribers. In Europe, different systems were deployed in different countries, which did not allow roaming between countries. In the Americas, a single Advanced Mobile Telephone Radio Technologies and Concepts for IMT-Advanced Martin D¨ottling, Werner Mohr and Afif Osseiran C 2009 Martin D¨ ottling, Werner Mohr, Afif Osseiran

2

Radio Technologies and Concepts for IMT-Advanced

System (AMPS) was deployed. Japan developed its own system as a derivative of AMPS [Wal02; S91; Lee90]. With respect to small national markets for different analogue systems in Europe, the Conf´erence Europ´eenne des Administrations des Postes et des T´el´ecommunications (CEPT) decided in 1982 to develop a pan-European second-generation mobile communication system. This was the starting point of the Global System for Mobile Communication (GSM), which was deployed internationally from 1991. In the beginning, the main objective of GSM was the support of voice telephony and international roaming with a single system across Europe. GSM is based on time division multiple access (TDMA) and digital signal processing, which is the main technical advance over first-generation systems [Hil02]. In parallel with GSM, other digital second-generation systems were developed globally and competed with each other. The USA followed a market-driven approach with competing technologies for the same service. Japan deployed a national second-generation system, Personal Digital Cellular (PDC) System, which was not compatible with other second-generation systems. Today, the GSM family (GSM, General Packet Radio Service (GPRS) and Enhanced Data Rates for GSM Evolution (EDGE)) is the dominant second-generation mobile communication standard with a global market share (at the end of the second quarter of 2008) of more than 86 % and 2.975 billion subscribers in more than 200 countries [GSA08]. The main competitor to GSM is IS-95 CDMA, which is based on code division multiple access (CDMA) [Vit95]. The CDMA system family, including third-generation systems, supports 451 million subscribers globally [CDG08]. Currently, the main subscriber growth markets for the GSM system are emerging markets, such as China with about 7 million new subscribers per month and India with about 5 million new subscribers per month. At the end of 2007, more than 3 billion subscribers in total were connected to mobile communications globally. Mobile and wireless communications are serving user needs with increasing penetration rates on a global basis. Mobile communication systems enable many new applications and allow more flexibility for users and thereby an improvement of quality of life and efficiency of business processes. In parallel with the fast growth of second-generation mobile communication, third-generation systems were developed from about 1988 and deployed globally since 2002. The introduction of third-generation (3G) mobile communication systems, International Mobile Telecommunications (IMT-2000), provided a mobile broadband access system, which increases the opportunities for data applications and new business models [UMTS05]. The main representatives are Universal Mobile Telecommunications System (UMTS), based on the integration of this new radio access system on the GSM network infrastructure, and cdma2000, a further development of IS-95 CDMA. The first UMTS systems supported peak data rates of 384 kbps. UMTS and cdma2000 are being further developed for increased performance. High Speed Packet Access (HSPA), a UMTS variant, supports peak downlink data rates from 7.2 Mbps to about 14.6 Mbps (see for example [HT07]) by using adaptive modulation and coding with higher-order modulation and multicode transmission. This system was launched in 2007. At the end of the second quarter of 2008, UMTS and its further developments reached about 235.5 million subscribers globally [GSA08]. UMTS is being further developed by the 3rd Generation Partnership Project (3GPP) [3GPP] towards the Long Term Evolution (LTE) system which has peak data rates of more than 100 Mbps, increased spectral efficiency and significantly shorter latency than today’s systems. LTE is based on orthogonal frequency division multiple access (OFDMA) and advanced spatial processing – multiple input multiple output (MIMO). Similar activities are ongoing for the further development of the cdma2000 family [3GPP2].

Introduction

3

The Next Generation Mobile Networks (NGMN) initiative, which is supported by international network operators, has formulated requirements on further developments of mobile communications [NGMN]. Such requirements are mainly related to a flat network architecture based on the Internet protocol (IP) for cost reduction, higher spectral efficiency for better use of the available frequency spectrum, lower latency and higher peak data rates with flexible allocation of data rates to users. Additional requirements are a high cell average throughput and sufficiently high cell edge capacity in order to cover the expected increasing data traffic with growing user density. LTE is already being developed towards these requirements, which is an important step towards IMT-Advanced (see Section 1.2). In parallel with these developments in the telecommunications industry, the wireless IT sector provides different IP-based access systems for different application areas. Wireless LAN (WLAN) systems, in the standards family IEEE802.11, are used for local and shortrange applications without mobility. WLAN systems are widely available globally. Wireless Personal Area Networks (WPAN) are standardised by IEEE802.15 for very short ranges and high throughput. Broadband wireless access (BWA) systems, according to IEEE802.16, are looking for higher ranges including the support of user mobility [IEEEIO; IEEESG]. The BWA WiMAX system is a member of the IMT-2000 family [WIMAX].

1.2 Expectations of Data Traffic Growth The expected economic impact of mobile and wireless communications providing improved productivity in business processes and access to information any time and anywhere is driving the further improvement of communication systems. Traffic over mobile and wireless systems is expected to increase significantly especially for data applications [UMTS05]. Data traffic is strongly increasing mainly due to Internet traffic. In preparation for the World Radiocommunication Conference (WRC) 2007, the European Commission initiated a market study of mobile and wireless communications [FBB05], which predicted a significant increase in traffic in various scenarios, confirming the expectations of fora such as the Wireless World Research Forum (WWRF) and the industry. In addition, the International Telecommunication Union – Radio Sector (ITU-R) has the objective of significantly increasing global teledensity, which should connect about 5 billion people by 2015. Based on these studies, ITU-R developed a market report and spectrum demand estimates. The EU Framework Programme 6 Wireless World Initiative New Radio (WINNER) project contributed to this activity [TW08]. In developed regions and countries, data traffic is mainly carried by fixed networks and minor amounts are carried via mobile and wireless systems. Data traffic per user is increasing significantly. If a small part of the traffic moves from fixed networks to mobile and wireless systems, this results in a huge increase in traffic on mobile and wireless systems [Wal07; WWRF]. In emerging regions and countries, a dense and broadband fixed network is not yet available especially in less densely populated areas. Therefore, the deployment of mobile and wireless systems is a faster and more economic solution. These trends show the need for increased radio network capacity and throughput by improved systems and the identification of additional frequency spectrum. WRC 2007 identified additional frequency spectrum to reply to these needs (Chapter 12). However, the amount of frequency spectrum identified is less than that required by market studies and is different in different regions and countries.

4

Radio Technologies and Concepts for IMT-Advanced

1.3 Development Towards IMT-Advanced These increasing traffic expectations require the further development of future mobile and wireless systems. Research activities on IMT-Advanced started in different regions even before the launch of third-generation systems. Around 1999, initial research work started in Europe on systems beyond 3G and in Japan even earlier on 4G systems. In Europe, the Wireless Strategic Initiative (WSI) project in Framework Programme 5 of the EU developed a first concept of systems beyond 3G. This project launched the global Wireless World Research Forum (WWRF) [WWRF] in 2001 as a platform for building consensus to further develop the vision and basic concepts and algorithms and to identify the major building blocks for future systems [Taf05; Taf06; MK00]. Today’s systems (see Section 1.1) are developed independently for different application areas such as cellular-based mobile communication systems for nationwide coverage and the support of full mobility and international roaming; short-range communications (WLAN-type systems) for nomadic use and local coverage; broadband wireless access systems for Digital Subscriber Line (DSL), last-mile applications and broadcast systems. Users want personalised mobile multimedia services with any content, anywhere, at any time, via any device and any access system. The necessary system complexity has to be hidden from the user via easy-tohandle user interfaces. From the user perspective, the vision for mobile communications can be described as a multisphere level concept (see Figure 1.1), which was developed in the WSI project around 2001 and further detailed in an early edition of the WWRF ‘book of visions’ [WWRF; Moh03a]. This basic vision will remain in the future. In the world beyond 3G, the user-centric approach will enable people to communicate anywhere, any time to anybody and will improve today’s user experience by seamless access to mobile and wireless systems. ITU-R developed and approved, in 2003, Recommendation M.1645 on the future development of IMT-2000 (3G) and systems beyond it [ITU03], which was the basis for the preparation of WRC 2007. Future systems will comprise a network of networks of cooperating heterogeneous access systems via horizontal handover within the same access system and vertical handover between different access systems for seamless access (see Figure 1.2). The access systems will be integrated on

Figure 1.1 The multisphere level concept: IMT-Advanced will cover different communication relations. (Reproduced with kind permission of Springer Science and Business Media © 2008).

Introduction

5

Services and Applications New Radio Interface download channel

return channel e.g. cellular

Digital Broadcast

IP based IP-based Core Network

Cellular 2nd gen. gen.

IMT-2000

xDSL Wireline xDSL WLAN WLAN WLAN type type

other entities Short Range Short-Range Connectivity

Figure 1.2 ITU-R vision for IMT-Advanced. (Reproduced by Permission of IEEE © 2009).

an IP-based core network platform. This overall vision will integrate wide-area and short-range systems as well as public licensed and unlicensed systems, which complement each other. The scope of this book is the new radio interface in Figure 1.2. It should support the following basic generic capabilities:

r New mobile access: Peak aggregate useful data rate up to approximately 100 Mbps and a mobile speed up to 250 km/h.

r New nomadic/local area wireless access: Peak aggregate useful data rate up to approximately 1 Gbps. These targets are summarised in a ‘van-diagram’ in terms of data rate versus mobility (see Figure 1.3). The international specification bodies 3GPP and 3GPP2 are already extending the capabilities of 3G systems towards higher peak data rates and lower latency (e.g. in the UMTS family: High Speed Downlink Packet Access (HSDPA); High Speed Uplink Packet Access (HSUPA); and LTE), which corresponds to enhanced IMT-2000. Supporting such capabilities with sufficient average cell throughput and cell edge capacity will require wider carrier bandwidth than today’s systems [Moh03b]. Such wider bandwidth may only be available in frequency bands above the bands for current systems. WRC 2007 identified additional frequency spectrum in different bands including the UHF band below 1 GHz and in the C band around 3.5 GHz. At higher frequency bands and for significantly higher data rates than today’s systems, the covered range will be smaller [MLM02]. In order to provide economic coverage under these conditions, new deployment schemes such as relay-based or multihop systems are getting much attention in the research community. The additional frequency spectrum will most probably not be available exclusively for mobile and wireless communications and sharing scenarios will play an important role.

6

Radio Technologies and Concepts for IMT-Advanced

Systems beyond IMT-2000 will encompass the capabilities of previous systems

Mobility

New capabilities of systems beyond IMT-2000

High

IMT-2000

Enhanced IMT-2000

New Mobile Access

Dashed line indicates that the exact data rates associated with systems beyond IMT-2000 are not yet determined.

Enhancement

New Nomadic / Local Area Wireless Access

Low

1

KEY:

10 1 00 Peak Useful Data Rate (Mb/s)

1 000

denotes interconnection between systems via networks, which allows flexible use in any environment without making users aware of constituent systems. Nomadic / Local Area Access Systems

Digital Broadcast Systems

Dark shading indicates existing capabilities; medium shading indicates enhancements to IMT-2000; and the lighter shading indicates new capabilities of systems beyond IMT-2000. Low mobility covers the speed pedestrian and high mobility covers speed highways or fast trains (60km/h to 250>km/h, or more).

Figure 1.3 The capabilities of IMT-2000 and systems beyond IMT-2000 [ITU03]. (Reproduced with kind permission of ITU © 2009).

1.4 Global Research Activities There are two trends of global research activities on future radio access systems [Moh08]. Research activities in Asian countries (China, Japan, Korea (CJK)) are mainly following the ITU-R Recommendation M.1645 [ITU03] with respect to Figures 1.2 and 1.3. In Europe and in telecommunication-oriented manufacturers in North America, research is based on requirements from ITU-R and bodies such as 3GPP. The global wireless IT sector is supporting application-specific radio access systems (see Section 1.1), e.g. in the context of IEEE standardisation [IEEEIO; IEEESG], which are directly connected to the Internet. Also the wireless IT sector is working towards ITU-R requirements in M.1645. The Chinese government has launched the research framework program 863. Future mobile communication systems are investigated in the FuTURE project. The concepts and results of this project are discussed with international partners in the FuTURE Forum [FF]. The system concept is based on distributed radio systems and antennas using radio over fibre in order to

Introduction

7

improve system capacity and coverage. The Chinese research community and industry are cooperating closely. In Japan, the activities are coordinated in the mobile IT Forum [mITF] and Advanced Wireless Communications Study Committee in the context of the Association of Radio Industry and Business [ARIB]. The concepts and requirements are summarised in reports [mITF03; mITF04; mITF05a; mITF05b]. The mITF reference model combines radio access as a heterogeneous environment of different systems, the packet-based core network, the service support platform and, on top of that, services and applications. The different radio access systems cooperate via the core network. This reference model is very similar to the ITU-R Recommendation M.1645 [ITU03]. Korea is following a similar approach to that of Japan. The Next Generation Mobile Committee [NGMC] is coordinating the national activities. Basic research activities are performed by the Electronics and Telecommunications Research Institute (ETRI) [ETRI]. There are different evolution and migration paths from Personal Area Networks (PANs), WLAN-type systems, wireless local loops (WWLs), Intelligent Traffic Systems (ITS), cellular and broadcast systems [Moh08]. In the different paths, the systems are evolving towards higher throughput values. The integration of these different systems results in the IMT-Advanced system similar to [ITU03]. In parallel, Korea is actively supporting the approach of the wireless IT sector based on IEEE802.16 as an intermediate step towards IMT-Advanced. China, Japan and Korea are cooperating at government level and between standards development organisations (SDOs) towards common objectives in the CJK initiative. In Europe, the European Commission addressed the area of systems beyond 3G in the Work Plan of Framework Programme 6, which is continuing in Framework Programme 7 [ECC]. Several complementary and cooperating projects have been launched in this domain (see Section 1.5). These European Commission Framework Programmes are complemented by related national programmes and projects in different Member States and Eureka Clusters at the European level [EUC], where nationally funded projects are cooperating. The global wireless IT sector is supporting systems for different radio access scenarios. The main activity is in the IEEE standardisation process [IEEEIO; IEEESG], where the following systems are currently under consideration:

r IEEE802.11 series for WLAN applications; r IEEE802.15 series for short-range communications; r IEEE802.16 series for fixed wireless access with a roadmap towards mobility; r IEEE802.20 series for cellular applications; r IEEE802.21 for interworking issues; r IEEE802.22 for wireless regional area networks. In the USA, significant effort is spent on software defined and cognitive radio systems for future applications and a different use of the frequency spectrum. The main research activities have been launched by the Defense Advanced Research Projects Agency (DARPA) in the neXt Generation (XG) communications program [DARPA] and the National Science Foundation (NSF) [NSF] mainly for the academic domain. In this global environment, the WINNER project in Framework Programme 6 of the European Commission was launched as an international consortium to develop a system concept including key algorithms, protocols and an architecture that support the ITU-R generic

8

Radio Technologies and Concepts for IMT-Advanced

requirements and ongoing developments in the global regulatory and standardisation domain [WIN].

1.5 WINNER Project The WINNER project was launched at the beginning of Framework Programme 6 of the European Commission. This project was organised in two phases, from January 2004 to December 2005 (WINNER I) and from January 2006 to December 2007 (WINNER II). This project was the response to the request from ITU-R to the research community to investigate the feasibility of generic requirements for IMT-Advanced. Major research challenges were derived from these requirements and other global research activities on IMT-Advanced (see Section 1.4). IMT-Advanced should provide significantly higher throughput than available systems. The related greater carrier bandwidth may only be available at higher carrier frequencies, which both reduces the available range and coverage [Moh03b; MLM02]. Therefore, finding economic methods of deployment for future systems was a major research challenge. With respect to the limited frequency bands, a high average cell spectral efficiency and cell edge capacity are important requirements. Link level procedures, advanced antenna concepts, resource management schemes, interference mitigation techniques, efficient protocols, and spectrum usage and sharing technologies are essential for efficient use of the available frequency spectrum. Only the best combination of these different technologies and their optimisation can ensure meeting the requirements. Therefore, system design and optimisation at the system level, taking into account the radio network in addition to the link level, is necessary. The overall objectives of the WINNER project can be summarised as follows [Moh07]:

r To develop a ubiquitous scalable radio access system based on common radio access technologies with enhanced capabilities, compared to existing systems and their evolutions, that will adapt to and be driven by user needs and a comprehensive range of short-range to wide-area mobile communication scenarios. This should be achieved by utilising advanced and flexible network topologies, physical layer technologies and frequency sharing methods, where the different scenarios are addressed by optimised parameter settings. r To base the design of the radio system on a horizontal integration for different radio environments and spectrum conditions in terms of frequency range and carrier bandwidth with respect to the availability of frequency spectrum. r To make efficient use of the radio spectrum in order to minimise the cost per bit. r To define the system in such a way that it can be realised through cost competitive infrastructure and terminals. All relevant areas of the radio interface have been investigated and concepts, algorithms and protocols have been developed. Major achievements of the project are in the following areas:

r the overall system concept and architecture including the multiple access system, modulation and coding, scheduling, medium access control (MAC), radio resource management (RRM) and radio link control (RLC);

Introduction

9

r the system architecture, which has a flat architecture, relay logical nodes, a spectrum server, cross-layer optimisation of protocols and a focus on scalability and flexibility;

r development and optimisation of coding schemes, such as LDPCC, DBTC and QC-BLDPC codes, and the link adaptation scheme for frequency-adaptive transmission and channel coding; r link level procedures such as channel estimation, link adaptation, a pilot channel scheme and synchronisation for SISO and MIMO systems; r interference averaging and mitigation techniques including random dynamic channel allocation; r deployment concepts, such as relaying for different radio environments including protocols, radio resource management and cost assessment; r cooperation (mobility management, congestion avoidance control and QoS-based management) between legacy systems and the WINNER concept; r the performance evaluation methodology for extensive system simulation, for optimisation and validation purposes; r channel models (path loss, wideband and MIMO) for system evaluation, contributed to international regulatory and standards bodies; r the spectrum requirements calculation tool, which was adopted by ITU-R for the preparation of WRC 2007 [TW08]; r extensive contributions to the global regulatory and standardisation process in ECC PT1, ITU-R, 3GPP and IEEE on spectrum requirements and spectrum sharing towards the preparation of WRC 2007, on channel modelling and indirectly on 3GPP LTE via project partners; r investigation of implementation issues and complexity. In addition, the project collected user requirements and traffic models. All building blocks are combined and optimised jointly to develop the overall system concept. These activities were carried out for wide-area, metropolitan-area and local-area scenarios. Finally, the project performed trials of key functions of the system concept. The WINNER project was part of a bigger research initiative, the Wireless World Initiative (WWI), where several projects in Framework Programme 6 were cooperating to investigate all major components of the value constellation of future mobile and wireless communications in the sense of end-to-end solutions from a system perspective [MA06]. In addition to the WINNER project, WWI comprised the following projects:

r Ambient Networks (AN): Development of a ‘seamless network’ for heterogeneous networks [NSZ07];

r End-to-End Reconfigurability (E2 R): Investigation of reconfigurability of networks [BE05]; r MobiLife: Looking at services and applications from the end user and terminal perspective [Kle07];

r Service Platform for Innovative Communication Environment (SPICE): Development of service platform concepts [MSK06].

1.6 Future Work The major activities in international standardisation (e.g. 3GPP and IEEE), the regulatory process in ITU-R, and WRC 2007 and its implementation are shown in Figure 1.4. International

10

Radio Technologies and Concepts for IMT-Advanced

HSDPA, start of deployment 3G evolution (LTE, NGMN) 3GPP - 3G evolution

HSUPA

IEEE802.11n / 16 / (WiMAX, WiBro in Korea) start of deployment IEEE and related activities Deployment Spectrum Implementation WRC 2007

Standardisation

Regulation (ITU-R Framework Recommendation) WINNER WINNER+ WINNER2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Figure 1.4 Timeline of activities.

bodies are now preparing themselves for the forthcoming standardisation process based on the conditions that were set by WRC 2007. The WINNER project contributed to the process in the pre-standardisation phase towards WRC 2007. In this phase, the research work was based on assumptions about the potential outcome of WRC 2007. After WRC 2007, the system concept has to be adapted. For example, the identified frequency bands in WRC 2007 will most probably not be available for exclusive use for mobile and wireless communications. The identified frequency bands provide constraints on the potential carrier bandwidth for IMT-Advanced in order to allow competition between different operators. Therefore, spectrum sharing concepts for the efficient common use of the spectrum will be developed. In addition, further requirements on increased spectral efficiency were raised with respect to the limited available frequency spectrum and bandwidth of identified bands (e.g. in [NGMN]). In parallel with the forthcoming standardisation process, the necessary research activities for adapting and optimising the system concept are continued in the European Eureka Celtic project WINNER+ [WIN+]. These developments and conditions result in the objectives of WINNER+, which are summarised as follows:

r To research and integrate the system concept and to evaluate innovations in areas with a high potential of exploitation in IMT-Advanced based on the LTE evolution and WINNER.

r To harmonise innovations in the pre-standardisation phase. r To contribute to regulatory and standard organisations technology elements that are suitable to IMT-Advanced.

r To participate in the evaluation of selected technology proposals. r To demonstrate the feasibility of selected key technologies.

References [3GPP] [3GPP2]

3rd Generation Partnership Project, www.3gpp.org. 3rd Generation Partnership Project 2, www.3gpp2.org.

Introduction

[ARIB] [BE05] [CDG08] [DARPA] [ECC] [ETRI] [EUC] [FBB05]

[FF] [GSA08] [Hil02] [HT07] [IEEEIO] [IEEESG] [ITU03] [Kle07] [Lee90] [MA06]

[mITF] [mITF03] [mITF04] [mITF05a] [mITF05b] [MK00] [MLM02]

[Moh03a] [Moh03b] [Moh07] [Moh08] [MSK06]

[NGMC] [NGMN] [NSF] [NSZ07]

11

ARIB, www.arib.or.jp/english/. Bourse, D. and El-Khazen, K. (2005) ‘End-to-End Reconfigurability (E2 R) Research Perspectives’, IEICE Transactions, 88-B(11):4148–57. CDMA Development Group (2007) ‘Subscriber statistics, end of first quarter of 2008’, www.cdg .org/worldwide/cdma world subscriber.asp. Defense Advanced Research Projects Agency, http://www.darpa.mil/sto/smallunitops/ xg.html. European Commission, http://cordis.europa.eu/home en.html. Electronics and Telecommunications Research Institute, www.etri.re.kr/eng/. Eureka Clusters, www.eureka.be/home.do. Forge, S., Blackman, C. and Bohlin, E. (2005) The Demand for Future Mobile Communications Markets and Services in Europe, Technical Report EUR 21673 EN, Institute for Prospective Technological Studies, Sevilla. FuTURE Forum, www.future-forum.org/en/index.asp. GSM Association (2008) GSM/3G Statistics, end of second quarter of 2008, www.gsacom .com/news/statistics.php4. Hillebrand, F. (2002) GSM and UMTS: The creation of global mobile communication, John Wiley & Sons, Ltd, Chichester. Holma, H. and Toskala, A. (2007) HSDPA/HSUPA for UMTS, John Wiley & Sons, Ltd., Chichester. IEEE-ISTO, www.ieee-isto.org. IEEE Standards Groups, http://grouper.ieee.org/groups/802/. ITU-R (2003) Framework and overall objectives of the future development of IMT-2000 and systems beyond IMT-2000, Recommendation M.1645. Klemettinen, M. (2007) Enabling Technologies for Mobile Services: The MobiLife Book, John Wiley & Sons, Ltd, Chichester. Lee, W.C.Y. (1990) Mobile cellular telecommunications systems, McGraw-Hill Book Company, Singapore. Mohr, W. and Aftelak, A. (2006) ‘The Wireless World Initiative: A Collaborative Approach to Creating the Building Blocks for Systems Beyond 3G’, IEEE Vehicular Technology Conference 2006 Fall, September 25–28, Montr´eal, Canada. mobile IT Forum, www.mitf.org/index e.html. mobile IT Forum (2003) Flying Carpet: Towards the 4th Generation Mobile Communications Systems, Version 1.00. mobile IT Forum (2004) Flying Carpet: Towards the 4th Generation Mobile Communications Systems, Version 2.00. mobile IT Forum (2005), Recent Mobile Commerce Trends and Perspective: Open Sesame, Version 1.00. mobile IT Forum (2005), 4G Mobile System Requirements Document, Version 1.1. Mohr, W. and Konh¨auser, W. (2000) ‘Access Network Evolution Beyond Third Generation Mobile Communications’, IEEE Communications Magazine, 38(12):122–33. Mohr, W., L¨uder, R. and M¨ohrmann, K.H. (2002) ‘Data Rate Estimates, Range Calculations and Spectrum Demand for New Elements of Systems Beyond IMT-2000’, IEEE 5th International Symposium on Wireless Personal Multimedia Communications, October 27–30, Honolulu, Hawaii, USA. Mohr, W. (2003) ‘The Wireless World Research Forum’, Computer Communications, 26(1):2–10. Mohr, W. (2003) ‘Spectrum Demand for Systems Beyond IMT-2000 Based on Data Rate Estimates’, Wireless Communications and Mobile Computing, 3(3):1–19. Mohr, W. (2007), ‘The WINNER (Wireless World Initiative New Radio) Project: Development of a Radio Interface for Systems Beyond 3G’, International Journal of Wireless Information Networks, 14(2):67–78. Mohr, W. (2008) ‘Vision for 2020?’, Wireless Personal Communications, 44(1):27–49. Mrohs, B., Steglich, S., Klemettinen, M., Salo, J.T., Aftelak, A., Cordier, C. and Carrez, F. (2006) ‘MobiLife Service Infrastructure and SPICE Architecture Principles’, 64th IEEE Vehicular Technology Conference (VTC-2006 Fall), September 25–28. Next Generation Mobile Committee, http://ngmcforum.org/ngmc2/eng ver/eng 1.html. NGMN Ltd, www.ngmn.org. National Science Foundation, www.nsf.gov/. Niebert, N., Schieder, A., Zander, J. and Hancock, R. (2007) Ambient Networks: Co-operative Mobile Networking for the Wireless World, John Wiley & Sons, Ltd, Chichester.

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[S91] [Taf05] [Taf06] [TW08]

Radio Technologies and Concepts for IMT-Advanced

Scheele, P. (1991) Mobilfunk in Europa, R.v. Decker’s Verlag, G. Schenck GmbH, Heidelberg. Tafazolli, R. (2005) Technologies for the wireless future, John Wiley & Sons, Ltd, Chichester. Tafazolli, R. (2006) Technologies for the wireless future, Volume 2, John Wiley & Sons, Ltd, Chichester. Takagi, H. and Walke, B.H. (2008) Spectrum requirement planning in wireless communications, John Wiley & Sons, Ltd, Chichester. [UMTS05] UMTS Forum (2005) Magic Mobile Future 2010–2020, Report No. 37, www.umts-forum.org/. [Vit95] Viterbi, A.J. (1995) CDMA: Principles of spread spectrum communication, Addison-Wesley Publishing Company, Reading, MA. [Wal02] Walke, B.H. (2002) Mobile radio networks, 2nd Edition, John Wiley & Sons, Ltd, Chichester. [Wal07] Walker, M. (2007) ‘Mobile Broadband: A 2020 Vision’, WWRF meeting #19, Chennai, India, November 5–7. [WIMAX] WiMAX Forum, www.wimaxforum.org/home/. [WIN] WINNER project, www.ist-winner.org/. [WIN+] WINNER+ project, www.celtic-initiative.org/Projects/WINNER+/abstractwinner+.asp. [WWRF] Wireless World Research Forum, www.wireless-world-research.org/.

2 Usage Scenarios and Technical Requirements Marc Werner1 and Paulo Jesus2 1 2

Qualcomm CDMA Technologies GmbH Portugal Telecom Inovac¸a˜ o

2.1 Introduction This chapter provides an overview of the scenario approach which was adopted to describe the different conditions in which the WINNER radio access networks (RAN) will be deployed and used. Usage scenarios, traffic models and service characteristics motivate the derivation of technical system requirements which are presented in this chapter. These requirements served as a backbone in the design of the WINNER system. As part of the user-centric paradigm that was followed in the WINNER system design, the relevant services were used to form service classes that encompass groups of important applications, taking into account the technical requirements (e.g. throughput, tolerable delays, quality of service (QoS), and mobility requirements) as well as the contextual aspect of the applications. By defining user and usage scenarios, the derivation of service requirements determines technological requirements for the system design. Models for predicting the acceptance and penetration of the WINNER technology in the end-user market can then be made.

2.2 Key Scenario Elements The WINNER system design is governed by the paradigm of communication “any time, anywhere, and with anyone”. While the services offered by the WINNER system will thus be offered ubiquitously, the individual user scenario and context (e.g., age, income, lifestyle, changing professional and personal roles over time, location, practical and social context, terminal type available) will influence the concrete form and mixture of services used. Similarly, usage scenarios can be formulated for a particular service with typical conditions Radio Technologies and Concepts for IMT-Advanced Martin D¨ottling, Werner Mohr and Afif Osseiran C 2009 Martin D¨ ottling, Werner Mohr, Afif Osseiran

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Radio Technologies and Concepts for IMT-Advanced

Table 2.1 WINNER scenario types. Key Question User Scenario

Usage Scenario

Location Scenario

Traffic Load Scenario

Deployment Scenario

Which services are needed by a user in a particular role and context? How is a particular service typically used? Which service mix and usage pattern results for a user population in a particular location? Which user distribution, density, and absolute traffic load are generated in a location? Which radio environment dominates and which technical solutions are deployed in a particular location?

Applicability

Averaging

Results

Single user

Instantaneous examples

Services per user type, role and context

Single service and device type Single location

Averaged over time

Typical usage conditions of a service Service mix, mobility, user distribution

Single location

Averaged over time and users

User density, traffic load

Single location

Averaged over time and users

Topology, radio environment, deployment parameters

Averaged over time and users

and environments. Location scenarios go along with a typical user distribution, mobility, and service mix. During the day, different traffic load scenarios are encountered in such a location scenario. Finally, a deployment scenario includes a particular location and traffic load scenario, as well as radio assumptions on the technical solutions that are deployed. Table 2.1 summarises the definition and relationship between the scenario types in the WINNER context. Different user profiles and service demands imply different usage percentages per service or application. The WINNER system concept supports a variety of services, with different requirements, and the end-to-end QoS should be negotiable and controllable. The association of the different scenario elements identifies a certain scenario type. A key scenario can be defined based on user, usage and location scenarios. Key scenarios focus on challenging requirements, e.g. when the defined service classes can be delivered only by technological innovations. Key scenarios are formed by the association of key scenario elements (environment type, coverage range, terminal type, user density and user profile), which can help in the definition of challenging technical requirements, e.g. a minimum cell throughput in a certain service usage, location and deployment context. The following sections list the values that each element can take. In addition to these sections, the following user profiles are considered: old, young, business, tourist, sport.

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15

2.2.1 Environment Type and Coverage Range The environment type is one of the most important elements in the key scenario characterisation. It is related to the physical characteristics of the environment and radio propagation properties:

r dense urban; r metropolitan typical urban; r bad urban; r metropolitan suburban; r rural; r indoor small (office/residential/commercial zones); r indoor to outdoor; r outdoor to indoor; r line of sight (LOS) – stationary feeder; r LOS – feeder; r Rural LOS – moving networks. Different environment types have been defined within the WINNER deployment scenarios (see Section Table 2.5), and are associated with coverage and cell types:

r in building (pico-cell); r hotspot (micro-cell); r rural (micro-cell); r rural (macro-cell); r urban (micro-cell); r urban (macro-cell); r umbrella. Specific environments within Local Area deployment scenarios (office, residential or commercial) are characterised mainly by low terminal mobility, short-range coverage (pico or micro-cells), high density of users and high data-rate applications. Metropolitan Area deployment environments (urban and suburban) normally require ubiquitous coverage (micro or macro-cells) and are characterised by medium–high density of traffic with medium terminal velocity. Wide Area deployment environments are characterised by a continuous and ubiquitous coverage (normally macro-cells), medium–high terminal velocity and a large proportion of low data-rate applications, mainly based on voice.

2.2.2 Terminal Type Different terminal types are envisaged in WINNER key scenarios in order to satisfy end-user requirements and to make use of all available services and applications. The terminal types can be derived from a set of physical characteristics and capabilities such as display size, portability (size and weight), power consumption, maximum transmission power (related to link budget and cell range):

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Radio Technologies and Concepts for IMT-Advanced

Table 2.2 User density and traffic parameters for CBR, ABR and UBR applications with multiple session attempts per day in specific environments. (Reproduced by Permission of European Commission, Joint Research Centre © 2005) Deployment scenario Local area

Metropolitan area Wide area

Environment type Indoor small (residential) or dense urban Indoor small (office) or dense urban Indoor small (residential) or suburban Typical urban Suburban Rural

User density (users/km2 )

Number of session attempts per day

Average session duration Dn,m (s)

26 400

5

120

30 000

10

150

6600

6

120

11 000 2000 200

3 10 6

100 120 120

r wearable terminal; r palmtop (PDA); r mobile phone; r laptop; r desktop.

2.2.3 User Density and Traffic Parameters User density depends highly on the environment type and can be defined by service or application type. The most demanding applications experience typical coverage limitations. Table 2.2 characterises the user density and traffic parameters for applications that are likely to have several session attempts per day: constant bit rate (CBR) voice applications, available bit rate (ABR) applications such as VoIP and SMS, and unspecified bit rate (UBR) MMS applications. Table 2.3 characterises the user density and traffic parameters for applications that are likely to have only one session attempt per day: CBR video interactive mobile TV applications, variable bit rate (VBR) applications such as telemedicine, streamed video (sports and events) and web radio, ABR applications such as video streaming, M-commerce and M-banking and UBR e-mail applications.

2.2.4 User Mobility User (or terminal) mobility plays an important role as a key scenario element, since system performance is directly affected in terms of radio resource management, traffic handling, location and QoS management. User mobility requirements have also to be taken into account in system design and architecture.

Usage Scenarios and Technical Requirements

17

Table 2.3 User density and traffic parameters for CBR, VBR, ABR and UBR applications with only one session attempt per day in specific environments. (Reproduced by Permission of European Commission, Joint Research Centre © 2005) Deployment scenario Local area

Metropolitan area Wide area

Environment type

User density (users/km2 )

Average session duration Dn,m (s)

2640

180

15 000

600

4400

180

1100 500 50

120 600 120

Indoor small (residential) or dense urban Indoor small (office) or dense urban Indoor small (residential) or suburban Typical urban Suburban Rural

Table 2.4 presents examples of the mobility ratio in different deployment scenarios and environment types [FBB05] of applications that are likely to have several session attempts per day: constant bit rate (CBR) voice applications, available bit rate (ABR) applications such as VoIP and SMS, and unspecified bit rate (UBR) MMS applications. Applications that are likely to have only one session attempt per day (such as CBR video interactive mobile TV applications, variable bit rate (VBR) applications such as telemedicine,

Table 2.4 Mobility ratio for CBR, ABR and UBR applications in specific environments. (Reproduced by Permission of European Commission, Joint Research Centre © 2005) Deployment scenario Local areaa

Mobility ratio (%) Environment type

0–10 km/h

10–100 km/h

100–250 km/h

>250 km/h

Indoor small (residential) or dense urban Indoor small (office) or dense urban Indoor small (residential) or suburban Typical urban

80

20

0

0

80

20

0

0

80

20

0

0

70

20

10

0

60 70

20 20

20 5

0 5

Metropolitan area Wide area Suburban Rural a

These values do not refer to indoor environments exclusively, but to indoor environments in dense urban and suburban areas [FBB05].

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Radio Technologies and Concepts for IMT-Advanced

Table 2.5 WINNER mobility models. Mobility model

Topology

Indoor small (office) environment Hotspot environment

‘Boxy’ – office rooms interconnected by corridors Unrestricted, within confined space

Outdoor to indoor (dense urban) Vehicular environment (typical urban)

Manhattan grid

Rural environment

Unrestricted

Urban and suburban areas outside the city centres, larger macro cells

Movement Stationary; low velocity Low velocity (less than 3 km/h) and stationary with a high probability of changing the direction of movement Low velocity (less than 3 km/h) and stationary Pseudorandom mobility model with semidirectional trajectories; medium to high velocities (50–100 km/h) Pseudorandom mobility model with semidirectional trajectories; high velocities (over 120 km/h)

streamed video (sports and events) and web radio, ABR applications such as video streaming, M-commerce and M-banking and UBR e-mail applications) have similar mobility characteristics (80 % at 0–10 km/h and 20 % at 10–100 km/h) regardless of the environment. Mobility models describe typical terminal movement. Many link and system-level simulators require knowledge of the terminal position in each simulated time step so that the effectiveness of studied techniques such as link adaptation, transmission diversity and beamforming may be evaluated. The WINNER project defined the mobility models listed in Table 2.5. More details can be found in [WIN2D6112].

2.2.5 Deployment Scenarios This section provides a high-level description of the deployment scenarios that were chosen as examples for dedicated system design and performance evaluations of the WINNER system. Further detail on the deployment and parameter assumptions are given in Chapter 13.

2.2.5.1 Wide Area The Wide Area (WA) scenario is meant to reflect the ubiquitous coverage deployment of WINNER in towns and cities, where it will overlap with deployments considered by the metropolitan and local area scenarios. This is similar to today’s macro-cell deployments. Ubiquitous coverage means that users should be able to use mobile communication services whilst on the move. At present, only aircraft are exempt from this expectation. It is assumed that services are provided to users in cars, buses, trains, etc. The most challenging of these

Usage Scenarios and Technical Requirements

19

transportation types is high-speed trains, which may reach speeds of up to 350 km/h. Where relaying is considered as a solution for this case, the in-train deployment is likely to be very similar to the solutions for a single-hop deployment (see Section 2.2.5.3). In the WA context, relaying is important for the communication from the base station (BS) to the train.

2.2.5.2 Metropolitan Area The Metropolitan Area (MA) scenario addresses the WINNER system design and assessment in urban and metropolitan areas where the user density and expected throughput requirements are high. The WINNER system must provide high system capacity in these environments. The MA deployment scenario provides contiguous coverage in urban areas and especially in the centres of large and medium-sized cities. Solutions that enhance system throughput will be prioritised over solutions that provide coverage. Based on these requirements, the Metropolitan Area deployment scenario needs to focus on micro-cellular deployments. In urban environments, base stations and relays are placed clearly below the rooftop level (in suburban deployments, the antenna placement can be above rooftop level). While the micro-cellular deployment helps in reaching the system throughput requirements, it is challenging from the radio propagation perspective. The requirement to support high user density means that MA deployments support outdoor hotspots (the Local Area scenario addresses indoor hotspots), the use of small cells, efficient multi-antenna techniques, and smart interference mitigation and avoidance schemes in order to provide the high system throughput. A basic assumption in the Metropolitan Area deployment scenario is that the deployment is realised outdoors, but outdoor-to-indoor coverage is addressed as well.

2.2.5.3 Local Area The two main environmental settings dominating the Local Area (LA) deployment scenario are the indoor environment and hotspot areas. The user mobility considered within such environments remains low (0–5 km/h), corresponding to a low coverage range (3–100 m). In the case of indoor residential areas, the main assumption is that the wireless link will be provided through a radio access point (RAP) connected to a backbone network. The indoor residential area is considered to be well isolated from neighbouring RAPs, so that there are no interference issues. Therefore, in case of home access, the main implications of the LA scenario relate to:

r deployment of efficient self-organisation schemes in order to allow cost-efficient deployment of many RAPs and to create provision for direct communication in a P2P mode, between WINNER capable terminals within the users’ residences; r efficient hand-over between WINNER deployments so as to allow for user mobility from one coverage area (home) to another (urban); r connectivity provisioning between home RAPs and the WINNER backbone.

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Radio Technologies and Concepts for IMT-Advanced

In the hotspot LA scenario, the main high-level assumptions are that this mode will cover larger areas than the ones encompassed in the residential usage and will need more than one RAP, therefore the assumption of limited interference may no longer be valid.

2.3 Service Classes and Service Requirements 2.3.1 Overview of Beyond-3G Applications Table 2.6 represents a list of service classes targeted by the WINNER platform and typical applications that fall into the scope of the respective classes. The typical target data rates, endto-end delays, and bit error rates (BER) have been derived from a user perspective and describe the desired performance at application layer. Because the higher-layer functions influencing these performance figures are out of scope for the definition of a radio access network, these values can only be regarded as guidelines for the derivation of requirements on lower layers. A similar classification of services based on data rate and delay requirements is used by ITU-R, see Chapter 12. All WINNER deployments are generally required to support a set of key services, e.g. real-time applications such as virtual reality and VoIP, and location-based, broadcast/multicast and emergency services.

2.3.2 Requirements for Service Provisioning Services are heterogeneous in nature (that is, they operate with different contents, QoS, delay, data rates, etc.). In fact, because of the heterogeneity of the network, the same service can be provided with different parameters according to a specific location. Certain requirements apply in the context of service provisioning [WIN2D6114]:

r network reconfigurability, content adaptation, and context awareness; r personalisation; r physical context information; r service discovery.

2.3.3 Mapping of Service Requirements to RAN Requirements As mentioned before, the service-level requirements represent the user perspective of a mobile communication system. Because the respective protocol layers that are responsible for user interaction were not in the focus of WINNER, the user-centric requirements need to be transformed into the lower-layer requirements presented in the following sections.

2.3.4 Traffic Models This section describes briefly the most important traffic models for applications and relevant statistical parameters, such as session arrival rate, session duration, packet call size, time between packets calls, etc., that are used as inputs for system-level simulations. For further details (in particular, traffic model parameters), the reader is referred to [WIN2D6137].

Usage Scenarios and Technical Requirements

21

Table 2.6 Service classes and associated applications. Service Class Real-time collaboration and gaming

Applications

Telepresence Videoconference Collaborative work Navigation systems Real-time gaming Geographic real-time datacast Real-time video streaming Collaborative work Short control messages and Alarms signalling Remote control Sensors Presence-driven transfer (lightweight content) Simple interactive applications Presence-driven transfer (heavy content) Interactive geographical maps (remote processing) High-quality interactive Rich data call multimedia Control Video broadcasting or streaming Geographic interactive Video broadcasting multimedia broadcast Streaming Localised map download Interactive ultra-high-quality High-quality video multimedia conference Collaborative work Simple telephony and Voice telephony messaging Instant messaging Lightweight multiplayer games Data and media telephony Audio streaming Video telephony (medium quality) Multiplayer games (high quality)

Data Rate

Delay

BER −6

1– 20 Mbps

highly interactive (<20 ms)

10 –10−9

2–5 Mbps

highly interactive (<20 ms)

10−6

8–64 Kbps

interactive/control (20–100 ms)

10−9

64–512 Kbps

interactive/control (20–100 ms)

10−6

2–5 Mbps

interactive/control (20–100 ms)

10−6

2–5 Mbps

interactive/control (20–100 ms)

10−6

10–50 Mbps

interactive/control (20–100 ms)

10−3 –10−6

8–64 Kbps

conversational (100–200 ms)

10−3 –10−6

64–512 Kbps

conversational (100–200 ms)

10−3 –10−6

(continued)

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Radio Technologies and Concepts for IMT-Advanced

Table 2.6 (Continued) Service Class Geographic datacast

Applications

Localised datacast or beacons Audio streaming Rich data and media telephony High-quality video telephony Collaborative work Standard data call LAN access and file services Access to databases Filesystems Multimedia messaging Messaging (data, voice or media) Web browsing (lightweight) Lightweight browsing Messaging (data, voice or media) (medium weight) Access to corporate database Audio on demand Web browsing Internet radio File exchange Access to databases (heavy weight) Filesystems Video download and upload Peer-to-peer file sharing Video streaming Video streaming (normal) High-quality video streaming Video streaming (archival) Large files exchange FTP (data)

Data Rate

Delay

BER

64–512 Kbps

conversational (100–200 ms)

10 –10−6

2–5 Mbps

conversational (100–200 ms)

10−3 –10−6

Up to 50 Mbps 8–64 Kbps

conversational (100–200 ms) few seconds (>200 ms)

10−6

64–512 Kbps

few seconds (>200 ms)

10−6

Up to 5 Mbps few seconds (>200 ms)

10−6

5 Mbps

10−6

Up to 30 Mbps Up to 50 Mbps

few seconds (>200 ms) few seconds (>200 ms) few seconds (>200 ms)

−3

10−6 –10−9

10−9 10−6

2.3.4.1 Internet Applications Internet and multimedia traffic can be characterised by frequent transitions between ON and OFF periods, active and inactive states. The ON period corresponds to the file-downloading period and the OFF period corresponds to the user-reading time. In a circuit-switched network, the dedicated bandwidth is wasted during the OFF period. However, packet-switched technology allows higher data transmission rates and uses the bandwidth only during ON periods. Table 2.7 summarises the statistical parameters of various types of Internet traffic.

Usage Scenarios and Technical Requirements

23

Table 2.7 Internet traffic model parameters. (Reproduced by Permission of IEEE © 2009) Application

Traffic Parameters and Distributions

Web browsing (HTTP)

Main object size (SM): Truncated Log-normal Embedded object size (SE): Truncated Log-normal Number of embedded objects per page (Nd): Truncated Pareto Reading time (Dpc): Exponential Parsing time (Tp): Exponential E-Mail Packet inter-arrival time: Poisson ON period length: Weibull OFF period length: Pareto E-mail attachment upload file size: Truncated Log-normal Instant Messaging for Multimedia (IMM) Session inter-arrival time: Exponential Session duration: Exponential Packet call size: Weibull Inactivity time: Pareto

2.3.4.2 Voice over IP Voice over Internet Protocol (VoIP) has emerged as a significant enabling technology and the adoption of industry standards has accelerated its deployment. VoIP technology is generating wide interest across several markets [Amd05]. There is now growing interest in delivering VoIP services over a range of wireless technologies, including 3G, WLAN, WiMAX and systems beyond 3G. VoIP applications require timely packet delivery with low latency, jitter and packet loss values. Three parameters emerge as the primary factors affecting voice quality within networks that offer VoIP technologies: clarity, end-to-end delay and echo. To support interactive voice applications on an Internet protocol (IP) network, four QoS categories must be controlled: bandwidth, latency, Laplacian-distributed jitter and packet loss [KlK01]. A VoIP source file with delay jitter applied is available as [3GPP206b]. 2.3.4.3 Video Telephony Video telephony is full-duplex, real-time, audio-visual communication between or among end users. The concept of video telephony has been available for more than 50 years, but only recently has it come to fruition. The primary challenge facing developers of video telephony is that full-motion, high-resolution video data requires far more bandwidth than audio data. Video telephony is an important but complex service and operators are working hard to promote the flagship service. Two source files are specified for typical video telephony traffic, one for audio and one for video [3GPP206a]. 2.3.4.4 Streaming Streaming applications have constantly been gaining ground in terms of popularity, mainly due to the abundance of bandwidth and hardware sophistication the end-user is experiencing.

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Radio Technologies and Concepts for IMT-Advanced

A simplistic video model represents self-similar video traffic with a local Hurst parameter ranging from 0.73 to 0.93, which is true of MPEG video at a rate of 25 fps [IEEE01]. Each video source in this framework is represented by a superposition of two interrupted renewal processes (IRP). The sojourn time in both (ON and OFF) states is Pareto distributed. Recent work [LaH01] on modelling of RealAudio, which is the most popular format for streaming audio applications, has indicated the multi-scale variance of this type of traffic. More specifically, in scales of tens of seconds, a single streaming audio flow has a constant rate while in smaller scales it behaves like a bursty ON–OFF source, with the OFF periods appearing in multiples of approximately 1.8 seconds; this bursty behaviour is also noticed in aggregate streaming audio flows. Other important characteristics incorporate the approximately fixed packet size and the strong correlation of flow requests with the time of the day. The main assumptions of the streaming audio model are the Pareto-distributed session duration and an exponentially distributed session inter-arrival time.

2.3.4.5 File Transfer [3GPP204] proposes a straightforward file transfer (FTP) traffic model. It incorporates two main parameters that describe the behaviour of an FTP transaction, namely the exponentially distributed reading time (the time between successive downloads of the same user) and the truncated log-normal distribution of the file size to be transferred.

2.3.4.6 Interactive Applications Interactive applications comprise a big part of current and forecast data exchanges. The popularity of online entertainment applications such as Internet based gaming is increasing at a fast pace. Other applications include telepresence, telesurgery and e-learning services. The topic of Internet game traffic modelling is relatively new and literature on this issue is scarce. However, Borella has done some important work on source models of network game traffic [Bor00] and Farber on the validation of the proposed traffic model on new, evolved and more demanding versions of games [Far02]. For a mathematical description of the distribution functions for inter-arrival time or packet size, it is necessary to find a function of similar shape and fit its parameters to empirical data. [Bor00] identified the Extreme Value distribution to fit best for specific game traffic and this function was also chosen in [Far02]. Functions similar to shifted log-normal or shifted Weibull also lead to acceptable fits.

2.4 Requirements for System Capabilities The WINNER concept offers significantly enhanced system capabilities compared to 3G systems. Additionally, basic capabilities fulfilled by legacy systems and relevant to the stakeholders have to be maintained. In this section, requirements are formulated that describe the minimum WINNER system capabilities. The requirements reproduced from the official WINNER requirements [WIN2D6114] are shown in a box.

Usage Scenarios and Technical Requirements

25

A fundamental requirement of a cellular system is its autonomy: WINNER shall be self-contained, allowing it to target the chosen requirements without the need for inter-working with other systems.

2.4.1 Generalised Mobility Support within WINNER A mobile radio system should be able to provide the user with a continuous connection regardless of the user’s location or movement from one cell to another. This support for mobility is one of the key features of a mobile system. In the WINNER system, where different deployment modes will operate together, it can support users moving from one operational mode to another or even to a different network, without losing their connection, by performing handovers. In WINNER there will be different types of handover, depending on whether the user is changing frequency, cell, mode, or network. In order to initiate a handover, many types of trigger are defined. The technical solutions to address these mobility requirements are considered in Chapters 4 and 10. The handover process shall respect and take into account the user’s service requirements especially in terms of delay, so the handover will not be noticeable at the application level. WINNER shall be able to provide the user terminal a list of neighbouring cells for which it should perform measurements in order to improve the handover process. These cells could be operating in a different mode or another network. WINNER shall be able to support not only radio but also IP handover for the user. Both radio and IP handovers shall be seamless and not noticeable by the end user. WINNER shall be able to support seamless intra-deployment handover of user sessions (or flows) inside the same RAP or between RAPs of one deployment mode (LA, MA or WA). WINNER shall be able to route each flow individually through the available deployments. This must be done taking into account the flow’s QoS requirements, the user’s preferences, and the capabilities, advantages and disadvantages of each deployment. WINNER shall support seamless handover of any individual flow between cells of different deployments. WINNER shall support resource management between deployments, including handover and load balancing between base stations of different deployments.

2.4.2 Generalised Mobility Support between WINNER and Legacy Networks The WINNER system shall provide an interface that supports cooperation with legacy systems in order to provide efficient interworking between WINNER radio access technologies (RATs) and legacy RATs. These RATs could belong to the same or

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Radio Technologies and Concepts for IMT-Advanced

different operators. This interface shall also support the seamless handover between WINNER and legacy networks.

2.4.3 Measurement Requirements for the WINNER System Measurements are essential inputs for radio resource management (RRM) algorithms and therefore mechanisms to configure, perform and report measurements must be defined for the WINNER system. Measurements affect the design of the PHY, MAC and RLC (or RRC) layers, from the physical procurement of the measurements to the transport of the measurements to the logical entities that need this information, including the definition of the protocols for the transport of the information. The WINNER system shall provide the RRM (or cooperative RRM) entity with a set of measurements for handover and other RRM functionalities. These measurements may include:

r The received signal strength, interference level and carrier-to-interference (C/I) ratio. These must allow a conclusion on the reception quality of the current flows and the possibility (or necessity) of doing a handover to other cell or radio access technology. In WINNER, these measurements will be based on the uplink (UL) and downlink (DL) synchronisation pilots (performed by terminals (UT), base stations (BS) and relay nodes (RN)), not only in the WINNER RAN but also on legacy RANs, when necessary. Three types of measurement should be available: intra-frequency, inter-frequency and inter-system (which should be performed by WINNER multi-system terminals). r The transmitted power setting at a precise moment. Path loss measurements can also be measured as the difference between the transmitted power and the received signal strength. For WINNER, this measurement should be performed by UT, BS and RN. r Quality measurements. These must allow statements on the quality perceived by the UT and RAN and comparison with the required quality. So it is necessary to do some measurements based on users’ flows in order to determine QoS levels and compare them with thresholds. QoS indicators could be the block error rate (BLER), the retransmitted block rate and the bit rate at different layers. r The cell load, which corresponds to the difference between available and used resources. Cell load can be measured at different levels, e.g., transmitted radio power (PHY layer), number of used chunks (MAC user plane), etc. r Terminal velocity and location. As a minimum requirement, the system should know the serving BS to which the UT is attached. To estimate the coverage area of the serving BS, a more detailed position should be determined using received signal strength measurements or satellite measurements (e.g. GPS). r A list of neighbouring base stations and relay nodes. During the inter-system handover from a legacy system to a WINNER cell, the target WINNER deployment mode and cell for the user performing the handover should be found. The selection should be based on the parameters that the current deployment mode or RAT

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and possible target deployment mode or RATs can offer to the terminal. Other information elements will be taken into account:

r Network capabilities: The legacy RANs cannot support many of the services that can be offered by networks beyond 3G (B3G), but B3G networks with limited bandwidth can only provide a limited subset of services. Information on network capabilities should be available (data rate, delay, etc). r Terminal capabilities: B3G terminals will be classified by performance (e.g., delay and data rates). It is expected that some terminals will have limited performance to achieve reduced size, longer battery duration, lower cost, etc. r User preferences: The user may select some characteristics, e.g., by pre-selecting the network that offers a lower cost per transmitted bit. r Operator classification of users: The operator could offer different classes of subscription (e.g. bronze, silver and premium) with different levels of performance. r Other high-level parameters: The architecture should be flexible enough to accommodate other information elements. In the WINNER system, UT, BS and RN should perform measurements, and the mechanisms to report these measurements to the UT, RN and admission control server (ACS) should be established. The measurements should be triggered periodically and on demand. The WINNER multi-system terminal should have the possibility of measuring the received signal strength of base stations and access points of legacy systems. Moreover, the RRM entity in WINNER, associated with the BS, should have the possibility of knowing through signalling and when necessary, the cells of legacy systems to which a fast intersystem handover is feasible. Most of the measurements described in the previous section are required not only for WINNER but also for the legacy networks. The possible measurements and signalling parameters include:

r received signal strength, interference level and C/I ratio; r transmitted power; r cell load; r terminal locations and velocities. These figures will be signalled either directly from the legacy network or reported to the WINNER RAN by the terminals. The WINNER system concept shall provide an interface to legacy RATs that supports seamless handover to minimise degradation of communication quality and enables further cooperation mechanisms, e.g., RRM. The WINNER concept shall include a signalling channel to report measurements of other RANs. In order to enable measurements of other systems by a WINNER transceiver, the WINNER MAC frame design shall allow for the reservation of time-frequency resources which are deliberately not used for transmission, but left free for measurements.

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The WINNER concept shall include a signalling channel to notify user terminals about other available RANs.

2.4.4 Support for QoS Mechanisms and Prioritisation of Flows The service classes introduced in Table 2.6 exhibit different target values of performance indicators. These targets are derived from general QoS requirements on the service level, which should be propagated through all layers of the WINNER protocol stack. Packet flows are classified by QoS requirements. The QoS of each flow shall be controlled individually. QoS related to the delivery of a requested application shall be negotiable, including renegotiation during an active session. To facilitate service-motivated QoS support mechanisms, prioritisation of user data flows should be applied to services with tight delay constraints (e.g., voice and interactive multimedia services). Usually, some timestamp information is available for incoming data related to such services (for VoIP, the RTP could be used). WINNER should be able to evaluate such information and to configure the prioritisation of the respective flows so that the data is available at the receiver within delay bounds. On the other hand, if the timestamp indicates that the delay limit has been already exceeded, the packet can be discarded in the network without consuming radio resources. WINNER should be able to signal prioritisation information to the external networks to which its outgoing flows are routed. Prioritisation of user data flows should also be applied to services with data rate constraints (e.g., real-time video streaming). Counters could be implemented at various protocol layers to ensure that the flow’s data rate remains between certain limits. Activated at certain thresholds, prioritisation mechanisms may lead to a rate stabilisation even for an increasing network load. Prioritisation mechanisms should be realised on different protocol layers:

r PHY: transmit power control, modulation and coding format for the subcarriers that carry a prioritised flow;

r MAC: resource scheduling algorithm; r RLC/RRC: RRM mechanisms such as admission control and load control. WINNER shall support prioritisation of flows to facilitate QoS support. Prioritisation mechanisms shall be implemented at all necessary protocol layers. Chapter 4 provides an overview of the QoS and flow class handling and its implementation in the logical node architecture of WINNER.

2.5 Terminal Requirements Today, many different kinds of terminals for mobile communication systems are available. In next generation networks, many more will exist with different sizes, shapes and functionalities.

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29

A wide range of different applications and services implies terminal heterogeneity, i.e., support of terminals that differ in display size, weight and dimensions, energy consumption, and complexity. The communication interface between human user and devices, if required, should be as natural as possible – like human talking or conversation – supporting audiovisual and other natural means of information exchange. Furthermore, high definition and quality video applications provided by broadcast or multicast (e.g., virtual sightseeing) will require, on the terminal, high performance in video processing (i.e. codecs) and display capabilities accompanied by extended autonomy and battery life. WINNER shall support different terminal classes, in order to accommodate terminals with a wide range of complexity, costs, capabilities and form factors. Maximum and average transmit power in terminals must comply with the EMF regulations for the corresponding frequency range. Talk and standby times shall be at least comparable to, and preferably better than, 2G and 3G systems. Power consumption shall be minimised in order to improve battery life. The WINNER system shall be able to make use of antenna diversity or multi-antenna processing techniques at the terminal to improve signal-to-noise ratio, data rates and system capacity. The WINNER system shall be able operate with basic functionality if only one transmit antenna is used at the terminal to save transmit power, complexity and cost. As a consequence, power-saving functionalities have been included in both radio resource control (RRC) states, and frame design includes the possibility for micro-sleep (see Chapters 10 and 9, respectively). The WINNER versatile multi-antenna processing scheme, including single-antenna operation, is explained in detail in Chapter 7.

2.6 Performance Requirements From the end user’s point of view, performance requirements can be derived from the supported services. Measuring the system performance has many aspects and the measurement criteria have to be selected in a way that the ‘real’ user experience is reflected. The user experience includes quantitative factors, e.g., the time to transfer a file of a certain size, the time to download a whole web page, the minimum response time for gaming applications or streaming services, and also qualitative assessments and feelings, e.g., about the voice quality. Note that the end-user experience is independent of the underlying transport protocol; the user doesn’t care whether FTP, HTTP or any other protocol is used. On the other hand, the measures should not only reflect the end user’s experience but should also measure the system performance, e.g., from an operator’s point of view. For the operator, the commercial operation of a mobile radio network and the usage of spectrum bands is coupled to significant costs, thus the operator’s target is to exploit the frequency spectrum

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efficiently by providing a full range of services to a large number of users per radio cell, with appropriate quality of service. The resulting technical performance requirements complement the requirements driven by the end-user experience. This section focuses on the technical performance requirements of the WINNER radio access network, covering all protocol layers below IP level. The performance of higher layers elements and functions (which also influence the end-user experience) was not considered in the technical work within WINNER. However, the WINNER RAN should be designed in such a way as not to prevent the fulfilment of service requirements (see Section 2.3). Therefore, certain higher-layer requirements that have an influence on the lower-layer performance requirements are addressed here.

2.6.1 Coverage Coverage is of major concern for WINNER. Therefore coverage aspects are inherently included in various other performance figures. A simple and viable way for a simulation to include coverage aspects in performance targets in loaded network conditions is to define the target values for a definite point in the cumulative distribution function of the respective performance measure. Within WINNER, the 95th percentile is used. Although no strict one-to-one mapping of the 95th percentile to coverage area is possible, it can be regarded as the target that will be exceeded in the main service area of a site, whereas the remaining 5% represent unfavourable situations, such as users in heavy shadowing at the cell edge. In order to ensure coverage and user satisfaction, a “satisfied-user criterion” has to be adopted in the definition of certain performance requirements, such as the spectral efficiency in typical, loaded conditions. The target spectral efficiency has then to be proven in an operational point that guarantees both coverage and user satisfaction. A detailed description of this satisfied-user criterion is given in Section 13.5. However it is necessary to keep in mind that the satisfied-user criterion, and therefore the coverage, depends on the type of application. As examples, the throughput and delay figures given for the service classes in Table 2.6 might serve as individual satisfied-user criteria.

2.6.2 Data Rate 2.6.2.1 Definition of User Throughput User throughput is defined as the throughput of correctly received information bits at IP packet layer during packet calls (known as ‘active session throughput’), for a specific link direction (uplink or downlink). The user throughput shall be measured considering all effects of packet loss and retransmissions, and taking into account the overhead due to guard bands, guard times, preambles, pilots, headers, and control signalling. Only the information bits in correctly received packets shall be counted. Packets are considered not correctly received if in error (e.g., failure of CRC or equivalent measures) or if they arrive with excessive delay compared to a service specific threshold. Impact of functions related to header compression, encryption, ciphering, and transport delays between base station and gateway shall not be considered, as these do not characterise the performance of the radio access technology itself.

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2.6.2.2 Peak Data Rate Peak data rate is the user throughput (according to the definition in Section 2.6.2.1) measured in ideal transmission conditions in a system which concentrates all of its resources on one user; it describes the fundamental performance limits due to system design. The WINNER system design shall achieve a downlink peak data rate of 100 Mbps in macro-cell deployments. The WINNER system design shall achieve a downlink peak data rate of 1 Gbps in local-area deployments. These requirements assume a 100 MHz system bandwidth allocation where half of the radio resources are assigned to each of uplink and downlink.

2.6.2.3 Sustainable Data Rate The sustainable data rate offered by a system is typically defined as the average user throughput achieved over the typical period of activity of a service in a cellular environment. This also requires definition of the number of simultaneous users that can be offered with the respective data rate under a service-dependent satisfied-user criterion. These figures are highly dependent on the deployment scenario, service and distribution of users. Assumptions include a realistic load in neighbouring cells (i.e., inter-cell interference) and the distribution of resources between users in the serving cell. Instead of defining the sustainable date rate, the spectral efficiency is measured for the maximum number of users that can be provided with a certain service fulfilling a specific satisfied-user criterion (see Section 2.6.5).

2.6.3 Allowable Error Rate As with most of the system requirements, the guarantees in terms of allowed residual error rates at the application layer also stem from the service classes table. The most demanding applications in terms of permissible error rates relate to critical functions and security transactions, such as remote control, authentications for remote payment and financial transactions, etc. In many packet-based transmissions, virtually error-free communication will be realised by means of reliable error detection and hybrid automatic repeat–request (ARQ) techniques, transforming transmission errors into additional delay. Therefore, no formal requirements regarding error rates are defined.

2.6.4 Delay 2.6.4.1 Definition of User-Plane Packet Delay The user-plane (U-plane) packet delay is defined in terms of the length of the one-way transmission time (from when a packet is available at the IP layer in the user terminal until when it is available at the IP layer in the base station, or vice versa). User-plane packet delay includes delay introduced by associated protocols and control signalling, assuming the user terminal

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is in the active state. The impact of functions related to header compression, encryption, ciphering, and transport delays between base station and gateway are not considered. 2.6.4.2 Achievable User-Plane Packet Delay The user-plane packet delay measured in ideal transmission condition in an unloaded system is known as the ‘achievable user-plane packet delay’. Performance is evaluated for a single user per cell, so as to describe the performance limits due to system design. The achievable delay is defined for a single-hop transmission of a minimum size of IP packet. The WINNER system shall enable an achievable user-plane packet delay of less than 1 ms in the downlink and 2 ms in the uplink of a single-hop transmission in unloaded conditions. The WINNER system shall enable an achievable user-plane packet delay of less than 5 ms in a two-hop transmission in unloaded conditions. These delay targets enable two features that give the WINNER system unique properties:

r Link retransmission (hybrid ARQ) can be used for flows from traffic classes with the most stringent delay requirements and delay jitter requirements. This results in improved link quality, as seen from higher layers. r Channel-aware link adaptation and scheduling can be used with respect to frequency selective fading channels, using channel quality information prediction and feedback, at vehicular velocities. This improves the performance by adaptation and multi-user scheduling gains. The QoS framework will provide guarantees for services that require high interactivity (a delay in the network level of less than 20 ms).

2.6.5 Spectral Efficiency Spectral efficiency is a performance measure used in widely different contexts throughout the literature. It is sensitive to a number of assumptions and parameters, including cell range, bandwidth, transmit power, sectorisation, antenna configuration, re-use factor, terminal capabilities, user mobility, number of users, and cell load. Furthermore, control overhead and real-world effects, such as imperfect synchronisation, channel estimation, control signalling and link adaptation, need to be considered. Peak spectral efficiencies can easily be calculated by dividing the peak data rate that can be carried per cell (see Section 2.6.2.2) by the system bandwidth. However, these figures are the result of the supported code rate, modulation, and spatial multiplexing. These are, therefore, simply the upper limits supported without any indication of whether and how frequently in practical deployments these values are reached. Relevant figures can be found in [WIN1D71]. For realistic comparison of air interface performance and for support of realistic economical projections, spectral efficiency has to be measured at an operational point of the system that

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33

ensures sufficient user satisfaction. Therefore the ‘satisfied-user criterion’ plays a crucial role in determination of spectral efficiency. In this section, the term spectral efficiency is used only with the definition including the satisfied-user criterion. In other texts this is also referred to as ‘achievable spectral efficiency’ or ‘average spectral efficiency’. These definitions of spectral efficiency should not be confused with peak spectral efficiency. The spectral efficiency is defined as the sum of user throughputs for all user terminals served by a certain radio cell, divided by the overall system bandwidth per link direction (uplink or downlink), calculated for the maximum load (number of users) that still allows fulfilling the satisfied-user criterion of a selected service in terms of data rate and delay. Furthermore, a sufficient statistic (the averaging effect) is needed in order to avoid singularities and misinterpretation. The satisfied-user criterion for WINNER phase II has been defined in [WIN2D6111] as 95% of the users having an average active session throughput greater than or equal to 2 Mbps. It was decided to formulate the required spectral efficiency per cell and not per site. In most deployments, the spectral efficiency per site can easily be increased by installing more independent radio sectors per base station. Furthermore, the definition per cell is in line with most standardisation and requirement definition activities. Compared to a definition per site, the efficiency figures given below should be multiplied by the number of sectors per site because, in most deployments, each sector represents an independent radio cell. As in [3GPP07], a cell is defined by a unique identification that is broadcast over a geographical area by a WINNER RAP. However, it is assumed that a relay node does not constitute an independent cell but adds to the capacity of its serving base station cell (this concept is also reflected in the widely used term ‘relay-enhanced cell’). WINNER shall provide a spectral efficiency in connected sites of 2 bps/Hz/cell for the downlink and 1 bps/Hz/cell for the uplink in wide-area deployments at an operation point that fulfils the satisfied-user criteria. WINNER shall provide a spectral efficiency in connected sites of 3 bps/Hz/cell for the downlink and 1.5 bps/Hz/cell for the uplink in metropolitan-area deployments at an operation point that fulfils the satisfied-user criteria. WINNER shall provide a spectral efficiency of 10 bps/Hz/cell for the downlink and 5 bps/Hz/cell for the uplink in isolated (non-contiguous) sites (i.e., the local area) at an operation point that fulfils the satisfied-user criteria. The above spectral efficiency figures are valid for typical WINNER deployments, e.g., a cellular deployment with a site-to-site distance of approximately 1 km and some moderate multi-antenna technology in a wide area; a deployment covering the Manhattan street grid with the help of relay nodes in the metropolitan area; and advanced MIMO deployment using remote radio heads in a local area [WIN2D6137]. It is important to understand that a direct comparison of spectral efficiency values (e.g., between WINNER and other systems) is only possible if the same satisfied-user criteria have

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been used. In particular, many results in research and standardisation use less challenging satisfied-user criteria (or even none at all), which is the main reason for high spectral efficiency values.

2.6.6 Maximum Terminal Speed The maximum user terminal speed is limited by the variability of the radio channel due to Doppler spread or shift related to the deployment scenarios and service type considered. Taking into account the wide-area deployment scenarios and assumptions, the challenge is to allow reliable links to high-speed trains at velocities up to 350 km/h or even up to 500 km/h, depending on the carrier frequency band and deployment environment. WINNER shall support at least low-rate services with reduced cell capacity for terminal speeds of up to 350 km/h, or even up to 500 km/h, depending on the Doppler shift related to the carrier frequency band and deployment environment.

2.7 Spectrum Requirements 2.7.1 WINNER Spectrum Range The WINNER system shall be able to operate anywhere in the range 2.7–5.0 GHz, taking into account that WINNER target characteristics have to be met.

2.7.2 Utilisation of Current Mobile Service Bands There may be situations where systems in current mobile bands need to be migrated into WINNER. Therefore the WINNER system concept needs to be able to utilise currently available bands for cellular use (i.e., 800–900 MHz, 1800–1900 MHz, 2 GHz and 2.6 GHz frequency ranges or other frequency bands that may become available below 2.7 GHz). Such a situation may arise from technical, commercial or regulatory reasons. In this case, due to the limited bandwidth, the forecast highest bit rate services will not be possible and the performance of the networks might be limited. The WINNER system shall be able to utilise current bands for cellular communication.

2.7.3 Spectrum Fragmentation From an implementation point of view, a channel bandwidth of 100 MHz is the currently agreed upper limit. This relatively broad channel bandwidth decreases the likelihood of achieving a sufficient spectrum assignment for a WINNER network or a number of WINNER networks in one single chunk. Available spectrum bandwidth may be fragmented due to practical geographical spectrum allocation. Indeed, some of the bands are expected to be available on a global basis while others might be available only in certain regions or countries. In this case, the WINNER system concept should be able to support a fragmented spectrum.

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The WINNER system shall be able to handle fragmented spectrum assignments efficiently.

2.7.4 Coexistence with Other Systems The WINNER system shall be able to coexist with non-WINNER systems and to share resources with minimal mutual interference. Operation in unlicensed bands shall be possible. Performance requirements shall be met if the interference caused by other systems operating in the same band does not prevent this.

2.7.5 Sharing Spectrum between WINNER RANs Significant advantages are expected, especially during the network deployment phase or if there is a severe lack of spectrum, when the spectrum can be shared between multiple parallel RANs using the same WINNER RAT and providing similar services. Most of the advantages result from the enhanced spectral scalability of the system since it should allow:

r the deployment of multiple RANs at the launch of the system, even when the spectrum can be made available gradually according to increasing traffic demands;

r system flexibility towards geographical differences in regulatory spectrum assignments; r more versatile operation of the networks; r adaptation of the spectrum available to a network to reflect changes in the number of subscribers as well as in daily traffic patterns.

The WINNER system shall be able to use spectrum shared between parallel network deployments (flexible spectrum utilisation).

2.7.6 Sharing Spectrum between Cell Layers of a WINNER System Further advantages are expected, especially during the network deployment phase or if there is a severe lack of spectrum, when the spectrum can be shared between different cell types of a WINNER network. The WINNER system shall be able to use spectrum shared between different cell types, e.g., between macro cells and micro cells, or between micro cells and hotspots. WINNER features addressing these spectrum requirements, such as operation in fragmented spectrum, spectrum sharing and flexible spectrum use, are treated in Chapter 11.

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2.7.7 System Bandwidth The maximum required physical data rate and other performance requirements determine the necessary system bandwidth. Due to implementation complexity and power consumption, an upper bound for the maximum continuous bandwidth of one radio link is assumed. A radio link is here defined to include all physical connections in a continuous frequency range between a terminal and the radio access network. This is intended to be the case for both frequency-division duplex (FDD) and time-division duplex (TDD) cases. The maximum required bandwidth for one radio link is 100 MHz. The upper bandwidth limit can be further explained by two aspects: the receiver channelselection filter (or automatic gain control (AGC)) integration and the analogue–digital converter (ADC) implementation. Regarding the filter, implementation issues arise for very high bandwidths with basic operational amplifier methods and the power consumption of this filter starts to increase. In the ADC, the bit count (number of bits), bandwidth, and required maximum modulation order (depth) are dependent on each other: the required sampling rate is at least twice the bandwidth. The total system bandwidth needed to fulfil the requirements should be minimised. Migration to systems beyond 3G requires the ability to operate in the spectrum bands made available in different regions of the world. A minimum required bandwidth is assumed for one duplex radio link. Performance in the minimum bandwidth may be significantly reduced from that in a ‘typical’ bandwidth. A minimum bandwidth of 1.25 MHz for TDD and 1.25 MHz per duplex FDD link, i.e., 1.25 MHz for uplink and 1.25 MHz for downlink, is required.

2.8 Dependency of Requirements It is clear that the different requirements presented in the previous sections are not mutually independent. Instead, the service-level requirements typically depend on the requirements of intermediate system layers to facilitate a certain QoS level. Figure 2.1 illustrates a mapping of higher-layer to lower-layer requirements. The dependencies indicated in the figure should be regarded as examples and are not exhaustive.

2.9 Conclusion As a starting point and motivation for the design of WINNER as a beyond-3G (B3G) mobile communication system with superior performance, this chapter provided a synopsis of possible usage scenarios for B3G services. Key scenario elements include the environment type, terminal type, and user mobility. With respect to system deployment, three scenarios were introduced as examples for detailed system design and performance evaluation: local area, metropolitan area, and wide area. The deployment and configuration of WINNER will

Usage Scenarios and Technical Requirements

service-level/ higher-layer requirements

37

service classes virtual reality

real-time applications

personalisation

security/privacy

service-level delay <20 ms

broadcast or multicast

emergency support location information

QoS mechanism

intra- or inter-handover deployment intra- or inter-RAT handover

requirements on RLC/MAC functions

flow prioritisation interdeployment RRM

reconfiguration and scalability arrows indicate dependency of requirements PHY requirements

system autonomy

1 ms – 5 ms IP packet delay

terminal TX power minimisation max. TX powers

measurements of other RATs

antenna diversity

spectral efficiencies in WA, MA, LA

max. data rate

coexistent or cooperative RRM spectrum sharing minimisation of necessary spectrum

spectrum range spectrum fragmentation min. and max. system bandwidth

Figure 2.1 Inter-dependency of requirements.

significantly differ between these scenarios, to ensure adaptation to the specific environments and user needs. Future mobile applications and service classes were then introduced and characterised, using statistical traffic models. The technical system requirements presented in this chapter were derived from the classification of services and from the envisioned usage scenarios. The service-level requirements characterise some of the key functionalities of the WINNER system that are immediately visible to the user. All of these requirements rely on the RAN system capabilities and performance, which are in turn characterised by requirements on the RLC, MAC and PHY layers. Most of these lower-layer requirements are expressed in quantitative figures and sometimes serve as targets for system-level or link-level simulations.

Acknowledgements The work described in this chapter has been carried out within WP1 ‘Scenarios’ and WP7 ‘System Engineering’ of WINNER I and Task 11 ‘Standardisation, Requirements, and Deployment’ of WINNER II. The authors would like to thank all colleagues who were active in the research work for the usage scenarios and technical requirements activities, and provided material for this chapter, especially:

r Carlos Silva, Portugal Telecom Inovac¸a˜ o, Portugal; r Albena Mihovska, Sofoklis Kyriazakos, Bayu Anggoro, and Satya Wardana, Aalborg University, Denmark;

r Pantelis Karamolegkos and George Karetsos, National Technical University of Athens, Greece;

r Yutao Zhu and Yi Wan, China Academy of Telecommunication Research of MII, China;

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r Bernard Hunt, Tim Moulsley, Philips Electronics, United Kingdom; r Jukka Henriksson, Pirjo Pasanen, Nokia Corporation, Finland; r Anne-Ga¨ele Acx, Ga¨el Champion, Christian Dale, Joe Wiart, France T´el´ecom (FTR&D), France;

r Juan Lara, Telefonica I&D, Spain; r Adam Pollard, Vodafone, United Kingdom; r Lino Moretti, Nokia Siemens Networks, Italy; r J¨orn von H¨afen, Nokia Siemens Networks, Germany; r Uwe Schwark, formerly with BenQ Mobile, Germany; r Bernhard Walke, Daniel C. Schultz and Lars Berlemann, RWTH Aachen University, Germany.

References [3GPP07] [3GPP204] [3GPP206a] [3GPP206b] [Amd05] [Bor00] [Far02] [FBB05]

[IEEE01] [KlK01]

[LaH01] [WIN1D71] [WIN2D6111]

[WIN2D6112]

[WIN2D6114] [WIN2D6137]

3GPP (2007) Vocabulary for 3GPP Specifications (Release 8), Technical Report TR 21.905 Version 8.0.0, Sophia Antipolis. 3GPP2 (2004) cdma2000 Evaluation Methodology, Specification C.R1002, Arlington. 3GPP2 (2006) Evaluation Methodology Text V5, Specification C30-20060911-061A, Arlington. 3GPP2 (2006) FL VoIP Packet Arrival with jitter.dat, Specification C30-20060823-005, Arlington. Amdekar, J. (2005) ‘Voice over Internet Protocol (VoIP) in the Hospitality and Gaming Sectors’, White paper, Infosys Technologies Ltd. Borella, M.P. (2000), ‘Source Models of Network Game Traffic’, Computer Communications, 23(4): 403–10, Amsterdam. Farber, J. (2002) ‘Network Game Traffic Modelling’, Proceedings of the first workshop on network and system support for games, ACM, New York. Forge, S., Blackman, C. and Bohlin, E. (2005) The Demand for Future Mobile Communications Markets and Services in Europe, Technical Report EUR 21673 EN, Institute for Prospective Technological Studies, Sevilla. Baugh, C.R. and Huang, J. (2001) Traffic Model for 802.16 TG3 MAC/PHY Simulations, IEEE 802.16 Broadband Wireless Access Working Group, Piscataway. Klepec, B. and Kos, A. (2001) ‘Performance of VoIP Applications in a Simple Differentiated Services Network Architecture’, Proc. of EUROCON, International Conference on Trends in Communications 2001, Bratislava, pp. 214–17. Lan, K. and Heidemann, J. (2001) Multi-scale Validation of Structural Models of Audio Traffic, Technical Report ISI-TR-544, USC Information Sciences Institute, Marina del Rey. WINNER I (2004) IST-2003-507581 System Requirements, Deliverable D7.1, July 2004, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+/. WINNER II (2006) IST-4-027756 Revised WINNER II System Requirements, Deliverable D6.11.1, June 2006, viewed 20 June 2009, http://projects.celticinitiative.org/winner+/. WINNER II (2006) IST-4-027756 Key Scenarios and Implications for WINNER II, Deliverable D6.11.2, September 2006, viewed 20 June 2009, http://projects.celticinitiative.org/winner+/. WINNER II (2007) IST-4-027756 Final WINNER II System Requirements, Deliverable D6.11.4, July 2007, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+/. WINNER II (2006) IST-4-027756 Test Scenarios and Calibration Cases Issue 2, Deliverable D6.13.7, December 2006, viewed 20 June 2009, http://projects.celticinitiative.org/winner+/.

3 WINNER II Channel Models Juha Meinil¨a, Pekka Ky¨osti, Tommi J¨ams¨a, and Lassi Hentil¨a Elektrobit Corporation

3.1 Introduction In the wireless mobile communications industry, channel models are needed for evaluation of technology proposals, for product design and development, for module and integration testing, and for type approval tests. It has been widely understood that radio propagation has a major impact on the performance of wireless communication systems. The impact on future broadband systems is even more important due to increased data rate, bandwidth, mobility, adaptivity, QoS, etc. Because of the major influence on the system performance and complexity, radio channel models and simulations have to be more versatile and accurate than in earlier systems. As the multi-antenna technologies develop, realistic spatial radio channel models are crucial for the purposes of testing and evaluation of system performance. This addresses the need for reliable and repeatable models that, on one hand, mimic the radio environment adequately well but, on the other hand, are easy to implement. The models should not be too computationally intensive and they should be easy to use in practical work. All these aspects had to be taken into account in the channel modelling work of the IST WINNER project, which was performed gradually over four years in the two phases of the WINNER project. In the first phase, the channel modelling work focused on wideband, multiple-input, multiple-output (MIMO) channel modelling at the 5 GHz-frequency range. Existing channel models were explored to find channel models for initial use in the WINNER project. Based on the literature survey, two standardised models were selected: the 3GPP/3GPP2 Spatial Channel Model (SCM) [3GPP03b] for outdoor simulations and IEEE 802.11n [IEEE04] for in indoor simulations. Because the bandwidth of the SCM model is only 5 MHz, a wideband extension (SCME) was developed [BHS05]. However, in spite of the modification, the initial models were not adequate for the advanced WINNER simulations. Therefore, new measurement-based models were developed. First, the WINNER generic model was created for the basis of the modelling work. The generic model allows creating an arbitrary,

Radio Technologies and Concepts for IMT-Advanced Martin D¨ottling, Werner Mohr and Afif Osseiran C 2009 Martin D¨ ottling, Werner Mohr, Afif Osseiran

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Radio Technologies and Concepts for IMT-Advanced

geometry-based radio channel model. The model is a ray-based, double-directional, multilink model that is antenna independent, scalable and capable of modelling channels for MIMO connections. Statistical distributions and channel parameters extracted by measurements at any propagation scenarios can be fitted to the generic model. The first WINNER channel models were based on channel measurements performed at the 2 and 5 GHz bands during the project. The models covered a limited set of propagation scenarios specified in the WINNER project. In the second phase of the project, the channel-modelling work package continued the modelling work and increased the number of scenarios. It added new features to the model and extended the frequency range to cover the frequencies from 2 to 6 GHz. The resulting models cover a wide scope of propagation scenarios and environments, including indoor, outdoor-toindoor (and vice versa), urban micro- and macrocell and corresponding bad urban scenarios, suburban and rural macrocell, moving network, and fixed feeder-links scenarios. They are based on a generic channel-modelling approach, which means that they offer the possibility of varying the number of antennas, the antenna configurations, the geometry and the antenna beam pattern without changing the basic propagation model. This method enables the use of the same channel data in different link-level and system-level simulations and it is well suited for evaluation of adaptive radio links, equalisation techniques, coding, modulation, and other transceiver techniques. This chapter describes the WINNER channel models. During phases I and II of the project, the models have evolved considerably. In this process, we have tried to conserve the model parameters from changes as much as possible. However, some changes have been inevitable. Therefore the models are not exactly the same today as they were at the beginning of the process. This chapter aims at giving a clear understanding about the principles of the channel model. Therefore many details have been omitted for brevity and to improve readability. When such detailed information is needed, the best reference is the WINNER deliverable [WIN2D112]. SCM, SCME, and the WINNER channel models have been implemented in the Matlab environment and are available via the WINNER web site [WIN08]. Section 3.2 describes important definitions, propagation scenarios and measurement tools. Section 3.3 defines the overall channel-modelling approach and Section 3.4 explains the generation of channel coefficients and describes the used path-loss models as well as parameters for generic models. Section 3.5 discusses the system level aspects of the channel models.

3.2 Modelling Considerations 3.2.1 Propagation Scenarios The following propagation scenarios are modelled in WINNER:

r A1: indoor; r A2: indoor-to-outdoor; r B1: urban microcell; r B2: corresponding bad urban scenario; r B3: indoor large hall; r B4: outdoor-to-indoor microcell; r B5: fixed feeder-link;

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Table 3.1 Selected WINNER propagation scenarios. Scenario

Definition

LOS/NLOS

Speed (km/h)

Frequency (GHz)

A1 B1 B4 C1 C2 D1

Indoor office Urban microcell Outdoor to indoor microcell Suburban Typical urban macrocell Rural macrocell

LOS/NLOS LOS/NLOS NLOS LOS/NLOS LOS/NLOS LOS/NLOS

0–5 0–70 0–5 0–120 0–120 0–200

2–6 2–6 2–6 2–6 2–6 2–6

r C1: suburban macrocell; r C2: urban macrocell; r C3: corresponding bad urban scenario; r C4: outdoor-to-indoor macrocell; r D1: rural macrocell; r D2: moving network. These scenarios cover a wide range of conditions, given that most scenarios include separate models for line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. Some scenarios also include sub-scenarios. Although all the scenarios have their basis in the project, only a subset of them and the corresponding channel models (see Table 3.1) is described here in more detail. The selection covers the most essential of the typical propagation environments. They have been specified according to the requirements agreed commonly in the WINNER project [WIN1D72]. The scenarios are not intended to cover all possible environments and conditions; e.g. mountainous and hilly rural environments are not covered. Similarly, the antenna heights do not cover all values that could be considered reasonable. Generally speaking, the environments considered are those found in urban areas of Europe and North America. The environments are described in two levels of detail. Most of the scenarios (non-gridbased models) use the ordinary way of placing transmitters and receivers, so that the only location parameter is the distance between the transmitter and the receiver. The other group of scenarios (A1, B1, and B4) is grid-based: the transmitters and receivers are located, e.g. by Cartesian coordinates, in a grid of streets or a building layout or both. Note that it is possible to use a non-grid-based model with a grid, but not to use a grid-based model without the grid. Stationary feeder scenarios (B5) are important for relay deployments but have not been included here for brevity. Interested readers are advised to consult [WIN2D112]. This selection of scenarios allows us to restrict the number of scenarios but still cover in a representative way the conditions encountered by radio equipment in the field. 3.2.1.1 A1: Indoor Office Scenario A1 has been modelled in [WIN1D54] and is shown in Figure 3.1. Base stations (BS) are assumed to be in the corridors. LOS cases occur within a corridor. NLOS cases occur between corridors and rooms and the basic path loss is calculated into the rooms adjacent to the corridor where the AP is situated. For rooms farther away from the corridor, wall losses

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Radio Technologies and Concepts for IMT-Advanced

Base station, e.g. 8-element array Mobile station

Figure 3.1 Layout of the A1 indoor scenario.

must be applied for the walls parallel to the corridors. For example, there are three walls to be taken into account between the mobile station (MS) at the bottom of Figure 3.1 and the room next to the upper corridor. Finally, we have to model the floor loss for propagation from floor to floor. It is assumed that all the floors are identical and that the floor loss is constant for the same distance between floors, but increases with the floor separation and has to be added to the path loss calculated for the same floor. It is easily seen that the indoor scenario forms a grid, where the location of BSs and MSs can be described by, e.g. x- and y-coordinates on one floor and x-, y- and z-coordinates over multiple floors.

3.2.1.2 B1: Urban Microcell In urban microcell scenarios, the height of the antenna at the BS and at the MS is assumed to be well below the tops of surrounding buildings. Both antennas are assumed to be outdoors in an area where streets are laid out in a grid. ‘The main street’ is the street in the coverage area where there is LOS from all locations to the BS (with an exception where the LOS is temporarily blocked by traffic). Streets that intersect the main street are referred to as perpendicular streets and those that run parallel to it are referred to as parallel streets. This scenario is defined for both the LOS and the NLOS cases. Cell shapes are defined by the surrounding buildings and energy reaches the NLOS streets mainly as a result of propagation around corners, and through and between buildings.

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3.2.1.3 B4: Outdoor to Indoor In outdoor-to-indoor urban microcell scenarios, the MS antenna height is assumed to be at 1–2 m (plus the floor height) and the BS antenna height is assumed to be below the rooftops, at 5–15 m depending on the height of the surrounding buildings (typically over four floors high). The outdoor environment is metropolitan area B1, i.e. a typical urban microcell where the user density is high and the requirements for system throughput and spectral efficiency are high. The corresponding indoor environment is A1, a typical indoor small office. Floors 1 to 3 are used in simulations, where floor 1 is the ground floor. The parameters of this scenario have been merged with the indoor-to-outdoor scenario. The comparison of outdoor-to-indoor and indoor-to-outdoor scenario characteristics is presented in [AHH+07; HACK07]. 3.2.1.4 C1: Suburban Macrocell In suburban macrocell scenarios, BSs are located well above the rooftops to allow wide-area coverage and MSs are outdoors at street level. Buildings are typically low, residential, detached houses with one or two floors or blocks of flats with a few floors. Occasional open areas such as parks or playgrounds between the houses make the environment rather open. Streets do not form an urban regular strict grid structure. Vegetation is modest. 3.2.1.5 C2: Urban Macrocell In typical urban macrocells, the MSs are located outdoors at street level and fixed BSs are clearly above the surrounding building heights. As for propagation conditions, obstructed or no line-of-sight is a common case, since street level is often reached by a single diffraction over the rooftops. The buildings can form either a regular urban grid or have more irregular locations. Typical building heights in urban environments are more than four floors. The height and density of buildings in a typical urban macrocell are mostly homogenous. 3.2.1.6 D1: Rural Macrocell Propagation scenario D1 represents radio propagation in large areas (radii up to 10 km) with low building density. The height of the BS antenna is typically in the range of 20 to 70 m, which is much higher than the average building height. Consequently, LOS conditions can be expected to exist in most of the coverage area. If the MS is located inside a building or a vehicle, an additional penetration loss may be modelled as a (frequency-dependent) constant value. The BS antenna location is fixed in this propagation scenario and the MS antenna velocity is in the range from 0 to 200 km/h. 3.2.1.7 B2 and C3: Bad Urban Conditions Bad urban conditions may happen in urban microcell (B1) and urban macrocell (C2) scenarios. However, propagation characteristics are such that multipath energy from distant objects can be received at some locations. This energy can have significant power (up to within a few dB of the earliest received energy), and exhibits long excess delays, because of reflecting from

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Radio Technologies and Concepts for IMT-Advanced

distant objects, often called far scatterers. Such situations typically occur when there is a clear radio path across open areas, such as large squares, parks or bodies of water. This makes for a difficult propagation condition; the channel must be in the NLOS propagation state.

3.2.2 Evolution of Channel Models from 2G to 4G The wireless mobile channel is dynamic in nature. The received signal is dispersed in time, frequency, and space due to the multipath propagation and mobile movement. Fading is caused by the fact that propagation paths may enter the detector as constructive or destructive complex sums in time. The Doppler frequency shift means a shift in the frequency domain, i.e. a phase velocity shift in the time domain. The signal is radiated in all directions from transmitter and it experiences multiple reflections due to buildings, ground, hills, etc. This phenomenon is called multipath propagation and it causes delay and angular dispersion to the signals. Thus, the radio channel can be characterised by different signal components entering the receiver at different delays and from different angles. Typically these phenomena are characterised by an impulse response and the simplest form of a radio channel model is a discretised impulse response [JY07]. As wireless access systems develop, there is a tendency for the applied bandwidths to get larger. The basic limitation of the early single-input, single-output (SISO) models is the bandwidth. There is no way to expand the models coherently (wideband models may always be filtered to narrowband models, but not vice versa). Moreover, these models do not include any MIMO characteristics of the radio propagation (SISO can be simplified from MIMO, but not vice versa). A MIMO radio channel model has to carefully describe correlations not only for the transmitter and receiver arrays, but for the parallel channels between the antenna arrays. In GSM [3GPP03a], 12-tap channel models were specified for three different environments. They are typical urban (TU), hilly terrain (HT), and rural area (RA). The specification allows simplification to a 6-tap version due to the fact that some fading emulators did not support 12-tap channels at the time of the specification work. Additionally, a 6-tap equalisation test (EQ) model was specified. The PCS Joint Technical Committee (JTC) recommended a radio channel model for one indoor and two outdoor scenarios [JTC94]. The principal structure of the JTC model is similar to the GSM model but there are more scenarios and more taps in some of them. The indoor models are classified into residential, office, and commercial models and the outdoor models cover urban high-rise, urban and suburban low-rise, and outdoor residential areas. The European Commission (EC) funded research program RACE II to study wideband air interfaces in the CODIT [PJ94] and ATDMA [USM95] projects. These projects developed further the SISO channel models to be applicable to bandwidths of 1–5 MHz. The two models characterise e.g. the vehicular, outdoor-to-indoor and pedestrian, and indoor office environments. The ATDMA and JTC channel models formed the basis of the ITU IMT-2000 recommended models [ITU-R]. Indeed, the 6-tap ATDMA delay line channel models are quite similar to the ITU models. The Stanford University Interim (SUI) models are specified for fixed wireless applications [IEEE16]. The basic assumptions include that cells are smaller than 10 km in radius, with a variety of terrain and tree density types. The system uses directional antennas at the receiver (2–10 m) and the transmitter antennas are mounted at 15–40 m heights. The SUI channel

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models state that the wireless channel is characterised by path loss, shadowing, multipath delay spread, fading characteristics, and Doppler spread. In 3GPP SISO channel models [3GPP07], there are six cases for multipath fading environments. The number of taps is two to four. Additionally, ITU Vehicular A and Pedestrian A and B models are specified for high-speed downlink packet access (HSDPA) and multimedia broadcast multicast service (MBMS) tests. Dynamic propagation conditions include two models: sinusoidal moving delay model and birth–death model. 3GPP deployment models are based on COST 259 and they consist of up to 20 taps [3GPP04]. The scope of the 3GPP/3GPP2 SCM ad hoc group was to develop and specify parameters and methods associated with spatial channel modelling that are common to the needs of the 3GPP and 3GPP2 organisations [3GPP06; JYTA06]. The model includes two sub-models:

r The calibration (link-level) channel model considers a MIMO link, where a single transmitter sends a signal through a radio channel to a single receiver. It is a spatial extension of the model in [ITU-R]. The spatial extension includes e.g. Laplacian distributed rms angular spread (AS) values for the base station, the mobile terminal and the propagation path directions in three environments: urban macrocell, urban microcell and suburban macrocell. r The simulation (system-level) model follows COST259 [Cor01]. The model is based on geometry but a subset of the parameters is stochastic. The target bandwidth is 5 MHz and the centre frequency is around 2 GHz. System-level simulations often include multiple base stations and multiple mobile terminals. A snapshot-based model is assumed for the generation of the clusters of scatterers. During a snapshot, the channel undergoes fast fading according to the mobile movement. However, delays are kept constant. Two consecutive snapshots are independent and include randomly located clusters, which make the channel model discontinuous. The channel model includes several parameters, such as the number of paths and sub-paths, mean angular spread at BS and MS, angular spread per path at BS and MS, angle-of-arrival (AoA) and angle-of-departure (AoD) distributions, variable delay spread, path loss, and shadowing. These parameters are fixed in the specification for a number of test cases. The number of paths in all test cases is six. The model also supports the LOS condition for the urban microcell scenario. An extended spatial channel model based on [BHS05] was proposed for 3GPP’s Long Term Evolution (LTE) and adopted for initial system evaluation [3GPP06]. The model has three midpaths (paths separated by delay) in each cluster. Thus, the number of simulation taps is three times higher than in the SCM model. The benefit of the model is that it describes more accurately the frequency correlation in frequency selective 20 MHz channels than the original SCM model. [3GPP08] summarises the study of radio requirements for the user equipment (UE) radio transmission and reception as part of the work item on Evolved Universal Terrestrial Radio Access (E-UTRA). The channel models in this document are even more simplified, with 9 taps and synthetic correlation (low, medium, and high) between MIMO channels, and do not necessarily reflect the reality. The simplifications were done for conformance tests and are not necessarily applicable for wider use. The IEEE 802.11n model [IEEE04] has been developed for indoor, WLAN, high-throughput applications. The models (A–F) cater for different environments from a small office to a large open space and include both LOS and NLOS cases. It is assumed that the propagation environment can be modelled via clusters. The number of clusters and multipath components

46

Radio Technologies and Concepts for IMT-Advanced

vary between models. A maximum number of 18 delay positions are considered. The 802.11n model uses tap-specific AoA and AoD characteristics and antenna geometry. Transmitter and receiver correlation matrices are calculated analytically from the geometry. The MIMO correlation matrix is obtained via the Kronecker product of the spatial correlation matrices at transmitter and receiver antenna arrays. MIMO channel models are not specified in the 802.16e standard. Therefore, channel model and test cases are standardised by the WiMAX Forum. The proposed models are based on ITU IMT-2000 tapped delay line models. Complex correlation matrices are defined for each tap. IEEE 802.16m has started recently and several channel model contributions have already been submitted. The COST 273 channel-modelling activities are a result of extensive research work, whereby the proposed models are step-wise generalised from path loss over delay dispersion to directional channel modelling (COST 207, COST 231, and COST 259). The recently published double directional model in [Cor06; JYTA06] is a geometry-based stochastic approach, which is based on a twin-cluster concept to represent multiple reflected or diffracted multipath components (MPCs). The location of the twin clusters can be chosen independently, in order to correctly reflect DoAs and DoDs. The transfer between the two clusters adds a delay that matches the measured delay of the MPCs. Furthermore, the model parameters are based on statistics derived from measurement data. Hence different environments can be modelled based on the same channel model core. A public version of the channel model implementation is available in [FTW06]. The WINNER channel models [WIN2D112] represent the latest development of the MIMO channel model. The SCM, SCME, and WINNER channel models are compared in [NST+07]. WINNER-type channel models have been proposed for IMT-Advanced evaluations by the ITU-R Working Party 5D.

3.2.3 Selection of Channel-modelling Approach Channel models can be roughly categorised by their basic modelling approach. A widely used categorisation separates channel models into stochastic and deterministic models.

r Stochastic channel modelling is based on a statistical view of the wireless channel. Realisations of stochastic radio channel models are based on pre-defined procedures and random number generations. Measurements are made in a large variety of locations and environments to obtain a data set with a good representation of the essential statistical properties. Parameters based on the environment characteristics can be used to refine the statistical accuracy for similar environments. As such, classification is an important tool in trading off accuracy against the universality of statements. Knowledge of statistical channel parameters allows for more general statements. Especially, they allow for the evaluation of the average properties and the usefulness of communication schemes in large-scale deployment. r Deterministic channel modelling is usually based on geometric descriptions of the propagation environment and antenna array configuration. Detailed environment data about the position, size, material and orientation of man-made objects (houses, buildings, bridges, roads, etc.) as well as natural objects (foliage or dominant plants, rocks, ground properties,

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47

etc.) are used in calculating the signal amplitudes, phases and delays. The basic idea is that if the propagation environment is known to a sufficient degree, wireless propagation is a deterministic process that allows the determination or prediction of its characteristics at every point in space. It is also referred to as propagation prediction and is the type of modelling used for cell planning, i.e., the analysis of optimum locations for BS deployment and the prediction of the resulting coverage, capacity, and data rates. In practice, it is difficult to model the environment accurately enough. Therefore, channel measurements are sometimes made in the same environment for which detailed data is available and then used to optimise the match between the prediction model and measurements. However, there are many models which cannot be categorised since they are partially stochastic and partially deterministic. Those models could be denoted as semi-deterministic or geometrically based stochastic channel models. Geometry-based stochastic models are based on measured parameters, but there is still channel directionality and antenna independency. The implementation of a geometry-based stochastic model can be based on either a correlation matrix or a sum of rays. A correlation-matrix model can be calculated from the geometry by fixing the antenna structure and parameters of the geometry-based stochastic model. A sumof-rays implementation is calculated from the geometry of multipath environment and antenna structure. The selection of the double-directional, geometry-based approach was agreed in the first phase of the WINNER project because only a geometry-based model is able to describe all the important characteristics of the channels. There was also a need for an antenna-independent model, for easy variation of large-scale parameters, and for a flexible system-level layer; these need can only be met easily by a geometry-based model. The double-directional subtype of the modelling principles was selected for its simplicity and because the approach had been successfully applied in the 3GPP/3GPP2 SCM [3GPP03b].

3.2.4 Modelling Process The WINNER channel-modelling process is depicted in Figure 3.2. The process is divided into three phases. The first phase starts from the definition of propagation scenarios, which means selection of the environments to be measured, antenna heights, mobility, and other general requirements. A generic model is needed to know what parameters have to be measured. The measurement campaign can be planned when the scenarios and generic model exist. Campaign planning has to be done carefully to take into account several aspects, e.g. channel sounder setup, measurement route, and link budget. Channel measurements are done according to the campaign planning and documented accurately. Measurement data is stored onto a mass memory device (e.g. magnetic tape or hard disk). The second phase of the channel-modelling process concentrates on data analysis. Depending on the required parameters, different analysis methods are applied. Output of data post-processing could be, e.g., a set of impulse responses, path-loss data, or extracted multidimensional propagation parameters. For the post-processed data, statistical analysis is done to obtain parameter probability density functions (PDFs).

48

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Radio Technologies and Concepts for IMT-Advanced

Generic model

2

Parameter generation 3 Array responses

Campaign planning

Channel measurements

Data postprocessing/ analysis

Parameter PDFs

MIMO Transfer matrix generation

Channel realisations

Measurement data

Simulations

Figure 3.2 WINNER channel-modelling process.

The third phase of the channel-modelling process covers the items required in the simulation. Parameters are generated according to the PDFs, by using random number generators and suitable filters. The MIMO transfer matrix is obtained by using the generated parameters and information about the antennas. In our approach, MIMO transfer matrices are generated by using the sum-of-rays method. Generated impulse responses, called ‘channel realisations’, which are then used in simulations. It should be noted that existing results achieved elsewhere are also taken into account, i.e. measurement data and parameters from literature can be included in the process.

3.2.5 Network Layout The WINNER MIMO radio channel model enables system-level simulations and testing. This means that multiple links are simulated (and evolved) simultaneously. System-level simulation may include multiple base stations (BS), multiple relay stations (FRS), and multiple mobile terminals (MS) as shown in Figure 3.3a. The channel segments are where large-scale parameters are fixed. Both link-level and system-level simulations can be done by modelling multiple segments or one segment (CDL model). The link-level simulation for one link is shown by the dashed ellipse and, enlarged, in Figure 3.3b. The parameters used in the models are also shown in Figure 3.3b. Each circle with several dots represents a scattering region causing one cluster. The number of clusters varies from one scenario to another. In the spatial channel model, the single link is defined by all MPCs between two radio stations. More complex network topologies include multihop links and cooperative relaying

WINNER II Channel Models

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MS

links

BS

Segments

FRS MS

(a)

N

ν σϕ

N

τ1

σφ

ΩMS

φ1 φ2 τ2

ϕ2

ϕ1

MS

BS

(b)

Figure 3.3 MIMO radio channel model: (a) system-level approach with several drops and (b) a single link.

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Radio Technologies and Concepts for IMT-Advanced

[Lan02], however, such peer-to-peer connections can easily be described as collections of direct radio links. Large-scale parameters (LSP) are used as control parameters, when generating the smallscale channel parameters. If we are analysing multiple MS positions (either for many MSs or for multiple positions of a single MS) we have a multiple link model for system-level simulations. It can be noted that different MSs at the same spatial position will experience the same LSPs. For multilink simulations some reference coordinate system has to be established in which positions and movement of radio stations can be described. The term ‘network layout’ designates the complete description of the relative positions of the system elements, as well as a vectored description of their movements (speeds). In general, the positions (coordinates) of scatterers are unknown. The only exceptions are related to far-cluster scatterers (FCS) that are actually positioned in the same coordinate system as radio stations. In multilink simulations, spatial correlations of channel parameters are important. In order to establish correlations between links at system level, the LSPs have been generated with the desired correlation properties.

3.2.6 Measurements 3.2.6.1 Measurement Tools Five different radio channel measurement systems were used for propagation measurement during the WINNER project. The main characteristics of the channel sounders used in WINNER II are summarised in this section. The measuring equipment used in WINNER I was described in [WIN1D54].

Propsound Multidimensional Radio-Channel Sounder The EB Propsound multidimensional radio channel sounder (CS) (see Figure 3.4) is a product of Elektrobit, Finland [Ele08], the University of Oulu, and Nokia. It was designed to enable realistic radio-channel measurements in both the temporal and spatial domains. The EB Propsound CS uses calibrated antenna arrays and time-domain, high-speed switching for measuring the channel-response matrix between the TX and RX arrays. The channel results are first processed by post-processing software which allows the user to view and export calibrated results to Matlab in various formats for further processing. The channel-sounding results are available in three basic formats: raw I/Q data, matched-filter correlated impulse responses, and ISIS channel estimates. The available channel dimensions are time, delay, angles of departure and arrival, polarisation and Doppler. The ISIS super-resolution algorithm is an implementation of the SAGE estimation algorithm developed by EB. The EB Propsound CS utilises accurate rubidium time-base clocks, integrated GPS receivers and high-speed switching technology to ensure the most accurate results and fastest data acquisition and location tracking. It is based on the spread spectrum sounding method in the delay domain. Together with optional super-resolution techniques (based on the SAGE algorithm [FXJS03]), this allows accurate measurements of SISO, SIMO, MIMO, geolocation

WINNER II Channel Models

(a)

51

(b)

Figure 3.4 Propsound terminals with a trolley: (a) transmitter and (b) receiver.

and multi-user propagation channels. Some key features of Propsound are presented in Table 3.2 and three antennas are described in Table 3.3. RUSK TUI-FAU Channel Sounder The RUSK TUI-FAU channel sounder used at the Technical University of Ilmenau for MIMO measurements was designed by Medav [Med08]. RUSK is a real-time, radio-channel, Table 3.2 Propsound characteristics. Propsound property RF bands Sustained measurement rate Maximum cycle (snapshot) rate Chip frequency Available code lengths Number of measurement channels Baseband sampling rate Transmitter output Control Synchronisation

Range of values 1.7–2.1, 2.0–2.7, 3.2–4.0 and 5.1–5.9 GHz Up to 30 000 CIR/s (code length: 255 chips) 1500 Hz up to 100 Mchips/s 31–4095 chips (M-sequences) up to 8448 up to 2 GSamples/s up to 26 dBm (400 mW), adjustable in 2 dB steps Windows notebook PC via Ethernet Rubidium clock

52 Rectangular array Dual (± 45◦ ) 5.25 32 (16 dual) Patch

Omnidirectional array Dual (± 45◦ ) 5.25

50 (25 dual)

Patch

Array structure Polarisation Centre frequency (Hz) Number of elements Element type

Elektrobit

PLA 5G25

Elektrobit

ODA 5G25

Owner

Name

Table 3.3 Propsound antennas.

Patch

42 (21 dual)

Radio Laboratory, Helsinki University of Technology Semi-spherical array Dual (h/v) 5

UCA 5G25

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Table 3.4 Key features of the Medav RUSK TUI-FAU channel sounder. RUSK TUI-FAU property RF bands Maximum measurement data storage rate Sequence length (defines maximum excess delay) Number of measurement channels Measurement modes Sampling frequency Transmitter output Control Synchronisation

Range of values 5–6 GHz (2x)a 160 MB/s 256–8192 spectral lines, depending on IR length Up to 65536 (216 ) SISO, SIMO, MIMO 640 MHz at Tx and Rx Up to 33 dBm (2 W) Windows PC Rubidium clock

a The rate is doubled with additional disk storage. The second storage device enables a shorter time gap between the Tx and Rx subchannels.

impulse-response measurement system that supports multiple transmit and receive antenna element configurations. The RUSK MIMO channel sounder measures the channel response matrix between all transmitting and receiving antenna elements sequentially by switching between (Tx, Rx) antenna-element pairs. This means that the sounder uses only one physical transmitter and receiver channel, which reduces sensitivity to channel imbalance. The switched-antenna approach offers a simple way of changing the effective number of antenna elements in the array. Additionally, since antennas are not transmitting at the same time, separation of transmitted signals at the receiver side is straightforward. To accomplish synchronous switching, rubidium reference oscillators are used at both the transmitter and the receiver. Timing and switching frame synchronisation is established during an initial synchronisation process prior to measurement data recording and must be maintained during the entire measurement. For channel excitation, RUSK uses a multicarrier spread spectrum signal (MCSSS) with an almost rectangular shape in the frequency domain. This approach allows precise concentration of the transmitted signal energy in the band of interest. Simultaneous sounding of multiple bands (e.g., separated up- and downlink bands in FDD) is supported by setting some spectral magnitudes to zero. Table 3.4 summarises the key features of the RUSK TUI-FAU channel sounder and an overview of measurement-relevant technical data for the antenna arrays used in the RUSK TUI-FAU campaigns is given in Table 3.5. CRC Sounder The CRC sounder used for measurements is the fourth generation of a pseudo-random noise (PN) sounder design that was first implemented with 20 MHz bandwidth at the Communications Research Centre (CRC), Canada in 1981. Its construction is breadboard style, with semi-rigid cables connecting various commercially available modules, such as phase-locked oscillators, power splitters, mixers, filter modules, and amplifiers. The bread-board style construction is maintained so as to allow easy reconfiguration and recalibration for different measurement tasks, with different operating frequencies and different bandwidths, as required. Its PN sequence generator is a CRC implementation that can generate sequences of

54

PULA8 (PULA8@10W)

IRK Dresden uniform linear array Polarisation dual (vertical+ horizontal) Centre frequency (GHz) 5.2 Bandwidth (MHz) 120 Max. power (dBm) 27 (40) Number of elements 8 Element type Patch Dimensioning Element spacing 0.4943 λ

Vendor Array structure

Name

Table 3.5 RUSK TUI-FAU antenna arrays.

5.2 120 27 16 Disk cone Diameter 10.85 cm

TU Ilmenau uniform circular array vertical

UCA16

UCPA24

IRK Dresden uniform circular array dual (vertical+ horizontal) 5.2 120 25 24 Patch Diameter 19.5 cm

P

Ring spacing 0.4943 λ

IRK Dresden stacked uniform circular array dual (vertical+ horizontal) 5.2 120 24 96 Patch Diameter 19.5 cm

SPUCPA4×24

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55

Table 3.6 CRC-Chanprobe operating characteristics. CRC-Chanprobe property RF bands Sustained measurement rate Maximum cycle (snapshot) rate Chip rate Useable code lengths Number of measurement channels Measurement modes Baseband sampling rate Transmitter output Control Synchronisation Transmit Antenna Receive Antenna

Range of values 0.95, 2.25, (4.9), 5.8, 30, 40, 60 GHz 10 000 snapshots/s 40 000 snapshots/s up to 50 Mchips/s 127–1021 chips (M-sequences) 32 switched Rx antennas; one Tx antenna SISO, SIMO 100 MSamples/s up to 42 dBm at 2.25 GHz, up to 30 dBm at other frequencies Windows PC Rubidium clock Vertical quarter-wavelength monopole, with drooping radials 32-element UCA of vertical quarter-wavelength monopoles with drooping radials

length between 127 and 1021 chips, and it can be clocked at rates up to 65 Mchips/s. Both CRC-Chanprobe’s transmitter and its receiver have two RF sections with operating bandwidths centred on 2.25 GHz and 5.8 GHz. The transmitter transmits continuously in both bands. Operation at other frequencies is made possible by substituting different up-converter phase-locked oscillators (PLOs) and bandpass filters. The receiver front ends are connected sequentially, using an RF switch, to its IF section. Operation at other centre frequencies is accomplished via an extra, external RF section, with frequency translation to either 2.25 or 5.8 GHz. Final downconversion is from IF to baseband via quadrature down-conversion circuitry. The in-phase (I) and quadrature (Q) baseband outputs can each be sampled at rates up to 100 MSamples/s. CRC-Chanprobe’s operating characteristics are summarised in Table 3.6.

3.2.6.2 Channel Measurements The following sections are intended to give an example of WINNER channel measurement campaigns and a hint of propagation parameter analysis. Both campaigns and analysis results are reported more extensively in Part II of [WIN2D112]. The measurement and analysis results have been presented in numerous publications, e.g. [AHY06; BSK+07; CBH+07; HACK07; HKP+05; HNM+08; NLS+07; RJK07; ZHM+07; ZKH+06; ZMH+07]. Measurement Campaigns Measurements for the C1 LOS (suburban macro) scenario were conducted by Elektrobit at the centre-frequency 5.25 GHz. Measurements were performed in Hein¨ap¨aa¨ relatively near to the centre of Oulu in an area where the houses are lower than in the centre of the town, with some

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Figure 3.5 Route for the moving MIMO measurement in BS location 1. (Reproduced by Permission of IEEE © 2009).

parking lots, parks and trees along the streets in between the houses. The height of the houses varied typically from 3 to 6 stories. Figure 3.5 shows the measurement environment. WINNER measurements for C1 NLOS scenario were made at 5.3 GHz centre frequency and 100 MHz chip rate in a typical Finnish suburban residential area with rather wide streets [RKJ05]. Buildings in the area are mainly one- or two-storey single or detached houses. There are open areas between the buildings, such as playgrounds, parks or small forest areas. BS height in the measurements was ∼25 metres, which is well above the surrounding buildings, and at or above the height of the highest neighbouring trees. Only close to the BS were there clear unobstructed LOS areas, and further away the MS–BS connection was obstructed mainly by trees. Deep NLOS conditions were achieved at long MS–BS distances. The maximum measured MS–BS distances were ∼1100 metres. Measurement for the B4 scenario (outdoor-to-indoor) was performed by University of Oulu. The measurements were conducted on a sunny summer day in the campus area of the University of Oulu. A centre frequency of 5.25 GHz together with 100 MHz bandwidth was applied using the Propsound Channel Sounder. ODA 5G25 and PLA 5G25 antennas (see Table 3.3) were employed in the Rx and Tx ends, respectively. From the campus area it is easy to find an urban-like outdoor environment with different indoor environments. Several measurement routes (see Figure 3.6) were investigated with the receiver moving indoors while the transmitter was fixed on a rooftop or a mobile platform. These routes were chosen to demonstrate different indoor

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Figure 3.6 Routes for outdoor-to-indoor measurements (in grey), from [HACK07]. (Reproduced by Permission of IEEE © 2009).

environments e.g. with big glass windows as in newer buildings or thick concrete walls as in older buildings. Also smaller and bigger rooms and different kinds of corridor were measured. Measurement routes covered mainly the first floor but a few routes were also measured on the second, third and fourth floors. First-floor routes and pictures of the outdoor and indoor environments are depicted in Figure 3.7. Urban microcellular (B1) measurements were conducted in Helsinki in WINNER Phase I and in inner-city Ottawa, Canada, in Phase II. A typical measurement site in Ottawa is depicted in Figure 3.8. Measurement Results Figure 3.9 shows the path-loss model and the shadow-fading (SF) distribution obtained from the Helsinki suburban macrocellular NLOS (C1) 5.3 GHz measurements. Two different BS sites, one with two sectors, were chosen, so data from three BS sectors were collected for data analysis during different measurement runs. More detailed descriptions on measurements can be found in [RKJ05]. The path-loss model for a suburban macrocellular environment, obtained from WINNER measurements, reads as: P L = 27.7 + 40.2 log10 (d [m])

(3.1)

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(a)

(b)

Figure 3.7 Environment seen from (a) a BS, from [S07] and (b) a MS, [AHH+07]. (Reproduced by Permission of IEEE © 2009).

In our WINNER measurement, we found that the shadow-fading component is log-normally distributed with standard deviation of 6.1 dB. In Figure 3.10, the distributions for the following parameters of the B4 (outdoor-to-indoor) scenario are shown: RMS delay spread (DS), Ricean K-factor (K), azimuth spread at Tx (Tx AS), and azimuth spread at Rx (Rx AS). The tested distributions proved to be log-normal (LN). Probability density functions to verify these distributions are given in Figure 3.10. Because the parameters are logarithmic or decibel values, the distributions are normal in the figures. Figure 3.11 shows plots of a typical clustered angular power spectrum and a local delayDoppler function.

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Figure 3.8 Measurement environment in the city centre of Ottawa (B1). (Reproduced by Permission of IEEE © 2009).

3.3 Channel-Modelling Approach The WINNER channel model is a geometry-based stochastic model. Geometry-based modelling of the radio channel enables separation of propagation parameters and antennas. The channel parameters for individual snapshots are determined stochastically, based on statistical distributions extracted from channel measurement. Antenna geometries and field patterns can be defined properly by the user of the model. Channel realisations are generated by geometrical principles by summing contributions of rays (plane waves) with specific small-scale parameters such as delay, power, AoA and AoD. Superposition results in correlation between antenna elements and temporal fading with a geometry-dependent Doppler spectrum [Cal+07]. A number of rays constitute a cluster. In the terminology of this document we equate the cluster with a propagation path diffused in space, either or both in delay and angle domains. Elements of the MIMO channel, i.e. antenna arrays at both link ends and propagation paths, are illustrated in Figure 3.12. The impulse response matrix of the MIMO channel is: H (t; τ ) =

N

Hn (t; τ )

(3.2)

n=1

It is composed of antenna array response matrices Ftx for the transmitter, Frx for the receiver and the propagation channel response matrix hn for cluster n as follows: Hn (t; τ ) =

T (φ)dφdϕ Frx (ϕ)hn (t; τ, φ, ϕ)Frx

(3.3)

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170 160 150 PL = 40.2*log10(d) + 27.7

Path loss [dB]

140 130 120

0.0 6.1 110 100 90 80 1.6

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2.4 2.6 log 10(d[m])

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(a) 0.08 0.07

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0.06 0.05 0.04 0.03 0.02 0.01 0

–20

–10

0 10 Shadow fading [dB]

20

(b)

Figure 3.9 Helsinki 5.3 GHz suburban measurements: (a) path-loss model and (b) shadow-fading distribution, from [RKJ05]. (Reproduced by Permission of IEEE © 2009).

61

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(c)

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–0.5 0 0.5 1 1.5 Azimuth spread in Tx [log10(degree)]

(a)

1 1.5 2 2.5 log10(RMS delay spread in ns)

Mean: 17.275 degrees Std: 12.582 degrees

0.5

Mean: 64.8315ns Std: 43.0305ns

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(b)

0 K-factor [dB]

Mean: 60.765 degrees Std: 16.9968 degrees

–10

Mean: 0.09 dB Std: 3.10 dB

10

2

2.2

15

Figure 3.10 Distributions of B4 parameters: (a) RMS delay spread, (b) Ricean K-factor, (c) Tx azimuth spread, and (d) Rx azimuth spread. (Reproduced by Permission of IEEE © 2009).

pdf

pdf pdf

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Power (dB)

110

120 40 20 0

50 0 −50

AoD[°]

AoA[°]

−20 −100

(a)

Normalised power [dB]

0 –50 –100 –150 –200 0 10

–500 20 30

Delay [µs]

40

500

0 Doppler shift [Hz}

(b)

Figure 3.11 Typical plots: (a) bi-azimuth power spectrum, from [ZKH+06] and (b) delay-Doppler spectrum. (Reproduced by Permission of IEEE © 2009).

The channel from Tx antenna element s to Rx element u for cluster n is T M Ft x,s,V φn,m Fr x,u,V ϕn,m αn,m,V V αn,m,V H Hu,s,n (t; τ ) = Ft x,s,H φn,m αn,m,H V αn,m,H H Fr x,u,H ϕn,m m=1 −1 −1 ¯ × exp j2π λ0 ϕ¯n,m · r¯r x,u exp j2π λ0 φn,m · r¯t x,s × exp j2π υn,m t δ τ − τn,m

(3.4)

where Frx,u,V and Frx,u,H are the antenna element u field patterns for vertical and horizontal polarisations respectively, α n,m,VV and α n,m,VH are the complex gains of vertical-to-vertical and horizontal-to-vertical polarisations of ray n,m respectively. Further, λ0 is the wave length of

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Figure 3.12 MIMO channel, from [MJKY08]. (Reproduced by Permission of IEEE © 2009).

the carrier frequency, φ¯ n.m is the AoD unit vector, ϕ¯ n.m is the AoA unit vector, r¯t x,s and r¯r x,u are the location vectors of element s and u respectively, and ν n,m is the Doppler frequency component of ray m of cluster n. If the radio channel is modelled dynamically, all the smallscale parameters are time variant, i.e. a function of t [SMB01]. For interested readers, a more detailed description of the modelling framework can be found in [WIN1D54].

3.3.1 WINNER Generic Channel Model The WINNER generic model is a system-level model that can describe an arbitrary number of propagation environment realisations for single or multiple radio links. It applies to all the defined scenarios for any desired antenna configurations, with one mathematical framework by different parameter sets. The generic model is a stochastic model with two (or three) levels of randomness. At first, large-scale (LS) parameters, such as shadow fading, delay and angular spreads, are drawn randomly from tabulated distribution functions. Next, the smallscale parameters, such as delays, powers and directions at arrival and departure, are drawn randomly according to tabulated distribution functions and random LS parameters (second moments). At this stage, the geometric setup is fixed and the only free variables remaining are the random initial phases of the scatterers. By picking (randomly) different initial phases, an unlimited number of different realisations of the model can be generated. When the initial phases are also fixed, the model is fully deterministic.

3.3.1.1 Modelled Parameters The parameters used in the WINNER II Channel Models are explained below. Parameter values are given in Section 3.4.

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Large-scale (LS) parameters are considered as an average over a typical channel segment i.e. distance of some tens of wavelengths. The first three large-scale parameters listed below are used to control the distributions of delay and angular parameters:

r delay spread and distribution; r angle of departure spread and distribution; r angle of arrival spread and distribution; r shadow-fading standard deviation; r Ricean K-factor. The support parameters are:

r scaling parameter for delay distribution; r cross-polarisation power ratios; r number of clusters; r cluster angle spread of departure; r cluster angle spread of arrival; r per cluster shadowing; r auto-correlations of the LS parameters; r cross-correlations of the LS parameters; r number of rays per cluster. All of these parameters have been specified from the measurement results or, in some cases, found from literature. The number of rays per cluster has been selected as 20 [3GPP03b]. Analysis of the measurement data for the different parameters is described in Part II of [WIN2D112]. In the WINNER channel models, the parameters are assumed not to depend on distance. Although this assumption is probably not strictly valid, it is used for simplicity of the model. In the basic case, the angles of arrival and departure are specified as two-dimensional, i.e. only azimuth angles are considered. For the indoor and outdoor-to-indoor cases, the angles can also be understood as solid angles, azimuth and elevation, and the modelling can be performed as three-dimensional. 3.3.1.2 Correlations Between Large-Scale Parameters For a single position of radio stations (one link), we can describe the inter-dependence of multiple control parameters (LSP) with a correlation coefficient matrix. Correlations of LSPs that are observed in measured data are not reflected in joint power or probability distributions. Instead LSPs are estimated from marginal power distributions (independently for angles and delays), and the necessary dependence is re-established through a cross-correlation measure: Cx y ρx y =

C x x C yy where C x y is the cross-covariance of LS parameters x and y.

(3.5)

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Figure 3.13 Links toward a common station will exhibit inter-correlations: (a) fixed common station and (b) mobile common station (from [NKM+07]). (Reproduced by Permission of IEEE © 2009).

At system level, two types of correlation can be defined: correlations between MSs connected to the same BS and correlations of links from the same MS to multiple BSs (see Figure 3.13). These correlations are mostly caused by some scatterers contributing to different links (similarity of the environment). In the first case, WINNER models use exponential correlation functions to describe the dependence of LSP changes over distance. In other words, LSPs of two MSs linked to the same BS would experience correlations that are proportional to their relative distance dMS . As a consequence, correlation coefficient matrices for neighbouring links (for MSs at a certain distance) are not independent and they also have to reflect the observed correlations over the distance dimension: C x y (d M S ) ρx y (d M S ) =

C x x C yy

(3.6)

For this reason, elements of the linked cross-correlation coefficient matrix should reflect exponential decay with distance, as shown in Figure 3.14. In the 3GPP SCM model, shadow fading for links from one MS to different BSs exhibits a constant correlation coefficient equal to 0.5. This correlation does not depend on distances between BSs or their relative angular positions as seen from a MS and therefore it is not layout dependent. Additionally, this property is estimated from few measurements and therefore it is not considered as being fully representative for different WINNER scenarios. This phenomenon has also been investigated in the WINNER project. Correlation properties of links from the same MS to multiple BSs (inter-site) were investigated in Phase I of the WINNER project [WIN1D54]. The results showed rather a high correlation for one measurement route and quite a low correlation for another. The amount of measurement data was limited, so that we could not specify the correlation as other than zero.

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Figure 3.14 Dependence of cross-correlation coefficient matrix over distance (from [NKM+07]). (Reproduced by Permission of IEEE © 2009).

The inter-site correlation of shadow fading is also investigated in the literature for the outdoor macrocell scenarios. In [Gud91; PCH01; WL02], the authors proposed that the intersite correlation is a function of the angle between BS directions being seen from the MS (θ ); while in [Sau99], the author studied the dependence of the inter-site correlation on the distance between BSs, d B S . Although some correlations can be found in these references, the results do not show clear correlation behaviour between different BSs. We believe that such correlation most probably exists in many scenarios, at least between base stations that are near to each other, but at this point we have decided to let the correlation be modelled as zero. Inter-correlations between links of one MS to multiple sectors of the same BS can be analysed in a similar way, by treating different sectors of the BS as independent one-sector BSs. As a matter of fact, the links from two different sectors to an MS are correlated so that the LS parameters for the links are the same. Correlation of large-scale parameters (LSPs) is achieved by using weighted sums of independent Gaussian random processes. If the i-th LSP, si , has a distribution that differs from normal, the required distribution is generated by applying a mapping from the random variable s˜i having Gaussian distribution. The random variable s˜i is referred to as a transformed LSP (TLSP). Prior to mapping s˜i to si , s˜i is correlated with TLSPs s˜ j , belonging to other LSPs or different links (being at certain distance) for system-level correlations. The process applied to introduce or to calculate correlations (from measured data) is illustrated in Figure 3.15. In cases when the mapping si = gi−1 (˜s ) is unknown, necessary relations between LSP and the transformed domain can be established using knowledge about the cumulative density function (CDF) of si , Fsi (s). In such cases, si can be generated from s˜i using the equation: s = gi−1 (˜s ) = Fs−1 Fs˜i (˜s ) . (3.7) i

WINNER II Channel Models

67

gi (.)

LSPi

gj (.)

TLSPi ~ S

Si

correlations

i

LSPj

TLSPj ~ S

Sj

j

gj–1 (.)

gi–1 (.)

Figure 3.15 Correlations of LSP are introduced in a transformed domain.

where Fs˜i (˜s ) is CDF of a normally distributed process that can be calculated using Q-function (or erf/erfc). In simpler cases, e.g. when the LSP is log-normally distributed, it is possible to use known mappings: s = gi−1 (˜s ) = 10s˜

(3.8)

s˜ = gi (s) = log10 (s)

(3.9)

The cross-correlation coefficient of Equation (3.4) is used as a correlation measure. It has been explained above that, for one link (a single position of a MS), the inter-dependence of multiple control parameters can be described with a correlation coefficient matrix. Additionally, if parameters of intra-site links are correlated according to the distance between MS positions, then the correlation matrix gets an additional dimension that describes changes in correlations over distance (see Figure 3.14). This means that, for each pair of TLSPs, we can define cross-correlation coefficient dependence over distance, as in Equation (3.5): Cr˜ s˜ (dk,l ) ρr˜k s˜l (dk,l ) = k l Cr˜k r˜k Cs˜l s˜l

(3.10)

Cross-variances Cr˜k s˜l (dk,l ) are calculated from measurement data using knowledge about positions of MS during measurement and, in general, have exponential decay over distance. If each link is controlled by M TLSPs and we have K links corresponding to MS locations at positions (xk , yk ), k = 1..K , then it is necessary to correlate values for N = M·K variables. Generation of N normally distributed and correlated TLSPs can be based on scaling and summation of N independent zero-mean and unit variance Gaussian random variables, ξ N (x, y) = [ξ1 (x1 , y1 ) , . . . , ξ N (x N , y N )]T . Using matrix notation, that can be expressed as: s˜ (x, y) = Q N x N ξ N (x, y)

(3.11)

This will ensure that the final distribution is also Gaussian. Scaling

coefficients have to be determined in such a way that cross-variances Cr˜k s˜l (dk,l ), dk,l = (xk − xl )2 + (yk − yl )2 correspond to the measured values. If element Ci, j of matrix C N x N represents the crossvariance between TLSPs s˜i and s˜ j , then the scaling matrix can be calculated as:

QN x N = CN x N (3.12)

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This approach is not appropriate for correlation of a large number of parameters, since dimensions of a scaling matrix increase proportional to the total number of TLSPs in all links (a squared dependence in the number of elements). For that reason, it is more convenient to generate separately the influence of LSP cross-correlation and exponential auto-correlation. Let us assume we have M LSPs per link and K correlated links, i.e. K MSs are linked to the same BS site at locations (xk ,yk ), where k = 1, . . . , K. Auto-correlation is generated to the LSPs the following way. First, we generate a uniform grid of locations based on coordinates of the K MSs. The size of the grid is (max(xk ) − min(xk ) + 2D) × (max(yk ) − min(yk ) + 2D). To each grid node, we assign M Gaussian independent identically distributed Gaussian random numbers with zero mean and unit variance, one for each LSP. Then the grid of random numbers is filtered with a two dimensional FIR filter to generate exponential auto-correlation. The impulse response of the filter for the mth LSP is d (3.13) h m (d) = exp −

m where d is distance and m is the correlation distance, both in metres (see Table 3.8). Each of the M random numbers in the nodes of the grid, representing M LSPs, is filtered with a specific filter, because the correlation distances may be different. After filtering, the correlated random numbers ξ M (xk , yk ) at K grid nodes (K MS locations) are saved and the redundant grid nodes are discarded. Cross-correlation is generated independently from the LSPs of K links by using the linear transformation

(3.14) s˜ (x k , yk ) = C M x M (0)ξ M (xk , yk ) where the elements of correlation matrix ⎡ Cs˜1 s˜1 (0) ⎢ .. C M x M (0) = ⎣ . Cs˜M s˜1 (0)

··· .. . ···

⎤ Cs˜1 s˜M (0) ⎥ .. ⎦ .

(3.15)

Cs˜M s˜M (0)

are defined in Table 3.8. The stochastic behaviour of large-scale parameters is illustrated in Figure 3.16. The auto correlation and the correlation distance are indicated by the smooth change of parameter values as a function of MS location. Cross correlations can be observed by comparing the curves on the left in Figure 3.16. Both empirical and theoretical distribution curves are depicted on the right in Figure 3.16. The parameters are log-normal distributed. Shadow fading is usually given in decibels and is normally distributed in the dB scale.

3.3.2 Channel Segments, Drops and Time Evolution A channel segment represents a period of quasi-stationarity during which the probability distributions of low-level parameters do not change noticeably. During this period, all largescale parameters are practically constant, as are the velocity and direction of travel for a mobile station (MS). To be physically feasible, the channel segment must be relatively confined in distance. The size depends on the environment, but it can be a few metres at maximum. The

WINNER II Channel Models

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50

0

0

200

200

100

100

0

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3000

3000

0

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40

40

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50

–40

0

500 location [m]

1000

–40 histogram

Figure 3.16 Large-scale parameter auto-correlations, cross correlations and distributions in scenario C2 NLOS. (Reproduced by Permission of IEEE © 2009).

correlation distances of different parameters describe roughly the proper size of the channel segment (see Section 3.4.4). Allowing the length of the channel segment to approach zero, we specify a drop: in a drop, all parameters are fixed except the phases of the rays. Motion within a drop is only virtual and causes fast fading and the Doppler effect by superposition of rotating phasors, rays. It can be said that a drop is an abstract representation of a channel segment, where the inaccuracies caused by the change of the terminal location have been removed. In a simulation, the duration of a drop can be selected as desired. It is common practice to use drops in simulations to simplify the simulation, because successive simulation runs do not need to be correlated. The drawback is that it is not possible to simulate cases where that need variable channel conditions or system aspects related to mobility, such as handover. Most simulations in WINNER are drop-based. In the final WINNER II channel models there is also an alternative to drop-based simulation, i.e. simulation with time evolution where correlated drops are used. In the WINNER II models, the propagation parameters may vary over time between the channel segments. In multi-segment modelling, two options are available: drops (stationary channel segments as in WINNER I) or continuous channel evolution with smooth transitions between segments. Another model, for continuous variation of small-scale channel parameters, is introduced in [BHS05]. The time evolution of propagation parameters is modelled by the WINNER model (see Figure 3.17). The route to be modelled is covered by adjacent channel segments. The distance

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Figure 3.17 Smooth transition between channel segments by power ramp-up and ramp-down of clusters (from [NKM+07]). (Reproduced by Permission of IEEE © 2009).

between segments is equal to the stationarity interval. Transition from segment to segment is carried out by replacing clusters of the ‘old’ segment by the clusters of the ‘new’ segment, one by one. The route between adjacent channel segments is divided to a number of sub-intervals equal to the maximum number of clusters within the channel segments. During each subinterval the power of one old cluster ramps down and one new cluster ramps up. Power ramps are linear in decibel scale. Clusters from the old and new segments are coupled based on their power. If the number of clusters is different in the channel segments, the weakest clusters are ramped up or down without a pair from other cluster.

3.3.3 Nomadic Channel Condition A propagation environment is ‘nomadic’ if the transmitter and receiver locations are normally fixed during the communication but may have moved between different uses of the network [OVC06]. In such conditions, we have to assume that some of the scatterers may move. Actually this is quite typical in many cases, for example, when there are people working in the vicinity of the transceiver. For the nomadic environment, it is also typical that an access point and user terminals can move, e.g. in the room and even going out of the room. However, the most important feature to be taken into account in channel modelling is the moving scatterers. Nomadic channels can be regarded as a special case of the WINNER generic model in Section 3.3.1. In principle, nomadic channels can exist in all the WINNER deployment scenarios, both in indoor and outdoor. An approach to the channel model in nomadic conditions is sketched in [WIN2D112].

3.4 Channel Models and Parameters In this section, we summarise all the channel models and parameters. The path-loss models are mainly based on 5 GHz and 2 GHz measurements. However, the frequency bands are extended for the 2−6 GHz range.

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3.4.1 Applicability 3.4.1.1 Environment Dependence Different radio-propagation environments cause different radio-channel characteristics. Instead of attempting to parameterise the environment directly (e.g. with street width, average building height, etc.), the WINNER models are use (temporal and spatial) propagation parameters obtained from channel measurements in different environments. In this context, environments in which measurements are conducted to observe radio-channel characteristics are called propagation scenarios. For each scenario measured, data is analysed and complemented with results from literature to obtain scenario-specific parameters. After this point, the same generic channel is used to model all scenarios, just by using different values of the channel parameters. Usually, even for the same scenario, the existence of a LOS component substantially influences the values of channel parameters. This means that most WINNER scenarios differentiate between LOS and NLOS conditions. To enable appropriate scenario modelling, the transition between LOS and NLOS cases has to be described. For this purpose, the distance-dependent probability of LOS is used in the model.

3.4.1.2 Frequency Dependence The WINNER channel models include carrier frequency dependence in the path-loss models, valid in the range of 2–6 GHz. The path-loss models are based on measurements that are mainly conducted in 2 and 5 GHz frequency ranges. In addition, the path-loss models are based on results from literature, such as [OOK+68; OTT+01], which have been extended to the desired frequency range. Path-loss frequency dependence is considered in more detail in Section 0. From WINNER measurement results and the literature survey, it was found that model parameters DS, AS and Ricean K-factor do not show significant frequency dependence [BHS05]. These parameters show only dependence on the environment (scenario). For modelling of systems with time-division duplex (TDD), all models use the same parameters for both uplink and downlink. If the system uses different carriers for duplexing (FDD), then (additionally to path loss) random phases of scatterer contributions between UL and DL are modelled as independent. For WINNER purposes, it is required that the channel model supports bandwidths up to 100 MHz. Following the approach described in [SV87] (for indoor propagation modelling) and further with SCME [BHS05], the WINNER II model introduces intra-cluster delay spread as a means of supporting the 100 MHz bandwidth and suppressing frequency correlation. Instead of the zero-delay-spread-cluster approach of the Phase I model, the two strongest clusters with 20 multipath components (MPCs) are subdivided into three zero-delay sub-clusters. Thus we keep the total number of MPCs constant, but introduce four additional delay taps per scenario.

3.4.2 Generation of Channel Coefficients The overall procedure for generation of the channel coefficient is depicted in Figure 3.18. A detailed description is given in [WIN2D112].

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General parameters: Set scenario, network layout and antenna parameters

Assign propagation condition (NLOS/ LOS)

Calculate path loss

Generate correlated large scale parameters (DS, AS, SF, K)

Small scale parameters:

Generate XPRs

Perform random coupling of rays

Generate arrival and departure angle

Generate cluster powers

Generate delays

Coefficient generations: Draw random initial phases

Generate channel coefficient

Apply path loss and shadowing

Figure 3.18 Channel coefficient generation procedure.

It has to be noted that the geometric description covers arrival angles from the last bounce scatterers and departure angles to the first scatterers interacted from the transmitting side. The propagation between the first and the last interaction is not defined. Thus this approach can also model multiple interactions with the scattering media. This indicates also that e.g. the delay of a multipath component cannot be determined by the geometry of the modelled scatterers. Bad urban channel realisations can be created as modified B1 and C2 NLOS procedures: For a bad urban scenario, five far scatterers (FS) are dropped in a hexagonal cell, within radii [FSmin, FSmax]. For each mobile user, we determine the closest two far scatterers, which are then used for calculating far scatterer cluster parameters (a detailed description of the procedure is given in [WIN2D112]). The actual channel statistics of the bad urban users depend somewhat on the cell size. It is worth noticing that, depending on the location of the mobile user within the cell, the FS clusters may appear at shorter delays than the maximum delay of the ordinary C2 or B1 NLOS cluster. In such cases, the far scatterers do not necessarily result in increased angular or delay dispersion. The output of the channel coefficient generation procedure is a time series of matrixformat channel impulse responses. Figure 3.19 represents a single realisation of the example scenario A1 (indoor, NLOS). The power azimuth spectra (Figure 3.19a) are approximately Gaussian shaped – the dashed line denotes the centre of gravity point and the vertical segment depicts the RMS spread. The power delay profile (Figure 3.19b) is close to exponential. In the joint Doppler-delay spectrum (Figure 3.19c), the centre frequency is 5.25 GHz and the mobile velocity is 3 m/s resulting in a maximum Doppler frequency of about 52 Hz. The fading profiles of 2×2 MIMO antenna pairs (Figure 3.19d) show that two of the channels are correlated and two others are quite uncorrelated. This is due to differences in the antenna separations at MS and BS, half-wavelength spacing at the MS results in correlated fading and four-wavelength spacing at the BS results uncorrelated fading.

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73

mag. [dB]

0 –10

rms AS = 20.7°

–20

–150

–100

–50

0

50

100

150

50

100

150

AoA [°]

mag. [dB]

0 –10

rms AS = 34.5°

–20

–150

–100

–50

0 AoD [°]

mag. [dB]

0 rms DS = 19.8 ns –10 –20 0

20

40

60

80

100

120

delay [ns] (a) 0

0 –5

magnitude [dB]

–5 –10 –15

–10

–20 200 50

100 0 0 AoD [°]

–15

–50 –100 –100

AoA [°] (b)

Figure 3.19 Scenario A1: (a) arrival and departure azimuth angles and delays of clusters; (b) magnitude, arrival and departure azimuth angles of clusters; (c) joint delay-Doppler spectrum; (d) fading profile of 2×2 MIMO channels.

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Radio Technologies and Concepts for IMT-Advanced

magnitude [dB]

0 –20 –40 –60 –80 300 –100

200

–50 0

Doppler frequency [Hz]

100 50

100

0

delay [ns]

(c) 0

–10

magnitude [dB]

–20

–30

–40

–50

–60

0

0.5

1 distance [m] (d)

Figure 3.19 (Continued)

1.5

2

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3.4.3 WINNER Path-loss Models Path-loss models for the various WINNER scenarios have been developed based on results of measurements carried out within WINNER, as well as results from the open literature. These path-loss models are typically of the form of Equation (3.16), where d is the distance between the transmitter and the receiver in metres, fc is the system frequency in GHz, the fitting parameter A includes the path-loss exponent, parameter B is the intercept, parameter C describes the path-loss frequency dependence, and X is an optional, environment-specific term (e.g., wall attenuation in the A1 NLOS scenario). f c [GHz] +X (3.16) P L = A log10 (d [m]) + B + C log10 5.0 The models can be applied in the frequency range from 2–6 GHz and for different antenna heights. 3.4.3.1 Frequency Dependencies of WINNER Path-loss Models The path-loss models shown in Table 3.7 are based on measured data obtained mainly at 2 and 5 GHz. These models have been extended to arbitrary frequencies in the range from 2–6 GHz with the aid of the path-loss frequency dependencies defined below. Table 3.7 either defines the variables of Equation (3.16) or explicitly provides a full path-loss formula. The free-space path loss PLfree referred to in the table can be written as P L free = 20log10 (d) + 46.4 + 20log10 ( f c /5.0)

(3.17)

The distribution of the shadow fading is log-normal, and the standard deviation for each scenario is given in the table. Following various results from the open literature, such as [RMB+06; CG99; JHH+05; Rudd03; SMI+02; KI04; YIT06], the frequency extensions are employed for the frequency coefficient C as shown in Table 3.7. As a summary, we can state that generally, the coefficient C lies between 20 and 23 dB. Sometimes this is shown implicitly, sometimes it is embedded in the coefficients, as in the formula for B1 NLOS. One peculiarity is the formula for LOS propagation beyond the break point, where the frequency dependence may be quite weak. It should also be noted that the break-point distance is also frequency-dependent. It can be seen that the frequency dependence is weaker in WINNER path-loss models than it is e.g. in the Okumura-Hata path-loss formula [OOK+68], where the frequency dependence coefficient C is 26 dB. This makes us assume that the frequency dependence is a function of frequency, and becomes smaller in the frequency range from 2 to 6 GHz. One useful action would be to generalise the WINNER path-loss models for frequencies in the range 0.4–2 GHz by merging them with some well-known current path-loss models. This has not yet been done, however. The NLOS path-loss model for scenario B1 is dependent on two distances, d1 and d2 . These distances are defined with respect to a rectangular street grid (see Figure 3.20), where the MS is shown moving along a street perpendicular to the street on which the BS is located (the LOS street). d1 is the distance from the BS to the centre of the perpendicular street and d2 is the distance of the MS along the perpendicular street, measured from the centre of the LOS street.

76

LOS

C1

a For

NLOS

B4

LOS

D1

30 m < d < dBP e

σ =4

50 m < d < 5 km, hBS = 32 m, hMS = 1.5 m

Same as C1 NLOS

σ =8

σ =8

10 m < d < d’BP c

σ =4

30 m < d < dBP 50 m < d < 5 km, hBS = 25 m, hMS = 1.5 m

σ =8

σ =4

σ =3 σ =4

A = 22.7, B = 41.0, C = 20 P L = min(P L(d1 , d2 ), P L(d2 , d1 )) where P L(dk , dl ) = P L LOS (dk ) + 20 − 12.5n j + 10n j log10 (dl ) + 3 log10 ( f c /5.0) n j = max(2.8 − 0.0024dk , 1.84), PLLOS is the path loss of B1 LOS scenario k,l ∈ {1,2} P L = P L b + P L tw + P L in , ⎧ ⎨ P L b = P L B1 (dout + din ) P L tw = 14 + 15(1 − cos(θ ))2 ⎩ P L in = 0.5din A = 23.8, B = 41.2, C = 20 P L = 44.9 − 6.55 log10 (h B S ) log 10(d) + 31.46 +5.83 log10 (h B S ) + 23 log10 f c 5.0 A = 26,B = 39, C = 20 P L = 44.9 − 6.55 log10 (h B S ) log 10(d) + 34.46 +5.83 log10 (h B S ) + 23 log10 f c 5.0 A = 21.5, B = 44.2, C =20 P L = 25.1 log10 (d) + 55.4 −0.13(h B S − 25) log10 (d/100) −0.9(h M S − 1.5) + 21.3 log10 f c 5.0

3 m < d < 100 m, hBS = hMS = 1. . .2.5 m same as A1 LOS, nw is the number, greater than 0, of walls between the BS and the MS 10 m < d1 < d’BP c 10 m < d1 < 5 km, w/2 < d2 < 2 kmd w = 20 m (street width), hBS = 10 m, hMS = 1.5 m When 0 < d2 < w/2, the LOS PL is applied. 3 m < dout + din < 1000 m, hBS = 10 m, hMS = 3 (nFl −1) + 1.5 m

σ =3 σ =4

A = 18.7, B = 46.8, C = 20 A = 36.8, B = 43.8, C = 20 and X = 5(nw–1 ) (light walls)

Applicability range, antenna height default values

σ =7

Shadow fading standard deviation (dB)

Path loss (dB)

b PL B1

one floor. For different floors, add floor loss (FL = 17 dB + 4(nf − 1), where nf > 0 is the number of floors between the BS and the MS). is the B1 path loss, PLC2 is the C2 path loss, dout is the distance between the outdoor terminal and the point on the wall that is nearest to the indoor terminal, din is the distance from the wall to the indoor terminal, θ is the angle between the outdoor path and the normal of the wall. nFl is the floor index (the ground floor has index 1). c d’ 8 BP = 4 h’BS h’MS fc /c, where fc is the centre frequency in Hz, c = 3.0 × 10 m/s is the propagation velocity in free space, and h’BS and h’MS are the effective antenna heights at the BS and the MS, respectively. The effective antenna heights h’BS and h’MS are computed as follows: h’BS = hBS – 1.0 m, h’MS = hMS – 1.0 m, where hBS and hMS are the actual antenna heights, and the effective environment height in urban environments is assumed to be equal to 1.0 m. d The distances d and d are defined in Figure 3.20. 1 2 e The break point distance, d , is computed as follows: d BP BP = 4 hBS hMS fc /c, where hBS , hMS , fc and c have the same definition as in Note c.

NLOS

LOS NLOSb

C2

NLOS

LOS NLOSa

NLOSa

LOSa

B1

A1

Scenario

Table 3.7 Summary of the WINNER path-loss models.

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+

MS

d2

d1

BS

d2

−

Figure 3.20 Geometry for d1 and d2 path-loss model in scenario B1. (Reproduced with the kind permission of ITU).

3.4.3.2 Transitions Between LOS and NLOS Conditions The WINNER channel model allows transitions between different propagation conditions, the most important of which are transitions between LOS and NLOS within the same WINNER scenario. In the A1 (indoor) and B1 (urban microcell) scenarios, transitions from LOS to NLOS can occur as a result of the MS turning from the corridor or street in which the BS is located (the LOS corridor or street) into a perpendicular corridor or street. An analysis of this specific case has indicated that such transitions can be adequately modelled by using the A1 or B1 LOS and NLOS path-loss models defined in Table 3.7. Let d1 and d2 denote the distances along the LOS corridor or street and the perpendicular corridor or street, respectively, as illustrated in Figure 3.20. The A1 LOS path-loss model is then considered to be applicable for values of d2 smaller than 3F 1 , where F 1 represents the radius of the first Fresnel zone (for a definition of Fresnel zones see [Sau99, Section 3.3.1]). For values of d2 greater than 3F 1 , the A1 NLOS path-loss model can be used. For the B1 scenario, a better fit to measured data was obtained by choosing the NLOS/LOS transition distance equal to 10F 1 . It is noted that, in most cases, reasonably good results can also be obtained by setting the transition distance equal to half the width of the LOS corridor or street, as reflected by the path-loss model for B1 NLOS in Table 3.7.

3.4.4 Values for Generic Channel Models Table 3.8 provides parameter values corresponding to the WINNER generic channel models. Parameter values related to elevation angles are provided in Table 3.9.

78

Cross-Correlationsc

Delay spread (DS) log10 ([s]) AoDa spread (ASD) log10 ([◦ ]) AoAa spread (ASA) log10 ([◦ ]) Shadow fading (SF) [dB] K-factor (K) [dB]

Scenarios

µ σ µ σ µ σ σ µ σ ASD vs DS ASA vs DS ASA vs SF ASD vs SF DS vs SF ASD vs ASA ASD vs K ASA vs K DS vs K SF vs K

Table 3.8 Parameters for generic models.

−7.42 0.27 1.64 0.31 1.65 0.26 3 7 6 0.7 0.8 −0.5 −0.5 −0.6 0.6 −0.6 −0.6 −0.6 0.4

LOS

A1

−7.60 0.19 1.73 0.23 1.69 0.14 4 N/A N/A −0.1 0.3 −0.4 0 −0.5 −0.3 N/A N/A N/A N/A

NLOS −7.39 0.36 1.25 0.42 1.76 0.16 7 N/A N/A 0.4 0.4 0.2 0 −0.5 0 N/A N/A N/A N/A

NLOS

B4

−7.44 0.25 0.40 0.37 1.40 0.20 3 9 6 0.5 0.8 −0.5 −0.5 −0.4 0.4 −0.3 −0.3 −0.7 0.5

LOS

B1

−7.12 0.12 1.19 0.21 1.55 0.20 4 N/A N/A 0.2 0.4 −0.4 0 −0.7 0.1 N/A N/A N/A N/A

NLOS −7.23 0.49 0.78 0.12 1.48 0.20 4/6b 9 7 0.2 0.8 −0.5 −0.5 −0.6 0.1 0.2 −0.2 −0.2 0

LOS

C1

−7.12 0.33 0.90 0.36 1.65 0.30 8 N/A N/A 0.3 0.7 −0.3 −0.4 −0.4 0.3 N/A N/A N/A N/A

NLOS −7.39 0.63 1 0.25 1.7 0.19 4/6b 7 3 0.4 0.8 −0.5 −0.5 −0.4 0.3 0.1 −0.2 −0.4 0.3

LOS

C2

−6.63 0.32 0.93 0.22 1.72 0.14 8 N/A N/A 0.4 0.6 −0.3 −0.6 −0.4 0.4 N/A N/A N/A N/A

NLOS

−7.80 0.57 0.78 0.21 1.20 0.18 4/6b 7 6 −0.1 0.2 −0.2 0.2 −0.5 −0.3 0 0.1 0 0

LOS

D1

−7.60 0.48 0.96 0.45 1.52 0.27 8 N/A N/A −0.4 0.1 0.1 0.6 −0.5 −0.2 N/A N/A N/A N/A

NLOS

−7.4 0.2 0.7 0.31 1.5 0.2 4 7 6 −0.1 0.2 −0.2 0.2 −0.5 −0.3 0 0.1 0 0

LOS

D2a

79

ζ [dB] DS ASD ASA SF K

10 4 16 20 5 5 3 4 5 3 4 N/A

11 4 12 20 5 5 6 7 6 2 6 6

2.4

8 5 4 10 11 17 7 N/A

9 11 12 20

2.2

Exp

NLOS

B4

3 18 3 9 13 12 14 10

9 3 8 20

3.2

Exp

LOS

B1 LOS

10 22 3 8 10 9 12 N/A

8 3 16 20

1 N

5 5 3 6 15 20 40 10

8 4 15 20

2.4

Uniform Exp ≤800 ns

NLOS

C1

2 10 3 40 30 30 50 N/A

4 3 14 20

1.5

Exp

NLOS

6 12 3 40 15 15 45 12

8 4 8 20

2.5

Exp

LOS

C2

2 15 3 40 50 50 50 N/A

7 3 20 20

2.3

Exp

NLOS

2 3 3 64 25 40 40 40

12 8 11 20

3.8

Exp

LOS

D1

2 3 3 36 30 40 120 N/A

7 4 10 20

1.7

Exp

NLOS

b

For determining the arrival and departure directions, we consider the downlink case, i.e. departure refers to BS and arrival refers to MS. The path loss models for the C1 LOS and D1 LOS scenarios contain separate shadowing standard deviations for distances smaller and greater than the breakpoint distance, respectively. c The sign of the shadow fading term is defined so that increasing values of SF correspond to increasing received power at the MS.

a

Number of clusters Number of rays per cluster Cluster ASD Cluster ASA Per cluster shadowing std Correlation distance [m]

µ σ

3

Wrapped Gaussian

AoD and AoA distribution Delay scaling parameter rτ XPR [dB]

Exp

Exp

Delay distribution

NLOS

LOS

Scenarios

A1

2 3 3 64 25 40 40 40

12 8 8 20

3.8

Exp

LOS

D2a

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Radio Technologies and Concepts for IMT-Advanced

Table 3.9 Elevation-related parameters for generic models. A1 Scenarios Elevation AoD spread (ESD)a Elevation AoA spread (ESA)a Cross-Correlations

µ σ µ σ ESD vs DS ESA vs DS ESA vs SF ESD vs SF ESD vs ESA

Elevation AoD and AoA distribution Cluster ESD Cluster ESA

B4

LOS

NLOS

NLOS

0.88 0.31 0.94 0.26 0.5 0.7 −0.1 −0.4 0.4 Gaussian

1.06 0.21 1.10 0.17 −0.6 −0.1 0.3 0.1 0.5 Gaussian

1.01 0.43 0.88 0.34 N/A 0.2 0.2 N/A N/A Gaussian

3 3

3 3

3 3

a

ESD and ESA refer to elevation angle spreads at the outdoor and indoor terminals, respectively.

System-level simulations require estimates of the probability of line of sight. For scenarios A2, B2, B4, C2 and C3, the LOS probability is approximated as zero. For the remaining scenarios, LOS probability models are provided in Table 3.10. These models are based on relatively limited data sets or specific assumptions and approximations regarding the location of obstacles in the direct path and should not, therefore, be considered exact. If the terminal locations are known with respect to a street grid or floor plan, which can be the case in grid-based scenarios such as A1 (indoor) and B1 (urban microcell), the WINNER channel model provides the option to determine the existence of NLOS/LOS propagation conditions deterministically. Table 3.11 provides median values of the large-scale parameters produced by the WINNER channel model for various scenarios.

Table 3.10 Line-of-sight probabilities. Scenario A1 B1 C1 C2 D1

LOS probability as a function of distance d (m) 1, d ≤ 2.5 3 1/3 PL O S = , d > 2.5 1 − 0.9 1 − 1.24 − 0.61 log10 (d) PLOS = min(18/d, 1) · (1 − exp(−d/36)) + exp(−d/36) d PLOS = exp − 200 PLOS = min(18/d, 1) · (1 − exp(−d/63)) + exp(−d/63) d PLOS = exp − 1000

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Table 3.11 Median output values of large-scale parameters: delay spread (DS), angular spread (AS), and elevation spread (ES). Scenario A1 B4 B1 C1 C2 D1 D2

LOS NLOS NLOS LOS NLOS LOS NLOS LOS NLOS LOS NLOS LOS

DS (ns)

AS at BS (◦ )

AS at MS (◦ )

ES at BS (◦ )

ES at MS (◦ )

40 25 49 36 76 59 75 41 234 16 37 39

44 53 18 3 15 6 8 10 8 6 9 5

45 49 58 25 35 30 45 50 53 16 33 32

8 11 10

9 13 10

3.5 Channel Model Usage The purpose of this section is to discuss issues concerning usage of the WINNER channel model for simulations.

3.5.1 System-level Description 3.5.1.1 Coordinate System Figure 3.21 shows a system layout in the Cartesian coordinate system. As shown in Table 3.12, all the BS and MS have (x,y) coordinates. MS and cells (sectors) also have array broadside orientation, where north (up) is the zero angle. The angles are positive in the clockwise direction (see Figure 3.22). Both the distance and line of sight (LOS) direction information of the radio links are calculated for the input of the model. A pairing matrix A is used to contain the information of simulated links. A link from celln to MSm that is not modelled has a value of 0 and a link that is modelled has a value of 1. The pairing matrix can be applied to select which radio links are generated.

3.5.1.2 Single User (Handover) Multicell Simulation A handover situation is characterised by a MS moving from the coverage area of one BS to the coverage area of another BS (see Figure 3.23). There are two base-stations or cells denoted c1 and c2, and one mobile station; each location of the mobile station on its path is assigned a unique label ms1 to msM. This is equivalent to a scenario with multiple mobile stations at different positions ms1 to msM. Path-loss is determined according to the geometry and large-scaley parameters correlate properly. The

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Radio Technologies and Concepts for IMT-Advanced

Y

cell3

cell1 MS1

cell2

MS2 BS1

cell4 cell6

MS3

cell5

BS2 x

Figure 3.21 System layout of multiple base stations and mobile stations.

resulting procedure is as follows:

1. Set base station locations c1 and c2 and array orientations according to geometry. 2. Set MS locations ms1 to msM and array orientations along the route. Choose the distance between adjacent locations according to the desired accuracy. 3. Set all the entries of the pairing matrix to 1. 4. Generate all the radio links at once to obtain the correct correlation properties. It is possible to generate more channel realisations, i.e. time samples, later for each channel segment. This can be done by applying the same values of small-scale parameters and restoring final phases of the rays. 5. Simulate channel segments consecutively to emulate motion along the route.

Table 3.12 Transceiver coordinates and orientations. Tranceiver BS1

BS2

MS1 MS2 MS3

cell1 cell2 cell3 cell4 cell5 cell6

Coordinates

Orientation (◦ )

(xbs1 ,ybs1 ) (xbs1 ,ybs1 ) (xbs1 ,ybs1 ) (xbs2 ,ybs2 ) (xbs2 ,ybs2 ) (xbs2 ,ybs2 ) (xms1 ,yms1 ) (xms2 ,yms2 ) (xms3 ,yms3 )

c1 c2 c3 c4 c5 c6 ms1 ms2 ms3

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83

N

BS array ΩBS

t

N qBSt , MSt

MS array

BS array broadside ΩMSt qMSt , BSt BS array broadside

Figure 3.22 BS and MS antenna array orientations.

It is also possible to model even more accurate time evolution between locations as described in Section 3.4. The clusters of the current channel segment (location) are replaced by clusters of the next channel segment one by one. Other cases of such a single-user multicell setup are found in the context of multiple-BS protocols, where a MS receives data from multiple BS simultaneously.

Figure 3.23 Handover scenario.

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Radio Technologies and Concepts for IMT-Advanced

Figure 3.24 A drive-by scenario (with multiple mobile stations).

3.5.1.3 Multi-user Multicell Simulation The extension of the single-user setup to multiple users (and one or more base stations) is straightforward. Because a location and a mobile station index are treated equivalently, it follows that all locations of all mobiles have to be defined. Consider the drive-by situation in Figure 3.24. Here, M locations of mobile station 1 and N locations of mobile station 2 are defined yielding a total of M+N points or labels. The resulting procedure is as follows. 1. 2. 3. 4.

Set BS locations c1 and c2 and array orientations according to layout. Set MS locations ms11 to ms2N and array orientations according to layout. Set the links to be modelled to 1 in the pairing matrix. Generate all the radio links at once to obtain the correct correlation properties. It is possible to generate more channel realisations, i.e. time samples, later for each channel segment. This can be done by applying the same values of small-scale parameters and restoring final phases of the rays. 5. Simulate channel segments in parallel or consecutively according to the desired motion of the mobiles.

3.5.2 SPACE–TIME Concept in Simulations In the end, the channel-sampling frequency has to be equal to the simulation-system sampling frequency. For the computational complexity to be feasible it is not possible to generate channel realisations on the sampling frequency of the system to be simulated. The channel realisations have to be generated on some lower sampling frequency and then interpolated to the desired frequency. A practical solution is e.g. to generate channel samples with sample density (over-sampling factor) of two, interpolate them accurately to sample density of 64 and apply zero-order hold interpolation to the system-sampling frequency. Channel impulse responses can be generated during the simulation or stored on a file before the simulation on low sample density. Interpolation can be done during the system simulation.

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To be able to obtain deep fades in NLOS scenarios, we suggest using 128 samples per wavelength. When obtaining channel parameters, quasi-stationarity has been assumed within intervals of 10–50 wavelengths. Therefore we propose to set the drop duration corresponding to the movement of up to 50 wavelengths.

3.5.3 Bandwidth and Frequency Dependence 3.5.3.1 Frequency Sampling The WINNER system is based on the OFDM access scheme. For simulations of the system, channel realisations in the time-frequency domain are needed. The output of a WINNER model is a channel in the time-delay domain. The time-frequency channel at any frequency can be obtained by applying the following two steps: 1. Define a vector of frequencies where the channel should be calculated. 2. Calculate the channel at defined frequencies using the Fourier transform. 3.5.3.2 Bandwidth Downscaling in the Delay Domain The channel models are delivered for the 100 MHz RF bandwidth. Some simulations may need smaller bandwidths. In describing how the downscaling should be performed, we assume that the channel parameters remain constant. There is a need for downscaling if the minimum delay sample spacing in the channel impulse response (CIR) is longer than 5 ns in the simulation. Five nanoseconds is the default minimum spacing for the channel model samples (taps) and defines thus the delay grid for the CIR taps. For all larger spacings, the model should be downscaled. The most precise way would be filtering by, e.g., a FIR filter. This would, however, create new taps in the CIR and this is not desirable. The preferred method in the delay domain is to move the original samples to the nearest location in the downsampled delay grid. In some cases, there are two such locations and the tap should be placed in the one that has the smaller delay. Sometimes two taps are located in the same delay position; they should be summed as complex numbers. 3.5.3.3 Bandwidth Downscaling in the Frequency Domain If desired, the downscaling can also be performed in the frequency domain. The original CIR specified in the delay domain is transformed into the frequency domain. The transformed CIR can be filtered, e.g. by removing the extra frequency samples or by resampling the frequency response. The maximum frequency sampling interval is determined by the coherence bandwidth: Bc =

1 Cστ

(3.18)

where σ τ is the rms delay spread and C is a scaling constant related to fading distribution. A typical value for C would be 2π for an arbitrary PDP [SMF05].

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Radio Technologies and Concepts for IMT-Advanced

3.5.3.4 FDD Modelling The following steps explain how to obtain both the uplink and downlink channel of an FDD system with bandwidths of 100 MHz. The centre carrier frequencies are fc and fc + fc . 1. 2. 3. 4. 5.

Define BS and MS positions. Calculate the channel for one link, e.g. BS to MS at a certain carrier frequency f c . Save the small-scale parameters. Exchange the positions of the BS and MS. Calculate the other link, in this example the MS to BS link by: r using saved small-scale parameters; r randomising the initial phases of rays; r changing the carrier frequency to f c + f .

3.5.4 Approximation of Channel Models The WINNER generic model is designed to apply to many different simulations and to cover a high number of scenarios with several combinations of large-scale and small-scale parameters. The generic model is the most accurate model and is recommended for use whenever possible. However, the channel model can be simplified (approximated) to reduce simulation complexity. It has to be done very carefully. When approximating the model, reality is reduced, and the impact of the approximation has to be understood. The impact depends on, e.g., the transceiver system, algorithms, modulation, coding, multi-antenna technology, and required accuracy of the simulation results. If you are uncertain whether the approximation affects the simulation results, it is better not to approximate. The following approximation steps should only be carried out by simulation experts. Firstly, we can approximate the model by assuming no correlation between large-scale parameters (angular spreads, delay spread, and shadowing). Secondly, all the large-scale parameters can be fixed to median values. Furthermore, we can reduce complexity of the model by fixing the delays, but keeping the angles as random. The fourth approximation can be done by freezing all propagation parameters to obtain the clustered delay line (CDL) model. If a correlation model is desired, the correlation matrices can be calculated from the CDL model by fixing the antenna structure. One set of possible approximations is shown below:

r generic model [WIN2D112]; r uncorrelated large-scale parameters; r fixed large-scale parameters; r constant delays, random angles (CDL with random angles); r CDL model (see Section 3.5.4.1). 3.5.4.1 Reduced Complexity Models A need has been identified for reduced-complexity channel models that can be used in rapid simulations having the objective of making comparisons between system alternatives at linklevel (e.g. modulation and coding choices). In this book, such models are referred to as reducedcomplexity models and have the character of the well-known tapped delay line class of fading

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channel models. Typical input to traditional tapped delay line models is the specification of relative powers, envelope-fading distributions, and fading rates. However more detailed information is required to address the needs of MIMO channel modelling. Specifically, multipath AoD and AoA information is inherent in the determination of tap fading characteristics. For these reasons, the reduced-complexity models reported here are referred to as cluster delay line (CDL) models. A cluster is centred at each tap. In general, each cluster consists of the vector sum of equal-powered MPCs (sinusoids), all of which have the same or similar delay and angles of arrival and departure. Each MPC has a varying phase, but has fixed AoA and AoD offsets. The angular offset depends on the angular spreads at the MS and the BS. The offset values were chosen to realise a specified Laplacian power azimuth spectrum (PAS) for each cluster, appropriate to the scenario being modelled. In cases where there is a desire to simulate Ricean-like fading, an extra MPC is added and given a power appropriate to the desired Rice factor and zero angular offset. The powers and delays of the clusters can be non-uniform, and can be chosen to realise the desired overall channel rms delay spread. Parameters of all CDL models reflect the expected values of those used in the more complex models described in other sections of this chapter. Doppler information is not specified explicitly for CDL models. This is because Doppler is determined by the AoAs of the MPCs, MS speed and direction, and the specified antenna patterns at the MS and BS, upon which there are no restrictions, except in fixed-feeder link scenarios. Although AoA and AoD values are fixed, it is recommended to have directional variation for e.g. beamforming simulations by adding network-layout-related angle parameters MS and BS to all tabulated angles (see Figure 3.22). In the CDL model, each cluster is composed of 20 rays with fixed offset angles and identical power. In the case of a cluster where a ray of dominant power exists, the cluster has 20+1 rays. This dominant ray has a zero angle offset. The departure and arrival rays are coupled randomly. The CDL table of all scenarios of interest are given in [WIN2D112]. The CDL models offer well-defined radio channels with fixed parameters to obtain comparable simulation results with relatively low-complexity channel models.

3.5.4.2 Comparison of Complexity of Modelling Methods Computational complexity of channel models is an important issue in system performance evaluation. A complexity comparison of the WINNER modelling approach with the popular correlation-matrix-based method is studied in [KJ07]. A common supposition is that the correlation method is simpler and computationally more effective than the geometric method. The conclusion of [KJ07] is that the complexity of both methods is about the same order of magnitude. With a high number (>16) MIMO antenna pairs, the correlation-based method is clearly more complex. The computation complexity is compared in terms of the number of ‘real operations’. With the term ‘real operations’ is equated the complexity of real multiplication, division, addition and table lookup. Figure 3.25a depicts the number of real operations per delay tap per MIMO channel time sample (matrix impulse response), with different M×N MIMO antenna numbers, assuming S = 10 or 20 rays (M in Equation (3.4)) and 8th order IIR filter in the correlation matrix method. It was also noted that the complexity of channel realisation generation (the bottom block in Figure 3.18) is several order of magnitudes lower than the computational

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Complexity of MIMO channel generation, MxN MIMO

4

5

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Figure 3.25 (a) Computational complexity comparison (b) Channel convolution complexity (from [KJ07]). (Reproduced by Permission of IEEE © 2009).

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complexity of the simulation of channel convolution (i.e. convolution of transmitted signal and channel impulse response). The computational complexity of channel simulation (i.e. convolution) is a linear function of simulated system bandwidth (see Figure 3.25b). For wideband, beyond-3G systems, the computational load of channel simulation is predominant, thus the method applied in channel coefficient generation has only marginal effect on the overall complexity. The channel simulation complexity is independent of the compared methods.

3.6 Conclusion During the two phases of the WINNER project, a comprehensive set of channel models was developed for 12 different propagation scenarios from indoor hotspot to metropolitan and wide-area scenarios. All models are based on one generic modelling principle, namely the geometry-based stochastic channel model. This means that all models can be obtained by changing only the model parameters to correspond to the propagation scenario. The parameters are obtained from measurements performed during the project and from other measurements presented in the literature. The generic model is complete in the sense that it describes the behaviour of all relevant phenomena in radio propagation: path-loss, shadowing, fast fading, temporal and spatial dispersion of propagation, cross-polarisation and various correlation properties of the propagation parameters. Due to its geometric nature, the modelling principle takes implicit care of the Doppler phenomenon that is caused by the movement of the terminal. The geometry-based modelling approach was selected because of some clear benefits compared to other approaches (such as the correlation-matrix-based approach): the geometry-based model is antenna independent – different antenna arrays and even different antenna radiation patterns can be covered with a single model; the geometry-based model utilises fully the propagation information. The WINNER channel model follows the same principles as some existing channel models, e.g., 3GPP/3GPP2 SCM. The strengths of the WINNER channel model are the following:

r parameters for large number of propagation scenarios; r support of arbitrary multi-antenna array; r variable large-scale parameters; r wide bandwidth. These characteristics make the WINNER channel models sufficient to be used in planning of future radio systems for very different environments, scalable RF bandwidths and different radio frequencies. In this respect, the WINNER model is unique. The channel models described in this book give a realistic picture of the principles of the model. The description does not cover the entire model with all the propagation scenarios and modelling details. Readers who would like to use the model are recommended to study the WINNER II deliverable [WIN2D112].

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Acknowledgements Authors would like to give thanks for the contributions of their colleagues in WINNER I and II, in particular, Mikko Alatossava, Daniel Baum, Robert Bultitude, Veli-Matti Holappa, Aihua Hong, Yvo de Jong, Marko Milojevi´c, Milan Narandˇzi´c, Terhi Rautiainen, Hassan ElSallabi, Christian Schneider, Reiner Thom¨a, Pertti Vainikainen, Juha Ylitalo, Per Zetterberg and Xiongwen Zhao.

References [3GPP03a] [3GPP03b] [3GPP04] [3GPP06] [3GPP07] [3GPP08]

[AHH+07]

[AHY06]

[BHS05] [BSK+07]

[Cal+07]

[CBH+07]

[CG99] [Cor01] [Cor06] [Ele08] [FTW06] [FXJS03]

3GPP (2003) Radio transmission and reception (Release 1999), TS 05.05 V8.16.0, Technical Specification Group GSM/EDGE Radio Access Network, 3GPP. 3GPP (2003) Spatial channel model for multiple input multiple output (MIMO) simulations (Release 6), TR 25.996, V6.1.0, Technical Specification Group Radio Access Network, 3GPP. 3GPP (2004) Deployment aspects (Release 6), TR 25.943, V6.0.0, Technical Specification Group Radio Access Network, 3GPP. 3GPP (2006) Physical layer aspects for evolved Universal Terrestrial Radio Access (UTRA), TR 25.814 V7.1.0, Technical Specification Group Radio Access Network, 3GPP. 3GPP (2007) User Equipment (UE) radio transmission and reception (FDD) (Release 7), TS 25.101, V7.7.0, Technical Specification Group Radio Access Network, 3GPP. 3GPP (2008) Evolved Universal Terrestrial Radio Access (E-UTRA); User Equipment (UE) radio transmission and reception, TR 36.803, V1.1.0, Technical Specification Group Radio Access Network, 3GPP. Alatossava, M., Hentil¨a, L., Holappa, V.-M. and Meinil¨a, J. (2007) ‘Comparison of Outdoor-to-Indoor and Indoor-to-Outdoor MIMO Propagation Characteristics at 5.25 GHz’, Proc. IEEE VTC 2007 Spring, Dublin, Ireland. Alatossava, M., Holappa, V.-M. and Ylitalo, J. (2006) ‘Outdoor to indoor MIMO radio channel measurements at 5.25 GHz: characterization of propagation parameters’, Proc. EuCAP 2006, Nice, France. Baum, D., Hansen, J., Salo, J., Del Galdo, G., Milojevic, M. and Ky¨osti, P. (2005) ‘An Interim Channel Model for Beyond-3G Systems’, Proc. IEEE VTC’05. Bultitude, R.J.C., Schenk, T.C.W., den Kamp, N.A.A.O. and Adnani, N. (2007) ‘A propagationmeasurement-based evaluation of channel characteristics and models pertinent to the expansion of mobile radio systems to frequencies beyond 2 GHz’, IEEE Trans. Vehicular Technology, 56(2): 382–8. Calcev, G., Chizhik, D., Goeransson, B., Howard, S., Huang, H., Kogiantis, A., Molisch, A.F., Moustakas, A.L., Reed, D. and Xu, H. (2007) ‘A Wideband Spatial Channel Model for System-Wide Simulations’, IEEE Trans. Vehicular Technology, March, pp. 389–403. Czink, N., Bonek, E., Hentil¨a, L., Nuutinen, J.-P. and Ylitalo, J. (2006) ‘Cluster-based MIMO channel model parameters extracted from indoor time-variant measurements’, Proc. IEEE GlobeCom 2006, San Francisco, USA. Chu, T.-S. and Greenstein, L.J. (1999) ‘A quantification of link budget differences between the cellular and PCS bands’, IEEE Trans. Vehicular Technology, 48: 60–65. Correia, L.M. (ed.) (2001) Wireless Flexible Personalised Communications: COST259: European Cooperation in Mobile Radio Research, John Wiley & Sons. Correia, L.M. (ed.) (2006) Mobile Broadband Multimedia Networks: Techniques, Models and Tools for 4G, Elsevier Science Publishers BV. Elektrobit Corporation (2008) EB Propsim Radio Channel Emulators, viewed 20 June 2009, www.propsim.com. FTW (2006) The COST 273 MIMO Channel Model Implementation, www.ftw.at/cost273. Fleury, B.F., Yin, X., Jourdan, P. and Stucki, A. (2003) ‘High-Resolution Channel Parameter Estimation for Communication Systems Equipped with Antenna Arrays’, Proc. SYSID 2003, Rotterdam.

WINNER II Channel Models

[Gud91] [HACK07]

[HKP+05]

[HNM+08] [IEEE04] [IEEE16] [ITU-R] [JHH+05] [JTC94] [JY07]

[JYTA06]

[KI04] [KJ07] [Lan02] [Med08] [MJKY08] [NKM+07]

[NLS+07]

[NST+07]

[OOK+68] [OTT+01]

[OVC06] [PCH01] [PJ94]

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Gudmundson, M. (1991) ‘Correlation model for shadow fading in mobile radio systems’, Electron. letter, 27: 2145–6, November. Hentil¨a, L., Alatossava, M., Czink, N. and Ky¨osti, P. (2007) ‘Cluster-level parameters at 5.25 GHz indoor-to-outdoor and outdoor-to-indoor MIMO radio channels’, Proc. 16th IST Mobile & Wireless Communications Summit 2007, Budapest, Hungary. Hentil¨a, L., Ky¨osti, P., Ylitalo, J., Zhao, X., Meinil¨a, J. and Nuutinen, J.-P. (2005) ‘Experimental Characterization of Multi-Dimensional Parameters at 2.45 GHz and 5.25 GHz Indoor Channels’, Proc. Wireless Personal Multimedia Communications, Aalborg, Denmark. Hentil¨a, L., Narandˇzi´c, M., Meinil¨a, J. and Ky¨osti, P. (2008) ‘Measurement based parameter extraction for WINNER radio channel’, Proc. URSI GA’08, Chicago, USA. IEEE (2004) IEEE P802.11 Wireless LANs: TGn Channel Models, IEEE 802.11-03/940r2. IEEE 802.16 Broadband Wireless Access Working Group (2001) Channel Models for Fixed Wireless Applications, www.ieee802.org/16/tg3/contrib/802163c-01 29r4.pdf. ITU-R (1997) Guidelines for Evaluation of Radio Transmission Technologies for IMT-2000 (Question ITU-R 39/8), Recommendation M.1225, International Telecommunication Union. J¨ams¨a, T., Hovinen, V., Hentil¨a, L. and Iinatti, J. (2005) ‘Comparisons of Wideband and Ultra-wideband channel measurements’, Proc. IEEE IWS2005/WPMC2005, Aalborg, Denmark. Joint Technical Committee of Committee T1 R1P1.4 and TIA TR46.3.3/TR45.4.4 on Wireless Access (1994) ‘Draft Final Report on RF Channel Characterization’, Paper No. JTC(AIR)/94.01.17-238R4. J¨ams¨a, T. and Ylitalo, J. (2007) ‘(New) White Paper on the standardization of radio channel models for wireless communications’, Proc. of 18th Wireless World Research Forum (WWRF) meeting, Helsinki. J¨ams¨a, T., Ylitalo, J., Thom¨a, R.S. and Alexiou, A., (eds) (2006) ‘Multi-Dimensional Radio Channel Measurement and Modeling for Future Mobile and Short-Range Wireless Systems’, Proc. of 17th Wireless World Research Forum (WWRF) meeting, Heidelberg, Germany. Kitao, K. and Ichitsubo, S. (2004) ‘Path loss prediction formula for microcell in 400 MHz to 8 GHz band’, Electronics Letters 40(11). Ky¨osti, P. and J¨ams¨a, T. (2007) ‘Complexity Comparison of MIMO Channel Modelling Methods’, Proc ISWCS’07, Trondheim, Norway. Laneman, J.N. (2002) ‘Cooperative Diversity in Wireless Networks: Algorithms and Architectures’, PhD thesis, Massachusetts Institute of Technology, Cambridge, MA, August. Medav (2008) Channelsounder, viewed 20 June 2009, www.channelsounder.de. Meinil¨a, J., J¨ams¨a, T., Ky¨osti, P. and Ylitalo, J. (2008) ‘Propagation modeling for evaluation of 4G systems’, in URSI General Assembly, Illinois, USA. Narandˇzi´c, M., Ky¨osti, P., Meinil¨a, J., Hentil¨a, L., Alatossava, M., Rautiainen, T., de Jong, Y.L.C., Schneider, C. and Thom¨a, R.S. (2007) ‘Advances in “Winner” Wideband MIMO System-Level Channel Modelling’, Proc. Second European Conference on Antennas and Propagation, pp. 1–7, Edinburgh, UK. Narandˇzi´c, M., Landmann, M., Schneider, C. and Thom¨a, R.S. (2007) ‘Influence of Extraction Procedures on Estimated Wideband MIMO Channel Parameters’, Proc. IST Mobile & Wireless Communication Summit, Budapest, Hungary. Narandzic, M., Schneider, C., Thom¨a, R.S., J¨ams¨a, T., Ky¨osti, P. and Zhao, X. (2007) ‘Comparison of SCM, SCME, and WINNER Channel Models’, Proc. IEEE Vehicular Technology Conference (VTC) Spring, Dublin. Okumura, Y., Ohmori, E., Kawano, T. and Fukuda, K. (1968) ‘Field strength and its variability in VHF and UHF land-mobile radio services’, Review of the Electrical Comm. Lab., 16(9). Oda, Y., Tsuchihashi, R., Tsunekawa, K. and Hata, M. (2001) ‘Measured path loss and multipath propagation characteristics in UHF and microwave frequency band for urban mobile communications’, Proc VTC 2001 Spring, 1: 337–41. Oestges, C., Vanhoenacker-Janvier, D., and Clerckx, B. (2006) ‘Channel Characterization of Indoor Wireless Personal Area Networks’, IEEE Transactions on Antennas and Propagation, 54(11): 3143–50. Perahia, E., Cox, D. and Ho, S. (2001) ‘Shadow fading cross-correlation between base stations’, Proc. IEEE VTC 2001, pp. 313–17. Perez, V. and Jimenez, J. (eds) (1994) Final Propagation Model, CoDiT Deliverable number R2020/TDE/PS/DS/P/040/bl.

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Rautiainen, T., Juntunen, J. and Kalliola, K. (2007) ‘Propagation analysis at 5.3 GHz in typical and bad urban macrocellular environments’, Proc. IEEE 65th Vehicular Technology Conference (VTC), Dublin, April. [RKJ05] Rautiainen, T., Kalliola, K., and Juntunen, J. (2005) ‘Wideband radio propagation characteristics at 5.3 GHz in suburban environments’, Proc. PIMRC Berlin, 2: 868–72. [RMB+06] Riback, M., Medbo, J., Berg, J.-E., Harrysson, F. and Asplund, H. (2006) ‘Carrier Frequency Effects on Path Loss’, Proc. IEEE VTC 2006 Spring, 6: 2717–21. [Rudd03] Rudd, R.F. (2003) ‘Building penetration loss for slant-paths at L-, S- and C-band.’, Proc. ICAP 2003. [S07] Suikkanen, E. (2007) ‘Wideband Radio Channel Measurements and Modelling of an Outdoor-to-Indoo Propagation Scenario at 5.25 GHz’, Master’s Thesis, University of Oulu, Finland. [Sau99] Saunders, S. (1999) Antenna and propagation for communication systems concept and design, John Wiley & Sons, Ltd, Chichester. [SMB01] Steinbauer, M., Molisch, A.F. and Bonek, E. (2001) ‘The double-directional radio channel’, IEEE Antennas and Propagation Mag., August, pp. 51–63. [SMF05] Sorensen, T.B., Mogensen, P.E., and Frederiksen, F. (2005) ‘Extension of the ITU channel models for wideband (OFDM) systems’, Proc. IEEE VTC2005-Fall, 1: 392–6, Dallas, USA. [SMI+02] Sakawa, K., Masui, H., Ishii, M., Shimizu, H. and Kobayashi, T. (2002) ‘Microwave path-loss characteristics in an urban area with base station antenna on top of a tall building’, Proc. Int. Zurich Seminar on Broadband Communications, pp. 31-1–31-4. [SV87] Saleh, A. and Valenzuela, R.A. (1987) ‘A statistical model for indoor multipath propagation’, IEEE J. Select. Areas Commun., SAC-5(2): 128–37. [USM95] Urie, A., Streeton, M. and Mourot, C. (1995) ‘An advanced TDMA Mobile Access System for UMTS’, IEEE Personal Communications Magazine, 2(1): 38–47. [WIN08] WINNER+ (2008) WINNER 1: Channel Model Implementations, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+. [WIN1D54] WINNER I (2005) Final Report on Link Level and System Level Channel Models, Deliverable D5.4, November 2005, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN1D72] WINNER I (2004) System Assessment Criteria Specification, Deliverable D7.2, v1.0, July 2004, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+. [WIN2D112] WINNER II (2007) WINNER II Channel Models, Part I Channel Models, Deliverable D1.1.2, v1.2, February 2008, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WL02] Weitzen, J. and Lowe, T. J. (2002) ‘Measurement of angular and distance correlation properties of log-normal shadowing at 1900 MHz and its application to design of PCS systems’, IEEE Transactions on Vehicular Technology, 51(2). [YIT06] Yonezawa, I., Ishikawa, H. and Takeuchi, Y. (2006) ‘Frequency range extension of path loss prediction formula for over-rooftops propagation in microwave band’, Proc. IEEE International Symp. Antennas Propagation, pp. 4747–50. [ZHM+07] Zhao, X., Hentil¨a, L., Meinil¨a, J., Jamsa, T., Ky¨osti, P. and Nuutinen, J.-P. (2007) ‘Correlations of wideband channel parameters in street canyon at 2.45 and 5.25 GHz’, IEEE Antennas Wireless Propagat. Letters, 6: 252–4. [ZKH+06] Zhao, X., Ky¨osti, P., Hentil¨a, L., J¨ams¨a, T., Meinil¨a, J., Laselva, D. and Nuutinen, J.-P. (2006) ‘Indoor, rural and suburban channel models and parameters for B3G link and system level simulations’, Proc. IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications, Helsinki, Finland. [ZMH+07] Zhao, X., Meinil¨a, J., Hentil¨a, L., J¨ams¨a, T., Ky¨osti, P. and Nuutinen, J.-P. (2007) ‘Effects of noise cut for extraction of wideband channel parameters’, Proc. IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2007, Athens, Greece.

4 System Concept and Architecture Martin D¨ottling,1 Mikael Sternad,2 Johan Nystr¨om,3 Niklas Johanson,3 and J¨orn von H¨afen1 1

Nokia Siemens Networks Uppsala University 3 Ericsson 2

4.1 Introduction This chapter reviews important design principles and uses a top-down approach to describe the WINNER Radio Access Network (WRAN) based on logical nodes and interfaces. Furthermore, the framework for the protocol and service architecture necessary for interoperability is described. Together, they represent the WINNER II system concept. An important requirement for communication systems and networks is interoperability between different vendors’ equipment. The main advantage of interoperability is that resources are exploited in an efficient way and economies of scale can prevail. This is of benefit to subscribers, service providers, manufacturers, and to the entire economy and society as a whole. This chapter describes the two most important characteristics of a system concept that must be standardised to enable interoperability: the logical node architecture (Section 4.3) and the protocol architecture (Section 4.4). We begin in Section 4.2 by reviewing the design principles and desirable characteristics of the WRAN that have influenced the resulting system concept. Numerous aspects and results of the WINNER projects are important for the performance and flexibility of the concept, but they would not necessarily have to be standardised from an interoperability point of view. The collection of algorithm proposals and best design guidelines that represent the WINNER II reference design is outlined in the other chapters. The logical node architecture is a framework that describes functions, groups them as logical nodes and connects them by well-defined, open interfaces. Logical nodes are therefore defined as the smallest entities in the radio access network (including user terminals) for which interoperable interfaces need to be defined that are independent of a specific vendor. The logical node architecture is partly inspired by the 3GPP LTE/SAE architecture, but also Radio Technologies and Concepts for IMT-Advanced Martin D¨ottling, Werner Mohr and Afif Osseiran C 2009 Martin D¨ ottling, Werner Mohr, Afif Osseiran

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contains many novel features such as a server for spectrum management and optional relaying functions. The protocol architecture describes the interfaces between logical nodes. It has a layered structure, but interaction between layers plays a crucial role in increasing the efficiency of the wireless packet data transfer. The scheduler function outlined in Section 4.4.3.3 plays a central role.

4.2 Design Principles and Main Characteristics In order to meet the requirements detailed in [PDH+06; WIN2D6114], the WINNER radio access network has been designed as a packet-oriented, user-centric, always-best concept. An always-best solution that provides competitive performance in a wide variety of situations is a challenging design goal. Different parameterisations are used to provide flexibility and maximum efficiency depending on the particular radio environment, usage scenario, economic model, etc. The always-best solution is further supported by the flexible protocol architecture of the WINNER radio interface and incorporates mechanisms for both long-term and shortterm adaptation. Both relaying and advanced spatial processing are integrated elements of the system architecture. The always-best solution is enabled by several innovative key components, including:

r A flexible logical node architecture that uses as few logical nodes as possible to keep the number of interfaces small. This is important for reducing cost and simplifying the number of interfaces that have to be taken into account by different vendors. The function grouping is defined such that it enables and encourages flat, flexible, scalable and cost-efficient physical node implementations. The logical nodes are defined in such a way that physical RAN implementations can efficiently use them as pooled resources (to avoid a single point of failure, for load-balancing, and trunking-gain purposes). r A flexible protocol architecture enabling efficient interworking between different system parameterisations is outlined in Section 4.4. The architecture focuses on the three lowest layers of the OSI stack. The two lowest layers, represented by the physical (PHY), medium access control (MAC) and radio link control (RLC) sublayers, are assumed to be present in all base stations (BS), user terminals (UT) and relay logical nodes (RN). The assumed co-location enables an efficient co-design of these layers and supports low latencies over the air interface. r Relay-enhanced cells are an integral part of the concept. A deployment can utilise advanced decode-and-forward layer-2 relay nodes, whenever this is deemed cost efficient. Such relay nodes can be used to optimise the deployment, reduce the cost, extend the range of transmission, cover shadowed areas, and re-distribute the offered capacity between cell centres and borders. Relay-enhanced cells are discussed in detail in Chapter 8 and in [WIN2D351; WIN2D352; WIN2D353]. r Design and support for operation in a shared spectrum and inter-system coordination. System functionalities that support shared spectrum use and inter-system coordination are presented in Chapters 10 and 11 of this book and in [WDK+08; WIN2D481]. r Highly optimised user-plane processing. In-band, packet control signalling is optimised across layers to provide a small overhead by means of segmentation and concatenation at RLC and multiplexing at MAC providing one single transport block per frame per UT. The

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cross-layer design of hybrid automatic repeat request (H-ARQ) and RLC-ARQ minimises overhead and status reporting. For both cases, multi-hop operation is supported efficiently. r MAC designed for short radio interface delays. A low-latency, over-the-air interface is desirable for several reasons: it enables adaptivity with respect to fast channel variations; it facilitates high throughput for TCP/IP traffic and low-latency services; and it enables fast link retransmission, which is an advantage for perceived performance at higher layers. The system concept provides a short-delay, over-the-air interface: 1 ms in downlinks and 2 ms in uplinks over single hops. It achieves this by a combination of short frame durations and tight feedback-control loops. r Advanced interference-control mechanisms. Multi-antenna transmission and reception is integrated into the WINNER system concept (see Chapter 7). It offers versatile tools for suppressing interference at receivers [WIN2D473] and intelligent transmission processing [WIN2D472]. The WINNER concept also enables the use of additional mechanisms such as resource allocation that targets interference avoidance by coordinated scheduling across base stations and relay nodes or the use of (spatial) precoding over distributed antennas. r An optimised physical layer design. The WINNER physical layer concept is based on generalised multi-carrier transmission. This technique is based on cyclic-prefix orthogonal frequency division multiplexing (OFDM) in combination with extra discrete Fourier transform (DFT) precoding steps. Both single-carrier and multi-carrier waveforms can be generated and received within one transmitter/receiver chain with low computational complexity [WIN1D210]. The basic resource elements assumed in the WINNER concept are outlined in Section 4.4.3.3. The general transmission technique can be applied in different configurations to ensure low complexity, high spectral efficiency, and high granularity of resource elements. The physical layer processing is described in detail in Chapters 5, 6, and 7 as well as in [WIN2D223; WIN2D233; WIN2D341]. r A spatial multi-user link adaptation concept that can be applied to a wide range of deployments, operational scenarios, propagation channels, service requirements, and terminal capabilities. The core elements are: r A scheduler that uses tight inter-layer interaction between the RLC, MAC, and PHY layers to accomplish joint link adaptation and channel-aware scheduling (see Chapter 9). r Two resource allocation principles (see Chapter 9): frequency-adaptive transmission uses individual link adaptation within rectangular time–frequency units (chunks), supported by efficient control signalling; Non-frequency-adaptive transmission uses smaller time–frequency blocks for diversity-based transmission. Both use the advanced channel coding schemes outlined in Chapter 5 (complete descriptions are available in [WIN2D223]). r Control signalling that supports adaptive transmission with reasonable overheads, achieved by a combination of efficient source and channel-coding techniques, as well as an adaptive control channel format to support usage scenarios from large wide-area cells to small local-area hotspots [SSD08; WIN2D61314]. r A generic multi-antenna transmit/receive scheme, which can be configured into various diversity, multiplexing and multi-user MIMO configurations (Chapter 7). The spatial transmission can be adjusted individually to the needs of different packet flows to and from a user terminal. r Pilot schemes that support various types of adaptive transmission and multi-antenna transmission with acceptable overheads (Chapter 6).

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4.3 Logical Node Architecture The WINNER system is designed to work well over a wide range of operating scenarios. The logical node architecture represents a high-level functional abstraction of all of these designs and shows the basic flow of user data, control data, and functional interactions without having to consider specific physical design implementations or scenario-dependent parameter settings. The logical node architecture is a framework for the description of the WINNER system concept that identifies functions, groups them into logical nodes and connects the nodes by well-defined, open interfaces. Logical nodes are defined as the smallest entities in the radio access network, including the user terminal, for which interoperable interfaces are defined that are independent of the implementation of a specific vendor. This enables operators to buy interoperable equipment from multiple vendors. Physical nodes are particular implementations, physical devices that may contain multiple logical nodes depending on the specific deployment scenario. The logical node architecture also offers a compact, top-down overview of the WINNER system starting from an abstract level and refining down to detailed protocol definitions.

4.3.1 Overview The WINNER Radio Access Network (WRAN) is connected to an external packet data network (e.g. the Internet) via the IG interface (see Figure 4.1). The WRAN provides the IWU radio interface to connect WINNER terminals (‘UT’). Strictly speaking, the WRAN also contains functionalities, such as gateway functionalities, that are more related to the core network than the radio network. Nevertheless, for simplicity we will use the term WRAN to denote the access network. In order to accommodate handover and other cooperation functions (e.g., spectrum sharing) between WRANs and non-WINNER access networks, WINNER has to comply with a cooperation architecture provided outside the WINNER RAN. Thus, each of the WRANs has to support its respective functionalities, e.g. to provide support functions such as measurements. As is shown in Figure 4.2, the WINNER logical node architecture [WDK+08] consists of the base station logical node, BSLN , the relay node logical node, RNLN , the user terminal logical node, UTLN , the SpectrumServerLN and RRMServerLN logical nodes and two types of gateway logical node: the IP anchor, GW IPALN , and the control, GW CLN .

Internet, operator services, etc. IG

WINNER Radio Access Network Interface Logical node

IWU

UTLN

Figure 4.1 Overview of the WINNER architecture.

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IG GW_IPALN

IGS

IGPC

SpectrumServerLN

IGPB

GW_CLN

IGCB

RRMserverLN IBRRM BSLN WINNER Radio Access Network

IWU UTLN

IBB

BSLN IWU UTLN

IBRN

RNLN IWU UTLN

Figure 4.2 WINNER logical nodes and interfaces.

The logical nodes are connected by logical interfaces denoted as ‘I’. Protocols are defined by the termination points and the interfaces between them. For a detailed description of the interfaces, the reader is referred to [WIN2D61314]. The GW IPALN provides access to external data networks (e.g., the Internet, corporate networks, or operator-controlled core networks) and operator services (e.g., Multimedia Broadcast Multicast Service (MBMS)). It also terminates flows on the network side and serves as the anchor point for external routing. Thus, all functions that operate on user data traffic are located here. It is accompanied by the GW CLN , which provides control functions for UTLN s that are not active (i.e., terminals that are in idle mode) and functions that control and configure the GW IPALN . In practice, there will most likely be several GW IPALN s present in the network as well as an independent number of GW CLN s. The WRAN contains two types of radio access point (RAP): BSLN and RNLN . The RNLN is a logical network node with relaying capabilities that is wirelessly connected to a BSLN or another RNLN . The RNLN communicates with the BSLN using the IBRN interface. A WINNER terminal, UTLN , is connected to the WRAN via the IWU interface and communicates either directly with the BSLN or indirectly to the BSLN via the RNLN . The IWU interface is exactly the same whether the connecting node is a base station or a relay node. The BSLN performs all radio-related functions for active terminals (i.e., terminals sending data) and is responsible for governing radio transmission to and reception from UTLN s and RNLN s in one cell. The BSLN also controls the resources the RNLN uses. The BSLN s are connected to each other by the IBB interface. The BSLN is connected to the GW IPALN and GW CLN pair via the IGPB and IGPC interfaces, respectively. These interfaces are multinode-to-multinode-capable interfaces, meaning that one BSLN can be connected to multiple GW IPALN and GW CLN pairs and, conversely, that one GW IPALN and GW CLN pair can be connected to multiple BSLN s. The GW IPALN and GW CLN form a pool of equipment that may cover large areas, e.g. cities. A UTLN is said to be associated with a GW IPALN and GW CLN pair when the GW IPALN provides an anchor point for the traffic to and from the UTLN and the GW CLN controls

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functionality for the UTLN . Two UTLN s connected to the same BSLN can be associated with different GW IPALN s and GW CLN s. Conversely, two UTLN s associated with the same GW IPALN and GW CLN pair can be connected to different BSLN s. The SpectrumServerLN is a logical node that contains centralised functions related to spectrum sharing and spectrum assignment [WDK+08]. It enables sharing and co-existence with other radio access technologies and spectrum assignment between WRANs. The SpectrumServerLN interfaces with the GW CLN to obtain information about associated UTLN s and their characteristics. The SpectrumServerLN communicates spectrum-sharing and spectrum-assignment decisions to the BSLN . The SpectrumServerLN is accessible via the GW IPALN for spectrum negotiations. The RRMserverLN is a logical node that contains centralised functions related to radioresource management.

4.3.2 Pool Concept and Micro Mobility The GWLN pool concept decouples the physical relation between a GWLN and a number of BSLN s in a pool area. The pool area is defined as an area in which a UTLN may roam without needing to change GWLN . Each GWLN is connected to each BSLN in the pool area. As is shown in Figure 4.3, there is at any moment an association between a UTLN and an IP anchor, GW IPALN , and between the UTLN and a control, GW CLN . The association between a GW IPALN and a GW CLN for one UTLN may be different from that of another UTLN connected to the same BSLN . For example, two UTLN s may be associated with the same GW CLN but different GW IPALN s. One advantage of defining these two types of gateways is improved dimensioning and scalability of the gateway functionalities according to the need in the deployed network. Furthermore, it gives vendors more freedom to build e.g. a physical base station product with the user-plane gateway functionalities close to the BSLN , while having a more centrally located

IG

IG

GW_IPALN IGS

GW_IPALN IGPC

IGPB

SpectrumServerLN

GW_CLN

RRMserverLN IGCB

IBRRM BSLN WINNER Radio Access Network

IWU UTLN

IBB

BSLN IWU UTLN

IBRN

RNLN IWU UTLN

Figure 4.3 One set of GW CLN may serve a (larger) set of GW IPALN . Each UTLN is associated with one GW CLN –GW IPALN pair (from [WDK+09]). (Reproduced by Permission of IEEE © 2009).

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control server. A drawback with the division of gateway functionalities in this way is that there is a need for a new standardised interface, IGPC. There is also an interface ICC between multiple instances of GW CLN for relocation and information transfer between two GW CLN . If the GW IPALN is relocated, or if two BSLN s are simultaneously relocated (a rare event since we have overlapping pool areas) and lack an IBB interface (also a rare event), packet forwarding between these two BSLN s is performed by the two GW IPALN s. Hence there is no need for an interface between the two GW IPALN s since no control signalling is performed between them. For simplicity and visual clarity in the figures, when this split is not of essence for the discussion, the GW IPALN –GW CLN pair is represented by one logical node, the GWLN . In addition, the interfaces IGPB and IGPC , can be represented by the interface IGB. In most of this document, we use this simplified view (see Figure 4.4). The logical association between a UTLN and the GWLN is independent of the BSLN s and can be kept during a handover (see Figure 4.5). Hence, the set of GWLN can be seen as a pool of resources. Within a pool, each BSLN can forward user traffic to or from any GWLN ; conversely, each GWLN can communicate with all BSLN s. Of course, the UTLN –GWLN association can change if necessary, e.g. for load-balancing purposes. This also facilitates network scalability. The GWLN pool concept decouples the physical relation between a GWLN and a number of BSLN s in a pool area. Instead, each GWLN is connected to each BSLN in the pool area. From that, the pool area is defined as an area in which a UTLN may roam without the need to change GWLN . The GWLN capacity of a pool area can be scaled simply by adding more GWLN s (more accurately, GW IPALN s and GW CLN s can be added independently). In contrast, in a hierarchical structure, each BSLN is connected to the one GWLN that serves its location area. There are several benefits of having the set of GWLN s as a pool of resources:

r It reduces the requirements for micro mobility since the GWLN is an anchor point for external routing.

r The pool capacity is easily optimised by adding or removing GWLN s. IG GW GW_C

GW_IPA

IGS SpectrumServerLN IGB RRMserverLN IBRRM BSLN WINNER Radio Access Network

IWU UTLN

IBB

BSLN IWU UTLN

IBRN

RNLN IWU UTLN

Figure 4.4 Simplified representation of the GW IPALN and GW CLN as GWLN .

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Internet, operator services, etc.

GW LN

GW LN

WINNER Radio Access Network Handover

BSLN

UTLN

BSLN

BSLN

UTLN

UTLN

Figure 4.5 The UTLN –GWLN association is normally kept during handover.

r Single points of failure are avoided: should a GWLN fail, the users can be handed over to any other GWLN .

r Load balancing can easily be achieved, e.g., to support services such as MBMS. The pool concept includes logical associations from each GWLN to each BSLN , which means that fully meshed interconnections (i.e. a switching or routing function) are needed (see Figure 4.6). For the routing, L3 technologies such as IP may be feasible provided they implement a virtual logical connection from each GWLN to each BSLN that allows micro mobility without reconfiguration of routing tables or change of IP addresses. An IP-based

GW1LN

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Routing function needed

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BS2LN

BS3LN

BS4LN

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BS6LN

Pool area Figure 4.6 Transport network for meshed RAN structure (pool concept).

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tunnelling protocol that provides tunnels between GWLN s and BSs is envisaged. The GWLN routes any data for a specific BSLN by addressing the corresponding tunnel. The aim for a large pool area may lead to a situation where a UTLN is associated with a GWLN that is located very far away from the BSLN it is connected to. This may increase the signalling load and memory requirements compared to a hierarchical structure in which the signalling is kept locally in the location area of a GWLN . However, each vendor may provide intelligent functions for optimising these associations with respect to delay and signalling load.

4.3.3 Equipment Sharing It is an important requirement that multiple operators can share part of the same physical RAN, for example, the transport network, and the physical BSs and RNs. This is supported by multiple sets of GWLN s, each connecting to different operator backbone networks. Each UTLN can then associate with the GWLN relevant for the operator in question (see Figure 4.7). The operators will thus be able to share the physical BSs and RNs, as well as the transport network (not depicted) connecting the BSs. The operators must have different sets of GWLN but nothing prevents a vendor from manufacturing physical GWs that can host multiple logical GWLN s belonging to different operators, thus enabling operators to share physical GWs.

Internet, operator services, etc.

GW LN

Internet, operator services, etc.

GW LN

GW LN

WINNER Radio Access Network

BSLN

UTLN

BSLN

UTLN

UTLN

Figure 4.7 Equipment-sharing support in the WINNER logical node architecture.

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4.3.4 Multicast and Broadcast Service Support Support for multicast and broadcast service (MBMS) has been identified as an important requirement for WINNER. While unicast traffic can be handled efficiently with a one-to-one association from a GW IPALN to a UTLN , the situation for MBMS is different. An efficient implementation of an MBMS service includes a central entity that multicasts the traffic to all base stations with connected UTLN s that are using this service. In a hierarchical concept, that is obviously simple because the MBMS traffic only has to be distributed to the GW IPALN s to which the UTLN s are connected. Forwarding within the location area is handled by the appropriate GW CLN and the signalling traffic is kept on the transport network that is logically behind this GW IPALN . UTLN s moving between location areas may require only an update of the routing to the GW IPALN s. In the pool concept, one GW IPALN has to take the role of a central anchor point that distributes the MBMS traffic to all UTLN s within the pool area that subscribe to this service. They may, however, be associated with different GW IPALN s for their unicast traffic. In addition, multiple MBMS sessions may be ongoing in parallel to many (partly overlapping) UTLN groups. In order to support MBMS, a UTLN is required to associate with multiple GW IPALN s. In this case, it is assumed that unicast traffic of a UTLN is still terminated by a single GW IPALN so that the one-to-one relation between the GWLN and the UTLN is kept for unicast traffic, see Figure 4.8. The multicast flows for each UTLN may however come from a different GW IPALN . In the pool concept, this means that the GW IPALN anchoring MBMS traffic has to be updated with all cell changes of UTLN s that are using this MBMS service. As the forwarding of all these UTLN s is handled by different GW IPALN s, in the general case (their unicast GWLN s), and no inter-GW IPALN interface is intended, the unicast forwarding

Internet, operator services, etc

Unicast

MBMS GW LN

GW LN

WINNER Access Network BSLN

UTLN

UTLN

BSLN

UTLN

Figure 4.8 Multiple associations between UTLN s and GWLN s.

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Internet, operator services, etc.

GW LN

GW LN

GW LN

WINNER

BS LN

BSLN

BS LN

Case A

UTLN

Case B

UTLN

Figure 4.9 Case A: Combined unicast and multicast traffic and Case B: MBMS using single frequency network.

procedures cannot be used to update the MBMS GWLN about any cell changes of the UTLN s. Thus, an additional forwarding update procedure for the MBMS GWLN must run in parallel to the unicast forwarding procedure. Broadcast and multicast traffic network may be transmitted in a single frequency network (SFN) network by multiple BSLN s in the same frequency band so that the UTLN s are able to utilise the multiple signals, see Figure 4.9. In this case, the broadcast or multicast traffic is transmitted from the GWLN to a serving BSLN . It is the same BSLN that controls the unicast traffic to a particular UTLN . In case A, the UT is associated with one GW for unicast traffic, and another for dedicated MBMS; in case B, multiple BSs form a single frequency network: the UT is associated with one MBMS GW and receives MBMS data from multiple BSs forming an SFN. Mechanisms for coordinating simultaneous multicast transmissions from multiple base stations to pools of user terminals are required. For WRAN, it is assumed that the GW CLN controls the respective BSLN in the single frequency network area.

4.3.5 Multiband Transmission from Different BSs The logical node architecture also supports simultaneous transmissions from multiple BSLN s to one UTLN (as shown in Figure 4.10) to support overlay networks. As an example, it enables flows (e.g. VoIP) that are characterised by a low data rate but are sensitive to delay and jitter are transmitted by an overlay wide-area cell to avoid frequent handover situations while highdata-rate flows that are delay- and jitter-insensitive are transmitted by a local-area cell. The traffic is distributed by the GWLN to the BSLN s. It also provides gains in load and admission

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IG GW GW_IPA

GW_C

IGS SpectrumServerLN

RRMserverLN

WINNER Radio Access Network

ISB IBRRM

IGB

BSPN BSLN@f1 @f1 IBB IWU UTLN

BSLN@f2

BSPN @f2

IWU UTPN

UTLN

Figure 4.10 Multiband transmission from two physically separated BSLN to one physical UT containing two UTLN .

control. In Figure 4.10, the physical implementations of the user terminal and base station nodes are denoted by UTPN and BSPN , respectively. It should be noted that this option addresses the situation where both cells operate in different bands and the physical UT supports multiple transceivers that are modelled by different UTLN . Furthermore, the network sees a physical terminal with two logical nodes as two separate entities. Transmission from two BSLN s in the same band to the same physical UTLN is currently indicative of MBMS traffic network.

4.3.6 Logical Nodes This section provides a description of the WINNER logical nodes network and the functions that they perform. These functions are defined in more detail in subsequent chapters. 4.3.6.1 Gateway Nodes: GW IPALN and GW CLN The gateway functionalities are performed by a GW IPALN and a GW CLN . Generally, a GW IPALN is an IP anchor (IPA) for the user, which also routes the data, whereas a GW CLN controls various aspects of the data flows in the GW IPALN . Certain functionalities are represented in both logical nodes; however, different aspects are captured in the different types of node. For example, policy enforcement is performed on the data by the GW IPALN but policy enforcement is controlled by the GW CLN (i.e. GW CLN configures the GW IPALN with respect to policy enforcement). Pooling a number of GW CLN or GW IPALN nodes eliminates the risk that one node failure will cause parts of the network to be out of service. It also enables load sharing between different GW CLN or GW IPALN nodes. The pooling is possible since there is a many-tomany interface between the BSLN , GW CLN and GW IPALN nodes; each BSLN is associated

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with a set of GW CLN and GW IPALN nodes, called a GW CLN –GW IPALN pool. When a UTLN attaches to the network, it is assigned to a GW CLN and a GW IPALN in this pool. No change of GW CLN or GW IPALN is normally required while the UTLN moves around among BSLN s. The GW IPALN and GW CLN provide the interface to the outside world. The GW IPALN acts as the IP anchor point for external routing functionality. One association of GW IPALN and UTLN is generally maintained for unicast traffic as long as the UTLN is located within the pool area. The GW IPALN contains functions that perform data forwarding, if the user terminal moves. In order to ensure data integrity and confidentiality within the WRAN, header compression and ciphering are performed in the GW IPALN (configured by the GW CLN ). A flow is a packet stream from one source to one or several destinations, classified by quality of service (QoS) requirements. A flow class is identified by its attributes (guaranteed bit rates versus non-guaranteed bit rates, maximum bit rates, delay budget, loss tolerance, etc.). These attributes are configurable and reconfigurable as new flows are set up or multiplexed onto an existing flow class and are governed by filters in the BS and UT that map flows onto flow classes. The flow class setup and release procedures are performed by the GW CLN . QoS is also addressed in this node to configure access network elements (e.g. routers within the WRAN); the details are beyond the scope of this book. In addition, the GW CLN communicates with an external authentication authorisation accounting (AAA) server to handle authentication and authorisation requests by the UTLN s. Once a UTLN has been authorised, the GW CLN is responsible for forwarding charging-related information to the external AAA server. The GW CLN encourages power saving by providing support for idle mode UTLN s. The GW CLN is informed by the UTLN about the current paging area when the UTLN moves and updates an internal database with this information. It initiates the paging procedure to when the UTLN state changes. When the UTLN is in an active state, the GW IPALN forwards the data towards the UTLN (using the BSLN to which the UTLN is attached). If an active UTLN moves, the pathswitch function in the GW CLN is informed by the serving BSLN about handover decisions. The GW CLN then reconfigures the GW IPALN so that the flows are routed via the target BSLN . MBMS traffic is supported by the GW CLN and GW IPALN in a similar way, except that a MBMS flow is used instead of a unicast flow. The flow types differ with respect to the maximum number of destination UTLN s. For each MBMS session, one MBMS flow is established to all BSLN s within a single frequency network area (i.e. several BSs use coordinated transmission to obtain downlink macro diversity – for more details, see [3GPP08]) in which at least one UTLN subscribes to that session. It is up to the BSLN to select an appropriate channel to transmit the data to the UTLN given in the MBMS flow. In summary, the following functions are performed by the GW IPALN :

r forwarding of unicast and multicast/broadcast services; r header compression; r ciphering; r policy enforcement; r traffic measurements (for charging purposes).

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In addition to the control and configuration of the functions performed in the GW IPALN , the following functions reside in the GW CLN :

r idle-mode mobility control; r support for micro mobility; r authorisation and authentication; r flow class establishment and release; r flow class admission control; r QoS signalling; r integrity protection; r paging. 4.3.6.2 Base Station Node: BSLN The BSLN performs all radio-related functions for both active and idle user terminals and is responsible for governing radio transmission to and reception from UTLN s and RNLN s in one or more cells. The BSLN is in control of relays (if used); it determines routes, forwards packets to the appropriate relay and takes care of flow control for the relays to ensure that they can forward the data to their associated UTLN s. User mobility in active mode UTLN s is handled by the BSLN . It determines the necessary handovers based on measurements obtained by the UTLN and information governed by neighbouring BSLN s. The handover decision between relays is determined in a similar manner, based on measurements obtained by the UTLN to collect the link information. Furthermore, the load situation at all involved nodes and the quality of the link between a BS or an RN and an RN is taken into account. Each UTLN that is served by the BSLN is represented by a user context that is kept in the BSLN . Each flow class that is transmitted via the BSLN is represented by a flow-class context in the BSLN . In the regular case, a UTLN handover involves transfer of the user context and all UTLN flow-class contexts to the target BSLN using the IBB interface. If a UTLN is connected to only one BSLN (the regular case), all flow-class contexts and the user context are linked and kept in the serving BSLN . In the special case of a dual transceiver (physical) UT that is served by two BSLN s operating in different bands, a handover (flow-class context transfer) of individual flow classes is possible. In this case, a separate user and flow class context is kept in each BSLN for each UTLN to which the physical UT is connected. In order to detect overload situations, load supervision is located in the BSLN . To prevent overload situations, the BSLN may perform countermeasures such as load balancing and flowclass admission. The following functions reside in the BSLN :

r flow class admission control; r flow control between RAPs within a relay-enhanced cell (REC); r packet scheduling over the radio interface; r outer ARQ; r buffer management; r lower layer QoS configuration;

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r route establishment within a REC; r user-context transfer; r flow-class-context transfer; r load supervision; r load sharing and control; r forwarding; r horizontal spectrum sharing with or without coordination; r short-term spectrum assignment; r micro mobility (for BS and RN); r resource partitioning; r lower-layer functions, such as scheduling and channel coding for delay and processing. Detailed descriptions of these functions are given in subsequent chapters.

4.3.6.3 Relay Node: RNLN The RNLN is a logical network node with relaying capabilities that is wirelessly connected to a BSLN , a UTLN or another RNLN . It contains a forwarding function and schedules packets on the radio interface. Furthermore, system information broadcast, provided by the BSLN , is relayed by the RNLN . In WINNER, an RNLN is assumed to operate as a decode-and-forward L2 relay. Decode-andforward L2 relays allow advanced forwarding and can take advantage of adaptive transmission with different modulation and coding schemes on the different hops. The relaying solution is not optimised to reside in a physical mobile device, but rather in a fixed installation. Nothing in the logical node architecture prevents mobile relays, but mechanisms for mobility support have not been studied. RNLN s serves UTLN s in the same way as BSLN s, which means the UTLN does not need an extra mode or reconfiguration. The relaying concept is primarily designed and optimised for two hops (BSLN –RNLN –UTLN ) in order to achieve a high performance relay deployment. Nevertheless, the concept aims at supporting any number of hops in order to allow a high degree of deployment flexibility, e.g. in areas where coverage has priority over performance. A tree topology is used, as it is less complex than, e.g., a mesh topology. In the case of node failure, the RNLN should autonomously connect itself to another RAP in its range. Although the re-association to the network is not seamless and can lead to some lost connections, the tree topology can still be assumed to be self-healing. The following functions reside in the RNLN :

r packet scheduling over the radio interface; r flow control between RAPs; r forwarding in the REC; r outer ARQ; r horizontal spectrum-sharing mechanisms related to lower-layer access in a shared medium; r lower-layer functions, such as scheduling and channel coding for delay and processing.

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4.3.6.4 User Terminal: UTLN The UTLN comprises all functionality necessary to communicate directly with the network, i.e. a BSLN or an RNLN . It contains functions to handle UTLN mobility in active and idle states, as well as functionality to perform an initial access to the network. It also contains functionality to initiate flow establishment. The function list below contains only those functions that are important for understanding the characteristics of the interfaces within the logical node architecture:

r initial access; r paging area update; r flow class establishment. 4.3.6.5 RRMserverLN The RRMserverLN terminates only network internal protocols (i.e. it does not have a direct relation to the user terminals). The following functions are included in a RRMserverLN :

r load sharing and control; r micro mobility; r admission control; r resource partitioning. However, further study must decide if these functions can be implemented in a distributed way in the mandatory logical nodes. Thus, if an RRMserverLN is present, the central instances of these functions can take over from the distributed instances and take decisions using information from a larger part of the network. 4.3.6.6 SpectrumServerLN The SpectrumServerLN is a logical node that contains centralised spectrum sharing and functions related to spectrum assignment. The SpectrumServerLN enables sharing and coexistence with other radio access technologies and spectrum assignment between WRANs. The SpectrumServerLN interfaces with the gateway. The SpectrumServerLN monitors the load, contains constraints on the available spectrum (e.g. information about exclusion zones), and keeps track of the available spectrum from spectrum sharing and spectrum assignment. The spectrum functions in the SpectrumServerLN interact with the spectrum functions in the base stations, so that spectrum availability can be communicated and local optimisations of the spectrum allocation can be made by mechanisms residing in the BSLN . The following functions (further detailed in Chapter 11) reside in the SpectrumServerLN :

r vertical sharing; r centralised component of horizontal sharing with coordination; r spectrum register; r long-term spectrum assignment.

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Control plane signalling

User plane information L3/RRC

RRC Data flows

IPCL

L2/IPCL

Flow classes

RLC

RLC

RLC

RLC RLC

L2/RLC

Logical Channels MAC (WA, MA, LA)

L2/MAC Transport Channels

PHY (WA, MA, LA)

L1/PHY

Figure 4.11 WINNER Radio Access Network protocol layers.

For horizontal sharing with coordination, most functions reside in the BSLN ; the SpectrumServerLN only contains the functions related to centralised coordination of horizontal sharing.

4.4 Protocol and Service Architecture In this section, the radio protocol architecture is described for the user plane and the control plane.

4.4.1 Overview The WINNER radio protocol stack is presented in Figure 4.11. It is composed of a physical layer (layer 1), a data link layer (layer 2) with sublayers MAC, RLC and IPCL, and a network RRC layer (layer 3). The protocol architecture is subdivided into the user (U) plane, composed of protocols devoted to user data-transfer services, and the control (C) plane, composed of the protocols created to control data transfer, user and network operation. The IPCL and RRC sublayers only exist in the user plane and the control plane, respectively.

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A user plane connection (an IPCL layer session) can generate several RLC layer flows using different QoS classes. Communication between consecutive layers and sublayers is carried out through service access points (SAPs), depicted as horizontal ellipses in Figure 4.11. Communication between the RRC control layer and the other layers is carried through control SAPs (C-SAPs), shown as vertical ellipses in Figure 4.11. The lower layers offer services to the upper layers via the SAPs. WINNER considers two physical layer modes: FDD and TDD. Within each mode, different parameterisations of the WINNER system can be used in order to have a scalable and optimised system for various deployments. The SAP between the MAC and PHY layers provide the transport channels and the SAPs between the MAC and RLC layers provide the logical channels. A service data unit (SDU) is a packet serving as input to a protocol layer, whereas the output of a protocol layer is denoted as (PDU). For example, a packet in a transport channel is a PHY SDU and a MAC PDU. The mapping of protocols to logical nodes is illustrated in Figure 4.12. The user plane is shown in darker grey boxes and the control plane is shown in lighter grey boxes. Note that the RRC in the BS terminates protocols initiated at both the RN and the UT. In the two-hop case (Figure 4.12b), the user plane protocols terminate on the network side at the BS except for the IPCL protocol, which ends at the GW. The network layer C-plane signalling is handled by RRC, which terminates in the UT and the BS. The control and configuration of the RNLN is performed by the sub-layer RRC2. The non-access stratum (NAS) is a functional layer in the protocol stack that supports signalling and traffic between the core network and the user terminal. The NAS protocols end at the GW in the WINNER RAN and in the Home Subscriber Server (HSS) in the core network. The GW communicates and connects with external networks and nodes using the IP protocol. The NAS protocol is not fully covered by the scope of this book and is only mentioned for reference of RRC functions that need interaction with this protocol. This protocol layer is used in idle-mode mobility management and paging; authentication, authorisation and accounting; and on data flow establishment management and release.

4.4.2 Layer 3: Radio Resource Control The radio resource control (RRC) layer controls the radio resources and configures the user terminal accordingly. The RRC layer includes measurement, exchange and control of indicators related to radio resources and commands between the WRAN and the UTLN s. The measurements include standardised radio-resource indicators that measure or assist in estimation of the available and potential radio resources. The exchange of radio-resource-related indicators includes the procedures and primitives between logical entities used for requesting and reporting such measurements or estimations. The resulting information may be made available implicitly, within the measuring stations using proprietary procedures and primitives that are not subject to standards, or explicitly, to a remote functional entity using standardised procedures and primitives. The control mechanism refers to the decisions made by the measuring station or remote entity to adjust radio resources based on the reported measurements, other information or the radio resource management (RRM) functions and communication of the adjustments to logical entities using standardised primitives. RRM functions are explained in further detail in Chapter 10.

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UT

BS

GW

NAS

NAS

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RRC IPCL

IPCL

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RLC

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PHY

PHY (a)

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GW NAS

RRC

RRC

RRC RRC2 IPCL

RLC

IPCL RLC

RLC

MAC

MAC

MAC

PHY

PHY

PHY

(b)

Figure 4.12 WRAN user and control plane architecture and termination: (a) single hop and (b) two hops.

The control and configuration of RNLN s is carried out by introducing a new sublayer (RRC2) within the RRC layer. The signalling from RRC2 is routed through a dedicated RLC instance for the sake of reliable data transfer. RRC2 instances are available only in BSLN s and RNLN s, i.e. UTLN functionality is not involved. This is also true of RLC instances that are used by the RRC2 protocol. The basic control plane functions performed by the RRC layer are as follows:

r Broadcast of system information: Basic cell identification and cell-specific information that changes frequently are candidates for transmission by the broadcast channel. Further WINNER information elements suitable for broadcast include: spectrum-sharing restriction parameters (slow), shared-band availability, cell ID, operator ID, Tx power mask, FDD/TDD duplex mode information, a pointer to the next important control channel (a super-frame

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allocation table), inter-system handover information, and basic properties of the random access channel (RACH). r Admission control: These functions admit new flow classes and renegotiate flow classes, accepting flow classes handed over from another BS. For more details of a possible implementation, see Chapter 10. r Establishment, maintenance and release of RRC connections: This includes the allocation of temporary identifiers between the UT and the WRAN and configuration of radio resources for RRC connection. r Security: This includes integrity protection and ciphering of RRC messages. r Establishment, maintenance, reconfiguration and release of flow classes for both unicast and multicast services. r Mobility: This includes paging, UT measurement reporting and control of the reporting for inter-cell and inter-RAT mobility, inter-cell handover, cell selection and re-selection, and UT context transfer between BSs. r Flow control between BS and RN. (Flow control to and from UTs is handled by the scheduler at the MAC layer.) r Cross-layer configuration of layer 2 protocol entities within the same node. In case of relaying, at least the following RRC functions are included in the RNLN :

r broadcast of system information; r QoS management functions; r paging. The RRC2 protocol includes the following control plane functions:

r establishment, maintenance and release of an RRC2 connection between the RN and WRAN; r conveying broadcast information to the RNs; r conveying information about establishment, maintenance, reconfiguration and release of flow classes for both unicast and multicast services for UTs served by the RN;

r conveying information related to resource partitioning; r conveying paging information; r cross-layer configuration of layer 2 protocol entities within the same node.

The RRC layer manages and controls the use of radio resources and therefore has links over control SAPs to all other layers, as shown in Figure 4.11. This interworking enables RRC to control and configure the other layers; it can receive measurement data from the lower layers as well as from the control functions in the other layers.

4.4.3 Layer 2 Layer 2 is split into three sublayers: the IP convergence layer (IPCL), the radio link control (RLC) layer, and the medium access control (MAC) layer. The main functions performed in these layers for the downlink and uplink are shown in Figure 4.13a and 4.13b, respectively. Both the IPCL and RLC layers are pure user plane protocols. The transmission may be to or from RNs or UTs.

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Figure 4.13 Layer 2 overview: (a) in the downlink and (b) in the uplink.

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4.4.3.1 IP Convergence Layer The IP Convergence Layer (IPCL) supports transfer of user data. IPCL receives an IPCL service data unit (SDU) and forwards it to the RLC layer and vice versa. The IP Convergence Layer is discussed in more detail in Chapter 10. The IPCL protocol supports header compression, decompression, ciphering, and insequence delivery of upper layer PDUs. Reordering is performed by the GWIPA in order to handle potential out-of-order packets that may arrive during a handover due to the different transmission delays of the packets from the involved BSs. Additionally, the IPCL performs duplicate detection of lower-layer SDUs. Duplication of packets may occur in either the uplink or the downlink direction when the acknowledgement of correctly received packets is not received by the transmitter, which then performs an unnecessary retransmission of the same packet. This layer also supports reordering of the downlink RLC SDUs at least during handover. Such reordering is performed by the UT in order to handle potential out-of-order packets that may happen during handover due to early forwarding of packets from the GWIPA . 4.4.3.2 Radio Link Control Layer The radio link control (RLC) layer protocol supports an unacknowledged mode (UM) and an acknowledged mode (AM). Whether UM or AM is used needs to be configured per flow class. Furthermore, the RLC layer supports segmentation and concatenation of RLC SDUs. Depending on the scheduler decision, a certain amount of data is selected from the RLC SDU buffer and segmented or concatenated, depending on the size of the SDUs. This selected data block becomes an RLC PDU to which a sequence number is assigned. This means that one transport block contains a single RLC PDU per flow class. However, there are two exception cases in which the RLC PDU is re-segmented:

r A retransmission PDU does not fit into the new transport block. r A received PDU at an RN does not fit entirely into the new transport block. In these two cases, a new sequence number is appended to the re-segmented packets by means of an extension header. The number of re-segmentations is not limited. It was shown that it is beneficial to keep the same RLC sequence number space on the entire path between the base station and the user terminal (see [WIN2D61314, Section 5.1.1] for further details) to enable fast retransmissions after a user terminal has moved from one node to another node within the relay-enhanced cell (REC). Hence, RLC SDUs are not re-assembled in the relay node and only re-segmentations are allowed, see Figure 4.14. In order to allow the RLC SDU to be reassembled at the receiver, the RLC header carries the required segmentation, re-segmentation and concatenation information. The RLC sequence number is also used at the receiver for in-sequence delivery to the RLC SDU reassembly entity. Details of the segmentation or reassembly process, its interaction with multiplexing at the MAC layer, and the associated signalling are described in [WIN2D61314, Section 5.1]. In AM, RLC is responsible for correcting residual H-ARQ errors by operating another ARQ protocol, the RLC-ARQ. The ARQ retransmission units are RLC PDUs or RLC PDU segments.

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Figure 4.14 RLC layer at the relay node (R-ARQ refers to relay ARQ, see Section 8.5.4).

If an RLC retransmission is required and the radio quality has changed significantly compared to the original RLC transmission, then the RLC protocol is able to perform a re-segmentation. In that case, RLC segments a PDU into smaller PDU segments. In case of relaying, the ARQ operation needs to be enhanced (e.g. with relay ARQ) in order to be able to determine which packets have been correctly received by the UT and RN (see Figure 4.14). Details of the ARQ operation, including its interactions with H-ARQ, are described in Section 8.5.4. Moreover, in case of relaying, the RLC also performs forwarding (i.e. mapping of an incoming SAP to an outgoing SAP). Finally RLC provides a means for protocol error detection and recovery (e.g. reset), duplicate detection, and SDU discard. 4.4.3.3 Medium Access Control Layer The WINNER medium access control (MAC) layer performs three main tasks:

r scheduling, which controls the transmission on the timescales of the frames; r multiplexing and demultiplexing; r H-ARQ, i.e. a retransmission procedure over one hop that uses incremental redundancy. The allocation of transmission resources is controlled by the network, not by the terminals, since we thereby attain the highest spectral efficiency. At the shortest timescales, this control is performed by schedulers at the MAC layer of the BSLN and RNLN . The schedulers receive inputs and constraints from the RRM functions, which perform resource allocation and flow control at slower timescales. As depicted in Figure 4.15, the scheduler controls the complete transmission chain on a packet-by-packet basis. It controls the segmentation at the RLC layer, the multiplexing at the MAC layer and the coding, modulation, multi-antenna processing and mapping onto transmission resources that are performed at the physical layer. The physical layer itself is

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Figure 4.15 Relationship of the MAC layer with other layers (from [WDK+09]). (Reproduced by Permission of IEEE © 2009).

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completely controlled by the scheduler in the MAC layer. This fast and tight interaction is possible since the RLC, MAC and PHY layers of a node are always assumed to be co-located and can therefore interact with negligible delays [WDK+08].1 If, in addition, several radios that use multiple bands are co-located, the scheduler can control the transmission resources within all the radio bands by controlling the multiple co-located physical layers.2 This particular functionality is called a multiband scheduler. A more detailed functional description of the scheduler is given in Section 4.4.3.3. Super-frames, Frames, Slots and Chunks The super-frame is a time–frequency–spatial resource allocation unit of the WINNER system concept. It fulfils two purposes:

r Each super-frame contains uplink and downlink synchronisation pilots that are used for self-organizing network synchronisation [WIN2D233]. The super-frame duration therefore scales the reaction time of this mechanism. r The resource partitioning (the division of transmission resources between cells and between RNs and BSs) is assumed to be specified one super-frame in advance. This provides a stable background for the resource allocation that is performed by the scheduler. The super-frame is assumed to have equal duration in the WINNER FDD and TDD modes. This facilitates inter-mode cooperation and multiband transmission. A super-frame consists of a short preamble with OFDM symbols used for pilots, followed by n frames. The n parameter is set to the sample value of eight in the sequel and for performance evaluations. In the frequency dimension, the super-frame comprises all frequencies (not necessarily adjacent) that are used within a cell. A frame is a temporal resource unit. The frame duration was set to the same value in the WINNER FDD and TDD modes. The frame duration contains two slots:

r In TDD mode, a frame consists of a downlink transmission slot followed by an uplink slot, separated by duplex guard-times.

r In FDD mode, one set of half-duplex terminals would receive downlinks in the first slot and transmit in the uplink in the second slot. A second set of terminals could do the opposite, since an FDD base station can be assumed to use full duplex. Full-duplex FDD terminals could transmit and receive in both slots, which doubles the maximal data rate. The super-frame structure (shown in Figure 4.16) has properties developed in WINNER Phase I [SSK06]. In the final WINNER II system concept, the super-frame was modified as follows:

r No contention-based uplink (DAC) channel is used, so separate transmission resources are not set aside for this purpose.

r The downlink physical broadcast channel (PBCH) and the uplink physical random access channel (PRACH) are no longer included in the preamble of each super-frame. This increases 1 The

scheduler of a BS or an RN will also control the resource allocation used for uplink transmission from UTs. be more precise, the MAC layer scheduler can control all PHY layer resources that can be controlled on a frame timescale without additional signalling delays. This could be used for co-located base stations that use different spectrum resources (FDD or TDD). 2 To

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8 frames= 8 x 0.6912 ms = 5.53 ms Frame

Frequencies used in this cell

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Figure 4.16 Super-frame for the WINNER TDD mode, with uplink:downlink asymmetry ratio 1:1. Light slots are for downlinks; dark slots are uplinks; and white bands represent duplex guard times between downlink and uplink slots.

the flexibility, since the timescale for broadcast transmission and random access opportunities are decoupled from the super-frame timescale. The broadcast channel is transmitted at a prespecified position every mth super-frame. The random access channel is available in every super-frame, to reduce the delays for UT initial access to the network. r To eliminate unused frequency resources, the downlink and uplink network synchronisation pilots were moved into the first frame of the super-frame, in the beginning of the downlink and uplink slots. As in the case of the PBCH and PRACH, they utilise only spectral bands that are available over a wide geographical area, to facilitate multi-cell coordination. They each comprise three consecutive OFDM symbols. This corresponds to the minimum B-EFDMA block size of the reference design (see Chapter 9), so it fits well into the WINNER II frame structure. r Both the FDD and the TDD super-frame preambles include one OFDM symbol that contains uplink pilots in order to provide the BS or RN with short-term channel state information (see Chapter 6). The super-frames thus consist of an uplink pilot preamble of one OFDM symbol followed by n frames. Figure 4.16 illustrates the case of TDD transmission with uplink:downlink asymmetry in a ratio of 1:1. With cyclic-prefix OFDM, the smallest time–frequency unit consists of one subcarrier by one OFDM symbol duration, here denoted as a channel symbol. Rectangular sets of ns subcarriers by nt OFDM symbols are assumed to be grouped into time–frequency units denoted chunks.

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Figure 4.17 Chunk sizes used for evaluation in WINNER II [WIN2D6137]. The figures show a slot (half a frame) in each case, assuming 1:1 TDD asymmetry. (Reproduced by Permission of IEEE © 2009).

A chunk within one spatial layer is denoted a chunk layer. The dimensions of the chunks used for evaluations in WINNER II are illustrated in Figure 4.17. The frame duration is the same in the FDD and TDD modes and has been set to 0.6912 ms throughout the WINNER projects. The slot duration is half a frame in FDD and in TDD with asymmetry ratio 1:1 and is thus 0.3456 ms. The slot and frame durations are the basic scales of delays and reaction speeds of the WINNER MAC layer. Functionalities The MAC layer may multiplex RLC PDUs that belong to different flow classes. The resulting packets form retransmission units, handled by the H-ARQ function that works over each hop in a multi-hop transmission. The WINNER system supports small retransmission delays allowing H-ARQ to be invoked for most flow classes, including delay-sensitive flows. The whole procedure is controlled by the multi-layer scheduler. The MAC layer can multiplex segments that belong to different flow classes. The aim is to reduce the overhead, in particular for smaller packets and in transmissions over relay links. Multiplexing is allowed for segments (RLC PDUs) that are transmitted to the same logical node, for example a relay node. Segments to be transmitted to different logical nodes are not multiplexed. A MAC MUX header (see [WIN2D61314, Section 5.1.2]) is appended to an RLC PDU (which may be multiplexed), resulting in a MAC PDU. If link retransmission is to be used, a cyclic redundancy check (CRC) code sequence is added to the MAC PDU. This represents the MAC retransmission unit (RTU), or transport block. An overview of the segmentation, concatenation, and multiplexing process is given in Figure 4.18.

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Figure 4.18 Segmentation, concatenation, and multiplexing process from higher layer PDUs to transport blocks.

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The retransmission scheme used over one link is described in more detail in [WIN2D223, Section 4.2]. It combines forward error correction and automatic repeat requests (ARQ) in a scheme that uses incremental redundancy: 1. The RTU is encoded as one or several forward error correction (FEC) code blocks. The preferred WINNER coding schemes are used: r rate 1/4 convolutional coding is used for blocks with less than 200 payload bits; r quasi-cyclic block LDPC with mother code rate 1/3 is used for larger block sizes. Both are assumed to be systematic codes, so the FEC blocks consist of uncoded bits followed by redundancy bits. Assume that the first N3 bits of the FEC block are systematic bits (the uncoded segment) and the remaining bits are redundancy bits. 2. A H-ARQ segment is transmitted for all FEC blocks that comprise one RTU. The initial H-ARQ segment uses the first N2 ≥ N3 bits of the FEC block, where N3 /N2 is the code rate that is specified by the link adaptation. 3. The CRC code of the RTU is used to detect transmission errors. 4. If retransmission is required, additional parity bits, known as incremental redundancy (IR) blocks, are transmitted by additional H-ARQ segments. Each IR block uses a link adaptation that is appropriate for that transmission. Soft-bit combining is used at the receiver. Since the CRC code spans the whole RTU, incremental redundancy transmissions have to be performed for all FEC blocks that belong to the RTU. 5. If the end of the FEC block is reached without a correct reception, the H-ARQ process uses the FEC block cyclically from the beginning, producing additional IR blocks until a maximum allowed number of retransmissions is reached. This scheme provides a seamless transition from the use of incremental redundancy for a few transmissions to chase combining for many retransmissions. The size of the IR blocks can be adjusted to a fraction of the initial H-ARQ segment size, see [WIN2D223, Section 4.2]. This choice is signalled as outband information at the initial transmission. An N-channel, stop-and-wait protocol with one-bit feedback is used as the retransmission protocol for a flow class. This means that we allow each flow class to use up to N parallel retransmission channels. A new RTU can be transmitted if there are fewer than N outstanding unacknowledged RTUs, i.e. if at least one channel is not in use. A scheduler coordinating the RLC, MAC and PHY layers is located in each BS and RN, see Figure 4.15, to provide efficient resource allocation. The resource allocator allocates all available transmission resources within the slot (i.e. it distributes time–frequency–spatial resources to different UTs). Some resources may be pre-allocated over multiple slots for transmission of flows whose packets have regular properties. The BS always performs the channel-aware resource allocation, whereas flow-class prioritisation is always performed by the transmitting part. This means that:

r For the downlink, the scheduler performs resource allocation and prioritises between different UT flow classes within those resources.

r For the uplink, the scheduler in the BS (or RN) performs resource allocation, however, a simpler scheduler in the UTs prioritise between the different uplink flow classes for that UT.

r In case of cooperative relaying (downlink transmission from multiple RAPs), the scheduler

at one coordinating RAP performs the scheduling (payload-selection-resource allocation and

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mapping, and link adaptation). It signals these decisions to the schedulers in all participating RAPs (see Chapter 8 for more details on cooperative relaying). The scheduler also selects the transport format and the spatial transmission scheme individually for each H-ARQ segment or code block. The WINNER concept supports individual link adaptation within the chunk layers that are used to transmit one H-ARQ segment. The following constraints and inputs on the scheduler are static or vary only slowly over time:

r antenna resources at the network side, multi-antenna and other capabilities of the BS, RNs and UTs involved;

r restrictions on the resource allocation that originate from the RRM functions (spectrum functions, resource partitioning between RN/BS, interference avoidance between cells, use of cooperative transmission) and are consolidated by the constraint processor; r properties and QoS constraints defined by the flow class of the packets; r the total load situation and distribution of users in the cell and the resulting possibilities and restrictions on using spatial division multiple access (SDMA). The following constraints and inputs on the scheduler vary on a frame timescale:

r transmission requests for uplink transmissions; r queue lengths and transmission demand for each flow class; r CQI and CSI information for time–frequency–spatial transmission resources within the frame to be scheduled;

r the need to transmit packets with high priorities, such as IR blocks (retransmissions); r scheduling constraints from cooperative relaying: one node determines the transmission parameters; the schedulers at the involved RAPs receive these decisions as constraints on the scheduling allocation; r time-to-live information of individual packets in delay-critical transmissions (depending on the number of hops, the delay budget, queue occupancy and relay-link, flow-control input). The scheduling has the overall aim of satisfying the QoS constraints of all flow classes. Channel-aware scheduling can also allocate transmission resources that are advantageous for each transmission, to optimise the network capacity or the terminal power consumption. The scheduling structure supports joint decisions that solve the following tasks (the exact implementation is manufacturer dependent):

r payload selection: a decision on the flow classes to transmit in the scheduled slot; r resource allocation: allocation of time–frequency–spatial transmission resources within a slot to specific MAC PDUs;

r resource mapping: a decision that determines the mapping of H-ARQ segments onto a set of transmission resources, using a set of link adaptation parameters (this decision is optimised jointly with a decision on segmentation and concatenation and the choice of link adaptation parameters); r link adaptation: a decision on the modulation parameters, the spatial layer or beam and the code rate used for transmitting a code block.

System Concept and Architecture

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Figure 4.19 Scheduler: main inputs and outputs (from [DWK07]). (Reproduced by Permission of IEEE © 2009).

The scheduler provides detailed allocation of packet segments to physical time– frequency–spatial transmission resources, including spatial link adaptation. This includes segmentation and concatenation commands to the RLC layer, multiplexing commands to the MAC layer, and instructions on the modulation, encoding and puncturing, spatial scheme, and time–frequency resource mapping performed at the physical layer. The scheduler also provides outband frame control information (i.e. control information not carried within any protocol header) that enables the receiver to decode the relevant part of a frame. This information is transmitted on the physical channels PDFCC and PUCH for the downlink and uplink directions, respectively. The outband frame control information and its corresponding overhead is discussed in more detail in [SSD08] and in [WIN2D61314, Annex]. Figure 4.19 illustrates the main input and output parameters of the scheduler in downlinks. It shows how the scheduler can be partitioned into a constraint pre-processor that handles various constraints on the transmission resources, a main scheduler and a sub-function that focuses on optimizing the link adaptation. Transmission sequences and transmission timing are discussed further in [WIN2D61314, Section 5.2]. A key functionality of the WINNER requirement to be able to handle fragmented spectrum efficiently (see Section 2.7) is the multiband scheduling concept. When the MAC layer controls several physical layers that are co-located so that control transmission delays are negligible, these multiple bands represent a widened resource pool as seen from the scheduler. One constraint imposed by the WINNER multiband scheduling concept is that the UT should never have to transmit and receive simultaneously in several bands that are not covered by the same radio (the same FFT), to simplify the terminal design. This means that MAC PDUs and their retransmissions should be transmitted in either band, not in all bands simultaneously.

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Downlink only Downlink only

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Figure 4.20 Mapping between logical channels and transport channels.

The scheduler may however decide to switch the transmission to or from one UT to another band, to obtain better load balancing. A H-ARQ channel is regarded as being associated with one particular radio band. When the transmission is switched to another band, the H-ARQ channel and state are associated with this new band and H-ARQ transmissions for previously transmitted packets proceed over the new band.3 For a detailed discussion of the WINNER multiband scheduling concept and its relation to flexible spectrum use, see Section 11.4.1. Logical Channels and Transport Channels Logical channels define types of communication between RLC peers. They are characterised by the type of information transmitted. Some are for downlink information, some for uplinks, and some are bi-directional. Transport channels define types of communication between MAC peers. They are characterised by how and with what characteristics the information is transmitted. Physical channels describe different distinct sets of physical resources that are required by various transmissions. They are described in Section 4.4.4.2. Figure 4.20 shows how the logical and transport channels are used in the WINNER concept. The logical channels are:

r Broadcast control channel (LBCCH): A downlink channel for broadcasting system control information. The LBCCH information is split into two: a static part and a dynamic part. The static part is transmitted every mth super-frame using the same pre-allocated physical resources. The location of the dynamic part is signalled in the static part. The dynamic part is transmitted every yth super-frame (where y is a multiple of m), using the shared transport channel TSCH. The static part contains information needed for an unknown UT to identify basic cell properties and to be able to initiate communication, e.g cell ID, operator ID, and FDD/TDD duplex mode information. Other candidates for inclusion in the static

3 This is possible with insignificant delays due to the assumed physical co-location of the radios (the physical layers). Multiband transmission that uses radios that are not co-located has to use the RRM handover functionality.

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part are spectrum-sharing restriction parameters and RACH properties. In the dynamic part, parameters such as transmission power mask are signalled. r Paging control channel (LPCCH): A downlink channel that transfers paging information (Chapter 10). This channel is used when the network does not have information about the location cell of the UT. r Common control channel (LCCCH): This channel is used by UTs that have no RRC connection with the network. It is used on initial access to the network. r Dedicated control channel (LDCCH): A point-to-point bi-directional channel that transmits dedicated control information between a UT and the network. It is used by UTs with an RRC connection (e.g. for messages setting up links between a RAP and a UT). Various RRM mechanisms (see Chapter 10) also use the LDCCH. r Multicast control channel (LMCCH): A point-to-multipoint downlink channel used for transmitting multicast and broadcast message service (MBMS) control information from the network to the UT, for one or several transport multicast channels (TMCHs). This channel is only used by UTs that receive MBMS. Messages for the setup and modification of multicast groups use the LMCCH. r Dedicated traffic channel (LDTCH): A point-to-point channel, dedicated to one UT, for the transfer of user information. An LDTCH can exist in both uplink and downlink. r Multicast traffic channel (LMTCH): A point-to-multipoint downlink channel for transmitting traffic data from the network to the UT. This channel is used only by UTs that receive MBMS services. The transport channels are:

r Broadcast channel (TBCH): A downlink channel for broadcasting system information, to all terminals inside the cell’s coverage area.

r Paging channel (TPCH): A downlink channel used for broadcast of paging information into an entire cell. It contains a paging indicator (PI) and a paging message (PM). The PI is a short message that indicates the range of UT addresses being paged within this cell. The PM contains the paging reason (>3 bits), the paging domain (one bit) and the paging identity (32 bits). r Random access channel (TRAC): A contention-based uplink channel for initial access to the network. This channel is used to obtain timing synchronisation (asynchronous random access). r Shared channel (TSCH): A point-to-point data channel for both the uplink and the downlink for both user and control data. It is possible to broadcast this channel in the downlink over the entire cell. r Multicast channel (TMCH): A separate transport channel for multicast transmission. It is to be utilised by MBMS. This channel is broadcast in the entire coverage area of the cell. Combining of multicast transmissions from multiple cells is supported.

4.4.4 Layer 1: Physical Layer The physical (PHY) layer handles the physical transmission of flows, measurements and control signalling that is directly related to the radio interface. The PHY layer is not separated

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into user plane and control plane, since it is assumed that all control functionality of the PHY layer resides within the MAC layer. The main characteristics of the PHY layer are discussed in detail in Chapters 5, 6, and 7. Some general aspects are discussed in the following sections. 4.4.4.1 Control Signalling Two main principles have been used in the WINNER concept for selecting the physical format for transmitting control signalling and measurement reports. Is the control signalling urgent or non-urgent, in the sense that timing allows it to use existing H-ARQ schemes?

r Any non-urgent control messages and measurement reports are treated in the MAC layer as packets to be transmitted using the TSCH transport channel. H-ARQ is used to reduce and control the error probability. r Urgent control messages need special FEC protection and may also have to be placed in special positions within the frame for feedback-loop timing reasons. Special control channels (PDCFC, PDFCC and PUCH) have been defined for these urgent control messages (see Section 4.4.4.2). Here, we use non-frequency-adaptive transmission with small block sizes to maximise frequency diversity and to obtain precise message timing within the frames, see Section 9.2.3. Whenever possible, frequency diversity is to be combined with spatial- or polarisation-diversity schemes to reduce the error probability.

Does the control signalling need to be broadcast or can it be multicast or unicast to UTs or groups of UTs?

r An optimisation is assumed to select the best type of transmission for each control message. Broadcasting results in transmission with the lowest spectral efficiency, since it has to be adjusted to the worst user, with possible not well-known SINR. Two channels that need to be broadcast have been defined: the physical broadcast channel (PBCH) and PDFCC for frame control messages. To keep the control overhead acceptable, it is important to minimise the payload that needs to be transmitted over these two channels. Downlink control messages to groups of users (multicast groups) may be transmitted individually to each user (unicast) or multicast. Multicasting is the most efficient scheme for downlink control information if the multicast groups contain sufficiently many members. Otherwise, control messages are preferably unicast. The limit between urgent and non-urgent messages is determined by the possibility of performing at least one H-ARQ retransmission. With the delays indicated in [WIN2D61314, Section 5.2], this limit is around 4 ms. The various measurements required for RRM and for MAC/PHY control, and their delay requirements, are summarised in [WIN2D233, Section 5]. WINNER supports two major transmission schemes that use frequency-adaptive or nonfrequency-adaptive transmission [SSD08] (see Chapter 9). Although they have differing

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127

Table 4.1 Control information and assumptions. Information bits per message

Information

Link direction

H-ARQ information

DL, UL

3+3

Per H-ARQ channel use

Transport block size, code rate Modulation information

DL, UL

5

DL, UL

2

Spatial processing DL, UL scheme Cell-specific user DL, UL address H-ARQ feedback DL, UL

5

Every transport block For every chunk layer in case of frequencyadaptive transmission. For every transport block in case of non-frequency adaptive transmission. Per MAC PDU

12

Per user or per flow Implicitly signalled

1

Per H-ARQ channel use Per user b Per user Per frame

CQI feedback CSI feedback Chunk allocation table

UL UL DL

flexible length

Message frequency

Comment 3-bit H-ARQ-ID: 2 bits for redundancy version, 1 bit new data indicator a

contains DL allocation and UL allocation of chunk layers to users

a

Assuming an asynchronous N-Channel stop-and-wait protocol supporting incremental redundancy. Update rates in frequency and time are not considered in this table, as they can be adjusted according to coherence properties of the channel as explained in [WIN1D24]. b

control signalling requirements, it has proved possible to design one control channel with different parameterisations that controls both schemes with acceptable downlink overhead. Table 4.1 gives a summary of the most important urgent control messages that need to be transmitted, their message sizes and their message frequency or urgency. Two physical control channels, PDCFC and PDFCC have been defined to support this frame control signalling. The control signalling scheme of WINNER allows flexible configuration of the control information on the PDCFC and PDFCC to a large range of operating conditions (e.g. ranging from a few high-rate users to many low-rate users and from a full load to a low load). In each frame, a minimal amount of information is broadcast to all users using a safe (but

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resource-demanding) coding and modulation scheme. More detailed information is transmitted to groups of users with differing SINRs, using tailored link adaptation. The total overhead is further minimised by using implicit information, as well as the adaptation of the control message format between a combinatorial and table-based approach to signal the resource allocation. Using such a flexible-length, multi-part, control message, the overhead can be kept below 24 % while having full scheduling flexibility for up to 1280 users in a 100 MHz [SSD08]. Further information on the transmission control sequences and their timing for downlink and uplink can be found in Section 9.2.4. It should be noted that a semi-static allocation of transmission resources can be used for some flow classes and users. This means that a fixed set of transmission resources (chunks or blocks and link adaptation parameters) is pre-allocated in multiple frames, which reduces the control overhead significantly as illustrated in [WIN2D61311, Section 4.7]. This is particularly possible for voice-over-IP flows, which are destined for slow-moving users. 4.4.4.2 Physical Channels and Mappings to Transport Channels This section gives a brief overview of the transmission technology and location in the superframe for the different physical channels. Detailed explanation of the chosen transmission technology can be found in Chapters 5, 7, and 9. Physical Broadcast Channel The physical broadcast channel (PBCH) is a downlink physical channel for broadcasting system information, etc., to all terminals inside the cell coverage area.

r Transmission technology: B-EFDMA allocation with smallest block size (four subcarriers by three OFDM symbols), to maximise frequency diversity, as described in Section 9.2.3. PBCH is sent by cell-wide spatial transmission that uses (Alamouti) space–frequency coding to achieve spatial diversity. 4-QAM with rate 1/4 convolutional coding and 6 x repetition coding is used, with a target minimum downlink SINR of −8 dB. r Position in super-frame (Figure 4.16): 3-OFDM symbol part of a downlink slot in frame j of every mth super-frame. It uses only frequency resources that are available within a wide area (multiple cells). The PBCH transmissions from different RAPs can potentially use a frequency re-use pattern to minimise interference between PBCHs of neighbouring BSs and RNs. Physical Downlink Control Format Indicator Channel The physical downlink control format indicator channel (PDCFC) is used for transmitting parameters indicating the location and layout of the PDFCC channel. As outlined in [SSD08; WIN2D61314], the downlink control information that signals the frame layout for downlinks and uplinks and for frequency-adaptive and non-frequencyadaptive transmission, is composed of several parts to minimise the control overhead. The PDCFC message contains a broadcast configuration table, CT, and an optional broadcast control message length indicator, LI. The PDCFC message is broadcast to all involved terminals.

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129

r Transmission technology: The transmissions use B-EFDMA non-frequency-adaptive transmission with small (4 × 3) block sizes and spatial diversity where possible. 4-QAM with rate 1 /4 convolutional coding is combined with 6 x repetition coding, to reach a target minimum downlink SINR of −8 dB. r Position in super-frame: Uses the set of downlink resources within the frame that are allocated to non-frequency-adaptive transmission. It is transmitted in the earliest three OFDM symbols of each downlink slot. Physical Downlink Frame Control Channel The physical downlink frame control channel (PDFCC) is used to signal information in a slot of a frame related to downlink data transmission within that slot and uplink data transmission in the subsequent slot. The PDFCC is used to define the resource allocation both for frequencyadaptive and non-frequency-adaptive transmission. The allocation tables (AT) and transport format tables (TFT) are transmitted on the PDFCC. The following information is conveyed on this channel:

r DL scheduling control: User terminal or relay node ID, resource allocation for PNDC that identifies the chunks or blocks have been allocated, the modulation and coding scheme, H-ARQ-related information (e.g. H-ARQ process ID and redundancy version), and ACK or NACK related to UL transmission. r UL scheduling grant: User terminal or relay node ID, resource allocation for PNDC that identifies the chunks that have been allocated, the modulation and coding scheme, and the (slow) uplink power control command. Not all types of table need be used in a particular parameterisation. Different table layouts are used for specifying frequency-adaptive and non-frequency-adaptive transmission. In the case of frequency-adaptive transmission, different table layouts are used when there are a few participating users or many users (for details, see [SDD08; WIN2D61314, Annex A.1]).

r Transmission technology: Uses B-EFDMA non-frequency-adaptive transmission with small (4 × 3) block sizes and spatial diversity where possible. Convolutional coding is used combined with repetition coding. The allocation and transport format tables are partitioned into sub-tables destined for different groups of users with different SINRs. Each such subtable is encoded with an appropriate code rate to limit the downlink control overhead. r Position in super-frame: Uses the set of downlink resources within the frame that are allocated to non-frequency-adaptive transmission. Tables for control of the downlink are transmitted in the earliest three OFDM symbols of each downlink slot. Tables for control of the subsequent uplink are transmitted in the following three OFDM symbols (numbers 4 to 6) of the slot. Physical Frequency-adaptive Data Channel The physical frequency-adaptive data channel (PADC) is used to transmit point-to-point, user data in downlinks and uplinks. A PDFCC is associated with each PADC to control the transmission.

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r Transmission technology: Modulation (BPSK, 4-QAM, 16-QAM, 64-QAM, 256-QAM) adjusted individually per chunk layer. An average code rate is computed for all chunk layers and is then used for the whole H-ARQ block. Convolutional codes are used for FEC block sizes of less than 200 payload bits. Quasi-cyclic block LDPC codes are used for larger FEC block sizes. r Position within super-frame: PADC uses a set of chunks within the slot that is pre-allocated for frequency-adaptive transmission by the resource division function. Physical Non-frequency-adaptive Data Channel The physical non-frequency-adaptive data channel (PNDC) is used for point-to-point, user-data transfer in downlinks and uplinks. A PDFCC is associated with each PNDC.

r Transmission technology: One modulation (BPSK, 4-QAM, 16-QAM, 46-QAM, or 256QAM) and code rate is chosen for the whole H-ARQ block. Convolutional codes used for FEC block sizes less than 200 payload bits. Quasi-cyclic block LDPC codes are used for larger FEC block sizes. r Position within super-frame: PNDC uses a set of chunks within the slot that is pre-allocated for non-frequency-adaptive transmission by the resource division function. The chunks are regularly spaced in frequency. Within that set, time–frequency blocks of size less than or equal to one chunk are allocated to transmissions. One code block uses a set of blocks that are all of the same size and are regularly spaced in frequency within one single slot. In downlinks, the scheme is denoted B-EFDMA. In uplinks, DFT-precoding is used (B-IFDMA) [SFF+07]. Please see Section 9.2.3 for a detailed discussion of these multiple access schemes. Physical Multicast Broadcast Channel The physical multicast broadcast channel (PMBC) is used to carry MBMS services. It uses B-EFDMA non-frequency-adaptive transmission with a modulation and code rate adjusted to the user with worst SINR in the multicast group. The PMBC transmission can support cooperative relaying.

r Transmission technology: As for PNDC downlinks, but the modulation and code rate is adjusted to the user with worst SINR within the multicast group.

r Position within super-frame: As for PNDC downlinks. Uses the set of downlink resources within frame that are allocated to non-frequency-adaptive transmission. Physical Uplink Control Channel The physical uplink control channel (PUCH) is used for urgent uplink control messages. The PUCH resources are pre-allocated to the UT and hence no UT ID needs to be conveyed in this message. The PUCH channel contains the H-ARQ ACK/NACK, triggered by DL data transmission and CQI messages. If no PADC or PDNC resources are assigned, it also contains scheduling requests. If PADC or PDNC uplink resources are assigned, then scheduling requests may be multiplexed into the PADC or the PDNC.

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131

r Transmission technology: The transmission is protected by convolutional coding in combination with repetition coding, adjusted to the average uplink SINR of the UT. It does not use a H-ARQ process. r Position within super-frame: Each active UT is given a small allocation in the uplink slot of each frame for the PUCH. The allocation is slowly time-varying with the SINR and the traffic load. B-IFDMA allocations are used. The allocation of PUCH resources is performed on initial access and is modified on a slow timescale when the traffic load of the UT varies significantly. Since the PUCH is limited to only few bits, detailed information needs to be carried on other physical channels, e.g. the PNDC. Physical Random Access Channel The physical random access channel (PRACH) is a contention-based uplink physical channel needed to acquire time alignment. The amount of resources used for PRACH is for further study, but it is important to schedule the PRACH resources in neighbouring cells such that the PRACH channel does not cause severe interference, especially to the PNDC and PADC channels.

r Transmission technology: An uplink PRACH transmission uses 4-QAM in one of the two OFDM symbols within the allocated uplink time slot.

r Position within super-frame: Uses a time slot with a duration of three OFDM symbols, that comprises two transmission OFDM symbol durations and synchronisation error guard times in both directions. This slot is positioned in the uplink slot of the jth frame of the super-frame. PRACH uses only frequency resources that are available within a wide area (just as with PBCH). 4.4.4.3 Synchronisation Pilots The downlink slot of the first frame in each super-frame contains downlink network synchronisation pilots that use the three OFDM symbols in the downlink slot. The uplink slot of the first frame of each super-frame contains uplink network synchronisation pilots that utilise the three OFDM symbols of the uplink slot. These pilots utilise only spectral bands that are available over a wide geographical area, to facilitate multi-cell coordination. In the allocated OFDM symbols, they utilise the whole of these bands. Payload transmission may proceed simultaneously in other bands.4 Figure 4.21 shows the mapping between transport channels and some of the physical channels. The remaining physical channels described above are used for control signalling between Layer 1 and Layer 2 and are not shown in Figure 4.21. The TBCH, TMCH and TRAC transport channels are directly mapped onto the corresponding physical channels. The transport shared channel (TSCH) may, for its physical transmission, use frequencyadaptive transmission (PADC), non-frequency-adaptive transmission (PNDC) or, for urgent uplink control and CQI measurements, the physical uplink control channel (PUCH).

4 To reduce interference to such transmissions, the network synchronisation pilots should use a transmit filter to suppress their interference within other sub-bands.

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Downlink only

TBCH

PBCH

TMCH

PMBC

Uplink only

Downlink or Uplink

TPCH

TSCH

PNDC

PADC

TRAC

PUCH

PRAC

Figure 4.21 Mapping of transport channels to physical channels (physical control channels are not shown). (Reproduced by Permission of IEEE © 2009).

The paging transport channel (TPCH) uses non-frequency-adaptive downlink transmission at physical locations that are known to all UTs. It uses low rate coding and modulation that is appropriate for broadcast control messages. No H-ARQ process is involved.

4.5 Conclusion This chapter has provided an overview of the WINNER system concept and architecture, which has been designed according to the principles of flexibility and cross-layer optimisation. Operation in a shared spectrum, relaying, low latency, optimised physical layer design, as well as advanced multi-antenna and interference control techniques are an integral part of the concept. A flat logical node architecture based on the pool concept enables scalable and efficient deployments, as well as equipment sharing, MBMS based on SFN, and multiband transmissions. A description of the logical nodes, protocols and channels provides a first overview and the necessary framework and terminology for the remaining chapters.

Acknowledgements The authors would like to thank all their colleagues from the WINNER I and II projects who contributed to the WINNER system concept. In particular, we acknowledge the contributions of Gunther Auer, Kari Kallioj¨arvi, Jijun Luo, Tommy Svensson, and Carl Wijting.

References [3GPP08]

[DWK07]

3GPP (2008) Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN), Stage 2, TR 36.300, V8.5.0, Technical Specification Group Radio Access Network, 3GPP. Doppler, K., Wijting, C.S., and Kermoal, J.-P. (2007) ‘Multi-band Scheduler for Future Communication Systems’, Proc. International Conference on Wireless Communications Networking and Mobile Computing (WICOM), pp. 6744–8.

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[PDH+06]

133

Pollard, A., D¨ottling, M., von H¨afen, J., Schulz, D. and Zimmermann, E. (2006) ‘WINNER: Towards Ubiquitous Wireless Access’, Proc. VTC 2006, pp. 42–6, IEEE, Melbourne, Australia. [SFF+07] Svensson, T., Frank, T., Falconer, D., Sternad, M., Costa, E. and Klein, A. (2007) ‘B-IFDMA: A Power Efficient Multiple Access Scheme for Non-frequency-adaptive Transmission’, Proc. 16th IST Mobile & Wireless Communications Summit, Budapest, Hungary. [SSD08] Sternad, M., Svensson, T. and D¨ottling, M. (2008), ‘Resource Allocation and Control Signalling in the WINNER Flexible MAC Concept’, Proc. IEEE VTC 2008 Fall, Calgary, Canada. [SSK06] Sternad, M., Svensson, T. and Klang, G. (2006) ‘The WINNER B3G System MAC Concept’, Proc. IEEE VTC 2006 Fall, pp. 1–5, Montreal, Canada. [WDK+08] Wijting, C., Doppler, K., Kallioj¨arvi, K., Johansson, N., Nystr¨om, J., Olsson, M., Osseiran, A., D¨ottling, M., Luo, J., Svensson, T., Sternad, M., Auer, G., Lestable, T. and Pfletschinger, S. (2008), ‘WINNER II System Concept: Advanced Radio Technologies for Future Wireless Systems’, Proc. ICT-Mobile Summit 2008 Conference, Paul Cunningham and Miriam Cunningham (eds), Sweden. [WDK+09] Wijting, C., Doppler, K., Kallioj¨arvi, K., Johansson, N., Nystr¨om, J., Olsson, M., Osseiran, A., D¨ottling, M., Luo, J., Svensson, T., Sternad, M., Auer, G., Lestable, T. and Pfletschinger, S. (2009), ‘Key Technologies for IMT-Advanced Mobile Communication Systems’, submitted to IEEE Wireless Communication Magazine. [WIN1D210] WINNER I (2005) IST-2003-507581 Final Report on identified RI key technologies, system concept, and their assessment, Deliverable D2.10, December 2005, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+. [WIN2D223] WINNER II (2007) IST-4-027756 Modulation and Coding Schemes for WINNER-II System, Deliverable D2.2.3, November 2007, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN1D24] WINNER I (2004) IST-2003-507581 Assessment of adaptive transmission technologies, Deliverable D2.4, February 2005, viewed 20 June 2009, http://projects. celtic-initiative.org/winner+/deliverables older.html. [WIN2D233] WINNER II (2007) IST-4-027756 Link Level Procedures for the WINNER System, Deliverable D2.3.3, November 2007, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D341] WINNER II (2006) IST-4-027756 The WINNER II Air Interface: Refined SpatialTemporal Processing Solutions, Deliverable D3.4.1, November 2006, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+. [WIN2D351] WINNER II (2006) IST-4-027756 Relaying concepts and supporting actions in the context of CGs, Deliverable D3.5.1, October 2006, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D352] WINNER II (2007) IST-4-027756 Assessment of relay based deployment concepts and detailed description of multi-hop capable RAN protocols as input for the concept group work, Deliverable D3.5.2, June 2007, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D353] WINNER II (2007) IST-4-027756 Final assessment of relaying concepts for all CGs scenarios under consideration of related WINNER L1 and L2 protocol functions, Deliverable D3.5.3, September 2007, viewed 20 June 2009, http://projects.celtic-initiative .org/winner+. [WIN2D472] WINNER II (2007) IST-4-027756 Interference avoidance concepts, Deliverable D4.7.2, June 2007, viewed 20 June 2009, http://projects.celtic-initiative.org/ winner+. [WIN2D473] WINNER II (2007) IST-4-027756 Smart antenna based interference mitigation, Deliverable D4.7.3, June 2007, viewed 20 June 2009, http://projects.celtic-initiative .org/winner+. [WIN2D481] WINNER II (2006) IST-4-027756 WINNER II Intramode and Intermode Cooperation Schemes Definition, Deliverable D4.8.1, June 2006, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D6114] WINNER II (2007) IST-4-027756 Final WINNER II System Requirements, Deliverable D6.11.4, July 2007, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+.

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[WIN2D6137] WINNER II (2006) IST-4-027756 Test Scenarios and Calibration Cases Issue 2, Deliverable D6.13.7, December 2006, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D61311] WINNER II (2007) IST-4-027756 Final CG ‘metropolitan area’ description for integration into overall System Concept and assessment of key technologies, Deliverable D6.13.11, November 2007, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+. [WIN2D61314] WINNER II (2008) IST-4-027756 WINNER II System Concept Description, Deliverable D6.13.14, January 2008, viewed 20 June 2009, http://projects.celticinitiative.org/winner+.

5 Modulation and Coding Techniques Thierry Lestable1 and Stephan Pfletschinger2 1 2

Samsung CTTC

5.1 Introduction This chapter captures leading-edge technologies related to channel coding, link adaptation and hybrid automatic repeat request (H-ARQ) algorithms that are essential to the WINNER System. Refinements and optimizations of advanced channel coding candidates (Section 5.3), specifically duo-binary turbo codes (DBTC) and quasi-cyclic block low-density parity-check (QCBLDPC) codes, have been focusing on enabling key features such as rate compatibility through puncturing (RCP) to make full use of the advantages of an incremental redundancy (IR) hybrid-ARQ scheme (Type-II), together with targeting higher codeword lengths (lifting of LDPC codes). Particular attention is paid to coding of control signalling information (short packets), especially focusing on broadcast channel (BCH) robustness, which directly impacts the coverage capabilities of the system. For this purpose, an existing solution based on optimum distance spectrum (ODS) convolutional codes is highlighted and promoted as a suitable and promising candidate. A brand new link adaptation algorithm (Section 5.4), based on a mutual-information approach, has been proposed, designed, and tuned with respect to the advanced channel coding candidates. In-depth evaluations and comparisons, taking into account multiple impairments (e.g. prediction errors) outline the outstanding performance enhancement brought by such new and innovative approach. Finally, in Section 5.5, an innovative framework of H-ARQ is introduced for the first time, leading to flexible and efficient handling of joint link adaptation, incremental redundancy and repetition coding. This enables the thorough evaluation of achievable throughput and delay whilst combining link adaptation with H-ARQ. Radio Technologies and Concepts for IMT-Advanced Martin D¨ottling, Werner Mohr and Afif Osseiran C 2009 Martin D¨ ottling, Werner Mohr, Afif Osseiran

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Figure 5.1 System model of BICM.

5.2 Basic Modulation and Coding Scheme The WINNER air interface has to adapt to a wide range of radio environments and thus to a wide range of SNR values. For high spectral efficiency, in general, coding and modulation have to be optimized jointly. This paradigm led to the development of trellis coded modulation (TCM) and was successfully applied in wireline channels. The drawback of these coded modulation schemes is the associated high system complexity and therefore it is highly desirable to treat coding and modulation separately. Fortunately, this separation is possible with hardly any compromise in performance and it consists in decoupling the coding from the modulation by a pseudo-random interleaver. This scheme is known as bit-interleaved coded modulation (BICM) and is depicted in Figure 5.1: the coded bits cn are permuted by the interleaver and then mapped to QAM symbols. A very detailed analysis of BICM in terms of capacity, cut-off rate and error probabilities was carried out by Caire et al. [CTB98]. This seminal work showed that BICM incurs hardly any loss in performance with respect to the more general coded modulation. This holds especially for fading channels and also applies to the outage capacity for quasi-static channels, as has been shown by Ekbal et al. [ESC03]. Figure 5.2 shows the capacity of BICM on an AWGN channel in comparison to the inputconstraint capacity of QAM, denoted as CM capacity. These capacities are given by: 2 ⎤ √ ˆ exp − w + γ (x − x) R1 1 ⎢ x∈χ ⎥ ˆ ⎥ ld IBICM,R1 (γ ) = R1 − 2−R1 E⎢ ⎣ √ 2 ⎦ q=1 b=0 x∈χbq w ˆ exp − w + γ (x − x) ⎡

q

ICM,R1 (γ ) = R1 − 2−R1

E ld

x∈χ w

ˆ b x∈χ

(5.1)

2 √ ˆ + |w|2 exp − w + γ (x − x)

ˆ x∈χ

R1 where, in both cases,

w ∼ CN(0, 1) and X denotes the set of constellation points of 2 -QAM. q The subset Xi = x = µ(b) : bq = i contains all constellation points whose corresponding bit vector has value i ∈ {0,1} in its q-th position. Note that while the CM capacity depends only on the QAM constellation, the BICM capacity is also a function of the bit labelling. For the values in Figure 5.2, Gray labelling has been assumed.

Modulation and Coding Techniques

8

Capacity [bits per channel use]

7

137

256−QAM

Unconstrained AWGN CM capacity BICM capacity

64−QAM

6 5 4

16−QAM

3 2

QPSK

1 0

BPSK

0

5

10

15 ES/N0 [dB]

20

25

30

Figure 5.2 BICM capacity in comparison with CM capacity and capacity of the unconstrained AWGN channel.

5.3 Coding Schemes This section presents two candidates for forward error correction (FEC) coding for medium and large packet lengths, which were chosen within the project: quasi-cyclic block low-density parity-check (QC-BLDPC, the FEC scheme for the WINNER reference design) codes in Section 5.3.1 and duo-binary turbo codes (DBTC) in Section 5.3.2. Both schemes yield a superior performance at packet lengths above 200 information bits and can be implemented efficiently. However, for smaller packets (e.g. those needed for broadcast control information), they are not applicable and a low-rate convolutional code (in Section 5.3.3) is proposed instead, for information lengths down to 25 information bits. The decoders for DBTC and QC-BLDPC codes are affected by several impairments of the overall system, the accuracy of channel estimation being the key parameter. Its influence on the decoding process is shown for the LDPC codes. These results can be used to assess the applicability of channel estimation algorithms. In Section 5.3.4, the choice of coding scheme for the system concept is justified and explained.

5.3.1 Low-density Parity-check Codes Among the increasing number of subsets of low-density parity-check (LDPC) codes, only a few are seen as serious candidates for next-generation wireless systems [LZ04, LR+06]. Indeed, for realistic future systems, many different constraints have to be taken into account simultaneously, such as performance, encoding and decoding complexity and decoder throughput (parallelism), resulting in what is called an ‘adequacy algorithm architecture’ approach

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[Dor07]. One of the most promising candidates is the family of quasi-cyclic block low-density parity-check codes (QC-BLDPCC) [Fos04]. QC-BLDPC codes are defined by sparse parity-check matrices of size M × N consisting of square submatrices (subblocks) of size Z × Z that are either zero or contain a cyclic-shifted identity matrix. M is the number of rows in the parity-check matrix, N is the code-length (number of columns) and the information size K is given by K = N − M. These parity-check matrices are derived from the base matrix Hb of size m × n and the expansion factor Z , which determines the subblock size and hence the size of the derived code. From one base matrix, different code lengths can be constructed using different expansion factors: N = Z ·n

(5.2)

There is one base matrix specified per mother code rate: R = K /N = 1 − m/n

(5.3)

The entries of the base matrix are integer values defining the content of the subblocks: Hb = ( pi j ) 1≤i≤m b

(5.4)

1≤ j≤n

In the expansion process each entry pi j is replaced by a Z × Z square matrix that is:

r a zero matrix 0 Z ×Z , if pi j < 0 r an identity matrix I Z ×Z shifted to the right by pi j mod Z , if pi j ≥ 0. The base matrix always consists of a systematic part Hs and a parity part Hp : Hb = Hs |Hp

(5.5)

Consequently a codeword c consists of a systematic part s and a parity part p: c = [s|p] = [s1 s2 · · · s K | p1 p2 · · · p M ]

(5.6)

The parity part of the base matrix is in an approximate lower-triangular form (see Figure 5.3). Therefore, the parity-check matrix resulting from the expansion process is also partially

Figure 5.3 Parity part of the base matrix.

Modulation and Coding Techniques

139

Figure 5.4 Structure of parity-check matrix after expansion.

lower-triangular and always has the structure introduced in [RSU01] (see Figure 5.4, in which the shaded area represents arbitrary sparse matrix entries). As reminder, this is equivalent to the notation introduced in [RU01] and shown in Figure 5.5 (with the initial notation, we have Z = p = g).

5.3.1.1 Encoding of BLDPC Codes Method 1 The parity-check matrix that was obtained from the expansion process has approximate lowertriangular form as depicted in Figure 5.4. Before encoding, the part of the matrix that is not lower-triangular, i.e. the last Z rows, have to be pre-processed. The pre-processing is done by Gaussian elimination and consists of the following steps: 1. The entries in the lower-right corner are eliminated to achieve the structure shown in Figure 5.6. Note that the area denoted by P is no longer sparse. 2. The last Z rows are processed to achieve an upper-triangular form as shown in Figure 5.7. The resulting parity-check matrix is denoted as H in the following discussion. 3. Determine the first Z parity bits by backward substitution using the last Z rows: pk =

N −M j=1

H M+1−k, j s j +

k−1

H M+1−k, j+N −M p j

k = 1, . . . , Z

(5.7)

j=1

Figure 5.5 Form of parity-check matrix, from [RU01]. (Reproduced by Permission of IEEE © 2009).

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Figure 5.6 Structure of parity-check matrix after pre-processing step 1.

4. Determine the remaining (M − Z ) parity bits by forward substitution using the first (M−Z ) rows: pk+Z =

N −M

H k, j s j +

N −M+Z j=N −M+1

j=1

H k, j p j +

N −M+Z +k

H k, j p j

k = 1, . . . , M − Z

j=N −M+Z +1

(5.8) Method 2 The second method follows strictly the instructions given in [RSU01], by taking advantage of the structure of the parity-check matrix [MYK05]. Indeed, it can be demonstrated (see Figure 5.5) that:

I −ET−1

A 0 H= (−ET−1 A + C) I

B T (−ET−1 B + D) 0

(5.9)

Then, since all codewords have to be orthogonal to the parity-check matrix: HxT = 0

(5.10)

We end up with the following system of equations: ⎧ ⎨ AsT + Bp1T + Tp2T = 0 ⎩ (−ET−1 A + C)sT + (−ET−1 B + D)pT = 0 1

Figure 5.7 Structure of party-check matrix after pre-processing.

(5.11)

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Figure 5.8 BLDPC code encoding pipeline structure.

Finally, by introducing the matrix = −ET−1 B + D, solutions of this system will give us the parity bits: T p1 = −−1 (−ET−1 A + C)sT (5.12) p2T = −T−1 (AsT + Bp1T ) This whole encoding process can be implemented in a high-throughput pipeline structure (see Figure 5.8). Thanks to the particular structure of the LDPC codes targeted within WINNER, we can take advantage of both the pipeline structure and reduced complexity, since operation (2) from Figure 5.8 is not needed. Indeed, as part of the joint design, we put specific constraints in order to end up with the identity matrix for the matrix. Operations (1) and (3) can easily be performed through simple back-substitution thanks to the double-diagonal structure of matrix T: ⎤ ⎡ IZ 0 K 0 ⎢ IZ IZ 0 K ⎥ ⎥ (5.13) T=⎢ ⎣0 0 K 0⎦ 0 K IZ IZ 5.3.1.2 Decoding Methods The standard algorithm for decoding LDPC codes is the ‘belief propagation algorithm’ (BPA) [MN96], of which several good approximations exist. This decoding algorithm computes the distribution of reliabilities of the Tanner graph by iteratively exchanging messages between the variable and check nodes. Tanner introduced an effective graphical representation for LDPC codes. The Tanner graph is equivalent to the structure of the parity-check matrix of the code. These graphs provide a complete representation and help to describe the decoding algorithm. Tanner graphs are bipartite graphs i.e. the nodes of the graph are separated into two distinctive sets and edges. There are two types of node: variable nodes (also called bit nodes) and check nodes. The way of switching between bit and check node updates is referred to scheduling; it will be discussed later on, as this can impact the decoder complexity. Decoding involves two major steps, the check node update and the bit node update. In Figure 5.9, intrinsic values from the channel feed first bit nodes (parents), then extrinsic

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(a)

(b)

Figure 5.9 Belief propagation decoding of LDPC codes, from [LZHS06]. (Reproduced by Permission of IEEE © 2009).

information is processed and forwarded to check nodes (children), that themselves produce new extrinsic information relying on parity-check constraints, feeding their connected bit nodes. The rules for updating the bit nodes are as follows: r extrinsic: Tn,m = In + E m ,n ; m ∈M(n)\m r a posteriori probability: Tn = In + E m ,n . m ∈M(n)

The rules for updating the check nodes are as follows: r sign processing: Sign(E n,m ) = Sign(Tn ,m ); n ∈N (m)\n x r magnitude: E n,m = Tn,m where (x) = − log tanh . 2 n ∈N (m)\n We have to introduce some notation:

r N (m): the set of bit nodes connected to the check node whose index is m. r M(n): the set of check nodes connected to the bit node whose index is n. In order to efficiently compute all messages, we need to follow the process involving some accumulated summation. The rules for updating the bit nodes with summation are as follows:

r extrinsic: Tn,m = Tn − E m,n ; r a posteriori probability: Tn = In + E m ,n . m ∈M(n)

The rules for updating the check nodes with summation are as follows: r sign processing: Sign(E n,m ) = Sign(Tn ,m ); n ∈N (m)\n r magnitude: Sm = Tn ,m , E n,m = Sm − Tn,m . n ∈N (m)

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5

y

4

3

2

1

0 0

1

2

3 x

4

5

6

Figure 5.10 Shape of non-linear Phi(x) function.

The sub-optimal min-sum algorithm (MSA) has been proposed in literature in order to decrease the complexity by avoiding any call to a non-linear function. This is the most famous sub-optimal decoding algorithm. As we’ll see, its creation and functioning is quite similar to the max-log-map decoding algorithm used with duo-binary turbo codes. The keystone of such a low-complexity algorithm is considering the following approximation: (|Tn ,m |) ≈ (|Tn 0 ,m |) n ∈N (m)\n

where n 0 = Arg min Tn ,m . n ∈N (m)\n

This is quite understandable when we consider the shape of the non-linear function in Figure 5.10. be seen that the X values give the highest Y values. This means that the lower It can lowest Tn,m is, the higher Tn,m will be. By exploiting the property [ (x)] = x, we get

E n,m = min Tn ,m = Tn ,m . 0 n ∈N (m)\n

In [CF02], Fossorier explained that the reason the min-sum algorithm is so degraded compared with log-likelihood ratio belief propagation (LLR-BP) is due to an overestimation of the reliabilities. In short: BP Min−Sum . E < E n,m n,m This overestimation can be compensated for by means of two simple countermeasures:

r a scaling factor, α; r an offset, β.

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One easy way to compensate for the overestimation is to apply a scaling factor in front of the LLRs: replace replace E n,m ← −−− α · E n,m and E n,m ←−−− α · Tn ,m 0

Another option is to subtract the amount of overestimation, then using the following new outgoing LLR: replace replace

E n,m ← −−− Max E n,m − β, 0 and E n,m ←−−− Max Tn 0 ,m − β, 0 The MAX operator ensures that all outgoing LLR whose magnitude is below the given threshold, β, are not considered. We can combine the counteracting efforts (i.e. we can use both α and β) by applying the following new updating rule: replace

E n,m ← −−− Max α · E n,m − β, 0 and replace

E n,m ← −−− Max α · Tn 0 ,m − β, 0

5.3.1.3 Scheduling Algorithms In the standard schedule of the BPA, which is often called the flooding schedule, all check nodes are updated first and then all variable nodes are updated. An alternative is ‘shuffled scheduling’ [Man02], also known as layered decoding, in which the BP decoder uses not only the messages from the last iteration but also information about the updates from the current iteration. This leads to a considerable increase in convergence speed. We can distinguish between horizontal and vertical shuffling. The vertical-shuffling schedule operates along the variable nodes: all check nodes connected to the current variable node are updated and the current variable node is updated. The horizontal-shuffling schedule operates along the check nodes: the current check node and all the variable nodes connected this check node are updated. As already emphasized, there are no theoretical constraints concerning the sequence of updating nodes. That means performance should be the same independent of the scheduling order. This is particularly true for graphs without cycles. Nevertheless, we can still observe some slight differences for codes whose graphs have some cycles. The main idea of finding smart-scheduling algorithms is to allow fast propagation of updating messages through the graph. As we’ll see, choosing the scheduling algorithm properly can result in twice as fast a speed of convergence. Flooding Scheduling This is the initial, or reference algorithm, where all check nodes, then all bit nodes are updated (Figure 5.11). Smarter scheduling algorithms can be implemented, resulting in faster convergence speed.

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Figure 5.11 Flooding scheduling process.

Horizontal Scheduling The horizontal scheduling algorithm (Figure 5.12) was proposed by Mansour [Man02]. In this case, we treat one check node after the other then, for each of them, we update their corresponding bit nodes N (m). Vertical Scheduling This scheduling algorithm (Figure 5.13), which is similar to the previous one, was proposed by Fossorier [ZF02]. Performance Assessment Comparing the performance of different scheduling algorithms has mainly focused on the convergence speed (see Figure 5.14). From these results, we can deduce that the convergence speed can be at least doubled by means of a smart scheduling algorithm. 5.3.1.4 Lifting Process of LDPC Codes In this section, we deal with new requirements from the WINNER system concept, ending up with a codeword length above 27 000 bits.

Figure 5.12 Horizontal scheduling process.

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Figure 5.13 Vertical scheduling process.

In order to ensure not only consistency, but backward compatibility with BLDPC codes developed under WINNER I (Rc = 1/2, 2/3, 3/4) and with the lowest coding rate Rc = 1/3, we will use the well-known ‘lifting’ method on our former parity-check matrices. As demonstrated in [MN96] and [MYK05], applying ‘lifting’ to existing LDPC codes, enables us to increase the maximum allowable codeword length, whilst keeping the same performance for the previous range of codeword lengths (backward compatibility). Our current constraints are as follows:

r Nb = 48: the codeword length is a multiple of 48 (compare with the dimension of base-model matrix);

r maximum codeword length is 96 ∗ 48 = 4608; r maximum expansion factor is Zfmax = 96. The new values are:

r maximum codeword length is 576 ∗ 48 = 27648; r maximum expansion factor is Zfmax = 576. With the notation introduced in [MN96], the resulting exponents E(Hk ) of the parity check matrix Hk corresponding to expansion factor L k are given by: E(Hk ) = E(H) mod (L k )

(5.14)

By applying step by step the modulo-lifting procedure described in [MN96], we have thus produced new parity-check matrices for the following coding rates: Rc = 1/3, 1/2, 2/3, and 3/4, leading to the performances shown in Figure 5.15. 5.3.1.5 Rate-Compatible Puncturing Codes Block LDPC codes are quasi-cyclic, i.e., a cyclic-shift by a number smaller than the subblock size Z of a codeword yields another codeword. From the symmetry of the codes, it follows that each bit within one subblock is equally important for the decoder and, hence, equally

147

Average Number of Iterations

100

101

102

1.6

1.8 (a)

2 2.2 Eb/No (dB)

2.4

2.6

2.8

0.8

1

1.2

1.4

1.6

1.8

2

2.2

1.4

1.6

1.8

2 2.2 Eb/No (dB) (b)

Horizontal Scheduling Vertical Scheduling

2.4

2.6

CV Speed Scheduling, LLR-BP, AWGN, BPSK

Figure 5.14 Convergence speed comparison: (a) absolute number of iterations and (b) ratio of number of iterations.

1.4

Horizontal Vertical Flooding

Convergence Speed Scheduling, LLR-BP, AWGN, BPSK

Ratio # iterations = CV Speed Gain

2.8

148 Figure 5.15 CWER performance results with lifted LDPC codes.

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suitable for puncturing. It is therefore reasonable to define the puncturing pattern as being ‘subblockwise’. For the R = 1/2 base matrix (which can be found on this book’s companion website), a ≤ R ≤ 24 . All these set of puncturing patterns was optimized for the code-rates in region 24 26 48 puncturing patterns are described by the priority vector P: P = [1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 30, 34, 38, 41, 42, . . . 13, 29, 46, 8, 32, 25, 37, 40, 44, 48, 27, 45, 33, 35, 36, 47, 31, 28, 26, 39, 9, 43, 22] (5.15) The priority vector P gives the order in which subblocks of the codeword should be sent in an H-ARQ process. It can be used to define an interleaver in order to implement arbitrary punctured code-rates elegantly. The currently available set of modulation and coding schemes (MCS) for rate-compatible punctured (RCP) BLDPC codes is limited to the combination of the following parameters: b = {1, 2, 4, 6, 8} R=

24 , 48 − P

P = 0, 2, 4, . . . , 22

(5.16) (5.17)

where b = log2 (M) is the number of bits per constellation symbol (M is the constellation size), R is the code rate, and P is the number of punctured subblocks from the codeword for mother code rate R = 1/2. The simulation results1 presented in Figure 5.16 were obtained through the Monte Carlo simulation using the following simulation chain:

r In the transmitter, each information packet of K = 288 or K = 1152 random bits2 has been encoded with the BLDPCC encoder, then rate-compatible punctured, interleaved using a pseudo-random bit interleaver and finally mapped into constellation symbols of b bits. Such a block of symbols has been transmitted through an AWGN channel. r In the receiver, a soft demodulation has been performed for each symbol of a block to obtain log-likelihood ratios (LLR). The demodulator assumed max-log-MAP approximation. The LLR block is deinterleaved, depunctured and sent to the BLDPC code decoder. The decoder employs a standard belief propagation algorithm in the LLR domain in parallel fashion (flooding schedule), i.e. all variable→check node messages are updated in one sweep and all check→variable node messages are updated in another sweep. The maximum number of decoding iterations has been set to 50.

5.3.1.6 SNR Mismatch Impact on LDPC Codes Whilst evaluating performance of advanced coding techniques, namely iterative coding such as turbo codes and LDPC codes, it is necessary to take into account multiple impairments resulting from the system in which such coding techniques are used. 1 The

database with the RCP BLDPC code BER and CWER performance results in the form of text files and plots can be found on the WINNER project web pages. 2 These two sizes of packet are taken from the baseline design assumptions.

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RCP BLDPC code; R = 24/48-P); K = 288; QPSK; AWGN channel P=0 P=2 P=4 P=6 P=8 P = 10 P = 12 P = 14 P = 16 P = 18 P = 20 P = 22

1.0e+00

CWER

1.0e-01

1.0e-02

1.0e-03

1.0e-04

1

2

3

4

5 Eb/No [dB]

6

7

8

9

Figure 5.16 CWER curves for QPSK (and BPSK) and K = 288.

As a result, although optimal decoding algorithms (such as log-MAP for turbo codes or LLRBP for LDPC codes) enable performance close to Shannon Capacity, they might experience severe degradation due to external impairments. One of the key parameters common to both decoders is the SNR estimation [SW98, Kha03, SBH05]. Therefore it is mandatory to evaluate the accuracy requested by SNR estimation algorithms (impacted by channel estimation) in order to avoid prohibitive performance degradations. In this section, we restrict ourselves to considering only LDPC codes. In order to obtain sufficient valuable and relevant results, various modulations have been taken into account: QPSK (see Figure 5.17), 16-QAM and 64-QAM, with a half-rate Rc = 1/2 LDPC codes, as defined in [WIN1D210]. Further detailed results can be found in [WIN2D223]. Depending on the acceptable degradation in performance (BER or CWER), these curves can be used for checking the suitability of channel-estimation algorithms through their impact on the SNR estimation. For instance, with QPSK for an operating point of E b /N0 = 3 dB, the SNR offset can be in the range [−3; +3] dB, if we want to avoid a BER above 10−5 . It’s worth noting that an offset of −5 dB (underestimation) will force such QPSK transmission (true E b /N0 = 3 dB) to be degraded up to a BER close to 0.1. On the contrary, even after +10 dB offset (overestimation), we are still around BER = 10−2 . We conclude that, even though the log-BP decoding of LDPC codes is optimal in terms of performance, it might lose this advantage due to mismatched SNR estimation. The sensitivity of such a decoding algorithm is more robust to overestimation than underestimation.

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Figure 5.17 SNR mismatch impact on LDPC codes, R = 1/2, QPSK.

5.3.2 Duo-Binary Turbo Codes The block diagram in Figure 5.18 shows the structure of the turbo encoder and decoder, which consists of a parallel concatenated convolutional encoder and the corresponding iterative decoder. The information message u is encoded twice: directly by the encoder C1 and a permuted version of the message by the encoder C2 . Both encoded bitstreams as well as the message itself are transmitted. At the receiver side, each coded bitstream is decoded separately by a soft-in soft-out decoder and the obtained information is used by the other decoder, which in turn returns new extrinsic information to the first decoder. After several iterations, the a posteriori L-values obtained are mapped to an estimate of the message u by hard decoding. In Figure 5.18, the received channel symbols are scaled appropriately and are demultiplexed into the L-values corresponding to the systematic bits, L u , those corresponding to the coded bits of encoder C1 , L c1 , and the L-values associated with the encoded bits of C2 , L c2 . E 1 denotes the extrinsic information of the first decoder, which becomes the a priori information A2 for the second decoder and vice versa for E 2 and A1 . Duo-binary turbo codes are used in several standards, e.g. [ETSI02, IEEE16e04], and have been found to offer very good performance in conjunction with higher-order modulation [BJD01]. The main enhancement from DBTC with respect to the original turbo codes lies in the component codes, which encode two bits at a time. As usual for parallel turbo codes, both

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Figure 5.18 Block diagram of a parallel concatenated turbo coding scheme.

component codes are identical. The term ‘duo-binary’ is somewhat misleading since the component codes are still binary convolutional codes (only the number of input bits per transition is k0 = 2) and all operations are carried out in the binary field GF(2). The transfer function matrix of the component encoders is given by ⎛ ⎜1 G(D) = ⎜ ⎝

0

0

1

1 + D2 + D3 1 + D + D3 1 + D + D2 + D3 1 + D + D3

⎞ 1 + D3 1 + D + D3 ⎟ ⎟ 1 + D2 ⎠ 1 + D + D3

(5.18)

As in all parallel turbo codes, the component codes are recursive. One of the salient features of turbo codes is that the component codes are relatively simple codes with low memory. This is also true here since the component codes defined by G(D) have only S = 8 states. The turbo encoder comprises two component encoders (see Figure 5.18); thus the mother code rate of the turbo encoder is 1/3, since for each input bit couple (u (1) , u (2) ) that is transmitted, two encoded bit couples (c1(1) , c1(2) ), (c2(1) , c2(2) ) are produced.

5.3.3 Low-Rate Convolutional Codes for Control Channel The modulation and coding requirements for control channel signalling are different from the ones for user data transmission. The information sent through the control channel is very important for proper functioning of the advanced protocols of the WINNER concept. Although the proposed BLDPCC and DBTC provide excellent coding performance as shown in [WIN1D210], they can’t be used for encoding the control information due to the very short packet sizes (25 information bits) being considered. Therefore low-rate convolutional codes, which can be used for encoding such short packets by choosing a tail-biting algorithm, are still considered for the WINNER reference design. The BER and CWER performance results presented in Figures 5.19 and 5.20 have been obtained for the convolutional code with the following generator polynomials: G B = [473, 513, 671, 765]oct . These results are compared with the results for the convolutional code

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Convolutional codes performance (BPSK; AWGN; tail biting; k = 25 inf. bits; GA = [575, 623, 727]oct; GB = [473, 513, 671, 765]oct) 1.0e+00

CWER, RB = 1/2, P = [3,0,0,3] CWER, RA = 1/3 CWER, RB = 1/4 BER, RB = 1/2, P = [3,0,0,3] BER, RA = 1/3 BER, RB = 1/4

BER / CWER

1.0e-01

1.0e-02

1.0e-03

1.0e-04 -8

-7

-6

-5

-4

-3 SNR [dB]

-2

-1

0

1

2

Figure 5.19 BER and CWER vs SNR results of R = 1/4 (ODS), R = 1/3 (MFD) and R = 1/2 (ODS, punctured) convolutional codes for K = 25 information bits (BPSK, AWGN, tail biting).

from WINNER Phase I. Additionally, R = 1/2 results have been obtained from the same mother convolutional code using the puncturing matrix from Equation (5.19). ⎡ ⎤ 1 1 ⎢0 0⎥ ⎥ P=⎢ (5.19) ⎣0 0⎦ 1 1 The complexity of the tail-biting Viterbi decoding needs to be taken into account. The ‘bruteforce’ tail-biting algorithm is 2k(L−1) times more complex than a standard Viterbi decoding with a known tail, where k represents the number of inputs of the convolutional code (R = k/n) and L is the constraint length. For a convolutional code with L = 9, this means an additional complexity factor of 256. Therefore other convolutional codes with shorter constraint lengths seem to be a good compromise between the decoding complexity and performance figures. Figure 5.20 compares CWER and BER results of a few R = 1/4 ODS convolutional codes with different constraint lengths, i.e. L = {6, 7, 8, 9}.3 The CWER performance of the shortest code in this group, i.e. with constraint length L = 6 is about 0.5 dB worse than the code with L = 9. On the other hand, the decoding complexity of this shortest code is 29–6 = 8 times lower than the longest one. following generator polynomials have been used for R = 1/4 convolutional codes: G 6 = [51, 55, 67, 77]oct , G 7 = [117, 127, 155, 171]oct , G 7 = [231, 273, 327, 375]oct , and G 7 = [473, 513, 671, 765]oct . All of them are optimum distance spectrum (ODS) convolutional codes [FOO+98]. 3 The

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Convolutional codes performance (BPSK; AWGN; tail biting; k = 25 inf. bits) 1.0e+00 CWER, R = 1/4, L = 6 CWER, R = 1/4, L = 7 CWER, R = 1/4, L = 8 CWER, R = 1/4, L = 9 CWER, R = 1/3, L = 9 BER, R = 1/4, L = 6 BER, R = 1/4, L = 7 BER, R = 1/4, L = 8 BER, R = 1/4, L = 9 BER, R = 1/3, L = 9

BER / CWER

1.0e-01

1.0e-02

1.0e-03

1.0e-04 -8

-7

-6

-5

-4

-3

-2

-1

0

SNR [dB]

Figure 5.20 BER and CWER vs SNR results of R = 1/4 ODS convolutional codes for various constraint lengths L and K = 25 information bits (BPSK, AWGN, tail biting).

5.3.4 Comparison of Coding Schemes 5.3.4.1 Performance Comparison The performance comparison (Figure 5.21) underlines the fact that the selection of an appropriate coding technique depends crucially on the target block length. For a code rate of 0.5, DBTC outperforms BLDPCC for block lengths up to 1728 (0.2 dB gain over BLDPCC for N = 576). Then BLDPCC starts progressively to outperform DBTC (0.1 dB better for N = 4308). In general, the performance loss by going from large to small block sizes is lower for DBTC than for BLDPCC. The threshold (in terms of block length) that separates these two regimes, however, depends on the code rate. When increasing the code rate to Rc = 3/4, a block length of 1152 is sufficient for the BDLPCC to achieve the same performance as the DBTC, and the difference observed for N = 576 is very small (see Figure 5.23). 5.3.4.2 Performance–Complexity Trade-Off Relying on the computational complexity assessment introduced in [WIN1D23] and [3GPP05], together with the relative cost of operations given by [WIN1D23, Table 5.1], the energy consumption and cycle counts were computed for both DBTC and BLDPCC. For the sake of clarity, we restricted the presentation of results to two extreme block length cases N = 2304 and N = 576. In the case of DBTC, only the max-log-MAP decoder with scaling of extrinsic

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Figure 5.21 Performance comparison between DBTC and QC-BLDPCC, Rc = 1/2, reproduced from [LZHS06]. (Reproduced by Permission of IEEE © 2009).

information (factor 0.5 in the first iteration, 0.75 in the second to seventh iterations and 1 in the last iteration) is used, as it has been proven to give performance similar to log-MAP decoding, with an attractive reduction in complexity. Similarly, the MinSum∗ algorithm (MSA∗ ) provides the best trade-off for LDPC decoding. Additionally, alternative schedulings (shuffled decoding) Table 5.1 Assessment of the most promising FEC techniques.

b

Performance (dB)

Short blocks Medium blocks Large blocks Memory requirements Code structure Decoding Encoding complexity (operations per information bit) Decoding complexity (operations per information bit) Maturity a

CCa

DBTC

BLDPCC

– <1–1.5 <2–2.5 Very low Low

<0.5 – <0.2 Low Medium

<1 <0.2–0.4 – Medium Medium

<1700 Medium

<1200 Medium

< 10 ∼2500 High

Memory 8 convolutional code, soft Viterbi decoding. Loss with respect to the best technique at codeword sizes 50, 576, and 4308 bits (AWGN, BPSK, target BLER 1 %). b

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Figure 5.22 Complexity–performance trade-off for QC-BLDPCC and DBTC, Rc = 1/2, from [LZHS06]. (Reproduced by Permission of IEEE © 2009).

should be used, as they lead to faster convergence of the decoding algorithm and the same performance at roughly half the iterations (compare with the results in Section 5.3.1.3). In the sequel, we tried to keep the notations already introduced into [WIN1D23]. Due to comparison between different packet lengths, minor modifications have been proposed. Packet lengths of N = 2304 bits and N = 576 bits are considered. For DBTC, the triplet (mx, iy, lgMPz) indicates the memory size ‘x’ of constituent encoders, the maximum number of iterations ‘y’, and max-log-MAP (z = 0) or log-MAP (z = 1) decoding algorithm. In the present discussion, only the max-log-MAP decoding algorithm is considered for DBTC (marked by a diamond in Figure 5.22). In the case of BLDPC codes, we also have to differentiate the number of iterations. A circle in Figure 5.22 denotes the maximum complexity (20 iterations) and a triangle the average complexity (average number of iterations). Energy and cycles are distinguished by the use of unfilled and filled markers, respectively. For the case of Rc = 1/2 (Figure 5.23, DBTC offers a better trade-off between complexity and performance than BLDPC codes with low block size (N = 576), as they perform better at only slightly more energy consumption (with respect to the maximum iteration case for BLDPCC). However, for a higher block length (N = 2304), BLDPC codes become more suitable, since their energy consumption saving is achieved at the expense of only minor performance degradation. These two trends are reinforced by the remaining results (see Figure 5.23). Indeed, the higher the coding rate the less energy or cycles are required by

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Figure 5.23 Complexity–performance trade-off for QC-BLDPCC and DBTC, Rc = 3/4 (from [LZHS06]. (Reproduced by Permission of IEEE © 2009).

BLDPCC (provided that the high code rate is achieved by using a code of a different rate rather than puncturing the rate 1/2 code). It might be worth mentioning that DBTC still outperforms BLDPCC for lower block length, but higher energy consumption (in the present case, of 8 internal decoder iterations) seems the price to be paid for such performance. This complexity–performance trade-off is a necessary step towards fair comparison of these two main channel coding candidates, DBTC and BLDPCC. Although these results are quite informative, they are not yet sufficient for any further technological decision. Indeed, the number of gates and the memory size requirements, together with performance robustness to quantization (fixed point simulations) should still be considered. Based on current assessment and knowledge, these two channel coding techniques seem more to be complementary than concurrent solutions (with respect to block length).

5.3.4.3 Domain of Suitability A large number of possible options for forward error correction are identified and discussed in [WIN1D21]. With the advent of iterative decoding, very powerful concatenated error correction schemes have been devised which exhibit a decoding complexity that is linear with respect to the block length – a key requirement for inclusion in any practical wireless communications system.

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Figure 5.24 Domain of suitability of DBTC and BLDPCC for a target BLER of 1 %, from [LZHS06]. (Reproduced by Permission of IEEE © 2009).

DBTC and QC-BLDPCC provide the necessary amount of flexibility in terms of block length and code rate. However, these iterative decoding schemes suffer from quite poor performance at short block lengths. Convolutional codes are, therefore, used with block lengths below 200 information bits. The domain of suitability (for a target BLER of 1 %) of all candidates is summarized in Figure 5.24 (see [WIN1D210] for details). Table 5.1 compares the most promising FEC techniques: convolutional codes, DBTC and BLDPCC. DBTC show very good performance at quite low block sizes (around 128 information bits) and may eventually be used instead of CC in such scenarios. BLDPCC are favoured over DBTC for large block sizes due to their superior error-correction performance in this regime (a lower bit SNR is required to achieve BLER of 1 %, see [WIN1D23]). All codes show the same high degree of flexibility in terms of block sizes and code rates (see [WIN1D210]) and support the construction of rate-compatible code sets. It should be emphasized that the impact on the overall system concept of selecting any one of the proposed FEC techniques is rather limited as soft-input soft-output versions of decoder algorithms exist for all the investigated techniques – the encoding and decoding sub-blocks can hence be regarded as a ‘black box’ by the rest of the system. 5.3.4.4 Implementation Issues: Flexibility, Parallelization and Throughput An important practical issue when dealing with coding schemes for adaptive radio interfaces is the flexibility in terms of block sizes and code rates. One typical way of constructing good

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LDPC codes is to first optimize the error correction performance of the code ensemble for the target code rate (e.g. optimize the degree distribution using EXIT chart analysis) and then generate one specific code using construction algorithms that ensure low error floors (e.g. the progressive edge growth algorithms). The disadvantage of this approach (for practical implementation) is that a new code needs to be designed for each block length and rate. On the other hand, structured LDPC codes, and especially quasi-cyclic and block LDPC codes [MY05; MYK05], have been shown to have good performance and high flexibility in terms of both code rates and block sizes. The general idea is to base the construction of the LDPCC on different basic elements, usually shifted or rotated versions of the identity matrix. Adaptation to different block lengths is easily done by expanding elements of the basic matrix (e.g. by replacing each ‘1’ in the parity check matrix with an identity matrix and each ‘0’ with an all-zeros matrix). Different code rates are obtained by appending more elements to the matrix in only one dimension (e.g., adding more variable nodes but no check nodes). The decoder must obviously be designed to support such changes in the code structure. The inherent parallelism of LDPCC is then exploited by allocating variable node units (VNU) and check node units (CNU) to groups of variable and check nodes, respectively, for processing of the decoding algorithm. The degree of parallelism is dictated by the number of elements in each unit. The throughput of such a semi-parallel decoder architecture can be expressed as follows: D = n b · Rc ·

P

% · Fck IT + P z

(5.20)

where Fck denotes the clock frequency, (n b , m b ) are the dimensions of the base model matrix, Rc is the code rate and IT is the iteration number. The degree of parallelism P is bounded as 1 ≤ P ≤ z, where z is the expansion factor. Note that in this architecture, the total number of VNUs and CNUs is P · n b and P · m b , respectively [LZ05]. Current technology does not yet allow the exploitation of this degree of parallelism in full at reasonable cost. Nevertheless, the flexibility of such architectures is quite attractive from a design point of view, as it allows scaling and tuning of the hardware cost very easily depending on the target throughput. Such semiparallel architectures are currently being investigated throughout the whole industry (some first complexity estimates can be found in [ZZ05]). As always when dealing with LDPCC, the number of iterations IT can vary between an average number required for convergence and a maximum number which represents the worst-case decoding delay (and strongly influences the error-correction performance). Duo-binary turbo codes [WIN1D21; WIN1D23] differ from classical PCCC by the fact that the information bits are encoded pairwise. The internal interleaver is based on an algorithmic permutation defined by a single equation. This PCCC can hence be adjusted to any frame size by modifying only four values which parameterize the internal interleaver, implying a high flexibility of this type of PCCC. Duo-binary PCCC not only achieve very good performance for long block lengths, but also perform well at smaller block sizes and have been standardized in DVB with block lengths as small as 128 bits. The performance has also been improved at low error rates, thanks to the improved internal interleaver. The decoder can be designed to decode two information bits per clock cycle and its throughput can hence be obtained by means of the following relation: D=

P · Fck IT

(5.21)

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Table 5.2 Achievable throughput for rate 0.5 and 200 MHz clock frequency. Code

Parallelism level

Number of iterations

Bit rate (decoded Mbps)

DBTC

6 12 4 8 2 4 1 2

8 8 5 5 50 (flooding schedule) 50 20 (shuffled decoding) 20

150 300 160 320 192 384 240 480

BLDPCC

The throughput is independent of the code rate, as code rates higher than 1/3 are obtained by puncturing. For the throughput analysis, we consider a clock frequency Fck of 200 MHz to be achievable, which corresponds to a technology of 0.13 µm and beyond [THB04]. By tuning the degree of parallelism P and the (maximum) number of iterations IT, we can match the requested decoder’s throughput to the system requirements. Table 5.2 summarizes the achievable throughput for the case of a code rate Rc = 0.5. It can thus be concluded that a parallelism level of 12 for DBTC (when using 8 internal decoder iterations) and 4 for BLDPCC (using a standard flooding schedule and corresponding to 192 VNUs and 96 CNUs) is sufficient to achieve a throughput of 300 Mbps (and beyond). We conclude that both coding candidates can offer a suitable throughput. It is, however, known that the inherent parallelism of BLDPC codes can be attractive from a designer’s point of view, as it allows tuning of the whole hardware and software implementation to the cost and performance constraints. Although very high data rates are theoretically achievable by BLDPC decoders, technology constraints still limit the degree of parallelism affordable and thus the data rates. To give the full picture, the respective gate count (and, thus, the area estimation) for DBTC and BLDPCC need to be considered.

5.4 Link Adaptation This section provides a detailed description of how modulation and coding is adapted to changing environments using link adaptation, also known as adaptive coding and modulation (ACM). The envisioned scheme for adaptive transmission within WINNER is based on the design presented in [WIN1D210]. Segmentation and FEC coding is performed in a flexible way. The segmentation can be performed before scheduling, using a fixed segment size [WIN1D210], or after scheduling, with a segment size adjusted to the allocated transmission resource. In either case, the segmentation supports the novel high-performance transmit scheme outlined below, that combines strong coding over large code blocks with fine-grained link adaptation within small resource units. With multiple users, it allows the scheduler to adaptively obtain multi-user scheduling gains [SFS06]. Two methods of adaptive transmission are supported:

r Frequency-adaptive transmission, where flows are given exclusive access to chunk layers and individual link adaptation is performed within the chunks, or chunk layers. The adaptation

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utilizes the frequency-selectivity of the channel and uses a very fast feedback loop, working on the time-scale of the frame to follow the short-term fading. r Non-frequency-adaptive transmission averages over the frequency variations of the channels. A code block is interleaved and mapped onto a wide frequency range. The whole code block utilizes the same modulation and coding scheme. Modulation and coding is adapted to the shadow fading and path loss, but not to the fast (frequency-selective) fading. The two methods are based on different principles: The first utilizes the fine-grained channel variations, while the other averages over them by diversity techniques. The frequency-adaptive transmission should typically be combined with multi-antenna transmit schemes that preserve the channel variability (such as spatial multiplexing), while the non-frequency-adaptive transmission is preferably combined with multi-antenna diversity techniques that further reduce the variability of the perceived channel. Frequency-adaptive transmission utilizes more channel quality information (CQI) at the transmitter: it requires the prediction of the SINR within each chunk. Non-frequency-adaptive transmission requires only an average SINR value for all allocated resources. This corresponds to a higher control and feedback overhead for frequency-adaptive transmission, but it provides the following types of potential gain:

r gains due to the individual adaptation of modulation and code rate within chunks; r multi-user scheduling gains: flows to and from a user can be given the chunks that are best for that particular user. Within a super-frame, different sets of chunks are pre-allocated for frequency adaptive and non-frequency adaptive transmissions. These sets are fixed over the whole super-frame, but may be changed between super-frames. Both sets of chunks should be well dispersed in frequency, since both transmission principles work better the more frequency selectivity they are provided with. The selection of either transmission mode depends especially on the quality of the available CQI at the transmitter side. Channel prediction is used to significantly extend the range of applicability of frequency-adaptive transmission. The resource allocation structure for non-frequency-adaptive transmission is denoted BIFDMA for the uplink and B-EFMDA for the downlink and is further discussed in Chapter 9. For the frequency-adaptive mode, the mutual-interference-based adaptive coding and modulation (MI-ACM) algorithm [SBC07] has been chosen due to its good performance and low complexity. Figure 5.25 shows the system model for a link which applies frequency-adaptive transmission to a set of chunks which have been previously allocated to this link by the scheduler. The input data flow is first encoded by an FEC encoder. After interleaving, the coded bits are adaptively modulated and mapped to the allocated chunks. The underlying transmission scheme is thus BICM, where the QAM constellation is adapted per chunk while the code rate is the same for all chunks of the link. The MI-ACM algorithm, which determines the QAM constellation for each chunk and the common code rate, was invented in the WINNER project [SBC07] and independently by another research group [LR07]. The key idea in this algorithm is mutual-informationbased averaging. This step allows the accurate consideration of channel coding without being restricted to a specific coding scheme. Hence, the MI-ACM algorithm is equally applicable to DBTC and LDPC codes.

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Figure 5.25 System model for frequency-adaptive transmission. (Reproduced by Permission of IEEE © 2009).

The MI-ACM algorithm has been evaluated for different scenarios and it has been compared to the Hughes-Hartogs algorithm [HH87], which is the optimum bit- and power-loading algorithm in terms of rate maximization or power minimization [PPS07]. In this investigation, the following outcomes have been found:

r Nearly the same throughput as with the Hughes-Hartogs algorithm is achieved in all considered cases. This indicates that the gain from power-loading is insignificant and that there is very little margin for improvement of the MI-ACM algorithm. r Non-square QAM constellations (8-, 32-, or 128-QAM) provide no additional gains. r The computational complexity of the MI-ACM algorithm is very low.

5.5 Link Level Aspects of H-ARQ In addition to the FEC techniques discussed in the previous sections, when a reverse channel is available it is possible to use an automatic retransmission request (ARQ) protocol, where the receiver sends requests for the sender to repeat data unit transmissions whenever errors are detected. In current standards, multiple retransmission functions are often located at different protocol layers on top of each other. Lower-layer retransmission aims to correct transmission errors on the physical channel over one hop, whereas higher-layer retransmission ensures reliable information transfer over the radio access network and its interfaces. A similar kind of function is proposed for the WINNER system. These hop-by-hop and end-to-end retransmission functions interact closely to ensure an efficient overall system. The focus in this section is on the selected retransmission technique for a single radio interface hop in the WINNER RAN.

5.5.1 Incremental Redundancy Scheme The selected approach for hop-by-hop retransmissions in the WINNER concept is based on a flexible H-ARQ Type II scheme using a soft bit interface. A soft bit interface is needed to enable iterative receivers for the FEC code, but it is also important for the operation of

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hop-by-hop retransmissions to take advantage of the soft information in the different redundancy versions without restricting the resource scheduler and the link adaptation process. The proposed H-ARQ scheme allows for a flexible implementation of incremental redundancy (IR) and repetition coding under a unified framework, providing a seamless transition from incremental redundancy to Chase combining through the parameterization of the retransmission (RT) size. The selection of the modulation and coding scheme (MCS) is controlled by an independent procedure (e.g. link adaptation), so it is a matter for the H-ARQ to handle retransmitted packets with different modulation formats and code rates. That implies the receiver handles soft values. During retransmissions, the corresponding likelihoods obtained for the associated modulation are combined in a similar fashion to the way in which Chase combining combines the proper likelihood values (L-values) of each retransmitted packet. Depending on the selected MCS, the number of coded bits is given by N = K /R for the initial transmission, while the message length K is fixed according to the MAC frame structure. Rate-compatible puncturing can be implemented with an interleaver which permutes the coded bits followed by bit selection which selects the first N bits of the permuted codeword (see Figure 5.26). This allows for a straightforward implementation of a cyclic incremental redundancy scheme, which generalizes the classical IR concept, which only retransmits the remaining parity bits: cyclic IR parts of the codeword are retransmitted in a cyclic fashion; for the initial transmission, the coded bits c0 , c1 , . . . , c N −1 are transmitted. The first retransmission continues with the next N bits. If less than N parity bits are left, then the missing bits are taken from the start of the codeword. The index range for the nth retransmission (n = 0 indicates the initial transmission) can be expressed concisely by a modulo operation: [mod (n N , 3K ) · · · mod ((n + 1)N , 3K )] Note, that for cyclic IR, it is irrelevant whether the channel encoder is systematic or not since no distinction is made between systematic or parity bits. Another feature of this scheme is that there is no restriction on the size of the retransmission unit (RTU). Due to cyclic repetition of the coded bits, the retransmission can be made as large as desired and it is also possible to vary the RTU size with the retransmission number according to some predefined strategy.

5.5.2 Throughput and Delay Analysis The strong performance degradation of ACM schemes with a non-negligible prediction error motivates the use of retransmissions. In this section, we evaluate the achievable throughput

Figure 5.26 Rate-compatible coding via interleaving and bit selection.

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and the delay for a simple H-ARQ scheme. In order to make the problem more tractable, we use some simplifying assumptions:

r The channel is modelled as Rayleigh block fading. r The long-term average SNR γ¯ is assumed to be constant and known at the transmitter side. For the simulations, it has been assumed that all codeword errors can be detected perfectly, which is a reasonable assumption as long as the probability of undetected error is well below the target packet error rate. Figure 5.27 shows the simulation results in terms of throughput and average delay for MCS and cyclic IR in comparison to Chase combining. Despite the high uncertainty of the channel state – only the long-term average SNR is assumed to be known – a surprisingly high throughput can be achieved, while the average delay is quite low. It can be observed that cyclic IR performs significantly better than Chase combining, especially if the code rate of the selected MCS is much higher than the mother code rate. When block fading, K = 288

i.i.d. Rayleigh channel, no retransmission HARQ with Repetition coding HARQ with cyclic IR

5

64−QAM, R=6/7

Throughput

4 3 16−QAM, R=2/3

2 1 0

QPSK, R=1/2

0

5

10

15 20 Average SNR [dB]

25

30

block fading, K = 288

6

Repetition coding Cyclic IR

Average delay

5 4 3

64−QAM, R=6/7

2 1 0 −5

16−QAM, R=2/3

QPSK, R=1/2

0

5

10 15 Average SNR [dB]

20

Figure 5.27 Throughput and average delay for K = 1152.

25

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165

the code rate corresponds to the mother code rate, cyclic IR is the same as Chase combining. These results show that, with cyclic IR, the lack of accurate channel state information does not result in a significant throughput reduction and the price to be paid in terms of additional delay is moderate. From these simulation results, we can make several interesting observations:

r The cyclic IR scheme outperforms Chase combining by taking advantage of the coding gain of the FEC scheme. This performance gain comes at hardly any additional implementation complexity. r The results allow fixed SNR thresholds for the MCS selection, taking both throughput and delay into account; e.g. we can maximize the throughput while constraining the delay. It is interesting to see that, even for maximum throughput, on average very few retransmissions are required. r With H-ARQ, the CWER or BER at the first transmission is just an intermediate parameter, which has no particular meaning.

5.6 Conclusions Investigation and comparison of advanced channel coding candidates represent a critical and sensitive topic, since ensuring outstanding, and efficient coding scheme is one of key enabler technologies for systems beyond 3G. Throughout the WINNER projects, DBTC and LDPC codes have been investigated thoroughly not only from a performance point of view, but taking into account the complexity and architecture constraints. Based on the greater maturity of DBTC when the project started, more effort has been spent on evaluating the suitability of LDPC codes with respect to mobile wireless systems, especially promoting their scalability to codeword length, high throughput decoding due to inherent parallelism, and finally the possibility of designing easily ratecompatible punctured LDPC codes, thus enabling their use within H-ARQ Type II schemes (incremental redundancy). The major outcome of our investigations on channel coding is that both families of codes, DBTC and LDPC codes, are now mature enough to target systems beyond 3G and that former drawbacks of LDPC codes are no longer relevant thanks to many different techniques developed recently and handled within our investigations. However, the final choice in favour of LDPC codes for the WINNER reference design is mostly motivated by the recent trend in standardized systems, candidates for IMT-Advanced, to request much higher codeword lengths, which are known to be the perfect playground for LDPC codes. Since it is well known that turbo codes and LDPC codes both have a sparse Tanner graph, and thus could be seen as the extreme cases of a more generic family of codes, it could be interesting to address the issue of a ‘generic’ decoder, proposing a suitable architecture for decoding without loss of performance in either family of codes. Concerning link adaptation, also referred as adaptive coding and modulation (ACM), the proposed breakthrough mutual-information-based algorithm is the confirmation that mutualinformation techniques are key to this era of digital communications through their use in code design (the ‘adequacy architecture’ algorithm), by means of EXIT charts, joint detection and decoding, or doping techniques that could be seen as an early stage of the proposed algorithm.

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It could be thus relevant to address the overall tuning, and parameterization of the whole transceiver chain using EXIT chart analysis and mutual-information exchange between all constituent modules of the PHY Layer. This would be dealing with a very complex system with many degrees of freedoms, but mutual information could bring sufficient consistency into the scheme to obtain a workable solution. The tuning could be done online, thanks to specific scheduling algorithms, and embedded within some H-ARQ schemes, enabling full usage of soft information over different protocol layers.

References [3GPP05]

3GPP (2005) ‘Comparison of Structured LDPC Codes and 3GPP Turbo-Codes’, TSG RAN WG1#42, R1-050840, ZTE, CATT, RITT, Huawei. [BJD01] Berrou, C., J´ez´equel, M., Douillard, C., Kerou´edan, S. and Conde Canencia, L. (2001) ‘Duo-binary turbo codes associated with high-order modulation’, ESA TTC’2001, Noordwijk, The Netherlands. [CF02] Chen, J. and Fossorier, M.P.C. (2002) ‘Near Optimal Universal Belief Propagation Based Decoding of Low-Density Parity Check Codes’, IEEE Transactions on Communications, 50(3):406–14. [CTB98] Caire, G., Taricco, G. and Biglieri, E. (1998) ‘Bit-interleaved coded modulation’, IEEE Transactions on Information Theory, 44(3):927–46. [Dor07] Dore, J.-B. (2007) ‘Optimisation conjointe de codes LDPC et de leurs architecture de decodage et mise en oeuvre sur FPGA’, Ph.D Thesis (French). [ESC03] Ekbal, A., Song, K.S. and Cioffi, J.M. (2003) ‘Outage capacity and cutoff rate of bit-interleaved coded OFDM under quasi-static frequency selective fading’, Proc. IEEE Global Telecommunications Conference, pp. 1054–8, San Francisco, USA. [ETSI02] ETSI (2002) EN 301 958 V1.1.1, Digital Video Broadcasting (DVB): Interaction channel for Digital Terrestrial Television (RCT) incorporating Multiple Access OFDM. [Fos04] Fossorier, M.P.C. (2004) ‘Quasi-Cyclic low density parity-check codes from circulant permutation matrices’, IEEE Transactions on Information Theory, 50:1788–94. [HH87] Hughes-Hartogs, D. (1987) Ensemble modem structure for imperfect transmission media, US patent 4 679 227. [IEEE16e04] IEEE (2004) Std 802.16-2004, Part 16: Air Interface for Fixed Broadband Wireless Access Systems, IEEE, New York, USA. [Kha03] Khalighi, M.A. (2003) ‘Effect of Mismatched SNR on the Performance of Log-MAP Turbo-Detector’, IEEE Transactions on Vehicular Technology, 52(5). [LR+06] Lestable, T., Ran, M., et al. (2006) ‘Error Control Coding Options for Next Generation Wireless Systems’, White Paper WG4, Wireless World Research Forum (WWRF) 17. [LR07] Li, Y. and Ryan, W.E. (2007) ‘Mutual-information-based adaptive bit-loading algorithms for LDPCcoded OFDM’, IEEE Transactions on Wireless Communications, 6(5)1670–80. [LZ04] Lestable, T. and Zimmerman, E. (2004) ‘LDPC Options for Next Generation Wireless Systems’, Wireless World Research Forum (WWRF) 15, Paris. [LZ05] Lestable, T. and Zimmermann, E. (2005) ‘LDPC Codes Options for Next Generation Wireless Systems’, Wireless World Research Forum (WWRF), WG5, San-Diego. [LZHS06] Lestable, T., Zimmerman, E., Hamon, M.-H. and Stiglmayr, S. (2006) ‘Block-LDPC Codes vs DuoBinary Turbo-Codes for European Next Generation Wireless Systems’, Proc. IEEE Vehicular Technology Conference Fall, Montreal, Canada. [Man02] Mansour, M.M. and Shanbhag, N.R. (2002) ‘Turbo-decoder Architectures for Low-Density ParityCheck Codes’, Proc. IEEE Global Telecommunications Conference, pp. 1383–8. [MN96] MacKay, D.J.C., Neal, R.M. (1996) ‘Near Shannon Limit Performance of Low-Density Parity-Check Codes’, Electronic Letters, 32:1645–6. [MY05] Myung, S., and Yang, K. (2005) ‘Extension of quasi-cyclic LDPC codes by lifting’, Proc. IEEE International Symposium on Information Theory, pp. 2305–9. [MYK05] Myung, S., Yang, K., and Kim, J. (2005) ‘Quasi-Cyclic LDPC codes for fast encoding’, IEEE Transactions on Information Theory, 51:2894–901.

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[PPS07]

[RSU01] [RU01] [SBC07] [SBH05] [SFS06]

[SW98] [THB04] [WIN1D21] [WIN1D23] [WIN1D210]

[WIN2D223]

[ZF02] [ZZ05]

167

Pfletschinger, S., Piatyszek, A., and Stiglmayr, S. (2007), ‘Frequency-selective Link Adaptation using Duo-Binary Turbo Codes in OFDM Systems’, Proc. IST Mobile and Wireless Communications Summit, Budapest. Richardson, T.J., Shokrollahi, A. and Urbanke, R. (2001) ‘Design of capacity-approaching low-density parity-check codes’, IEEE Transactions on Information Theory, 47:619–37. Richardson, T.J. and Urbanke, R. (2001) ‘Efficient encoding of low-density parity-check codes’, IEEE Transactions on Information Theory, 47:638–56. Stiglmayr, S., Bossert, M. and Costa, E. (2007) ‘Adaptive coding and modulation in OFDM systems using BICM and rate-compatible punctured codes’, European Wireless, Paris, April. Saeedi, H., Banihashemi, A.H. and Hong, Q. (2005) ‘Asymptotic Performance Analysis of LDPC Codes with Channel Estimation Error’, Proc IEEE Vehicular Technology Conference Spring. Svensson, T., Falahati, S. and Sternad, M. (2006) ‘Coding and Resource Scheduling in Packet Oriented Adaptive TDMA/OFDMA Systems’, Proc. IEEE Vehicular Technology Conference Spring, Melbourne, Australia. Summers, T.A. and Wilson, S.G. (1998) ‘SNR Mismatch and Online Estimation in Turbo Decoding’, IEEE Transactions on Communication, 46(4). Tousch, J., Hamon, M.H. and Benko, J. (2004) ‘Turbo-Codes Complexity Estimates’, IEEE 802.11n Proposal 1385-r1, November. WINNER I (2004) IST-2003-507581 Identification of Radio-Link Technologies, Deliverable D2.1, June 2004, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+/. WINNER I (2005) IST-2003-507581 Assessment of Radio-Link Technologies, Deliverable D2.3, February 2005, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+/. WINNER I (2005) IST-2003-507581 Final Report on identified RI key technologies, system concept, and their assessment, Deliverable D2.10, December 2005, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+/. WINNER II (2007) IST-4-027756 Modulation and Coding Schemes for WINNER-II System, Deliverable D2.2.3, November 2007, viewed 20 June 2009, http://projects.celticinitiative.org/winner+/. Zhang, J. and Fossorier, M. (2002) ‘Shuffled Belief Propagation Decoding’, Proc. 36th Annual Asilomar Conference on Signals, Systems and Computers, pp. 8–15, IEEE. Zhong, H. and Zhang, T. (2005) ‘Block-LDPC: A practical LDPC coding system design approach’, IEEE Transactions on Circuits and Systems I: Reg. Papers, 52(4).

6 Link Level Procedures Ivan Cosovic,1 Gunther Auer,1 and David Falconer2 1 DOCOMO Euro-Labs 2 Carleton University

6.1 Introduction This chapter presents and assesses techniques for link level procedures essential for the WINNER system and other future wireless systems. These techniques are indispensable for higher-level radio access procedures, which rely on estimation of the channel parameters and on the fact that system elements are synchronised. Various topics such as pilot design, channel estimation, measurement and signalling, RF and synchronisation imperfections, link-level and self-organised network synchronisation are discussed. Also, a final specification of pilot design, channel estimation schemes, and synchronisation methods for the WINNER system transmission modes is presented.

6.2 Pilot Design Reference symbols known to the receiver (pilots) are used for implementing certain physical layer support functions, e.g. connection setup, synchronisation, mobility support, power control, channel quality indicator (CQI) measurements and, most importantly, channel estimation. On the other hand, pilots add overhead and consume transmission power. Thus, a proper pilot design should enable accurate and reliable channel estimation but also keep the induced spectral and power efficiency losses at an acceptable level. For channel estimation purposes, the following means of multiplexing pilots are predicted:

r for OFDM downlink and uplinks, a scattered pilot grid is used for channel estimation and channel prediction;

r for uplink (frequency-domain-generated) serial modulation, pilot patterns may be generated in the frequency domain equivalent to OFDM; this enables the use of a scattered pilot grid Radio Technologies and Concepts for IMT-Advanced Martin D¨ottling, Werner Mohr and Afif Osseiran C 2009 Martin D¨ ottling, Werner Mohr, Afif Osseiran

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and establishes a common framework for the pilot grid of generalised multi-carrier (GMC) signals. The WINNER system concept heavily relies on provision of accurate channel state information (CSI) at both receiver and transmitter. To this end, two types of channel estimation must be distinguished:

r channel estimation for data reception, where the receiver needs to measure the effective channel, including the effect of the spatial processing at the transmitter, referred to as effective CSI (ECSI); r channel estimation (or, more accurately, channel prediction) for adaptive transmit processing based on CSI at the transmitter (CSIT), typically provided through return link feedback or measurements, where additional extrapolation or prediction in time is required. One key issue for pilot design is the spatial transmit processing, which is selected based on the available CSI or CQI at the transmitter. The following combinations of available CSI/CQI are considered in the WINNER system concept:

r Short-term CSIT: An accurate estimate of the full instantaneous channel matrix (i.e. the channel responses between all transmit and receive antennas) is assumed. Short-term CSIT allows for the most advanced multi-user (MU) MIMO schemes, but requires a slowly fading channel, i.e. it is applicable only to users with pedestrian velocities. r Long-term CSIT–short-term CQI: Long-term CSIT refers to the spatial structure of the channel expressed by the averaged transmit correlation matrix while short-term CQI is a measure of the instantaneous channel gain, e.g. the received SINR. Frequency-adaptive transmission with some kind of beamforming is possible. r No CSIT–short-term CQI: The instantaneous CQI is available at the transmitter, but no knowledge of the channel spatial structure is assumed, enabling frequency-adaptive transmission with linear dispersion codes (LDC). r No CSIT–long-term CQI: Only the average CQI is available at the transmitter, reflecting the channel characteristics due to path-loss and shadowing. The transmitter resorts to nonfrequency-adaptive transmission. The pilot design for any specific embodiment of MU-MIMO (see Chapter 7) is a challenging task on its own and the optimum choices for the specific MU-MIMO schemes may be fundamentally different. Moreover, various flavours of multiple antenna transmission schemes are to be flexibly combined with opportunistic multi-user scheduling and link adaptation, within the same air interface. To this end, a straightforward combination of the individual best choices would result in a combination of a large number of types of pilot (dedicated or common pilots per beam or antenna), which inevitably leads to prohibitive overheads. Hence, the objective of the pilot design is to re-use pilots for as many different functions as possible. The pilot design is a modular concept consisting of basic building blocks defined at the chunk level:

r The pilot pattern specifies the position of pilots on the chunk. The pilot positions are chosen such that a globally regular pilot pattern is obtained, i.e. a two dimensional (2D) grid with equidistantly spaced pilots by Df and Dt in time and frequency, which is advantageous for channel estimation by interpolation.

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r The pilot type specifies whether pilots include user-specific transmit processing. r The orthogonal pilot set specifies whether pilots associated with different spatial streams are orthogonally separated in time or frequency or whether pilots are spatially re-used, i.e. pilots of two spatial streams are placed on the same subcarriers at the same time. This modular concept avoids several pilot patterns corresponding to different pilot types being inserted within a frame. Instead, only one pilot grid is inserted into the frame and the pilot type is determined by the chunk-specific spatial transmit processing. Thus, a highly flexible and adaptive system concept can be supported with a modest pilot overhead [WIN2D61314].

6.2.1 Types of Pilot A scattered pilot grid with orthogonally spaced pilot symbols in time and frequency was proposed in [HKR97] and has been identified as suitable for the WINNER system [WIN1D21] and [WIN1D210]. However, it may result in prohibitive pilot overheads. For instance, a local area deployment is intended to have a distributed antenna array with up to 32 antenna elements. Fortunately, spatial precoding schemes forming beams that are spatially well separated allow the spatial re-use of pilot symbols [WIN2D233]. Figure 6.1 illustrates a scattered pilot grid. Pilots associated with beams that are spatially well separated are multiplexed. For instance, pilots 1 and 3 as well as 2 and 4 do not spatially overlap, so they are located on the same subcarrier, yielding a pilot re-use of two. On the other hand, pilots associated with beams with significant spatial overlap (for instance, pilots 1 and 2, 2 and 3, and 3 and 4) need to be orthogonally separated in time or frequency, i.e. placed on a different subcarrier. Depending on the transmit direction (uplink or downlink) and the kind of spatial processing being used, several types of pilots are distinguished [WIN1D210]:

r Common pilots have the property not to include user-specific transmit processing and thus interpolation in frequency is possible. r Common pilots per antenna (CPA) are used to obtain the unweighted channel matrix, H, which describes the propagation channel between any combination of transmit and receive antennas in the MIMO case.

Figure 6.1 Scattered pilot grid. Pilots are orthogonally separated in time and frequency. Pilots associated with beams that are well separated are spatially multiplexed.

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r Common pilots per beam (CPB) are useful to estimate the effective channel (including the beamforming weights) and perform CQI measurements for the associated beam for fixed beamforming approaches. Note that measurements on such pilots in neighbouring beams can be used for beam handover. Also, the common pilots per beam benefit from the beamforming gain, which reduces the transmit power required for a target channel estimation error and coverage area. r Dedicated pilots may be required if user-specific transmit processing (i.e. a user-specific adaptation of amplitude and phase) is applied to the data symbols. The amplitude and phase induced on common pilots by a fading channel deviates from those of the data symbols (which are induced by a combination of user-specific transmit processing and fading channel) and therefore the receiver cannot detect them based on common pilots. Dedicated pilots are subject to the same transmit processing as the data symbols and therefore allow the receiver to estimate the effective channel. The use of dedicated pilots for other purposes, such as CQI measurements, is limited, since they contain a user-specific component, giving rise to biased measurements. Two different types of dedicated pilots can be distinguished: r Dedicated pilots per antenna (DPA) are either chunk specific or over the full band (DPAFB). r Dedicated pilots per beam (DPB) are also either chunk specific or over the full band (DPB-FB). DPB are useful for estimating the effective channel (including the beamforming weights) and performing CQI measurements for the associated beam for adaptive beamforming approaches. While DPB-FB allow for interpolation over frequency, the resulting pilot overhead may be prohibitive for multi-user MIMO-OFDM. Due to the fact that common pilots can be used by several users, they are attractive for downlink processing, since the overall energy to perform the associated functions has only to be spent once and the pilot symbols can be spread over all resources. Also they provide a basis for un-biased CQI measurements. However, certain user-specific spatial processing techniques require estimation of the effective channel (the channel including the spatial precoding), which is typically provided by dedicated pilots. When user-specific spatial processing is combined with multi-user OFDMA an increasing number of dedicated pilots is needed, even on the downlink. On the other hand, dedicated pilots fail to deliver the unweighted CSI and CQI estimates that are needed for adaptive transmission in a straightforward way.

6.2.2 Reference Pilot Design 6.2.2.1 In-band Pilot Patterns A generic framework for pilot patterns is briefly summarised as:

r Pilot symbols in frequency and time, with respective spacings of Df subcarriers and Dt OFDM symbols, should be placed sufficiently close to satisfy the sampling theorem [SS00] allowing to reconstruct the channel response through interpolation. To allow for realisable filters, oversampling factors should be at least 20 % and 100 % for common and dedicated pilots, respectively. r The pilot pattern only determines the position of the pilots within the frame. The type of pilot is determined entirely by the spatial transmit processing scheme that is used in a particular

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chunk, e.g., a grid of beams (GoB), successive minimum mean-squared error (SMMSE) or linear dispersion codes (LDC). This ensures that only one pilot grid is necessary to support all flavours of MU-MIMO schemes and is therefore the key to keep the resulting pilot overheads at an acceptable level. The receiver implicitly knows which type of pilot is transmitted, as it is uniquely determined by the spatial scheme selection and the transmission mode (FDD or TDD). r The pilot spacing in frequency Df is to be chosen such that the chunk width is an integer multiple of Df . This is an important requirement to allow for a chunk-specific selection of the pilot type. r Pilots from different spatial streams are re-used if the associated beams are well spatially separated. In the case of overlapping beams or unweighted transmit signals, pilots are orthogonally multiplexed in time and frequency. r For TDD and half-duplex FDD systems, pilots should be placed near the beginning and end of a slot in the time direction. The rationale here is that interpolation between pilots exhibits a smaller estimation error than extrapolation near the beginning and end of the slot. r Dedicated pilots should be placed near the corners of a chunk, as interpolation between pilots exhibits a smaller estimation error than extrapolation at the chunk edges. The optimum placement of pilots, such that the channel estimation error in terms of MSE is minimised, within a chunk and over consecutive multiple chunks are addressed in [LMT07a] and [LMT07b], respectively. While the selected pilot patterns of the pilot design results in sub-optimum pilot placement for one or two spatial layers, for four transmit spatial layers the optimum placement is retained. Table 6.1 shows the associated pilot spacing Df and Dt in frequency and time, as well as the resulting overhead p . Up to four orthogonal sets of pilots are allocated. Due to spatial re-use of pilots, the actual number of spatial streams can be significantly higher than four. In many cases, the number of spatial streams is below four or the associated beams are well separated; in this case, the number of orthogonal pilot sets Pn that are actually used may be smaller than four. For instance, only one orthogonal pilot set is used for SISO, while two orthogonal pilot sets are required for linear dispersion codes (LDC) with two antennas. In Table 6.1, it is seen that the pilot spacing in frequency, Df , is always four. This is due to the chunk dimension in frequency of eight subcarriers in both the TDD and FDD cases Table 6.1 Pilot spacing and overheads for the pilot design.

Df Dt p c

FDD

TDD

4 10 4.16 % · Pn (5.2 % · Pn )a Pn = {1,2,3,4}

4 12 3.33 % · Pn (1.67 % · Pn )b Pn = {1,2,3,4}

B-IFDMA (see Section 9.2.3.2) 4 3 8.33 % · Pn , Pn = {1,2}

a Chunks of high-velocity users with speeds exceeding 150 km/h, where additional pilots are inserted. b Chunks of low-velocity users with speeds below 10 km/h. c The overhead is given as a function of the number of orthogonal pilot sets Pn .

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(see Section 4.4.3.3). With Df = 4, there are two pilots in frequency per chunk and a globally regular pilot pattern is retained, where the pilot locations are independent relative to the start of the chunk. This is the key requirement for the WINNER pilot design, as it allows selection of the pilot type (common or dedicated pilot per beam or antenna) at the chunk level. A further advantage of having the chunk width in frequency a multiple of Df is for uplink transmission: if a user is allocated several adjacent chunks, interpolation over frequency on those chunks is possible in the case of dedicated pilots per antenna. This is particularly beneficial for relays in relay-enhanced cells (REC) as well as high data-rate users; as a relay is forwarding data of several users on the uplink in a localised sub-band, so interpolation in frequency over that sub-band is possible.

Pilot Sequences The in-band pilot pattern allows pilots from multiple beams to share the same orthogonal pilot set, which causes inter-beam interference. Moreover, interference from adjacent cells further corrupts the pilots. Hence, pilots originating from the various cells and beams that are spatially multiplexed are randomised through a cell- and beam-specific scrambling sequence. The scrambling does not remove the inter-beam and inter-cell interference, but removes the correlation between the pilots. In order to reduce the peaks inherent to multi-carrier signals, the pilot signal should preferably exhibit a uniform envelope and power spectrum. The recommendation for the pilot sequence is the DFT of a Chu sequence [Chu72] (or the DFT of a sequence with similar constant amplitude, constant spectrum magnitude properties), as it can be shown to produce a constant magnitude sequence in the time domain.

Pilot Boost The effect of a pilot boost on the performance of a MIMO-OFDM system is investigated in [CA07]. Only the link level was studied, so cellular interference is not taken into account. The optimum pilot boost is shown to decrease as the number of transmit antennas increases. In general, most of the attainable gains of a pilot boost are captured by setting the pilot boost to Sp = 3 dB.

Pilots in FDD Mode On the downlink, common pilots per antenna or beam (CPA and CPB) are used, while on the uplink dedicated pilots per antenna (DPA) are used. Since common pilots are not subject to user-specific processing, interpolation in frequency is possible and edge effects are less problematic. According to the WINNER pilot design, the selection of CPA or CPB is determined by the spatial processing within a particular chunk: for GoB we choose CPB, while for LDC we choose CPA. On the other hand, the pilot design is independent of the multiple access scheme, i.e. OFDMA or B-EFDMA (see Section 9.2.3.2). In the case of LDC with two transmit antennas, only two sets of orthogonal common pilots are required. The pilot spacing in frequency and time are Df = 4 and Dt = 10, as illustrated in Figure 6.2. For chunks associated with high-velocity users (speeds exceeding 150 km/h), one additional pilot per orthogonal pilot set is inserted to better track the large time variations on the channel response.

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Figure 6.2 Pilot grids for the FDD mode.1 (a)

(b)

Figure 6.3 Pilot grids for the TDD mode: (a) downlink and (b) uplink.

Downlink Pilots in TDD Mode For TDD mode, four pilots per chunk per orthogonal pilot set are arranged in a rhombus shape (see Figure 6.3). The spacing in the time direction Dt should be adjusted to the uplink–downlink asymmetry ratio. With a link asymmetry ratio of 1:1, the pilot spacing becomes Df = 4 and Dt = 12 in frequency and time. For low mobility users with velocities below 10 km/h, one pilot in the time direction is sufficient, which cuts the pilot overhead by a factor of two. With spatial re-use of pilots, the Pn = 4 orthogonal pilot sets allow for a number of spatial layers of up to 32, with a modest pilot overhead of p = 13.3 % and 6.7 % for mobile and pedestrian velocities, respectively. Uplink Dedicated Pilots in TDD Mode It is apparent from Figure 6.3 that the pilot pattern for TDD uplinks closely follows the TDD downlink. Dedicated pilots per antenna (DPA) are always used on the uplink. 1 The pilots near the center of the chunk are optionally inserted for high mobility users with velocities > 150 km/h.

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The difference from the downlink is an additional set of pilots on the last OFDM symbol of the uplink slot. For MU-MIMO schemes with spatial precoding based on short-term CSIT on the downlink, the last uplink OFDM symbol of each chunk is reserved for CSI transfer of downlink streams. Unfortunately, users that receive data on the downlink may not always have data to transmit on the uplink at the same time, and vice versa. Hence, in-band uplink pilots can, in general, not be used for updating the spatial precoding matrix at the base station (BS), as the uplink pilots may not be associated with the same downlink user. Therefore, user terminals (UTs) insert two pilots with frequency spacing Dff = 4 per UT antenna on the last OFDM symbol of an uplink slot, on chunks where the UT is receiving data on the downlink. With eight subcarriers per chunk, up to four orthogonal pilot sets are available, which is sufficient to provide CSIT for four spatial layers on the downlink. This means that users scheduled for CSIT spatial precoding on the downlink are reserved the last OFDM symbol of the corresponding uplink chunk to transmit pilots. These pilots provide the BS with the necessary CSIT to update the spatial precoding matrix for the next downlink transmission. Uplink Dedicated Pilots for B-IFDMA When using B-IFDMA for non-frequency-adaptive transmission (Section 9.2.3.3) one pilot symbol is included within each block, if possible located near the centre of the block. The smallest assumed block size is four subcarriers by three OFDM symbols (abbreviated as ‘4x3 block’). The resulting pilot pattern is depicted in Figure 6.4. A larger number of pilots (one per 4x3 block per layer instead of four pilots per chunk layer) are thus required for nonfrequency-adaptive transmission, as compared to the frequency-adaptive transmission. With eight blocks per chunk in FDD and ten blocks per chunk in TDD, the pilot overhead becomes

(a)

(b)

Figure 6.4 Uplink pilot grids for non-frequency-adaptive transmission with B-IFDMA: (a) FDD mode and (b) TDD mode.

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8/96 and 10/120, respectively. The specification for B-IFDMA recommends that not more than two spatial layers are used because of high pilot overheads and the fact that dedicated pilots cannot be placed near the centre of a chunk, which severely affects channel estimation accuracy. Thus, aggregation of these smallest blocks is beneficial to channel estimation (see Section 9.2.3.3 for further discussion of block allocations). 6.2.2.2 Uplink Super-Frame Pilot Preamble The dedicated pilot symbols per stream alone provide estimates of effective channel gains, i.e., channel gains affected by the spatial precoding scheme. To provide the BS with short-term CSI and CQI, an uplink pilot preamble is inserted at the beginning of each super-frame in both FDD and TDD modes, to obtain estimates of the unweighted channel matrix (see Section 4.4.3.3 for an overview of super-frame layout). As uplink pilots over the full band are very expensive in terms of overhead and UT power consumption, these full-band pilots are inserted at a lower rate (once per super-frame). This essentially limits the maximum velocity for adaptive transmission to 10 km/h. Only users with sufficiently low velocities and which are scheduled by the BS for adaptive transmission, transmit pilots at the super-frame preamble. Pilots are orthogonally multiplexed in frequency. With a pilot spacing of Df = 8, it can be shown that up to eight such users can be supported in the band of frequencies, called a competition band, to which they are scheduled. 6.2.2.3 Case Study for the Reference Pilot Design In order to demonstrate how the pilot design is able to flexibly support various flavours of MU-MIMO schemes, this section describes a case study for a typical metropolitan area (MA) urban micro-cell deployment [WIN2D61311].2 For this scenario, the TDD mode applies with the pilot grids shown in Figure 6.3. Furthermore an uplink super-frame pilot preamble is intended to enable MU-MIMO with CSIT on the downlink for slowly moving users. For the case study, the BS is equipped with four cross-polarised antenna elements, giving nT = 8 antennas, while UTs are equipped with nR = 2 cross-polarised antenna elements. Downlink The BS is equipped with nT = 8 transmit antennas and up to four spatial streams per chunk can be transmitted simultaneously. A number of MIMO schemes need to be supported [WIN2D61311]:

r spatial precoding based on short-term CSIT: slowly moving users (velocity v ≤ 10 km/h) with high median SNR;

r no CSIT–short-term CQI: users with medium velocities (10 < v ≤ 50 km/h) and high median SNR;

r no CSIT–long-term CQI: users with high velocities (v > 50 km/h) or low median SNR. The MA downlink is characterised by a variety of MIMO schemes. The fact that all these MIMO schemes are to be flexibly combined imposes great challenges for the reference design. 2 For

a detailed description of the WINNER test scenarios, see Chapter 13.

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Figure 6.5 Pilot design to enable various flavours of MU-MIMO.

Figure 6.5 illustrates how the reference pilot design facilitates the coexistence of all considered flavours of MU-MIMO. The chunk allocation works as follows: 1. Downlink users scheduled for MU-MIMO with ST-CSIT transmit uplink pilots in the super-frame preamble. 2. The BS selects up to four users that transmit simultaneously in one chunk. The most appropriate chunks for these users are assigned and the spatial precoding matrix is computed. The chunk allocation for MU-MIMO with ST-CSIT persists until the next super-frame pilot preamble is transmitted. This is reasonable since the low mobility suggests that the channel conditions do not significantly change during one super-frame. More importantly, the user-specific uplink pilots for estimation of CSI used for link adaptation only need to be transmitted on the allocated downlink chunks, not over the full competition band. This is a key requisite for keeping at an acceptable level the pilot overhead for CSI transfer on the uplink, while maintaining a regular update of the spatial precoding matrix on a super-frame-by-super-frame basis. 3. The remaining chunks are then assigned to MU-MIMO without CSIT, but with CQI at the transmitter (short-term or long-term). Since the BS transmits unweighted downlink pilots to these users, the UTs can measure the CQI on all the remaining chunks that are not reserved for MU-MIMO with CSIT. In other words, unbiased CQI is available at the UTs as no user-specific spatial precoding is applied to the pilots, which is a key requirement for multi-user scheduling based on short-term CQI on a frame-by-frame basis. 4. CQI is reported to the BS on the uplink as encoded data packets, by discrete cosine transform (DCT) data compression [WIN1D24; ESA02]. This is a key enabler for frequency-adaptive transmission up to mobile velocities. We note that no uplink pilots for CSI transfer are needed for high-velocity users, due to the TDD channel reciprocity, thus avoiding prohibitive overheads for feedback on the uplink.

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To conclude, flexible operation and coexistence of various flavours of MU-MIMO is established on the downlink; the overall burden on the uplink for feedback of CQI and CSI is kept remarkably low. Uplink In MA uplinks, dedicated pilots per antenna (DPA) are used. For OFDMA, the pilot pattern specified in Figure 6.3 is applied; for B-IFDMA, the pattern shown in Figure 6.4 is used. The reference design is such that the spatial processing schemes chosen for both downlink and uplink have the same requirements in terms of signalling. We therefore benefit from the same pilot design for both downlink and uplink reference design [WIN2D341]. As the MU-MIMO schemes with CSIT on the downlink require uplink pilots to update the spatial precoding matrix, the last OFDM symbol is reserved for pilots of those users which require short-term CSI at the BS on that chunk. As this applies to only a subset of users, one bit of signalling per chunk per super-frame is necessary, to inform the UT whether or not the last OFDM symbol of a particular chunk is reserved for CSI feedback.

6.2.3 Capacity-Achieving Pilot Design [CA07] establishes an analytical framework to dimension the pilot grid for MIMO-OFDM in terms of overhead and energy allocation. The optimum pilot grid that maximises the pilot (or training) capacity for OFDM operating in time-variant frequency selective channels is identified. Analytical expressions for optimal pilot distance, optimal pilot boost and number of transmit antennas are derived assuming ideal low-pass interpolation filter (LPIF). It is shown that, for pilot-aided channel estimation (PACE), the maximum capacity is achieved by placing pilots with maximum equidistant spacing given by the sampling theorem, if LPIF is assumed and pilots are appropriately boosted. Allowing for realisable and possibly sub-optimum estimators where interpolation is not perfect, the following semi-analytical method is proposed: 1. Specify the filter parameters so that the sampling theorem is satisfied. 2. Choose the maximum possible pilot spacing and estimator dimensions in time and frequency that maintain a certain interpolation error. This determines the minimum pilot overhead and the estimator gain. 3. Determine the optimum pilot boost using the analytical expression obtained for the ideal LPIF. 4. Calculate the optimum number of transmit antennas using the analytical expression obtained for the ideal LPIF. To illustrate the achievable capacity and confirm the developed analytical framework, several numerical examples are given in [CA07] assuming different pilot boost overheads and number of transmit antennas.

6.3 Channel Estimation In the WINNER system, channel estimation (CE) is aided by the transmission of pilots, frequency-multiplexed with data. Pilots create extra overhead, and it is therefore important to use channel estimation techniques which make most efficient use of pilots. Channel estimation

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is essential for satisfactory performance of equalisers, smart antennas, multi-user scheduling, transmitter array processing and precoding, optimum or near-optimum detection and decoding, and signal quality estimation. Channel estimation for generalised multi-carrier signals (GMC) produces explicit estimates of the frequency response (channel transfer function) between transmitters (with or without spatial processing) and receivers. Section 6.3.1 describes the CE reference design. Section 6.3.2 describes pilot-aided channel estimation (PACE) by interpolation in time and frequency utilizing pilot symbols and Section 6.3.3 describes variants of iterative channel estimation (ICE) which supplement the pilot-aided estimates. Advanced ICE variants include genetic algorithm-aided iterative techniques for OFDM and decision-directed iterative techniques for DFT-precoded OFDM, which suppress in-cell and out-of-cell interference with the aid of least squares adaptation.

6.3.1 Channel Estimation Reference Design The CE reference design utilises a scattered pilot grid as summarised in Section 6.2.2. Interpolation between pilots over time and frequency by PACE provides initial channel estimates for the entire frame. Interpolation over time and frequency is separated and realised by two one-dimensional FIR filters, referred to as 2x1D PACE which is described in Section 6.3.2. The FIR interpolation filters are implemented by a Wiener interpolation filter (WIF) with model mismatch. As the pilot design in Section 6.2.2 requires dedicated pilots, in some cases, as well as allowing for spatial re-use of pilot symbols, the attainable performance of PACE may be insufficient to meet the ambitious targets of the WINNER system. Two enhancements for the CE reference design are suggested:

r Channel estimation over multiple frames exploits the correlation in time, in the way that pilot symbols from previous OFDM symbols provide significantly improved channel estimates. A Kalman filter is an efficient means of exploiting the correlation of pilots over multiple frames [WIN2D233], in the downlink [SA03; SFS+05] or in the uplink [AS07a; AS07b]. r Iterative channel estimation (ICE) can be efficiently implemented in terminals that contain turbo receivers that utilise soft information in the form of log-likelihood ratios (LLRs) [WIN1D210; WIN2D233]. In this case the channel estimation unit is included in the turbo loop and additional computational complexity is acceptable. PACE estimates are used as initial estimates for ICE and feedback needed for ICE is derived from extrinsic or a posteriori information, as explained in Section 6.3.3. Application of ICE leads to significant performance improvements over PACE. Although channel estimation over multiple frames and ICE are both optional extensions of PACE, they should both be implemented for the reference CE to achieve the ambitious spectral efficiency targets of the WINNER system. In order to improve performance in a multi-user MIMO scenario where channel estimation is more challenging, ICE can be extended by genetic algorithm (GA) aided joint ICE and multi-user detector (MUD) at the expense of additional computational complexity. For DFT-precoded OFDM, a decision-directed iterative technique, which suppresses in-cell and out-of-cell interference with the aid of least squares adaptation, is applied. To adaptively suppress out-of-cell interference, without explicitly estimating it, a least-squares processing

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over several successive FFT blocks can be applied. In order that least-squares processing functions properly, it is required that the channels do not change much over observed blocks.

6.3.2 Pilot-Aided Channel Estimation Pilot-aided channel estimation (PACE) is based on mismatched 2D Wiener filter interpolation. For PACE, a cascaded channel estimator is used, consisting of two one-dimensional (1D) estimators, called 2x1D PACE. First, channel estimation is performed in the frequency direction at OFDM symbols containing pilot symbols, yielding tentative estimates for all subcarriers of that OFDM symbol. The second step is to use these tentative estimates as new pilots, in order to estimate the channel for the entire frame. It was demonstrated in [HKR97] that 2x1D PACE is significantly less complex to implement with respect to optimum 2D channel estimation, with little degradation in performance. The estimator is to be designed such that it covers a great variety of power delay profiles and Doppler power spectra. A rectangular power delay profile with maximum delay τ w and a rectangular Doppler power spectrum with maximum Doppler frequency f D,w are used. The parameters of the robust estimator should always be equal to or larger than the worst case channel conditions, i.e. the largest propagation delays and maximum expected velocity of the mobile user: τw ≥ τmax and f D,w ≥ f D,max . Furthermore, the estimated average SNR at the filter input, which is used to generate the filter coefficients, γ w , should be equal to or larger than actual average SNR, so γw ≥ γ . The filter coefficients of a WIF with model mismatch are generated with the following prior knowledge about channel statistics: the maximum delay of the channel τ max , Doppler frequency f D,max and average SINR are assumed to be known; however, no further knowledge of the second-order statistics (i.e. the channel covariance matrices in time and frequency) are assumed. If the required measurements of τ max , f D,max and average SINR are unavailable at the receiver, the worst case design of the WIF is adopted: the maximum delay of the channel is set equal to the CP-length, the maximum expected velocity is set with respect to the deployment scenario (local area: 3 km/h; metropolitan area: 70 km/h; wide area: 250 km/h), and the highest expected SINR is set to 30 dB. We note that the worst case design of the WIF will have significantly poorer performance, so it is recommended to implement and utilise means to measure τ max and f D,max . The Wiener filter with model mismatch is closely related to a low-pass interpolation filter (LPIF), in the way that signals with spectral components within the range [0, τ w ] and [0, f D,max ] pass the filter undistorted, while spectral components outside this range are blocked. In fact, it can be shown that a mismatched WIF approaches an ideal LPIF if the number of filter coefficients approaches infinity, {Mf , Mt } → ∞ [AK05]. By using a scattered pilot grid, the received OFDM frame is sampled in two dimensions, with rates Df /T and Dt T sym in frequency and time, respectively, where 1/T is the inter-subcarrier spacing and Tsym is the OFDM symbol duration, including the cyclic prefix. In order to reconstruct the signal, there exists a maximum spacing of Df and Dt , dependent on the filter parameters, τw ≥ τmax , and f D,w ≥ f D,max , that is [SS00]: 1 Df τw < 1, = T βf

2 Dt f D,w Tsym =

1 <1 βt

(6.1)

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where β f and β t denote the oversampling factor in frequency and time. The sampling theorem states that the theoretical limit is approached by an ideal LPIF, having a rectangular filter transfer function, so Df ≤ T /τw and Dt ≤ 1/(2 f D,w Tsym ). In order to allow for a realisable filter with finite M f and M t , however, the oversampling factors β f and β t should be larger than one. Multiple spatially-multiplexed in-cell users (ICUs) may share common frequency–time channels; i.e. they may occupy different layers of the same chunk. Each such interfering ICU has dedicated frequency–time resources for its pilots, which are used for channel estimation. Thus ICU pilots are orthogonal and do not interfere with one another. However ICU pilots may be affected by out-of-cell interferers which, as a result of frequency re-use partitioning, are at relatively low average received power levels (see Chapter 9). Basic channel estimation strategies thus use the above 2x1D PACE interpolation algorithm with orthogonal ICU pilots and ignore low-level out-of-cell interference. [SDF07a] describes a linear equalisation MMSE algorithm for uplink DFT-precoded OFDM, using estimated ICU channels, together with estimated variance of the receiver noise and out-of-cell interferers. The basic non-iterative and iterative equalisation and channel estimation algorithms are designed to combat the effects of fading, in-cell interference as well as channel frequency selectivity, ignoring the effects of low-level out-of-cell interferers. Further refinement using direct least squares adaptation works to suppress out-of-cell interference without explicitly estimating its channels.

6.3.3 Iterative Channel Estimation A turbo receiver consists of an inner and an outer receiver which exchange extrinsic information [Hag97]. For iterative channel estimation (ICE), the channel estimation unit is included in the turbo loop [VW01; SJ03]. ICE is particularly powerful when dedicated pilots are used, e.g. on the uplink, where interpolation over several chunks is not possible [BA06a]. A separated approach is adopted here, in which initial channel estimates are provided by PACE and refined in subsequent iterations by utilising symbol estimates fed back from the decoder, which serve as auxiliary pilot symbols. A separated approach is more computationally efficient and allows for a flexible and modular receiver design, where the channel estimation unit and the detector or decoder are mostly independent building blocks. Moreover, since an initial estimate already exists, ICE may be implemented optionally, e.g. for high-end terminals. On the other hand, the data symbol estimates are subject to decision feedback errors, which, in particular for low SNR, may cancel out parts of the potential performance gains of ICE. To this end, feedback derived from a posteriori information, shown in Figure 6.6, is preferred over extrinsic information feedback, as it is generally more reliable. Further means to reduce the sensitivity of ICE to decision feedback errors are identified in [BA06b; AB07]. 6.3.3.1 Channel Estimation for Single-Input, Single-Output Scenarios In this section, the performance of ICE is evaluated for non-frequency-adaptive B-EFDMA transmission in FDD mode with 1024 subcarriers in a total utilised bandwidth of 40 MHz on a C2 NLOS channel which is described in [WIN2D112, Chapter 3]. Each user transmits one codeword on 8x12 blocks which are placed equidistantly, once every 32 subcarriers. The

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Figure 6.6 Iterative receiver structure; a posteriori (APP) information is used for ICE.

channel encoder is a memory 6 CC with generator polynomials (133,171) in octal form. The performance of two variants of channel estimation units is compared:

r Scenario A: the maximum channel delay is set equal to the CP duration and the maximum Doppler frequency is set corresponding to 100 km/h mobile velocities.

r Scenario B: the maximum channel delays and the maximum Doppler frequency are assumed to be known. Figure 6.7 shows the frame error rate (FER) against the SNR for PACE and ICE. It is seen that ICE is very effective for higher order modulation such as 16-QAM. ICE utilizing an estimator, in Scenario B, deviates only about 1 and 1.5 dB from perfect CSI for common and dedicated pilots, respectively. [WIN2D233, Appendix C; LAD+07; LFD07; LFD08] show similar results for OFDM, full-band DFT-precoded OFDM, IFDMA and B-IFDMA with rate 1 coded QPSK signal constellation. 2 Table 6.2 displays a summary of simulation results for channel estimation schemes, and their penalties relative to perfect channel state information (CSI), for the wide area FDD scenario with single input-single output (SISO). Full-band (40 MHz, 40 Mbps, 1024 subcarriers spaced at 39.0625 KHz) and 1.25 Mbps IFDMA, B-EFDMA and B-IFDMA are shown. All results are for QPSK, except for a set of results for 16-QAM B-EFDMA with full-chunk (8 × 12) blocks. Two channel estimation approaches are used:

r a non-iterative purely pilot-aided approach with four pilots per chunk, using Wiener interpolation in the full-band case and a single pilot per block without interpolation in the cases of B-EFDMA, B-IFDMA and IFDMA; r a decision-directed iterative approach (DFICE) to supplement the pilot-aided estimation, using decoder outputs [AB07; BA06a; LFD07; LFD08].

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(a)

(b)

Figure 6.7 FER vs SNR for PACE and ICE with (a) common and (b) dedicated pilots: 16-QAM, SISO, FDD mode, channel C2 NLOS, and UT velocity 50 km/h.

In addition, the improvement available from estimating channel parameters over several successive frames is also shown for full-band DFT-precoded OFDM, B-IFDMA and IFDMA, OFDM and B-EFDMA systems using linear equalisation and interference rejection as well as CSI-aware decoding. DFT-precoded OFDM, IFDMA and B-IFDMA systems used iterative block decision feedback equalisation (IBDFE), described in [BT05; DGE03; LFD08], with decoding in the iteration loop. Table 6.2 SNR and SNR degradation for frame error rate = 10−2 for noniterative and iterative channel estimation SISO schemes (wide area scenario). SNR (dB) with channel estimation (degradation compared to ideal CSI)

Pilot schemes QPSK OFDM full banda QPSK DFT-precoded OFDM full banda QPSK B-EFDMAb QPSK B-EFDMAc 16-QAM B-EFDMAd QPSK B-IFDMAb QPSK IFDMAe a

SNR for ideal CSI (dB) for 10−2 FER 10.5 9.4 8.2 8.9 12.2 8.4 8.5

Non-ICE Wiener pilot Interpolation (2 × 1D PACE) 1-frame CE 4-frame CE

1-frame CE 4-frame CE 1-frame CE 4-frame CE

4.1 % pilot overhead. 4 × 3 blocks; 32-subcarrier spacing; 8.3 % pilot overhead. c 8 × 6 blocks; 32-subcarrier spacing; 8.3 % pilot overhead. d 8 × 12 blocks; 32-subcarrier spacing; 4.1 % pilot overhead. e 32-subcarrier spacing; 16.7 % pilot overhead. b

ICE with decoding in iteration loop 12.0 (1.5) 11.5 (2.1) 10.9 (1.5) 12.2(4.0) 12.2 (3.3) ∼16.2 (∼4) 12.7 (4.3) 12.0 (3.6) 11.3 (2.8) 10.4 (1.9)

10.7 (0.2) 9.8 (0.4) 9.4 (0.0) 11.0 (2.8) 10.7 (1.8) 13.2 (1.0) 11.4 (3.0) 10.8 (2.4) 10.0 (1.5) 9.5 (1.0)

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Table 6.2 displays the required SNR to achieve a 10−2 frame error rate, and also the SNR degradation between ideal CSI and non-ideal CSI with the various channel estimation schemes. B-IFDMA, B-EFDMA and IFDMA require significantly higher pilot overhead and exhibit higher SNR degradation due to channel estimation errors than do full-band OFDM and DFT-precoded OFDM. This is mainly due to the reduced opportunity to interpolate pilot estimates and exploit correlation in the frequency domain. There is in fact no frequency domain interpolation in the B-EFDMA, B-IFDMA and IFDMA cases, since there is only one pilot per 4 × 3 block, and blocks are separated in frequency by more than the correlation bandwidth. The use of larger block sizes (e.g. 8 × 6), with more pilots, gives more scope for interpolation between pilot locations in frequency and time, while keeping pilot overhead reasonable. Table 6.2 also shows that ICE can yield significant improvement relative to non-iterative estimation and that estimation over multiple frames yields 0.5 to 1 dB improvement, at least for the moderate vehicle speed of 50 km/h.

6.3.3.2 Channel Estimation for Multiple-Input, Multiple-Output Scenarios The channel estimator component shown in the bottom-left of Figure 6.6 can be extended to uplink multi-user detection at the base station with a multi-antenna receiving array, in a MIMO or SDMA scenario (e.g. as described in Chapter 7), by using a GA-aided joint ICE and multi-user detector (MUD) or a multi-user ICE for DFT-precoded OFDM. With the aid of the turbo-processing framework illustrated in Figure 6.6, the performance of the GA-ICE can be further improved at the expense of a relatively higher computational complexity. Genetic-Algorithm-Aided ICE The genetic algorithm (GA) was first introduced by Holland [Hol75] during the 1960s. Since then, a growing interest in GAs resulted in a rapid development in this area [GB89; GOL89; MUH91], where GAs have been shown to perform well in numerous robust global search and optimisation problems, which may not be conveniently solved by using traditional search methods. This section provides a brief introduction to GAs in the context of channel estimation in multi-user MIMO-OFDM systems [JAH07]. At the beginning of the GA-based optimisation process, an initial population consisting of X number of individuals is created, each representing a possible solution of the optimisation problem considered, with the aid of a priori knowledge concerning the optimum solution. In our case, the goal is to optimise the frequency-domain (FD) channel transfer function (CTF) such that they match their true counterparts sufficiently closely. The xth individual of the population of the yth generation of subcarrier n at OFDM symbol k, is expressed as ⎧ (1) (2) (Nt ) ⎪ ˜ ˜ ˜ ˜ s [n, k] = [n, k], s [n, k], · · · , s [n, k] s ⎪ (y,x) (y,x) (y,x) (y,x) ⎪ ⎪ ⎤ ⎡ (1) ⎪ (2) ⎪ ˜ ˜ (Nt ) [n, k] ˜ ⎪ [n, k] H [n, k] · · · H H ⎪ 1,(y,x) 1,(y,x) 1,(y,x) ⎪ ⎨ ⎥ ⎢ (1) (2) (Nt ) ⎥ ⎢ H˜ (6.2) ⎢ 2,(y,x) [n, k] H˜ 2,(y,x) [n, k] · · · H˜ 2,(y,x) [n, k] ⎥ ⎪ ⎥ ⎢ ˜ ⎪ H(y,x) [n, k] = ⎢ ⎪ .. .. .. ⎥ .. ⎪ ⎪ ⎥ ⎢ . . . . ⎪ ⎪ ⎦ ⎣ ⎪ ⎪ ⎩ [n, k] H˜ (2) [n, k] · · · H˜ (Nt ) [n, k] H˜ (1) Nr ,(y,x)

Nr ,(y,x)

Nr ,(y,x)

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which can be an arbitrary combination of a length-Nt multi-user constellation symbol vector ˜ (y,x) [n, k]. Then the GA’s task is s˜ (y,x) [n, k] and an (Nr × Nt )-dimensional FD-CTF matrix H to find an individual, which is considered optimum or near-optimum in terms of minimizing the objective function (OF) defined by 2

˜ ˜ H[n, k], s˜ [n, k] = x[n, k] − H[n, k] · s˜ [n, k]

(6.3)

For each individual, the OF’s output, referred to as an objective score (OS), is converted to a corresponding fitness value, which indicates the fitness of the specific individual in the sense of Equation (6.3). Then a number of individuals that have the highest fitness values are selected to create a ‘mating pool’, based on specific criteria such as those used in [GOL89]. The individuals in the mating pool are appropriately paired to produce offspring. More explicitly, for each parent, genetic operations referred to as cross-over and mutation [MIT96] are invoked; they follow specific rules to exchange, combine, and change parts of the parent individuals, resulting in offspring individuals having statistically better fitness values. The genetic operation cycle forms the basis of GA-aided optimisation, yielding an offspring population with improved average fitness. This evolution continues until the generation index reaches its maximum. Then the operation of the GA is terminated and the highest-fitness individual of the last population is considered as the final solution. It is a specific combination of the symbol vector and the FD-CTF matrix, which contains the jointly optimised estimates of the transmitted multi-user symbols and the associated FD-CTFs, respectively, for the OFDM subcarrier considered. The structure of the GA-ICE-aided, MIMO-OFDM receiver is detailed in [JLC07b; HK06]. Naturally, with the aid of the FEC decoder, the accuracy of the detected signal can be substantially enhanced, which is expected to assist the GA-ICE in improving the channel estimates and vice versa. Benefiting from the exchange of soft information between the inner and the outer iterations, the system’s achievable performance can be significantly boosted [JLC07a]. The results for a 2x2 uplink SDMA OFDM system are summarised in Table 6.3. The turbo-processing assisted GA-ICE was employed with a pilot overhead of 6.7 %. As seen from Table 6.3, the performance of the system improves as the number of inner or outer iterations increases (an iteration of 0 means that there was no iteration). Note that the largest performance Table 6.3 SNR degradation recorded in 2x2 uplink full-band SDMA OFDM with no OCI. SNR (dB) with channel estimation (degradation compared to perfect CSI) SNR for perfect CSI (dB) for 10−2 FER Outer (turbo) iteration = 0 Outer iteration = 1 Outer iteration = 2 Outer iteration = 3

Inner (GA) Inner Inner Inner iteration = 0 iteration = 1 iteration = 2 iteration = 3

3.92

6.19 (2.27)

5.83 (1.91)

5.68 (1.76)

5.65 (1.73)

2.70 2.53 2.49

4.84 (2.14) 4.64 (2.11) 4.44 (1.95)

4.43 (1.73) 4.42 (1.72) 4.08 (1.59)

4.42 (1.72) 4.41 (1.71) 4.08 (1.59)

4.39 (1.69) 4.02 (1.49) 3.88 (1.39)

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improvement was achieved by the first iteration, while the best result attained was an SNR degradation of 1.39 dB (in comparison to the ideal CSI-aided scenario) when three inner and three outer iterations were employed. Other assumptions are as follows: two transmit and two receive antennas; independent B1 channel links between all antenna pairs; half-rate LDPC code [LZH+06]; 4-QAM; terminal speed of 70 km/h; perfect synchronisation between all SDMA streams. Out-of-cell interference (OCI) is not considered. ICE for Uplink Multi-user DFT-Precoded OFDM The basic linear receiver structure for DFT-precoded OFDM (or serial modulation) can be iterated [BT05; DGE03] so that, at each iteration, estimated co-channel and inter-symbol interference derived from soft detector outputs is subtracted. This structure is called ‘iterated block decision feedback equaliser’ (IBDFE). This soft inter-symbol interference subtraction yields substantially lower error probability for frequency selective channels. When the iteration loop includes soft outputs from the decoder, it is turbo equalisation [NLF07]. IBDFE can also include soft cancellation of co-channel interference as well as inter-symbol interference for DFT-precoded OFDM [SDF07b; SDF08]. The IBDFE algorithm with co-channel interference, at the ith iteration, for user k (k = 1, 2, . . . , NT ), based on previous iteration equaliser outputs {um,k (i−1) }, is summarised in [WIN2D233, Section 4.3.2; SDF07b]. The equaliser frequency domain output at frequency f (f = 0, 1, . . . , M-1, where M is the DFT block length) for the kth user (k = 1, 2, . . . , NT ) at the ith IBDFE iteration is: U (i) f (k)

=

H W(i) f (k)

Rf −

NT

ˆ ( j−1) () A¯ (i−1) () H f f

+ A¯ (i−1) (k) f

(6.4)

=1

(i) The equaliser time domain output samples are u (i) m (k) = IFFT({U f (k)}). In Equation (6.4), Rf is the frequency domain channel output vector whose dimension ˆ ( j−1) () is the channel estimate from the is the number of receiving antenna elements; H f

th user at the (j-1)th ICE iteration; and W(i) f (k) is the forward equaliser tap vector used at (i−1) ¯ the ith iteration for the kth user. A f () is the soft estimate of the th user’s data from the (i-1)th IBDFE iteration; it is formed from U (i−1) () or from the soft decoder output as f (i) in [NLF07]. W f (k) is recomputed at each IBDFE iteration, based on the current channel ˆ ( j−1) ()}, estimates of noise variance, and correlations based on the { A¯ (i−1) ()}. estimates {H f f (i) ()} and W (k) are in [WIN2D233, Section 4.3.2; SDF07b]. Expressions for { A¯ (i−1) f f A fixed number, IE , of IBDFE iterations occurs within one ICE iteration. In the initial ICE ˆ (0) () is the estimated channel response vector for the th in-cell user iteration, j =1 and H f obtained from that user’s pilots. At the end of j ICE iterations, the number of IBDFE iterations ( j,i) is i = jIE and a hard decision decoder output Aˆ f (k) is obtained from {U (i) f (k)} for each in-cell user k. At the jth ICE iteration, the channel estimate is updated as ( j,i)

ˆ ( j) (k) = H f

X f (k) ( j,i) Aˆ f (k)

.

(6.5)

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followed by Wiener frequency domain smoothing, where ( j,i)

X f (k) = R f −

NT

ˆ ( j−1) () Aˆ ( j,i) () H f f

(6.6)

=1, =k

represents an estimate of the kth user’s signal without in-cell interference. The problem of ( j,i) noise enhancement at frequencies where Aˆ f (k) has a small magnitude can be avoided by ˆ ( j) (k) by its initial estimate derived from pilots or its estimate from a previous replacing H f

( j,i)

OFDM symbol, if | A f (k)| is less than a certain threshold. It is likely that, in most scenarios of interest, the out-of-cell interferers’ (OCI) channels are difficult to measure, because their received signal powers are low. If only the in-cell users’ (ICU) channels are known, NT is the number of in-cell transmitters and the out-ofcell interferers are ignored, acting as added noise. Adaptive suppression of the out-of-cell interference can be done without explicitly estimating out-of-cell interference data or channels by least-squares processing over a series of F successive FFT blocks, m = 1, 2, . . . , F, assuming that channels do not change much over the F blocks. After IE IBDFE iterations and hard decoder (i, j) decisions, X f (k, m) is formed using Equation (6.6), where m refers to the mth block. With in-cell interference thus removed, the output of a linear least squares equaliser for the kth data source is: (i) H (i,i) Y (i) f (k, m) = V f (k) X f (k, m),

k = 1, 2, . . . , N T ,

m = 1, 2, . . . F

(6.7)

The coefficient vector V(i) f (k) is obtained by a least squares estimate over F blocks: V(i) f (k)

=

F

m=1

−1 ( j,i) ( j,i) X f (m)X f (m) H

F

( j,i) ( j,i) X f (m) Aˆ f (k, m)∗

(6.8)

m=1

Hard decoder decisions that are made from the time-domain-transformed {Y (i) f (k, m)} are then used in the ICE updating of Equation (6.5) and eventually for the final decoder outputs. Note that Equation (6.7) describes the output of a linear spatial equaliser. However it is robust to channel frequency selectivity without incurring noise enhancement since it represents the processing of an in-cell, interference-free signal received over multiple diversity paths. Inter-cell pilot interference can be minimised by adopting a frequency re-use partitioning or dynamic channel allocation strategy [WIN2D472; HNO06], in which user terminals with low path loss to their base stations, but which are in different cells, have frequency re-use of one, while user terminals experiencing higher path loss have a higher frequency re-use factor, and thus experience out-of-cell interference only from more distant cells [WIN2D472, Section 4.3.1]. A representative frequency re-use partitioning scenario is presented and analysed in [WIN2D233, Appendix A]; in that scenario, the frequency re-use factors for terminals within and beyond 70 % of the cell radius are 1 and 3, respectively. It is shown, based on a WINNER wide area propagation model, that average received power from each OCI in this deployment scenario is at least 15 dB below that of in-cell users. For evaluation of channel estimation performance for SDMA with out-of-cell interference, we assume that one or more of these techniques have been applied, so that uplink out-of-cell interferer signals arrive at a victim

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Table 6.4 One-frame estimation for uplink DFT-precoded OFDM with 2 × 4 SDMA.

MA schemes Full-band DFT-precoded OFDMa

Full-band DFT-precoded OFDMa with turbo equalisation and rate 1/2 LDPC codes B-IFDMAb B-IFDMAc

OCI characteristics

SNR per receive antenna for ideal CSI for 10−2 FER (dB)

None

4 OCIs, at −15 dB 4 OCIs, at −15 dB

4 OCIs, at −15 dB 4 OCIs, at −15 dB

SNR (dB) per receive antenna with channel estimation (degradation due to channel estimation) Non-ICE

ICE

ICE plus least squares

0.3

4.2 (3.9)

3.6 (3.3)

2.3 (2.0)

1.3

6.3 (5.0)

5.0 (3.7)

3.5 (2.2)

−0.4

3.7 (4.1)

3.0 (3.4)

2.3 (2.7)

−1.0

7.5 (8.5)

5.0 (6.0)

5.0 (6.0)

0.3

6.8 (6.5)

5.4 (5.1)

4.7 (4.4)

a

8.3 % pilot overhead. 4 × 3 blocks; 32-subcarrier spacing; 33.3 % pilot overhead. c 8 × 6 blocks; 32-subcarrier spacing; 16.6 % pilot overhead. b

base station with an average received power of −15 dB relative to the average power of each in-cell received signal. Table 6.4 is based on simulations of an uplink DFT-precoded SDMA system with two in-cell user terminals sharing a common channel, and (in all but the first row of the table) four out-of-cell interferers (OCIs), each with an average received power 15 dB below that of each in-cell user’s average received power. The base station has four receiving antennas (NR = 4). Independently fading C2 channel models with 50 km/h Doppler are assumed between each transmitting and receiving antenna pair. The base station’s MMSE-based receiver uses the IBDFE equalisation algorithm, for the multi-antenna, multi-user case, described in [SDF07b], except in the third row of the table. In that case, a rate 1/2 regular (3,6) LDPC code with 4608 block length and turbo equalisation, instead of IBDFE, is used. As in the SISO cases, non-iterative channel estimation based on interpolation of frequency-multiplexed pilots, is evaluated, as is iterative channel estimation. Both the pilot-based and iterative channel estimation schemes estimate only the in-cell user channels, while OCIs are ignored. The last column in the table shows results for the least-squares, decision-directed algorithm (described by Equations (6.7) and (6.8)), which is used in addition to pilot interpolation and iterative ICU estimation. Full-band DFT-precoded OFDM uses four orthogonal pilots per

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chunk per in-cell user. Table 6.4 also shows the required SNR and SNR degradation for BIFDMA with 4 × 3 blocks and for 8 × 6 blocks. For the 4 × 3 case, there are two pilots per block per in-cell user; thus the pilot overhead is 33.3 %. For the 8 × 6 case, there are four pilots per block per user and the corresponding total pilot overhead is 16.6 %. SNR degradation is reduced by ICE and is further reduced in the full band case by the application of least-squares processing using receiver hard decisions to suppress OCI interference. It is also evident from the table that the use of turbo equalisation along with LDPC block coding gives a further SNR advantage over IBDFE. Least-squares processing produces no benefit in the 4 × 3 B-IFDMA case, since the number of OFDM symbols per block (3) is inadequate for least-squares averaging. While the 8 × 6 B-IFDMA case has 1.3 dB higher required SNR for perfect CSI (due to its slightly diminished frequency diversity), its SNR degradation for non-perfect CSI with ICE and least-squares processing is 1.6 dB less than that for the 4 × 3 case. The main reason for this is the use of four, instead of two pilots in the 8 × 6 block, allowing better interpolation from pilot estimates, while also reducing overhead. The resulting gain in channel estimation accuracy more than compensates for the reduced diversity of the 8 × 6 case. The required SNR in this case is 4.7 dB, while for 4 × 3 it is 5.0 dB. The LDPC–turbo equalisation combination reduces required SNR by roughly 1.5 dB, but the SNR degradation due to non-ideal CSI remains roughly the same. Note that multi-frame channel estimation would improve in the case of small blocks, at least at modest velocities, and is possible whenever persistent scheduling is used. A comparison of Table 6.4 with Table 6.2 shows that SNR degradation due to channel estimation errors is in general larger for the SDMA case, with multiple ICUs, OCIs and receiving antennas. There are several reasons for this:

r the sensitivity of the equaliser output, Equation (6.4), to errors in multiple interferers’ channels and data;

r uncompensated OCI interference to data (unless least squares is used) and to pilots; r the required SNR per antenna for M = 4 antennas to attain a frame error rate of 10−2 is much lower than that required for M = 1 antenna and one user terminal, therefore channel estimates at each receiving antenna will be plagued by more noise; r channel frequency correlation, in the case of B-IFDMA and IFDMA, cannot be exploited as effectively to interpolate to non-pilot frequencies and to smooth noise.

While the FER performance of B-IFDMA and B-EFDMA with ideal CSI is much better than that of the full-band system (due to the enhanced frequency diversity and smaller number of data symbols per frame of the B-IFDMA and B-EFDMA systems), the SNR degradation for non-ideal CSI is significantly larger than that for full-band transmission, even when pilot overhead is about 33 %. The use of larger B-IFDMA blocks, with more pilots per block, significantly improves channel estimation accuracy and enables reduced pilot overhead.

6.3.4 Channel Prediction One key enabler for adaptive transmission is the provision of accurate short-term CQI and CSI to the transmitter. Channel prediction aims at assessing the CQI and CSI at the time of

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transmission, given outdated channel estimates computed in preceding frames. In WINNER, substantial work was performed on channel prediction on the downlink [SA03; SFS+05; WIN1D24] and on the uplink [AS07a; AS07b]. Below we give a short overview of these results and describe how the choice of the WINNER pilot design affects the feasibility of frequency-adaptive transmission. In-depth analysis and performance evaluation of channel prediction schemes are provided in [Ekm02; Aro07]. The complex baseband channel h and the associated channel power gain c = |h|2 are important inputs for CSI and CQI to facilitate link adaptation and scheduling decisions at the transmitter. An MMSE optimal (linear) prediction hˆ of the complex channel h provides an MMSE-optimal prediction of the channel power c = |h|2 , by using ‘unbiased’ channel power prediction [ESA02]: ˆ 2 + σ2 cˆ = E|h|2 = |h|

(6.9)

ˆ If the noise and interference where σ 2 is the variance of the complex channel prediction h. is assumed to be Gaussian, then the MMSE optimal channel prediction hˆ is provided by the Kalman predictor. The Kalman algorithm utilises the received signals at positions with known inputs (pilots) and the assumed correlation properties of the channel in time and frequency, so as to optimally extrapolate the channel in time. In [SA03; Aro07], it is shown that if orthogonally placed pilot signals with constant modulus, such as 4-QAM, are used, then updating of a quadratic state-space Riccati difference equation can be avoided. This update is responsible for the dominant computational load in Kalman algorithms. Instead, one may use pre-computed steady-state solutions to the Riccati equation. Furthermore, in [SFS+05] it was shown that frequency-adaptive transmission is feasible and beneficial when using Kalman-based channel prediction up to vehicular speeds in WINNER scenarios. The attainable prediction accuracy for a radio link depends on

r the required prediction horizon, scaled in carrier wavelengths; r the average SINR of the channel; r the pilot density; r the type of fading statistics (the shape of the Doppler spectrum). The prediction accuracy depends on the prediction horizon L scaled in wavelengths, which in turn depends on the velocity v, the prediction horizon in time D and the carrier wavelength λ via the relation L = v D/λ. The prediction accuracy also depends on the SINR. Thus, adaptive transmission to and from a terminal is feasible up to a maximal velocity for a given SINR, or equivalently, down to a limiting SINR at a given velocity. The prediction accuracy is stated in terms of the normalised, mean-square prediction error (NMSE) of the complex channel, σ 2 /E|h|2 . The NMSE for the FDD downlink is shown in Figure 6.8 as a function of the prediction horizon scaled in carrier wavelengths and as function of the SINR. The maximum velocities for which frequency-adaptive transmission is feasible can be extracted from Figure 6.8. The NMSE should typically not exceed 0.1. Then the prediction horizon L for a given SINR can be extracted, which translates to the maximum tolerable mobile velocity v.

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Figure 6.8 Normalised mean-square prediction error (NMSE) for the complex channel, as a function of the prediction horizon and the SINR, from [MSO+07]. (Reproduced by Permission of IEEE © 2009).

6.4 Radio Frequency Impairments In this section, two topics related to radio frequency hardware impairments are discussed. First, the effects of high power amplifier (HPA) nonlinearities on properties of transmitted signals are considered, in particular the transmitted power spectrum. Secondly the modelling and suppression of oscillator phase noise and frequency offsets are addressed for both OFDM with spatial multiplexing and DFT precoded OFDM. This step is especially important for OFDM systems, as phase noise causes inter-carrier interference.

6.4.1 HPA Non-Linearities Neighbouring desired and undesired user spectra may be received with large power variability due to differing path losses. Avoidance of adjacent channel interference then requires low transmitted power spectral sidelobes and rather stringent spectral masks. For example, allowable interference to adjacent-frequency receivers is usually specified in terms of maximum interference power at a certain distance and at a certain frequency offset from the interferer’s carrier. Under typical transmitted power and path-loss conditions, this may imply spectral masks with as much as 40 to 60 dB of out-of-band attenuation. A WINNER spectral mask,

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10

10 DFT-precoded OFDMA, dB back off=7 OFDMA, dB back off=9 Spectral mask

−10 −20 −30 −40 −50 −60 −70 −80 0

DFT-precoded OFDMA, dB back off=6 OFDMA, dB back off=8 Spectral mask

0 Power spectrum (dB)

Power spectrum (dB)

0

−10 −20 −30 −40 −50 −60 −70

20

40

60 80 100 120 140 160 180 Frequency (MHz) (a)

−80 0

20

40

60 80 100 120 140 160 180 Frequency (MHz) (b)

Figure 6.9 HPA output power spectra for QPSK OFDMA and DFT-precoded OFDMA signals, corresponding to B-EFDMA and B-IFDMA respectively, with block width = 4 and with 40 MHz nominal bandwidth. HPA has Rapp model nonlinearity with (a) parameter p = 2 and (b) parameter p = 10.

scaled to fit the current assumed wide-area system bandwidth, is illustrated in Figure 6.9. Control of power spectrum sidelobe levels to obey a spectral mask is normally achieved by an appropriate power backoff at the HPA. Signal processing techniques to reduce the dynamic range of the transmitted waveform can also be used to reduce spectrum sidelobe levels, and are discussed and evaluated in [DFL+08]. Minimising the power backoff required for high power amplifiers is very important in terms of cost and battery recharging intervals, especially for mobile terminals. Large backoff lowers amplifier efficiency and increases the maximum output power required from the HPA, thus increasing its cost and battery drain. The minimum required power backoff depends on several factors:

r The distribution of the transmitted signal’s amplitude: A large dynamic range implies larger minimum to maximum amplitude swings and, hence, larger backoff to minimise distortion. A commonly used, but not necessarily very useful, criterion is peak-to-average power ratio (PAPR). r The nonlinear input–output characteristic of the HPA: In our studies, we have used the Rapp model [Rap91], which is considered reasonably typical for solid-state power amplifiers, for amplitude to amplitude conversion. The model has one parameter, p. A low value, e.g. p = 2, results in an input–output characteristic which has a visible nonlinearity below the saturated output. It may be typical of a moderate-cost HPA. A higher quality HPA, or one whose input–output characteristic below saturation is linearised by adaptive pre-distortion, has a higher value of p, such as p = 10. r The power spectrum mask to which the HPA output power spectrum must be confined: It is determined by consideration of allowed power leakage into adjacent users’ spectral allocations. In this study, we use the spectral mask that was derived for WINNER narrowband mobile terminal outputs in [WIN1D25], scaled to the wide-area signal bandwidths of 40 MHz and 10 MHz.

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In general, different nonlinearity characteristics and spectral masks will change the absolute values of backoffs for different types of signal, but would not be expected to change the relative values. The backoff requirements for QPSK B-EFDMA and B-IFDMA signals with a foursubcarrier block width, with 32 blocks spaced at 32 subcarrier intervals, are shown in Figure 6.9 for p = 2 and p = 10. Each signal block is time-windowed with a raised cosine window after the IFFT operation. For each signal, the average power was adjusted by trial and error so that the power spectrum barely grazed the spectral mask. The difference between that average power and the saturated output power from the nonlinearity was determined to be the signal’s required power backoff. Although the B-IFDMA signal waveform is not exactly equivalent to a single carrier waveform, its required backoff is close to that of the full-bandwidth case. Results presented in [DFL+08] indicate that IFDMA has similar backoff properties to B-IFDMA and full-band DFT-precoded OFDM. The power spectra shown in Figure 6.9 are for signal waveforms which do not include frequency-multiplexed pilots for channel estimation and synchronisation. The PAPR and HPA output spectra for full-band and B-IFDMA signals will be affected, since frequency multiplexed pilots essentially add another waveform to the data waveform. It is to be expected that the power spectrum for B-EFDMA will be less affected by frequency multiplexed pilots, since the signal is the sum of many waveforms in parallel. As shown in [DFL+08], the presence of pilots in the B-IFDMA signal increases the backoff required from 7.0 dB to 7.8 dB and the difference from B-EFDMA decreases from 1.9 dB to 1 dB. Note that only a fraction of OFDM symbols contain pilots and need this extra backoff. Thus the effect of frequency multiplexed pilots on the required power backoff is minimal. Table 6.5 summarises the required power backoff results, for in-band signal to nonlinear distortion and for signal to ‘adjacent user’ distortion. For the full-band (40 MHz) cases, ‘adjacent user’ means the adjacent 40 MHz frequency band. For B-EFDMA and B-IFDMA, ‘adjacent user’ means the frequencies occupied by a user whose blocks are immediately adjacent and interleaved in frequency with those of the transmitting user; e.g. the transmitting user occupies subcarriers [1, 2, 3, 4, 33, 34, 35, 36, . . .] and the adjacent user occupies subcarriers [5, 6, 7, 8, 37, 38, 39, 40, . . .]. To summarise the results of these and related studies in [WIN2D233; [DFL+08]:

r The backoff required to satisfy the spectral mask does not increase significantly in going from QPSK to 256-QAM.

r The same holds true for backoffs required to maintain a given signal to nonlinear distortion ratio (SNDR). For example, for p = 2 Rapp model nonlinearity, backoffs required to satisfy spectral mask also yield approximately 32 dB SNDR for QPSK through 256-QAM. (Although it would probably be desirable to have a higher SNDR and therefore somewhat more backoff, for 256-QAM.) r DFT-precoded OFDM and B-IFDMA have 1.5 to 2 dB less required power backoff than do OFDM and B-EFDMA, respectively (∼2 dB for QPSK, ∼1.5 dB for 256-QAM). r For all modulations, and for Rapp parameter p = 10 (HPA approximating an ideal linear clipper), the required backoff is roughly 1 dB less than that for p = 2 (corresponding to more nonlinearity in the unsaturated region of the HPA). Note that p = 10 also reduces SNDR by about 5 dB.

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Table 6.5 Required power backoffs and signal to distortion ratios to comply with WINNER spectral mask, for Rapp HPA parameter values 2 and 10. Rapp parameter p = 2

Rapp parameter p = 10

Signal to Signal to Required Signal to adjacent Required Signal to adjacent power in-band user power in-band user backoff distortion distortion backoff distortion distortion (dB) ratio (dB) ratio (dB) (dB) ratio (dB) ratio (dB) QPSK OFDM (full-band) (4-QAM) DFT-precoded OFDM (full-band) B-EFDMA B-IFDMA 16-QAM OFDM (full-band) DFT-precoded OFDM (full-band) B-EFDMA B-IFDMA 64-QAM OFDM (full-band) DFT-precoded OFDM (full-band) 256-QAM OFDM (full-band) DFT-precoded OFDM (full-band)

8.8

32.7

38.6

7.9

40.2

44.7

6.8

37.0

39.5

5.7

49.3

51.3

9.0 7.0 8.8 7.0

35.7 36.3 31.8 33.3

41.7 40.2 38.5 38.2

8.0 6.0 7.8 6.0

42.9 44.3 37.8 39.6

47.6 47.9 44.4 46.9

8.8 7.3 8.7 7.0

29.8 29.8 31.5 32.6

40.8 39.6 38.1 37.8

8.0 6.5 7.9 6.1

32.1 32.1 37.5 38.6

49.1 49.3 44.7 45.8

8.7 7.0

31.6 32.6

38.2 37.6

7.9 6.1

37.3 38.4

44.5 45.6

Thus DFT-precoded OFDM and higher quality (or adaptively-predistorted) HPAs are desirable, especially for uplink transmission high-level modulations. The required backoff of course depends on the choice of the spectral mask.

6.4.2 Phase Noise Once frame timing and carrier frequency acquisition have been achieved by a receiver, fine carrier phase synchronisation and symbol timing must be carried out. Carrier phase synchronisation is complicated by time variation of the phase of the received carrier – in the form of frequency offset and phase noise in the transmitter frequency synthesisers and oscillators used for up- and down-conversion. A complex baseband received signal with frequency offset and phase noise can be represented as: Re(s(t) · e j·(2·π ·δ f ·t+ϕ(t)) )

(6.10)

where δf represents the frequency offset and ϕ(t) represents random phase noise. For cost reasons, it is likely that the worst-case frequency offset and phase noise impairments will be in the user terminals. Frequency offset and phase jitter cause inter-carrier interference in OFDM systems (see for example [PVM95] and [Sto98]). Avoidance of severe performance

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degradation requires that frequency offsets be kept lower than about 1 % of the subcarrier spacing. 6.4.2.1 Phase Noise Model Phase noise is a random process characterised by the power spectral density of ϕ(t). Many studies, such as [PVM95], model it as a Wiener process, with a Lorentzian power spectrum behaving like f –2 . More realistic phase noise spectra have somewhat more complex shapes, but still typically exhibit f –2 behaviour over part of their range [Lar96]. For simplicity we will restrict ourselves to the case of a free running oscillator (Wiener process). The output of a noisy oscillator is modelled as a stochastic time shift [DMR00]: xs (t + α(t)) where xs (t) is the noiseless periodic steady state response of an oscillator. The phase offset at the time instance t is now given as ϕ(t) = 2πfm α(t), with fm as carrier frequency. The properties of the time shift α(t) can be considered as a Brownian motion (Wiener) process. Thus the time shift can be expressed using the Wiener process V(t) as follows: √ (6.11) α(t) = cvco V (t). The oscillator constant cvco can be related to the 3 dB bandwidth of the phase noise spectrum, relative to the subcarrier spacing f by [WIN2D233]: cvco =

f f 3dB = δ3dB 2 . 2 π fm π fm

(6.12)

where fm is the carrier frequency. The discrete time phase noise model for a free running oscillator is based on a sampled Brownian motion process. The phase noise at the kth sample is related to the previous one as: ϕ(k) = ϕ(k − 1) + w

(6.13)

where w is a Gaussian distributed random variable, with zero mean and variance σ 2 = 4π fm 2 cvco TS . In this notation, TS describes the sample interval. 6.4.2.2 Phase Noise Suppression in OFDM with Spatial Multiplexing Phase noise induces the following two effects in an OFDM system: a rotation of the received constellation, which is referred to as common phase error (CPE), and an inter-carrier interference (ICI). In comparison with the frequency offset (which can be understood as a linear phase shift), phase noise is a random process and changes rapidly within one OFDM symbol. For that reason the complexity of suppressing phase noise is larger than for suppressing frequency offsets, which is normally taken as constant within one burst. For single-input single-output (SISO) systems, a lot of phase noise correction methods have already been investigated [PVM95; WB02], however, phase noise compensation in a MIMO environment is only rarely discussed (for example, in [LWB06; STS+04; STS+05]). Most of the proposed phase noise correction methods only compensate the CPE, which is a common rotation to all subcarriers. However, in the existence of strong phase noise, this correction can easily become insufficient, because higher order spectral components of the phase noise process are still present leading to ICI. Therefore an extension and optimisation of the phase

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noise suppression algorithm initially proposed for the SISO case in [PRF03] is applied to the case of multiple transmit and receive antennas. The approach is described in detail in [WIN2D233, Section 6.3.3] and is briefly summarised in the following discussion. For a coded MIMO environment the idea is to use the soft information of the coded bits supplied from the decoder iteratively to improve the linear minimum mean squared error (LMMSE) estimation of the Fourier coefficients of the phase noise realisations. For the LMMSE estimation, a selection based on the soft information is performed to identify the most reliable symbols which are used for the estimation. As stated above, phase noise in the time domain is a multiplicative distortion resulting in a convolution of the spectra in the frequency domain. In order not to perform multiple transformations, the phase noise influence in the frequency domain is compensated by performing a deconvolution with the estimated spectrum. The received frequency domain signal affected by phase noise can be written as: R = YHS + η

(6.14)

where the block diagonal matrix H is the frequency domain representation of the channel matrix and S represents the coded frequency domain symbols. Phase noise at the receiver causes inter-carrier interference between the received symbols during downconversion, which is modelled by y. The AWGN noise is also transformed in the frequency domain, keeping the same statistical behaviour. The phase noise matrix representation is given by: ⎞ ⎛ E −1 · · · E −(NC −1) E0 ⎜ E1 E 0 · · · E −(NC −2) ⎟ ⎟ ⎜ (6.15) Y=⎜ . ⎟ ⊗ I Rx .. .. .. ⎠ ⎝ .. . . . E NC −1 E NC −2 · · ·

E0

where Ei =

1 NC exp(− j2πik/NC ) exp( jϕ(k)) k=0 NC

(6.16)

is the frequency domain representation of the phase noise process. The main tasks are to compensate the influence of the phase noise and to solve the joint detection problem due to spatial multiplexing on each subcarrier. After initial correction of the common phase error using pilots, and decoding, an iterative estimation of higher order harmonics of the phase noise is carried out, using the idea of joint linear minimum mean square error (LMMSE) estimation [BRF07]. The key point of the linear MMSE approach as one way to estimate the phase noise is the fact that only a few low frequency components of the phase noise spectrum are usually sufficient to give a reasonable approximation of the phase noise realisation during a given OFDM symbol. The selection of the most reliable subcarriers or symbols to use for the iterative estimation process is described in [BRF07]. To further reduce the number of equations, only the best receive antenna per subcarrier is selected, specified by the maximal row norm of the channel matrix H. The already Fourier-transformed received symbols and the phase noise distortion can be deconvolved using the frequency reversed conjugate spectrum of the phase noise estimation according to: ˆ =R⊗E ˆ∗ R

(6.17)

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100

100

10−1

10−1 FER

FER

198

10−2 10−3

No PN 1st lter./CPE 2nd lter./ICI1

10−2 10−3

3rd lter./ICI2

10−4 10

4th lter./ICI3

15

20 Eb/N0 [dB]

(a)

25

30

10−4 10

No PN 1st lter./CPE 2nd lter./ICI1

15

20 Eb/N0 [dB]

25

30

(b)

Figure 6.10 Phase noise compensation, 16-QAM, TDD Mode, A1 NLOS, memory 6 convolution code, (a) δ 3dB = 0.001; (b) δ 3dB = 0.00005.

Following this step, an MMSE-based MIMO signal detection is performed, resulting in the estimate of S. The proposed algorithm was tested under the WINNER TDD parameter. The initial CPE correction is based on the known pilots using the defined pilot grid. For the channel code, a rate 1/2 convolutional code with generator polynomial G=[133, 171]8 was used. An estimate of the transmitted symbols based on the correction of the common phase error only is not very reliable. Hence, an additional estimation of higher order phase noise components is necessary in order to further improve the systems performance. Figure 6.10(a) shows the performance results in terms of frame error rates for a 16-QAM system with a relative oscillator linewidth of 0.1 % of the subcarrier spacing. Compensation of the phase noise up to the 3rd harmonic (ICI3 ) provides a significant performance improvement compared to the single common phase error correction. Figure 6.10(b) presents the inter-carrier interference correction for a relative oscillator linewidth of 5 × 10−5 of the subcarrier spacing. Only two detector–decoder iterations are sufficient to achieve an almost phase-noise-free transmission.

6.4.2.3 Phase Noise Suppression for DFT-Precoded OFDM (Serial Modulation) It is pointed out in [WIN1D22, Section 2.1.1.7], that frequency offset and phase noise affect the receiver output in serial modulated systems in a different (and more easily correctable) way than in parallel modulated systems such as OFDM. Whereas these impairments cause inter-symbol interference through frequency domain convolution in multi-carrier systems, they simply cause a slowly varying phase rotation to single carrier (DFT-precoded OFDM) data symbols, which can be easily estimated, tracked and compensated by decision-directed techniques. Phase noise would trace a similar trajectory, but with a slowly and smoothly varying profile instead of the linear increase or decrease of frequency offset. The time-varying phase process {n } can be tracked with a second-order, soft decision-directed, phase-locked loop (PLL),

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100

BER

10−1

10−2

10−3

δf.T=0.1, γ2=0.001 δf.T=0.1, γ2=0.001, synchronization δf.T=0, γ2=0

10−4 0

1

2

4 3 Eb/N0 [dB]

5

6

7

8

Figure 6.11 Phase noise and frequency offset compensation, QPSK, from [SF08]. (Reproduced by Permission of IEEE © 2009).

which uses log likelihood ratio (LLR) information from a turbo equaliser as described in [SF08]. Simulation results for this system are shown in Figure 6.11. The code used here is a regular (3,6) LDPC code with a 504 × 1008 parity check matrix. The belief propagation (BP) algorithm is used for decoding. The number of iterations in the LDPC decoder and the number of iterations in the turbo equaliser are 4. The code block length is N = 1008. The FFT length is also 1008. The bandwidth is 40 MHz and the channel is the C2 channel. The figure presents the BER achieved for QPSK versus Eb /N0 . δ f · T is the frequency offset, normalised to the FFT block duration (the subcarrier spacing). δ f · T = 0.1 represents a large frequency offset: 10 % of the subcarrier spacing. The value of γ 2 = 0.001 corresponds to a phase noise power spectrum bandwidth of about 6.4 KHz. These represent very severe degrees of frequency offset and phase noise, but the compensation approach results in a performance loss of only about 1 to 1.5 dB. The above discussion was for the case where no other user signals occupy immediately adjacent subcarriers. If there are adjacent users, frequency offset and phase noise, which cause inter-subcarrier interference, will obviously cause interference between user signals occupying adjacent subcarriers. Assuming that interference-causing adjacent channel signals are from the same cell or sector, their average received powers will be equal due to power control, and the effect of the adjacent channel interference will be similar to that of same-signal, intersubcarrier interference (detailed in Section 6.4.2.2). For full-band transmission, the adjacent channel interference will be mostly concentrated on the subcarriers located at the band edge, and so the effect should be relatively minor. The types of signals most vulnerable to the adjacent channel interference would be those in which different users’ signals are interleaved

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in frequency, such as IFDMA and B-IFDMA. Explicit attempts to compensate would be complicated because each adjacent channel-interfering signal will, in general, have different phase noise and frequency offset. [WIN1D22] includes simulation results showing the effects of uncompensated frequency offset and phase noise on multi-user IFDMA signals.

6.5 Measurements and Signalling Measurements play an important role in modern communications systems, allowing nodes within the system to understand other nodes of the system, signals within the system, and their context. This information can be used to aid various system functions, from initial system discovery to link adaptation and handover. Measurement metrics, which may be required, and which are tabulated in Sections 5.2.1 and 5.2.2 of [WIN2D233], include in band and out of band channel responses, channel quality index, SNR and SINR, propagation delay, channel state information, transmit and receive powers, Doppler, UT position as well as information about interferers. For each measurement four categories are relevant: range of measurement, granularity / accuracy, frequency of measurement and timeliness of measurement. Since the node which desires certain measurement information may not be the node which is able to perform that particular measurement, signalling of some measurement information is also necessary. The amount of information to be signalled, the accuracy and frequency of signalling, and the reliability of signalling all have a system cost in terms of overheads and capacity reduction for end user data. Conversely, given sufficient measurements information, it is possible to use more advanced radio transmission and reception techniques, which increase the available system capacity and performance. Therefore, in practical communications systems, design of measurement procedures and associated signalling consists of a trade-off between a number of factors, such that performance gains are not taken up by increased overheads. The trade-offs between achievable performance and overheads for measurement and signalling procedures are a matter for specific system design and implementation. To gain a better understanding of the types of trade-offs involved, an example study on the impact of quantised CSI signalling on spatial-temporal transmission schemes is given in [YuA08]. This study is briefly reviewed in the following. For some spatial-temporal transmission schemes such as dominant eigen-mode transmission and the multi-user downlink MIMO transmission with MMSE precoding, the CSI is required at the transmitter side. In FDD systems, this means a feedback link is necessary for the receiver to send the CSI information back to the transmitter. Thus, the CSI is quantised at the receiver side and the transmitter exploits received imperfect CSI that contains quantisation errors. Note that another important source of CSI errors at the transmitter is the feedback delay for the CSI to be fed back to the transmitter [YuA08]. To perform quantisation of the CSI vector, a random vector quantisation (RVQ) [TFF07] is considered. To quantise a 1xM CSI vector hk , first at the k-th receiver direction of the vector is determined hk . h˜ k = ||hk ||

(6.18)

Then the direction of hk is quantised using a user-specific random codebook Ck = {ck,1 , . . . , ck,N } that contains N unit norm column vectors, i.e., codewords. The N

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quantisation codewords are chosen from an isotropic distribution on the M-dimensional unit sphere independently. By minimising the chordal distance, the quantised direction of channel hk can be expressed as hˆ k = arg max |h˜ k ck, j |. {ck, j } j=1,...,N

(6.19)

The numerical results from [YuA08] show that when the receiver knows the CSI perfectly, the quantisation error due to 8-bits RVQ only degrades the performance of dominant eigenmode transmission without an error floor. On the other hand, for multi-user downlink MIMO transmission with MMSE precoding, both the BER performance and the throughput are limited at high SNR region even for 12-bits RVQ due to interference among different users.

6.6 Link Level Synchronisation In order to demodulate the received data stream, robust and accurate synchronisation of the transmitted MAC frames are essential. Link level synchronisation aims at aligning the local clocks between a transmitter and a receiver, and can be divided into the following categories [SFF01]:

r time synchronisation, further subdivided into frame and symbol synchronisation; r frequency synchronisation, further subdivided into carrier frequency and carrier phase synchronisation. Carrier phase synchronisation could be considered as a part of channel estimation and is beyond the scope of this section, which discusses link layer synchronisation procedures for the WINNER system. Two scenarios are considered:

r no interference originating from non-WINNER systems, which corresponds to the exclusive operation of WINNER in the assigned frequency band;

r some narrowband interference, e.g., as in the case of licence-exempt bands or multi-band operation in which several non-continuous frequency bands exist between which other nonWINNER systems operate. Section 6.6.1 describes the WINNER synchronisation preamble. Sections 6.6.2 and 6.6.3, respectively, describe link layer synchronisation algorithms suitable for operation in licensed bands where the WINNER system does not suffer from any outside-world interference and a scenario where the WINNER system is affected by narrowband interference.

6.6.1 Synchronisation Preamble Design Time and frequency synchronisation should be performed during one DL Synch slot at the beginning of the WINNER MAC super-frame (see Figure 4.16). The first symbol of the slot, called the T-Pilot, is dedicated for time and frequency synchronisation and its time-domain structure is illustrated in Figure 6.12 [Lan07].

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Figure 6.12 Time-domain structure of the T-Pilot, from [Lan07]. (Reproduced by Permission of IEEE © 2009).

The samples c(m) modify the signs of the A sub-symbols and the corresponding vector is defined as follows c = [c(0), c(1), c(2), c(3), c(4), c(5), c(6), c(7)] = [−1, 1, 1, 1, 1, 1, 1].

(6.20)

The T-Pilot consists of eight equal sub-symbols A, each of length N FFT /8, where A is generated from the IFFT of a Gold sequence. To ease timing synchronisation, the sign of the first sub-symbol, c(0), is negated. In order to create the time-domain structure of Figure 6.12, pilot tones should be transmitted on every 8th subcarrier, here called ‘active’ subcarriers. Moreover, some active subcarriers are excluded to ease integer frequency offset estimation. The preamble should not only have good correlation properties but also low peak-toaverage-power ratio (PAPR). The lowest PAPR of the preamble was achieved with BPSK modulated Gold sequence of degree 9 and shift registers which states were initiated by 247oct and 503oct used as pilot tones, transmitted on the active subcarriers of T-Pilot. Indices of excluded subcarriers were found using the PAPR reduction algorithm [SS06]. The algorithm finds pilot tones that have the greatest influence on PAPR. Those pilots are excluded from the pattern and not predistorted as in the original version of the algorithm. The following set of excluded pilot tones indices minimising PAPR was found: 40, 384, 392, 408, 568, 1480, 1640, 1664, 2008 for the indoor and micro-cellular scenarios; and 88, 96, 264, 464, 488, 1560, 1584, 1784, 1952, 1960 for the urban scenario. The PAPR of the designed preamble equals 5.98 dB.

6.6.2 Synchronisation in a Licensed Band 6.6.2.1 Coarse Symbol Timing Synchronisation In order to perform the coarse timing synchronisation, only four A sub-symbols of the preamble presented in Figure 6.12 are used. The remaining symbols are used for fine frequency synchronisation in the time domain and fine timing synchronisation in the frequency domain. The following time metric is applied 2 2 L−1 1 ∗ r (d − n − (3 − m)L)r (d − n − (2 − m)L) c(m) 3 m=0 n=0 M(d) = L−1 2 |r (d − n − L)|2

(6.21)

n=0

where L = N /8 and N is the number of subcarriers. Furthermore, r(d) are the received time domain samples of the transmitted signal. In Equation (6.21) the numerator is an averaged

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value of three cross-correlations computed between four consecutive sample blocks of length L. Thus, it facilitates noise averaging improving the quality of the time metric. In order to detect the second peak of the time metric in Equation (6.21), the metric is compared with a detection threshold m . The first peak is a result of only one correlation of two sub-symbols A, whereas the second peak is a result of summing three successive correlations of sub-symbols A, thus the timing offset estimate achieved with the second peak is more reliable. Detection of the maximum value of Equation (6.21) determines the coarse timing synchronisation. Subsequently, frequency synchronisation is performed.

6.6.2.2 Frequency Offset Estimation Aided by a preamble in the form shown in Figure 6.12, we are able to estimate the frequency offset using a similar procedure as for timing estimation. The argument of correlation between two subsequent pilot symbols determines the frequency offset:

γ (m) =

m+L−1

r (n)r ∗ (n + L)

(6.22)

n=m

δˆ f =

1 ˆ + l f arg(γ (θ)) 2π L

(6.23)

where θˆ is the previously estimated symbol timing. Calculating the argument of the correlation function as the basis of the frequency offset estimation results in a frequency offset ambiguity, due to the fact that the argument is calculated modulo π. Thus, such an algorithm is only able to estimate the fractional part of the frequency offset; its integer part l f in terms of the multiples of the subcarrier spacing f must be estimated in another way. The distance between the used subcarriers in the A sub-symbol is equal to 8 f , so that ±4 f is the maximum frequency offset which can be estimated. The shorter A, the wider the range of the frequency offset that is possible to estimate in the time domain. However, shortening A degrades the attainable estimation accuracy. The quality of the frequency offset estimate can be improved by extending the correlation window over two A sub-symbols. Hence, the range of the frequency offsets that can be estimated is shortened to ±2 f . The desired frequency offset estimate is achieved after five A sub-symbols, due to the fact that the sign of the first sub-symbol is negated. Thus, the frequency offset estimation quality may be further improved by averaging estimates computed during the last three sub-symbols. After correcting the fractional part of the frequency offset, the integer part is estimated. As the integer frequency offset l is equal to an integer multiple of the subcarrier spacing, l f can easily be detected in the frequency domain (after an N/4-point DFT), by scanning the specific signature of the received frequency domain preamble. As a subset of modulated subcarriers are set to zero, finding these zeros provides an estimate of the integer frequency offset l [Dlu02], [DW02], [Dlu03]. Further details about the applied coarse or fine time and frequency synchronisation procedures can be found in [Lan07].

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6.6.3 Synchronisation in Shared Spectrum The synchronisation algorithm described in this section is suitable for several spectrum sharing scenarios: horizontal sharing (HS) with or without coordination and vertical sharing (VS) (see Chapter 11). Note, the operation in licence-exempt bands corresponds to the HS scenario without coordination. Operating in licence-exempt bands, the transmission signal can be disturbed by narrowband interference (NBI), e.g. caused by Bluetooth signals. Unfortunately, NBI severely corrupts the common approach for timing and frequency acquisition, with a dedicated training block composed of several repeated parts. Thus, in order to achieve good synchronisation in a spectrum sharing scenario, it is necessary to explicitly take care of NBI. A preamble block composed of 3N time domain samples is used, corresponding to three OFDM symbols, that performs the following operations:

r detection of NBI sources; r cancellation of detected NBI; r timing synchronisation; r estimation of carrier frequency offset. After the NBI sources have been detected and the interfered subcarriers have been set to zero (NBI cancellation phase), the virtually interference-free time domain samples of the preamble are fed to the synchronisation unit. The block diagram of the receiver is shown in Figure 6.13. The synchronisation preamble is divided into two semi-blocks of size 3N/2 symbols each. The first semi-block contains the sequence {b1 (n)}, n = 1, . . . , 3N /2 and the second semiblock contains the sequence{b2 (n)}, n = 1, . . . , 3N /2, which is a replica of b1 (n) rotated by a frequency corresponding to the subcarrier spacing f = 1/(N T ). The proposed NBI detection algorithm relies on this specific structure of the preamble, which is robust to framesynchronisation errors θ up to N/2 [WIN2D233]. The presence of NBI on each of the subcarriers is detected by comparing the metric which calculates the difference between two consecutive frequency domain OFDM symbols with a threshold value. Let be the set of the subcarriers that have been detected as interfered; interference is removed by setting to zero the

Figure 6.13 Receiver block diagram.

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Figure 6.14 NBI cancellation block diagram.

subcarriers in so that the timing synchronisation and the carrier frequency estimation algorithms can work reliably. Figure 6.14 shows how NBI cancellation is implemented [MC07]: 1. The time domain symbol (TDS) r (n), n = 1, . . . , N1 of the received signal are fed to a DFT device that yields R(m), m = 1, . . . , N1 . The DFT length N1 is chosen to include the whole synchronisation preamble, so that all the interference is removed at the same time. 2. Most of the interference is removed by setting to zero all the subcarriers in . The resulting symbols are fed to an IFFT device to generate the virtually interference-free TDS. After NBI cancellation, symbol and timing estimation are performed according the Schmidl and Cox algorithm (SCA) [ScC97]. As already mentioned, the second semi-block contains the sequence b2 (n), which is a replica of the sequence transmitted in the first semiblock b1 (n) rotated by a frequency corresponding to the subcarrier spacing. Thus, the difference from standard SCA is that the TDS of the second semi-block needs to be counter-rotated before being correlated with the TDS of the first semi-block.

6.7 Network Synchronisation Network synchronisation is defined as aligning all internal time references within the network, so that all nodes agree on the start and end of a super-frame. To do so in a self-organised manner, an algorithm is applied that is inspired by the synchronised flashing of fireflies.

6.7.1 Firefly Synchronisation The phenomenon of spontaneous synchronised flashing of fireflies can be modelled by the theory of pulse-coupled oscillators [MS90]. A firefly is modelled as a pulse-coupled oscillator that flashes periodically and interacts with other nodes through pulses. These systems are known to show interesting phenomena ranging from perfect synchrony to pattern formation [GDGL+00]. This section describes how time synchronisation is achieved between pulse-coupled oscillators, i.e. to ensure all oscillators pulse simultaneously. First, a mathematical model is

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Figure 6.15 Time evolution of the phase function (a) for an isolated node, (b) upon reception of a pulse at instant τ j , from [TAB07; TA06]. (Reproduced with kind permission of Springer Science and Business Media © 2008).

associated with these oscillators. Then the scheme and conditions for a system of N oscillators to synchronise are presented. 6.7.1.1 Mathematical Model As a simple mathematical representation, a pulse-coupled oscillator is described by its phase function φi (t) which evolves linearly over time: when the phase reaches a threshold value φth , the oscillator is said to fire, meaning that it will transmit a pulse and reset its phase. If not coupled to any other oscillator, it will naturally oscillate and fire with a period T. Figure 6.15(a) plots the evolution of the phase function during one period when the oscillator is isolated. The phase function can be seen as an internal counter that dictates when a pulse should be emitted. The goal of the synchronisation algorithm is to align all internal counters, so that all nodes agree on a common firing instant. To do so, the phase function needs to be adjusted. In the following discussion, we consider that all nodes have the same dynamics, i.e. clock jitter is considered negligible. 6.7.1.2 Synchronisation of Coupled Oscillators When coupled to others, an oscillator i is receptive to the pulses of its neighbours. Phase adjustment is performed upon the reception of a single pulse and depends on the current phase value at the receiver. When receiving a pulse at instant τ j , a node instantly increments its phase by an amount that depends on the current value: φi (τ j ) → φi (τ j ) + φ(φi (τ j )) Figure 6.15(b) plots the time evolution of the phase when receiving a pulse. The received pulse causes the oscillator to fire early. By appropriate selection of φ(φi ), a system of N identical oscillators forming a fully meshed network is able to synchronise their firing instants within a few periods [MS90]. The phase increment φ(φi (τ j )) is determined by the phase response curve (PRC), which was chosen to be linear in [MS90]: φi (τ j ) + φ(φi (τ j )) = min(α · φi (τ j ) + β, φth )

(6.24)

where α and β determine the coupling between oscillators. Here, the threshold φ th is normalised to 1. It was shown in [MS90] that if the network is fully meshed, the system always converges, i.e. all oscillators will agree on a common firing instant, for α > 1 and β > 0.

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Figure 6.16 Synchronisation emerges from an initially random situation, from [TA08]. (Reproduced by Permission of IEEE © 2009).

An example of the synchronisation of pulse-coupled oscillators is shown in Figure 6.16. Initially all nodes start with a random phase, which increments until one phase reaches the threshold. At this instant and each time a phase reaches the threshold value φ th , all nodes increment their phase. Over time, order emerges from a seemingly chaotic situation where nodes fire randomly: after six periods in Figure 6.16, all nodes fire together. This synchronisation property is very appealing. Nodes do not need to distinguish between transmitters; they simply need to adjust their internal clock φi (t) by a phase increment when receiving a pulse and transmit a pulse when firing. After some time, synchronisation emerges from an initially unsynchronised situation and pulses are transmitted synchronously. 6.7.1.3 Refractory Period When delays, such as propagation delays, are introduced into a system of pulse-coupled oscillators, it becomes unstable and is unable to synchronise [EPG95]. To regain stability, a refractory period of duration Trefr is introduced after transmitting. During this period, no phase increment is possible [HS03]. A node’s receiver is switched on during this period, but the phase function stays equal to 0 even if a synchronisation message is received. An appropriate choice for Trefr is important and may be determined through simulations. In general, the value of Trefr should not be too small as it helps to limit the number of interactions per period (and thus remain stable) and it should not be too large so as to enable some interactions between nodes.

6.7.2 Synchronisation Rules The synchronisation of pulse-coupled oscillators presents the advantage that synchronisation emerges from any random initial situation, and does not have pre-requisites regarding the distribution of initial firing instants. Thus self-organised synchronisation enables the system to cope with changes in the topology, which is especially interesting in mobile systems, where wireless communications do not guarantee that all nodes in the network are connected. Application of firefly synchronisation to wireless networks has been previously considered [HS03]. A modification to the original model was presented in [TAB07] to cope with the induced delays associated with long synchronisation words, being far more practical for implementation

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Figure 6.17 State machines of network synchronisation units for coarse misalignments, from [TA08]. (Reproduced by Permission of IEEE © 2009).

in wireless systems than a transmission of a single pulse. Based on the biologically inspired slot synchronisation scheme of [TAB07], a decentralised network synchronisation protocol is presented that was successfully integrated into the WINNER system concept [WIN2D61314; TA09]. Initially when a user terminal (UT) accesses the network, it needs to synchronise with its base station (BS) by performing link synchronisation. Synchronisation within the entire wireless network requires interactions between BSs and UTs located in different cells. Interactions occur when a node of one type (either a BS or a UT) transmits and nodes from the other group detect the transmission. Detection of the distinct synchronisation words is done by the link synchronisation described in Section 6.6. Given the WINNER super-frame structure, Figure 6.17 presents the two state machines defined for BSs and UTs as well as the super-frame preamble structure, when nodes are synchronised. As the WINNER MAC frame structure distinguishes between downlink and uplink transmission slots, both BSs and UTs need to be synchronised such that BSs transmit on downlink slots and UTs transmit on uplink slots. To force the formation of two groups, one of BSs and one of UTs, requires two distinct synchronisation sequences: ‘UL Synch’ and ‘DL Synch’. Based on the two state machines of Figure 6.17, interactions occur between the two groups (BSs and UTs) when a node transmits and nodes from the other group detect this transmission. Detection of the distinct synchronisation words is done by the PHY link synchronisation. It is demonstrated in [STM+08] that OFDM link synchronisation algorithms, as described in Section 6.6, are sufficiently reliable to allow for network synchronisation. Based on the super-frame structure, the listening time (the time where a node is receptive to the synchronisation words of its neighbours) for UTs and BSs is equal to: TUL,Rx = (Tpreamble + TSF ) − (TUL,Sync + Trefr,UL )

(6.25)

TDL,Rx = (Tpreamble + TSF ) − (TDL,Sync + Trefr,DL )

(6.26)

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Based on the firefly synchronisation rules, slot synchronisation requires all nodes to maintain a phase function that is adjusted. Thus all user terminals maintain a phase function, which increments linearly over time: 1 dφi (t) = dt TUL,Rx

(6.27)

where TUL,Rx is the listening period of a user terminal. Similarly, all base stations maintain a phase function: 1 dφi (t) = dt TDL,Rx

(6.28)

where TDL,Rx is the listening period of a user terminal. The nodes are separated into the two groups as follows:

r If, at instant τ j , a BS node i is in ‘Listen’ state, where its phase function φi linearly increments over time, and a UT node j, which can communicate with i, started transmitting TUL,Synch + TDL,dec before, then node i increments its current phase φi : φi (τ j ) → φi (τ j ) + φBS (φi (τ j )) where φ + φBS (φ) = αBS · φ + βBS

(6.29)

r If, at instant τi , a UT node j is in ‘Listen’ state, where its phase function φ j linearly increments over time, and a BS node i, which can communicate with j, started transmitting TUL,Synch + TDL,dec before, then node j increments its current phase φ j : φ j (τi ) → φ j (τi ) + φUT (φ j (τi )) where φ + φUT (φ) = αUT · φ + βUT

(6.30)

Thanks to this strategy, the formation of the two groups is controlled: starting from a random initial state, where all nodes fire randomly, after following the simple coupling rules, UTs and BSs separate over time into two groups, all BSs firing TUL after UTs and all UTs firing TDL after BSs, corresponding to a time-slotted access for uplink and downlink.

6.7.3 Compensating for Propagation Delays: Timing Advance Propagation delays are problematic in uplink transmissions. In fact, propagation delays exceeding the CP duration result in inter-symbol interference (ISI) as well as inter-carrier interference (ICI). Thus it is particularly important to compensate for propagation delays in deployments with large cell sizes. Table 6.6 summarises the maximum propagation delays considered for the three scenarios in WINNER (wide area (WA), metropolitan area (MA) and local area (LA)) Table 6.6 Propagation delays. WA MA

LA

Guard Interval Duration [µs] 3.20 2.00 2.00 Maximum Inter-node distance [m] 1000 325 100 Propagation delay [µs] 3.33 1.08 0.333

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and the considered guard interval durations [WIN2D6137]. From this table, compensating for propagation delays on the uplink is particularly important for wide area deployment. A common procedure to compensate for propagation delay is for terminals to advance their transmission by the propagation delay, known as ‘timing advance’ in GSM networks. The firing instant, or ‘timing reference instant’, of a user terminal τUT,i is advanced by the propagation delay with its own base station θUT,i,BS(i) , so that uplink transmissions are effectively performed according to the new timing reference instant: τUT,i → τUT,i − θUT,i,BS(i)

(6.31)

By implementing timing advance with firefly synchronisation, a synchronisation accuracy not exceeding 1 µs is retained (see [WIN2D233, Section 8.4]).

6.7.4 Imposing a Global Time Reference on Firefly Synchronisation In a cellular network, an issue with self-organised network synchronisation is scalability. In very large networks, it has been shown that synchronisation can fall apart due to loops in the network and a synchronised state is never reached. This problem is effectively mitigated by imposing a global time reference onto the network. To do so, a subset of BSs within the network needs to have access to a primary reference clock; such a clock is available, for example, through the global positioning system (GPS). The reference BSs then redistribute the reference timing to the entire network. When nodes are following the slot synchronisation strategy shown in Figure 6.17, the reference node corresponds to an oscillator that periodically transmits the ‘DL Synch’ word at the start of every super-frame without modifying its phase [TA07]. To enforce the time reference on the network, reference nodes should have slightly shortened symbol duration with respect to normal nodes [TA07]. One possible means of achieving this is to shorten the duration of the ‘Wait,DL’ state, so that TWait,ref < TDG , where T DG is the duplex guard that is introduced to switch between uplink and downlink transmissions. The time evolution of reference base stations is shown in Figure 6.18.

Figure 6.18 Time evolution of reference base stations.

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6.8 Conclusion 6.8.1 Pilot Design Pilot design is an important building block of the WINNER system concept, as it enables adaptive transmission and multiple antenna transmission schemes. Several studies aiming to optimise the placement and power allocation of pilot symbols were conducted. Both dedicated pilots, where only the pilots that are within the chunk can be utilised for channel estimation, and common pilots, where interpolation in frequency over consecutive chunks is possible, were addressed. These studies provide valuable insights not only on the placement of pilot symbols, but also on the attainable spectral efficiency of a MIMO-OFDM system taking into account realistic channel estimation schemes. When it comes to a general framework for the WINNER pilot design, there are many constraints other than the placement of pilots that need to be taken into consideration. As pilot overheads tend to increase when more antennas are allowed into the system, it is crucial that the inserted pilots can be efficiently re-used for several functions. Apart from channel estimates at the receiver, these functions are measurements of CQI and CSI which are utilised for link adaptation. The re-use of pilots as well as the flexibility to support various flavours of multi-user MIMO and multiple access schemes is one of the main features of the WINNER pilot design. A modular concept is established that consists of basic building blocks: the pilot pattern, the pilot type and orthogonal pilot sets. The pilot pattern defines the position of pilot symbols within the chunk and resembles a regular 2D grid with equidistantly spaced pilots on the macroscopic view of the OFDM frame. The spatial transmit processing scheme then defines the pilot type that is inserted on a particular chunk. The introduction of orthogonal pilot sets specifies whether pilots associated with different spatial streams are orthogonally separated in time or frequency, or, if the spatial separation between beams is sufficient, the pilot symbol can be re-used. Thanks to this modular concept, the pilot overhead stays within acceptable limits.

6.8.2 Channel Estimation Channel estimation in WINNER II is based on pilot subcarriers placed in a scattered pilot grid, with in-cell users’ pilots placed so as not to interfere with one another. When block sizes are large enough to permit several pilots per block, Wiener filter interpolation, in frequency and time over one of more frames, provides channel estimates for all used subcarriers and OFDM symbols. Pilot-based CE can be enhanced by iterative CE and, for MIMO and SDMA, can be further aided by application of genetic algorithm or least squares techniques. There is always some performance loss (SNR degradation relative to the idealised situation where all channels are known by the receiver) resulting from CE. In general, larger CE performance losses occur:

r when interpolation over a wide range of subcarrier frequencies and OFDM symbol times is not possible (for example, for IFDMA, B-IFDMA and B-EFDMA); the effective advantage of diversity over full-band or local FDMA of multiple access schemes, is thus diminished; r when interference is imposed on pilots and data transmitters from outside the cell or sector. Such interference should be kept to a low level by frequency re-use partitioning or intercell dynamic channel assignment and can be combated at the receiver by least-squares techniques which essentially attempt to estimate the autocorrelation matrix of the out-ofcell interference.

212

Radio Technologies and Concepts for IMT-Advanced

Performance and limitations on channel prediction, using Kalman filter techniques, was evaluated for use in predicting CQI for channel estimation and frequency-adaptive transmission. The Kalman algorithm uses assumed channel time and frequency correlation properties to optimally estimate the future time evolution of the channel. Its use is favoured for channels corresponding to relatively slow mobile terminals and where auxiliary channel correlation measurements are made.

6.8.3 RF Imperfections Minimising the power backoff required for high power amplifiers is very important in terms of cost and battery recharging intervals, especially for mobile terminals. Large backoff lowers amplifier efficiency and increases the maximum output power required from the HPA, thus increasing its cost, and battery drain. The effect of HPA nonlinearity was evaluated for different systems. DFT-precoded OFDM, IFDMA and B-IFDMA require less power backoff than OFDM and B-EFDMA. Backoff is also reduced for all modulations by using a high quality (linear below the saturation level) or an adaptively linearised power amplifier. In practice, of course, the required backoff depends on the choice of spectral mask. The phase noise compensation algorithm for multi-carrier systems is applicable for any pilot patterns and does not need any additional overhead. For single carrier, the time-varying phase process can be tracked with a second-order, soft decision-directed, phase-locked loop (PLL), which uses log-likelihood ratio information from a turbo equaliser. For both schemes (multi and single carrier), the complexity of the correction algorithm can be neglected compared to the decoding complexity.

6.8.4 Link Layer Synchronisation Link layer synchronisation is based on the synchronisation symbol placed in the downlink synch slot of the preamble. In the licensed case, a T-Pilot synchronisation scheme is used. The synchronisation is performed in two stages and both are performed during one OFDM symbol. The first stage includes coarse timing synchronisation and fractional frequency offset estimation, both utilising a modified Schmidl and Cox algorithm. The latter stage includes integer frequency offset estimation and fine timing synchronisation, both performed in the frequency domain. According to the presented simulation results the synchronisation scheme guarantees inter-block interference-free signal reception and acceptably low inter-carrier interference, however it is not robust against narrowband interference (NBI). In the presence of NBI, as in licence-exempt and spectrum-sharing cases, the algorithm utilising three OFDM symbols should be used. The algorithm detects and removes the interference in the frequency domain. The corrected signal is then used for time and frequency synchronisation in the time domain. Simulation results proved the efficiency of the algorithm even for very low signal to interference power ratio.

6.8.5 Self-Organised Network Synchronisation Section 6.7 presented a firefly synchronisation scheme that was appropriately modified to fit into the WINNER frame structure. Thanks to this modification, a network is able to correctly synchronise: base stations and user terminals agree on a common time-slotted structure, starting from any random misalignment. The algorithm was further modified in order to cope

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with the presence of relays in the network and with access to a primary reference clock. This last property is necessary in order to stabilise the network, i.e. avoid synchronisation loops, and in order to reduce scalability issues that are common in self-organised networks. Thanks to these modifications, the proposed network synchronisation algorithm is well suited for the WINNER system concept [WIN2D61314].

Acknowledgments The authors would like to thank all partners involved in the link level procedures task: Angeliki Alexiou, Daniel Aronsson, Steffen Bittner, Florence Danilo-Lemoine, Bernard Hunt, Ming Jiang, Chan-Tong Lam, Adrian Langowski, Hongju Liu, Yi Ma, Keith Roberts, Maryam Sabbaghian, Fayyaz Siddiqui, Mikael Sternad, Tommy Svensson, Alexander Tyrrell and Kai Yu.

References [AB07]

[AK05]

[Aro07] [AS07a]

[AS07b]

[BA06a] [BA06b] [BRF07]

[BT05] [CA07] [Chu72] [DFL+08]

[DGE03] [Dlu02]

Auer, G. and Bonnet, J. (2007) ‘Threshold Controlled Iterative Channel Estimation for Coded OFDM’, Proc. of IEEE Vehicular Technology Conference (VTC 2007 spring), IEEE, Dublin, Ireland, pp. 1737–41. Auer, G. and Karipidis, E. (2005) ‘Pilot aided channel estimation for OFDM: a separated approach for smoothing and interpolation’, Proc. of IEEE International Conference on Communications (ICC 2005), IEEE, Seoul, Korea, pp. 2173–8. Aronsson, D. (2007) Channel Estimation and Prediction from a Bayesian Perspective, Licentiate Thesis, Uppsala University, Uppsala. Aronsson, D. and Sternad, M. (2007) ‘Kalman Predictor Design for Frequency-adaptive Scheduling of FDD OFDMA Uplinks’, Proc. of IEEE Symp. Personal Indoor and Mobile Radio Communications (PIMRC 2007), IEEE, Athens, Greece. Aronsson, D. and Sternad, M. (2007) ‘OFDMA Uplink Channel Prediction to Enable FrequencyAdaptive Multiuser Scheduling’, Proc. of European Signal Processing Conference (EUSIPCO 2007), Poznan, Poland. Bonnet, J. and Auer, G. (2006) ‘Chunk-based Channel Estimation for Uplink OFDM’ Proc. of IEEE Vehicular Technology Conference (VTC 2006 spring), IEEE, Melbourne, Australia. Bonnet, J. and Auer, G. (2006) ‘Optimized Iterative Channel Estimation for OFDM’, Proc. of IEEE Vehicular Technology Conference (VTC 2006 fall), IEEE, Montreal, Canada, pp. 1–5. Bittner, S., Rave, W. and Fettweis, G. (2007) ‘Phase Noise Suppression in OFDM with Spatial Multiplexing’, Proc. of IEEE Vehicular Technology Conference (VTC 2007 spring), IEEE, Dublin, Ireland. Benvenuto, N. and Tomasin, S. (2005) ‘Block Iterative DFE for Single Carrier Modulation’, IEEE Transactions on Communications, 53(11): 1867–75. Cosovic, I. and Auer, G. (2007) ‘Capacity of MIMO-OFDM with Pilot Aided Channel Estimation’, Wireless Communications and Networking (EURASIP JWCN), Article ID 32460. Chu, D. (1972) ‘Polyphase Codes with Good Periodic Correlation Properties’, IEEE Transactions on Information Theory, 18(4): 531–2. Danilo-Lemoine, F., Falconer, D., Lam, C.-T., Sabbaghian, M. and Wesołowski, K. (2008) ‘Power Backoff Reduction Techniques for Generalized Multicarrier Waveforms’, Wireless Communications and Networking (EURASIP JWCN), Article ID 437801. Dinis, R., Gusm˜ao, A. and Esteves, N. (2003) ‘On Broadband Block Transmission over Strongly Frequency-Selective Fading Channels’, Proc. of IEEE Wireless, IEEE, Calgary, Canada. Długaszewski, Z. (2002) ‘Simple Coarse Frequency Offset Estimation Scheme for OFDM Burst Transmission Using Specially Designed Preamble Symbols’, Proc. of International OFDMWorkshop, Hamburg, Germany, pp. 227–31.

214

[Dlu03] [DMR00]

[DW02]

[Ekm02] [EPG95] [ESA02] [GB89]

[GDGL+00]

[GOL89] [Hag97] [HK06] [HKR97]

[HNO06]

[Hol75] [HS03]

[JAH07]

[JLC07a]

[JLC07b]

[LAD+07]

[Lan07] [Lar96] [LFD07]

Radio Technologies and Concepts for IMT-Advanced

Długaszewski, Z. (2003) Methods of OFDM Transmission on Fading Channels, Doctoral Thesis, Poznan University of Technology, Poznan. Demir, A., Mehrotra, A. and Roychowdhury, J. (2000) ‘Phase Noise in Oscillators: A Unifying Theory and Numerical Methods for Characterization’, IEEE Transactions on Circuits and Systems I, 47(5): 655–74. Długaszewski, Z. and Wesołowski, K. (2002) ‘Simple Coarse Frequency Offset Estimation Schemes for OFDM Burst Transmission’, Proc. of IEEE Symposium on Personal Indoor and Mobile Radio Communications (PIMRC 2002), IEEE, Lisbon, Portugal, pp. 567–71. Ekman, T. (2002) Prediction of Mobile Radio Channels. Modelling and Design. Ph.D. Thesis, Uppsala University, Uppsala. Ernst, U., Pawelzik, K. and Geisel, T. (1995) ‘Synchronisation induced by temporal delays in pulsecoupled oscillators’, Physical Review Letters, 74(9): 1570–3. Ekman, T., Sternad, M. and Ahlen, A. (2002) ‘Unbiased power prediction on broadband channels’, Proc. of IEEE Vehicular Technology Conference (VTC 2002 fall), IEEE, Vancouver, Canada. Grefenstette, J.J. and Baker J.E. (1989) ‘How Genetic Algorithms Work: A critical look at implicit parallelism’, Proc. of International Conference on Genetic Algorithms, California, USA, pp. 20–7. Guardiola, X., Diaz-Guilera, A., Llas, M., and Perez, C.J. (2000) ‘Synchronisation, diversity, and topology of networks of integrate and fire oscillators’, Physical Review E, 62(4): 5565– 70. Goldberg, D.E. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning, AddisonWesley, Reading, Massachusetts. Hagenauer, J. (1997) ‘The Turbo principle: Tutorial introduction and state of the art’, Proc. of International Symposium on Turbo Codes, Brest, France. Hanzo, L. and Keller, T. (2006) An OFDM and MC-CDMA Primer, Wiley-IEEE Press. H¨oher, P., Kaiser, S. and Robertson, P. (1997) ‘Pilot-Symbol-Aided Channel Estimation in Time and Frequency’, Proc. Communication Theory Mini-Conf. (CTMC) within IEEE Global Telecommun. Conf. (Globecom’97), Phoenix, USA, pp. 90–96. Haas, H., Nguyen, V.D., Omiyi, P., Nedev, N.H. and Auer, G. (2006) ‘Interference Aware Medium Access in Cellular OFDMA/TDD Network’, Proc. of IEEE International Conference on Communications (ICC 2006), IEEE, Istanbul, Turkey. Holland, J. (1975) Adaptation in Natural and Artificial Systems, Ann Arbor, Michigan, University of Michigan Press. Hong, Y.W. and Scaglione, A. (2003) ‘Time synchronisation and reach-back communications with pulse-coupled oscillators for UWB wireless ad hoc networks’, Proc. of IEEE Conference on Ultra Wideband Systems and Technologies, IEEE, Reston, VA, USA. Jiang, M., Akhtman, J. and Hanzo, L. (2007) ‘Iterative Joint Channel Estimation and Multi-User Detection for Multiple-Antenna Aided OFDM Systems’, IEEE Transactions on Wireless Communications, 6(8): 2904–14. Jiang, M., Lestable, T. and Cho, Y. (2007) ‘Uplink Multi-User MIMO OFDM Enhancement Using Genetically Improved Turbo Receiver’, Proc. of IEEE Vehicular Technology Conference (VTC 2007 fall), IEEE, Baltimore, USA. Jiang, M., Lestable, T. and Cho, Y. (2007) ‘Iterative MIMO Channel Estimation for Next Generation Wireless Systems’, Proc. of IEEE Global Communications Conference (Globecom 2007), IEEE, Washington DC, USA. Lam, C.-T., Auer, G., Danilo-Lemoine, F. and Falconer, D. (2007) ‘Design of Time and Frequency Domain Pilots for Generalized Multicarrier Systems’, Proc. of IEEE International. Conference on Communications (ICC 2007), Glasgow, UK. Langowski, A. (2007) ‘Fast and Accurate OFDM Time and Frequency Synchronisation’, Proc. of IEEE Int. Symp. Wireless Communication Systems (ISWCS 2007), Trondheim, Norway. Larson, L.E. (ed.) (1996) RF and Microwave Circuit Design for Wireless Communications, Artech House, Norwood, MA. Lam, C.-T., Danilo-Lemoine, F. and Falconer, D. (2007) ‘A Low Complexity Frequency Domain Iterative Decision-Directed Channel estimation Technique for Single Carrier Systems’, Proc. of IEEE Vehicular Technology Conference (VTC 2007 spring), IEEE, Dublin, Ireland.

Link Level Procedures

[LFD08]

[LMT07a]

[LMT07b]

[LWB06]

[LZH+06]

[MC07]

[MIT96] [MS90] [MSO+07]

[MUH91] [NLF07]

[PRF03] [PVM95]

[Rap91] [SA03] [ScC97] [SDF07a]

[SDF07b]

[SDF08] [SF08] [SFF01]

[SFS+05]

215

Lam, C.-T., Danilo-Lemoine, F. and Falconer, D. (2008) ‘Iterative Frequency Domain Channel estimation for DFT-precoded OFDM Systems Using In-Band Pilots’, IEEE Journal on Selected Areas in Communications, 26(2): 348–58. Liu, H., Ma, Y. and Tafazolli, R. (2007) ‘Optimum Pilot Placement for Chunk-Based OFDMA Uplink: Single Chunk Scenario’, Proc. of IEEE Vehicular Technology Conference (VTC 2007 fall), Baltimore, USA. Liu, H., Ma, Y. and Tafazolli, R. (2007) ‘Sub-Optimum Pilot Placement for Chunk-Based OFDMA Uplink: Consecutive Chunks Scenario’, Proc. of IEEE Int. Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2007), Athens, Greece. Liu, P., Wu, S. and Bar-Ness, Y. (2006) ‘A Phase Noise Mitigation Scheme for MIMO WLANs with Spatially Correlated and Imperfectly Estimated Channels’, IEEE Communications Letters, 10(3): 141–3. Lestable, T., Zimmerman, E., Hamon, M.-H. and Stiglmayr, S. (2006) ‘Block-LDPC Codes vs Duo-Binary Turbo-Codes for European Next Generation Wireless Systems’, Proc. IEEE Vehicular Technology Conference Fall, Montreal, Canada. Moretti, M. and Cosovic, I. (2007) ‘OFDM synchronization in an uncoordinated spectrum sharing scenario’, Proc. of IEEE Global Communications Conference (Globecom 2007), Washington DC, USA. Mitchell, M. (1996) An Introduction to Genetic Algorithms, MIT Press, Cambridge, Massachusetts. Mirollo, R.E. and Strogatz, S.H. (1990) ‘Synchronisation of pulse-coupled biological oscillators’, SIAM Journal of Applied Math, 50(6): 1645–62. Sternad, M., Svensson, T., Ottosson, T., Ahln, A., Svensson, A. and Bunstrom, A. (2007) ‘Towards systems beyond 3G based on adaptive OFDMA transmission’, Proc. of the IEEE, Special Issue on Adaptive Transmission, 95(12): 2432–2455. M¨uhlenbein, H. (1991) ‘Evolution in time and space: the parallel genetic algorithm’, Foundations of Genetic Algorithms, in G. Rawlins (ed.), California, USA, Morgan Kaufmann, pp. 316–37. Ng, B.K., Lam, C.-T. and Falconer, D. (2007) ‘Turbo Frequency Domain Equalization for SingleCarrier Broadband Wireless Systems’, IEEE Transactions on Wireless Communications, 6(2): 759–67. Petrovic, D., Rave, W. and Fettweis, G. (2003) ‘Phase Noise Suppression in OFDM Including Intercarrier interference’, Proc. of International OFDM Workshop (InOWo), Hamburg, Germany. Pollet, T., Bladel, M. van and Moeneclaey, M. (1995) ‘BER Sensitivity of OFDM Systems to Carrier Frequency Offset and Wiener Phase Noise’, IEEE Transactions on Communications, 43(234): 191–3. Rapp, C. (1991) ‘Effects of HPA Nonlinearity on a 4DPSK/OFDM Signal for a Digital Sound Broadcasting System’, Proc. 2nd European Conference on Satellite Communications, Liege, Belgium. Sternad, M. and Aronsson, D. (2003) ‘Channel Estimation and Prediction for Adaptive OFDM Downlinks’, Proc. of IEEE Vehicular Technology Conference (VTC 2003 fall), Orlando, USA. Schmidl, T.M. and Cox, D.C. (1997) ‘Robust Frequency and Timing Synchronization for OFDM’, IEEE Transactions on Communications, 45(12): 1613–21. Siddiqui, F., Danilo-Lemoine, F. and Falconer, D. (2007) ‘On Interference in Uplink SDMA SCFDE System’, Proc. of Communication Networks and Services Research (CNSR 2007) Fredericton, Canada. Siddiqui, F., Danilo-Lemoine, F. and Falconer, D. (2007) ‘PIC-Assisted IBDFE-Based Iterative Spatial Channel estimation with Intra- and Inter-Cell Interference in SC-FDE System’, Proc. of IEEE Vehicular Technology Conference (VTC 2007 fall), IEEE, Baltimore, USA. Siddiqui, F., Danilo-Lemoine, F. and Falconer, D. (2008) ‘Iterative Interference Cancellation and Channel estimation for Mobile SC-FDE Systems’, IEEE Communication Letters, 12(2): 746–8. Sabbaghian, M. and Falconer, D. (2008) ‘Joint Turbo Frequency Domain Equalization and Carrier Synchronization’, IEEE Transactions on Wireless Communications, 7(1): 204–12. Speth, M., Fechtel, S., Fock, G. and Meyr, H. (2001) ‘Optimum Receiver Design for OFDM-Based Broadband Transmission-Part II: A Case Study’, IEEE Transactions on Communications, 49(4): 571–8. Sternad, M., Falahati, S., Svensson, T. and Aronsson, D. (2005) ‘Adaptive TDMA/OFDMA for Wide-Area Coverage and Vehicular Velocities’, Proc. of IST Summit, Dresden, Germany.

216

[SJS03] [SS00] [SS06] [STM+08]

[Sto98] [STS+04]

[STS+05] [TA06]

[TA07]

[TA08] [TAB07]

[TA09] [TFF07]

[VW01]

[WB02] [WIN1D21]

[WIN1D22]

[WIN1D24]

[WIN1D25]

[WIN1D210]

[WIN2D112]

Radio Technologies and Concepts for IMT-Advanced

Sanzi, F., Jelting, S. and Speidel, J. (2003) ‘A comparative study of iterative channel estimators for mobile OFDM systems’, IEEE Transactions on Wireless Communications, 5(2): 849–59. Sanzi, F. and Speidel, J. (2000) ‘An Adaptive Two-Dimensional Channel Estimator for Wireless OFDM with Application to Mobile DVB-T’, IEEE Transactions on Broadcasting, 46(2): 128–33. Sezginer, S. and Sari, H. (2006) ‘OFDM Peak Power Reduction with simple amplitude predistortion’, IEEE Communications Letters, 10(2): 65–7. Sanguinetti, L., Tyrrell, A., Morelli, M. and Auer, A. (2008) ‘On the Performance of BiologicallyInspired Slot Synchronization in Multicarrier Ad Hoc Networks’, Proc. of IEEE Vehicular Technology Conference (VTC 2008 spring), Singapore. Stott, J. (1998) ‘The Effects of Phase Noise in COFDM’, European Broadcasting Union Review Technical (276): 12–25. Schenk, T.C.W., Tao, X.-J., Smulders, P.F.M. and Fledderus, E.R. (2004) ‘Influence and Suppression of Phase Noise in Multi-Antenna OFDM’, Proc. of IEEE Vehicular Technology Conference (VTC 2004 fall), IEEE, Los Angeles, USA, pp. 1443–7. Schenk, T.C.W., Tao, X.-J., Smulders, P.F.M. and Fledderus, E.R. (2005) ‘On the Influence of Phase Noise Induced ICI in MIMO OFDM Systems’, IEEE Communications Letters, 9(8): 682–4. Tyrrell, A. and Auer, G. (2006) ‘Firefly Synchronization in Ad Hoc Networks’, Proc. International Conference on Bio Inspired mOdels of NETwork, Information and Computing Systems (BIONETICS 2006), Cavalese, Italy. Tyrrell, A. and Auer, G. (2007) ‘Imposing a Reference Timing onto Firefly Synchronisation in Wireless Networks’, Proc. of IEEE Vehicular Technology Conference (VTC 2007 spring), Dublin, Ireland. Tyrrell, A. and Auer, G. (2007) ‘Decentralized Inter-Base Station Synchronization Inspired from Nature’, Proc. of IEEE Vehicular Technology Conference (VTC 2008 fall), pp. 1–5, Calgary, Canada. Tyrrell, A., Auer, G. and Bettstetter, C. (2007) ‘Biologically inspired synchronization for wireless networks’, in F. Dressler and I. Carreras (eds), Advances in Biologically Inspired Information Systems: Models, Methods, and Tools, Springer. Tyrrell, A. and Auer, G. (2009) ‘Biologically Inspired Intercellular Slot Synchronization’, EURASIP Journal on Wireless Communications and Networking, vol. 2009, Article ID 854087, 12 pages. Trivellato, M., Boccardi, F. and Tosato, F. (2007) ‘User Selection Schemes for MIMO Broadcast Channels with Limited Feedback’, Proc. of IEEE Vehicular Technology Conference (VTC 2007 spring), Dublin, Ireland. Valenti, M.C. and Woerner, B.D. (2001) ‘Iterative channel estimation and decoding of pilot symbol assisted turbo codes over flat-fading channels’, IEEE Journal on Selected Areas in Communications, 19(9): 1697–105. Wu, S. and Bar-Ness, Y. (2002) ‘A Phase Noise Suppression Algorithm for OFDM based WLANs’, IEEE Communications Letters, 6(12): 535–7. WINNER I (2004) IST-2003-507581 Identification of Radio-Link Technologies, Deliverable D2.1, June 2004, viewed 20 June 2009, http://projects.celtic-initiative. org/winner+. WINNER I (2004) IST-2003-507581 Feasibility of Multi-Bandwidth Transmissions, Deliverable D2.2, October 2004, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. WINNER I (2005) IST-2003-507581 Assessment of adaptive transmission technologies, Deliverable D2.4, February 2005, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. WINNER I (2004) IST-2003-507581 Duplex Arrangements for Future Broadband Radio Interface, Deliverable D2.5, October 2004, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. WINNER I (2004) IST-2003-507581 Final Report on identified RI key technologies, system concept, and their assessment, Deliverable D2.10, December 2005, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+. WINNER II (2007) IST-4-027756 WINNER II Channel Models, Deliverable D1.1.2, September 2007, viewed 20 June 2009, http://projects.celtic-initiative. org/winner+.

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[WIN2D233]

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WINNER II (2007) IST-4-027756 Link Level Procedures for the WINNER System, Deliverable D2.3.3, November 2007, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D472] WINNER II (2007) IST-4-027756 Interference avoidance concepts, Deliverable D4.7.2, June 2007, viewed 20 June 2009, http://projects.celtic-initiative. org/winner+. [WIN2D341] WINNER II (2007) IST-4-027756 The WINNER II Air Interface: Refined SpatialTemporal Processing Solutions, Deliverable D3.4.1, November 2006, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+. [WIN2D6137] WINNER II (2006) IST-4-027756 WINNER II Test Scenarios and Calibration Cases Issue 2, Deliverable D6.13.7, November 2006, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D61311] WINNER II (2006) IST-4-027756 CG “metropolitan area” description for integration into overall System Concept and assessment of key technologies, Deliverable D6.13.11, October 2007, viewed 20 June 2009, http://projects.celtic-initiative. org/winner+. [WIN2D61314] WINNER II (2008) IST-4-027756 WINNER II System Concept Description, Deliverable D6.13.14, January 2008, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [YuA08] Yu, K. and Alexiou, A. (2008) ‘On the Impact of Channel Estimation and Quantization Errors for Various Spatio-Temporal Transmission Schemes’, in I. Frigyes, J. Bito, and P. Bakki (eds), Advances in Mobile and Wireless Communications: Views of the 16th IST Mobile and Wireless Communication Summit, Springer.

7 Advanced Antennas Concept for 4G Afif Osseiran Ericsson

7.1 Introduction The beginning of the nineteenth century witnessed the first transatlantic radio wave transmission performed by Marconi. At that time, the antenna system consisted of more than 50 wires supported by two 60-meter wooden poles (equivalent to several transmitting antenna elements). Two decades later, Beverage and Peterson were the first to install space diversity stations [Rog02; BP31]. World War Two gave a great boost to wireless communications in general and the use of antenna arrays was exclusively used for radar systems. The interest in multiple antennas at the receiver and transmitter in the research community took a major leap with the appearance of articles in the late 1980s and 1990s [Win87; Fos96; FJ98; Tel99]. Since then, it has been one of the dominant research topics in the communications field. Initially, the research focused on investigating the capacity limits of systems using multiple-input multiple-output (MIMO), the ‘information theory trend’ of MIMO, to derive the upper bound expressions of the capacity. [G+03; GJJV03] are among many articles in which this aspect is treated. MIMO has given rise to a plethora of articles related to every imaginable future wireless technology at various frequency bands from indoors to outdoors radio channel environments [ML02; AH04; MH04; S+02; G+02]. MIMO is the default setting for any future wireless communication system. The utilisation of the term MIMO is meant to differentiate it from a classical wireless system where a single antenna is used at both ends of the transmitting and receiving part, called ‘single-input single-output’ (SISO). More generally, it is common to define an antenna system related to the number of antennas at the receiver and transmitter. Figure 7.1 shows four antenna configurations that can characterise any wireless radio communication system [PNG03; PP97]: SISO, single-input multiple-output (SIMO) (which is characterised by a single Radio Technologies and Concepts for IMT-Advanced Martin D¨ottling, Werner Mohr and Afif Osseiran C 2009 Martin D¨ ottling, Werner Mohr, Afif Osseiran

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Rx

Tx

Tx

(a)

Tx

Rx

(b)

Rx

Tx

(c)

Rx

(d)

Figure 7.1 Antenna configurations: (a) SISO, (b) SIMO, (c) MISO, (d) MIMO.

transmit antenna and multiple receive antennas), multiple-input single-output (MISO) (which is characterised by multiple transmit antennas and a single receive antenna), and MIMO, where multiple antennas are used at both ends of the transmission. The use of multiple antennas at transmitters and receivers in a wireless communication system offers additional degrees of freedom which can be used for adaptive directivity, diversity and multiplexing. It implies that reception and transmission in different directions may be controlled, redundancy in the spatial domain may be used, and that the time and frequency resources may be re-used for parallel transmission to one or several users. MIMO can be used as a generic term to cover, among others, the following transmission schemes:

r beamforming; r receive and transmit diversity and space–time coding; r spatial multiplexing; r linear dispersion codes. These multi-antenna transmission methods exploit different properties of the radio channel in order to accomplish performance improvements. Typically, this exploitation is realised by the spatial processing component of the multi-antenna method that leverages, more or less, one or more of the following basic gains [P+04]:

r array gain; r diversity gain; r spatial multiplexing gain. Array gain is the average increase in the receive SNR due to a coherent combining from multiple antennas at the receiver or transmitter or both. Array gain requires partial or full channel knowledge at the transmitter or receiver and depends on the number of transmit and receive antennas. A typical multiple access scheme that provides array gain is spatial-division

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multiple access (SDMA) where the BS transmits, on the same frequency and time, multiple data streams to spatially separated users in the cell. The diversity gain is the gain obtained from receiving independently (or partially correlated) faded replicas of the signal. Diversity can be achieved by transmitting the signal over multiple independently fading paths (in time, frequency or space). In the following discussion, the focus is on diversity gains that can be achieved from using multiple antennas. Spatial diversity is preferred as it is both rate- and bandwidth-efficient. Wittneben [Wit93] and Winters [Win94] were the first to propose and evaluate spatial transmit diversity based on a linear transformbased transmitter. Non-linear operations (i.e. transforms) such as coding the information across transmit antennas were proposed later. This method is called space–time coding (STC) [NSC00]. STC allows the receiver to extract the information and exploit spatial diversity (possibly while providing coding gain). Spatial multiplexing gain is the increase of data rate at no additional power consumption obtained by simultaneously transmitting independent data signals from several transmit antennas [MH04]. It exploits the multiple dimensions by creating several parallel or linearly independent sub-channels (or channel eigenmodes) that can be obtained via the single value decomposition (SVD) of the MIMO channel. Linear dispersion codes (LDC) [HH02] represent a more general framework than STC. They address the problem of flexibility in terms of system design. LDC is also called ‘matrix modulation’, where the symbols are dispersed by means of matrices that can be optimised with respect to various criteria. LDC allows trading spatial diversity gain with spatial multiplexing gain when the diversity gain is not enough from other dimensions [ZT03].

7.2 Multiple Antennas Concept The WINNER multi-antenna concept is designed to work with varying degree of available channel knowledge at the transmitter and to foster flexible combinations of spatial multiplexing, SDMA, spatial diversity, beamforming, and enhanced interference management. Conceptual work has been conducted [WIN1D210] with the following objectives:

r supporting all spatial processing gains, i.e., spatial diversity, spatial multiplexing, beamforming, and interference management by spatial processing, in order to ensure high performance in all major scenarios, usage conditions, and for all terminal classes; r using techniques with scalability to different amounts of channel knowledge at the transmitter; r concentrating complexity in the network and minimising it in the mobile terminals; r supporting concurrent transmission to different subscribers using different spatial processing techniques and different degrees of adaptivity; r ensuring the possibility of future evolution of spatial processing.

7.2.1 Generic Transmitter The generic transmitter is developed in order to support a wide range of antenna techniques (techniques that perform well under practical conditions while satisfying a set of generic requirements, see Chapter 2). The idea is to group several antenna techniques under a

Radio Technologies and Concepts for IMT-Advanced

an t. s

Seg. FECk

pr oc e

O

um m

transport block

FD M

at io n

ss i

ng

222

space fre qu en cy

Seg. FECk

Multiplexing

transport block

Mod GMC LDC

LP

link adaptation and resource assignment outer coding and chunk layer multiplexing

chunk processing

OFDM

FEC: Mod: LDC: LP: BF:

forward error coding modulation linear dispersion code linear precoding beamforming

Figure 7.2 Generic transmitter.

common umbrella which allows selection of the appropriate spatial mode based on the radio environment. This concept consists of the following basic components:

r (linear) dispersion codes; r directive transmission (beamforming); r per stream rate control (spatial multiplexing). These basic components are supported for a single carrier (interleaved FDMA (IFDMA) and equidistant FDMA (EFDMA)) as well as for an OFDM system; see Chapter 9 for details on the multiple access schemes. The generic transmitter supports the use of MC-CDMA even though it is not part of the WINNER reference design. The generic configurable transmitter was first introduced in [WIN1D27] and later refined in [WIN1D210; WIN2D341] to include generalised multi-carrier (GMC) processing. In this chapter, the generic transmitter with the latest modifications and additions is described. A three dimensional illustration of the generic transmitter is shown in Figure 7.2. The received transport block from a higher layer (e.g. the multiple access control (MAC) layer) goes through the following stages:

r segmentation; r channel encoding (also called FEC); r multiplexing; r modulation; r GMC; r LDC; r linear precoding (LP); r antenna summation; r OFDM processing.

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Figure 7.3 The generic transmitter in relation to the radio interface structure.

Depending on the desired transmission technique, one or several of these modules might be activated. Before we describe each of these modules, the relation of the generic transmitter to higher layers and a simpler top-down representation are explained. The relation of the generic transmitter to the overall radio interface structure can be seen in Figure 7.3. The MAC layer controls the generic transmitter configuration. The data units are handed from the transmitting MAC entity through the physical (PHY) service access point (SAP) to the transmitting PHY entity and the generic transmitter is configured by the same transmitting MAC entity through a separate control SAP. The generic transmitter is essentially invoked once per slot (i.e. time-resource unit) when incoming Layer 2 (L2) data units, referred to as transport blocks, are to be transmitted. Figure 7.4 shows a top-down (two-dimensional) representation of the generic transmitter where the input and output of each module is specified. Firstly, each transport block is segmented and each segment is separately channel encoded in a forward error correction (FEC) entity. These encoded segments of transport blocks are referred to as FEC blocks. The number of parallel FECs associated with a user is equal to the number of data streams associated with that same user. The FEC entity supports hybrid automatic repeat request (HARQ) functions so that for each (re)transmission slot the relevant subset of bits is selected, as determined by the HARQ protocol status. The retransmission entity is the transport block.

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transport block 1

transport block k

tr ansport block K transpor

segmentation

segmentation

segmentation

FEC

FEC

FEC

FEC

FEC

FEC

FEC

FEC

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encoded substreams MUX chunk layers chunk C

chunk c

chunk 1 MOD

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MOD

MOD

GMC modulated chunk layers LDC

LDC

LP

LP

virtual antenna chunks

antenna chunks Antenna Summation

Assembly of chunks to raw symbol data

IFFT

IFFT per antenna

CP

CP extension To RF

Figure 7.4 Generic transmitter for GMC.

The FEC blocks, i.e. encoded segments of transport blocks, are then multiplexed onto one or several ‘chunk’ layers. The multiplexing operation spreads the user data over one or several chunk layers. Additionally interleaving can be considered a part of the multiplexing module. In the case of single carrier transmission, a chunk layer consists of one or several consecutive FEC blocks belonging to the same transport block. It should be noted that the concept of chunk layer herein is general and does not only represent the spatial dimension. For instance, for an FDMA SISO system, each chunk layer corresponds to the allocated frequency or tone for a specific user. On the other hand, in the case of an OFDM system, the chunk layer corresponds to the spatial dimension in most cases. The multiple chunks may derive from the same user or from different users. If they derive from the same user, a single chunk layer may correspond to the data that will be mapped to the several physical antennas as in the case of Alamouti1 or it may correspond to a stream as in the case of spatial multiplexing. If chunks derive from different users, the multiple layers of a 1 A single chunk is mapped to two virtual antennas by the LDC module and subsequently each virtual antenna is mapped directly to each physical antenna.

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chunk allow several users to share the resources of a chunk, for instance, by means of spatial re-use in the form of spatial division multiple access (SDMA) per chunk. It is interesting to note that the maximum number of layers in a chunk can be different for different chunks and arbitrary in relation to the number of physical antennas. The time and frequency resources of a chunk may be re-used in the sense that several FEC blocks of one or several transport blocks may be mapped onto the same chunk. It is possible for the receivers to demodulate the data of the different layers by means of appropriate dispersion of the layers onto the time, frequency and antenna resources of the chunk. In its simplest form, a layer may be mapped directly to a physical antenna to support horizontal encoding spatial multiplexing. Thereafter, the bits mapped to each chunk layer are separately modulated. The multiplexing operation precedes the modulation since the type of modulation might differ for each of the chunk layers. The modulated chunk layers are then subject to a processing technique that is referred to as generalised multi-carrier (GMC) processing. The GMC function operates on an OFDM symbol basis in the frequency domain over all chunks allocated to a transport block. More specifically, these modulated layers of chunks are jointly processed by a discrete Fourier transform (DFT) function and then split and dispersed over the chunks again. The data link layer controls the segmentation and multiplexing (MUX) function and controls the modulation and GMC processing. In order to allow some flexibility in terms of the modulation and GMC processing, the generic transmitter considers two forms of GMC [WIN1D210]:

r for the uplink power limited case, GMC configured as DFT-precoded cyclic prefix (CP)OFDM;

r in all other cases, GMC configured as standard CP-OFDM. GMC with DFT precoding is thus used in the UTs and all chunks undergo the same spatial processing (see [WIN1D210]). All chunks are concatenated in the frequency domain to super-chunks covering the entire frequency band. For localised TDMA/FDMA, the output of the DFT is mapped by to consecutive carriers; in the case of IFDMA, the mapping is done uniformly on non-adjacent carriers. Note that the mapping function can also be performed in the multiplexing box when FDMA is used where each chunk layer can be considered to correspond to a user but for consistency the mapping operation will be retained in the GMC module. To reduce the UT processing at the expense of BS processing, non-linear precoding is not used. The resulting transmitter is shown in Figure 7.5. For the case with standard CP-OFDM, the GMC processing entity is fully transparent. At the output of the GMC module, the modulated chunk layers are dispersed or spread onto virtual antenna chunks with a linear dispersion code which is a three-dimensional entity spanning the adjacent subcarriers of the consecutive OFDM symbols in time and frequency corresponding to the chunk in addition to the spatial dimension which has been added. Each chunk layer is thus in the general case mapped onto a three-dimensional virtual antenna chunk. Each virtual antenna may correspond or not to a physical antenna. In the case of Alamouti encoding or horizontal or vertical spatial multiplexing encoding, each virtual antenna chunk corresponds to a physical antenna. Afterward, the linear precoding module maps each virtual antenna chunk of each layer onto a physical antenna chunk. Each virtual antenna might correspond to a different user (i.e. to a different transmit antenna weight vector).

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Figure 7.5 DFT-precoded CP-OFDM for limited uplink power.

Finally, the layers’ physical antenna chunks are summed per antenna to form a threedimensional antenna chunk, which is passed to assembly and OFDM modulation per antenna. In fact, once the antenna transmit weight vector corresponding to a virtual antenna is applied to all antennas, the output is summed up at each antenna in order to avoid redundant operations (e.g. IFFT, CP, etc.). In the following discussion, some configurations of the generic transmitter are exemplified and discussed. 7.2.1.1 Per Stream Rate Control The per antenna rate control (PARC) represents the case when the generic transmitter provides pure spatial multiplexing gain. In that case, the user data flow or transport block is segmented and divided into several data streams. Figure 7.6 shows an example of horizontal encoding where each data stream is encoded separately. Thereafter the data are interleaved and modulated. Then the total available power is split between the transmitted streams. Finally, each stream is OFDM modulated. It is interesting to mention that, in the case of horizontal encoding, a chunk layer corresponds to a virtual antenna chunk which is mapped directly to

227

O FD M

pr oc es si ng

Advanced Antennas Concept for 4G

FECk Πk

P

Resource Scheduler

space fre q.

one user flow

LDC Mod

P P

link adaptation and resource assignment segmentation, coding, chunk layer multiplexing

chunk processing

OFDM

FEC: Mod: LDC: P:

forward error coding modulation linear dispersion code power allocation

Figure 7.6 Per antenna rate control, from [D+05]. (Reproduced by Permission of IEEE © 2009).

a physical antenna. In the case of diagonal encoding, the chunk layers are mapped to virtual antennas via the LDC module where the spatial multiplexing of each stream is executed.

7.2.1.2 Space–Time Block Code

fre q.

space

one user flow Seg. FECk Πk

Resource Scheduler

O

FD M

pr oc e

ss

in g

In the second example, the usage of diversity is emphasised. In contrast to the first example, the user has only one flow of data which will be encoded, interleaved and modulated before being dispersed by a set of defined linear code (see Figure 7.7). An example is Alamouti for two s1 s2 , on the first antenna, the data transmit antennas where, for an input data sequence of antenna the symbol sequences are identically transmitted (e.g. s1 s2 ), and on the second order is inverted and their phase changed (the output sequence is s1∗ − s2∗ . Afterward the power is split among the transmitted physical antennas.

Mod

P

link adaptation and resource assignment segmentation, coding, , chunk processing chunk layer multiplexing

FEC: forward error coding Mod: modulation

OFDM

Figure 7.7 Space–time block code.

P:

power allocation

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Radio Technologies and Concepts for IMT-Advanced

w21 User 2

FECk

w2n 2 n tt

User 1

FECk

Multiplexing

Mod

w11

Mod

w1n tt

Figure 7.8 SDMA and the generic transmitter.

7.2.1.3 SDMA In the case of pure SDMA in an OFDM system, the GMC and LDC blocks are not used. Assume that two users (user 1 and user 2) are scheduled simultaneously (see Figure 7.8). The data flow of each user is encoded. The multiplexing block decides how to map the encoded bits to chunks. In the simplest form, user 1 and user 2 might get the whole bandwidth. Each is placed on a different layer (users 1 and 2 are assigned respectively layers 1 and 2) and the bits of all three-dimensional chunks are modulated. A specific weight vector is applied to each layer (w1 and w2 ), where wmn is the weight applied to the nth transmit antenna of the mth layer. Finally the modulated and weighted data are summed at each antenna before applying the IFFT operation and cyclic prefix. The generic transmitter ‘antenna summation’ entity sums all signals of the weighted streams for each antenna to generate the superimposed transmit signal.

7.2.2 Control Signalling Control signalling serves several purposes, including for example indication of transmission format, communication of uplink scheduling information, ACK/NACK feedback, and measurement requests and reports. Different control information may concern different layers in the protocol architecture and be included either in-band or out-band. In this discussion, we briefly touch upon what control signalling information is needed to specify the configuration and parameterisation of the generic transmitter, and how the different types of control signalling are mapped onto radio resources by the PHY layer within the framework of the generic transmitter structure; for more information on control signalling, see Chapter 6. Control signalling must normally convey information about the detailed state of all generic transmitter blocks. Different information may, however, be conveyed on different timescales, on different protocol layers, and to different users. Since the network in general controls transmission in both the uplink and the downlink, most control signalling related to the transmitter configuration goes in the downlink. Some control information may, however, also be transmitted in the uplink, e.g. channel state information (CSI) or channel quality indicator (CQI). Information related to the generic transmitter blocks includes segmentation (or the

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absence of it), modulation, channel coding, LDC, and mapping onto chunks. It should also be noted that some parameters of the transmitter configuration need not be communicated; this applies in particular to LP weights and certain LDC schemes when dedicated pilots are used, since the setting is then transparent to the receiving terminal. Further information about what has to be communicated, and on what timescales, can be found in the rest of this chapter. Regardless of the layer that has generated the signalling information, it is handled very much the same way by the PHY layer. Control signalling generated by any layer above PHY is handled just as any other regular transport block received via the PHY SAP (see Figure 7.3). Control signalling generated by the PHY layer, either independently or based on information provided by higher layers (e.g. via the PHY control SAP), can also conveniently be treated in a similar way within the framework of the generic transmitter structure. The PHY layer simply forms an internal transport block, which is then processed by the generic transmitter configured by the PHY layer in the correct way. The MUX block is used to map the control information onto chunk layers, which are then mapped onto the desired resource elements by the LDC blocks, in a manner similar to when this block is used to achieve TDMA and FDMA, as discussed above. Hence, from the perspective of the generic transmitter structure, all control information can be seen and handled as normal transport blocks.

7.3 Spatial Adaptation The process of selecting the spatial scheme is called ‘spatial adaptation’. It consists of selecting the appropriate spatial scheme for transmission or reception in such a way that the spatial scheme is adapted continuously to the spatial properties of the channel. Spatial adaptation aims at improving the signal reception at the receiver and managing the downlink or uplink interference that can be tackled in conjunction with the scheduler by advanced receivers, such as interference rejection combining (IRC) with successive interference cancellation (SIC). In order to be able to choose a capacity-achieving scheme, the spatial adaptation involves grouping the users into various spatial groups. User grouping coordinates the transmission of transport blocks in such a way that the intra-cell interference experienced at the mobile terminals during downlink transmission or at the base station during uplink transmission is reduced. This section gives a description of the generic user selection algorithm. More specifically, we present a general framework for spatial user selection schemes under linear precoding. Consider a downlink transmission with M transmit antennas and K users with N antennas each. Let S, T , and P be the sets of possible user allocations, possible transceiver strategies and power allocations, respectively. The optimum (capacity-achieving) scheme can be found by solving the following expression max

¯ ¯ S∈S, T¯ ∈T, P∈P

K

¯ T¯ , P¯ , αk log 1 + γk S,

(7.1)

k=1

¯ T¯ , P) ¯ is the signal to noise-plus-interference ratio (SINR) of the kth user for where γk ( S, ¯ T¯ and P, ¯ and αk is the quality of service (QoS) weight given by the a given choice of S, scheduler. The capacity-achieving scheme is the so-called dirty paper coding (DPC) scheme [CS03; VT03; YC04; WSS06] where the signal of different users are successively encoded at the

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transmitter side. The optimum receive and transmit coefficients are calculated using the duality concept [VT03]. The user selection stage is embedded in the standard power control technique applied to the dual channel [BTC06]. Suboptimum linear precoding (also known as beamforming) techniques have also been proposed in which the transmitted signal is a linear combination of the users’ data signals. With respect to DPC, linear techniques provide lower performance at a reduced computational complexity. Moreover, due to the fact that there is no cancellation of the interference at the transmitter side, linear precoding schemes can be extended to the case of imperfect channel state information at the transmitter side. Differently from DPC, the user selection stage is not embedded in the precoder design stage. In the following, we present a general framework for spatial user selection schemes under linear precoding. For simplicity, we first treat the case where one stream is transmitted for each user and then we present the multi-stream case.

7.3.1 Single Stream Per User ¯ be the achievable rate for a given set of users S¯ Let R( S) ¯ = R( S)

max

K

¯ T¯ ∈T, P∈P

¯ T¯ , P¯ . αk log 1 + γk S,

(7.2)

k=1

Assuming the total number of users, K , we need to consider all possible sets of users with ¯ ≤ min(M, K ). cardinality | S| The user selection problem can be written as ¯ R(S ∗ ) = max R( S), ¯ S∈S

(7.3)

where S ∗ is the optimum set of users. Finding S ∗ requires abrute force search over all possible sets of 1, 2, . . . , K, min(K, M) ) K! . So the complexity of search becomes unacceptably users, whose number is min(M,K j=1 (K − j)! j! high for large K . Different suboptimal greedy user selection schemes have been proposed in order to lower the computational complexity. The general idea is that a new user is added to the set of allocated users at each iteration of the algorithm, in order to maximise a given metric. Let S ∗(i−1) be the set of users allocated at iteration (i − 1) of the algorithm. At the ith iteration, the set of selected users is updated as follows: A new set of candidate users C (i) ∈ {1, . . . , K } is generated such that C (i) ∩ S ∗(i−1) = , where is the empty set. The best candidate is chosen in order to maximise a given metric ξ : (7.4) c∗ = arg max ξ S ∗(i−1) ∪ c . c∈C (i)

For example in [DS05; FDGH05], the previous user selection approach is used with a zero-forcing beamforming technique. In [DS05], the set of available is simply C (i) = ∗(i−1) users ∗(i−1) ∗(i−1) {1, . . . , K } \S and the metric is the sum-rate ξ S ∪c = R S ∪ c . To reduce the complexity, approaches such as ProSched ([FDGH05; FDGH07]) may be applied to

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approximate the metric using orthogonally projected channels of the users. An implementation guide is available in [WIN2D341]. A similar approach has been taken in [YG05], where the set of candidate users is chosen in order to have semi-orthogonal channels with respect to the users’ channels chosen in the previous iterations. Such approaches can also be successfully applied to non-ZF techniques.

7.3.2 Multiple Streams Per User Let us assume that multiple independent streams can be sent to each user. The described approach can be extended to the case of multiple streams per user, under the assumption of a block diagonalisation transceiver design [BH07]. The achievable rate can be written for a given ¯ The problem is then reduced to find the optimum set selection of users and eigenmodes E. ∗ of eigenmodes, E . The optimisation is performed for a given user and eigenmode selection. A generalisation of the greedy allocation algorithm can be used instead of the brute-force allocation in order to avoid the high computational complexity. Let E ∗(i−1) be the set of eigenmodes allocated at iteration i − 1 of the algorithm. At the ith iteration, the set of selected eigenmodes is updated: a new set of candidate eigenmodes C E(i) is generated such that C E(i) ∩ E ∗(i−1) = . The best candidate is chosen in order to maximise a given metric ξ : c∗ = arg max ξ E ∗(i−1) ∪ c .

(7.5)

c∈C E(i)

7.4 Spatial Schemes Before describing the major techniques that have been investigated, we present a short reminder of receive diversity.

7.4.1 Receive Diversity Receive diversity is a spatial diversity scheme where multiple receive antennas are used to combine or select the desired signal. The combining can be done in several ways which vary in complexity and overall performance. The separation between antennas is generally such that the fading amplitudes corresponding to each antenna are approximately independent (greater than the coherence distance). Beverage and Peterson were the first to install space diversity stations in Riverhead, New York by the late 1920s. Their work was published in the 1930s [BP31]. In the 1950s, receive diversity become widely known and studied. Descriptions of the linear receive diversity techniques are given in [Bre59] (later reprinted by IRE in 2003). The choice of the weight vector applied to the received signal depends on the desired combining method. The most commonly used in a MIMO system are maximum ratio combining (MRC) and minimum mean square error (MMSE) method. While the former aims at maximising the combined SNR, the latter aims at reducing the square error between the transmitted and received signal. Let us define a system model before describing the MRC and MMSE methods. Let x and y be the signal of interest (i.e. the transmitted signal from one antenna) and the received signal vector, respectively. The received vector for a frequency flat channel

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can be written as y = hx + n,

(7.6)

where h and n, are the channel vector and the noise vector between the transmit and all receive antennas, respectively. In MRC, the signal at each received antenna is multiplied by the corresponding complexvalued channel gain (which implies that the signal is co-phased and weighted by a factor that is proportional to the signal strength) [Pro95]. The weight vectors, w, are chosen such the received SNR is maximised: H H hH w hh w = , (7.7) wMRC = argmax H w Rn w diag(Rn ) w where Rn = E nn H is the noise (i.e. interference) covariance matrix and diag(M) is the diagonal of the matrix M. In the MMSE algorithm, the weights are chosen to minimise the mean square error (MSE) between the antenna array output and x the transmitted symbol. The output of the antenna is equal to the weight vector by the received signal and the cost function for the MMSE is given by (see [LR99]):

2 (7.8) J (w) = E w H y − x . [Hay02] shows that the optimal MMSE solution is wMMSE = argmin {J(w)} = R−1 p,

(7.9)

w

where R = E yy H is the covariance matrix of the received vector signal and p = E xy H is the cross-correlation between the received signal and the reference signal. The main disadvantage of the MMSE algorithm is that it requires a generation of a reference signal. But, on the other hand it does not require knowledge the direction of arrival (DoA) of the desired signal. MMSE can be seen as receive beamforming with null steering. In fact, a receiver equipped with nr receive antennas can perfectly reject n r − 1 interfering sources, provided the interfering signals are received with different spatial signatures compared to the signal of interest. It is interesting to note that the estimation of the covariance matrix R can be performed either using a parametric approach or using an average sample product method. While the former method relies on knowledge of the structure of the covariance matrix, the latter method estimates the covariance matrix of the received signals and averages it over time or frequency, as in the case in an OFDMA system. Note that several names maybe be used to designate an MMSE receiver optimum combining [Win84], also sometimes referred to as IRC. In [BJ95], it was shown that the IRC and MMSE solutions are identical in the case of a flat fading channel.

7.4.2 Beamforming The process of combining the signals from different antenna elements is known as beamforming. A beamformer can be regarded as a spatial filter that separates the desired signal

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from interfering signals given that all the signals share the same frequency band and originate from different spatial locations. It weights and sums the signals from the different antenna elements (thus adjusting phases and amplitudes of transmit antennas) according to a certain objective function. Throughout the literature, many terms are used to express exactly the same concept; for instance, beamforming system, adaptive antennas, antenna arrays, phased arrays, space-time processing, smart antennas and, more recently, linear precoding are terms used interchangeably. The beamforming operation includes power allocation and in the generic transmitter this is done by multiplication of the virtual antenna chunk signals with a complex weight matrix. Physically this results in changing the array beam pattern, thus introducing directivity into the transmitted signal. Some well-known beamforming objective functions are

r optimising the quality of the desired signal; r minimising the unwanted interference for other users; r optimising the signal-to-interference-and-noise ratio (SINR); r optimising sum capacity for several users. The transmit weights are either chosen from a predefined set (a codebook, interpretable as ‘fixed beams’, also called a ‘grid of beams’ (GoB)) or calculated adaptively. The design of the transmit weights requires a certain knowledge of the channel available at the transmitter. Direction-based beamforming uses the angles of arrival (AoA) estimated from the uplink. All other linear precoding and beamforming techniques make use of either short-term or long-term CSI. Linear precoding can be done on a spatially layered basis, allowing for transmission of several FEC blocks using the same space–time–frequency resources. It can be shown (assuming that only second-order statistics are available) that the same weight vector can be applied to the whole frequency band with negligible capacity loss. By default, one weight vector per chunk is assumed but it could e.g. be constant for the whole frequency band. By using beamforming at the transmitter, the directivity is increased toward the receiving node of interest. Hence the same transmitter may serve several nodes, which are spatially separated, on the same physical resource. This is referred as spatial division multiple access. In Figure 7.9, a BS is serving three users (the ones surrounded by a square) simultaneously on the same physical resource. It can be noticed that the three served user are spatially separated due to the use of beamforming at the BS. An alternative way of applying beamforming per sector (GoB per sector) is to increase the number of sectors per site in order to enhance the directivity toward the users of interest. This method is called higher-order sectorisation (HOS) and can be considered as a special implementation case of GoB where each sector of the site is considered a beam. Figure 7.10 illustrates 3- and 12-sector sites. 7.4.2.1 Signal Model Let n t and n r be the number of transmit and receive antennas, respectively. Then the received signal model for beamforming can be written as: y = Hw px + n,

(7.10)

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Figure 7.9 Beamforming and SDMA. (Reproduced by Permission of IEEE © 2009).

(a)

(b)

Figure 7.10 (a) A 3-sector site and (b) a 12-sector site, from [OSO07]. (Reproduced by Permission of IEEE © 2009).

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235

where x is the transmitted signal transmitted with power p, w is the transmit weight vector of size n t × 1, H is the channel matrix of size n r × n t and n is the thermal noise vector of size n r × 1. The antenna weight vector is set according to the desired method. For instance, in the case of GoB, the weight vector is chosen from a fixed set, i.e. w ∈ {w1 w2 . . . wn b }, where n b is the number of fixed beams. It is interesting to note that the simplest beam selection can be based on long-term channel statistics. In an FDD system, the UT can simply measure the received power per beam and feed back the beam index, bmax , that has the maximum receive power. bmax is equal to the element index of the vector Pr , of size 1 × n r : Pr = E 11×nr |HW| • |HW| ,

(7.11)

where E[.] is the expectation function over time and frequency, the operator • represents the Hadamard product, |.| is the absolute value of a matrix, 11×nr is a column vector where all the elements are equal to one and W = w1 w2 . . . wn b is the beamforming network matrix.

7.4.2.2 Results In this section, we investigate the performance of HOS to SDMA based on GoB in a multicell radio network for the WINNER wide area (WA) scenario (described in detail in Section 13.4). The WA concept provides ubiquitous coverage for rural, suburban and urban areas. Additional details on the simulation environment and system model can be found in [OSO07]. The normalised site throughput versus the cell radius for the SDMA based on GoB in a 3-sector (3-Sec) is shown in Figure 7.11. The number of simultaneous scheduled users is either two, in the case of SDMA-2, or four, for SDMA-4. Moreover, the number of generated fixed beams at the BS is equal to the number of antenna elements in the uniform linear array (ULA). It is assumed that one user is scheduled per beam. The reference case is a single-user (SU) 3-sector site equipped with a single antenna. SDMA-4 offers a gain of up to 2.4 times compared to the SU 3-sector site. The gain SDMA-2 is roughly 1.4 times compared to the SU 3-sector site case. The HOS 12-sector site offers up to 3.5 times relative gain over the SU 3-sector site while the HOS 6-sector site offers 1.8 times relative gain. It is interesting to note that the relative gain of SDMA and HOS increases for a large cell size. In fact, for a large cell size the capacity is limited by the noise, and not the interference as is the case for small cell sizes. Hence increasing the antenna gain will increase the coverage and allow larger number of users with a better SINR to be served. Doubling the number of beams from one to two yields slightly more than 40 % relative cell throughput gain for SDMA while going from a 3- to 6-sector site provided 80 % relative gain. On the other hand, doubling the number beams from two to four yields substantially higher relative cell throughput gain in the order of 70 %. The same observation was noted for WCDMA in [OL06a]. In fact, the low gain of an SDMA-2 system is due to the method used to generate the beam patterns. The 6-sector antenna pattern has higher maximum antenna gain and lower side lobes compared to a 3-sector site each covered by two fixed beams (e.g. SDMA-2 3-sector). Finally, the

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3.5 (SU) 3-sector SDMA−2 (3-sector) SDMA−4 (3-sector) (SU) 6-sector (SU) 12-sector

Normalised site throughput

3

2.5

2

1.5

1 500

1000

1500

2000

Cell radius [m]

Figure 7.11 The normalised site throughput versus the cell radius for SDMA based on 3-sector sites and HOS for 6- and 12-sector sites, from [OSO07]. (Reproduced by Permission of IEEE © 2009).

cross-over beam region is wider for the SDMA-2 case. Consequently, the intra-beam interference increases and yields a lower cell throughput. The relative system throughput gains for HOS and SDMA compared to SU 3-sector are summarised in Table 7.1. It is interesting to note that a large number of antenna configuration parameters can be optimised in order to increase further the downlink system capacity. For instance, the antenna pattern e.g. side-lobes level, beam-width, antenna tilting and antenna spacing have been shown to impact the signal quality [NL03; OL06b]. Moreover, the number of the beams can be optimised for a GoB concept [MPM04] in order to further increase the capacity gain for a FB system. It was shown in [WIN2D341] that tapering (see Section 9.3.4.1) improves the spectral efficiency for SDMA based on GoB. It should be noted that SDMA based on adaptive beamforming compared to SDMA based on GoB yields up to 20 % improvement in terms of cell spectral efficiency. Additional system level results on beamforming and MIMO techniques can be found in Sections 9.3.4.4, 13.6.1 and 13.6.3.

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Table 7.1 Relative spectral efficiency gain to SISO for a wide area scenario. TX × RX antenna per sector 1×1 2×1 4×1 2×1 4×1 4×1 4×2 4×2 a b

Scheme HOS HOS HOS GoB and SDMA GoB GoB and SDMA GoB and SDMA Adaptive beamforming

Sectors per site

TX per site

Combining method at RX

Relative spectral efficiency

3 6 12 3 3 3 3 3

3 12 48 6 12 12 12 12

– – – – – – MMSE MMSE

1 1.8 3.5 1.4 2.0 to 2.2a 2.4 B = 2.9a 1.2Bb

Results extracted from [WIN2D341]. This is valid when the number of users is large [WIN2D341].

7.4.3 Diversity and Spatial Multiplexing Linear dispersion codes (LDC) are used to disperse modulated precoded chunk layers to virtual antenna chunks. Depending on the code used, a trade-off can be obtained between diversity gain and multiplexing gain. The present design considers a set of LDC which can be used to achieve certain points on the trade-off curve. The dispersion code maps a sequence of Q modulated symbols onto MT sequences of length T , i.e., it achieves a rate of R = Q/T . LDC may be represented by a complex modulation matrix X of size MT × T . Let the sq = Re{sq } + jIm{sq }, k = 1, . . . , K ; be the K complex symbols at the input of the encoder. Then the modulation matrix X is given by X = [x1

x2

...

xT ] =

Q

Aq Re{sq } + Bq Im{sq },

(7.12)

q=1

where Re{·} and Im{·} are the real and imaginary parts of a complex number, respectively. The matrices Aq and Bq of size MT × T , are called dispersion matrices. These matrices define the code and distribute each symbol over the two dimensions. For example, Alamouti’s code uses Q = MT = T = 2 with

1 0 0 1 1 0 0 1 , A2 = , B1 = , B2 = . (7.13) A1 = 0 1 −1 0 0 −1 1 0 As proposed in [LXG02], the two-dimensional LDC or space-time codes described in this section can be combined with a unitary transformation in order to enable multipath diversity and spatial diversity to arrive at three-dimensional (equivalent to the chunk size) space–time–frequency codes. The most common linear dispersion codes are summarised in Table 7.2. We consider adaptive linear dispersion codes (LDC) in the downlink in a metropolitan area for a multi-cell system. The LDCs here cover a 2 × 2 system with Alamouti space–time block coding (STBC) for single stream transmission and V-BLAST for dual-stream transmission. The adaptation for LDCs is based both on the eigenvalues of the MIMO channel and on the

238 1

MT

≥1

≥1

2

2

3

Antenna hopping

Vector modulation

Alamouti

Twisted STTD

Orthogonal space– time block code

0.75

2

1

Rate Q/T

Code

(Virtual) antennas MT

Table 7.2 Linear dispersion codes.

⎥ ⎥ ⎥ ⎦

⎤

⎡

s MT ⎤ s1 ⎥ ⎢ ⎢ s2 ⎥ X MU X s1 , . . . , s MT = ⎢ . ⎥ ⎣ .. ⎦ s MT

s2 s1 X Ala (s1 , s2 ) = −s2∗ s1∗

s2 s4 s3 s1 XTSTTD = + ∗ U −s2∗ s1∗ s4 −s3∗ ,

1 + j −1 + 2 j 1 √ U= 7 1 + 2j 1− j ⎡ ⎤ s1 −s2∗ s3∗ 0 s1∗ 0 s3∗ ⎦ X3 (s1 , s2 , s3 ) = ⎣ s2 ∗ s3 0 −s1 −s2∗

Modulation matrix ⎡ s1 s2 ⎢ ⎢ X H S1 , . . . , SMT = ⎢ .. ⎣ .

See [TH02].

See [WIN1D27].

See [Ala98].

See [WIN1D27].

Possibly used when the rate of the concatenated code is below 1/3.

Comment and reference

239

4

4

Diagonal ABBA

Quasi-orthogonal space–time block code (QOSTBC) 1

1

XQOSTBC =

XB XA , X B U −X A U

s2 s1 X A (s1 , s2 ) = , −s2∗ s1∗

s4 s3 , X B (s3 , s4 ) = −s4∗ s3∗

1 0 U= 0 −1

√1 2

0 XA + XB , 0 −X B + X A

s2 s1 X A (s1 , s2 )= ∗ ∗ , −s2 s1

iπ/4 eiπ/4 s4 e s3 X B (s3 , s4 )= −iπ/4 ∗ −iπ/4 ∗ −e s4 e s3 XDiag-ABBA =

(continued)

See also [SJ05] for an extension to more than four antennas.

May be used if the rate of the concatenated channel code is ≥0.33; the matrix modulation targets full transmit diversity; see [WIN1D27].

240

Double ABBA

Stacked Alamouti

Code

Table 7.2 (Continued)

4

Even

(Virtual) antennas MT

2

1

Rate Q/T

X A (s1 , s2 ) =

s2 s1 ∗ ∗ , −s2 s1 iπ/4 e s3 X B (s3 , s4 ) = −e−iπ/4 s4∗

s6 s5 XC (s5 , s6 ) = ∗ ∗ , −s6 s5 iπ/4 e s7 X D (s7 , s8 ) = −e−iπ/4 s8∗

eiπ/4 s8 e−iπ/4 s7∗

eiπ/4 s4 e−iπ/4 s3∗

Modulation matrix ⎤ ⎡ s1 s2 ⎢ −s2∗ s1∗ ⎥ ⎥ ⎢ ⎢ .. .. ⎥ XSt−Ala s1 , . . . , s MT = ⎢ . ⎥ . ⎥ ⎢ ⎣ s M −1 s M ⎦ T T ∗ ∗ −s M sM T −1 T XC + X D X A + XB 1 XDABBA = √2 −X D + XC −X B + X A

May be used if the receiver has two antennas, or if it has more than two antennas and there is significant antenna correlation; the target is to have as high throughput as possible, so the concatenated channel codes have high rates ≈ 0.75; see [WIN1D27].

See [SJ05].

Comment and reference

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Table 7.3 Simulation assumptions for adaptive LDC in MA downlink. Channel model User velocity Modulation and coding

Retransmission Scheduling LDC adaptation granularity Receiver Channel estimation CSI and SINR at Tx

B1 (see Chapter 3) 50 km/h BPSK LDPC coding 1/2, 2/3; QPSK LDPC coding 1/2, 2/3, 3/4; 16-QAM LDPC coding 1/2, 2/3, 3/4; 64-QAM LDPC coding 2/3, 3/4 HARQ with Chase combining Proportional fair, ideal link adaptation One frame MMSE or MRC Ideal Perfectly known

received SINR [WIN2D341]. The capacity is calculated from these two factors for selecting the transmission rank. The adaptation period is one frame and the LDC is selected for the full user bandwidth, i.e. non-frequency-adaptive transmission is assumed. We assume that perfect channel and SINR information are known at the transmitter due to channel reciprocity. SISO and SIMO MRC are also simulated for comparison. The deployment scenario and system parameters are the ones specified for the microcellular scenario (the Manhattan grid deployment, as described in Section 13.4 and [WIN2D6137]) but only outdoor users are considered. Some simulation parameters are given in Table 7.3. Figure 7.12 shows the simulation results for average cell spectral efficiency and 5-percentile user throughput. The number of users per cell is fixed at five. The overhead considered only includes a common pilot and is assumed to be two symbols per chunk per antenna. Single stream transmission and receive diversity with MRC can provide 34 % gain over the SISO case. The user throughput gain from MRC is also quite large. Further gain can be achieved by using an MMSE receiver. Adaptive LDC provides 42 % cell gain over 1 × 2 MRC thanks to possible dual-stream transmission. However, only a small gain is obtained for the 5-percentile user throughput through adaptive LDCs. The Manhattan grid deployment results in a high median SINR in the downlink due to the line-of-sight channels arising in the narrow street canyons. Therefore, for adaptive LDCs it has been seen that the number of users benefiting from dual-stream transmission is increased, which results in large cell throughput improvement, although only a small gain from dual-stream transmission is obtained for cell edge users. The cell spectral efficiency of the 2 × 2 LDC scheme investigated is 1.8 bps/Hz/cell. The spectral efficiency would substantially increase for a higher number of receive antennas (e.g. four), as illustrated by the 2 × 4 and 4 × 4 configurations investigated for the metropolitan area in [WIN2D341], at the expense of higher receiver complexity at the UT. Table 7.4 gives the values of spectral efficiency per sector and 5-percentile user throughput when the number of user per sector is 5.

7.4.4 Beamforming and Spatial Multiplexing The deployment of a specific antenna configuration (e.g. beamforming) in a multi-cell scenario may be beneficial for a set or a fraction of users in the system. For instance, using GoB

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1.8

Spectral Efficiency [bit/s/Hz/sector]

1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0

1x1

1x2 MRC

2x2 Adaptive LDC

(a)

3.5

5% user throughput [Mbit/s]

3

2.5

2

1.5

1

0.5

0 1x1

1x2 MRC (b)

2x2 Adaptive LDC

Figure 7.12 Metropolitan area downlink (5 users per cell): (a) spectral efficiency per cell and (b) 5-percentile user throughput. (Reproduced by Permission of IEEE © 2009).

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Table 7.4 Spectral efficiency and cell edge user throughput in the MA downlink with 5 users per sector. Case 1×1 1 × 2 MRC 1 × 2 MMSEa 2 × 2 Adaptive LDC a

Spectral efficiency (bps/Hz/sector)

5-percentile user throughput (Mbps)

B = 0.925 1.34B 1.5B 1.91B

C = 1.722 1.87C – 1.99C

Result extracted from [WIN2D341].

increases the spectral efficiency for the cell edge users compared to the ones close to the BS. In fact, the latter users may prefer to obtain higher data rates instead of interference reduction, hence the idea of having an antenna setting which allows simultaneous relief of users experiencing deep fades, reduction in interference for the ones experiencing higher interference and satisfying of those requesting higher data rates. The desired setting can be obtained either by having a hybrid antenna configuration, e.g. clustered arrays, that combines beamforming and spatial multiplexing gain [ZK07] or by using a structure, called an ‘adaptive multi-user transceiver’, that allows switching between various modes (e.g. diversity, spatial multiplexing, beamforming, and MU-MIMO) depending on the channel conditions and the system requirements [SJSC06; STJH07; TJSZ07]. In this section, we show that the hybrid antenna architecture and the adaptive transceiver scheme improves the spectral efficiency of the system or offers more than one of the MIMO gains (e.g. array and spatial multiplexing gain). First, we present the clustered array structure and show its performance for the adaptive transceiver scheme in a downlink multi-cell scenario. Then we show that the uplink may also benefit from combining SDMA and spatial multiplexing. 7.4.4.1 Clustered Array Structure Zangi and Krasny [ZK07] showed that grouping the antennas in two independent clusters is an optimal geometry for SU-MIMO for almost all SNRs (except for very low SNRs where it is best to use transmit antennas to form a uniform linear array). Figure 7.13 illustrates the clustered array geometry for four transmit antennas, grouped in two clusters, with the antennas in each cluster are closely placed together. The spacing between the antennas belonging to the same cluster is half a wavelength, whereas the spacing between clusters is 10 times the wavelength. As shown in Figure 7.15, the data bits are de-multiplexed into two streams which are separately encoded, interleaved and modulated. It is evident that the number of streams is equal to the number of clusters. A beamforming weight is applied on each stream of each cluster before the data symbols are transmitted. Note that all clusters use the same set of beamforming weights. Further, it will be shown that transmitting two streams and restricting the beamforming matrix to take a finite set of fixed values yields a substantial system capacity improvement compared to PARC with SIC. 7.4.4.2 Results The performance of a 2-clustered array is investigated in a multi-cell radio network for the WINNER wide area scenario (see Section 13.4). We assume that the transmitter has access only

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w FEC

Π

λ

MOD w

2

10λ

De-MUX

w FEC

Π

λ

MOD w

2

Figure 7.13 Two clusters of transmit beamforming, each with two transmit antennas, from [OZH08]. (Reproduced by Permission of IEEE © 2009).

to the second-order statistics of the channel and that the receiver knows the channel perfectly. Figure 7.14(a) shows the 5-percentile user throughput of 1 × 2 SIMO, 4 × 2 PARC with successive interference cancellation and 4 × 2 clustered array versus the spectral efficiency2 in for a round-robin scheduler. It can be seen that the 2-clustered architecture provides 47 % spectral efficiency gain at a target user bit rate of 2 Mbps. It is interesting to notice that PARC-SIC yields negligible gain compared to SIMO. In order to check the potential gain of the 2-clustered array in terms of spatial multiplexing, it is interesting to look at the rate of users in good channel conditions. The 95th percentile of the user bit rate versus the spectral efficiency is shown in Figure 7.14b. For a user bit rate of 50 Mbps the 2-clustered array yields 83 % and 40 % gain compared to 1 × 2 SIMO and 4 × 2 PARC-SIC, respectively. The cumulative density function (CDF) of the user bit rate is show in Figure 7.15. It can be noticed that CDF for the 2-clustered array scheme provides substantially higher user throughput than SIMO and PARC-SIC. For instance, the data rate is increased by 30 % for 20 % of the users. The 5th percentile spectral efficiency relative to SIMO, of the PARC, 2-clustured array and adaptive multi-user transceiver configuration is shown in Table 7.5. It is interesting to notice that adaptive switching between all transmission modes (single and multi-stream) yields substantial gain compared to SIMO. It is expected that the spectral efficiency of the 2-clustered array increases substantially in the case of MU-MIMO (two users can be scheduled simultaneously, each on a different beam). We now evaluate different uplink SU-MIMO and MU-MIMO configurations for different offered traffic loads (average numbers of users per cell) in the base coverage urban scenario. A base station deployment with 1000 m inter-site distance is considered and the base station 2 Such graphs are used to describe spectral efficiency under QoS constraints in WINNER as explained in Section 13.5.

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5 % User bit rate (Mbps)

7 SIMO PARC-SIC 4x2, 2Str. 2-Clustered 4x2

6 5 4 3 2 1 0 1

1.5 2 Spectral efficiency (bps/Hz/sector)

2.5

(a)

95 % User bit rate (Mbps)

100 SIMO PARC-SIC 4x2, 2Str. 2-Clustered 4x2

80

60

40

20

0 1

1.5 2 Spectral efficiency (bps/Hz/sector)

2.5

(b)

Figure 7.14 User bit rate versus the sector spectral efficiency for SIMO 1 × 2 and PARC-SIC 4 × 2 with two streams active and 2-clustered 4 × 2 arrays: (a) 5 percentile user bit rate and (b) 95 percentile user bit rate; from [OZH08]. (Reproduced by Permission of IEEE © 2009).

receivers are equipped with four antennas using ten-wavelength antenna spacing. MMSE combining and SIC after-channel decoding are employed at the receiver side. One, two or four SDMA users are scheduled for simultaneous transmission (SU, SDMA-2, and SDMA4, respectively) and each user terminal transmits one or two streams (single stream and dual stream PARC). The total number of transmitted streams per cell is limited to four. Note that SU is a reference case that corresponds to receive diversity. Round-robin scheduling is employed and a scheduled user is assigned the entire transmission bandwidth. The 5th percentile, in Figure 7.16(a), and the 95th percentile, in Figure 7.16(b), user throughput are plotted against the average cell throughput. At high loads, the cell edge users,

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100 90 80 70

CDF

60 50 40 30

SIMO PARC−SIC 4x2, 2Str. 2−Clustered 4x2

20 10 0 0

5

10

15 20 User bit rate (Mbps)

25

30

Figure 7.15 The CDF of the user bit rate for SIMO 1 × 2, PARC-SIC 4 × 2 with 2 streams active and 2-clustered 4 × 2 arrays, from [OZH08]. (Reproduced by Permission of IEEE © 2009).

here represented by the 5th percentile value, benefit slightly from the use of SDMA (in comparison to multi-stream transmission). High SINR users, on the other hand, represented by the 95th percentile user throughput, benefit greatly from dual-stream transmission, in particular at low network load levels. With dual-stream transmission, the 95th percentile user data rate may increase significantly in comparison to single-stream transmission (with and without SDMA). Table 7.6 provides the uplink spectral efficiency estimates for the studied techniques. In the uplink, the satisfied user criterion (SUC) is defined as a user throughput of 1.3 Mbps and the spectral efficiency is measured at the highest load for which the user data rates for 95 % of the users exceed the SUC (see Section 13.5).

Table 7.5 Relative spectral efficiency gain to SIMO for the 5th percentile users. TX and RX per sector 1×2 2×2 4×2 4×2 a

Scheme SIMO Adaptive Switching between GoB, SU-MIMO, SDMA PARC 2-clustered array (SU-MIMO)

Result extracted from [WIN2D61310].

Combining method at RX

Relative spectral efficiency

MRC MRC/MMSE

1 1.55a

MMSE-SIC MMSE-SIC

1.1 1.55

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5th percentile user throughput (Mbps)

15 SDMA-2, 2-Streams SDMA-4, 1-Stream SDMA-2, 1-Stream SU, 2-Streams SU, 1-Stream

10

5

0 0.5

1

1.5

2

2.5

3

3.5

4

3.5

4

Spectral efficiency (bps/Hz/Cell) (a) 95th percentile user throughput (Mbps)

45 40 35 30 25 20

5

SDMA-2, 2-Streams SDMA-4, 1-Stream SDMA-2, 1-Stream SU, 2-Streams SU, 1-Stream

0 0.5

1

15 10

1.5

2

2.5

3

Spectral efficiency (bps/Hz/Cell) (b) Figure 7.16 User throughput versus sector (i.e. cell) spectral efficiency at: (a) 5th percentile user throughput and (b) 95th percentile user throughput; from [OSO07]. (Reproduced by Permission of IEEE © 2009).

7.4.5 Linear MU-MIMO: SMMSE and RBD MU-MIMO designates the case when in the downlink, a BS equipped with multiple antennas is transmitting simultaneously to more than one user or when in the uplink (UL), several users are simultaneously transmitting to a common BS equipped with multiple antennas.

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Table 7.6 Uplink spectral efficiency accounting for the SUC (user data rates of 1.3 Mbps for at least 95 % of the users). Uplink scheme SDMA-1, single stream SDMA-1, dual stream SDMA-2, single stream SDMA-2, dual stream SDMA-4, single stream

Spectral efficiency (bps/Hz/sector) 2.8 2.9 3.3 3.1 3.4

In the downlink case, the BS may attempt a priori to mitigate or eliminate the multiple access interference (MAI) by intelligently designing the transmitted signal [PSSH04] and consequently precoding it, hence the name ‘MU-MIMO via precoding’. When the precoding is based on linear processing, it is called MU-MIMO via linear precoding and the transmitted signal is generated by linear combination of the data symbols. A simple way to handle interuser interference is by forcing all the interference terms to be zero; this can be done by precoding the vector symbol with the inverse or pseudo inverse of the channel matrix. Multi-user precoding exploits the CSI available at the transmitter in order to mitigate or completely eliminate the multi-user interference. In a TDD system, using the reciprocity principle it is possible to use the estimated uplink channel for downlink transmission [PNG03]. This information can be used to perform joint precoding at the BS of the users’ signals. When the channel is varying too rapidly, precoding based on the transmit correlation matrix Rt = E H H H can be used instead of the short term CSI to reduce or eliminate multi-user interference. MU-MIMO precoding requires knowledge of the CSI at the BS. In a TDD system, the CSI is obtained based on the UL measurements and requires compensating for RF impairments. The precoding can be performed using only the measurements on the UL channel. However, if the users are inactive for a period of time longer than the coherence interval, even though the user is static or slowly moving, we perform the precoding using the second-order statistics instead of perfect CSI. The drawback of linear precoding based on channel inversion are the stringent requirements that the interference be identically zero at the receiver (channel inversion requires a large normalisation factor at low SNR, consequently reducing the SNR at the receivers). One simple way to get around this problem is to allow limited interference at each receiver by using an approach based on linear MMSE. MMSE precoding was first proposed in [JKG+02]. It improves the system performance by introducing a certain amount of interference especially for users equipped with a single antenna. At low SNRs, it balances the multi-user interference and the noise enhancement in order to extract a higher diversity gain. However, since it was defined for single antenna receivers, it suffers from a performance loss when it attempts to mitigate the interference between two closely spaced antennas if the user terminal is equipped with more than one receive antenna. In order to reduce the performance loss due to the cancellation of interference between two closely spaced antennas at the same terminal, the precoding matrix F is generated by successively calculating its columns for each of the receive antennas separately. This technique is called successive MMSE (SMMSE) [SH04]. The columns in the precoding matrix F, each

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corresponding to one receive antenna, are calculated successively. With perfect CSI available at the BS, the equivalent combined channel matrix of all users after the precoding is also block diagonal for high SNRs. Regularised block diagonalisation (RBD) was first proposed in [SH06; SH08] and offers higher diversity and array gain compared to SMMSE. Similar to the case of SMMSE, the precoding matrix F = βFa Fb is designed in two steps. The matrix Fa is used to suppress the MUI interference first, and then the matrix Fb is used to optimise the system performance according to a specific criterion assuming that the MU-MIMO channel has been transformed into a set of parallel SU-MIMO channels. We can now apply any SU-MIMO technique to each user’s equivalent channel matrix. If we want to maximise the capacity of the system, we use water-filling (WF) on the singular modes of all users’ effective channel matrices. If we want to extract the maximum array and diversity gain, we transmit only on the dominant singular mode of each user. Dominant eigenmode transmission (DET) provides maximum SNR at the receiver and minimum BER performance. If we assume that the channel varies too rapidly to be tractable, the information regarding the relative geometry of the propagation paths is captured by a non-white correlation matrix. Let us define the average correlation matrix of the ith user in the kth chunk as Ri(k) =

1

Nchunk

j

(k, j)H

Hi

(k, j)

Hi

,

(7.14) (k, j)

and its singular-value decomposition (SVD) as Ri(k) = Vi(k) Qi(k) Vi(k)H , where Hi is the ith user channel matrix in the kth chunk. The MU-MIMO precoding is now performed on the equivalent channel defined as follows: ˆ (k) = Q(k)1/2 V(k)H . H i i i

(7.15)

ˆ i depends on the rank of ith user’s The number of rows of the ith user’s equivalent channel H (k) average correlation matrix Ri , and can have maximum value of MT . ˆ (k) we also facilitate easier adaptation from perfect CSI to By using the equivalent channel H i ˆ (k) was constructed based on the second-order CSI. This is a consequence of the way matrix H i (k) (k) ˆ contains all the information about the left singular average correlation matrix Ri . Matrix H i vectors that are needed to perform the precoding regardless of the channel variability rate. The same algorithm is used in cases when we have short-term channel knowledge at the transmitter, but instead of the exact channel we use the equivalent channel. In this way, we achieve substantial complexity reduction since the precoding is performed only once per chunk instead of once per symbol in that chunk. 7.4.5.1 System Models An example of a downlink MU-MIMO precoding is shown in Figure 7.17. The BS is equipped with MT antennas and each UT with MR,k = 2 antennas. There are K users in total out of which via SDMA. Consequently, the total number K cc are served simultaneously within one chunk K cc M R,k = 2 · K cc . Each user receives rk = 1 data of receiving antennas is equal to M R = k=1 K cc rk = K cc . The data streams; the total number of data streams is therefore equal to r = k=1

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Figure 7.17 System model for the downlink. (Reproduced by Permission of IEEE © 2009).

model can be summarised as y = D · (H · F · x + n),

(7.16)

where D ∈ Cr×MR is a block-diagonal matrix containing the receive filters Dk ∈ Crk ∈MR,k , the matrix H ∈ C MR ×MT represents the MIMO channel matrix, F = [F 1 , . . . , F K ] ∈ C MT ×r denotes the overall precoding matrix and the vectors x, y and n represent the vectors of sent symbols, received symbols and additive noise at the receive antennas, respectively. The task of the linear precoder is to compute the precoding matrices F k ∈ C MT ×rk such that the multi-user interference is minimised while balancing it with noise enhancement. Figure 7.18 depicts an example of a system model for the UL MU-MIMO linear precoding. Here, ‘Ala/BF’ indicates the fact that we may use either Alamouti or beamforming to preprocess the data at the user terminals. The input data streams are denoted by x k , the decoded streams at the base station by yk . The base station can use either SMMSE or RBD decoding to cancel multi-user interference. Additionally, SIC may be used on top. 7.4.5.2 Results The results can be found in Section 7.7.2.2.

7.5 Interference Mitigation The deployment of multiple antennas in radio networks impacts the spatial or temporal variation of the interference. In particular, the interference variation is more acute when precoding is used at the transmitter. The spatial change is due to the fact that the transmitted energy would be beamformed in different directions when the precoder’s weights are rapidly changing. In addition, if the radio channel is varying quickly, both the temporal and spatial interference will be fluctuating rapidly.

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Ala / BF SIC+SMMSE/RBD

Ala / BF

Ala / BF

Figure 7.18 System model for the uplink. (Reproduced by Permission of IEEE © 2009).

Most studies tend to neglect the impact of rapid interference variation on the system performance. Rapid variation of interference makes any kind of required feedback to a system (e.g. CQI) obsolete, yielding a ‘CQI mismatch’, also called a ‘flashlight effect’. This phenomenon was already known in the HSDPA system [OL05]. In [WIN2D341], it was shown that linear MU-MIMO precoding schemes such as SMMSE and RBD in a multi-cell scenario perform worse than a SISO system. It can be easily understood that any kind of scheme where the precoding weights are changing rapidly (e.g. adaptive beamforming, clustered array etc.) will cause a fast spatial interference variation at other cells, consequently making it impossible to predict the interference. The observation is especially true at medium to high system loads where the interference (and noise) is mostly dominated by inter-cell interference which is changing fast. In the case of isolated cells, such as in the local area scenario, the performance degradation would be minor due to low or nonexistent inter-cell interference. Section 7.7.2.2 shows that the performance degradation of linear MU-MIMO precoding schemes such as SMMSE and RBD in isolated cells is minor in cases of a mismatch in channel estimation and imperfect CSI. Predicting the interference variation appears to be a challenging task. Instead it is more practical to try to stabilise, reduce or eliminate the interference. In the following discussion, we present a method (slow fixed beam re-use) that allows mitigation of the spatial and temporal interference: 1. Restrict the beamforming matrix to take a finite number of fixed values. 2. Assign in each cell a portion of subcarriers to a given beam. 3. Change the beam allocation synchronously and slowly. While the first two steps stabilise the interference spatially, the slow update of the beam weight stabilises the interference temporally. An illustration of slow fixed beam re-use is depicted in Figure 7.19, where two fixed beams are defined by the angles θ 1 and θ 2 and are updated every T seconds. In a given cell, the portion

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f

θ 1 θ1

θ1 θ 2 θ 2

θ 2 θ1 θ 1

θ2 θ2

θ2 θ2 θ2

θ 2 θ1 θ 1

θ 1 θ1

θ1 θ 1 θ 1

θ 1 θ1 θ 1

t T

Figure 7.19 Slow fixed beam re-use of period T , with two beams defined by θ 1 and θ 2 , from [OZHK08]. (Reproduced by Permission of IEEE © 2009).

of subcarriers assigned to each beam can be proportional to the total traffic generated by the UTs having this particular beam as their favourite beam. Typically, T is chosen much larger than duration of one transmission time interval (TTI). With fixed beam re-use, the interference seen by each UT changes at most once every T seconds compared to every TTI without fixed beam re-use. Hence, fixed beam re-use, with a properly chosen T , can significantly reduce the variations in interference observed by the UTs. In order to assess the impact of the CQI delay on a precoded MIMO system, the clustered array configuration is evaluated in a multi-cell urban scenario (i.e. the WINNER wide area scenario) with ideal and delayed CQI. Figure 7.20 plots the spectral efficiency (for the best user in a sector) for a 4 × 2 clustered array scheme in the case of ideal CQI and a CQI delay of three TTIs (i.e. δ = 3). It can be observed that a CQI delay of three TTI already causes a substantial spectral efficiency loss of 35 % (at 20 Mbps user rate). The proposed method, slow fixed beam (FB) re-use, was applied to the 4 × 2 2-clustered array scheme and evaluated in a multi-cell scenario. The following cases were simulated for comparison purposes:

r 2-clustered 4 × 2: one user scheduled per cell and assuming perfect CQI; r 2-clustered 4 × 2, FB re-use: one user scheduled per beam and assuming perfect CQI; r 2-clustered 4 × 2, δ = 3: one user scheduled per cell with a CQI delay of three TTIs; r 2-clustered 4 × 2, FB re-use, δ = 3: one user scheduled per beam with a CQI delay of three TTIs;

r 2-clustered 4 × 2, FB re-use, δ = 3: one user scheduled per beam with a CQI delay of three TTIs and slow update of the FB re-use. The sector spectral efficiency of the simulated cases is shown in Figure 7.21. The FB re-use bridges the gap between ideal and delayed CQI (for 5 Mbps user rate) to around 11 %. Further when the weight vector is updated at a slower pace (see the case of FB re-use, with slow update), the CQI delay of three TTIs only causes 5 % loss compared to 35 % when the FB re-use method was not used. It is interesting to notice that at very low load the CQI delay has a minor impact on the performance since the inter-cell interference is low. As mentioned previously, the fast change of the transmit weight vector means the SINR (and consequently the CQI) may become obsolete. In Figure 7.22, the CDF of the difference

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120

95 % User bit rate (Mbps)

100

2−Clustered 4x2 2−Clustered 4x2, δ=3

80

60

40

20

0 1

1.5 2 Spectral efficiency (bps/Hz/sector)

2.5

Figure 7.20 The 95th percentile user bit rate versus the spectral efficiency for the 4 × 2 2-clustered array with and without a CQI delay of three frames (δ = 3), from [OZHK08]. (Reproduced by Permission of IEEE © 2009).

between the instantaneous SINR at time t and the estimated SINR at time (t − δ) is plotted. It can be seen that the mismatch (i.e. either overestimation or underestimation) is zero when there is no delay. In the case of a three TTIs delay, more than 20 % of the users will experience a SINR mismatch greater than 2 dB. When the FB re-use is applied to the 4 × 2 2-clustered array, then the percentage of users experiencing a mismatch greater than 2 dB reduces to 10 %. Finally, when slow FB re-use is applied then the SINR mismatch vanishes.

7.6 Pilots, Feedback and Measurements Most multi-antenna techniques require partial or full knowledge of the radio propagation channel. The knowledge can be obtained by measuring the pilot signalling or from feedback information obtained from the other end (receiver). Most multi-antenna techniques rely on various types of measurement of spatio-temporal processing. This section gives a short overview of the required number and types of pilots, feedbacks and measurements when multi-antenna techniques are deployed. For more information about the pilot design the reader can be referred to Chapter 6.

7.6.1 Pilots In a radio transmission, the propagation channel will modify the phase and the amplitude of the signal. Hence in order to recover the original signal it is crucial to estimate the channel.

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100 90

95 % User bit rate (Mbps)

80 70

2−Clustered 4x2, FB Reuse 2−Clustered 4x2, FB Reuse, δ=3 2−Clustered 4x2, δ=3 2−Clustered 4x2, FB Reuse, δ=3, Slow update

60 50 40 30 20 10 0 1

1.5 2 Spectral efficiency (bps/Hz/sector)

2.5

Figure 7.21 The 95th percentile user bit rate versus the spectral efficiency for the 4 × 2 2-clustered array, from [OZHK08]. (Reproduced by Permission of IEEE © 2009).

The channel estimation is either based on pilots i.e. known transmitted signals, or blind i.e. based on exploiting certain properties of the transmitted signal. Common pilot is the most widely used type of pilot. It is broadcast in the whole cell, is designated for all users, and does not include user-specific transmit processing. Different variants of common pilots exist (see Section 6.2.1):

r Common pilots per cell (CPC) or sector are used to support mobility-related functions. It allows obtaining an unweighted channel coefficient H .

r Common pilots per antenna (CPA) are used to obtain the unweighted channel matrix H , which describes the propagation channel between any combination of transmit and receive antennas in the MIMO case. r Common pilots per beam (CPB) are used to estimate the effective channel and perform CQI measurements for the associated beam for fixed beamforming approaches. Due to the fact that (common) pilots can be used by several users, they are appealing for the downlink processing, since the overall energy to perform the associated functions has only to be spent once and the pilot symbols can be spread over all resources. Also they provide a basis for un-biased CQI measurements. Multi-antenna transmissions, in particular beamforming and precoding, bring additional requirements and need dedicated pilots (or sophisticated receiver processing). One way around it is to reduce the pilot density by not designing the density of the common pilots according to the worst case (e.g. the high-speed user). Instead the density can be planned according to

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100 90 80 70

CDF

60

2−Clustered 4x2, FB Reuse 2−Clustered 4x2, FB Reuse, δ=3 2−Clustered 4x2, δ=3 2−Clustered 4x2, FB Reuse, δ=3, Slow update

50 40 30 20 10 0 −6

−4

−2 0 2 − δ) [dB] SINR(t) − SINR(t

4

6

Figure 7.22 The CDF of the difference between the estimated SINR at time (t − δ) and the experienced SINR at time t, from [OZHK08]. (Reproduced by Permission of IEEE © 2009).

users moving at medium speed, and transmit additional dedicated pilots on demand (e.g. to users that cannot make appropriate channel estimation with the system setting). In [GO08], it is shown that by appropriately combining these dedicated pilots with the common ones, the pilot density can be substantially decreased without any noticeable performance loss. The most commonly used pilot pattern in an OFDM system is a regular pattern (i.e. the pilots are equidistantly placed in frequency and time; see Chapter 6 for more details). For a multi-cell system where each cell is equipped with several antennas, users need to distinguish the pilots originating from the various cells and antennas. Additionally, in a non-frame synchronised system, the number of orthogonal pilots might be insufficient. A solution is to use irregular pilot patterns such as Costas arrays [Gue07], which provide substantially more detectable pilot sequences, have a lower peak power and offer better frequency offset estimation. The pilot type and density for the evaluated MIMO techniques are shown in Table 7.7.

7.6.2 Feedback When two nodes (e.g. a transmitter and a receiver) are communicating with each other, the transmitter may need to have some information about the radio propagation channel on the forward link. Ideally, the transmitter may send a specific data to the receiver node which in turn will measure that sequence and feed it back to the transmitter. In practice the feedback rate depends on the transmission type, TDD or FDD transmission.

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Table 7.7 Pilot type and density for adaptive system. Tx and Rx antennas

Pilot density

Tx schemes and receiver algorithm

FDD

TDD

CPC CPC CPB

4.2 % 4.2 % 16.8 %

1.7 % 1.7 % 6.8 %

Pure SU-MIMO dual stream techniques 2×2 PARC-MMSE 4×2 PARC-MMSE 4×2 2-clustered – MMSE (2 streams)

CPA CPA Common per stream (CPS)

8.4 % 16.8 % 8.4 %

3.4 % 6.8 % 3.4 %

Pure MU-MIMO scheme 4×2 SDMA (GoB) – MMSE (4beams)

CPB

16.8 %

6.8 %

Pure single stream techniques 1×1 SISO 1×2 SIMO (MRC and IRC) 4×2 GoB – MMSE (4 beams)

Pilot type

MU-MIMO adaptive scheme switching between single stream and multiple stream 2×2 SDMA (GoB), TDMA CPA 8.4 % (GoB), IRC MU-MIMO chunk-wise adaptive precoding/decoding (any) × 2 Required pilots in the Dedicated per stream, downlink fractional pilot re-use 0.5 (any) × 2 Required pilots in the Full-band (contention band) uplink dedicated pilots per antenna

6.7 % —

—

— —

In a TDD transmission, reciprocity may be assumed (i.e. the channel may be considered identical in both directions). The reciprocity assumption reduces the effort of conveying measurements to the transmitter by being able to re-use a downlink measurement in the uplink and vice versa. As result of exploiting reciprocity, there is no feedback of short-term CSI in order to avoid very costly return link signalling. The reciprocity principle in TDD holds only for the propagation channel which does not take into account the RF front-end components. The imperfections of the RF front-ends (e.g. due to ADC, baseband filter, IQ imbalance, phase noise, and amplifiers) can prevent exploitation of the reciprocity principle. Hence, calibration is required to compensate for these impairments and restore the channel reciprocity. In an FDD transmission, the transmitter and the receiver communicate on a different frequency. Although the instantaneous channel realisations of the uplink and downlink may be uncorrelated, the physical parameters, such as directions and relative delays of the waves are commonly assumed to be frequency independent. Hence the second-order statistics, such as the transmit and receive correlation matrix, may be successfully transformed from one frequency band to the other. In addition, the short-term and long-term CSI can be combined in order to reduce the overhead [KBLS08].

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Table 7.8 Feedback rate for WA scenario assuming a CQI of 8 levels. Tx and Rx antennas

Tx schemes and receiver algorithm

Pure single stream techniques 1×1 SISO 1×2 SIMO (MRC and IRC) 4×2 GoB – MMSE (4 beams) Pure SU-MIMO dual stream techniques 2×2 PARC- MMSE 4×2 PARC- MMSE 4×2 2-clustered MMSE (2 streams)

Feedback rate

Comment

R = 706 kbpsa R

Per UT Per UT

R

Per UT, assuming the beam selection is done in the UL

2R+.4 kbps 4R+.8 kbps 2R+.0358 bpsb

MU-MIMO adaptive scheme switching between single stream and multiple stream 2×2 SDMA (GoB) + R+21.7 kbps Per UT, each user provides a TDMA (GoB), (single CQI value per supported IRC stream) stream 2R (dual stream) a

The rate is extremely high, which is due to the fact that an update of the values per frame is assumed. Significant feedback rate reduction can be achieved by adapting it to the coherence time and frequency of the user. b The beam index is updated on the super-frame basis (i.e. 5.8896 ms) which includes the preamble duration.

The feedback rate for the techniques presented in this chapter is shown in Table 7.8. It is assumed that the UT reports every frame for frequency-adaptive transmission.

7.6.3 Measurements We distinguish the following types of measurements required for spatial processing (more information about measurements and signalling can be found in Section 6.4):

r channel state information (CSI); r channel quality indicators (CQI); r effective CSI (ECSI), including the effect of spatial transmit processing; r scalar noise plus interference power (NIP); r frequency-dependent interference power (IP). CSI, CQI and ECSI can be short term (ST) or long term (LT). In case the ST observations are not meaningful because they vary too much, LT CSI can be created in the form of averaged correlation matrices. Note that the CQI term used here may encompass a set measurement and have a broader sense than the one used by the standardisation body, 3GPP.

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In TDD mode, the CSI of the downlink channel is obtained by assuming reciprocity of uplink measurements after proper calibration (a more elaborate discussion on reciprocity in the two modes is given in [WIN2D341]). ECSI requires dedicated pilots to include the effect of spatial processing. In FDD mode, mainly feedback is used. CQIs can be fed back or computed from already transmitted CSI, depending on the technique in use. Table 7.9 lists requirements per MIMO technique.

7.7 MIMO Aspects in Relaying In the previous sections, we have shown that use of multiple antennas at the transmitter or receiver may provide array, diversity and spatial multiplexing gain relative to single antenna systems. Further, it is shown in Chapter 8 that relaying can be used either with, or additionally to, a MIMO system in order to extend the network connectivity providing macro-diversity gain in addition to the path loss reduction. A relaying system is a conventional radio network that is complemented with relay nodes. The relay nodes communicate wirelessly with other network element (e.g. BS, another relay or a UT). Relaying systems are treated more in depth in Chapter 8. Analogous to a multiple antenna system where the antennas are co-located, distributed antennas can cooperate jointly in order to mimic a MIMO system. By doing so, the hardware complexity at the BS or UT can be relaxed. Two categories can be distinguished: cooperative relaying (also called cooperative communication) and distributed antenna systems (DAS). It should be noted that the distinction is loose: DAS may imply a more general connotation but it simply indicates that distributed antenna are cooperating or controlled by a central unit. Moreover, as in a conventional co-located antenna system, spatio-temporal processing algorithms are applied to the distributed antennas. A cooperative relaying system is a relaying system where the information sent to an intended destination is conveyed through various routes and combined at the destination. Each route can consist of one or more hops utilising the relay nodes. In addition, the destination may receive the direct signal from the source. From a theoretical point of view, it seems to be intuitive that the same spatio-temporal transmit processing algorithms should be applicable to both co-located and distributed antennas. In practice however, there exist impairments in the distributed antenna case: signalling delay between the antennas; CSI processing delay, in the case of dislocated processing; and possible separate power constraints for each antenna site. Only recently, it has been shown theoretically that both types of systems are indeed equivalent in many aspects, while still neglecting the practical aspects: in [MH05; MH06] it was shown that any spatially correlated MIMO system employing an orthogonal space–time block code (OSTBC) can be transformed into a distributed OSTBC system having an equivalent average symbol error probability. As a result, one can conclude that the same design rules for optimality of an OSTBC scheme apply in both cases. In [MH05], this proof was extended to the equivalence of the two types of MIMO systems in the case of flat Rayleigh fading channels which follow the Kronecker correlation model in the sense that systems with equivalent capacity distributions exist. The same paper concludes that both types of system implicate the same design rules, e.g., sufficient antenna spacing with respect to spatial processing schemes. One should however not conclude the optimality of either one of the two approaches, which can be highly scenario dependent. In the following sections, several examples of cooperative relaying and a distributed antenna systems are described and assessed.

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Table 7.9 Measurement requirements of specific space–time processing techniques, from [O+07]. (Reproduced by Permission of IEEE © 2009). Required at TX (purpose) Technique

CSI

Adaptive linear multi-user MIMO precoding and multi-user scheduling (LA downlink frequency-adaptive mode)

LT/ST or mixed

Matrix LDCs (STBCs) (uplink; LA, WA, MA downlink nonfrequency-adaptive mode) LT beamforming (possible alternative WA non-frequencyadaptive) Fixed GoB, linear precoding and multi-user scheduling (WA downlink frequency-adaptive mode)

—

Adaptive GoB, linear precoding and multi-user scheduling (downlink) (using variable codebook size or possible transition to LT-CSI-based precoding depending on feedback technique)

LT/ST or mixed (beam adaptation)

LT

—

Required at RX (purpose)

CQI

CSI

CQI

LT/ST NIP (precoding, scheduling, adaptive modulation and coding (AMC)), other CQI (scheduling, technique dependent) can be obtained from CSI IP (interference avoidance scheduling) may be obtained from CSI LT/ST NIP (AMC, LDC selection), condition number (LDC selection, technique dependent) statistical indicators for SINR distribution in front of detector

ECSI (spatial equaliser)

LT/ST NIP (spatial equaliser)

—

—

ECSI

LT/ST NIP (spatial equaliser)

ST SINR per user and per beam (adaptive beam selection and scheduling) LT/ST NIP (AMC, beam selection), obtainable from SINR and CSI, IP (interference avoidance scheduling) may be obtained from CSI ST SINR per beam (beam selection, scheduling), LT/ST NIP (AMC, beam selection) obtainable from SINR and CSI, IP (interference avoidance scheduling) may be obtained from CSI

ECSI (spatial equaliser) from CSI because of fixed weights

LT/ST NIP (spatial equaliser)

ECSI (spatial equaliser)

LT/ST NIP (spatial equaliser)

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7.7.1 Cooperative Relaying Cooperative relaying systems are typically limited to only two (or a few) hops. In the literature, several names are in use, such as cooperative diversity [Lan02; LW01], cooperative coding [SE04], and virtual antenna arrays [DLA02]. A conventional two-hop system is shown in Figure 7.23(a), where an RN is deployed between the BS and the UT. Figure 7.23(b) depicts a two-hop cooperative relaying system, where a BS, a UT and several RNs each equipped with a single antenna are shown. The RNs and the BS cooperate as if multiple antennas are used at the transmitter (i.e. the BS). A cooperative relaying system can be divided into numerous categories based on desired parameters. For instance, the way the signal is forwarded and encoded at the relay station can

RN

UT BS

(a)

RN

UT BS RN

(b)

Figure 7.23 Two-hop system: (a) conventional and (b) cooperative, with multiple RNs emulating a MIMO system. (Reproduced by Permission of IEEE © 2009).

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be classified into two categories: amplify-and-forward and decode-and-forward. As its name indicates, in amplify-and-forward the RNs simply amplify and forward the received signal. In the decode-and-forward case, the RNs demodulate and decode the signal prior to re-encoding and retransmission. Additional details on cooperative relaying and its performance can be found in Sections 8.4.6 and 13.6.1.5. 7.7.1.1 Cooperative Diversity Relaying Inducing multipath diversity by means of cooperative communications nodes have already been proposed for a single carrier system [A+02; BS04; SH03; WGV04; RXG04; SO06]. At the destination, the signal consists of several delayed copies due to the introduced delay at the nodes or asynchronous operation, the propagation delay or the processing delay. The diversity is exploited either by increasing the data symbol length [SH03] in order to avoid ISI or by using a complex equaliser such as a generalised decision feedback equaliser [WGV04] or by introducing a cyclic prefix (CP) in conjunction with frequency domain equalisation (FDE) [SO06]. This section investigates and evaluates in a multi-cell (radio network) scenario several distributed diversity schemes: relay cyclic delay diversity (RCDD), relay Alamouti, relay coherent combining and relay selection diversity. RCDD [Oss06; OLSL07] consists of using a set of distributed RNs each associated with a different cyclic shift. In analogy with CDD and in order to provide frequency selectivity and spatial diversity in a cooperative relaying wireless communication system, a set of distributed RNs is treated as a single entity composed of multiple antennas; at each RN transmit antenna, a cyclic shift is applied to the OFDM symbol that is forwarded between the BS and the UT. In relay Alamouti diversity [A+02; BS04; AK06], two antennas of the RNs act jointly as in the case of two transmitting antennas for a conventional STTD. The only difference is that each of the antennas is attached to a different antenna system instead of being controlled by the same radio unit. Relay coherent combining (RCC) was first proposed in [Lar03]. It consists of multiplying the transmitted signal at each RN by a phase that compensates the one introduced by the channel. In fact the effective channel at the UT is a constructive summation of all the RN signals transmitting to the desired UT; for more details on distributed diversity schemes, see [OLS08]. In order to compare the above methods, the following schemes were evaluated in the wide area scenario (see Chapter 2):

r the reference case, i.e. a single-hop system, designated in the figures ‘1-hop’; r the relay cyclic delay diversity, denoted ‘RCDD’; r the relay selection diversity (when only the relay is allowed to forward the information from the BS to the UT), denoted ‘2-hop OneR’;

r the relay Alamouti method, denoted ‘RALA’; r the relay coherent combining, designated ‘RCC’. In the simulation environment, an error-free, first hop transmission was assumed. In addition, a higher order modulation was assumed (up to 256-QAM). The ‘2-hop OneR’ scheme

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Normalized cell throughput

2.5

2 1-hop 2-hop OneR RCDD RALA RCC 1.5

1 500

1000

1500

2000

Cell Raduis (m)

Figure 7.24 Normalised cell throughput versus the cell radius for 1-hop, 2-hop, RCDD, RALA and RCC for max-SINR scheduling assuming 10 dB demodulating loss. (Reproduced by Permission of IEEE © 2009).

experiences less interference than the other 2-hop schemes and, hence, can take better advantage of the variation of the fading multipath channel. This improvement in the received SINR translates into better system cell throughput as seen in Figure 7.24. A cell throughput gain of about double3 is obtained by the 2-hop relaying schemes in comparison with the 1-hop scheme. Besides that, the Alamouti diversity order is limited to two and has shown little system capacity gain in a WCDMA system [RZL06]. 7.7.1.2 Two-Dimensional Cyclic Prefix The cooperative diversity schemes discussed in the previous section require two transmission phases for each DL and UL direction: for instance, in the DL, in the first transmission phase, the BS transmits to the RN and, in the second transmission phase, the RN transmits to the UT. The two phase transmission methods may effectively reduce the data throughput by half. Two-dimensional cyclic prefix (2D-CP) [Oss06; OL09] is a method that circumvents such a drawback. It requires only a single transmission phase for each direction in a cooperative relaying system. The transmission scheme of 2D-CP is illustrated in Figure 7.25. In contrast to the classical 2-hop system, we assume here relay nodes which are able to support fullduplex operation, i.e., to transmit and receive on the same time–frequency resource. In fact, the BS will transmit two different data signals (x2n and x2n+1 ) during two consecutive phases 3 The calculation of the normalised cell throughput took into account the fact that 2-hop systems require twice the amount of time compared to single-hop systems.

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x2 n −1

x2 n

x2 n

x2 n+1

RN

RN

x2 n

x2 n +1

BS

BS

UT

UT

(a)

(b)

Figure 7.25 (a) Full duplex relay, 2D-CP slot 2n and (b) full rate relay, 2D-CP slot 2n + 1. (Reproduced by Permission of IEEE © 2009).

(i.e. 2n and 2n + 1). As shown in Figure 7.25, the BS transmits to the RNs and to the UT. The RN forwards information received from the BS to the UT with one symbol delay (see Figure 7.25(b)). For instance, during phase 2n, the RN forwards data x2n−1 , which was received from the BS at the previous phase, 2n − 1. The basic time–frequency resource unit in an OFDM system is defined as a chunk. Each chunk entity comprises N subcarriers and spans a time window of M OFDM symbols. Let B denote the N × M matrix of the chunk (see Chapter 4). A two-dimensional IFFT is applied to the data block B. The output of the IFFT is denoted by X. X is subject to a two-dimensional cyclic prefix. This is simply done by pre-pending the last OFDM symbol of X to X. The pre-pending operation which consists of adding column-wise a cyclic prefix, eliminates the interference from the simultaneous transmission of the data from the BS and RN. The transmission process of the block data X at the BS and the RN is illustrated in Figure 7.26. During the first phase, the last symbol x M is transmitted by the BS and a noisy version of the signal (denoted by y0 ) is received by the UT. The RN forwards x M during the subsequent Block of OFDM symbols

TX at of BS

x

TX at of RN

RX at of UT

y

x

x

···

x

x

x

x

x

···

x

y

y

···

y

y t

Figure 7.26 A block of M OFDM symbols transmitted at the BS, relayed by the RN and received at the UT. (Reproduced by Permission of IEEE © 2009).

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100 90 80 70

CDF

60 50 40 30 20

1-hop, R=1000m 2-hop, R=1000m 2D-CP, R=1000m

10 0 -10

-5

0

5

10

15 20 SINR [dB]

25

30

35

40

Figure 7.27 CDF of the SINR for 1-hop, 2-hop and 2D-CP schemes for a cell radius of 1 km.

phase. At the next time instant, the BS transmits the first symbol x1 in the data block and the UT receives a linear combination of x M from the RN and x1 from the BS. The process is repeated until the BS transmits the entire data sequence in X. At the UT, the signal estimation can be carried out only when the entire OFDM chunk has been sent by the BS. As the data of the whole chunk is jointly encoded, the RNs cannot decode the data but use amplify and forward. The cumulative density function (CDF) of the received SINR of 1-hop and 2-hop systems for a cell radius of 1 km is shown in Figure 7.27. The proposed method yields 9 dB SINR gain at the median value relative to the single-hop system. On the other hand, the conventional 2-hop system offers slightly higher SINR gain (11 dB) at the expense of doubling the latency (two transmission phases are required). Despite the interference increase with the 2D-CP method (BS and RNs transmit simultaneously), the SINR gain of the proposed method translates into a substantial cell throughput gain as shown in Table 7.10. This comes of course at the expense of more complexity and costs in the relays (i.e. full-duplex RN). As was mentioned previously, additional results on relaying in general and cooperative relaying in particular can be found in Chapter 8 and Section 13.6. Compared to those results, it should be noted that more advantageous assumptions for relaying systems were assumed in this chapter: an error-free first hop, a higher-order modulation and an inter-site distance greater than 1.5 km.

7.7.2 Distributed Antenna Systems In order to increase the frequency on the downlink, one can either deploy orthogonal cells (i.e. orthogonal sectors) or allow cooperation between adjacent sectors or cells in order to eliminate

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Table 7.10 Relative cell throughput (or spectral efficiency) gain for Max-SINR scheduler, a cell radius of 1 km and assuming 10 dB demodulation loss. Method

Relative Gain

1-hop Relay Alamouti Relay Coherent Combining Relay Cyclic Delay Diversity Relay Selection Diversity 2D-CP

1 1.94 1.98 2.01 2.25 3.5

the interference in the system. In [FDGH06], it was observed that distributed antenna systems are beneficial only when a lot of shadowing exists and when the positions of the antennas are favourable from the user point of view. For many channel realisations, joint scheduling between the BSs using low complexity interference estimates was able to outperform the distributed antenna approach. In this section, various cooperative deployment scenarios in an indoor environment are investigated. Then the performance of linear MU-MIMO precoding techniques, such as SMMSE and RBD, using distributed antennas are presented. 7.7.2.1 Distributed MIMO Configuration In order to investigate the cell and user throughput assuming multiple BSs/antenna arrays with various degrees of cooperation between them, the following scenarios are considered in local area testing (see Section 13.4):

r Scenario 1: There are two BSs positioned as in Figure 7.28. Each BS has a triangular array with four antennas per ‘sector’ (12 in total). Each user in the system is assigned a unique pilot pattern for channel estimation regardless of the BS or ‘sector’ of the BS with which it is

Scenario 1 and 2: 2 BSs full/no cooperation Scenario 3: Single BS Scenario 4: 3 antenna arrays, full cooperation

Figure 7.28 Distributed MIMO configurations for an office scenario assuming light walls.

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communicating. There is a central unit where the full CSI (from all BSs) is used to generate the precoding matrices. r Scenario 2: There are two BSs positioned as in Scenario 1. There is no cooperation between the BSs. Each BS is assigned 50 MHz out of the total bandwidth of 100 MHz. Each BS operates as if it were an isolated cell. r Scenario 3: There is one BS in the centre of the office floor with a triangular array. Each sector has eight antennas (24 in total). r Scenario 4: There are three antenna arrays positioned as in Figure 7.28. Each array has eight antennas. Scenarios 2 and 3 correspond to non-cooperative MIMO and are used here for comparison. Further, it was assumed that there were six pilots per chunk for channel estimations; seven spatial layers; and 50 % of users transmitting two data streams, except in Scenario 2 where each user transmits only one data stream (SDMA only). From Figure 7.29(a) where the complementary cumulative density function (CCDF) of the cell throughput is shown, it can be seen that by introducing cooperation between antennas we gain more than 500 Mbps of cell throughput compared to the scenarios where the two BSs do not cooperate or we have only one BS. User throughput is not improved much compared to Scenario 2, but on average we gain around 10 Mbps (see Figure 7.29(b)). Note that two noncooperating BSs which work in frequency orthogonal sub-bands provide the same 90 % cell throughput as a single BS or cooperating antenna array in the corners of the floor. By deploying antenna arrays in the corners of the floor, we gain on average 200 Mbps compared to Scenario 3.

7.7.2.2 Performance of Linear MU-MIMO Precoding In this section, we evaluate the performance of the proposed MU-MIMO scheme for local area deployments under realistic assumptions (see Chapter 2). For LA TDD, the user mobility is rather low, so instantaneous CSI can be assumed to be available at the transmitter. However, in practice this CSI may be erroneous due to channel estimation errors as well as RF impairments. Both sources of CSI quality degradation were included in the simulations through appropriate models, following [WIN2D6137] for the channel estimation errors and the self-calibration approach from [BCK03] for the calibration errors. At the BS, four distributed antenna arrays are assumed, each having eight antennas (four cross-polarised elements in an ULA) which yields a total of MT = 32 antennas (see Section 13.4.2 for more information on local network deployment). The UTs are equipped with two antennas (one cross-polarised element). At the BS SMMSE or RBD, precoding is performed. Since the instantaneous CSI is imperfect, the user terminals suffer from a certain degree of multi-user interference. At the user terminals, MMSE receivers are assumed. In order to find suitable groups of users that can be served jointly using SDMA, a scheduling decision for each chunk is required. It is not feasible to find the best groups through an exhaustive search: for 32 antennas at the base station and a total of 50 users in the system, the total number of possible combinations that would have to be tested for every chunk is ≈1015 . A suitable alternative is given by the ProSched proportional-fair-based approach, which reduces the complexity by replacing the exhaustive search with a tree-based greedy search [FDGH05].

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system throughput 1 Scenario 1. Scenario 2. Scenario 3. Scenario 4. SISO

0.9 0.8 0.7

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0

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0

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60 80 100 data rate [Mbps]

120

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(b)

Figure 7.29 Complementary CDF for distributed MIMO: (a) cell throughput and (b) user throughput.

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1 0.9 0.8 0.7

TDMA/FDMA SMMSE RBD

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15 20 25 30 35 Spectral efficiency [Bits/s/Hz]

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CCDF

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0

0.5

1 1.5 2 Spectral efficiency [Bits/s/Hz]

2.5

3

(b)

Figure 7.30 Local area downlink with SMMSE or RBD precoding, ProSched scheduling and proportional fair (PF): (a) cell throughput and (b) user throughput.

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The CCDF of the cell spectral efficiency is shown in Figure 7.30(a). It can be seen that both SMMSE and RBD offer impressive spectral efficiency gain compared to a TDMA/FDMA system. The results demonstrate that the performance of SMMSE and RBD is similar. The cell spectral efficiency is above 35 bps/Hz and is up to 46 bps/Hz. The CCDF of the user spectral efficiency for SISO, SMMSE and RBD is shown in Figure 7.30(b). It can be seen that the spectral efficiency was more than doubled for 50 % of the UTs. The guard band overhead, super-frame control overhead and in-chunk pilot and control overhead were taken into account. Eight (twelve) symbols were reserved for pilots (controls) within each chunk. For TDMA/FDMA, only four pilot symbols were subtracted as overhead. In the rest of this section, we discuss the system-level simulation results for MU-MIMO in the uplink. At the UTs, we used Alamouti space–time precoding. The BS was performing RBD or SMMSE decoding to cancel the multi-user interference and then decoding the Alamouti transmissions user by user. At the UTs where no CSI is required, two antennas (one element with cross-polarisation) were employed. The total number of users in the system K was set to 32, from which 16 were served at the same time, and frequency resource through SDMA. These groups were rotated in a round-robin fashion over all available chunks. Twelve symbols per chunk were reserved for in-chunk control overhead. The guard band overhead is also taken into account in the throughput. In order to set the downlink precoding weight and allow the scheduling decisions, the BS needs to know the unweighted channel matrix for all the UTs. This is obtained by transmitting dedicated pilots on the competition bands4 (i.e. the users may be divided into groups that share competition bands). The results of the proposed scheme are shown in Figure 7.31. SMMSE and RBD for perfect CSI yield 19 times the spectral efficiency gain of TDMA/FDMA for 90 % of the users. It can be seen that the relative gain decreases to 10 times under imperfect CSI. This is mostly due to the sensitivity of the Alamouti precoding scheme to CSI errors. Additional results on linear MU-MIMO precoding can be found in [OORW08; RFH08].

7.8 Conclusion In this chapter, we presented a MIMO framework for 4G radio systems. The proposed framework allowed the capture (one or more at a time) of the known MIMO gains (i.e. array gain, diversity, spatial multiplexing) depending on the radio deployment scenario. The developed generic multi-antenna architecture aims to choose the appropriate spatial scheme at the transceiver by means of spatial adaptation, in order to adapt continuously to the spatial properties of the channel. In fact, it was shown that the most efficient multiantenna transmission scheme is highly dependent on the scenario and propagation conditions. The bulk of the multi-antenna schemes supported by the generic transceiver were presented and evaluated in radio network environments. The performance of such schemes under their preferred deployment scenario was presented.

7.8.1 Beamforming It was shown that beamforming techniques are preferred for an FDD wide area scenario where only long-term channel state information (CSI) is available at the transmitter. 4 Competition bands are orthogonal subsets of the complete set of chunks, which can be introduced in order to reduce complexity.

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1 0.9 0.8 0.7

CCDF

0.6 0.5 0.4 0.3

TDMA/FDMA ALA SMMSE ALA imperfect CSI RBD ALA imperfect CSI SMMSE ALA perfect CSI RBD ALA perfect CSI

0.2 0.1 0

0

0.2

0.4 0.6 0.8 1 Spectral efficiency [Bits/s/Hz]

1.2

1.4

Figure 7.31 User throughput for the Local Area uplink with RBD decoding and Alamouti precoding. Round-robin scheduling.

For instance, single user (SU) MIMO transmission GoB in a three-sector site with a fourelement uniform linear array (ULA) antenna provided a throughput gain in the order of 25 % to 40 % compared to a single transmit antenna. SU-MIMO GoB can be improved by using a clustered antenna configuration hence allowing the combinations of spatial multiplexing and beamforming. A 4 × 2 clustered array provided 55 % increase in terms of sector throughput for the 5 % worst users and 83 % spectral efficiency gain for the 5 % best users. In order to further lift the performance, spatial division multiple access (SDMA) can be used on top of GoB, consequently outperforming single-stream GoB by 60 %, yielding up to 3.3 bps/Hz/sector (≈10 bps/Hz/site). Furthermore, SDMA based on adaptive beams can additionally increase the performance by 10–20 % at the expense of requiring dedicated pilots. Finally, higher-order sectorisation (HOS), which consists of using one ULA per sector, improved the performance up to a factor of 3.5 for 12-sector sites using four antennas per sector.

7.8.2 Diversity and Linear Dispersion Codes Adaptive linear dispersion codes (LDC) were shown to be more adequate in a high SINR radio environment. LDCs were investigated in downlink in the metropolitan area scenario. A 2 × 2 adaptive LDC system yielded an increase of 90 % in terms of the mean sector spectral efficiency and provided at most 10 % spectral efficiency gain for the 5th-percentile users (corresponding to the cell edge users).

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7.8.3 Multi-User MIMO Precoding Multi-user MIMO precoding based on regularised block diagonalisation (RBD) or successive MMSE (SMMSE) schemes were presented and evaluated. These schemes exploit short-term CSI at the transmitter to multiplex streams to several users in order to provide very high performance. The investigations in an indoor scenario for the system with 32 transmit antennas operating as four 8-element ULAs provided more than 40 bps/Hz/site.

7.8.4 Distributed Antenna Systems and Cooperative Relaying Distributed antenna systems and cooperative relaying were presented and highlighted as an add-on or alternative to a classical co-located antenna system. In particular, it was shown that a two-hop cooperative diversity scheme can more than double the spectral efficiency compared to a single-hop system. The gain can even reach 350 % when a more advanced transmission scheme, such as two-dimensional cyclic prefix, is used. Several antenna configurations for distributed antennas in indoors scenario were investigated. It was shown that BS cooperation improves substantially the cell and user spectral efficiency, up to 50 % compared to noncooperative BSs.

Acknowledgements I would like to thank all colleagues involved in the spatio-temporal task during 2006 and 2007 that made it possible to write this chapter. Note that some of the material presented in this chapter is extracted from or based on several of the public deliverables of the European project WINNER. Hence several persons contributed indirectly to the chapter. In particular, Johan Axn¨as and Kai-Erik Sunell contributed to the generic transmitter and multiple antennas concepts. Federico Boccardi and Eduard Jorswieck contributed to spatial adaptation. Yong Teng contributed to the LDC results. Per Skillermark contributed to the wide area uplink results. Florian R¨omer contributed on MU-MIMO. Martin Fuchs contributed on measurements. Veljko Stankovic contributed on distributed antennas. Moreover, special thanks for Magnus Olsson, Malte Schellmann and Thorsten Wild for constructive and fruitful discussions. I would also like to thank David Astely and Martin D¨ottling, who set the basis of the multi-antennas concept during the first phase of the project. Finally, I would like to dedicate this chapter to my mother Hayat, who passed away young and unexpectedly at the last stage of the book review. I owe her the largest debt of gratitude for constant encouragement and support in order for me to devote my utmost energy and effort to writing this chapter.

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Simane, S.B. and Osseiran, A. (2006) ‘Relay Communication with Delay Diversity for Future communication Systems’, Proceedings of the IEEE Vehicular Technology Conference (VTC) Fall, Montreal, Canada. [STJH07] Schellmann, M., Thiele, L., Jungnickel, V. and Haustein, T. (2007) ‘A Fair Score-Based Scheduler for Spatial Transmission Mode Selection’, IEEE 41st Asilomar Conference on Signals, Systems and Computers, Monterey, USA. [Tel99] Telatar, I.E. (1999) ‘Capacity of multiantenna Gaussian channels’, European Transactions on Teleommunications, 10(6):585–95. [TH02] Tirkkonen, O. and Hottinen, A. (2002) ‘Square-matrix embeddable space-time block codes for complex signal constellations’, IEEE Transactions on Information Theory, 48:1122–6. [TJSZ07] Thiele, L., Jungnickel, V., Schellmann, M. and Zirwas, W. (2007) ‘Capacity Scaling of Multi-User MIMO with Limited Feedback in a Multi-Cell Environment’, IEEE 41st Asilomar Conference on Signals, Systems and Computers, Monterey, USA. [VT03] Viswanath, P. and Tse, D. (2003) ‘Sum capacity of the vector Gaussian broadcast channel and uplink-downlink duality’, IEEE Transactions on Information Theory, 49(8):1912–21. [WGV04] Wei, S., Goeckel, D. and Valenti, M. (2004) ‘Asynchronous cooperative diversity’, Conference on Information Sciences and Systems. [WIN1D27] WINNER I (2005) IST-2003-507581 Assessment of advanced beamforming and MIMO technologies, Deliverable D2.7, February 2005, viewed 20 June 2009, http://projects.celticinitiative.org/winner+/. [WIN1D210] WINNER I (2005) IST-2003-507581 Final Report on identified RI key technologies, system concept, and their assessment, Deliverable D2.10, December 2005, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+/. [WIN2D341] WINNER II (2006) IST-4-027756 The WINNER II Air Interface: Refined SpatialTemporal Processing Solutions, Deliverable D3.4.1, November 2006, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+/. [WIN2D6131] WINNER II (2006) IST-4-027756 Test Scenarios and Calibration Cases Issue 1, Deliverable D6.13.1, December 2006, viewed 20 June 2009, http://projects.celticinitiative.org/winner+/. [WIN2D6137] WINNER II (2006) IST-4-027756 WINNER II Test Scenarios and Calibration Cases Issue 2, Deliverable D6.13.7, November 2006, viewed 20 June 2009, http://projects.celticinitiative.org/winner+/. [WIN2D61310] WINNER II (2007) IST-4-027756 Final CG ‘wide area’ description for integration into overall System Concept and assessment of key technologies, D6.13.10, November 2007 v1, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+/. [Win84] Winter, J. (1984) ‘Optimum combining in digital mobile radio with co-channel interference’, IEEE Journal on Selected Areas in Communications, 2(4):528–39. [Win87] Winter, J. (1987) ‘On the Capacity of Radio Communication Systems with Diversity in Rayleigh Fading Environment’, IEEE Journal on Selected Areas in Communications, 5:871–8. [Win94] Winter, J. (1994) ‘The diversity gain of transmit diversity in wireless systems with Rayleigh fading’, IEEE International Conference on Communications (ICC), Illinois, USA, pp. 1121–5. [Wit93] Wittneben, A. (1993) ‘A New Bandwidth Efficient Transmit Antenna Modulation Diversity Scheme for Linear Digital Modulation’, IEEE International Conference on Communications (ICC), pp. 1630–34. [WSS06] Weingarten, H., Steinberg, Y. and Shamai, S. (2006) ‘The capacity region of the Gaussian multipleinput multiple-output broadcast channel’, IEEE Transactions on Information Theory, 52:3936–64. [YC04] Yu, W. and Cioffi, J. (2004) ‘Sum capacity of Gaussian vector broadcast channels’, IEEE Transactions on Information Theory, 50:1875–92. [YG05] Yoo, T. and Goldsmith, A. (2005) ‘Optimality of zero-forcing beamforming with multiuser diversity’, Proceedings of the IEEE International Conference on Communications (ICC). [ZK07] Zangi, K. and Krasny, L. (2007) ‘Impact of Transmit Antenna Array Geometry on Downlink Data Rates in MIMO Systems’, European Wireless, Paris, France. [ZT03] Zheng, L. and Tse, D.N.C. (2003) ‘Diversity and Multiplexing: A fundamental tradeoff in multiple antenna channels’, IEEE Transactions on Information Theory, 49(5):1073–96.

8 Layer-2 Relays for IMT-Advanced Cellular Networks Bernhard H. Walke,1 Klaus Doppler,2 Ralf Pabst,1 Daniel C. Schultz,1 and Simone Redana3 1

RWTH Aachen Nokia 3 Nokia Siemens Networks 2

8.1 Introduction The European Research Project WINNER has identified relaying as a key enabler to allow cost efficient ubiquitous broadband radio coverage. Relaying is expected to provide a means of achieving high capacity in different network deployments with various types of traffic demands and of facilitating rapid system roll-out and low-cost initial system deployments. The main reason for introducing relay nodes (RNs) to a radio cell is mainly to reduce deployment cost, resulting in lower cost per bit transmitted, since relays do not need a fixed or microwave-based backbone connection. Thus the objectives of the WINNER relaying concept are the design of a cost-efficient RN that is able to meet the WINNER performance goals and its integration into the network architecture. Other reasons for introducing RNs are to increase the user data rate close to the cell border and in cell areas shadowed from the base station (BS). In general, relaying is a novelty to cellular networks, since all of them are single-hop cellular networks (SCNs), i.e. no mobile or fixed relay stations are specified in current 3GPP or 3GPP2 standards.

8.1.1 Rationale for Relays in Cellular Networks The transmission range of a broadband radio interface, such as the one envisaged by WINNER is limited by the high attenuation of radio waves at high carrier frequencies (beyond 3.4 GHz), limited transmission power (EIRP) owing to regulatory constraints, and unfavourable Radio Technologies and Concepts for IMT-Advanced Martin D¨ottling, Werner Mohr and Afif Osseiran C 2009 Martin D¨ ottling, Werner Mohr, Afif Osseiran

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Figure 8.1 Motivation for introducing relays to cellular networks, from [WWS06]. (Reproduced by Permission of IEEE © 2009).

radio propagation conditions, e.g., in metropolitan areas and indoors. Conventional cellular radio network deployments require a very high density of base stations to achieve sufficient radio coverage there. It is known that an increased data rate (for a given power and carrier frequency) would require an increased SINR budget leading to a reduced radio range and that the available data rate, in general, decreases with increased distance from a BS as illustrated in Figure 8.1. A high data rate tends only to be available close to the BS. Progress in signal processing and semiconductor technology allows for a steady increase of the maximum data rate provided by wireless and mobile systems as shown in the graph. However, the range of a BS tends to decrease and the difference in data rate available close to the BS and at the cell border tends to increase. In general, the service quality (in terms of data rate, delay, outage probability, etc.) experienced by the user should not depend on its location in a cell or distance from the BS. Assuming a constant number of user terminals (UTs) per area element in a cell, the number of UTs increases linearly with the distance d from the BS (see Figure 8.1). Accordingly, most UTs are roaming at medium to large distances from the BS where the data rate tends to be medium or low. It appears reasonable that the requirements on future broadband radio systems in terms of capacity, delay, user-experienced data rate and deployment cost are difficult to meet using conventional single-hop cellular infrastructure concepts. The property exploited in relay-enhanced cells as developed by WINNER is a link budget saving on the two consecutive relay links connecting BS to RN and RN to UT, compared to the budget for direct communication between BS and UT. This link budget saving has to outweigh the resources required to transmit data twice over the radio interface. An RN introduced to the communication path between UT and BS induces two hops. Assuming the RN is positioned half-way between BS and UT, the two hops each reach half the distance of the single-hop link.1 Since the path loss is a power law of distance, a total path loss reduction can be expected that grows with the path loss coefficient, making relays attractive, especially, in metropolitan areas.

1 Clearly,

the position of the RN between the BS and UT depends on the power applied by the BS and the RN. Half-way positioning has been shown optimum under the same transmit power and path-loss model assumptions for a BS–RN–UT configuration.

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Current wireless and mobile systems apply link adaptation (LA) based on combinations of modulation and FEC coding schemes (MCS). In addition, these schemes are supplemented by automatic repeat request (ARQ) protocols for error correction by repeated transmission. LA is characterized by a highly non-linear throughput-versus-distance relationship that may exploit the link budget savings mentioned. Increased effective throughput on a two-hop link may be achieved in two ways. Reduced path loss at each hop under a given MCS reduces the probability of retransmission. Even more important, reduced path loss enables use of a less robust but higher data rate MCS. MIMO transmission techniques may be applied, in addition, to exploit the spatial dimension of the channel, leading to an increased throughput performance. If carefully engineered, the benefits gained from higher throughput per relay link outweigh the effort of transmitting data twice, resulting in a higher spectrum efficiency of multiple hops compared to a single hop. In addition to the potential performance increase, the relays have to be cost efficient to meet the goal of a low-cost radio network deployment in the envisioned deployment scenarios, i.e. base urban coverage, metropolitan area and local area (see Chapter 13). Relay stations differ from BSs in not requiring a wired or out-of-band wireless backbone access. An RN only requires access to mains or other means of power supply, e.g. solar-panel-fed battery. This reduces the capital and operations expenditure (CAPEX and OPEX) of the radio access network and, in addition, introduces high flexibility in relay positioning allowing a fast network rollout and adaptive traffic capacity engineering. Relays in cellular networks may also be used to provide indoor coverage from an outdoor BS. WINNER has been following the relaying concept from the project start in 2003. The potential of relaying has also shown up in standardization bodies, especially in IEEE Project 802. In Task Group (TG) 802.11s, a draft addition for a multi-hop mesh network has recently been adopted to the IEEE 802.11 standard; it specifies meshing of IEEE 802.11 access points, called ‘mesh points’. A mesh point is an RN in the terminology of WINNER. In Task Group IEEE 802.16j a standard on Mobile Multi-hop Relaying (MMR) has been drafted in July 2007 [P80216j]; it follows a very similar to that proposed by the WINNER project [PWS04]. Unlike the TGs IEEE802.16j and IEEE 802.11s, WINNER considers relaying as an integral part of the system concept. This is why no backwards compatibility issues are considered. Nevertheless, the relaying concept outlined in this chapter can be applied to all orthogonal frequency division, multiple-access (OFDMA) radio interface technologies, such as WiMAX or 3GPP UTRAN Long-Term Evolution (LTE) and the assessment results give insights into the potential benefits of relays also for these systems. Relays in an infrastructure-based WINNER deployment concept are, in most cases and similarly to 802.11s and 802.16j, positioned at fixed locations, denoted in this discussion as fixed relay nodes (FRNs) or short RNs. RNs introduced to a cell, form a relay-enhanced cell (REC); the number of RNs per cell is a design parameter. To be able to meet the performance goals specified by ITU-R for IMT-Advanced systems as envisaged by WINNER and described in Chapter 2, RNs are designed as decode-and-forward, Layer 2 (L2) relays. L2 relays allow forwarding of error-free data packets secured by an ARQ protocol on a per-hop basis. Advantage can be taken of adaptive transmission with different modulation and coding schemes applied to the links in tandem used to connect a UT with the BS. RNs may be operated under radio resource control so that interference avoidance and mitigation algorithms can be applied efficiently. The RN is designed such that a nearby UT is served by it as if the RN were the BS, which means the UT does not need relay-specific hardware or software. Just like the BS, an RN

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defines the WINNER medium access control (MAC) super-frame in its vicinity by sending regular preambles and broadcast messages. Consequently the UT does not suffer from extra complexity introduced by the RN. The relaying concept in WINNER is primarily designed and optimized for two-hop operation (BS–RN–UT) in order to achieve a maximum performance of the relay-enhanced cellular system. Nevertheless, the concept is able to support a larger number of hops in order to allow for high deployment flexibility, e.g. in areas where coverage has priority over throughput capacity. A tree topology has been chosen to connect UTs via RNs to the BS as it is less complex than a mesh topology. In the rare event of node failure, the RN will autonomously connect itself to another radio access point (RAP) in its range that may be either a BS or RN. Although the re-association of an RN to the network is not seamless and can lead to some lost packets, the tree topology can still be assumed to be self-healing.

8.1.2 Organization of this Chapter We review previous work on relay-based cellular systems in Section 8.2; fixed relays as introduced by WINNER appear to be a new technology that has a significant potential to become a key technology for 4G systems. We address opportunity-driven multiple access (ODMA) as the first technology introducing multiple hops to cellular networks, which is based on the ad-hoc network paradigm. Section 8.3 is devoted to a description of relay-based deployment concepts and strategies that are suited for capacity enhancement and range extension of a radio cell. Further, we describe the relay deployments for the three main scenarios considered in WINNER, i.e. base urban coverage (wide area), metropolitan area and indoor office (local area). We also address the cost efficiency of relay-based systems, which are discussed in more detail in Chapter 14. Section 8.4 introduces the design options in a relay-based system and justifies the decisions taken in the WINNER relaying concept, e.g. the choice of decode-and-forward L2 instead of amplify-and-forward L1 relaying, fixed RN instead of mobile relaying (as applied in ODMA), radio resource management in a relay-enhanced cell, and the support of cooperative relaying. In Section 8.5, we focus on system and network functions, especially enhancements required to protocols to enable the operation of WINNER relays. The implementation of the broadcast control channel (BCH) function is discussed. We also address radio resource unit allocation to RNs and UTs. Section 8.6 presents system-level simulation results characterizing the performance of relay-enhanced cellular systems in comparison to a conventional singlehop system. The assumptions made for the simulation study, the deployment concepts of relay-based systems, and the way radio resource units are allocated are described and the performance evaluation results gained from a simulator are presented by graphs. The discussion of the results serves to explain the pros and cons of alternate ways to allocate radio resources for operation of RNs in the WINNER system. Our conclusion summarizes the findings of the chapter.

8.2 Motivation for Layer-2 Relays and Prior Work Calculations [MLM02] motivated by [EVW00, E01, W01, W02] prove that mobile broadband services in reasonably large areas – as envisioned for 4G systems – are not feasible with

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conventional cellular architectures under transmit power constraints, owing to the propagation characteristics of carrier frequencies beyond 3 GHz where sufficient bandwidth is expected to be available for broadband services. Instead, a 30 dB shortage in the radio link budget of 4G systems will be faced in these bands compared to current systems. Consequently, a relay-based radio network infrastructure has been suggested as a solution to this problem. A conventional approach would be to increase the density of base stations, significantly, by introducing a large number of micro BSs each serving a very small cell area. This would result in a considerable increase in radio network infrastructure deployment costs, which would be economically feasible only if the number of subscribers and their traffic load to the system would also increase, equivalently, and higher average revenue per user would be accepted in the market than is common today. These conditions, apparently, are not realistic, since the penetration of mobile terminals is close to saturation in highly developed countries and subscribers expect the cost of using mobile services to decrease, not increase. There is clearly a demand towards a much higher transmission rate for mobile Internet access with a speed similar to that experienced in homes from DSL fixed network access, but for comparable cost – and this is really a challenge to 4G systems. A substantial increase in the number of base stations appears only to be economically justifiable if low-cost 4G BSs were available and the operation expenditure for connecting BSs to the core network were very low. This cannot be expected to be realistic in the near future. Besides evolutionary concepts, such as ‘many more base stations’, there is clearly a need for disruptive solutions, such as the introduction of Layer-2 relays to cellular networks to reduce the deployment cost. Today’s cellular radio systems employ, in rare cases, two-way repeaters to amplify and forward (AF) layer-1 signals received between the BS and the UT. Repeaters increase the noise and interference level in the system and therefore they are mostly used to cover otherwise shadowed areas, e.g. underground car parks. The multi-hop cellular network (MCN) concept has been considered in the 3GPP UTRAN option opportunity-driven multiple access (ODMA), an ad-hoc, multi-hop relaying protocol that was believed to improve system coverage and possibly enhance capacity [3GPP99]. In ODMA, some UTs are assumed to be able to operate as store-and-forward relays to connect other UTs to the BS. This concept is based on [S78; RMH01]. ODMA content was removed from the 3GPP specifications in March 2000 because of concerns about complexity, battery life of UTs on standby, and signalling overhead issues. Despite the problem of building initial coverage during network roll-out, ODMA remains an attractive prospect for future mobile communication systems, due to the advantages it offers (see [HN00]):

r reduction in transmission power; r potentially enhanced coverage; r a greater trade-off possible between quality-of-service (QoS) and capacity in the extended coverage region;

r increased capacity under certain circumstances. An MCN differs from a single-hop cellular network (SCN) by employing forwarding of data blocks by UTs operating as layer-2 RNs to route a packet between a source UT via the RN to the BS and vice versa. Relaying of data blocks is not allowed in SCNs. Lin and Hsu presented performance analysis results, [LH00], such as mean hop-count, throughput per hop and throughput capacity comparing MCN and SCN concepts. They

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differentiate between two possible MCN architectures: area optimizing, called ‘MCN-b’, and power-minimizing, called ‘MCN-p’. Although the transmission range adopted in MCN-b is the same as that in the equivalent SCN, the number of BSs is reduced so that the distance between two adjacent BSs becomes k times the distance in the SCN. In MCN-p, the transmission ranges of BSs and UTs is reduced by 1/k compared to the equivalent SCN, but the number of BSs is not changed. In [AT01], the GSM protocol stack is proposed to be upgraded by incorporating an ad hoc component to UTs, enabling it to operate as a relay in MCN-p. It is shown in [SLC04] that, in an MCN-p, with an increased number of RNs it becomes highly probable that UTs can find their RN and save their power. The gain of power saving through two-hop relaying is much greater when the path-loss exponent becomes greater.

8.3 Relay-based Deployments In this section, we first introduce a typical relay-based system. Then we illustrate the multitude of applications for an RN within a relay-enhanced cell (REC) in Section 8.3.1. The capacity of sub-cells within a REC depends on many parameters that are analyzed in Section 8.3.2. Section 8.3.3 presents the example relay-based test scenarios that have been used for simulation studies in WINNER. Section 8.3.4 gives insights on how to achieve cost-efficient, relay-based deployments. It is worth noting that, by introducing RNs to cellular communication systems, a large number of new parameters are added to the system and hence, designing an MCN is a very complex task. For example, the layout of a REC, the basic building block of an MCN, depends on:

r the number of hops; r the number of RNs served by one BS; r the positioning of the RN in relation to the cell radius under environmental constraints; r the transmit power of a node (BS or RN) in relation to the transmit power of adjacent nodes; r spatial temporal processing; r the radio environment (LOS/NLOS probabilities). Based on these parameters, a multitude of deployments may be envisioned and may be employed differently in each REC. Therefore no unique answer to the question about the size of a REC or its performance can be given. Nevertheless, in WINNER three typical deployment scenarios have been identified: the base urban coverage (wide area), the metropolitan area and an indoor office (local area) scenario. Typical relay deployments for all of these scenarios are discussed. Mobile broadband radio coverage can be provided in an area by means of a hierarchical layered system architecture combining different relaying concepts, as depicted in Figure 8.2. In this example, the BS and up to two tiers of RNs establish a REC (delimited by the thick line in the right hand side of the figure). The shaded areas around BS and RNs, with shapes formed by the nearby buildings, represent sub-cells in a REC. In this example, the RECs are in the lowest hierarchy of the broadband cellular radio, serving UTs at low to medium speed of mobility. The BS of the REC may be connected to the Internet either by fibre or by a second hierarchy layer of radio service, namely a point-to-multipoint radio operated above roof-tops, see the BS on the building in the centre. Wide area network

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Figure 8.2 Relay-based cellular mobile broadband system in cities, embedded in a wide area cellular radio network.

BSs (visible outside the circle) serve highly mobile UTs, covering the same area as the multihop broadband network. Broadband BSs are based on multi-radio transceivers operating the WINNER air-interface and, where applicable, the air-interface of the point-to-multipoint radio network. UTs are equipped with multi-radios, too, since they may connect to the WINNER air interface and to a wide area network, alternating (or even concurrently). A REC may, in fact, extend into buildings if a suitable RN is found to provide coverage for indoor services (see bottom of Figure 8.2).

8.3.1 RN Deployment Concepts RNs may be applied either to increase the cell coverage range or to the cell throughput capacity. RNs may be useful, in addition, to reduce the percentage of shadowed locations in the cell. Often RNs are deployed within a REC to cover locations with insufficient signal to interference plus noise ratio (SINR). Three goals for RN deployment can be distinguished, as shown in Figure 8.3:

r capacity optimization: UT#2 is located at the cell edge within the range of the BS. By connecting to RN#1 instead of to the BS, the (overall) link budget may be improved so much that the efficiency of the two-hop link is better than a direct link to the BS. r coverage improvement: RN#2 is just within the coverage area of the BS and helps to serve UT#3 which would be in outage without a support by the relay. Applying advanced antenna technologies and a predictable LOS connection, RN#2 can even be placed outside the cell covered by the BS. r coverage of shadowed areas: RN#3 can serve an area that is shadowed from the BS (e.g. by a large building).

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Figure 8.3 Reasons for deploying an RN: capacity enhancement (RN#1), increase of the radio coverage (RN#2) and service to a shadowed area (RN#3).

8.3.1.1 Relaying for Coverage Improvement If an RN is placed outside the range of the BS, antenna gain is needed to connect BS and RN. The higher the antenna gain used on the BS–RN link, the higher is the modulation and coding rate applicable and the higher is the capacity that can be transferred from the BS to the RN. If RNs are placed outside the coverage area of a BS (Figure 8.4), UTs served by RNs cannot receive broadcast information from the BS and the RN has to transmit a broadcast channel as well. The throughput achieved at a given area element in the REC is represented by the intensity of the grey shading. Clearly, the REC is made-up from sub-cells each served either by a BS or an RN.

Figure 8.4 A cell of an SCN (left) and a REC of an MCN (right) with RNs serving to extend cell coverage, from [WMB06]. Low path-loss connectivity is assumed between the BS and the RNs, resulting from LOS propagation or antenna gains applied. (B.H. Walke, S. Mangold, L. Berlemann IEEE 802 Wireless Systems, Reproduced by Permission of John Wiley & Sons Ltd. © 2006).

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Figure 8.5 A cell of an SCN (left) and a REC of an MCN (right) with RNs increasing cell capacity and balancing capacity per area element, from [WMB06]. (B.H. Walke, S. Mangold, L. Berlemann IEEE 802 Wireless Systems, Reproduced by Permission of John Wiley & Sons Ltd. © 2006).

RNs are considered a main trend for providing radio coverage in wireless and mobile broadband networks, especially to extend the service range of a BS. Since there is a clear trend towards increased transmission rate of radio transceivers resulting from technology progress (see Figure 8.1), the BS transceivers tend to provide excess capacity for the ever-shrinking local service area of a BS, because higher data rate under given power limits always tends to reduce radio range. As illustrated, relay nodes substantially increase the size of the service area of a BS by multi-hop communication and may increase thereby the chance that the high capacity offered by a BS will be used effectively. 8.3.1.2 Relaying for Capacity Optimization at Outer Cell Regions In the scenarios shown on the right of both Figure 8.4 and Figure 8.5, the dark shaded areas indicate high capacity per area elements. The capacity offered by a REC matches the capacity required by the user (see Figure 8.1) more closely than possible from a single-hop cell. For a cellular radio deployment the channel re-use distance is minimized when receive antenna gain is used instead of transmit antenna gain to connect BS and RNs. Relaying, as shown in Figure 8.5, can also be used to minimise the transmission power needed by UTs, BSs and RNs [LH00]. Reduced energy consumption of UTs and reduced hardware cost of BSs and RNs can be achieved owing to the reduced output power. 8.3.1.3 Relaying to Cover Shadowed Areas In current SCNs, layer-1 relays are applied to cover shadowed areas but are known to increase intra-cell and inter-cell interference levels. The example in Figure 8.6 applies to an MCN in a Manhattan grid scenario, where three-hop communication is used to serve a UT shadowed from the BS by buildings. A single hop path to the UT may not be available or may offer only a low data rate. The three hops of the multi-hop path may each operate under LOS propagation

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Figure 8.6 Multi-hop communication to reach a UT shadowed from the BS, from [WMB06]. (B.H. Walke, S. Mangold, L. Berlemann IEEE 802 Wireless Systems, Reproduced by Permission of John Wiley & Sons Ltd. © 2006).

conditions and the data rate may therefore be high. Besides that, re-use of the radio channel may be possible for the resources assigned to some hops of the multi-hop connection, e.g., in the sub-cells serving hop 1 and hop 3, or in sub-cells serving other connections concurrently in nearby sub-cells. The top left of Figure 8.6 suggests how an MCN can be deployed to provide multi-cellular radio coverage to the Manhattan grid scenario, e.g., by applying a re-use of two, based on two channel groups 1 and 2, each comprising a number of disjoint frequency channels used in a REC.

8.3.2 Sub-cell Capacity of a Relay-enhanced Cell In a relay-based cellular network, sufficient capacity is required not only in the sub-cell served by the BS but also for UTs served by multi-hop sub-cells. In this section, we present analytical estimates of basic parameters of a REC:

r multi-hop throughput in cellular deployment; r sub-cell capacity served by an RN; r capacity of multi-hop links under delay constraint. The multi-hop system we consider is assumed to use OFDM-based transmission with link adaptation similar to IEEE 802.16. Link level results [KSW+99] for the packet error rate (PER) as a function of the signal to noise ratio (SNR) at 5 GHz at a 20 MHz channel bandwidth have

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been used as a basis for the analysis. The physical-layer throughput in a multi-hop system has been calculated without considering protocol header overhead. Further assumptions are an ideal link adaptation and selective repeat (SR) ARQ protocol. 8.3.2.1 Multi-hop Throughput in Cellular Deployment For a hexagonal cellular deployment, the maximum end-to-end throughput of a UT in a REC was evaluated [EWP04]. Figure 8.7(a) shows the throughput over distance from the BS in an SCN deployment with re-use of seven. The end-to-end throughput for a multi-cellular network (MCN) with re-use of seven is shown in Figure 8.7(b), where a REC has three RNs. For fairness of comparison, the radius of the single-hop cell is set to 346 m, whilst the radius of the sub-cells in the REC is set to 200 m, resulting in equal service areas for both the single-hop cell and the REC. Interference from the first ring of six co-channel cells or RECs is taken into account for calculating throughput, assuming the worst case, in which all co-channel cells are fully loaded and transmit simultaneously. The analysis models both the BS–RN and the RN–UT transmissions for multi-hop connections. Further, a receive antenna gain of 12 dB of the BS–RN link was assumed. In Figure 8.7, a slight reduction of the achievable throughput is visible close to the BS in the REC compared to the single-hop cell, owing to the signalling overhead required for relaying. These were early studies and the actual protocol overhead in WINNER is much less. When designing the relaying protocols, minimal overhead has been one of the main design criteria; for example, a simple tree topology has been selected to avoid complex routing protocols, resource partitioning within the REC has been chosen instead of centralized scheduling, etc. (see Section 8.4). In return, the introduction of RNs considerably increases the throughput in the outer regions of the REC and the capacity per area element is more evenly distributed over the cell area compared to the single-hop cell. Apparently, RNs are well suited to smooth the local capacity available to a UT in a cell. 8.3.2.2 Sub-cell Capacity Served by an RN It is interesting to know the traffic capacity of a multi-hop sub-cell in an REC, when assuming homogeneously distributed UTs in the service area, as a function of the antenna gain on the BS–RN link. Figure 8.8(a) shows the Manhattan scenario and Figure 8.8(b) shows the capacity of the sub-cell served by the BS and the capacity of the sub-cell served by RN1, both versus the receive antenna gain available for the link BS–RN1. The capacity in the sub-cell served by the BS in the single-hop operation is independent of the antenna gain and amounts to 22.5 Mbps. The capacity available in the sub-cell served by RN1, when the whole capacity of the BS is transferred to it, grows with increased antenna gain from values of 2.7 Mbps (no gain) to 15.9 Mbps at 30 dBi antenna gain. An amount of 6.7 Mbps of the BS capacity is required for relaying traffic from the BS to the sub-cell served by RN1. In this example, an antenna gain higher than 28 dBi does not increase the capacity the subcell served by RN1 any more, since at this value the highest modulation and coding scheme (MCS) of the air interface is used on the BS–RN link and the packet error rate is close to zero. For the scenario considered and parameters chosen, approximately 29 % of the original single-hop cell capacity is spent transferring 71 % of the RAP capacity to one RN. The result

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RN receive antenna gain (dBi) Figure 8.8 Capacity at Relay Node #1 in Manhattan scenario with variable antenna gain, from [WMB06]. (B.H. Walke, S. Mangold, L. Berlemann IEEE 802 Wireless Systems, Reproduced by Permission of John Wiley & Sons Ltd. © 2006).

is encouraging when taking into account that multiple sub-cells can be operated in parallel, thereby compensating for the overhead consumed to serve multi-hop sub-cells. 8.3.2.3 Capacity of a Multi-hop Connection under Delay Constraint One concern is the delay introduced by relaying packets and the throughput possible on a multihop connection under delay constraint. Figure 8.9(a) compares the end-to-end throughput achieved with single-hop and two-hop transmission under LOS radio propagation conditions for the scenarios depicted in the top right corner. The end-to-end delay is restricted to 10 ms for both the single- and the two-hop connection. We assume the RN is placed in the middle between BS and UT. Other placements have been studied in [PEW05]. It turns out that the twohop link from a distance of 240 m onwards delivers a slightly higher throughput than the onehop link (the light shaded area in Figure 8.9(a)). It is worth noting that the range of the one-hop connection is limited to about 380 m, whilst the two-hop connection has a range of 500 m, still meeting the 10 ms delay bound. Under the somewhat weaker constraint of 10 ms delay per hop (instead of ‘per connection’), the range of the two-hop link even extends to 750 m (the dark shaded area in Figure 8.9(a)). Relay-based two-hop communication provides another considerable benefit: it may reduce signal shadowing caused by obstacles obstructing the radio path between BS and UT. In Figure 8.9(b), the throughput gain of the two-hop link compared to the single-hop link is highlighted (the light-shaded area represents a 10 ms end-to-end delay constraint; the dark shading represents the 10 ms per-hop delay constraint). In the scenario in the top right of Figure 8.9(b), BS and UT are shadowed from each other by two walls forming a rectangular corner, such as a street corner. A modified COST259 propagation model with 12 dB attenuation by each wall is assumed in the calculations. The distance is measured between BS and UT. The shaded area highlights that the two-hop link is superior to the single hop, starting at a distance of about 50 m and extending the range of the BS up to about 380 m. Both examples in Figure 8.9

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Figure 8.9 Maximum end-to-end throughput versus distance for one- and two-hop connections under SR-ARQ protocol with 10 ms maximum end-toend-delay restriction: (a) LOS propagation on both links in tandem and (b) two LOS links in tandem, but around an obstacle; from [WMB06]. (B.H. Walke, S. Mangold, L. Berlemann IEEE 802 Wireless Systems, Reproduced by Permission of John Wiley & Sons Ltd. © 2006).

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clearly show that relaying may be of advantage both for increasing throughput close to the cell border of a BS and for providing radio coverage to areas otherwise shadowed from the BS.

8.3.3 WINNER Test Scenarios The WINNER project defined three test scenarios that represent the typical BS deployments expected of IMT-Advanced communication systems. For each scenario, a baseline relay deployment has been defined to study the properties of the relay network (see Chapter 13 for details). 8.3.3.1 Base Urban Coverage Test Scenario In the base urban coverage test scenario, an urban macro-cellular deployment is assumed that uses the FDD physical layer mode of WINNER. The RNs considered in this scenario are deployed below rooftop with a single antenna and a significantly lower output power of 37 dBm compared to the 46 dBm for the macro BS, resulting in smaller equipment and allowing for much higher deployment flexibility. In addition to the test scenario for one RN per sector as depicted in Figure 8.10(a) and described in Chapter 13 we have also studied a scenario with three RNs per sector (Figure 8.10(b)). The exact deployment of the RNs is outlined in Table 8.1. Figure 8.10 clearly shows Table 8.1 RN deployment in base urban coverage test scenario with an inter-site distance of 1000 m. RNs per sector 1 3

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Table 8.2 Spectral efficiency comparison of BS only and RN deployment adopting connection-based scheduling (CbS). Scenario

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that three RNs can cover big parts of the border areas to other cells, which is not the case for one RN per sector. The results in Table 8.2 were obtained by adopting the connection-based scheduling (CbS) [WIN2D351; RCC+07], which is a resource request and assignment strategy designed for multi-hop relay networks with the support of flow prioritization according to the users’ service. CbS is specified as optional in IEEE802.16 TGj mobile multi-hop relaying (MMR). We can observe that relaying in conjunction with CbS, even with only one RN per sector, provides 10 % increase in spectral efficiency as compared to the BSonly deployment. By deploying three RNs per sector, the spectral efficiency is increased by 25 % even without coordinating the relay transmissions with the interference coordination scheme at the BS (see Section 8.4.4), since three relays can cover the majority of the cell edge.

8.3.3.2 Metropolitan Area Test Scenario The metropolitan area test scenario is an urban micro-cellular scenario, modelled by a twodimensional Manhattan grid, using the TDD physical layer mode of WINNER. In the baseline scenario of Chapter 13, the BS deployment follows the UMTS 30.03 recommendation [ETSI97] and an RN has been added to both BS sectors. Both the BS and the RN are deployed below rooftop and in streets. It models the important scenario of an operator that wants to upgrade an already deployed 3G network to an IMT-Advanced system. The BS locations are re-used and relays are utilized to extend the coverage area and to distribute the capacity more evenly. The baseline scenario is depicted in Figure 8.11(a). However, cost comparison studies in [DWV08] have shown that the scenario presented in Figure 8.11(b) is more cost efficient. Most of the cell throughput gain is achieved by adding one relay per BS and the additional increase from the second RN is less than 4 % while the overall deployment costs increase by more than 7 %. The RNs in both deployments are equipped with two antennas, one directional (sector) antenna to communicate with the BS and the other one to serve its UTs. In Figure 8.11(a), the UT antenna is a directional sector antenna pointing away from the BS; in Figure 8.11(b) it is an omni-directional antenna. Even so, only one transceiver chain is required because the RN does not transmit to the BS and serve its UT at the same time. The transmit power is limited to 5 W for each BS sector and to 1 W for the RN. The lower transmit power of the RN results in smaller equipment, which allows for more flexibility in deployment than a micro-BS with two sectors. Consequently the RN can, for example, be deployed on lamp posts.

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8.3.4 Cost Efficiency of RNs One argument for introducing relays to cellular systems is the expected reduction of deployment cost: in many deployment scenarios, a relay-based deployment can provide the same performance as a single-hop deployment for less cost. (Besides this cost argument, it should be kept in mind that RNs are the only choice for extending the range of a BS, if no backhaul connection is available. This may be typical of deployments in developing countries.) Other arguments for introducing RNs to cellular systems are:

r local capacity in a REC that is much more balanced throughout the cell area than in SCNs; r higher spectral efficiency; r higher cell capacity. The deployment costs for relays include the installation, power connection and initial configuration costs. They depend on the functionalities provided by the relay and on additional requirements they have to fulfil. Hardware parts of the relay that determine the device costs comprise the power amplifier and RF chain, the memory and the antenna configuration. Their costs can be much lower than for a base station due to, e.g., smaller and less complex hardware, the lack of a backhaul connection and no reliability requirement that would require back-up batteries. To achieve low hardware costs and a small size, the WINNER relay output power has been set to 5 W in the wide area scenario and 1 W in the metropolitan area scenario. The number of antennas at the RN has been limited to two and in most studies in WINNER only one transceiver chain has been assumed. The size of the relay will also determine the

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Total Cost (k€ ) 397.8 42.2 12.4 26.6 6.1

Pico BS without fixed line connection

deployment flexibility and thus the site acquisition and rental costs, which are the main cost drivers for radio access points (see Chapter 14). Micro and pico RNs deployed as add-on to macro and micro BSs are seen as the most likely scenario for MCNs in highly developed countries. However, macro RNs may be beneficial in some specific scenarios, e.g. to cover large rural areas with low traffic density. The low output power and small size of the RN will increase public acceptance of broadband radio networks. The possible cost savings from deploying small (micro or pico) RNs in cellular systems are derived in [SW07]. Table 8.3 summarizes example total capital expenditures (CAPEX) and operational expenditures (OPEX) for BS and RN, assuming a traffic load per UT as typical in 3G systems (see Chapter 14 for more details). The actual costs will vary depending on the specific deployment scenario. Nevertheless the cost examples give some guidance on the achievable cost ratios between different BS and RN types. The properties of the metropolitan area relay equipment are closer to a pico BS than to a micro BS and therefore also the CAPEX will be similar. The relay equipment in both the wide and metropolitan area achieves its cost benefits mostly by reduced OPEX because it does not require a backhaul connection and the higher flexibility in deployment results in lower site rental costs. Since the WINNER system is broadband requiring up to 1 Gbps access lines in MA and 100 Mbps in WA, the OPEX assumed for micro and macro BS in Table 8.3 (and in Chapter 14) will likely turn out to be too low in areas not well developed with fibre-based infrastructure. The required backhaul capacity cannot be delivered by low-cost xDSL lines; much more expensive solutions are required and, in some locations, the backhaul operators might even be unable to offer high-capacity, fixed network access to connect BSs at reasonable costs, leaving RNs as the only choice. Cost assessment studies have been performed in the WINNER project for the wide area deployment scenario using the indifference curve method [WNJ+08]. These studies have concluded that, for unequal traffic density in a wide area deployment, it is clearly more cost efficient to add RNs to macro BSs than to increase the macro BS density. The studies also gave evidence that it is already cost efficient to add RNs instead of micro BSs to a macro BS deployment for a cost ratio of 1.5 between micro BS and RN (see Chapter 14). In these studies, intelligent placement of RNs in the cell has been assumed, i.e. line of sight (LOS) to the BS. Intelligent placement sounds complex, but an arrow marked on the housing of a (small) RN might be sufficient for a non-expert to mount an RN correctly, pointing LOS to the BS. Generic cost studies with random placement find the break-even point to be at a BS–RN cost ratio of about 9 assuming similar propagation conditions for both BS–UT and RN–UT links. A transmit power of 20 W for the BS and 5 W for the RN has been assumed. From

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Table 8.3 it can be seen that such a cost ratio is not achievable and therefore an intelligent placement of RN has generally been assumed in WINNER. Cost comparison studies have also been performed in the metropolitan area scenario. Due to limited radio access point (RAP) placement possibilities in the MA scenario, the indifference curve method cannot be applied. Instead selected deployments of BSs and RNs have been compared, representing typical deployments in the Manhattan grid. For a given performance target of 250 MB/s/km2 and uniform traffic densities, these studies conclude that the breakeven point is already achieved for a two-sector, micro-BS–RN cost ratio of three [DWV08]. An example cost ratio of about six between a two-sector, micro-BS and an RN can be derived from Table 8.3, showing that the relay-based deployment is clearly more cost efficient. These studies also concluded that an RN deployment with only one RN per BS site is more cost efficient than a deployment with an RN added to each micro-BS sector. However, a break-even analysis considers only average area throughput, which does not reflect one other major advantage of RNs that provides cost-efficient coverage. A comparison of outdoor-to-indoor coverage in the metropolitan area shows that the indoor area covered by an RN deployment with similar costs is 9 % higher than for a BS-only deployment with micro-BS (see Chapter 13). It was concluded in [WPS03] that new sites in wide area urban deployments should use small and aesthetically discreet network elements. New micro-RN should be deployed with the following characteristics:

r open-air operation, without air conditioning or forced ventilation: the equipment should

be able to work in temperatures of −20 to +60 ◦ C, with high humidity levels and with air-salinity conditions encountered in coastal areas; r small size: front or surface linear dimensions should be measured in tens of centimetres, not in meters; equipment depth should not exceed a few tens of centimetres; r low weight, in the range of few kilograms: it should be possible to mount the equipment on an outside wall or a lamp post, without any special safety precautions; r rugged construction, to withstand handling associated with street equipment installation; r small visual profile, particularly in relation to antennas: whenever possible, flat antenna enclosures that can be placed on a building wall and painted the same colour as the wall should be employed. More visionary concepts [WPS03] talk of a ‘hamburger-sized RN’ as an ‘invisible’ infrastructure element of IMT-Advanced systems. The rationale behind this is that the equipment can be hung on an outside wall, or placed on a roof, with minimum disturbance to the building owner and without attracting much outside attention. If the power consumption is not very high, e.g., similar to a house appliance, a new electrical installation might not be required. With these considerations, the site rental fee paid to the building owner can be significantly lower than with a traditional base station siting.

8.4 Design Choices for Relay-based Cellular Networks In this section, the fundamental design options for relay-based cellular networks are introduced and the choices for the WINNER system are justified.

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8.4.1 Half-duplex Saves Costs and Improves Deployment Flexibility The choice between half-duplex and full-duplex may be different for systems that utilize time division duplex (TDD) and those that use frequency division duplex (FDD). In WINNER, we have chosen half-duplex relays in both cases. For high-capacity metropolitan WINNER deployment, TDD transmission has been selected (see Chapter 4). To operate in full-duplex mode, the RN has to be equipped with at least two transceivers and the isolation between the respective antennas has to be sufficiently high to keep self-interference low. The second antenna adds costs to the RN and the required antenna isolation limits the deployment flexibility of the RN. Therefore half-duplex RNs were chosen for TDD mode. In wide area WINNER networks, FDD transmission has been selected. Similar to metropolitan areas, half-duplex RNs are chosen in order to reduce hardware cost.

8.4.2 Decode-and-Forward Relaying Exploits Adaptive Modulation and Coding Today’s cellular radio systems employ, in rare cases, two-way repeaters to amplify and forward (AF) layer-1 signals received between the BS and the UT. Repeaters increase the noise and interference level in the system and therefore they are mostly used to cover otherwise shadowed areas, e.g. underground car parks. In addition, AF relays cannot take advantage of the higher quality of the BS link compared to the UT link by using the optimal modulation and coding scheme for each link. In WINNER, we typically assume an intelligent placement of the RN, e.g. with LOS to the BS where a decode-and-forward RN can exploit the higher SINR of this link by using a higher modulation and coding scheme. Therefore, decode-and-forward (DF) relaying has been found in WINNER to be a key technology for low-cost mobile broadband 4G systems [PWS04]. Compared to AF, each radio link may be handled, separately, e.g., by applying an ARQ protocol per hop to increase the reliability of the multi-hop connection.

8.4.3 Fixed Relays in MCN Assist Fast and Cheap Network Roll-out An MCN may employ mobile, movable or fixed RNs to connect a UT by means of a multi-hop link to the BS. Fixed RNs and multi-hop connections introduced to cellular networks may contribute to more efficient use of the radio resource, may reduce deployment and operations costs, and may substantially contribute to increase capacity at cell edges. In [SYF03], it is shown that MCN enhanced by mobile RNs can attain a significant enhancement in high data rate coverage with a proper relay design, relaying channel, and relay power selection. Mobile relays typically follow the ODMA concept presented in Section 8.2, where UTs are supplemented with relaying functionality and are chosen opportunistically to forward packets on an ad-hoc basis. For these concepts to work, the UT density has to be sufficiently high to provide coverage in the whole area. In the WINNER system, we assume that an operator will deploy the network and want to build coverage into the initial network roll-out. However, initially the UT density will be low and therefore the operator cannot rely on mobile relays to provide coverage. Hence the

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operator will have to build coverage with BSs and potential cost savings from a less dense BS deployment are lost. The high potential of MCNs that are based on fixed RNs to enlarge the coverage of a BS and also to serve areas shadowed from the BS by RNs is demonstrated in WINNER [PEW05] and other work reaching back to 2000 [EVW00]. Consequently, fixed RNs make the difference, as was found by the WINNER project: when RNs are fixed (or movable) and RNs are linked to RNs and the BS in a tree topology, routing becomes simple and efficient, and the network can be designed to be reliable and to meet the WINNER performance targets. RNs may be positioned at pre-planned locations in the cell according to need and plug-and-play placement of RNs may be applied during network evolution. The notion of the ‘relay-enhanced cell’ (REC) was introduced by WINNER to name a cell served by one base station supplemented by fixed RNs in a cellular layout.

8.4.4 Flexible Radio Resource Management Adapts to the Environment In high-capacity broadband radio networks based on OFDMA transmission, such as WINNER, IEEE 802.16j/m and LTE, the radio resources are allocated to UTs (and RNs) in terms of resource units (known as ‘chunks’ in WINNER) consisting of a number of sub-carriers allocated over several OFDM symbols. The BS broadcasts the allocated chunks periodically to inform multiple UTs of the time instant at which to receive and transmit on a specific set of sub-carriers with a specific MCS during a limited time horizon, called a ‘medium access control (MAC) frame’ (see Chapter 6). It has been found that the allocation of chunks to RNs by the radio resource control (RRC) layer can be handled in the same way as for UTs [EVW00; W02; E01; WPS03]. Scheduling of chunks to be spatially re-used by different links in the same REC and for spatial re-use across adjacent RECs has proven attractive to increase the system capacity. In densely built-up areas, such as cities, signal shadowing may contribute much to exploitation of spatial re-use of chunks in the same REC [SYF02]. Further, relay-based systems have a much higher potential than SCNs for exploiting spatial diversity as discussed in [LTW04]. Radio resource management within a relay-enhanced cell (REC) is of crucial importance to exploiting these potential benefits of relay-based deployments. In WINNER, a ‘distributed’ MAC protocol is applied: the BS, dynamically, assigns resources to itself and to the RNs in the REC (see [DRW+09, WIN2D352]). The RNs can then independently allocate these resources and, thus, frequency-adaptive transmission and multi-antenna control for UTs served by RNs can be supported without forwarding all the required feedback signalling to the BS. Figure 8.12 illustrates the flexible assignment of radio resources within a super-frame for an example scenario with three relays in the REC. Different allocations between base station and relays (RAPs) are possible: a frame can be shared between all RAPs and part of a frame can be allocated to a limited number of RAPs or to a unique RAP. As well as assigning resources to the RN when it acts as a BS, the resource assignment also decides in which frames the RN acts as a UT towards the BS. This allows the resources spent on the BS–RN link and the RN–UT links to be balanced in time. The actual resources that are assigned depend on delay requirements, traffic load, and the interference coordination scheme. In WINNER, we investigated three different resource assignment strategies: static load-based resource partitioning between RAPs in a REC for MA and WA deployment; dynamic resource sharing in the WA scenario coordinating beams used

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Figure 8.12 Allocation of a super-frame using the flexible resource partitioning scheme in a REC with three RNs. The super-frame has a preamble and an eight-frame payload. The BS allocates (part of) the resources to the RNs; the RNs independently schedule their associated UTs on cost of their allocations.

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by the RAPs in a REC; soft frequency re-use in combination with static load-based resource partitioning for MA deployment. 8.4.4.1 Static Load-based Resource Partitioning Even though flexible resource partitioning allows the BS to change the resource assignment to the RAPs in the REC in each super-frame, it appears advantageous to aim at a slowly changing resource allocation pattern. By assigning the same chunks to BS and RNs in a REC over multiple sequential super-frames, chunk-based TDMA channels are introduced. These TDMA channels may be used to flexibly serve the associated UTs in UL and DL directions. The size of these TDMA channels may fluctuate over time according to the needs of the respective RAPs. A fairly static, TDMA-based resource allocation results in predictable interference, which is well suited to support self-organizing, multi-cell interference coordination and the spectral efficiency of the network can thereby be enhanced. Assigning chunks exclusively to one sub-cell in a REC is sub-optimal especially if the subcells are (partly) shadowed from each other. The capacity of a two-hop REC can be significantly increased by exploiting spatial separation of sub-cells through simultaneous transmissions. In Figure 8.13, pairs of sub-cells, e.g., (RN#1, RN#2) and (RN#3, RN#4), respectively can serve UTs simultaneously, since they are mutually shadowed from each other. RN#1 does not cause harmful interference to UTs served by RN#2 and UTs in the sub-cell served by RN#1

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Figure 8.13 Static time-domain relaying with spatial re-use of one chunk: one OFDMA subchannel and shadowing is exploited. Two groups of RNs serve their UTs simultaneously and the BS then serves its UTs. The cycle repeats in successive MAC frames. The diagram is from [WMB06]. (Reproduced by Permission of John Wiley & Sons Ltd. © 2006).

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Figure 8.14 Groups of BSs that are spatially well separated are given resources in orthogonal timeslots (phases) or subchannels and the schedule is repeated periodically. (B.H. Walke, S. Mangold, L. Berlemann IEEE 802 Wireless Systems, Reproduced by Permission of John Wiley & Sons Ltd. © 2006).

do not cause harmful interference to RN#2. This can be exploited for spatial re-use of a chunk in two sub-cells. In a similar way, the transmissions over multiple adjacent RECs can be coordinated in large Manhattan grids while keeping the interference low, see Figure 8.14 and [SPI03]. A full cycle of chunk allocations, serving all sub-cells of the REC, starts with serving RN-based sub-cells; then two out of four RNs serve their UTs simultaneously (so RN#1 and RN#2 serve their UTs, followed by RN#3 and RN#4 serving their UTs), leading to a concurrent allocation of the same chunk for different communication relationships; and, finally, the BS serves its UTs. The end-to-end performance assessment results in Section 8.6 utilize static load-based resource partitioning. 8.4.4.2 Dynamic-resource Sharing in Wide Area Deployment with Beamforming Figure 8.15 illustrates a strategy for dynamic resource sharing (DRS) within a REC. UTs served by a RAP that are unable to share the same resources are dynamically grouped in logical beams (LBs).2 It should be noted that the LBs represent groups of users and they can be seen as a dynamic version of the sectors in a RAP. For example, in Figure 8.15, users A and B are grouped within the same beam because they are spatially correlated and cannot share the same resources. Users of different LBs over multiple sub-cells with the same fill pattern in Figure 8.15 can share the same resource. The single antenna relays studied in WINNER have only a single ‘beam’ and a multi-antenna BS coordinates the use of its own beams with the resources that it has assigned to the RNs. 2A

logical beam must be distinguished from an ‘antenna beam’ formed by steering an antenna array.

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D C

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Figure 8.15 Logical beam configuration using dynamic resource sharing (DRS). (Reproduced with kind permission of VDE © 2009).

Three main steps can be identified: creation of the beams, grouping of the beams and resource partitioning. The procedure proposed in [CFR+07] for the beam creation is derived from the tree-based structure in [FDH05]. In the original algorithm, compatible users are grouped together, while in our implementation the outputs are groups of incompatible users. Different beam creation metrics could be used, such as maximization of the average inter-user correlation within a beam or maximization of the minimum inter-user correlation. Furthermore, under reasonable assumptions the correlation can be considered as a function of the angular spread between the users. Since grouping of UTs is a nondeterministic polynomial-time (NP) hard problem, run-time efficient heuristics have been developed, for example in [HEP+07]. In order to select the beams that might be grouped and share the same resources, a simplified estimation of the transmission rate of a beam is used [CFR+07]. The computation of this term makes use of estimates of the inter-beam interference, i.e. a measure of the interference experienced by users of a beam when a transmission is occurring in another beam. The actual value can be calculated in different ways, e.g. through the maximum or average inter-user interference. The resource partitioning algorithm that we investigated in [FRC+07] aims to achieve the maximum possible cell throughput by allocating a chunk to the groups of beams having the highest total rate. It further keeps the balance between the relay and access link allocation by making sure that enough resources are assigned to the relay link for each allocation on the access link. The chosen approach consists of having a stepwise alternating allocation between the two links.

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Figure 8.16 Soft frequency re-use with two power mask levels assigned to neighbouring radio access points. (Reproduced with kind Permission of VDE, © 2009).

Simulation results, presented in Section 13.6.1.4, give evidence that the proposed DRS strategy applied to an MCN outperforms the SCN in the base coverage urban scenario: the spectral efficiency is increased by about 25 %. 8.4.4.3 Soft Frequency Re-use and Static Load-based Resource Partitioning Soft frequency re-use (SFR) assigns different power masks to neighbouring radio access points (RAP). In Figure 8.16, the 230 chunks have been subdivided in the frequency domain into two equal-sized groups and the relative power level per chunk P = {1, 0.25} has been assigned to each group. The absolute power level depends on the maximum transmit power of the RAP. The SINR variations introduced by SFR can be utilized to schedule high power resources to UTs with strong interference (for example at street crossings or between two RNs in the same street). Suitable phased scheduling algorithm has been developed in WINNER that allocates high power resources to low SINR users and low power resources to high SINR users close to the RAP. By utilizing soft frequency re-use in combination with static load-based resource partitioning, the cell throughput in the scenario depicted in Figure 8.17 is increased by 28 % compared to a BS only scenario (see Chapter 13 and [DWV08]). Further, in [DHW+07] it has been shown that a power mask adaptation according to the traffic load of the RAPs in a REC can increase the throughput experienced by users that otherwise would reach a low throughput.

8.4.5 MIMO Techniques Boost Capacity MIMO is one of the key technologies in the WINNER concept (see Chapter 7), where UTs are expected to employ multiple antennas (at least two) in most scenarios. Since the complexity (and thus the cost) of the RNs is envisaged to be more than that of the UTs (but less than that of the BSs), RNs can also support multiple antennas. However, the efficient utilization of these multiple antennas in BS–RN and RN–UT links are likely to be different. The characteristics of the RN–UT links are expected to be similar in many ways to those of the BS-UT links. Therefore, employing MIMO techniques in the RN–UT links, similar to the MIMO techniques in the BS-UT links, will be the most efficient approach. However, the characteristics of the BS–RN links are quite different due to the point-topoint (wireless feeder) nature of such links. RNs will be deployed above the clutter (as much

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Power masks:

REC

[1, 0.25] RN

2 sector BS

RN

[0.25 1]

Figure 8.17 Soft frequency re-use power mask assignment for the metropolitan area deployment scenario used in performance evaluations.

as possible) in carefully selected locations in order to establish very good links with the corresponding BSs (low propagation exponent, mild shadowing, and mild fading). As a result, in many cases, the BS–RN links will have a strong LOS flavour. As is well known, MIMO techniques cannot profit in LOS conditions due to the lack of sufficient dispersion to make the channel matrix full-rank. Therefore, MIMO is not the preferred multi-antenna technique in BS–RN links. The best way to utilize the existing multiple antennas at RNs is, in most cases, to form highly directional beams towards the corresponding BSs. Then, high spectral efficiencies can be achieved using very high adaptive modulation levels. Antenna gain can be used at both transmitter and receiver. To keep inter-cell interference as low as possible, receive gain is applied wherever possible. It is worth noting that a BS–RN link is a stationary link; therefore, there is no need for steering after a beam is formed; this makes the implementation very feasible. By the same token, a BS can reach its multiple RNs through space-division multiple access (SDMA); once again, the static nature of the environment enables the SDMA implementation to be very feasible. One major argument often advanced against relaying has been the ‘bandwidth loss’ as a result of the half-duplex nature of the relays. The high-gain beams enabling high spectral efficiency levels and the dense channel re-use as a result of the SDMA technology minimize the ‘lost bandwidth’ in facilitating the BS–RN links. In fact, it has been proven that RECs using high-gain antennas to connect BS and RNs may have a higher spectral efficiency than SCNs in the same scenario [PEW05].

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Virtual Antenna Array

Figure 8.18 Combination of source signal and repeated signal in cooperative relaying, from [WMB06]. (Reproduced by Permission of John Wiley & Sons Ltd. © 2006).

8.4.6 Cooperative Relaying Boosts Performance Cooperative relaying (CR) is an option in the WINNER system. Today, the related complexity and processing power consumption appears to be too high for a low-cost RN, however, this may not apply in the future when WINNER-like systems are deployed. CR is a technique studied in [CG79] for Gaussian channels with and without feedback. The receiver may exploit macro diversity gain by processing the original signal (data block) received from the source and the repeated signal (data block) received from an RN to improve the SINR of the signal at a receiver. Further, two or more RAPs (BS–RN or RN–RN) might form a virtual antenna area and MIMO techniques can be applied (see Figure 8.18). In this section, we refer to traditional relaying without cooperation as ‘single-path3 relaying’ to distinguish it from cooperative relaying (CR). In WINNER, two forms of cooperative relaying have been investigated:

r exploitation of macro-diversity by combining the user signal received at a receiver from both the RN and the transmitter preceding the RN in two separate time slots;

r application of multi-user pre-coding techniques to BS antennas augmented by RN antennas. Two-hop relaying protocols that exploit macro-diversity operate in two phases: in the first phase, the BS transmits data to the RN; in the second phase, the RN forwards the data. To gain on large-scale spatial diversity, most cooperative relaying protocols benefit from the combination of first phase and second phase transmissions at the UT. For single-antenna BS and RN, significant performance increase can be achieved by this form of cooperative relaying (see Section 7.7). In a multiple-antenna system, this implies that dedicated MIMO algorithms (for instance, beamforming and other SDMA algorithms) cannot be applied, since one stream is only optimized to one destination. Furthermore, as the position of an RN can be chosen 3 ‘Single-path’

refers to decoding at the receiver of one signal out of many multi-path signals.

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Table 8.4 Link throughput in the urban coverage and dense urban scenario; all values are given in Mbps. Protocol

δ5%

δ

δmax

Direct Single-path relaying Cooperative relaying

0.24 1.12 3.23

3.42 4.99 7.64

13.76 24.35 40.19

to obtain good channel conditions, the data rate on the relay link is likely to exceed the data rate on the RN–UT links. This inhibits cooperation on a physical layer because the links use different modulations and information combining and the UT would be unable to decode the BS transmission. One solution might be to decrease the data rate on the relay link and to forbear from using MIMO algorithms, which could lead to significant performance degradation. In order to gain insights into the performance of a MIMO relay-based system, we compare the performance of direct transmission for a BS-only deployment, single-path relaying and cooperative relaying. The BS is equipped with four antennas, the RN with two antennas and single antenna terminals are assumed. In all cases, we utilize LQ transmit pre-coding as described in [KFV06] where we restrict the number of concurrently served users to four. This is not the default multi-user pre-coding scheme in WINNER, which is successive minimum mean square error (SMMSE) pre-coding (see Chapter 7). Nevertheless, we expect similar results for SMMSE pre-coding and the insights are still valid. In the case of cooperative relaying, the pre-coding is done over both the BS and RN antennas. In single-path relaying, the pre-coding is separately done over the BS and RN antennas. This section analyzes only the performance; it does not take the additional signalling overhead into account. Event-driven, stochastic, system simulation studies were performed in a Manhattan-like dense urban scenario. The direct scenario involved 55 BSs without RNs at street crossings and the relaying scenarios involved 20 BSs with four RNs each. We use the following measures to evaluate the system performance: the fifth percentiles4 δ5% , the average user throughput δ = E x,y δ (x, y), and the maximum throughput δmax = maxx,y δ(x, y). Table 8.4 and Figure 8.19 summarize the obtained results. In Table 8.4, δ5% shows that relaying reduces the number of UTs with low throughput. Cooperative relaying increases the fifth percentiles of the user throughput CDF 13-fold compared to direct transmission and threefold compared to single-path relaying. Also the maximum and average throughputs are significantly increased by both single-path and cooperative relaying strategies. Under the given assumptions and scenario setup, cooperative relaying more than doubles the average throughput compared to direct transmission. Cooperative relaying offers the potential to further improve the gain of single-path relaying by more than 50 % in the metropolitan area scenario. More details on the system performance of MIMO cooperative relaying can be found in [RBF08]. The WINNER relaying concept does not require the UT to distinguish between a BS and an RN, and the RN appears like a BS to the UT. The RN transmits its own BCH and signals resource allocation to its associated UT. Thus, it is not practical to perform control signalling 4 A fifth percentile gives the value of a cumulative distribution function that is only missed by 5 % of the realizations of the variable considered.

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1.0

Pr {δ (.,.) ≤ δ}

0.8 0.6 0.4

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0.2 0 0

5

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15 20 25 Throughput δ in Mbps

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Figure 8.19 User throughput CDF (direct transmission, single-path relaying and cooperative relaying), from [RBF08]. (B.H. Walke, S. Mangold, L. Berlemann IEEE 802 Wireless Systems, Reproduced by Permission of John Wiley & Sons Ltd. © 2006).

cooperatively between multiple RAPs. One of the RAPs is a serving RAP which signals the cooperative resource allocation, i.e. even when exploiting large-scale spatial diversity by combining transmissions of two hops at the destination, one of the RAPs signals both allocations to the UT. The serving RAP also coordinates retransmissions, which are performed by the serving RAP itself or cooperatively. When applying multi-antenna techniques by forming a ‘virtual antenna array’ over multiple RAPs, the cooperative transmissions take place on the same resources and they have to be coordinated between the cooperating RAPs. In the WINNER cooperative relaying concept, the first common node in the tree topology, i.e. the BS in two-hop deployments, decides about the resource allocation to cooperatively served UTs. It then forwards the data with the resource allocation and the transmission format and, if applicable, pre-coding weights to the cooperating nodes. For the cooperating RAPs, these allocations are taken into account by the constraint processor in the scheduler (see Chapter 4) and the UTs that are not cooperatively served are scheduled on other resources. The delay between resource allocation decisions and the cooperative transmission should be as short as possible or frequency adaptive scheduling gains will be reduced. Without cooperative relaying, feedback from UTs can be taken into account when scheduling the next frame. In the case of cooperative relaying in a two hop setup, the feedback has to be forwarded to the BS which has to signal the resource allocation, resulting in an additional delay of two frames (1.4 ms in the WINNER system). The delay increases further, when delaying the cooperative transmission to allow for a retransmission on the BS–RN link. Therefore for deployments with stable BS–RN link quality, the target packet error rate on the BS–RN should be kept low and the cooperative transmissions should be scheduled immediately, assuming a successful first-hop transmission.

8.5 System and Network Aspects From a system architecture point of view, the RN is an integral part of the radio access network (RAN). Regardless of the RAP, which can be either a BS or an RN, the UT is connected to

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Figure 8.20 MCN system architecture as investigated by WINNER. (Reproduced by Permission of IEEE © 2009).

the WINNER RAN via the UT interface (IWU ) that is supported by both BS and RN. The IWU protocols terminate in the gateway (GW) and the BS in the single-hop scenario. In the multi-hop scenario, the protocols terminate in the RN, BS and GW. In MCN, the BS and its RNs form a REC. Figure 8.20 shows the basic elements of a multi-hop connection. The UT is always connected via an access link to a RAP. The link between two RAPs is called a relay link. The multi-hop link is comprised of at least one relay link and one access link. The integration of the RN into the system relates to two groups of functions and protocols:

r the role of the RN in the e2e-ARQ protocol as part of an end-to-end (e2e) connection; r the RN interface towards the UTs or other RNs, i.e. the BS functionalities provided by the RN, such as the transmission of broadcast channels. The integration of the RN to the control plane of the WINNER system is depicted in Figure 8.21. A detailed description of the WINNER system architecture and the protocol layers can be found in Chapter 4. This section describes protocols and functions required in a relay-enhanced RAN:

r forwarding of user data (Section 8.5.1); r broadcast information transmitted by the RN and resource allocation (Section 8.5.2); r flexible resource partitioning (Section 8.5.3); r ARQ (Section 8.5.4). The functions described here represent the most important tasks to be considered when designing a relay-enhanced system; other functions required but not addressed here are flow control, admission control, handovers and load balancing (which are covered in Chapter 10).

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RRC

RRC

RRC2

RRC2

RLC

RLC

RLC

MAC

MAC

MAC

PHY

PHY

PHY

Transport Network

Transport Network

GW

IBRN

Iw RN UT

BS

Figure 8.21 Control plane of the WINNER architecture enhanced by RN.

8.5.1 Relaying by the WINNER MAC Protocol The MAC protocol of the WINNER cellular system is based on MAC frames representing orthogonal, dedicated uplink (UL) and downlink (DL) resources for a well structured access preventing BS–BS and UT–UT interference. With respect to the chosen duplex scheme, the UL and DL resources are separated either in the time (TDD) or frequency domain (FDD). Figure 8.22 shows the WINNER MAC frame in TDD mode and example roles of the RN in each frame (see Chapter 4 for more details about the WINNER frame structure). It is structured at the highest level into super-frames that are composed of a number of MAC frames (in WINNER, parameterized to eight frames per super-frame), each 0.6912 ms long. Due to the short MAC frame duration, delay and round trip time (RTT) of the system are kept short. Dedicated parts of the resources within the super-frame are reserved to transmit the broadcast channel (BCH) and synchronization symbols (SYNCH) on the DL. Some UL resources are reserved for the random access channel (RACH). These resources appear periodically according to the period of the MAC super-frame. The main function of the RN is to forward user data received from the UT (UL), the BS (DL) or a preceding RN (DL). All DL communication takes place during the DL phases and all UL data is transmitted during the UL phases in order to avoid harmful interference to adjacent RECs. In WINNER, a single transceiver is specified for both RN and UT. The half duplex approach forces the RN to switch between UT and BS operation modes. These two alternating modes of operation are highlighted in Figure 8.22: The RN can either serve its UTs (RN↔UT) in BS mode or communicate with the BS (BS↔RN) in UT mode. In other words, the RN is not able to serve its UTs while communicating with the BS. On the other hand, the BS may serve its one-hop UTs while serving an RN and while an RN is serving the UTs of its cell sharing the same resources available from the OFDMA-based MAC frame.

8.5.2 Cell Broadcast and Resource Allocation UTs connected to the network must be able to receive both the BCH and the synchronization symbols broadcast per super-frame period. Besides the BCH, the UTs have to receive scheduling information related to allocation of radio resources in each MAC frame.

309

Synch RACH

Synch

RACH

Figure 8.22 MAC frames grouped in a super-frame for TDD operation: broadcast channel (BCH), synchronization preamble (Synch) and random access channel (RACH).

RACH

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The BCH serves to convey control information to the UTs, e.g. to enable initial communication between a UT and the system. The BCH is usually transmitted using a low modulation and coding scheme and therefore is well protected against transmission errors. The range of error-free reception of the BCH determines the cell edge. The WINNER RNs act in the cellular system like a BS, i.e. the RN transmits broadcast information addressed to the sub-cell (as part of the REC) it is responsible for, using its own dedicated resources. In this case, the RN also signals the resource allocation within its one or more (if sectors are used) sub-cells.

8.5.3 Radio Resource Partitioning Radio Resource Partitioning (RP) serves to manage radio resources such that intra-REC and inter-REC interference may be controlled (Section 8.4.4). This is done by partitioning the resources and putting restrictions on any set of resources when used by any specific RAP (the restrictions are RAP specific and may be different in DL and UL). This procedure is governed by the RP function, which dynamically performs the required actions and further distributes the information to the affected RAPs. In order to limit the overhead and to ensure stability of the system, these actions should be carried out infrequently. The three alternatives for RP in RECs that are described in Section 8.4.4 fit the same resource partitioning framework. The following essential elements have to be defined within the RP framework:

r resources to be partitioned: r frames in super-frame where RN serves UT or communicates with BS; r chunks assigned to RN; r power mask for chunks; r granularity of resources: r group of four chunks in the frequency domain; r one frame in the time domain (∼0.7 ms); r measurements required: r CQI feedback; r received signal strength to neighbouring RAP (used for updates of power mask (in MA) and to identify RAPs that can transmit in parallel: measurements used for handover purposes are sufficient); r what performs measurements: UT and RN; r estimate of required resources (in chunks) to serve UT; r how often: new measurement and message every 100 ms; r what collects information: serving RAP; r what uses information: BS; r information push or poll: push; r resource-partitioning message: r content power mask (in MA); r frames assigned to serve UT in super-frame; r chunks assigned within the UL/DL frames to the RN; r timing constraints: new partitioning at most with every super-frame but preferably every 100 ms.

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Figure 8.23 Outer-ARQ with relay as part of the process.

The minimum granularity for RP is assumed to be one MAC frame (due to in-band signalling) in the time domain and one OFDMA resource block (chunk) in the frequency domain. If the network uses a central network control node for inter-cell interference coordination, the central node generates the RP information for all RAPs under its control. The central node can also be exercised by a group of BSs together, negotiating the RP amongst themselves. The BS conveys the RP information by transmitting a Radio Resource Control (RRC) message to all the RNs under its control. The RRC messages may be protected by end-toend ARQ. In a multi-hop (>2) network the resource partitioning information has also to be conveyed over all the hops in the network. As a consequence, an additional delay will be added to the RRC messages compared to a system without RNs. Further, also the information used to calculate the RP has to be forwarded from the RNs to the BS. Thus, the number of hops and the utilized ARQ scheme will put restrictions on the inter-cell interference coordination scheme.

8.5.4 Relay ARQ Transmission in an MCN should result in the same reliability as in an SCN to guarantee a required maximum residual packet error rate. In order to achieve the required maximum residual packet error rate over multiple hops with a separate retransmission protocol on every hop, the residual packet error rate on each hop has to be even lower than in the single-hop case. This results in a higher overhead than in the single-hop case and it further emphasizes the need for two layers of retransmission protocols, Moreover, by incorporating an outer-layer RLC-ARQ scheme (that terminates in the BS and UT, see Chapter 4), resource-efficient (and lossless) intra-REC handovers may be supported as retransmissions may be initiated from the BS after handovers. WINNER utilises an outer-layer RLC-ARQ not only between the BS and UT but also at the RN (see Figure 8.23). The benefit of this approach is that Relay-ARQ may be used for local error recovery over multiple hops which lead to faster recovery (HARQ errors on the second link lead to retransmissions only on the second link). Moreover, the BS may poll the RN over one hop to release packets and during handover the BS need only poll the RN for UT status (if needed, the RN may poll the UT for its status). In addition, these messages implicitly indicate the respective buffer status for each node along the path and may hence be used for flow control (e.g. if there is a large discrepancy between the number of acknowledged packets in the RN and the UT, it suggests that the transmission rate should be decreased). Depending on the frequency of polling, two options have been defined for RLC-ARQ in the RN:

r Two bits per entry are used to indicate the buffer occupancy status for the RN and for the BS or UT. That is, outer-ARQ is enhanced with Relay-ARQ at the RN (i.e. the RN may respond to the transmitter with NACK (not received by peer), RACK (received by peer but not by the final receiver), or ACK (received by final receiver).

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r The RN is polled for RN or UT buffer occupancy status separately using one bit per entry. In the status report, one bit in the RLC-ARQ message indicates whether the RLC-ARQ feedback is sent on behalf of the UT or the RN. The second option reduces the overhead in scenarios in which RLC-ARQ at the RN is mainly used to recover from errors of the inner HARQ between BS and RN, e.g. NACK to ACK errors. Combining the acknowledgements of the RN and the UT (the first option) reduces the overhead in scenarios in which frequent polling is implemented, since only one message has to be sent. Thus neither of the options has been selected as the preferred option but one of them should be implemented in a relay-based deployment.

8.6 System-level Performance Evaluation In this section, we present in detail the end-to-end performance assessment results of the relay-based WINNER radio access network. Similar to previous results in this chapter, they have been obtained by system level (i.e. multi-cellular) simulations. Additional results for the end-to-end performance of relay-based deployments can be found in Chapter 13 and in [WIN2D353, DRW+09]. The example results are presented for the base urban coverage WA scenario to present the advantages of RECs in a system context. The performance gain over a traditional SCN deployment derived from a relay-based MCN is presented and the impact of static resourcepartitioning schemes is discussed (Section 8.6.3). The partitioning schemes investigated are different flavours of the static, load-based partitioning and dynamic resource sharing schemes described in Section 8.4.4.

8.6.1 Scenario and Traffic Modelling We study a scenario similar to the one introduced in Section 8.4. A macro-BS deployment provides the base urban wide area coverage, modelled by a regular hexagonal grid with a frequency re-use factor of one. Three RNs are added to each BS in areas with high user density and deployed below rooftop. The RNs are intelligently placed with LOS connection to the BS and to the UT in its vicinity, which might require a second antenna towards the wide-area BS. From the perspective of the UT there is no difference when connected to either of the two RAP types. To achieve the highest possible spectral efficiency, the re-use factor for adjacent RECs is assumed to be one. Maximum possible inter-cell interference results from this assumption. User data traffic is modelled according to a per-UT Poisson arrival process of packets. All simulation experiments are made assuming a fixed number of 44 UTs per REC. The overall traffic load per cell is varied by scaling the share of traffic of each UT, e.g., at 50 Mbps cell load, each UT individually offers 50 Mbps/44 = 1.136 Mbps. At a fixed packet size of 128 bytes, this corresponds to a mean of 1110 packets per second and UT. UTs in a REC are assumed to be evenly distributed in circular areas around the RAPs, see Figure 8.24. In the presented set of simulations, the 44 UTs in the REC are divided into 26 UTs directly connected to the BS and 18 UTs connected to one of the three RNs (six UTs per RN). In the SCN case, used as a reference system, all 44 UTs are directly connected to the BS. UTs are assumed to be stationary in this study. While all seven RECs are fully modelled to provide a realistic interference environment, the results have been collected only from the centre REC.

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313

x [m]

Figure 8.24 Multi-cellular wide-area MCN: 44 randomly placed UTs per REC and three RNs per BS, with the BSs and RNs regularly repeated.

8.6.2 System Model Most of the assumptions of the system model follow the assumptions outlined in Chapter 13. Important aspects of the model and deviations from the baseline assumptions are explained in this section to help the understanding of the presented simulation results. The MAC super-frame of the system investigated is shown in Figure 8.25. A super-frame comprises eight MAC frames. Relay nodes in alternating MAC frames switch between behaving as UTs towards the BS and as BSs towards the UTs in their respective sub-cells. The available physical resources of the radio channel are separated into chunks as proposed for non-frequency-adaptive transmission (see Chapter 9). The result is 18 disjoint frequency sub-channels, each based on 64 exclusive OFDM sub-carriers chosen sub-channel-specific

tWINNER MAC Super-Frame tMAC-Frame BS RN

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BS

Figure 8.25 WINNER WA (FDD) mode MAC super-frame.

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out of a 20 MHz wide band. The sub-carriers of sub-channels are mutually interleaved to provide sufficient frequency diversity. A chunk spans three OFDM symbols in the time domain. Consequently, a chunk comprises 192 symbols. Each MAC frame spans 24 OFDM symbols, the first three of which (=12.5 %) account for frame organization and signalling overhead. The overall spectral efficiency of a relay-based deployment depends on the degree of spatial re-use of the radio resources, not only by relay-based sub-cells (as outlined above) but also by the relay link. It is assumed here that BSs apply SDMA based on beamforming to serve both the RNs and the UTs in their sub-cells. The spatial grouping at the base station is performed according to a tree-based algorithm applying a direction of arrival (DoA) heuristic as described in [HEP+07]. DL scheduling of chunks is based on the exhaustive round-robin principle. Spatial groups of up to four users (RN, UT) are scheduled at the same time by the BS and individual UTs are scheduled by the RNs. In addition, distinct groups of RNs and UTs are able to fully exploit the good channel conditions on the relay link and to account for different amounts of traffic of RNs and UTs without causing the scheduling inefficiencies that result from unbalanced groups. In overload conditions, a bottleneck node may drop packets if the buffer capacity is exceeded. BSs, UTs and RNs have ‘output’ buffers. RNs additionally have ‘store-and-forward’ buffers. The assumption made on the buffer sizes is that base stations and relay nodes have equally sized MAC buffers (250 kbit per supported flow class), while the relays have a relatively small forwarding buffer that can store 100 RLC retransmission units. Link adaptation is performed based on mean values estimated for received power and interference levels, taking into account the expected beamforming gains. A fixed code rate of 12 without puncturing is chosen. The MCS is chosen according to the predicted SINR and thresholds for switching between MCSs are chosen as shown in [PSW08]. The link-level mapping is performed using a mutual information-based approach as described in [BAS+05] and is based on a low-density parity check (LDPC) code with about 2000 bit code word length. The applied propagation models follow the scenario assumptions outlined above and the used channel and propagation models are taken from [WIN2D111], see also Chapter 3. The propagation conditions assumed between the RAPs are given in Table 8.5. These assumptions are specific for the present scenario and the LOS assumption between RN and UT has not been used in other simulation results presented in this chapter. The scenario considers the deployment density as a parameter. The distance d between BSs has been set to 800 m and 1200 m. The distance between BS and RNs is 45 % of the BS–BS

Table 8.5 Applied propagation models. Relation

Model

LOS/NLOS

BS ↔ RN BS ↔ RN (interferer link) BS ↔ UT RN↔ UT RN↔UT (interferer link)

C1 C2 C2 B1 B1

LOS NLOS NLOS LOS NLOS

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Table 8.6 Simulation parameters and their values. Parameter

Value

Carrier frequency Channel bandwidth Number of cells Users per cell Inter-site deployment Distance BS–RN Traffic load

3.95 GHz DL; 3.7 GHz UL 2 × 50 MHz 7 (centre evaluated) 44 (26 one-hop and 18 two-hop) 800 m; 1200 m 45 % of distance BS–BS DL only; 10–120 Mbps; individual arrival processes per user 11 (uniform circular array) 1 1 46 dBm 37 dBm 24 dBm Equal load from each UT in cell No No Yes No Exhaustive round robin

BS number of antennas RN number of antenna UT number of antenna BS transmission power RN transmission power UT transmission power Traffic model Retransmissions (ARQ, HARQ) Segmentation and reassembly Link adaptation Mobility Resource scheduling

distance, placing the RNs relatively close to the cell edge. Table 8.6 summarizes the most relevant parameter values used in the simulation study.

8.6.3 Resource Partitioning The system’s time–frequency resources are assigned to groups of RAPs. At one extreme, each RAP belongs to a distinct group; at the other extreme, all RAPs belong to the same group. The groups serve as objects for intra-cell frequency planning by the partitioning scheme: RAPs belonging to the same group may re-use the same radio resources. In order to exploit resources of a REC efficiently, one may try to identify RAPs in the REC that are sufficiently well separated from each other in terms of path loss or shadowing to enable re-use of the same resources. Under central resource-partitioning control, the groups may form a basis for inter-cell interference minimization planning. The optimal partitioning and allocation of resources to sub-cells of a REC depends greatly on the distribution of UTs in the area and its related traffic load. Figure 8.26 illustrates the example resource partitioning pattern of a REC with three RNs per BS as investigated in the presented study. The pattern marked MCN eliminates intra-REC interference between RN-based sub-cells by assigning individual resources to each sub-cell. All RNs serve as RAPs in the same MAC frame and all chunks are evenly shared among them. During the frame where the RNs are active in their RAP role, the BS is silent, similar to the schedule introduced by Figure 8.22. Corresponding RNs in neighbouring RECs re-use the same resources.

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Figure 8.26 Resource partitioning: (a) SCN as a reference, with all resources used by BS, and (b) MCN with separate chunks for access links to RNs and with common chunks for relay and access link to BS.

Together with the role of the RN within the MAC super-frame, illustrated in Figure 8.22, this determines the resource partitioning in the REC. It is not the optimal resource partitioning for this scenario and represents the baseline assumption as outlined in Chapter 13. Thus, the results using this resource partitioning as introduced in Figure 8.13 are not the optimum, since no re-use of resources is performed in the REC of this scenario. Nevertheless, the results illustrate some important properties of relay-based deployments. An SCN scenario with re-use factor one is also considered, for comparison.

8.6.4 Uplink Power Control and Resource Allocation The bidirectional (UL and DL) nature of the communication in mobile and wireless networks requires establishing a balanced link budget (UL and DL cell sizes), because the cell size is determined by whichever link has the smaller range. Unbalanced transmission ranges imply resource under-utilization. Factors that influence link budgets are the peak output powers of different station types; the noise figures; the receiver gains and sensitivities; the service SINR requirements; and power control constraints. An additional requirement is to cope with the effects of frequency synchronization errors and – more severely – Doppler shift. Multi-carrier systems with an FDMA component in the

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UL require an equalized reception of UL transmissions at the BS within a tolerable dynamic range [WCL+99]. Accordingly, there is a need for an uplink power control scheme that takes into account the above constraints. This scheme will consequently have interrelations with the resource allocation (resource scheduling). Terminals located at the cell edge have to concentrate their available transmission power on a small number of frequency resources. In turn, users close to the centre of the cell may not be allowed to use all their available power and may also not be able to use the optimal modulation and coding if scheduled in parallel with a ‘bad’ (i.e. cell-edge) user. Separating good and bad users in the time domain could hence contribute to an increase in system capacity, a technique which is beyond the scope of the simulations presented here. It is a trivial observation that, for a balanced link budget, the UL power spectral density has to be in the same order of magnitude as in the DL. Eventual differences are mainly the result of different noise figures, receiver gains and sensitivities, and SINR requirements caused by different UL and DL traffic and service characteristics. As a consequence of what was said above and owing to lower peak power in UTs, less spectral resources can be used. The allocation of spectral resources to UL users is not only influenced by their power and channel condition but also by the amount of capacity the users have requested for their UL transmissions. Therefore, a joint resource-reservation and UL power-control scheme has been applied in the shown simulations (also see [PSW08]). Another novel aspect of the proposed scheme is that it is extended to relay-based deployments, taking into account the different UL power classes of RNs and UTs and the substantially different capacity requirements between RNs and UTs. The power control algorithm takes into account the above considerations by applying the following procedure: 1. Determine the UT in the (sub-)cell which has the highest path loss to the BS or RN. 2. Determine the maximum number of parallel resources this UT can use (and estimate the resulting received power at the BS or RN). 3. Taking into account their respective path loss, set the number of parallel resources and the transmission power per spectral resource for all other UTs to yield that target received power (plus a dynamic range). A fair-scheduling algorithm is further considered that takes into account:

r the constraints on parallel resources and the allowable power limits as identified by the power control algorithm above;

r the resources requested by UL users (i.e. both RNs and UTs). The resource allocation strategy calculates the overall sum of resources requested by the individual UL stations and grants each station a share that corresponds to the percentage of overall resources that the respective station has requested. In overload situations, this leads to a graceful degradation, i.e. if the overall demand can not be satisfied, each user’s granted resources are reduced by an equal percentage.

8.6.5 Simulation Results This section presents the performance evaluation results obtained from the simulations outlined in the previous sections.

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8.6.5.1 Baseline Resource Partitioning Figure 8.27(a) shows the carried DL throughput versus the total offered DL traffic. The MCN improves radio coverage from a slightly better performance at low to medium traffic loads. With increasing cell size, the SCN cannot serve all users, leading to a deviation of the carried traffic from the ideal line shown, already at low load. The MCN shows a linear increase in carried traffic with increased offered traffic (indicating good radio coverage in the REC) and – for larger cells – a higher saturation throughput than the SCN. At 1200 m inter-BS distance, a cell throughput of about 80 Mbps is achieved, 25 % higher than the saturation throughput of the SCN (55 Mbps). This capacity increase goes along with a coverage increase of another 20 %. In the uplink, gains are even bigger: for 1200 m inter-site distance, the coverage is improved by roughly 100 %, see Figure 8.27(b). Figure 8.28 shows the achieved quality of service (QoS) by means of the 95th percentile of the DL and UL packet delay versus the carried traffic: 95 % of the UTs experience a smaller 0.02 0.018

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delay than shown in the graph. Clearly, under low load the SCN deployment exhibits about 30 % shorter delay than the MCN deployment. However, all the results for MCNs show a less harsh delay increase close to saturation and overall a higher saturation throughput (the throughput where the delay starts increasing sharply) than the SCN with the same inter-BS distance. The resulting UL capacity increase (i.e. the amount of traffic that can be carried under certain QoS guarantees) amounts to more than 200 %, as shown in Figure 8.28(b); while in the DL, the increase is of the order of 25 %. The results show that the low link budget available on the uplink results in substantially lower traffic capacity provided by the SCN compared to the MCN, for all the inter-BS distances considered. 8.6.5.2 Downlink Performance of Infinite Buffer and Optimum Resource Partitioning For the same scenario with 1000 m inter-site distance, three sectors instead of a circular array and an infinite buffer traffic model, we evaluate the DL throughput for different static resourcepartitioning options. In particular, we varied the number of frames in which the RN acts as a BS, allowed the BS and the RN to serve the UT at the same time, and allowed both the RN and the BS to use the whole bandwidth. The highest cell throughput in this scenario was achieved when the RN acts as a BS in five out of eight frames, when the BS serves the UT at the same time as the RN, and when both the RN and the BS use the whole bandwidth. The cell throughput of the BS-only scenario was 139 Mbps, a bit less for the baseline resource-partitioning scheme (133 Mbps) and 263 Mbps for the optimum resource-partitioning scheme. Contrary to the previous results, the infinite buffer traffic model favours high throughput users. The user throughput CDF in Figure 8.29 illustrates that even though the average cell throughput might be a bit lower for the baseline resource partitioning there are many fewer users with low throughput and the fairness is increased. On the other hand, for the optimal resource partitioning the user throughput is significantly enhanced for the whole CDF, illustrating that the cell throughput of almost double is not achieved at the cost of low throughput users.

8.7 Conclusion Multi-hop cellular networks (MCN) based on fixed decode-and-forward (layer-2) relay nodes (RNs) have been extensively researched by the WINNER I and WINNER II projects and, thereby, have been established as a new technology proven to be both spectrally efficient and cost efficient. Cellular radio networks based on relay-enhanced cells (RECs) may significantly improve the radio range of a base station and the signal coverage in a cell. Relay-based deployments have been proven to effectively compensate for the uplink power limitations of UTs, while achieving better throughput capacity figures than single-hop cellular networks (SCN) under a comparable BS density. The imbalance in terms of throughput capacity of a UT (which, in an SCN, very much depends on its distance from the BS) has been found to be reduced substantially in MCNs and cell edges have been shown to be much better served. MCNs have been shown to be well suited to improving the performance and lowering the cost in any scenario studied in WINNER: wide area, urban, metropolitan and shortrange indoors. The relay equipment defined in WINNER achieves its cost benefits mostly by reduced OPEX because it does not require a backhaul connection and the higher flexibility in

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deployment results in lower site rental costs. The higher deployment flexibility also reduces the site acquisition costs. Part of these cost advantages still remain even if micro BSs with exactly the same properties as RNs are deployed. Since the WINNER system is broadband requiring up to 1 Gbps access lines in a metropolitan area and 100 Mbps in a wide area, the required backhaul capacity cannot be delivered by low cost xDSL lines but requires much more expensive solutions. However, in areas where a high capacity backhaul connection based on, for example, fibres is cheaply available, the cost advantage of relays vanishes but the spectral efficiency remains. Compared to mobile RNs proposed as an ad-hoc component for 3G systems, e.g., opportunity driven multiple access (ODMA), which failed to meet basic requirements, such as a smooth roll-out of a network and low-cost robust routing of multi-hop messages, fixed RNs have been found to be well suited to improve network infrastructure deployment flexibility by plug-and-play placement, ‘invisibility’ owing to the small size, EMC compatibility to humans owing to the small transmit power applied, indoors service from outdoors, low cost, and network reliability. Project IEEE 802 Working Group 16 has taken up fixed, multi-hop, layer-2 relays in its Draft Amendment 802.16j for mobile WiMAX systems, thereby acknowledging the WINNER work on REC as a basic building block of an MCN. Higher throughput capacity in the same service area directly translates into higher spectral efficiency of an MCN compared to an SCN. WINNER has found that a gain in spectral

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efficiency of MCNs, compared to SCNs, can only be realized when applying intelligent RN positioning (mainly to achieve line-of-sight connection of RN to BS) and dynamic radio resource-partitioning strategies. Radio resource management complexity has been found to scale well with the resultant network capacity, which is motivating further research in that domain especially in the context of self-optimizing networks.

Acknowledgements The authors would like to thank their colleagues of WINNER II Task 5 (Relaying) for fruitful cooperation and discussions. Special thanks go to Niklas Johansson for his very much appreciated contributions to relay-enhanced protocols and to system design in general, to Peter Rost and Michał W´odczak for their valuable work on integrating cooperative relaying into the WINNER relay-based concept and to Mark Naden, Peter Moberg and Marc Werner for providing us with insights into the cost assessment of relay-based deployments.

References [3GPP99] [AT01] [BAS+05]

[CFR+07] [CG79] [DHW+07] [DRW+09]

[DWV08] [E01]

[ETSI97] [EVW00] [EWP04]

[FDH05]

[FRC+07]

3GPP (1999) ‘Opportunity Driven Multiple Access’, Technical Report TR25.924, Version 1.0.0. Agg´elou, G.N. and Tafazolli, R. (2001) ‘On the Relaying Capability of Next-Generation GSM Cellular Networks’, IEEE Personal Communications, 8(1):40–47. Brueninghaus, K., Astely, D., Salzer, T., Visuri, S., Alexiou, A., Karger, S. and Seraji, G.A. (2005) ‘Link performance models for system level simulations of broadband radio access systems’, Proc. of IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 4:2306–11, Berlin, Germany. Costa, E., Frediani, A., Redana, S. and Zhang, Y. (2007) ‘Dynamic Resource Sharing in Relay Enhanced Cells’, Proc. of European Wireless Conference, Paris, France. Cover, T. and Gamal, A.E. (1979) ‘Capacity theorems for the relay channel’, IEEE Trans. on Information Theory, 25(5):572–84. Doppler, K., He, X., Wijting, C. and Sorri, A. (2007) ‘Adaptive Soft Reuse for Relay Enhanced Cells’, Proc. of IEEE 65th Vehicular Technology Conference (VTC 2007 Spring), IEEE, Dublin, Ireland. Doppler, K., Redana, S., Wodczak, M., Rost, P. and Wichman, R. (2009) ‘Dynamic resource assignment and cooperative relaying for cellular networks: Concept and performance assessment’, submitted to EURASIP Journal on Communications and Networking. Doppler, K., Wijting, C. and Valkealahti, K. (2008) ‘On the Benefits of Relays in a Metropolitan Area Network’, Proc. of IEEE 67th Vehicular Technology Conference (VTC 2008-Spring), Singapore. Esseling, N. (2001) ‘Extending the Range of HiperLAN/2 Cells in Infrastructure Mode using Forward Mobile Terminals’, Proc. of European Personal Mobile Communication Conference (EPMCC 2001), Vienna, Austria. ETSI (1997) Recommendation TR. 30.03, ‘Selection procedure for the choice of radio transmission technologies of the UMTS’. Esseling, N., Vandra, H.S. and Walke, B. (2000) ‘A Forwarding Concept for Hiper-LAN/2’, Proc. of European Wireless Conference, Dresden, Germany. Esseling, N., Walke, B. and Pabst, R. (2004) ‘Performance Evaluation of a Fixed Relay Concept for Next Generation Wireless Systems’, Proc. of IEEE 15th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2004), Barcelona, Spain. Fuchs, M., Del Galdo, G. and Haardt, M. (2005) ‘A novel tree-based scheduling algorithm for the downlink of multi-user MIMO systems with ZF beamforming’, Proc of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 3:1121–4, Philadelphia, PA. Frediani, A., Redana, S., Costa, E., Capone, A. and Zhang, Y. (2007) ‘Dynamic Resource Allocation in Relay Enhanced Cells based on WINNER System’, Proc. of IST Mobile Summit, Budapest, Hungary.

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[SPI03]

[SW07]

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Hoymann, C., Ellenbeck, J., Pabst, R. and Schinnenburg, M. (2007) ‘Evaluation of Grouping Strategies for an Hierarchical SDMA/TDMA Scheduling Process’, Proc. of IEEE International Conference on Communications (ICC 2007), Glasgow, Scotland. Harold, T.J., and Nix, A.R. (2000) ‘Capacity enhancement using intelligent relaying for future personal communications systems’, Proc. of IEEE 53rd Vehicular Technology Conference (VTC-Fall, 2000), Boston, MA, pp. 2115–20. Karakayali, K., Foschini, G., Valenzuela, R. and Yates, R. (2006) ‘On the maximum common rate achievable in a coordinated network’, Proc. of IEEE International Conference on Communications (ICC 2006), Istanbul, Turkey, 9:4333–8. Khun-Jush, J., Schramm, P., Wachsmann, U. and Wegener, F. (1999) ‘Structure and Performance of the HiperLAN/2 Physical Layer’, Proc. of IEEE 50th Vehicular Technology Conference (VTC 1999-Fall), Amsterdam, Netherlands. Lin, Y.D. and Hsu, Y.C. (2000) ‘Multi-hop cellular: A new architecture for wireless communications’, Proc. of IEEE 19th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM 2000), 3:1273–82, Tel Aviv, Israel. Laneman, J.N., Tse, D.N.C. and Wornell, G.W. (2004) ‘Cooperative Diversity in Wireless Networks: Efficient Protocols and Outage Behavior’, IEEE Transactions on Information Theory, 50(12):3062–80. Mohr, W., L¨uder, R. and M¨ohrmann, K.H. (2002) ‘Data Rate Estimates, Range Calculations and Spectrum Demand for New Elements of Systems Beyond IMT-2000’, IEEE 5th International Symposium on Wireless Personal Multimedia Communications, Honolulu, Hawaii, USA. Task Group IEEE802.16j (2007) P802.16j/D1 Draft Standard for Local and Metropolitan Area Networks-Part 16: Air Interface for Fixed and Mobile Broadband Wireless Access Systems – Multi-hop Relay Specification, IEEE. Pabst, R., Esseling, N., and Walke, B.H. (2005) ‘Fixed Relays for Next Generation Wireless Systems: System Concept and Performance Evaluation’, Journal of Communications and Networks, Special Issue on ‘Towards the Next Generation Mobile Communications’, 7(2):104–14. Pabst, R., Schultz, D.C. and Walke B. (2008) ‘Combined Uplink Resource Reservation and Power Control for Relay-Based OFDMA Systems’, Proc. of IEEE 67th Vehicular Technology Conference (VTC 2008-Spring), Singapore. Pabst, R., Walke, B.H., Schultz, D.C., et al. (2006) ‘Relay-based deployment concepts for wireless and mobile broadband radio’, IEEE Communications Magazine, 42:80–89. Rost, P. Boye, F. and Fettweis, G. (2008) ‘System Performance Analysis of Single-Path and Cooperative MIMO Relaying’, Proc. of 68th IEEE Vehicular Technology Conference (VTC’08), Calgary, Canada. Redana, S., Coletti, L., Capone, A. and Moretti, L. (2007) ‘A Novel Resource Request and Allocation Strategy for Relay based Beyond 3G Networks’, Proc. of 8th World Wireless Congress (WWC 2007), May. Rouse, T., McLaughlin, S. and Haas, H. (2001) ‘Coverage-capacity analysis of opportunity driven multiple access (ODMA) in UTRA TDD’, Proc. of 2nd International Conference on 3G Mobile Communication Technologies. Salbu Research and Development (Proprietary) Ltd. (1978) ‘Adaptive Communication System’, SA Patent. Song, J., Lee, H.J. and Cho, D.H. (2004) ‘Power Consumption Reduction by Multi-hop Transmission in Cellular Networks’, Proc. of IEEE 60th Vehicular Technology Conference (VTC 2004-Fall), Los Angeles, USA. Schultz, D.C., Pabst, R. and Irnich, T. (2003) ‘Multi-hop based Radio Network Deployment for efficient Broadband Radio Coverage’, Proc. of IEEE Wireless Personal Multimedia Communications Symposium 2003 (WPMC 2003), 2:377–81, Yokosuka, Japan. Schultz, D.C. and Walke, B.H. (2007) ‘Fixed Relays for Cost Efficient 4G Network Deployments: An Evaluation’, Proc. of IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2007), Athens, Greece. Sreng, V., Yanikomeroglu, H. and Falconer, D.D. (2002), ‘Coverage Enhancement through TwoHop Relaying in Cellular Radio Systems’, Proc. of IEEE Wireless Communications and Networking Conference (WCNC) 2002, 2:881–5, Orlando, Florida. Sreng, V., Yanikomeroglu, H. and Falconer, D.D. (2003) ‘Relayer Selection Strategies in Cellular Networks with Peer-to-Peer Relaying’, Proc. of IEEE 58th Vehicular Technology Conference, 2003 (VTC 2003-Fall), Orlando, Florida.

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[W01] [W02] [WCL+99]

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Walke, B.H. (2001), ‘On the Importance of WLANs for 3G Cellular Radio to Become a Success’, Proc. 10th Aachen Symposium on Signal Theory-Mobile Communications, 13–24. Walke, B.H. (2001) ‘The Wireless Media System – a Candidate Next Generation System’, Proc. of 6th Wireless World Research Forum Workshop, WWRF-6, Tempe, Arizona, USA. Wong, C., Cheng, R.S., Lataief, K.B. and Murch R.D. (1999) ‘Multiuser OFDM with adaptive subcarrier, bit, and power allocation’, IEEE Journal on Selected Areas in Communications, 17(10):1747– 58. WINNER II (2006) IST-4-027756 WINNER II Interim Channel Models, Deliverable D1.1.1, November 2006, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+/. WINNER II (2006) IST-4-027756 Relaying concepts and supporting actions in the context of CGs, Deliverable D3.5.1, October 2006, viewed 20 June 2009, http://projects.celticinitiative.org/winner+/. WINNER II (2007) IST-4-027756 Assessment of relay based deployment concepts and detailed description of multi-hop capable RAN protocols as input for the concept group work, Deliverable D3.5.2, June 2007, viewed 20 June 2009, http://projects.celtic-initiative.org/ winner+/. WINNER II (2007) IST-4-027756 Final assessment of relaying concepts for all CGs scenarios under consideration of related WINNER L1 and L2 protocol functions, Deliverable D3.5.3, September 2007, viewed 20 June 2009, http://projects.celtic-initiative.org/ winner+/. Walke, B., Mangold, S. and Berlemann L. (2006) IEEE 802 Wireless Systems: Protocols, Multi-Hop Mesh/Relaying, Performance and Spectrum Coexistence, John Wiley & Sons. Werner, M., Naden, M., Jesus, P., Silva, C., Moberg, P., Skillermark, P. and Warzanskyj, W. (2008) ‘Cost Assessment and Optimization Methods for Multi-Node Radio Access Networks’, Proc. of IEEE 67th Vehicular Technology Conference (VTC 2008-Spring), Singapore, May. Walke, B.H., Pabst, R. and Schultz, D. (2003) ‘A Mobile Broadband System based on Fixed Wireless Routers’, Proc. IEEE International Conference on Computer Communications (ICCT-2003), Beijing, China. Walke, B.H., Wijaya, H. and Schultz, D.C. (2006) ‘Layer-2 Relays in Cellular Mobile Radio Networks’, Proc. of IEEE 63rd Vehicular Technology Conference (VTC 2006-Spring), Melbourne, Australia.

9 Multiple Access Schemes and Inter-cell Interference Mitigation Techniques Jean-Philippe Javaudin,1 Genevi`eve Mange,2 Tommy Svensson,3 and Mikael Sternad4 1

Orange France Telecom Alcatel-Lucent Bell Laboratories 3 Chalmers University of Technology 4 Uppsala University 2

9.1 Introduction The definition of efficient access of multiple users to a single transmission medium is a problem that has been widely investigated in the literature. The main dimensions, such as time, frequency and space, have been explored. The choice of multiple access scheme is one of the crucial decisions made in the design of a communication system and is particularly important in the case of mobile radio systems. The aim of a multiple access scheme is to enable sharing common resources available to the system (such as frequency and time) among many users, for the transmission and provision of dedicated streams either on downlinks or uplinks. Thanks to the access scheme, the signals generated by users can be effectively separated at the receivers. Its design largely influences the total system design including the fixed part of the network and determines the quality of the system operation to a large extent. Inter-cell interference becomes a crucial issue in developing a single ubiquitous radio access system adaptable to a wide range of mobile communication scenarios. The designed system needs to integrate enhanced capabilities in order to provide users with superior quality wireless access. Wireless links may experience different kinds of interference such as intersymbol interference (ISI), multiple access interference (MAI), and interference from external

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sources. MAI can be further categorised into intra-cell and inter-cell interference depending on whether it is generated from its own cell or from other cells, respectively. Inter-cell interference mitigation has been identified as a key issue as it is directly coupled with achievable re-use of the scarce spectrum. Three main types of inter-cell interference mitigation techniques are defined and investigated: interference averaging, interference avoidance and interference mitigation based on smart antennas.

9.2 Multiple Access Schemes 9.2.1 Classic Multiple Access Schemes Time and frequency are the two conventional dimensions that can be used for the resource allocation to multiple users, but code is sometimes considered as a third one. The corresponding multiple access schemes are, respectively, frequency division multiple access (FDMA), time division multiple access (TDMA) and code division multiple access (CDMA). They are often implemented in a hybrid form as a combination of at least two of them. They can be additionally supported by the application of antenna arrays allowing for space division multiple access (SDMA). Recently, orthogonal frequency division multiple access (OFDMA), which can be considered as a special type of FDMA, has become a serious candidate for the multiple access scheme in certain applications. The multiple access schemes are sometimes grouped into narrowband and wideband systems, depending on the bandwidth allocated to a single user with respect to the coherence bandwidth of the channel. Basic multiple access schemes have been described in the literature [Rap96; Pro02; Skl88; LM88; Wes02; PL95]. The performance and quality achieved due to the multiple access scheme applied in a mobile or wireless system has to be considered in a particular environment. The results of comparison of several multiple access schemes can be different if we consider them in a single- or multiple-cell environment. Propagation conditions also play an important role in such comparisons. It is well known that in a single-cell environment with AWGN channels, all orthogonal multiple access schemes are equivalent with respect to capacity and possibility of separation of user signals at the receivers [Bai94]. The differences between multiple access schemes become visible when transmission channels exhibit frequency selectivity and time variability. These are some of the reasons why multiple access schemes need to be carefully studied. 9.2.1.1 Frequency Division Multiple Access In FDMA, individual frequency bands which define transmission channels are assigned to individual users. In [Rap96] one can find basic features of the FDMA scheme, the most important of which are:

r Adjacent channels are separated on the frequency axis by guard bands, which are necessary due to the finite slope between the pass-band and stop-band of the channel filter characteristics. This in turn decreases the FDMA spectral efficiency. r The bandwidths of FDMA channels are relatively narrow because each channel is used by only one connection at a time. In this sense, FDMA can be analysed as a narrowband approach, although the total utilised bandwidth may be much larger than the coherence bandwidth.

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r In narrowband traditional FDMA systems in which the channel is almost flat fading within each frequency slot, the data symbol period is large compared to the average delay spread. Therefore, the inter-symbol interference is small or moderate and in such cases no or only simple channel equalisation is needed. r A base station (BS) power amplifier amplifies the signal, which is the sum of many individual channel signals; thus, the amplifier has to be highly linear due to a high envelope variation of the aggregated signal. In the multi-cell environment typical in mobile systems, the exclusive choice of FDMA as a multiple access scheme results in the necessity of frequency re-use to cope with intercell interference issues (a frequency re-use factor smaller than one) and hard handover. It also requires careful radio network design taking frequency planning into account. All these factors are considered as drawbacks of this multiple access scheme. Application of FDMA as a multiple access component that is used together with other principles relaxes these requirements and allows for higher flexibility of resource management in multi-service and multi-operator environments [BJK96]. 9.2.1.2 Time Division Multiple Access TDMA is a well-understood access scheme, which has been successfully applied in many wired and wireless digital transmission systems. In TDMA, the time axis is typically divided into a sequence of periodically repeating time slots. In each slot, only one user is allowed to transmit or receive. Typically, a user has periodic access to the time slot assigned to him. The time slots are organised in frames. TDMA is accompanied by either TDD or FDD duplex schemes. The hybrid TDMA/FDMA/FDD version is used in GSM [Meh97]. A basic description of TDMA can be found in [Rap96]. TDMA applied in mobile communications is characterised by the following main properties:

r A single-carrier frequency is shared by a number of users. Each of them transmits or receives a signal in non-overlapping time slots.

r Data transmission has a bursty on–off nature. For a certain fraction of time the mobile station can be in an idle state, thus battery energy can be saved. Outside of slots in which the mobile station transmits or receives, it can monitor surrounding BSs. This enables and simplifies a mobile-assisted handover procedure. r Data bursts in the uplink have to be separated by guard periods to account for time misalignments as a consequence of synchronisation imperfections in the terminals. In TDD, a guard period is also required to account for the time interval needed to switch from receive to transmit mode and vice versa. r A relatively large overhead is required for frame and slot synchronisation. Application of TDMA allows for flexible time slot assignment, so that the number of time slots can be adjusted to the needs of particular users (see, for example, the GPRS operation rules [SSP03]). This has facilitated the introduction of packet switching. When TDMA is combined with FDMA, as is common, careful frequency planning has to be applied in a multicell environment. Hard handover is the only practical possibility for TDMA/FDMA systems, although mobile assisted handover can be utilised.

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9.2.1.3 Code Division Multiple Access In CDMA, all users transmit and receive their signals in the same band, applying unique code sequences assigned to them [Vit95]. The code sequence chip rate is N times higher than the data rate. All applied user code sequences are mutually fully orthogonal or quasi-orthogonal. Good examples of fully orthogonal sets of code sequences are the Walsh-Hadamard sequences applied as channelisation codes in IS-95 [LM98] and the orthogonal variable spreading factor (OVSF) codes applied in UMTS [HT00; SW02]. In the quasi-orthogonal case, pseudo noise (PN) m-sequences or Gold sequences derived from maximum length linear feedback shift registers (LFSR) are used. Particular cells of a cellular CDMA system are typically distinguished by application of appropriate scrambling codes based on PN or Gold sequences, in addition to the channelisation codes. Selection of either a fully orthogonal or a quasi-orthogonal set of spreading sequences is an important factor of CDMA system design. In [Rap96], the basic features of a classical CDMA scheme are summarised:

r CDMA systems using pseudo-noise sequences exhibit a soft capacity limit. Influence of multipath fading is potentially substantially reduced due to signal spreading over a large spectrum. r The frequency re-use factor is normally selected to be equal to one. Thus, all surrounding cells use the same frequency band. As a result, soft handover is possible, in which the mobile terminal temporarily receives signals from more than one BS or the signal from the mobile terminal is received by more than one BS. r Of special concern in CDMA single-user receivers is the near–far problem. The performance of such receivers is very sensitive to the quality of power control.

9.2.2 Multi-carrier Multiple Access Schemes 9.2.2.1 Orthogonal Frequency Division Multiple Access Orthogonal frequency division multiple access (OFDMA) is a multi-carrier-based multiple access scheme derived from orthogonal frequency division multiplexing (OFDM). Early publications of OFDMA include [SLK96; SLK97]. In OFDMA, an individual sub-carrier or a group of sub-carriers is assigned to each user. For high instantaneous data rates per user, several sub-carriers need to be assigned in parallel to each user. There are several methods for allocating sub-carriers to the users, where the two basic approaches are to define groups of sub-carriers consisting of adjacent sub-carriers or dispersed sub-carriers. Grouping sub-carriers minimises the inter-user interference, but the grouped sub-carriers can experience collective fading. A dispersed sub-carrier allocation is less susceptible to fading but more susceptible to inter-user interference in case of imperfectly aligned users in the frequency domain, e.g. due to synchronisation errors, phase noise and Doppler spread. In addition to OFDMA, hybrid TDMA/OFDMA can also be used to allocate time-frequency resources to users in the system. The advantages of OFDMA or hybrid TDMA/OFDMA are:

r simple frequency domain equalisation, as with OFDM; r small and controlled multiple access interference (MAI) in frequency-selective fading channels (as compared to CDMA-based schemes);

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r easy data rate adjustment; r possibility of frequency-domain scheduling and adaptation; r potentially high spectral efficiency (as compared to FDMA), due to the lack of guard bands; r distribution of total power and data rate over the sub-carriers, according to the current channel conditions. In turn, the disadvantages of OFDMA or hybrid TDMA/OFDMA are:

r the requirement for tight sub-carrier synchronisation; r the need for a mitigation or avoidance scheme at high loads to avoid performance degradation in multi-cell environment due to co-channel interference from other cells;

r other disadvantages related to OFDM, such as large signal envelope variations (i.e. high peak-to-average power ratio), requiring highly linear transceivers. The possibility with OFDMA of adapting rate and power in the frequency domain has opened an area of intensive research into the problem of finding the optimal sub-carrier and power allocation scheme for multiple users with certain quality of service (QoS) requirements as well as other constraints, such as power limitations, see [WCLM99; YL00; KLL03; KJ04], due to the different and varying channels between the BS and the user terminals (UTs). 9.2.2.2 Multi-Carrier Code Division Multiple Access Multi-carrier code division multiple access (MC-CDMA) potentially combines the benefits of multi-carrier transmission and spread spectrum and was proposed in 1993 by both Kondo [KM93] and Yee [YLF93]. In the original scheme, the data stream is spread using a given spreading code and then a different sub-carrier is modulated with each chip [CBJ93; FP93; YLF93]. As a result, MC-CDMA can be seen as a serial concatenation of direct sequence spreading and multi-carrier modulation. Due to the more or less independent flat fading on each sub-channel this distortion is compensated by equalisation at the receiver side. The received chips are equalised by using a low complex linear minimum mean square error (MMSE), one-tap equaliser. Due to the spreading component, the MC-CDMA system has to cope with multiple MAI [Kai95]. Another basic approach of combining multi-carrier transmission and CDMA is the concept of spread spectrum multi-carrier multiple access (SS-MC-MA) [KF97] which separates the users with a frequency division multiplexing component. Each user applies the whole set of orthogonal spreading codes. Therefore, each sub-carrier is exclusively used by one user. The major benefit of SS-MC-MA is similar to MC-CDMA, i.e. a larger exploitation of frequency diversity compared to OFDMA. The frequency diversity is somewhat less than in MC-CDMA, but the advantage is that the MAI at the receiver side is replaced by self-interference (SI) only, since each user applies the whole set of orthogonal spreading codes by itself. Variable spreading factor orthogonal frequency code division multiplexing (VSF-OFCDM) [Saw03] is a flexible downlink scheme based on MC-CDMA. VSF-OFCDM realises code multiplexing of physical channels in addition to time- and frequency-domain multiplexing. The VSF-OFCDM system can change the spreading factor according to the cell layout. Thus inter-cell interference can be mitigated by means of long spreading codes, which are generated as a combination of an orthogonal short channelisation code and a cell-specific long scrambling

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code. In cells where inter-cell interference is dominant, the spreading factor can be increased, whereas in isolated cells the spreading factor can be decreased to unity. The advantages of spread spectrum multi-carrier transmission compared to OFDMA are:

r larger frequency diversity; r more robustness with cell frequency re-use of one. In turn, the disadvantages of spread spectrum multi-carrier transmission are:

r inter-code interference resulting in MAI (MC-CDMA) or SI (SS-MC-MA); r the need for more complex receivers; r high peak-to-average power ratio (PAPR), as for OFDMA. In addition to the basic schemes mentioned above, there exists a multitude of variants on how to do the spreading operation, see e.g. the overview in [WIN1D26; FK03] and the references therein.

9.2.3 WINNER Multiple Access and Medium Access Control Concept One of the goals of the WINNER project has been to design and propose a flexible and efficient set of multiple access schemes. The design of the WINNER multiple access and medium access control concept is based on the following design principles and desired properties:

r Fully synchronous users within a cell enable the use of orthogonal time-frequency transmission resources within cells, to minimise intra-cell interference and guard overheads. This approach relies on the network synchronisation algorithm described in Chapter 6. r Short frame length enables support for low latency in the radio access network, even with multiple hops. It also enables frequency-adaptive transmission techniques to improve the spectral efficiency of the system. In addition, it enables reliable links for delay-critical services with the use of fast retransmission schemes. However, a short frame length also implies that there is little time-diversity to collect within a frame duration to mitigate fading channels. r Utilisation of a large system bandwidth, whenever available, to enable a robust transmission by utilising the large frequency diversity. This is especially important in deployment scenarios with little spatial diversity available, since a short frame length limits the available time diversity in the system. A large system bandwidth also enables large instantaneous data rates per user, which improves the service latency and makes it possible to improve the battery life in UTs with the use of sleep mode. In addition, large packet transfer per frame enables the use of strong channel coding schemes that are terminated within the frame, which simplifies the implementation of iterative channel estimation and detection schemes. r High spectral efficiency can be achieved, compared to traditional diversity-based multiple access techniques, because the short frame length enables tracking of the channel resources, which can be used for multi-user scheduling and link adaptation gains. r Efficient transmission of various packet sizes is provided by a flexible resource allocation scheme that enables a fully packet-oriented system that supports all foreseeable QoS requirements.

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Table 9.1 WINNER preferred multiple access schemes.

DL UL

Frequency-adaptive

Non-frequency-adaptive

TDMA/OFDMA TDMA/OFDMA

B-EFDMA B-IFDMA

r Energy efficiency, especially in the UTs, is important for sustainable mobile computing. r Scalability and flexibility in different deployment scenarios. The WINNER system concept is a multi-carrier-based system that supports both TDD and FDD. Two basic transmission modes are defined and, with them, different associated multiple access schemes (summarised in Table 9.1):

r Frequency-adaptive: The multiple access scheme for this spectrally efficient transmission mode is based on a hybrid TDMA and OFDMA scheme called ‘chunk-wise adaptive TDMA/OFDMA’. It is basically the same in both the downlink and the uplink for both TDD and FDD. r Non-frequency-adaptive: The multiple access scheme for this robust transmission mode is based on a hybrid TDMA and dispersed OFDMA scheme in both TDD and FDD. In the downlink, this scheme is called ‘block equidistant frequency division multiple access’ (B-EFDMA) and, in the uplink, it is called ‘block interleaved frequency division multiple access’ (B-IFDMA). The latter includes a precoding step to improve the envelope properties of the signal. The signals in Table 9.1 can all be generated and received with a generic transceiver structure as shown in Figure 9.1. The transmitter and receiver of TDMA/OFDMA and B-EFDMA use all blocks except the grey dotted blocks, whereas B-IFDMA uses of all the blocks. In the case of TDMA/OFDMA and B-EFDMA, the modulated data undergoes a serial to parallel operation followed by a sub-carrier mapping which depends on the desired transmission mode (frequency-adaptive or non-frequency-adaptive). The sub-carrier mapping block allocates desired sub-carriers to the user of interest. The data stream is then subject to OFDM modulation, i.e. an N-point IDFT, where N is the total number of sub-carriers in the system. The parallel to serial operation follows and a cyclic prefix is added to the data stream. Finally, an up-conversion is applied before transmission. As shown in Figure 9.1(a), in the case of B-IFDMA the data stream undergoes an additional operation compared to the TDMA/OFDMA and B-EFDMA cases. The operation is a Q-point DFT that precedes the sub-carrier mapping, where Q ≤ N is the number of sub-carriers assigned to the user of interest. At the receiver, the operations shown at the transmitter are inverted as illustrated in Figure 9.1(b). The only additional block is the equalisation block (labelled EQ. in Figure 9.1(b)). We now describe these multiple access schemes in more detail. The description is made jointly for the two supported duplex modes, TDD and FDD, in both the downlink and the uplink to highlight the integrated design and strong similarities of the solutions for the two transmission directions in the WINNER system concept.

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Mod.

S/P

Qpoint DFT

Npoint IDFT

Subcarrier Mapping

P/S

Add CP

Up Conv.

(a)

Demod.

P/S

Qpoint IDFT

EQ.

Subcarrier De-Mapping

Npoint DFT

S/P

Rem CP

Down Conv.

(b)

Figure 9.1 Generic transceiver of the preferred multiple access schemes: (a) transmitter and (b) receiver.

9.2.3.1 Chunk-wise Adaptive TDMA/OFDMA The chunk-wise adaptive TDMA/OFDMA scheme for frequency-adaptive transmission relies on channel quality information (CQI) at the transmitter. Based on reliable CQI, a resource scheduler can obtain multi-user scheduling gains by allocating mainly good time-frequency chunks to each user. Together with link adaptation, large spectral efficiency gains can be obtained, compared to traditional diversity-based schemes that try to average the channel conditions for all the users. Important design elements for this scheme are:

r chunk size; r CQI measurement; r resource scheduler; r resource allocation and link adaptation signalling; r packet segmentation; r channel coding and retransmission scheme. Figure 9.2 illustrates how a time–frequency selective channel can be tracked by a channel gain predictor. The chunk size should be selected such that the channel gain is essentially flat within the chunk in order to dynamically characterise the channel quality by a scalar CQI depending on the SINR in the chunk. With multiple antennas, chunks can be spatially re-used and a scalar CQI value is assigned to each spatial chunk layer. However, to make the system efficient, it is not enough to optimise the chunk size only towards the channel selectivity; a too large chunk size is inefficient for small packets and a too small chunk size is inefficient for control channel and pilot overhead. The chunk size in the WINNER test scenarios has

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(a)

(b)

Figure 9.2 (a) A time–frequency selective channel and (b) per sub-carrier prediction of channel gain.

been selected as 96 channel symbols in FDD (12 × 8 OFDM symbols over sub-carriers) and 120 channel symbols (15 × 8 OFDM symbols over sub-carriers) in the TDD case (when 1:1 symmetry of the up- and the downlink is used); see Chapter 13 for further details of the WINNER parameters in the test scenarios. At low UT speeds, it is possible to base the multi-user scheduling and link adaptation on outdated channel estimates, but at higher speeds such a delayed CQI measure becomes

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Figure 9.3 Multi-user scheduling gains with and without channel prediction errors, see [SFS+05; WIN1D24] for simulation details.

unreliable, especially at high carrier frequencies. With a channel gain predictor, it is possible to obtain a reliable CQI up to vehicular speeds even at a high carrier frequency, as illustrated in Chapter 6. Even with some CQI uncertainty, there is a large gain in spectral efficiency by using a multiuser resource scheduler to dynamically assign distinct chunks to users with good chunk CQI along with link adaptation per chunk. Figure 9.3 illustrates the spectral efficiency gain versus the number of users in scenarios with and without CQI errors in the form of the normalised mean square prediction error of the complex channel (shown as the prediction error variance in Figure 9.3). Thus, this transmission mode can be used in conditions when the SINR is above and the UT velocity is below certain thresholds; it can then provide high data rates for such users, thus improving the system capacity. In terms of channel gain, channel reciprocity holds for TDD, i.e. TDD implies physical channel reciprocity with respect to the propagation conditions. The different RF front-ends in uplink and downlink have to be (dynamically) compensated for in the UT and the BS, especially for multiple-antenna systems. In terms of channel SINR, reciprocity does not hold, in general. The reason is that the interference experienced by the terminal may not be the same as experienced by the BS due to intra- and inter-cell interference. In this case, the interference level at the receiving station has to be continuously fed back to the transmitter in an adaptive system or, as in the WINNER system concept, users are mapped on orthogonal resources in the cell and inter-cell coordination and mitigation techniques are used to keep the interference levels low.

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With secured channel reciprocity in TDD, channel prediction can be made either in the UTs or the BS for both the uplink and the downlink channels. In FDD, channel reciprocity cannot be used and the UTs have to provide pilot signals in the uplink to the channel predictor located in the BS. Similarly, the BS has to provide pilot signals in the downlink to assist the downlink channel predictors located in each UT. In the WINNER system concept, the channel predictors in TDD are also located in the UT to limit the pilot overhead, at the cost of a somewhat larger prediction horizon; see [SSO+07] for further discussion of designing the adaptation loop. The short frame length in the WINNER system concept enables a short adaptation loop, which shortens the required prediction horizon and thus enables support for vehicular users. In the WINNER system concept, the uplink control is based on a request for transmission in frame j − 2. If granted, the transmission is scheduled and prepared during frame j − 1 and is then performed in the uplink slot of frame j. The corresponding out-band frame control information is transmitted in the preceding downlink slot of frame j. The downlink transmission in frame j is scheduled during frame j − 1. The data transmission and the associated out-band control signalling then follows in the downlink slot of frame j. See Chapter 4 for the frame duration in the WINNER test scenarios. To realise the frequency-adaptive transmission, a significant but still reasonable control and feedback overhead is required. With adaptive hierarchical design of the resource allocation signalling, the CQI quality is the main limiting factor for frequency-adaptive multi-user transmission in the downlink, down to a few dB user SINR and up to vehicular speeds in the WINNER test scenarios, see [SFS+05]. The same is true in the TDD uplink, since channel reciprocity can be used. However, in the FDD uplink, the pilot overhead is the limiting factor. With the WINNER pilot design in Chapter 6, frequency-adaptive multi-user transmission in the FDD uplink is limited to pedestrian speeds. Since the channel predictors are located in the UTs, the CQI reports have to be transmitted to the BS. Here feedback compression of the CQI reports is important to keep the feedback overhead low. The feedback signalling can be reduced to acceptable rates, around 0.25 bits per chunk layer at 50 km/h, by using its time and frequency correlation to compress the CQI feedback [WIN1D24; EO07]. The results in Figure 9.3 were obtained with a proportional fair scheduler, but with equal average user SINR. In such a scenario, the scheduler corresponds to a maximum throughput scheduler, which assigns a chunk to the user with the best CQI. In general, the users have different average SINRs and a scheduler such as the proportional fair scheduler is needed to introduce service fairness among the users. Thus, in general, some of the multi-user scheduling gains have to be sacrificed for the benefit of fairness among the users. Section 9.2.4 elaborates further on the tasks of the MAC scheduler, of which the resource scheduler is one component. In order to take advantage of both small-grained channel adaptation and strong channel coding, a forward error correction (FEC) concept is developed that supports large FEC blocks that span multiple chunks with individually calculated link adaptation parameters (i.e. code rate and modulation level); see Chapter 5 for further discussion of this algorithm. Frequency-adaptive transmission should typically be combined with multi-antenna transmit schemes that preserve the channel variability. This is due to the well-known effect that transmit diversity reduces the channel variations in frequency. In this case, the amount of channel variations that can be exploited by the scheduler is lower, which reduces the multi-user scheduling gain.

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9.2.3.2 Block Interleaved and Block Equidistant Frequency Division Multiple Access The multiple access scheme for the frequency-adaptive transmission mode is optimised for spectral efficiency to support transmission of large data volumes and high instantaneous data rates for low latency. It also improves the system capacity since more bits can be transmitted in a given bandwidth. On the other hand, the multiple access schemes for the non-frequencyadaptive uplink and downlink transmission mode are diversity-based schemes, which are optimised for robustness for bad channel conditions as well as for low-latency control signals that cannot benefit from a retransmission scheme. The uplink scheme also increases energy efficiency in the UTs. These schemes are more efficient for transmission of small amount of data than the frequency-adaptive scheme in which the adaptation loop introduces a startup phase before full performance can be achieved. Non-frequency-adaptive transmission is also the choice for multicast transmission to multiple users with widely varying channels. With the non-frequency-adaptive multiple access schemes, bits from each flow are allocated onto sets of chunks that are dispersed in frequency or space. Forward error correction (FEC) coding and interleaving are used to combat the small-scale frequency selective fading. Link adaptation may be performed with respect to shadow fading, but not with respect to frequencyselective fading. This means that the same modulation and coding is used in all frequency and spatial resources allocated to the flow in a frame slot (i.e. chunks and chunk layers). However, the user flow scheduling is as fast as for the frequency-adaptive case. Evaluations in the WINNER project have shown a rather small performance difference between the different multi-carrier based resource allocation schemes, i.e. multi-carrier CDMA and OFDMA schemes with dispersed sub-carrier allocations [WIN1D26; WIN1D210; WIN2D461]. MC-CDMA, symbol-based TDMA and sub-carrier-based OFDMA are investigated in [WIN1D210, Appendix G]. MC-CDMA provides the highest diversity, however if spreading is applied across several chunks, MC-CDMA suffers from imperfect user separation at the UTs with increasing load and OFDMA performs similarly or slightly better. In addition, equalisation considerably increases the complexity of MC-CDMA in the UT. Spreading within a chunk only, chunk-based MC-CDMA, enables low complexity user separation at the UTs without large performance penalties due to the essentially flat fading characteristic within each chunk. The results indicated that dispersed OFDMA performs slightly worse than chunk-based MC-CDMA. Thus, with sufficiently small resources dispersed over the system bandwidth and reasonably large FEC blocks, most frequency diversity can be collected without code multiplexing different users onto the same resource and, since the chunks are reasonably flat, there is just a small extra diversity to gain by spreading each bit within the chunk. With multiple antennas, additional diversity can be collected in the spatial domain. The better resistance to inter-cell interference with MC-CDMA at low loads can be compensated by spatial interference rejection techniques; see Section 9.3 for further details on interference mitigation techniques. In the non-frequency-adaptive uplink, much more MAI is introduced at the BS with codemultiplexed users and, with a large system bandwidth, enough frequency-diversity can still be obtained without a CDMA component. It was therefore decided early on not to differentiate users by using different spreading codes (code multiplexing). The possible use of a spreading component (such as repetition coding) within the resources allocated to each user was left for further evaluation. It was decided to use repetition coding as a complement to other coding when needing very low code rates (below 1/3), to enable transmissions at the lowest low SINRs, down to −8 dB which is the assumed cut-off SINR within WINNER.

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The WINNER project early identified generalised multi-carrier modulation (GMC) as a good candidate for the uplink. This scheme incorporates a discrete Fourier transform (DFT) precoding step before mapping onto selected sub-carriers, which lowers the envelope variations (PAPR) compared to a pure OFDMA signal, and low signal envelope variations enable the high power amplifier (HPA) in the UT to work at a higher efficiency operation point. Although spatial diversity is seen as the primary source of diversity due to the simplification in resource partitioning, it should be possible to deploy the system in scenarios where low spatial diversity is available, e.g. in non-rich scattering environments. One candidate within the GMC concept has been interleaved frequency division multiple access (IFDMA) [SBS98], which employs an equidistantly dispersed sub-carrier allocation to obtain large frequency diversity; after the DFT precoding step, the signal exhibits single-carrier envelope characteristics. Good envelope characteristics are not the only important aspect for power efficiency in the UT. As important is to localise the transmission in time to enable the power amplifier to operate as close as possible to its designed optimal target output power level. Thus, especially at low data rates, a more time-localised scheme is beneficial. Periods with no transmission can then also be used to turn off selected parts of the transceiver chain, called ‘sleep mode’, to save energy in the UT. OFDM-symbol-based TDMA is the most extreme scheme; see [WIN2D461] for further discussions on this topic. The short frame structure in the WINNER system concept makes it hard to introduce a TDMA component in the IFDMA scheme, mainly due to the pilot overhead for channel estimation. Thus, in the WINNER system concept, a multiple access scheme has been developed for the non-frequency-adaptive uplink that provides a trade-off between the good envelope properties of IFDMA and the time-localisation of TDMA. B-IFDMA aims to maximise frequency diversity, to enable micro-sleep-mode gains and to simultaneously enable low envelope variations of transmitted uplink signals. In B-IFDMA equidistantly frequency-separated blocks, each consisting of a few subcarriers, are allocated to each user and a DFT precoding step is performed on an OFDM symbol time basis before transmission. Figure 9.4(a) shows the resource allocation in B-IFDMA, IFDMA and the localised frequency division multiple access (LFDMA) scheme adopted in 3GPP LTE [DPS+07].1 Assuming perfect channel estimation, Figure 9.4(b) shows that LFDMA collects much less frequency diversity than B-IFDMA and IFDMA, but frequency hopping (FH-LFDMA) can improve its performance (at a cost of larger pilot overhead and increased delays). Note that B-IFDMA with a block size of one sub-carrier and full chunk duration corresponds to the IFDMA scheme and, with only one block assigned to each user, B-IFDMA corresponds to the LFDMA scheme, i.e. B-IFDMA is a generalisation of these two schemes. Compared to IFDMA, the same amount of frequency-diversity can be collected, but with a higher instantaneous data rate to optimise the power amplifier operation point. The BIFDMA scheme provides better robustness to frequency offsets, phase noise and Doppler spread compared to IFDMA, at the cost of a somewhat larger required power amplifier backoff. The required backoff is still smaller than for OFDMA, as illustrated in Figure 9.5. B-IFDMA also enables blocks shorter than a chunk duration, which can be used for sleep mode gains. 1 In 3GPP LTE IFDMA is known as single-carrier frequency division multiple access (SC-FDMA) with distributed

mapping and LFDMA is denoted localised DFTS-OFDM or SC-FDMA with localised mapping [DPS+07].

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(a)

(b)

Figure 9.4 B-IFDMA, IFDMA and LFDMA: (a) resource allocation and (b) performance on a frequency-selective channel, assuming perfect channel estimation. In the simulation, N = 1024 subcarriers are assumed in the system, and each user is assigned Q = 32 subcarriers. B-IFDMA is configured with P = 32 blocks with dimension M = 4 subcarriers and 3 OFDM symbols. Additional simulation assumptions are detailed in [SFF+07].

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(a)

(b)

Figure 9.5 Power spectral density with a power amplifier backoff keeping the WINNER spectral mask for B-IFDMA with M = 4 sub-carriers per block and 32 sub-carriers per user and for OFDMA with corresponding sub-carrier allocation using QPSK modulation. The HPA non-linearity is modelled by a Rapp model with nonlinearity parameter p = 2 [Rap91]; see [WIN2D61310] for further details.

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The WINNER multiple access scheme for the non-frequency adaptive downlink is mainly derived from the insights in the uplink. Recall that the chunk-based multiplexing strategies investigated did not show significant differences in their performance. In [WIN2D461] sleep mode is identified to be almost as beneficial for the UT in receive mode as in transmit mode. In addition, the scheme for the non-frequency-adaptive downlink is also intended to be used for delay critical control channels, which need a specific timing within the chunks to support the adaptation loop in the frequency-adaptive transmission. In the downlink, the BS has to transmit a sum of parallel frequency-adaptive and non-frequency-adaptive flows over the entire system bandwidth, where some users are close to the BS and can use high-order modulation. Thus, there is no performance or power-saving advantage to use a DFT-precoding per flow of the non-frequency-adaptive downlink flows. For these reasons, B-EFDMA, a similar scheme as for the uplink has been adopted for the downlink, but without the DFT precoding step. As for B-IFDMA, the structured allocation pattern is beneficial for keeping low control information overhead. The use of a similar allocation structure in downlink as in uplink is beneficial for low system complexity and HW implementation. The two proposed schemes are used for both FDD and TDD deployment, but can be configured differently depending on the deployment scenario. 9.2.3.3 Configuration of Non-Frequency-Adaptive Multiple Access Schemes The benefit of configuration flexibility motivates the introduction of a small basic block consisting of four sub-carriers of three OFDM symbols in B-IFDMA/B-EFDMA to enable adaptive block allocation in different scenarios. In a specific scenario, a block size can be defined as a multiple of the basic block. The block size definition should take into account:

r UT energy efficiency (power amplifier operation point and backoff requirement and possibilities for sleep mode gains);

r transmit robustness for small packets, especially when HARQ cannot be used as in the case of fast and broadcast control signals;

r amount of spatial diversity available; r performance of channel estimation; r amount of MAI due to carrier frequency offsets (CFOs) and Doppler spread expected in the cell;

r allocated bandwidth for the non-frequency-adaptive transmission; r employed power control algorithm. Below we outline some block allocations that are recommendations based on the insights gained during the WINNER project. The block allocations should be seen as the union of the allocations used in different deployments. However, the number of block allocations should be smaller in any given cell in order to maintain a low resource allocation complexity, and the allocation should be optimised based on the deployment scenario, instantaneous data rate and expected composition of users’ capabilities and demands. The following general guidelines apply:

r Several frequency-separated small blocks are beneficial for large frequency diversity. r Large blocks are beneficial for channel estimation performance with a given pilot overhead requirement.

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r Whenever enough power can be generated, blocks with a short duration enable high HPA efficiency and sleep mode gains, especially in UTs.

r Whenever transmit power is a limitation, blocks with a long duration generate sufficient signal energy at a given data rate.

r Blocks with few sub-carriers are beneficial for high HPA efficiency. r A few large blocks are sufficient when enough spatial diversity is available. Studies in [WIN2D461; WIN2D233] and other results in WINNER, partly included in [WIN2D61310], have quantified performance with different block allocations (with respect to power amplifier backoff) and frequency-diversity gains (with respect to channel estimation loss, coding gains and performance of receiver equalisers). FDD Uplink and Downlink In SISO deployment, frequency diversity collected by up to 8–16 blocks in B-IFDMA seems appropriate for resource allocation units (RAUs) corresponding to 1–2 physical chunks (see Figure 9.6). A block size equal to the basic block of 4 × 3 (sub-carriers × OFDM symbols) is then appropriate for small packets, with a repetition distance of 2–4 chunks in the frequency direction (see Figure 9.6(a)). The short 4 × 3 blocks are also needed for timely carrying of the control signals for frequency-adaptive transmission. (See [WIN1D26] for a discussion of the timing loops for channel prediction in frequency-adaptive transmission.) Larger RAUs should use larger blocks. The investigations in [WIN2D233] shows that a block size of 8 × 6 is appropriate since it enables interpolation of channel estimates based on the dedicated pilots and the resulting channel estimation gain is larger than the loss in

Figure 9.6 Configuration of (a) B-IFDMA and (b) B-EFDMA in FDD wide area scenario.

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frequency diversity. However, taking into account also uplink power control, power amplifier backoff and the optimum operation point for the power amplifier, a resource block of 4 × 12 seems most appropriate for bad SINR users. With spatial diversity available, less frequency diversity is needed. With 1 × 2 or 2 × 2 spatial diversity, 4–8 blocks in the frequency direction per frame could be sufficient, which enables allocation of larger blocks per chunk. Taking the power efficiency and power control aspects into account, a block size of 4 × 6 for small packets and 4 × 12 for large packets seems appropriate. An even number of OFDM symbols in the RAU enables use of space–time Alamouti precoding. With an odd number of OFDM symbols, a space–frequency diversity scheme can be applied instead also with DFT precoding [JWI06]. In the FDD downlink, the B-EFDMA scheme does not apply a DFT precoding step and the envelope property of the signal does not significantly depend on the block allocation. Since the RAP should be able to generate enough power to signal with a constant power spectral density over the allocated system bandwidth (it essentially defines the cell size), power shortage in the transmitter is not an issue as for the uplink. Thus, localising the transmission in time is of no disadvantage for the RAP and it enables the UT, and also the RAP in low load situations, to benefit from micro-sleep within chunks. Common pilots are used for the downlink. The UT could either apply a basic channel estimation algorithm and improve its performance by listening to pilots in blocks not allocated for it or use a more advanced channel estimator and benefit from micro-sleep in parts of the chunk. Thus, compared to the FDD uplink, in the FDD downlink a preference for short and wide blocks should be beneficial as shown in Figure 9.6(b), with block size 4 × 3, 8 × 3 or 8 × 6 depending on the packet size and number of available antennas. Since no DFT precoding is used, standard Alamouti precoding can be applied in the frequency direction for block allocation that uses an odd number of OFDM symbols.

TDD Uplink and Downlink In local area deployments, most users will be served by frequency-adaptive transmission, but fast control channels would benefit from large diversity. Typically, multiple antennas are used and, in deployment in licensed bands, limited frequency diversity might be needed. Thus, referring to Figure 9.7, a few rather large blocks could be used consisting of 8 × 6 (sub-carriers × OFDM symbols) for payload data. Control signals for frequency-adaptive transmission still need short blocks for timing reasons, and the 8 × 3 blocks might be the best choice. In deployments in unlicensed bands with narrowband interferers, the smallest 4 × 3 blocks would be needed to maximise robustness towards the narrowband interferers. In metropolitan area scenarios, there is typically a mix of users with good SINR that can benefit from frequency-adaptive transmission and users with bad SINR that cannot benefit from frequency-adaptive transmission. Thus the preferred block allocations are basically the same as for the FDD uplink and downlink. In low load scenarios with low inter-cell interference levels, the UT could however be less power limited than in the FDD scenario due to the smaller cells. This would enable a preference for wider blocks 8 × 3 and 8 × 6, keeping the same number of blocks in the frequency direction for the same frequency-diversity gains, thus localising the transmission more in time and still operating with the power amplifier close to the optimal operation point (but with somewhat larger heat dissipation due to larger backoff requirement of the power amplifier).

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Figure 9.7 Configuration of (a) B-IFDMA and (b) B-EFDMA in TDD (assuming uplink:downlink symmetry 1:1).

On the other hand, some users could be severely shadowed (indoor-to-outdoor) and they would be severely limited under a power control scheme with a target power spectral density per block. Thus, these users would benefit from a few long blocks (4 × 9, in Figure 9.7(b)) and large spatial diversity. 9.2.3.4 Co-existence and Switching Both frequency-adaptive and non-frequency-adaptive transmission benefit from a large resource pool with independent fading statistics. Non-frequency-adaptive transmission needs independent resources to combat small-scale fading by diversity combining techniques. Frequency-adaptive transmission benefits from independent resources to minimise the service outage probability for semi-static users and to enable large multi-user diversity gains. The default assumption is frequency multiplexing of the resources for frequency-adaptive and non-frequency-adaptive transmission but, for deployment with low system bandwidth and low carrier frequency, time multiplexing of resources is preferred. As shown in Figure 9.8(a), different sets of chunks are pre-allocated within a frame for each of the two transmission modes, frequency-adaptive and non-frequency-adaptive. These sets are fixed within the whole super-frame but may be changed between super-frames. An important enabler for efficient co-existence and switching of the two transmission modes is the cross-layer design of the MAC layer, as illustrated in Figure 9.8(b). Efficient switching is supported by the common approach for channel coding and retransmission with the

344 (b)

Figure 9.8 Multiple access schemes for frequency-adaptive and non-frequency-adaptive transmission: (a) frequency multiplexing and (b) integration into the MAC layer.

(a)

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hop-by-hop ARQ, as described in Chapter 5. The packets are encoded and controlled by a typeII hybrid ARQ function that works over each hop in a similar way, independent of the resource allocation mode. The single low rate mother code is punctured to obtain multiple higher coding rates, i.e. rate-compatible punctured code (RCPC), to adapt to the channel conditions. A soft-bit interface at the receiver side enables a seamless switching of transmission mode for packets subject to retransmission. The multiple access scheme selection is made at the flow set-up, but switching between multiple access schemes can be made on an ongoing flow on a super-frame timescale, if needed. Below, we present criteria and mechanisms for switching of multiple access schemes within a cell. During handover to other cells, there are cases when switching is needed. This re-negotiation has to be made as part of the handover process. The initial selection of the multiple access scheme for a flow is based on pre-selection criteria and switching criteria. The pre-selection criteria are rather static, they are determined by the capability and the configuration of the BSs, the spectrum constraints, and the UT capabilities and their distribution in the cell. They do not rely on measurements. The switching criteria are more related to parameters that change dynamically within the cell and for the flow. They are monitored and used for possible switching of multiple access schemes for already established flows. The identified set of pre-selection criteria is:

r flow class (defined by attributes such as guaranteed versus non-guaranteed bit rates, maximum bit rates, delay budget, packet loss tolerance, etc.);

r next hop node type (BS, relay node or UT); r UT capabilities (from a UT database); r chunk resources and their current constraints; r cell load.

For example, relay links are characterised by good semi-static channel conditions, possible antenna directivity gains and large, more or less continuous data transfer rate due to aggregation of many user flows served by the relay node. This calls for selecting frequency-adaptive transmission, while keeping the CQI update rate, and hence the feedback and pilot overhead, rather low. The use of some of the criteria for the initial selection of frequency-adaptive transmission or non-frequency-adaptive transmission for a newly set-up scheduled flow is exemplified in [WIN2D461]. The identified set of switching criteria is:

r CQI reliability; r user terminal velocity (e.g. based on Doppler spread or satellite-aided measurements); r average SINR; r downlink SINR, which determines the reliability of downlink control information and the associated required coding overhead of this control information;

r the number of recipients of a multicast transmission.

The WINNER pilot patterns and pilot schemes in Chapter 6 affect the possibilities of using frequency-adaptive transmission and the preferable combinations of frequency-adaptive

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transmission and multi-antenna schemes. These aspects are summarised as follows:

r For FDD downlinks, a fixed grid of beams (GoB) is used with common pilots per beam. These pilots are present in each chunk and they thereby support frequency-adaptive transmission at vehicular velocities. r In FDD uplinks, the uplink pilots used for channel prediction are assumed to be transmitted only once per super-frame to limit the uplink pilot overhead. This reduces the accuracy of channel prediction so it may not be possible to use frequency-adaptive FDD uplinks at vehicular velocities. r For TDD systems, frequency-adaptive transmission in downlinks would be integrated with one of several possible multi-user MIMO-OFDM schemes as outlined in Chapter 7. For downlinks that use the successive minimum mean square error (SMMSE) multi-user MIMO scheme with short term CSI at the transmitter, the appropriate pilots would be uplink pilots transmitted in the super-frame preamble from all UTs that take part in the competition for a set of frequency resources. This SMMSE transmit scheme is limited to users at below 10 km/h; the super-frame preamble pilots allow frequency-adaptive transmission to be used at these velocities. Spatial multiplexing with per antenna rate control is the preferred scheme at velocities of 10–50 km/h in metropolitan area deployments. In such cases, unweighted pilots would be transmitted from each antenna in each downlink slot. The UTs can generate CQI estimates on all chunks where the downlink pilots are transmitted. These CQI estimates are compressed as described in [WIN1D24] and transmitted to the BS over the uplink. This enables the use of frequency-adaptive transmission in both downlinks and uplinks, due to the TDD channel reciprocity, up to velocities determined by the vehicle velocity and the Doppler spectrum properties of each channel. r For non-frequency-adaptive B-IFDMA transmission in uplinks, the pilot scheme uses one pilot symbol per 4 × 3 basic block. See [WIN2D233] for investigations of the resulting channel estimation errors, with and without iterative channel estimation schemes.

9.2.4 MAC Transmission Control The WINNER system concept uses scheduled transmission in both uplink and downlink. The multi-layer scheduler (Section 4.4.4) controls all aspects of the physical layer transmission and reception and the interaction between these layers is extremely tight. The outcomes of this interaction are the scheduling decisions. They define allocation of chunk layers (for frequencyadaptive transmission) and sets of blocks (in the case of non-frequency adaptive transmission). They also govern the transmission and reception control sequences for each segment. The control information that defines the resource allocation within a frame is transmitted early in the downlink slot of the same frame, using the PBCH, PDCFC, and PDFCC physical channels described in Section 4.4.4.2. The B-EFDMA scheme for non-frequency-adaptive downlink transmission is assumed to be used to transmit this information safely. The resulting transmission control sequences for downlink and uplink are outlined briefly below. We then discuss the assumed timing of these sequences and the resulting delays over the air interface. 9.2.4.1 Transmission Control Sequences for Downlinks For downlinks, the scheduler at the transmitter side performs the scheduling and controls the transmission. Downlink transmission control for frequency-adaptive and

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non-frequency-adaptive transmission is outlined in detail in [WIN2D461, Sections 4.1 and 4.2]. The transmission sequence is as follows: 1. The scheduler decides which flow classes are to share the downlink slot to be scheduled. For frequency-adaptive transmission, channel quality predictions are also obtained in the BS for relevant transmission resources and terminals. If necessary, the resulting predictions are reported to the transmitter over the uplink. 2. The downlink slot is transmitted, with associated out-band downlink frame control information (PDCFC and PDFCC) transmitted in the same slot. 3. All receivers receive and buffer the first three OFDM symbols of the slot where the PDCFC and PDFCC are located. 4. Each receiver detects and decodes the PDCFC and relevant parts of the PDFCC. 5. The payload is detected and decoded by the receiver.2 6. When all HARQ segments that comprise a transport block have been received, an ACK/NACK is generated based on the CRC code of the transport block. The ACK/NACK is transmitted over the PUCH in the next available uplink slot. 9.2.4.2 Transmission Control Sequences for Uplinks Uplink transmission is controlled by the scheduler located at the BS, so it has to be preceded by a transmission request. Initial transmission requests are in general transmitted over the PUCH. This uplink channel uses a dedicated transmission resource for each UT with active flows and it therefore generates a significant overhead. To keep this overhead acceptable, only very small request messages can be transmitted within this resource. The request message contains information that the UT has data to transmit, but not how much data. The transmission request evokes a grant to send an initial transport block. The size of this block is a scheduling decision and may be based on, e.g. flow class, available resources, CQI/CSI, etc. For some flow classes, this is all that is needed to transmit all queued data. For others, additional transport blocks will need to be sent in subsequent frames. The complete sequence is as follows: 1. An initial request (one or two bits) is transmitted over the PUCH. 2. The scheduler may grant the terminal permission to send an initial transport block within the next frame. 3. If granted, a transmission grant message is sent over the DL, using the PDCFC and PDFFC. It specifies the modulation, channel coding and utilised transmission resources for this initial transport block. 4. The UT sends the initial transport block over the UL. 5. If more detailed information is needed to send the rest of the queued data, a detailed uplink transmission request is sent, piggybacked onto the initial transport block, specifying the queued data size per flow class.

2 HARQ segments do not span multiple frames, so decoding can be attempted directly after the slot has been received. Iterative channel estimation and decoding (turbo processing) can be used.

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6. The scheduler sends a response to the detailed request over the downlink. The response specifies the transmission resources, modulation, coding scheme, etc. for the next uplink slot. 7. The transmission commences as outlined in [WIN2D461, Sections 3.1 and 3.2]. 9.2.4.3 Transmission and Retransmission Delays The timing of the transmission control loops has as a target to attain a very short delay over the air interface. In the transmission control systems, a scheduling computation delay of maximum 0.1 ms has been assumed. This is the delay from the arrival of the last information needed for scheduling a complete slot, until the scheduling is finalised. The computation delay for channel quality or state prediction is likewise assumed to be at most 0.1 ms, counted from the latest pilot symbol on which the prediction update is based. Both channel prediction and scheduling can then be finalised within one slot. The delay of decoding is added to the delay of ACK/NACK for (link) retransmission. The results of [WIN1D210, Table B.2] show that a delay of below one clock cycle per decoded bit is attainable, with appropriate parallel implementations of LDPC and DBTC decoders. For example, for an assumed FEC block size up to 1200 bits, this corresponds to less than 6 µs when 200 MHz of the total clock cycles are allocated to decoding. We below allow the total receiver processing delay to use up to one slot (345.6 µs). Decoding of FEC blocks of size up to 1520 bytes should require less than 60 µs, so this also provides ample time for iterative turbo decoding and channel estimation. In the TDD physical layer mode, frames comprise a DL slot followed by a UL slot. In the FDD mode, half-duplex terminals are assigned to one of two groups. Group 1 transmits in the downlink in the first slot of the frame and in the uplink in the second slot. Group 2 transmits and receives in the opposite way (Section 4.4.3.3). Consider a transmission (either TDD or FDD) where the DL slot precedes the UL slot within the frame. Let ULi and DLi denote the uplink and downlink slots, respectively, of frame i. The resulting delays over the air interface are summarised in Table 9.2. It is assumed that uplink transmissions request resources on a slot-by-slot basis and do not make multi-slot reservations. The transmission requests refer to the initial requests, which determine the initial delay. If transmission of additional transport blocks is required, this adds to the delay. Furthermore, the whole RTU is assumed to be transmitted within one slot, so a Table 9.2 Transmission plus decoding delays. Initial Transmit CQI Requests predictiona Frequency-adaptive UL Non-frequency-adaptive UL Frequency-adaptive DL Non-frequency-adaptive DL a b

ULj-2 ULj-2

Update (U) and scheduling (S). This delay includes decoding.

U,S:ULj-1 S: framej-1 U,S:ULj-1 S: ULj-1

DL 1-hop 1-hop frame Transdelayb delay control mission Decoding (frames) (ms) DLj DLj DLj DLj

ULj ULj DLj DLj

DLj+1 DLj+1 ULj ULj

3.0 3.0 1.5 1.5

2.1 2.1 1.0 1.0

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Table 9.3 Retransmission delays.

Downlink Uplink

RTU received

ACK/NACK transmission

Retransmission performed

Retransmission delay (frames)

Retransmission delay (ms)

DLj ULj

ULj+1 DLj+2

DLj+2 ULj+2

2.0 2.0

1.4 1.4

decision for retransmission can be taken immediately after its decoding. A PUCH reserved resource, which contains reserved bits for ACK/NACK, is present in each frame.3 For example, in frequency-adaptive downlinks, the packet is assumed to arrive at the end of the downlink slot of frame j − 1. Late during this slot and at the beginning of the uplink slot of frame j − 1, the channel predictions are updated. In FDD systems, this is done by predictors in the UTs and is based on downlink pilots up to the latter part of the downlink slot of frame j − 1. CQI feedback is transmitted in the beginning of the uplink part of frame j − 1 and scheduling is then performed during the latter part of this slot. The transmission then commences in the downlink slot of frame j. The downlink frame control tables (PDCFC and PDFCC) are transmitted in the beginning of the same slot. After the whole slot has been received, the decoding then takes place during the uplink slot of frame j. For a packet arriving at the BS at the end of the downlink slot of frame j − 1, the transmission delay over one hop, including decoding, is 3 slots or 1.5 frames or 1 ms. This corresponds to the minimum delay over the air interface that has been targeted for the WINNER system. (Packets arriving up to one frame earlier cannot be transmitted earlier and they therefore experience correspondingly larger delays, up to 2.5 frames.) The attainable delays involved in a retransmission are summarised in Table 9.3. It can be seen that the achievable user plane packet delay requirements (see Section 2.6.4.2) of 1 ms and 2 ms in the downlink and uplink, respectively, are met by this system design. Note that uplink retransmissions are assumed not to require separate (re)transmission requests. They use sets of transmission resources and modulation and coding schemes that are assumed to be known beforehand by both transmitter and receiver.

9.3 Inter-cell Interference Mitigation Schemes Interference can be defined as an undesired signal superimposed at the receiver antenna element, originating at the same source as or a different from the desired signal. This definition implies that we are looking at the antenna and viewing interference from the physical point of radio-wave propagation. Thus we include the geometry of the antenna element and its possible angle-dependent capability to capture the energy of an approaching radio wave. In the case where we have an antenna array, this angle dependency can be controlled by proper weighting and combining of the individual receive signals. Because of this combining, the receiver may be able to notch individual interferers and the interference may be cancelled out. This implies a certain level of intelligence regarding interference at the receiver and thus motivates us to define interference on a per-antenna-element basis in order to not depend 3 For a large number of UTs with active flows within the cell, this may create a large reverse link overhead. If PUCH slots are placed in every mth frame to reduce the overhead, the retransmission delays increase accordingly.

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on receiver intelligence. For broadband communication over frequency selective channels, the power density of the interferer over the frequency axis may be constant, in which case the interference is said to be white, or variable, in which case the interference is said to be coloured. Depending on the origin of the interference it is possible to further detail the interference definitions into two major classes. The interference can be either from the same cell or from any other cell in the system. Based on this classification, we distinguish between inter-cell and intra-cell interference. By inter-cell interference, we mean any signal coming from any source cell other than the desired one. Intra-cell interference refers to interference whose origin is in the same cell as the desired signal. Different methods are available to mitigate inter-cell interference. Interference averaging techniques aim at averaging the interference over all users, thereby reducing the interference experienced by some users located close to the cell borders. Interference avoidance techniques on the other hand, aim at explicitly coordinating and avoiding interference, e.g. by setting restrictions on how the radio resources are used. Smart antennas can be used to mitigate inter-cell interference as well as by, e.g., beamforming techniques or transmit/receive diversity techniques.

9.3.1 Modelling Inter-cell Interference Adequate modelling of inter-cell interference is a key aspect of the evaluation of its effects. Indeed, it drives the final comparison of the methods investigated, as well as the reliability of the results. The dilemma of inter-cell interference modelling can be summarised as follows. On the one hand, all the properties of inter-cell interference exploited by the mitigation techniques, or having an influence on the performance results, have to be reproduced in the most realistic way. On the other hand, accurate simulation of all the radio links can be computationally intensive, leading to impractical simulation durations. Therefore, a trade-off has to be found between the accuracy of the models and their implementation complexity. On top of this, a universal model is difficult to achieve since specific model simplifications can be drawn according to the mitigation technique considered and because specific inter-cell interference features have to be accounted for with respect to different types of simulators. 9.3.1.1 Link-Level Model SISO links have been assumed throughout this study. Nevertheless, when orthogonal spatial block coding (STBC), such as the Alamouti scheme, is used, the addition of two Gaussian variables leads to a Gaussian variable since the coding is orthogonal. In that case, the nature of the inter-cell interference remains Gaussian. For other multiple antenna schemes, the validity of the Gaussian approximation is not guaranteed, and further studies would be required in order to examine it. Nevertheless, we expect the farthest surrounding cells (typically the second ring in a hexagonal deployment) to be quite accurately modelled as the sum of their contribution is expected to be generally weakly spatially correlated. Provided there are sufficient interferers in each bandwidth, it may be possible to split the overall bandwidth into sub-bands and have a Gaussian approximation with different power levels in each sub-band. Another example is when different duplex modes are used in interfering

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cells; again, it may be possible to use a Gaussian-based approximation, but with step changes in power in the time domain. 9.3.1.2 System-Level Model When analysing or assessing the performance of a radio network, it is typically not sufficient to study the performance of a single radio link. Instead, one would like to assess the overall network performance, accounting for the fact that several pairs of communicating nodes must share a common radio resource. For instance, in a cellular network it must be considered that the resources in a cell are shared among all UTs associated with the cell and it is further of great importance to account for interference from neighbouring cells. Multi-cell evaluations of cellular networks are often performed by computer simulations, here referred to as system-level simulations.4 Furthermore, in a system-level simulation it is typical that there is no explicit modelling of physical layer procedures such as modulation and coding. Instead, less complex link performance models are used to estimate the performance of single links. Such a link performance model is often referred to as a link-to-system interface. The link-to-system interface needs as input a measure of the radio link quality and delivers as output an estimate of the packet error probability. Often, the SINR is used as a measure of the radio link quality, which means that system-level simulations must include the calculation of the received SINR. Here we can distinguish between actual value interfaces, where the SINR depends on the fast fading realisations of the channel [BrAs05], and average value interfaces, where channel quality metrics are an average of the instantaneous SINRs over the fast fading channel. An overview of classes of system-level simulators and the WINNER actual value link-tosystem interface based on the mutual information effective SINR metric (MIESM) is provided in Chapter 13. A further modelling issue is an assumption about the information available for RRM algorithms. For example, in order to apply adaptive scheduling algorithms, we need to track the interference (more precisely, the channel quality) of each user having data in a scheduling queue at the rate of fast fading. Since this might cause a high signalling overhead in real systems, one of the modelling issues would be to allow variable amounts of signalling for RRM in order to enable investigations of the trade-offs between signalling and performance.

9.3.2 Inter-cell Interference Averaging Techniques Two basic strategies targetted at reducing inter-cell interference are presented in this section: interference cancellation and combinations of dynamic channel allocation and scheduling techniques. Interference cancellation involves estimating the interfering signal and then subtracting it from the received signal before detection. A processing gain has to be present in the system in order to be able to separate the interference from the signal of interest. This processing gain can be obtained through spreading (as in MC-CDMA or the SS-MC-MA scheme [FK03]) or using low-rate error correcting codes (e.g., in OFDMA). Note that the spatial dimension (i.e. multiple antenna reception and IRC) also provides processing gains for signal separation. 4 More

details on system level modelling can be found in Section 13.2.

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Well-known techniques are successive interference cancellation (SIC) [RLJ00] and parallel interference cancellation (PIC) [DSR98], which can be applied iteratively, possibly taking advantage of the channel coding through a turbo-equalisation process [WP99]. Random and minimum interference channel allocation techniques coupled with several scheduling algorithms represent another way to average the inter-cell interference. It is expected that these combinations of allocation and scheduling algorithms may lead to a different level of interference. Besides the interference averaging ability, the implementation complexity and signalling and measurement overhead is important. For example, random dynamic channel allocation cannot attain the performance of an optimal channel allocation algorithm, due to its simplicity; however, under certain conditions it may provide sufficient performance with minimum signalling and measurement overhead. Therefore, when assessing the most promising combination, it is imperative to take all these aspects into account. 9.3.2.1 Inter-cell Interference Cancellation Figure 9.9 illustrates the basic principle of interference cancellation (IC). The signal received from interfering BSs is first estimated and then subtracted from the received signal. Due to the orthogonal spread of data symbols and the resulting redundancy in transmission, MC-CDMA provides the possibility of iteratively removing the inter-cell interference at the receiver side without the need to perform a highly complex channel-decoding step in the interference estimation loop (leading to turbo-equalisation schemes). Therefore, MC-CDMA provides the means to perform inter-cell interference cancellation at the price of a small amount of additional complexity. Two approaches are proposed that differ in the way the inter-cell interference signal is estimated. The first approach, called ‘hard inter-cell interference cancellation’, is based on the use of the hard decision of the demodulator to reconstruct the received signals. This approach is one of the simplest ways of realising interference cancellation; however, it is not efficiently applicable to OFDMA since the only source of processing gain in this case is the channel coding present in the system (at the exclusion of the spatial dimension), which is not exploited. The second approach, called ‘soft inter-cell interference cancellation’, is based on the use of more reliable soft values provided by the decoding process. This approach requires a significantly higher amount of complexity, since a channel decoding step has to be performed for each interferer. Implementations according to the two approaches are presented under the framework of MC-CDMA, with indications of how to apply them in the OFDMA case. Link-level evaluations of the proposed implementations are carried out in an MC-CDMA context. Upper bounds on the system-level performance are then provided for OFDMA, assuming perfect interference cancellation of one or two dominant interferers.

Figure 9.9 Principle of interference cancellation schemes.

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Hard Inter-cell Interference Cancellation The inter-cell interference cancellation (IIC) techniques that we assume here are all based on parallel architectures. Parallel interference cancellation (PIC) receivers are proposed in the literature in order to suppress MAI [KH97]. Here we intend to use the idea of the PIC structure in order to suppress inter-cell interference, which will result in parallel inter-cell interference cancellation (PIIC) schemes. We are interested in a multi-cellular DL MC-CDMA system, however the PIIC concept can be applied in an OFDMA context as well; in the OFDMA case, a channel decoding step has to be present in the interference estimation process in order to ensure reliable performance. For simplicity of explanation of the process, we consider that there exist only two synchronised cells: Cell 0 will denote the cell of interest, while Cell 1 is the interfering cell. At time instant n, the received signal on the downlink (after OFDM demodulation) has the following form: y(n) = x0 (n) + x1 (n) + w(n)

(9.1)

where x0 (n), x1 (n) are the signals from Cell 0 and Cell 1, respectively, and w(n) denotes the AWGN. Figure 9.10 presents the main blocks of a PIIC receiver, which provides an estimation of the signal that is emitted from BSi. The PIIC scheme assumes that FFT and guard interval removal have been already performed. Note that the PIIC process is identical in the OFDMA case, except that the spreading or despreading and equalisation (MMSE) blocks have to be removed (in MC-CDMA, the equaliser is needed to restore, at least partially, the orthogonality between the spreading codes before despreading). We should mention here that every PIIC receiver that is discussed below has exactly the structure presented in Figure 9.10. It is sufficient to specify the cell that the PIIC receiver estimates to define which scrambling sequence and channel coefficients the receiver uses. The same scrambling sequences as used in UMTS are implemented [3GPP07]. Direct IC This scheme is based on a PIC structure, suitably tuned in order to directly estimate and remove the inter-cell interference. Consider that the received signal has the form as presented

Figure 9.10 Structure of a PIIC for Cell i (W-H stands for Walsh-Hadamard).

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Figure 9.11 Structure of a DPIIC receiver.

in Equation (9.1). Then the direct IC receiver, as is evident from its name, directly estimates the signal x1 (n) of the interfering cell. This receiver has the structure shown in Figure 9.11. We deduce that applying the direct PIIC (DPIIC) receiver is equivalent to applying the PIIC in order to estimate the interfering cell (here Cell 1) and subtracting this information from the received signal. The resulting signal z d (n) constitutes an estimate of the signal of interest plus noise. Thus, in case of an ideal (perfect) estimation of the interfering signal, we get z d (n) = x0 (n) + w(n). We should mention that apart from the scrambling and descrambling inherent to the PIIC structure, this receiver provides no novel information. Indirect IC In most cases the signal of interest x0 (n) is stronger than the interfering one x1 (n), and therefore applying the DPIIC scheme presented in Figure 9.11, may provide a poor estimation of x1 (n). Here we propose to proceed by first estimating x0 (n), subtracting it from the received signal y(n), and then estimating and subtracting the interfering signal x1 (n). This constitutes an indirect way to estimate the interfering BS, which is why we call the receiver an indirect PIIC (IdPIIC). The structure of the IdPIIC scheme is depicted in Figure 9.12. We expect the proposed IdPIIC receiver in Figure 9.12 to have better performance than the DPIIC scheme in positive SIRs. Here again, in the case of an ideal (perfect) estimation of the interfering signal we get: z id (n) = x0 (n) + w(n)

(9.2)

It should be stated here that the IdPIIC constitutes a novel approach to performing interference cancelling. Even in the case of the classical IC literature, where we seek to suppress the MAI, all the proposed techniques estimate and subtract the interfering signal directly. None of them performs an initial rough estimation of the signal of interest, in order to better estimate the interfering signal.

Figure 9.12 Structure of a IdPIIC receiver for Cell i.

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Figure 9.13 Structure of the MPIIC receiver.

Mean IC A mean IC (MPIIC) constitutes a parallel implementation of the direct and indirect PIIC approaches. The interference estimation consists in averaging the outputs of DPIIC and IdPIIC. Then, this information is subtracted from the received signal (see Figure 9.13). It is straightforward to show that: z m (n) =

z d (n) + z id (n) 2

(9.3)

The addition in Figure 9.13 is done after estimating the interfering cell (i.e., Cell 1). One might expect that using the MPIIC receiver generates a BER performance somewhere between the DPIIC and IdPIIC. However, this is rarely the case. The lemma of this affirmation is presented in [DL07]. Soft Inter-cell Interference Cancellation A more sophisticated approach to cancelling the inter-cell interference is based on the use of more reliable soft values [CHL07]. Figure 9.14 shows the block diagram of the proposed soft IIC for MC-CDMA (for OFDMA, the diagram would be the same except that spreader, despreader and equalisation blocks would be removed). The received signal y is processed in respect to its specific cell parameters j for the desired and inter-cell interference signal. In contrast to the hard IIC process, the demodulator computes from the received symbols’ E . The demodulator, and therefore soft-demodulated extrinsic log-likelihood ratio values L Demod E A , with L Demod , exploits the knowledge of a priori LLR values, L Demod A L Demod = log

P(c = 0) P(c = 1)

E coming from the decoder. L Demod is given by A (c) P c = 0|d, L Demod E A − L Demod L Demod = log A P c = 1|d, L Demod (c)

(9.4)

(9.5)

where c represents the coded bits at the transmitter side, c the code sequence, and d the output A , are set of the despreader. In the initial iteration, the LLR values for the demodulator, L Demod to zero.

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Figure 9.14 Structure of soft IIC.

E After deinterleaving, the extrinsic LLR values L Demod become the a priori LLR values of the channel decoder. The channel decoder computes for all code bits the LLR values using the max-log-MAP algorithm [RVH95] with

A L Decod E L Decod

E = log L Decod

P(c = 0|q) A − L Decod P(c = 1|q)

(9.6)

where q represents the entry of the coded sequence of the decoder. The extrinsic LLR values E A are then interleaved to become the a priori LLR values L Demod used in the next iteration L Decod in the demodulator. The signals of the desired cell Ydes and the interfering cell Yint are reconstructed and for the next iteration step the inputs of the processing blocks are Yˆdes = Y − Yint

Yˆint = Y − Ydes

(9.7)

With this iterative approach the inter-cell interference is stepwise removed from the received signal. Combination with Spatial Processing at the UT Interference cancellation can be combined with spatial processing when multiple antennas are present at the UT. Two spatial combining schemes are considered here: maximum ratio combining (MRC) and interference rejection combining (IRC) can be implemented at the UT without implicating the transmitter. The MRC and IRC receive schemes are described more in

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detail in Section 7.4.1. Here we recall that the MRC combines coherently the signals output by the various sensors, by applying weights depending on the signal-to-noise ratio (SNR) at each antenna output, whereas the IRC accounts for the spatial structure of the interference, which allows the interference to be partly rejected in the spatial domain. The MRC and IRC schemes can be combined with an interference cancellation step, at the price of further increased complexity. The dominant interfering signal can then be subtracted either before (hence for each antenna) or after the spatial combining step. In practical implementations, the IRC can be used to detect both the signal of interest and the dominant interfering signal. For interference detection, specific IRC weights have to be used that enhance the dominant interfering signal contribution and reject the signal of interest in the spatial domain. This way, the spatial dimension provides additional separation between the signal of interest and interference.

9.3.2.2 Dynamic Channel Allocation and Scheduling In the following discussion, we investigate how efficiently the various combinations of elementary channel allocation and scheduling algorithms mitigate the inter-cell interference, thereby improving system performance. Description One role of channel allocation algorithms is to organise channel re-use in order to minimise the probability that the carrier-to-interference ratio drops below a desired value. In principle, this can be accomplished in a fixed or dynamic way. In a fixed channel allocation (FCA) scheme, a set of channels is permanently allocated to each cell. In cases where the traffic fluctuates, FCA cannot maintain the high quality of service and capacity achieved under static traffic conditions. In contrast, dynamic channel allocation (DCA) allocates channels to each cell temporarily depending on traffic intensity. Hence, DCA allows a network to utilise radio resources more efficiently, leading to improved capacity figures. DCA schemes can be implemented in a centralised or distributed manner. The centralised approach requires the use of a central controller, selecting channels for each cell; in the distributed approach, channel allocation decisions are made by individual cells based on locally available information. Theoretically, the centralised DCA leads to the best performance. Therefore, it is often regarded as an optimal DCA strategy. On the other hand, the optimisation achieved with centralised schemes requires an increased amount of signalling and channel allocation time, making them technically undesirable and computationally demanding. Without significant performance degradation, distributed schemes offer remarkable advantages, such as reduced allocation time, scalability and distributed signalling. The distributed approach thus represents a trade-off between performance and signalling overhead, a suboptimal solution. Hence, we limit the focus of our attention to distributed DCA algorithms. More specifically, we deal with minimum interference DCA (MIDCA) and random DCA (RDCA) strategies. The MIDCA algorithm temporarily allocates channels with the lowest inter-cell interference from a set of free channels, whereas RDCA chooses channels randomly in each allocation period. The gain of the MIDCA algorithm results from maximising SINR. However, in the case of a

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high load almost all channels are assigned in each allocation period. Hence, a negligible gain is achieved in comparison to RDCA. Another technique often considered to lower inter-cell interference experienced by users is opportunistic scheduling. By ‘opportunistic’, we mean the ability to exploit the variation of channel conditions. In cellular systems, the level of inter-cell interference measured on a given resource is user- and time-dependent. Opportunistic scheduling can exploit this multi-user diversity to average the user-experienced interference. The amount of interference reduction depends on the scheduling policy used. We investigate three common types of scheduling algorithms: proportional fair (PF), round robin (RR) and best channel quality indicator (BCQI). The scheduling policy that shares resources but does not exploit the channel conditions is the well-known RR scheme. It is simple and easy to implement, but the overall system performance may not be optimal since the multi-user diversity is not taken into account. On the other hand, BCQI always selects the user with the best channel. In this way, it is efficient in terms of overall throughput but the users with poor channel quality, typically located at the cell border, may never get scheduled. The PF scheduler represents a trade-off between efficient throughput and fairness, selecting the user according to the ratio between possible (estimated according to actual SINR) and already achieved data rates. In our investigations, RDCA and MIDCA operate on a long-term basis; i.e., a new set of chunks is allocated to each cell on the time scale of hundreds of milliseconds. To track the short-term variations of traffic as well as channel quality, an opportunistic scheduler is coupled with DCA to provide the effective sharing of DCA-allocated chunks between the users of a given cell. The performance of the various combinations of DCA and scheduling techniques is investigated by means of dynamic system-level simulations.

Simulation Results Figure 9.15 shows the system-level performance of ideal IIC techniques, as well as their combination with spatial combining at the UT. The IIC schemes are said to be ideal because the inter-cell interference coming from the processed dominant interferers is assumed to be perfectly estimated and cancelled. In Figure 9.15, IIC(1) denotes that the receiver cancels the dominant interferer, whereas IIC(1+2) indicates that the receiver cancels the two dominant interferers. The powerful IIC techniques are shown to remove almost all the interference, allowing large performance gains. These schemes however demand high baseband complexity on the UT side and also put constraints on the overall system design. Figure 9.16 presents results obtained for the mean inter-cell interference in the system for a different number of users in cells. The lowest interference is obtained by the BCQI scheduler, followed by PF and RR. The difference in the performance of BCQI and PF schedulers in terms of mean interference is not very large (about 0.25 dBm) compared to the gain obtained by these algorithms over RR (about 1.25 dBm). On the other hand, the impact on interference by DCA is rather low: about 0.1 dBm gain due to MIDCA for the BCQI scheduler and a negligible gain for the RR scheduler. The reason for a relatively low gain obtained by MIDCA is that the ‘best users’ are already selected by BCQI or PF schedulers that take into account the channel quality information in the smaller timescale (on the order of milliseconds). Therefore, there is a little room left for further improvement by DCA operating in the order of seconds.

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Relative performance improvement w.r.t. MRC 60%

IIC(1+2)

MRC

In sector throughput

50%

IRC IIC(1+2)

40%

IIC(1) 30%

IIC(1) 20%

no IIC 10%

non adaptive mode no IIC 0% 0%

400% 200% 300% In cell-edge throughput

100%

500%

600%

Figure 9.15 Inter-cell interference cancellation (IIC) at the receiver combined with spatial rejection schemes, improvement of average sector throughput and cell edge throughput.

–103.5

MIDCA/BCQI MIDCA/PF MIDCA/RR RDCA/BCQI RDCA/PF RDCA/RR

Mean Interference [dBm]

–104

–104.5

–105

–105.5 10

20

30

40

50 60 Users per cell

70

80

90

Figure 9.16 Mean inter-cell interference in dependence of load for different combinations of DCA and scheduling algorithms.

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9.3.3 Inter-cell Interference Avoidance Techniques In general, inter-cell-interference avoidance techniques for the downlink are based on restrictions or specific allocation schemes applied to the resources available for transmission towards the UTs. They usually apply in a coordinated way between cells so that inter-cell interference avoidance is often mentioned together with inter-cell interference coordination. The following classification is used for the avoidance techniques investigated:

r resource management and partitioning; r resource allocation and scheduling. Resource management and partitioning techniques are based on restrictions of the transmit power over the resources. Pre-defined frequency re-use schemes may apply as well as techniques for flexible or even self-adaptive re-use partitioning (where frequency re-use varies inside the cells). Resource allocation and scheduling techniques (such as, dynamic chunk allocation using cell coordination) intrinsically take a frequency re-use value of 1 into account. Scheduling schemes based on a cost function or QoS provisioning and aware of inter-cell interference are considered here as well. Three kinds of coordination may apply:

r static coordination: reconfiguration of the restrictions is done on a timescale corresponding to days; inter-node communication is very limited, since it corresponds only to the set-up of restrictions; r semi-static coordination: reconfiguration of the restrictions is done on a timescale corresponding to seconds or longer; the corresponding signalling rate is generally in the order of tens of seconds to minutes; r dynamic interference coordination: the timescale usually corresponds to several tens to hundreds of milliseconds, as does the signalling rate. 9.3.3.1 Resource Management by Restriction of Transmit Power Description This section investigates inter-cell coordination schemes using static power restrictions in the frequency domain in pre-defined frequency subsets. The definition of these frequency subsets follows a frequency re-use scheme which determines the subset of resources restricted for each cell in a coordinated way with its neighbours. The aim of these coordinated power restrictions is to create particular frequency subsets with enhanced average channel conditions, which are assigned to the cell-edge UTs in order to improve their SINR. Various types of power restrictions can be applied [R1060864; Alc06]. Here we study two families of transmit power restriction schemes: soft frequency re-use (SFR) and fractional frequency re-use (FFR). In SFR, each cell uses the whole frequency band but a specific subset is reduced in power according to a specific frequency re-use scheme, the remaining ones using equally full power [Alc05]. At the cell edge, UTs are assigned the subsets with reduced power of their strongest interfering BS. This scheme keeps the inter-cell interference at a low level while still using all of the available frequency resources for the transmission. Increasing the re-use value leads

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to restricting the transmit power in a smaller portion of the resources; e.g. in the case of soft frequency re-use of 7/6 it is 1/7 of the bandwidth available which is reduced in power. FFR typically involves a sub-band commonly used by all cells (i.e. with a frequency re-use of 1) with equal power, while the power allocation of the remaining sub-bands is coordinated among the neighbouring cells in order to create one sub-band (the cell-edge band) with a lower inter-cell interference level in each cell. FFR schemes can be further subdivided into FFR with full transmit power isolation (FFR FI) and FFR with only partial isolation (FFR PI). Full power isolation means the cells are not allowed to transmit on the cell-edge sub-band of their neighbours, which suppresses inter-cell interference from the neighbouring cells in the cell-edge sub-bands at the expense of a reduction in the amount of available resources. With partial power isolation, all the system bandwidth is available in all cells; therefore, the number of available frequency resources is the same than for the re-use 1 scheme, similarly to interference coordination through cell-specific transmit power reduction. In the FFR PI scheme considered here, the enhanced channel quality is achieved by allocating more power to the cell-edge sub-band, and consequently reducing the transmit power of the remaining sub-bands in order to maintain the same total transmit power. Note that the term ‘frequency re-use’ is misleading in the case of partial power isolation, since all the frequencies are used at all cells. In the case of partial power isolation, ‘frequency re-use’ refers to the re-use of the frequency resources benefiting from reduced interference levels. Scheduling Policies Three scheduling policies are considered for prioritising the cell-edge UTs into the sub-bands with enhanced channel conditions (or cell-edge sub-bands). We assume in this description that the cell-edge sub-band can be UT dependent. UTs are classified as ‘cell-edge’ if their SINR averaged over the whole allowed bandwidth is below a given threshold. These schemes assume that each UT is allocated a number of chunks (possibly depending on the UT) known by the scheduler at the time when the prioritisation schemes are applied. Priority Scheme 1: Cell-edge UTs First This scheme is restricted to the frequency-adaptive mode. The chunk assignment is performed in two steps: first, the cell-edge UTs select their best chunks (i.e. the chunks with the highest CQI) in priority order. When the cell-edge UTs have been allocated all their chunks, the chunk assignment process is performed for the non-cell-edge UTs using the remaining chunks. This scheme is of no interest in the non-frequency-adaptive mode since the chunk CQI is not taken into account there. However, this scheme can also be used for the re-use 1 scheme in frequency-adaptive mode. Priority Scheme 2: Cell-edge UTs Constrained to the Cell-edge Band As in Priority Scheme 1, this scheme assigns chunks first to the cell-edge UTs but, in addition, the cell-edge UTs are constrained to be allocated chunks from their cell-edge band. When the cell-edge UTs are too numerous for all their chunks to be allocated in the cell-edge band, the scheme shares the cell-edge band evenly between its associated UTs. In the non-frequencyadaptive mode, this scheme reduces to allocating the cell-edge UTs in their cell-edge band first, in an even manner if the cell-edge band is too narrow to contain all the chunks of the associated cell-edge UTs.

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Priority Scheme 3: First N Cell-edge UTs Constrained to the Cell-edge Band This scheme differs from Priority Scheme 2 only when the number of cell-edge UTs selected for transmission is too large compared to the available cell-edge band. In this case, only the first N cell-edge UTs are constrained to be allocated in the cell-edge band, where N is chosen so that all the chunks of the N UTs can be allocated within the cell-edge band. The remaining cell-edge UTs are then allocated outside the cell-edge band, but still have priority over the non cell-edge UTs in the frequency-adaptive mode. 9.3.3.2 Self-adaptive Re-use Partitioning Self-adaptive re-use partitioning (SARP) represents an interference avoidance technique exploiting priority-based partitioning of chunks available to a given cell. We refer to this technique as self-adaptive since chunk priorities are evaluated independently by individual cells, taking into account locally available information (interference along with a reuse factor). Each of the resource subsets obtained by partitioning is assigned to a designated category of UTs. In this discussion, we categorise UTs based on their path-loss measurements. Within each category, the channels are distributed between individual UTs by means of cost-function-based scheduling (CFBS) which further decreases the experienced interference. The priority P(c) of the channel c is obtained by evaluating the following priority function: P(c) = W I I (c) + W R R(c),

(9.8)

where I (c) denotes the mean interference observed on the channel c normalised to the interval 0, 1, and R(c) represents the re-use factor of the channel c. The weights W I and W R are real-valued constants. When UT categories based on path-loss are used, SARP assigns chunks with the highest priority and, consequently, the highest quality to UTs with path-loss exceeding a specified threshold. The remaining chunks with lower priority values are assigned to UTs with better transmission conditions located closer to the centre of the given cell. Since the priority calculation for chunks can be influenced by changing the weights or introducing chunks with re-use greater than 1, SARP is a flexible technique that can easily be adjusted to fit the given network or traffic conditions. In order to compare the performance of flavours of SARP that aim at avoiding the inter-cell interference, we consider the following cases:

r Random SARP (RSARP): Channel priority is set randomly. All chunks are transmitted with a re-use factor of 1 and their assignment to UT categories is performed independently in each cell in a random manner. r Re-use 1 interference-based SARP (ISARP): Channel priority is determined with the weight settings W I = −1 and W R = 0. As W I is negative, channels with lower interference have higher priority and channel re-use is not taken into account. Here, the channels with lower interference are allocated to UTs with path-loss higher than a specified threshold (115 dB). We can interpret this as a dynamic resource partitioning technique. r Re-use 3 SARP (R3SARP): Channel priority is determined with the weight settings W I = −1 and W R = 1. Thus, the channel with higher re-use has higher priority. At the cell border, a

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re-use of 3 is deployed, i.e., 1/4 of all channels are used in this zone. We use the path-loss threshold of 115 dB to specify the radius of central re-use 1 zone. The channels with lower interference are allocated to UTs with a path-loss higher than 118 dB. 9.3.3.3 Cost-function-based Scheduling Inter-cell interference mitigation by means of a scheduler based on a cost function (CF) is investigated in this section. The allocation of UTs to resources is done by the scheduler, which takes into consideration QoS, channel quality of the UTs (interference, path-loss) and data rate achieved by the UTs so far. This scheduler evaluates a cost function for all eligible combinations of users and resources. The combinations leading to a minimum cost function value are scheduled for transmission. In our investigations, we have considered a cost function of the form CF(u, r u) = W R R(u) + W I I (u, r u) + W L L(u, r u) + C,

(9.9)

where W R , W I , W L and C are real-valued weights, u denotes the user and r u denotes the resource unit. Further, R(u) stands for the data rate achieved by the user u. The terms I (u, r u) and L(u, r u) represent the interference and loss (geometrical path-loss, shadowing and fastfading) measured by the user u on the resource unit r u. The values of R(u), I (u, r u) and L(u, r u) are normalised and R(u), I (u, r u), L(u, r u) ∈ 0, 1. Clearly, changing the values of the weights W R , W I , W L and C varies the resource distribution and thus the inter-cell interference experienced by the UTs. In order to obtain an optimal performance of scheduling even for different loads, it is necessary to automatically adapt the values of W L , W I , W R and C. We present here a solution based on the LMS algorithm that is widely used in signal processing, neural networks and machine learning. The network utility (Utilityn ) encountered is considered and the weights are updated according to the following equation W (n + 1) = W (n) − λUtilityn

∂CF , ∂W

(9.10)

where λ is a parameter (0 ≤ λ ≤ 1) and Utilityn is the difference between the desired utility and the actual utility i.e. the number of satisfied users and average data rate. A simple adaptation of weights can be achieved by keeping weights W L = W I = 1 and C = 0 fixed and changing the weight W R according to WR (n + 1) = WR (n) + λ (PSU min − PSU)

WR , λ ∈ 0, 1

(9.11)

where PSUmin is the minimum required percentage of satisfied users (90 % in our case), PSU refers to the actual percentage of satisfied users averaged over certain time period and λ is a parameter controlling the speed of parameter change. From Equation (9.11) it can be seen that when PSU is above PSUmin , the weight W R is decreased. Consequently, the impact of the data rate on the cost function value obtained by Equation (9.9) is lowered. It means that UTs with a higher channel gain (lower loss and interference) are scheduled more frequently, which increases the throughput in the system. When PSU is lower than PSUmin , the weight WR increases. Therefore, the probability that a user will be scheduled increases with the decreasing average data rate of the given user. This increases the fairness in the system, represented by the PSU, but also reduces overall throughput.

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Figure 9.17 FFR FI bandwidth allocation with re-use 3 at the cell edge.

9.3.3.4 Simulation Results Figure 9.17 illustrates the FFR method investigated with the priority schemes described in Section 9.3.3.1. Table 9.4 contains the corresponding system performance results obtained using two performance metrics: the average sector throughput and the cell-edge throughput (measured at 5 % of the user throughput CDF). Fractional frequency re-use with full power isolation shows significant throughput improvements at the cell border but at the expense of non-negligible sector throughput degradation. The system performance obtained for the SARP method, and its variants, described in Section 9.3.3.2 is depicted in Figure 9.18. The metrics used are the end-to-end user throughput and its 5th percentile. The highest mean throughput is obtained by ISARP within the considered range of loads. With increasing load, the mean throughput for ISARP, R3SARP-115 and R3SARP-118 (R3SARP with path-loss thresholds of, respectively, 115 and 118 dB) decreases and converges to the same value. The lowest mean throughput is achieved by RSARP, when chunks are partitioned in a random fashion. Further results comparing SFR and FFR schemes in a microcellular deployment can be found in Chapter 13.

Table 9.4 Performance results for FFR FI. Coordination scheme Sector service throughput (Mbps) Cell-edge service throughput (kbps)

FR 1

FFR FI No priority

FFR FI Priority Scheme 2

FFR FI Priority Scheme 3

47 + 0% 120 + 0%

41 − 13% 195 + 60%

41 − 12% 270 + 125%

40 − 14% 310 + 160%

E2E Throughput (kbps)

145

RSARP ISARP R3SARP-115 R3SARP-118

140 135 130 125 120 115 110

0

10

20

30

40 50 60 Users per cell

70

80

90

5th Percentile E2E Throughput (kbps)

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40

RSARP ISARP R3SARP-115 R3SARP-118

35 30 25 20 15

0

10 20 30 40 50 60 70 80 90 Users per cell

(a)

(b)

Figure 9.18 SARP schemes: (a) mean E2E user throughput and (b) E2E user throughput at 5th percentile.

9.3.4 Inter-cell Interference Mitigation Techniques Based on Smart Antennas In this section, we investigate methods based on smart antennas to mitigate inter-cell interference. These include transmit beamforming, transmit diversity at the cell border (also known as macro diversity), and receive diversity and spatial interference suppression.

9.3.4.1 Beamforming Techniques Beamforming is an efficient means to combat inter-cell interference and, in particular, to protect the users at the cell border. By transmitting in a narrow beam directed towards the desired user instead of a sector-wide beam, it is possible to significantly reduce the interference spread to other cells in the system. Beamforming can be carried out in different ways; at the highest level, we distinguish adaptive and fixed beamforming. In adaptive beamforming, the antenna weights are set in order to optimise the antenna pattern. This can be done according to several different optimisation criteria and based on different amounts of channel knowledge [WIN2D341]. In fixed beamforming or grid of beams (GoB) approaches, a certain number of pre-defined beams are used and the beamforming problem reduces to beam selection, which requires less feedback information than adaptive approaches. For both adaptive and fixed beamforming correlated antennas are preferred, e.g. with half a wavelength element separation. Transmit beamforming is in principle applicable both at BS and UT, but in practice it will most probably be limited to the BS. It is also possible to implement SDMA as a further multiple access component on top of beamforming in order to serve several users at the same time on the same resources, but spatially separated. With appropriate scheduling this allows significant improvements in system performance [WIN2D341]. However, for users at the cell border it might be a disadvantage due to the partly lost directivity of the interference compared to single stream beamforming.

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In the following discussion, we focus on a traditional adaptive beamforming scheme, a simple single stream GoB scheme, and an SDMA variant built on GoB optimised with ‘beam tapering’. Adaptive Beamforming In adaptive beamforming, the antenna pattern is adapted in order to provide optimal reception for the scheduled user. This optimisation can be done according to different criteria. The adaptation is based on channel knowledge, typically long-term CSI in the form of a spatial transmit covariance matrix. Note that there exist several other versions and optimisations; details of some of them can be found in e.g. [WIN1D27]. We consider the downlink of a wireless communication system. Beamforming in a MIMO channel H means that an input signal s is transmitted with power p and the antenna weight vector v, resulting in the receive vector y in the presence of interference plus noise z: y

M R ×1

=

H

y

p

s + z ,

M R ×MT MT ×1 1×1 1×1

M R ×1

(9.12)

where M R and MT are the number of receive and transmit antennas, respectively. The antenna weight vector, or the beamforming vector, v, is set according to some method. One method is eigenbeamforming [HBD00], where the beamforming vector is set to the eigenvector corresponding to the largest eigenvalue of the spatial transmit covariance matrix. Fixed Beamforming or Grid of Beams Adaptive beamforming adapts the transmit weights for each user to long-term CSI at the transmitter. A low-complexity approach to such adaptive beamforming is to use a finite set of fixed antenna weights that generate a set of beams matched to the long-term transmit covariance matrices of different parts of the coverage area. All users in the coverage area share the set of beams and the problem of beamforming reduces to beam selection. The amount of channel knowledge required at the transmitter, once the beams have been designed, is small and since the adaptation is typically done to long-term CSI, the required amount of feedback signalling is low or none if the uplink received signals are used to determine which beam is the best. For further details about the fixed beamforming method and the signal model, see Section 7.4.2. Let {v1 v2 . . . v I } be the set of weight vectors corresponding to the total number of fixed beams. Then, the antenna weights vi , are calculated from the main beam direction ϑi of beam i and from the mth transmit antenna element position dm for all M elements, with k = 2π/λ being the wave number, according to: 1 vi (ϑi ) = √ [vi1 (ϑi ) vi2 (ϑi ) M

...

vi M (ϑi )]T

(9.13)

with vim (ϑ) = exp(− jkdm sin(ϑi )). A four-element uniform linear array with λ/2 spacing has the element positions: 1 3 dm = ± λ, ± λ 4 4

(9.14)

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Figure 9.19 Fixed beam pattern for a 120◦ sector with 70◦ half power beam width, eight beams created by four elements.

For a 120◦ sector with four antenna elements, eight beams are chosen with beam directions according to equal spacing in beamspace (resulting in equal beam-crossing levels in the directivity pattern), which gives the following beam directions ϑi in degrees: [−49.30

−32.80

−19

−6.20

6.20

19

32.80

49.30]

The resulting antenna pattern is shown in Figure 9.19. Fixed Beam Design and Scheduling for GoB SDMA SDMA can be implemented on top of beamforming as an additional multiple access component, in order to enhance the system performance via spatial re-use of the radio resources. Several enhancements are possible for GoB when using SDMA. The shape of the beam directivity pattern can be altered by tapering. Adaptive scheduling gives additional performance enhancements, e.g. the score-based scheduler can be modified. Tapering can be used on top of the GoB to improve the shape of the beam directivity pattern for SDMA. Here Chebyshev tapering [Dol46] was chosen as it is optimal in the following sense: for a given side-lobe level, the width of the main lobe is minimised. When using SDMA with GoB, increasing tapering decreases cross-talk between side lobes and increases robustness due to beam mismatch. Figure 9.20 illustrates a beam pattern with and without the side-lobe suppression resulting from tapering. For single stream GoB (non-SDMA), tapering is not recommended as lower side lobes are not needed. The calculation of the tapering vector gives real-valued filter coefficients, denoted as t. The baseline GoB scheme defines the design of the untapered weights wbaseline based on the steering vector and the beam directions which are equidistant in a cosine space. Tapering for each beam k can now be done by element-wise multiplying the amplitudes of the complex weights of baseline GoB by the tapering vector of the desired approach (with i denoting the index of the antenna element): wtapered,i,k = wbaseline,i,k · ti

(9.15)

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(a)

(b)

Figure 9.20 Beam pattern for eight transmit antenna elements creating 16 beams (a) with and (b) without side lobe suppression.

Tapering results in unequal transmit power for each antenna element. A key question here is the efficiency of the power amplifier. A possibility of rebalancing the power between the transmit amplifiers is to shift single- or dual-antenna traffic (such as non-preamble broadcast channels) to the outer elements of the array where the shared data channel for tapered SDMA GoB uses less transmit power than in the centre elements. Diversity schemes would benefit from the antenna spacing and the power amplifiers could be used efficiently. One important aspect of SDMA is how to select and schedule users. Here a method based on scheduling score and best beam index is described. The score is calculated as follows: for the best beam, a CQI feedback per chunk is needed. For a given frame, for each chunk in the frequency direction, CQI feedback is available and the ranking of each user’s latest CQI feedback determines its score. Each chunk now is an independent instance for user allocation. The user with the highest score is allocated first. When users, and thus corresponding beams, are allocated, neighbour beams on each side are blocked in order to avoid significant intra-cell interference. The number of blocked beams depends on the scenario, the SNR and number of beams used. Now the user with the second highest score is allocated, except its corresponding beam is already blocked. This approach continues until all users are checked or a desired maximum number of spatial streams (e.g. four) is reached. 9.3.4.2 Transmit Diversity Techniques With multi-antenna concepts at the transmitter, transmit diversity can be applied to increase the performance at the receiver side. The idea is to mitigate the deteriorating effects of fading; by transmitting the signals from different antennas, it is possible to improve the reception at the other end of the link due to the increased diversity. This allows reduction of the total transmitted power and thereby the interference spread in the system. In a cellular system, transmit diversity techniques can be applied by utilising neighbouring BSs as the multi-antenna configuration, which is known as macro diversity. From an interference mitigation point of view, this is valuable since the user at the cell border between two cells

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typically experiences a high level of interference. By utilising macro-diversity techniques, the situation for the cell border users is improved due to the increased diversity, meaning that the total transmitted power, and hence the interference, may be reduced. Transmit diversity techniques can be based on, e.g., space time block codes (STBCs) or antenna diversity schemes [DK01]. These multi-antenna techniques can be transferred to a cellular scenario by using the neighbouring BSs as the multi-antenna setting. Therefore, transmit diversity is transformed into macro diversity. In 2002, Inoue et al. proposed this within the application of STBCs [IFN02]. In contrast, the application of cyclic delay diversity (CDD) to a cellular environment (cellular CDD, or C-CDD) [PD06] offers the exploitation of the increased transmit diversity at the receiver without any change on the receiver side. Transmitting the same signal from several BSs including cyclic delays will be observed as a channel with higher frequency selectivity at the receiver. This resulting additional frequency diversity can be collected by a channel code for instance. There exists no rate loss for higher numbers of transmit antennas/BSs, and there are no requirements regarding constant channel properties over several sub-carriers or symbols and transmit antenna/BS numbers. The structure of C-CDD is presented in Figure 9.21 without considering the random choice of cyclic delays and power adaptation blocks. At the cell border of a conventional OFDMA system, inter-cell interference exists due to double allocated sub-carriers, and therefore, the frequency resources are partially used. This decreases the exploitation of the sub-carrier resources per cell site. C-CDD takes advantage of the aforementioned resources. The main goal is to increase performance by avoiding interference and increasing diversity at the most critical environment directly at the cell border. The C-CDD technique offers improved performance especially at the critical cell border without the need for any information about the channel state information on the transmitter side. On the other side, inter-BS communication is necessary to guarantee the transmission of the desired signals on the same sub-carriers. Furthermore, the transmission from the BSs must ensure that the reception of both signals is within the guard interval, and therefore, the involved BSs have to be almost synchronised.

Figure 9.21 Principle of cellular cyclic delay diversity.

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Additionally, the inter-cell interference within a cell or sector is reduced by lowering the transmit power for sub-carriers which are assigned to C-CDD. There is still a performance gain due to the existing macro diversity. Macro diversity is considered as a possible enhancement to multimedia broadcast and multicast services (MBMS). Basically, the diversity combining concept consists of receiving redundantly the same signal over two or more fading channels, and combine these multiple replicas at the receiver in order to increase the overall received SNR. The drawback of cancelled out cyclic delays due to the geographical set-up can be avoided by using a randomised choice of cyclic delays. The area with higher diversity can be also broadened by applying an adaptive transmit power control (see Figure 9.21). This adaptation needs feedback of the received signal power information. 9.3.4.3 Receive Diversity and Interference Suppression Techniques By equipping radio receivers with multiple receive antennas, it is possible to implement different combining schemes in the baseband signal processing. Since the radio channels (from a transmit antenna) to the receive antennas tend to fade differently, multi-antenna receivers provide receive diversity – both for the signal of interest and for the interference. With appropriate selection of the antenna combining weights, accounting for such things as the radio channel, the interference power and the spatial colouring of the interference, such multiantenna receivers may provide increased robustness to both fading and interference. This, in turn, may improve the radio network coverage, capacity and user data rates. Maximum ratio combining (MRC) and interference rejection combining (IRC) are two well-known combining schemes. With MRC, the combining weights are selected accounting for the radio channel (of the desired signal), the noise power and the interference power at the different receive antennas. IRC is an extension of MRC which also takes the spatial characteristics of the receive signals into account and therefore enables interference suppression at the receiver. IRC determines the combining weights based on the channel and the (spatial) noise and interference covariance matrix, i.e., not only the interference power but also the spatial colouring of the interference is taken into account. The MRC and IRC receive diversity are described in more detail in Section 7.4.1. MRC and IRC can be applied both at the BS receiver and at the UT receiver and can, consequently, be used to improve both the downlink and uplink performance. 9.3.4.4 Simulation Results Figure 9.22 depicts the overall system performance obtained by the several spatial processing schemes and their combinations. Interference coordination (FFR with re-use 1 in the inner part of the cell and re-use 3 at the cell border) has also been considered here. The performance gains are evaluated in comparison with the MRC scheme used solely. The results show that the IRC scheme improves both cell-edge and sector throughput whereby GoB provides tremendous gains when combined with IRC. Furthermore SDMA on top improves the sector throughput but degrades slightly at the cell edge. On the other hand FFR improves at cell edge, but degrades sector throughput. GoB with coordination exhibits a small increase in cell edge throughput but significant decrease in sector throughput. Hence interference coordination should not be used in conjunction with GoB. Note that additional system-level results for GoB and SDMA can be found in Chapters 7 and 13.

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In sector throughput

Relative performance improvement w.r.t. 1T x MRC 100% 1 Tx GoB + IRC SDMA + MRC 4 Tx 80% GoB + MRC

60%

GoB + IRC + coord

40% IRC

20% MRC 0% 0% –20%

coord. + IRC 200%

400%

600%

800%

1000%

coord. In cell-edge throughput

Figure 9.22 Performance improvement of spatial processing schemes and their combinations relative to a single antenna Tx with a 2 Rx antenna MRC receiver.

Figure 9.23 illustrates performance of C-CDD by means of the bit error rate (BER) versus the carrier to interference (C/I) ratio in comparison with no transmit diversity technique used and with a random independently chosen sub-carrier allocation in each cell. The reference system is half loaded (RL = 0.5) and fully loaded (RL = 1.0). The term C/I describes the ratio of the power from the desired BS to the temporary BS. A large performance gain is observed

Figure 9.23 BER versus C/I for SNR = 5 dB using C-CDD with full power and halved power per sub-carrier and no transmit diversity technique.

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close to the cell border (C/I ∈ [ −10, 10 ] dB) for the C-CDD technique. Furthermore, C-CDD enables an additional substantial performance gain compared to pure macro diversity by transmitting identical signals from both cells at the cell border.

9.4 Conclusion In this chapter, we first presented multi-user access techniques for cellular environments in the context of next generation mobile networks. It has been shown that in the case of transmissions over slow varying channels, frequency adaptive techniques are preferred. The introduction of a tight feedback loop enables the frequency-adaptive transmission to utilise fine-grained channel variations, by relying on the prediction of the SINR within each chunk (layer). These channel variations differ between channels to different UTs. We therefore obtain significant multi-user scheduling gains when flows can be allocated to chunks that provide the best channel gains and interference conditions. The proposed multiple access scheme for frequency-adaptive uplink and downlink is based on chunk-wise frequency-adaptive TDMA/OFDMA in both the FDD and the TDD modes. When channel conditions cannot allow exploitation of feedback information (because of too fast variation or bad SINR, for instance), non-frequency-adaptive transmissions offer robustness. Various access schemes are proposed, the preferred ones being B-EFDMA over downlinks and B-IDFMA over uplinks. In practice, both frequency-adaptive and non-frequency adaptive modes will coexist. Further, we have also shown that inter-cell interference mitigation was directly coupled with achievable re-use of the available spectrum, the ultimate goal being to achieve high system capacity using an optimal effective frequency re-use factor. Three main types of intercell interference mitigation techniques were investigated: inter-cell interference averaging, inter-cell interference avoidance and inter-cell interference mitigation using smart antennas. Interference cancellation algorithms have been shown to significantly improve the system performance, especially in the cell border areas, but this improvement comes at the expense of increased baseband complexity of mobile receivers and additional time-synchronisation of the network. The investigations showed that random and minimum interference dynamic channel allocation enhanced by specific scheduling provide similar performance, especially when grid of beams is used. Compared to minimum interference dynamic channel allocation, random dynamic channel allocation represents a low-complexity solution with minimum signalling overhead. For interference avoidance based on resource management, significant throughput improvements can be achieved by fractional frequency re-use with full power isolation (FFR FI) at the cell border. But this occurs at the expense of non-negligible sector throughput degradation. In consequence, this scheme should be reserved for data transmission when the network load remains low to medium. In these cases, such a degradation can be afforded, in order to enhance the user experience at the cell-edge. Concerning self-adaptive re-use partitioning, both the interference-based variant with re-use 1 (ISARP) or the variant with re-use 3 at the cell border (R3SARP) achieve additional performance gains that can be used for the VoIP service. This would also hold for other real-time services (e.g. streaming) or when the load increases in the network. This technique works in a decentralised manner and therefore does not require any additional inter-BS coordination.

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The use of smart antennas significantly improves the system robustness to inter-cell interference. A fixed grid of beams (GoB) is an especially efficient way to reduce the interference spread in the system and to protect users at the cell border. With SDMA on top, the overall system performance is further improved at the expense of slightly less protection of the cell edge users. Additionally receive diversity combining schemes such as MRC provide considerable improvements when used at UTs in downlink and at BSs in uplink. Further improvements are achieved with IRC, in particular for downlink reception at UTs. When combined, transmit beamforming at the BSs and multi-antenna reception with IRC at UTs provide tremendous performance gains. Furthermore, the macro diversity method based on cyclic delay diversity (CDD) improves the inter-cell interference situation at cell border areas as well.

Acknowledgements The authors would like to thank their colleagues from the WINNER and WINNER 2 projects involved in the tasks related to multiple access and inter-cell interference. Thanks go especially to Sorour Falahati, Tobias Frank, Elena Costa, David Falconer and Gunther Auer for our fruitful collaboration on the WINNER multiple access concept and to Mugdim Bublin, Magnus Olsson and Eric Hardouin for their deep involvement and sharing their expertise in the WINNER inter-cell interference mitigation domain.

References [3GPP07] [Alc05] [Alc06] [Bai94] [BJK96]

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[DL07] [Dol46] [DPS+07]

3GPP (2007) Spreading and Modulation (FDD), TS25.213 TSG Radio Access Network, Release 7. Alcatel (2005) ‘OFDM air interface with QoS at cell edge’, 3GPP TSG RAN WG1 #40bis, Beijing, China. Alcatel (2006) ‘Comparison of efficiency of DL Interference Coordination schemes and view on measurements on intra-frequency neighbour cells’, 3GPP TSG RAN WG1 #46, Tallinn, Estonia. Baier, P.W. (1994) ‘CDMA or TDMA? CDMA for GSM?’, Proc. of IEEE PIMRC 1994, The Hague, pp. 1280–84. Baier, P.W., Jung, P. and Klein, A. (1996) ‘Taking Challenge of Multiple Access for Third-Generation Cellular Mobile Radio Systems – A European View’, IEEE Communications Magazine, February 1996, pp. 82–89. Brueninghaus, K., Astely, D., Salzer, T., Visuri, S., Alexiou, A., Karger, S. and Seraji, G.A. (2005) ‘Link performance models for system level simulations of broadband radio access systems’, Proc. of IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 4:2306–11, Berlin, Germany. Chouly, A., Brajal, A. and Jourdan, S. (1993) ‘Orthogonal multi-carrier techniques applied to direct sequence spread spectrum CDMA systems’, Proc. of IEEE GLOBECOM’93, Houston, pp. 1723–8. Chacun, M., Helard, M. and Legouable, R. (2007) ‘Iterative Intercell Interference Cancellation for DL MC-CDMA Systems, MC-SS 2007, Oberpfaffenhofen, Germany, 7–9 May. Dammann, D. and Kaiser, S. (2001) ‘Standard conformable antenna diversity techniques for OFDM and its application to the DVB-T system’, in Proceedings IEEE Global Telecommunications Conference (GLOBECOM 2001), pp. 3100–105, San Antonio, TX, USA. Doukopoulos, X.G. and Legouable, R. (2007) ‘Intercell Interference Cancellation for MC-CDMA Systems’, VTC 2007, Dublin, Ireland. Dolph, C.L. (1946) ‘A current distribution for broadside arrays which optimises the relationship between beam width and side-lobe level’, Proceedings of the IRE and Waves and Electrons. Dahlman, E., Parkvall, S., Sk¨old, J. and Beming, P. (2007) 3G Evolution: HSPA and LTE for Mobile Broadband, Academic Press Inc.

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Divsalar, D., Simon, M.K. and Raphaeli, D. (1998) ‘Improved parallel interference cancellation for CDMA’, IEEE Transactions on Communications, 46(2):258–68. Eriksson, T. and Ottosson, T. (2007) ‘Compression of Feedback for Adaptive Transmission and Scheduling’, Proceedings of the IEEE, 95(12):2314–21. Fazel, K. and Kaiser, S. (2003) Multi-Carrier and Spread Spectrum Systems, John Wiley & Sons. Fazel, K. and Papke, L. (1993) ‘On the Performance of Convolutionally-Coded CDMA/OFDM for Mobile Communication System’, Proc. of IEEE PIMRC’93, Yokohama, pp. 468–72. Hammerschmidt, J.S., Brunner, C. and Drewes, C. (2000) ‘Eigenbeamforming: a novel concept in array signal processing’, Proc. VDE/ITG European Wireless Conference, Dresden, Germany. Holma, H. and Toskala, A. (eds) (2000) WCDMA for UMTS, John Wiley & Sons, Chichester, UK. Inoue, M., Fujii, T. and Nakagawa, M. (2002) ‘Space time transmit site diversity for OFDM multi base station system’, in Proceedings IEEE Mobile and Wireless Communication Networks (MWCN 2002), pp. 3100–105, Stockholm, Sweden. Jang, J.H., Won, H.C. and Im, G.H. (2006) ‘Cyclic Prefixed Single Carrier Transmission with SFBC over Mobile Wireless Channels’, IEEE Signal Processing Letters. Kaiser, S. (1995) ‘Analytical performance evaluation of OFDM-CDMA mobile radio systems’, in Proceedings First European Personal and Mobile Communications Conference (EPMCC’95), Bologna, Italy, pp. 215–20. Kaiser, S. and Fazel, K. (1997) ‘A flexible spread-spectrum multi-carrier multiple-access system formulti-media applications’, Proc. 8th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, 1:100–104. Kaiser, S. and Hagenauer, J. (1997) ‘Multi-Carrier CDMA with Iterative Decoding and SoftInterference Cancellation’, in IEEE GLOBECOM’97, Phoenix, USA, pp. 6–10. Kittipiyakul, S. and Javidi, T. (2004) Resource Allocation in OFDMA: How Load-Balancing Maximizes Throughput when Water-Filling Fails, UWEE Technical Report Nr UWEETR-2004-0007, Dept. of Electrical Engineering, University of Washington, Seattle. Kivanc, D., Li, G. and Liu, H. (2003) ‘Computationally Efficient Bandwidth Allocation and Power Control for OFDMA’, IEEE Transactions on Communications, 2(6):1150–58. Kondo, S. and Milstein, L.B. (1993) ‘On the Use of Multi-carrier Direct Sequence Spread Spectrum Systems’, Proc. of IEEE MILCOM’93, Boston, pp. 52–6. Lee, E.A. and Messerschmitt, D.G. (1998) Digital Communication, Kluwer Academic Publishers, Boston. Lee, J.S. and Miller, L.E. (1998) CDMA Systems Engineering Handbook, Artech House, Boston. Mehrotra, A. (1997) GSM System Engineering, Artech House, Boston. Plass, S. and Dammann, A. (2006) ‘Cellular cyclic delay diversity for next generation mobile systems’, Proc. 64th IEEE Vehicular Technology Conference (VTC 2006-Fall), Montreal, Canada. Pahlavan, K. and Levesque, A.H. (1995) Wireless Information Networks, John Wiley & Sons, New York. Proakis, J. (2002) Digital Communications, McGraw Hill. 3GPP TSG RAN WG1 (2006) ‘Overview of Resource Management techniques for Interference Mitigation in EUTRA’, Texas Instruments, 3GPP TSG RAN WG1 #44bis, Athens, Greece. Rapp, C. (1991) ‘Effects of HPA-nonlinearity on a 4-DPSK/OFDM-signal for a digital sound broadcasting signal’, in Proc. Second European Conference on Satellite Communications (ECSC-2), pp. 179–84. Rappaport, T.S. (1996) Wireless Communications, Principles and Practice. Prentice Hall, Upper Saddle River, New Jersey. Rasmussen, L.K., Lim, T.J. and Johansson, A. (2000) ‘A matrix-algebraic approach to successive interference cancellation in CDMA’, IEEE Transactions on Communications, 48(1): 145–51. Robertson, P., Villebrun, E. and Hoeher, P. (1995) ‘A comparison of optimal and sub-optimal map decoding algorithms operating in the log domain’, in Proceedings IEEE International Conference on Communications (ICC 1995), Seattle, USA. Sawahashi, M. (2003) ‘Broadband Packet Wireless Access Supporting Cellular System and Hot-spot Environments and Its Experiments’, Proc. of 5th Smart Antenna Workshop with Emphasis on SDR Applications, April.

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[SFS+05]

[Skl88] [SLK96]

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[SSP03] [SW02] [Vit95] [WCLM99]

[Wes02] [WIN1D24]

[WIN1D26]

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Sorger, U., De Broeck, L. and Schnell, M. (1998) ‘Interleaved FDMA-a new spread-spectrum multiple-access scheme’, Proc. of ICC’98, pp. 1013–17, Atlanta, Georgia. Svensson, T., Frank, T., Falconer, D., Sternad, M., Costa, E. and Klein, A. (2007) ‘B-IFDMA: A Power Efficient Multiple Access Scheme for Non-frequency-adaptive Transmission’, Proc. 16th IST Mobile & Wireless Communications Summit, Budapest, Hungary. Sternad, M., Falahati, S., Svensson, T. and Aronsson, D. (2005) ‘Adaptive TDMA/OFDMA for WideArea Coverage and Vehicular Velocities’, 14th IST Mobile & Wireless Communications Summit, Dresden, Germany. Sklar, B. (1998) Digital Communications: Fundamentals and Applications, Prentice Hall PTR, Englewood Cliffs, NJ. Sari, H., Levy, Y. and Karam, G. (1996) ‘Orthogonal Frequency-Division Multiple Access for the Return Channel on CATV Networks’, Proc. of Int. Conference on Telecommunications (ICT’96), Istanbul, pp. 602–7. Sari, H., Levy, Y. and Karam, G. (1997) ‘An Analysis of Orthogonal Frequency-Division Multiple Access’, Proc. of IEEE GLOBECOM’97, pp. 1635–9. Sternad, M., Svensson, T., Ottosson, T., Ahln, A., Svensson, A. and Brunstr¨om, A. (2007) ‘Towards systems beyond 3G based on adaptive OFDMA transmission’, Proc. of the IEEE, Special Issue on Adaptive Transmission, 95(12):2432–2455. Seurre, E., Savelli, P. and Pietri, P.J. (2003) GPRS for Mobile Internet, Artech House, Boston. Springer, A. and Weigel, R. (2002) UMTS: The Physical Layer of Universal Mobile Telecommunications System, Springer Verlag, Berlin. Viterbi, A.J. (1995) CDMA: Principles of Spread Spectrum Communications, Addison-Wesley, Reading, MA. Wong, C., Cheng, R.S., Lataief, K.B. and Murch, R.D. (1999) ‘Multiuser OFDM with adaptive subcarrier, bit, and power allocation’, IEEE Journal on Selected Areas in Communications, 17(10):1747–58. Wesolowski, K. (2002) Mobile Communication Systems, John Wiley & Sons, Chichester. WINNER I (2005) IST-2003-507581 Assessment of adaptive transmission technologies, Deliverable D2.4, February 2005, viewed 20 June 2009, http://projects.celticinitiative.org/winner+/. WINNER I (2004) IST-2003-507581 Assessment of Multiple Access Technologies, Deliverable D2.6, October 2004, viewed 20 June 2009, http://projects.celticinitiative.org/winner+/. WINNER I (2005) IST-2003-507581 Assessment of advanced beamforming and MIMO technologies, Deliverable D2.7, February 2005, viewed 20 June 2009, http://projects.celticinitiative.org/winner+/. WINNER I (2005) IST-2003-507581 Final Report on identified RI key technologies, system concept, and their assessment, Deliverable D2.10, December 2005, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+/. WINNER II (2007) IST-4-027756 Link Level Procedures for the WINNER System, Deliverable D2.3.3, November 2007, viewed 20 June 2009, http://projects.celticinitiative.org/winner+/. WINNER II (2006) IST-4-027756 The WINNER II Air Interface: Refined SpatialTemporal Processing Solutions, Deliverable D3.4.1, November 2006, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+/. WINNER II (2006) IST-4-027756 The WINNER II Air Interface: Refined multiple access concepts, Deliverable D4.6.1, November 2006, viewed 20 June 2009, http://projects.celticinitiative.org/winner+/. WINNER II (2007) IST-4-027756 Interference averaging concepts, Deliverable D4.7.1, June 2007, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+/. WINNER II (2007) IST-4-027756 Interference avoidance concepts, Deliverable D4.7.2, June 2007, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+/. WINNER II (2007) IST-4-027756 Smart antenna based interference mitigation, Deliverable D4.7.3, June 2007, viewed 20 June 2009, http://projects.celtic-initiative. org/winner+/.

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10 Radio Resource Control and System Level Functions Emilio Mino,1 Jijun Luo,2 Elias Tragos,3 Albena Mihovska,4 and Roberta Fracchia5 1

Telef´onica Investigaci´on y Desarrollo Nokia Siemens Networks 3 National University of Athens 4 Aalborg University 5 Motorola 2

10.1 Introduction This chapter describes functions of the Radio Resource Control (RRC) layer and the IP Convergence Layer (IPCL) of the WINNER system. The overall protocol stack and the mapping to the logical node architecture are described in Section 4.4. To cope with the new scenario on broadband wireless networks, in this chapter updates of the RRC functions are proposed. One of the principal motivations is the optimisation of data rate offered by the network, to facilitate the user plane traffic. As an example, in the case of handover, the baseline is the coverage in terms of signal to interference plus noise ratio (SINR), similar to current systems, but in the case of cells with similar SINR and overlapping coverage, handover algorithms should include the data rate offered to the user by neighbouring cells (see simulation results in Section 10.5). Furthermore novel approaches are proposed, such as fuzzy logic applied to intersystem handover between heterogeneous systems, admission control, integrating congestion into the backbone, combined radio and IP handover [TMM+07a], new architectures for efficient user plane data handling, etc. New broadband systems have a reduced number of nodes, to decrease inter-node user plane signalling, e.g., in UMTS between the node and the Internet, the data traffic has to pass through the RNC, SGSN and GGSN. In WINNER, the gateway (GW) is the only intermediate node that routes traffic to the Internet. However, the use of an optional centralised radio Radio Technologies and Concepts for IMT-Advanced Martin D¨ottling, Werner Mohr and Afif Osseiran C 2009 Martin D¨ ottling, Werner Mohr, Afif Osseiran

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resource management (RRM) server is proposed to offer superior performance in situations of medium to high load in the network [MJT07; TMM+07b], when central coordination performance will provide system functionalities and gains not achievable by a pure distributed RRM [3GPP08a]. The changes in the architecture require changes in system layers and RRM functions [MLT07]: now the base station (BS) includes the RLC MAC and RRC layers and new RRM functions, e.g. distributed handover algorithms and IPCL security keys reside at the BS. Finally, the performance of the proposed intermode handover algorithms is analysed and the simulation results are shown.

10.2 IPCL Layer The IPCL layer functions are classified into two groups: the ones related to the transfer of user plane data between two IPCL layers in different nodes (e.g. in the terminal and the gateway) and the IPCL functions for handover. In the case of transfer of user data, the IPCL layer adapts the higher-layer data flows (e.g., IP packets) to the transmission modes of the RLC layer, establishing the transfer data protocol with a peer IPCL entity, compressing the long IP headers and ciphering the IP payload (see Figure 10.1). Figure 10.2 shows the process of conversion of TCP /UDP/IP packets to IPCL protocol data unit (PDU) packets (composed of an IPCL header and an IPCL service data unit (SDU)). The IPCL layer uses the following RLC layer services: transparent data transfer, unacknowledged data transfer and acknowledged data transfer. Another group of IPCL functions is associated with handover: in sequence delivery of upper layer PDUs, duplicate detection of lower layer SDUs, and the effects on the IPCL layer during the inter-gateway handover.

10.2.1 Transfer of User Data Between IPCL Entities The transfer of user data function is used by the UT and the GW to transfer IP/UDP packets between them. Above this protocol there are TCP/UDP/IP layers in the UT and GW. Figure 10.1 shows the IPCL data transfer protocol, using the acknowledged mode, in which an application requests data from a remote node. If the header compression is configured, the transmitter IPCL entity: 1. Performs header compression upon reception of IPCL SDU from upper layers. 2. Increments the sequence number. 3. Submits the IPCL PDU to the RLC layer in the sequence in which they have been received from upper layers. When the receiver IPCL entity receives an IPCL PDU, with a compressed header, from the RLC layer, it: 1. Performs header decompression of the IPCL PDU to obtain the IPCL SDU. 2. Checks that it has received the expected consecutive sequence number. 3. Delivers the IPCL PDU to the upper layer, in the order received from the lower layer.

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Figure 10.1 IPCL transmission using the RLC services in acknowledged mode.

There are two data transfer methods using the underlying acknowledged and unacknowledged RLC modes. A typical example of IPCL transmission could be an application (the IPCL user in Figure 10.1) downloading data from a server located on the Internet. There is no acknowledgement between the IPCL layer entities, as we can see in Figure 10.1: in acknowledged mode the transmission reliability is based on an RLC layer acknowledgment. 10.2.1.1 IPCL Header Compression The aim of the header compression and decompression protocols of IP data streams (e.g., TCP/IP and RTP/UDP/IP headers) is to reduce the amount of redundant transmitted header information. As an example, the TCP/IP headers of IPv4 have around 20+20 bytes, which can be reduced to only 3 octets; in the case of TCP/IP headers of IPv6 the gain is even more noticeable, the header could have around 60 bits, due to the IP address space change from 32 to 128 bits. Figure 10.2 shows the conversion process. It includes an IP header with 24 bytes, not a protocol transport header (e.g. TCP), which could have similar size. The first time, the full

Figure 10.2 Conversion of an IP packet to an IPCL PDU.

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header is transmitted; after that, only the incremental changes to this header are signalled. From time to time, a refresh packet is sent with the full header. A header compression adapted to mobile communications, such as [IETF01] could be used. IPCL PDUs are composed of the following fields:

r PDU type: this is an indicator of the IPCL PDU (with or without sequence number) with a length of three bits.

r Packet Identifier (PID): This is an indicator of the use compression method (or not). The header compression method is specific to the particular network layer (IP, PPP, etc.), transport layer (TCP, RTP, UDP, etc.) or upper layer protocol combinations e.g. TCP/IP or RTP/UDP/IP. Its length is five bits. r Sequence number (SN): It is used in the case of flows of compressed packets in acknowledged mode and has a length of 16 bits. r Data. There are three types of data: r uncompressed IPCL SDU; r header compressed IPCL SDU; r header compression protocol, including feedback information. 10.2.1.2 IPCL Data Ciphering and Ciphering Keys In the IPCL layer, the ciphering can guarantee the confidentiality for the UP traffic. In other words, it ensures that unauthorised users have no access to data belonging to other users. Ciphering is required to prevent the eavesdropping of messages and the unlawful interception of packets. Moreover, ciphering is also able to ensure, to some degree, integrity and authentication as it proves the possession of the corresponding keys shared by the communicating entities. Figure 10.3 depicts the process followed to obtain the IPCL key. Initially, it is supposed that there is a permanent key shared between the UT (in the SIM of the UT, which is supposed to be tamper-resistant) and the core network. Alternatively, a common key can be used, which could be a private–public key, where the private key is on the UT SIM and the corresponding

Figure 10.3 Process for generation and transmission of IPCL NAS and RRC security keys.

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public key is only in the home subscriber server (HSS) database. After each authentication, a set of ciphering and integrity protection keys is generated using this permanent key. The IPCL key is derived from an initial permanent key stored in the UT SIM and in the authentication centre (AuC), which is connected to HSS. The HSS chooses a random temporal IPCL key, which is encrypted using the initial common key. The GW receives the transferred IPCL key with and without ciphering. The encrypted temporal key is transferred by the air to the UT, which is able to decrypt the key generated by the HSS. The ciphering algorithms for the IPCL and NAS communications could be UEA2 or UIA2 [3GPP06]. The IPCL ciphering temporal key could be exchanged between GWs (e.g. interGW load balance) if the connection between GWs is secure enough (i.e., the inter-GW key exchange takes place when GWs belong to the same service pool of the same operator). The RRC and NAS keys are used to protect the signalling messages. The most critical key is the NAS key; it protects access to the system confidential data. The most exposed key is the RRC key, which should be transmitted without protection to the base station; the other keys should be transmitted to the GW, usually a less accessible network element, for example in the case of macro deployments, where the GW could be at the operator premises. In the case of UMTS, a single key is required and is located in a secure place, the RNC, which terminates the layer 2 and 3 protocols. With the integration of some RNC system layers and functions in the base station (BS), the BS should be also included in the ciphering process and additional securitisation measures should be provided to the BS.

10.2.2 IPCL and Handover The concept of a pool of gateways has been introduced to minimise the number of UT IP handovers (see Sections 4.3.2 and 10.3.3.2). IP handover is triggered when there is a change of GW controlling a particular UT. In the GW resides the anchor point of the user plane connection. The IP handover is more time consuming than the radio handover, but should be used in cases such as load balancing between GWs, GW failure and inter-operator handover. IP handover can be implemented using such protocols as mobile IP (MIP), hierarchy MIP (HMIP) and fast MIP (FMIP). The IP packets transferred from the corresponding node are not processed by IPCL in the target GW as long as the old packets forwarded by the source GW are not processed. It is recommended to restart the IPCL sequence number at least after the tunnelling phase between the source and target GW when applicable. When requested by the RRC layer mobility management (MM) module, for each flow configured to support lossless inter-GW relocation, the IPCL sub-layer in the source GW should forward the following messages to the target GW:

r the UL Receive IPCL SN (UL means uplink) of the next IPCL SDU expected to be received from the UT;

r the DL Send IPCL SN (DL means downlink) of the first transmitted but not yet acknowledged IPCL SDU;

r the transmitted but not yet acknowledged IPCL SDUs together with their related DL Send IPCL SNs;

r the not yet transmitted IPCL SDU.

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10.2.2.1 In-Sequence Delivery of Upper Layer PDUs In the downlink, data may arrive out of order at the target BS, if the data transmitted directly from the GW to the target BS arrive before the data that are forwarded from the source to the target BS during handover. In the uplink, the GW may receive data out of order, if there are gaps in the data transmitted from the source BS before handover and retransmissions of the missing data arrive from the target BS after handover [BHK+07]. In 3GPP LTE, out-of-order data may degrade TCP performance severely [BHK+07; RTR07]. In contrast to IPCL in WINNER, the PDCP in LTE cannot provide reordering and in-sequence delivery, since it is terminated in the BS and the UT.1 In WINNER, degradation of the TCP performance due to out-of-order segments should be less of a problem, since IPCL terminates in the GW and UT. Even if IPCL can reorder data to provide in-sequence delivery to higher protocol layers, outof-order data should be avoided when possible, since out-of-order data increases delay at the higher protocol layers. In [BHK+07], a separate service class for forwarded data is proposed. To reduce delay of forwarded data, the scheduler in the source BS is proposed to give priority to forwarded data. In [RTR07], forwarded data is proposed to be prioritised over data from the GW in the target BS for transmission to the UT (which will only work if the buffer of forwarded data is not emptied before all forwarded data have been received). We recommend that forwarded data are prioritised in WINNER, in order to avoid out-of-order data.

10.2.2.2 Duplicate Detection of Lower Layer SDUs Data may be duplicated after handover, if a lower layer PDU, MAC or RLC (in the case of no PDU context transfer), that is received before handover is delivered to higher layers and the acknowledgement is lost [BHK+07]. Duplicates may have a negative impact on higher layer protocols, especially on TCP [BHK+07; RTR07]. In the UL, IPCL in the GW should remove duplicates before data is delivered to higher layers. In the DL, there are two alternatives:

r The RLC in the target BS uses the IPCL sequence number to detect duplicated data (IPCL PDUs).

r The IPCL in the UT detects and removes duplicated data (IPCL PDUs). If RLC in the BS performs duplicate detection, then RLC has to look into the IPCL sequence numbers, which violates the protocol layering. On the other hand, if IPCL in the UT performs duplicate detection, then layering is preserved, but duplicates are transmitted over the air to the UT. If there is enough capacity between the BS and the UT, then the UT could detect and remove duplicates and violation of protocol layering could be avoided. Duplicates are not expected to occur often. Therefore, we recommend that IPCL in the UT performs duplicate detection. In environments in which duplicates occur frequently, RLC in the BS could perform duplicate detection as a value added function.

1 In the latest version of [3GPP08a], a solution is proposed. PDCP in the source BS informs the target BS about the next PDCP sequence number to use.

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10.3 Radio Resource Control Radio resource control (RRC) is an important radio interface protocol. It includes measurement, exchange and control of radio resource-related indicators and commands between the RAN and UTs. Section 4.4.2 lists the RRM functions of the WINNER system. In the following sections, we describe the main RRM functions and part of the related RRC signalling. The described functions include: mobility management in idle mode (paging, cell re-selection), mobility management in active mode (intramode, intermode, intersystem, IP, radio and hybrid handover), admission control, mobility management, load control, paging, and flow control.

10.3.1 RRC States Future systems will have simplified RRC states compared to legacy networks (e.g., UTRAN). In order for WINNER to be harmonised and compatible with this general trend (e.g., LTE of 3GPP), similar RRC states are proposed (see Figure 10.4). Connected and detached modes are the two basic RRC modes for UTs. Inside the connected main state, there is a separation of the states according to the UT activity, namely active and idle. Furthermore, we can distinguish inside the active mode a submode called dormant, in which the UT could be waiting for the assignment of radio resources or the change to idle mode. Reduction of transition times with respect to current systems, from idle to active and from detached to active RRC modes is a design goal; the integration of IP address assignment is an integral part of active mode. 10.3.1.1 UT Detached State The network is not aware of this UT (e.g. UT is switched off or not operating on the current system). There is not an RRC context in the network and neither a UT RRC identity nor an IP address are assigned.

Figure 10.4 User Terminal RRC states.

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10.3.1.2 UT Idle State After switching on, the UT camps in the most appropriate cell (e.g. the cell with the highest ranking based on signal strength). The UT is able to send and receive system and cell information. UT is handled by the GW node. The UT is maintained in this state until it is going to send or receive user information. This is the inherently power saving state: the UT should be able to maintain this state for several days. UT performs a periodic search for a higher priority mode or RAN, autonomously selects or re-selects a cell and monitors broadcast and paging channels. UT belongs to a tracking area or paging group of cells controlled by a GW (after the initial connection, the UT can change its position without notifying the network). Assuming that previously the UT has been in active mode, the idle mode parameters are:

r RRC context in the network: information to allow switching to active mode (e.g. IPCL–NAS ciphering key);

r UT RRC identity: none (but any previously assigned IP address remains); r UT tracking area: included in the temporal mobile subscriber identity (TMSI) provided in active mode, the tracking area (TA) identifies a pool of cells controlled by a GW;

r UT position: the tracking area ID (a group of several cells); r mobility: cell re-selection; r DL/UL activity: Listens to broadcast channel, configuration of the discontinuous reception period (DRX). 10.3.1.3 UT Active State This state could be similar to the UTRAN CELL DCH state [3GPP08b]. The UT is moved to this state from idle when transmitting or receiving data on traffic channels and it monitors the dedicated and common control channels continuously. The BS handles the UT, which selects the best cell from the list of candidates provided by network. There is a power saving sub-state within the Active state. This is the dormant sub-state where the UT is ready to transmit or receive data on traffic channels and monitors the control channels discontinuously. The active mode parameters are:

r RRC context in the network: all needed information is provided; r UT RRC identity: a short (8 bits) identifier for a UT in a cell (e.g. for assignment of radio resources); any assigned IP address is also given;

r UT TMSI: the TMSI, which includes the TA identification; r UT position: cell level (identified by the serving BS); r mobility: cell re-selection; r DL/UL activity: the UT can be configured for the dormant mode, with a discontinuous reception period (DRX); UL/DL information is indicated in the broadcast channel. The change from active to dormant state is activated when no user plane transmission or reception is expected by the UT. The aim is to reduce power consumption in the

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active state, with a reduced activity cycle. The user plane connection between BS and UT is maintained. The change from the active to the idle state is launched by an active state timer in the BS. The BS informs the GW-IPALN which then removes the user plane connection between BS and UT.

10.3.2 Mobility Management in Idle Mode In the UT idle state, the UT is configured for discontinuous reception (DRX) to allow power saving. The UT is allowed to switch to active once per DRX cycle to check whether it is currently being paged or not. For the UT idle state, the network has to broadcast the system information through its Broadcast Control Channel (BCCH). Based on the BCCH and the pilot signals channels, the UT is able to camp in a cell, which is better than other candidates according to the cell reselection criteria. In the DRX active period, the UT has the chance to re-select the cell when it realises a change in the cell ranking. Thanks to the hybrid information system (HIS) [WIN2D481], even if, for instance, a TDD cell has higher rank than the FDD cell, however, for high vehicle speed UTs, such a TDD cell should not be selected as the cell for the UT to camp on. HIS is an optional database that contains the periodic UT and BS measurements, including its position. The HIS is proposed for improvement of the intermode handover increasing the cell size by precisely identifying the cell borders. HIS allows for including UT speed in the ranking for cell (re-) selection. A unique ID must be assigned to the idle UTs within one tracking area. After being assigned to a tracking area, the UT must wake up from time to time to check the paging channel for the relevant paging message. 10.3.2.1 Paging Current cellular networks locate idle mode mobile devices using a mechanism called paging, which is based on the transmission of a paging channel (LPCCH) that is a downlink transport channel (see Section 4.4.3.3 for the description of logical and transport channels). The LPCCH is always transmitted over the entire cell, to support efficient idle and sleep mode procedures. Since cellular network subscribers are in idle mode most of the time, paging is necessary to support the idle mode mobility management with reduced power consumption and signalling. Indeed, it permits support of a large number of users, using reduced radio resources. It should be noted that paging can also be used to wake up a UT in active mode (including dormant mode, in which a UT wakes up periodically to check for pending data addressed to it), in order to establish an additional data session with the UT. In this case, the network is already aware of a UT association at the cell level, thus the paging request can be directly sent to the UT. Since active mode paging is relatively easy to realise, we focus on idle mode paging. 10.3.2.2 Tracking Area GSM defines the location area (LA) and the routing area (RA). UTRAN defines three areas used: the UTRAN registration area (URA), the LA and the RA. In contrast, WINNER tracks an idle mode UT by only one predefined tracking area (TA), in order to simplify the management.

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Having a flat architecture, it is important to divide the coverage of a WINNER system into a number of tracking areas to limit the broadcast overhead due to paging requests. For multi-mode UTs, it is possible that a WINNER UT is assigned to multiple TAs. It is also true that a UT can temporarily be attached to multiple TAs, since the TAs may overlap for the purpose of reducing the signalling overhead. A tracking area normally includes a number of radio cells, which may overlap due to the FDD and TDD deployment following the hierarchical cellular system principle, where, e.g., several TDD cells can be contained in a large FDD cell. The WINNER network is aware of the UT location through the registered tracking area of the UT. An idle UT is paged in all cells of the tracking area. It is normally necessary to define several tracking areas, especially in the case when the WINNER network is large. Such a method limits the overhead caused by the broadcast of paging requests and decreases the paging delay. A tracking area is composed of a number of BSs. In order to control the tracking areas in a WINNER system, a logical entity or function, a radio paging controller (RPC), is required. The RPC can be integrated into the GW as an additional function or can be placed in the core network. Such an RPC function takes care of the tracking area definition and registration of idle UTs. There are four reasons to perform tracking area registration:

r transition from detached RRC state to the idle state; r non-validation of the previous paging area; r tracking area update timer expiry; r HIS triggered tracking area update.

10.3.3 Mobility Management in Active Mode The purpose of active mode mobility management is to maintain connectivity and to support QoS in a radio resource efficient manner (seamless handover). A handover may be triggered due to: mobility changes in the radio environment, changes in the QoS requirements or for other reasons (e.g. load balancing). Two aspects have been considered in mobility management in active mode: micro-mobility and macro-mobility. 10.3.3.1 Micro Mobility Micro mobility is usually separated into two mobility schemes: active mode mobility and idle mode mobility [3GPP08b]. This section deals with mobility management in active mode. Henceforth, ‘handover’ is used synonymously with ‘active mode mobility’. The purpose of active mode mobility is to maintain connectivity and support QoS in a radio resource efficient manner for mobile terminals. In WINNER, the concept of a radio access point (RAP) is defined. A RAP could be a base station or a relay node: in principle, a UT does not distinguish between them. A handover from one RAP to another (i.e. the UT will alter its point of attachment to the RAN from one RAP to another RAP) may be triggered for a number of reasons. The most essential trigger for handover is the signal strength, which may vary due to mobility or changes in the radio environment. However, the load situation within a cell, the interference

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Figure 10.5 Radio handover flow chart between two BSs.

experienced by the UT, and the location of the UT or changes in the UT speed may also trigger a handover. A typical flow chart of radio handover between BSs is shown in Figure 10.5. The mechanism of handover can be optionally enhanced by the introduction of additional system entities. In the centralised approach, all the signalling and decisions go through a central entity, the RRM server. Radio handover takes place when the UT changes its attachment to a RAP, but maintains the same IP address after completion of the handover. This seamless handover is based only on a switching process of serving BS or RN. Since the UT has the same IP address after the handover process, the data flow is not interrupted since the application does not see any changes. Only the routing of the packets to the destination BS or RN is changed. This is the most common type of handover, when the source and target BS or RN belong to the same network domain. In WINNER, three different types of radio handover are considered intermode, intramode and intersystem handover (with or without IP handover depending on the coupling degree of the networks), and the BSs involved are controlled by the same gateway or pool of gateways. Some additional signalling is required if an RN is involved in the handover process. A typical flow chart of a handover between RNs of the same cell is depicted in Figure 10.6. The handover candidates have to be forwarded by the source RN to its serving BS. The BS then decides on the target RAP. If the target RAP is an RN of another cell, the handover request has to be sent to the BS of the other cell and the BS of the other cell then forwards it to the target RN. The target RN confirms the handover request and the BS sends the confirmation to the source RN, which forwards it to the UT. Some of this signalling is over the air and adds delay to the handover process. In order to save time, the handover confirmation can be sent by the BS, because it is aware of the operational state of the RNs in its cell. In addition to the signalling delay, the forwarding of the data packets causes some additional delay from the BS to the target RN. More details on the handover process can be found in [WIN2D351].

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Figure 10.6 Radio handover flow chart between two RNs in the same cell.

The common sequence numbers for RLC PDUs at the BS and the RN, and the end-to-end ARQ process between the BS and the MS ease the handover process. The same end-to-end ARQ process can be continued in case of an intra-relay-enhanced cell (intra-REC) handover and the BS can perform a PDU context transfer without requiring a data transfer from the source RN to the BS. Further, the BS can poll the source RN for its buffer status and can delay the handover in order to empty first the buffer of the source RN. The handover mechanism for REC is explained in detail in Chapter 8.

10.3.3.2 Macro Mobility Macro-mobility handover or IP handover means moving between two domains representing two different sub-networks, i.e. macro mobility or IP handover takes place when the UT changes the gateway it is associated with and thus enters a different IP domain with a new IP address. Thus, macro-mobility is also named inter-domain mobility. The domains might belong to one or several network operators. The mobility management mechanism in WINNER also covers macro mobility. Macro mobility includes mobility support and associated address registration but also security and context transfer are important aspects. Mobile IP can be seen as a means to provide macro mobility. The mobility management mechanism in WINNER combines the radio and IP handover into ‘integrated handover’ (see Figure 10.7). The two layers of handover are executed independently. The micro mobility (radio handover) would exist between BSs of the same pool of GWs and the macro mobility (IP handover) would exist in handovers between BSs that belong

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Figure 10.7 Integrated radio and IP handover.

to different pools of GWs, or when load balancing between GWs takes place. Macro mobility is also used in intersystem handovers between WINNER and legacy RANs that are not tightly or very tightly coupled, i.e., the two systems do not share the same GWs. According to the WINNER architecture, the two layers of handover are executed independently, that is when moving from one overlapping pool area to another, the time for the macro mobility handover is not dependent on the time for the micro mobility handover. This is also one of the arguments for having overlapping GW pool areas. The benefit of independent execution of micro and macro mobility is that it avoids the need for a large amount of signalling (change of BS, GW, and IP address) to be exchanged in very short time. A one-step process would be complicated and could fail in a high network load situation.

10.3.3.3 Intramode Handover Intramode handover is the handover RA operating in the same physical layer mode (i.e. FDD or TDD). There are three possibilities: intra-BS and intra-RN handover as well as handover between RNs and BSs of the same mode. This type of handover includes the intra-cell handover where the user remains in the same mode (for example, the change of frequency in the same cell) and intercell handover between cells of the same mode. The basic trigger for intercell handover is the received signal strength, the load of the neighbouring cells, congestion situations, increased interference, the location of the user, etc. In the WINNER system, a user terminal may be in the coverage area of several cells of the different WINNER modes. So the number of cells used to perform measurements could be very large and cause limitations to the handover process, but the use of lists containing

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neighbouring cells that are the most likely to fulfil the signal strength or quality requirements at the mobile terminal could very much improve the handover process. The criteria for choosing the users (and their number) that perform the handover must be determined: it could be services-based criteria (speech users are handed over first, then another service, etc.) or resource-based criteria (users consuming lot of resources are transferred first, etc.) or user-based criteria (bronze users are handed over first, then silver users, then gold users, etc.). In Figure 10.8, a general flowchart for intramode handover is presented. In Section 10.5.2.2 the intramode algorithm is presented and simulated. The algorithm in the UT performs periodic measurements and, when a trigger for intramode handover is activated, the signals for cells included in the neighbouring cell list are measured and ranked as candidates for handover. Then the cell with the highest ranking is selected as the target cell. The admission control accepts or rejects the UT. If the decision is positive, the UT performs the handover, otherwise it selects another cell from the target list. 10.3.3.4 Intermode Handover In WINNER, two physical layer have been defined, namely FDD and TDD. Within each mode, different parameterisations of the WINNER system can be used in order to have a scalable and optimised system always offering the best network performance in largely varying deployments, such as wide area, metropolitan area, and local area. While in this order, mobility requirements are reduced, data rate requirements increase. In the exemplary WINNER test scenarios described in Chapter 13, a wide area deployment is based on the FDD mode, whereas metropolitan and local area deployments use TDD mode. Intermode handover is defined as the switching process between two cells of different WINNER modes. The typical WINNER scenario is expected to be the one where the cells of the different modes overlap either completely or partially. The triggers for intermode handover are:

r insufficient signal strength or signal quality; r congestion in one mode; r user mobility; r terminal location; r other physical layer triggers. In the case of handover from a wide area cell using FDD mode to a local area cell (TDD) there is a specific trigger, the need for higher data rate services. In that case and in the case of handover from local area to wide area the most important trigger is the UT velocity. Figure 10.9 shows the basic flowchart for an intermode handover. As we can see, the bad signal strength and quality (BER) as well as cell congestion trigger the intramode handover process, and then a BS in the same mode to which the handover can take place is sought. If no BS in the same mode is found, then an intermode handover is triggered. There are specific triggers that directly activate intermode handover as new services request or release and velocity changes. In Section 10.5.2.2 the intermode handover algorithm is presented and simulated. When there is an intermode handover trigger, the algorithm tries to find the best suitable mode for the user to handover to. This decision is based on several criteria (analysed above)

391

Figure 10.8 WINNER intramode handover flowchart.

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Figure 10.9 WINNER intermode handover flowchart, including its relationship with the intramode and inter-RAN processes.

and also on the trigger that requested the handover. The target cells are listed and ordered by preference according to the above criteria. The Admission Control function checks to which cell the user can be admitted. 10.3.3.5 Intersystem Handover In the intersystem handover, a UT connected to the WINNER network performs a handover to a legacy network such as UMTS or WLAN. It is assumed that a UT camped in WINNER could handover to a legacy RAN when it loses coverage of the system. This situation is relevant especially in the initial deployment with a very limited number of BSs, congestion in WINNER cells or due to possible user preference. The two systems belong to different networks and thus to different domains. These domains are IP domains, so when a user moves from a WINNER BS to a legacy BS then it has to change its associated IP address. This means that this type of handover is an IP handover

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Figure 10.10 Intersystem handover.

and the mobile IP protocol can be used for the execution of the handover process in order to provide a seamless handover without re-initiating the user’s session. The intersystem handover is presented in Figure 10.10. The UT changes network using an IP handover. In [WIN2D483] the intersystem handover algorithm, based on fuzzy logic, is presented and simulated. 10.3.3.6 Inter GW Handover and Load Balancing To avoid the use of IP handover, which is not as efficient in terms of delay and traffic discontinuity as radio handover, the concept of a pool of gateways is introduced in the WINNER system (see also Section 4.3.2). The interactions between GW IPCL layers have been presented in Section 10.2.2 for inter-GW handover. The pool of gateways share the resources of the pool of base stations to which they are connected. When there is a handover between BSs in the same pool, the GW normally does not change and thus the IP address remains the same. The logical association between the UT and GW is preserved independently of the serving BSs. Indeed, each GW may be associated with each BS in the pool area. Such an association avoids IP handover between GWs in the given pool area (see Figure 10.11). The traditional approach is a hierarchical structure, where each GW is associated with a set of BSs serving their own location area and providing a direct mapping between a GW and the area covered by associated BS. The GW is an anchor point for external routing and also the bridge between the UT and the external world outside the WINNER RAN, through the IG interface.

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Figure 10.11 Load balancing in a same pool of gateways.

The total capacity of a pool of GWs is easily optimised by adding or removing GWs. Furthermore the pool concept provides redundancy, that is, in the case of a GW failure, the users can be handed over to other GWs in the pool, and at the same time load balancing between GWs can easily be achieved. Therefore, the GW association is preserved even when a user hands over to a BS that is controlled by a different GW, belonging to the same pool. In that case, a change of IP address is not necessary. The handover between GWs in the WINNER system is a new approach that will not be common but should be considered in order to provide the system with advanced RRM functionality, such as load balancing between gateways. The inter-GW handover uses an IP handover between GWs that belong to the same or different pools. Load balancing between GWs (see Figure 10.11) is needed when a GW is congested or in order to achieve balanced distribution of load between the GWs. A GW can be congested when there are too many active users associated with it, or there are highly loaded users associated with it and the total load exceeds a predefined threshold in relation to the GW capabilities. Then there would be a need to decongest the GW. Decongestion of the GW can be performed by decreasing the quality of service (QoS) level of the associated users, by dropping some of the associated users, or by the handover of a number of the associated users to a less-loaded GW. The load balancing between GWs is another option for preventing congestion situations in the WINNER network, since the network load is distributed to all the gateways and the BSs of the network.

10.3.4 Flow Admission Control Admission control (AC) is the mechanism that receives the requests for new flows (whether they come from a new user or from ongoing users), checks if the users are authenticated to

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the network and checks if the network has sufficient resources compared to the resources requested by the new session. Depending on the different systems and implementations there are several criteria that the AC algorithms use for accepting or rejecting a flow. Many algorithms are power-based, which means that they use periodic measurements of the transmitted power, computing the interference at the user’s receiver. Based on that calculation, they make the decision of admitting or rejecting the user. In throughput-based algorithms, the throughput that can be delivered by the system is determined according to some dimensioning calculations, assuming certain conditions in the system. There are also algorithms that use the equivalent capacity of aggregated traffic, which is an estimation of the arrival rate of a class of traffic. Other algorithms check that the load of the system does not exceed a pre-defined threshold and check the bandwidth and delay constraints of each flow, according to the current system’s data. In the IP environment not all sessions have the same characteristics and requirements. For example, a voice session (telephony) has a very low delay requirement, while e-mail delivery is very tolerant to delay and needs more bandwidth. That’s why in radio access networks, different service classes are defined for users according to the services the networks offers and to the different characteristics of each service (see Section 2.3). Future wireless networks will have to consider many service classes to meet the user requirements for best quality of service. The service classes considered have different delay, throughput and BER requirements. The AC algorithm, in terms of resource allocation and especially in terms of prioritisation, must acknowledge the different service classes of the RANs. Different service classes will have different priorities. The criteria for each class’s priority should be based on the characteristics and requirements of the class, i.e. the delay sensitivity, the bandwidth requirements, etc. A class with high priority should be checked (admitted or rejected) before a low-priority class, even if the lowpriority class arrives first; a high-priority class could be admitted though a low-priority class is rejected. Due to the nature of the future service classes, there could be services that should have higher priority irrespective of if the call is new or from handover. For example, if a network has defined a service class for ‘emergency calls’ (e.g. calls for police or ambulance services during a car accident), these calls (which are new calls) should have the highest priority, even over handover calls. The options of the WINNER RRM architecture also affect the admission control procedure, so there are three different options:

r Centralised AC can only be done when a central entity (such as the RRM server in WINNER) exists in the network. The RRM server receives periodic real time traffic measurements (RTTMs) from the BSs and the GWs, so it permanently has knowledge about the load of the BSs and the GWs. When a user tries to change his state from idle to active, the AC is handled by the RRM server; based on the load information, it can find the most suitable (and not overloaded) mode and BS to serve the user. r Distributed AC means that the BS that the user is trying to connect to takes the AC decisions. This is very similar to the existing AC procedure for independently operated systems. If a user is not admitted to a specific BS, it tries to be admitted to other BSs in the same area. This approach is very fast and does not need a lot of signalling exchange between the network entities.

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r Scalable/hybrid admission control in WINNER exploits the advantages of the two previous options. In this approach, the BS handles the AC but can request assistance from the RRM server to take a better decision. The hybrid/scalable AC solution is usually fast since the BS takes the decision and does not need so much signalling between the entities, since the information exchanges between the BSs and the RRM server are performed only on demand and not all the time. The current BS is the one that communicates with the neighbouring BS in order to take the decision about admitting the new user request. The current BS also communicates with the RRM Server in order to get advice for the target BS, so it can take the best decision for the user. The BS can also complete the AC procedure without asking the RRM Server for advice and this can happen in low-load situations. When the admission control (AC) process admits a new data flow, the RRC layer is in charge of the control signalling for flow establishment, maintenance, reconfiguration and release. The RRC layer may receive a flow setup or reconfiguration request from the peer RRC layer, a higher layer or the IPCL layer. For the setup of a flow, first the IPCL layer has to be configured, for example, a header compression scheme is selected for the flow in question (see Section 10.2.1). After configuration of the IPCL layer, the RLC layer has to be configured. A logical channel is set up between the RLC layer and the MAC layer. One data flow uses one independent RLC entity, which then uses one logical channel. On the RLC level, first its operating mode has to be selected: acknowledged mode (AM), unacknowledged mode (UM) or transparent mode (TM). Also the RLC has to decide which logical channel is to be used and hence the interface to the MAC can be determined. The transport channels are interposed between the MAC and the PHY layers. A variety of transport channels are defined for different purposes such as broadcasting, paging, forward access or transmitting normal unicast data. The transport channel and physical channel parameters are included in the flow setup or reconfiguration procedures but can also be configured separately with the transport channel and physical channel dedicated procedures. An overview of the WINNER logical, transport and physical channels is given in Chapter 4. After the flow transmission is terminated or the radio handover is completed, the RRC releases the flow and reconfigures the related lower layers and their interfaces. Allocated radio resources are also released.

10.3.5 Congestion Avoidance Control 10.3.5.1 Admission Control: Two-Stage Approach The WINNER system employs load-dependent multi-stage distributed admission control, which is characterised by the following features:

r The number of entities involved in the end-to-end communication system are subject to admission control and admission is not decided by only one entity.

r A ‘token’ is assigned to the entity whose controlling domain has the most critical situation, e.g., lowest capacity.

r The involved entities send ‘Flags’ with different tags (cng, soft and adm) to others and each tag has its own meaning.

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r Admission control is not immediately done when an entity receives an adm flag: further checking of the instantaneous situation is performed.

r The rejection command is immediately sent when the cng flag is received. r A shared resource is re-partitioned when a soft flag is received. Compared to conventional systems, this protocol has the following characteristics [MAL08]:

r Admission control is distributed but jointly developed by all involved entities. r Decision polling follows a load-dependent sequence, i.e., ranking of the intermediate decisions is dynamic and depends on the load. The entity with highest load, receives the token to perform the admission check at the first step. r The distributed decision is controlled by passing tokens between the involved entities and assigning flags to entities that immediately reject new admissions. r The token is scalable depending on the service: a token for high data rate service may be located in the GW and a token for real-time data service may be located in the BS. Such scalability is also applied to the DL and UL cases. r A soft-flag concept is used in relay enhanced cells for resource re-partitioning. Gateway and BS Basic Scenario In Figure 10.12, some basic network elements of the future network are shown, comprising the GW, BS, UT, and RN. The GW provides the interface to the Internet and communicates with external routing functional entities. The GW also provides the anchor point for inter-RAN communication. It is expected that, in the future, GWs will connect different RANs and the RANs will be considered as elements of the whole communication network.

Figure 10.12 Admission control function distribution over GW and BS.

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The BS performs almost all radio-related functions for active terminals (i.e., terminals sending data) and is responsible for governing radio transmission to and reception from UTs and RNs in one or more cells. The BS is in control of relays (if used) and determines routes, forwards packets to the respective relay and takes care of flow control for the relays to ensure that they can forward the data to their associated UT. The RN is equipped with relaying capabilities and is wirelessly connected to a BS, UT or another RN. As such it contains a forwarding function and schedules packets on the radio interface. Furthermore, a system information broadcast provided by the BS is relayed by the RN for an extension of the system coverage. One BS may communicate with UTs through multiple RNs, i.e., multi-hop. As shown in Figure 10.12, the admission control function is distributed among the BSs and the GW. The BSs take care of the radio part during AC, while the GW takes care of congestion avoidance within the core network or other sub-networks. Decision-Making Sequence According to Network Context Let D1 be the decision made by the BS and D2 be the decision performed by the GW: Di = 0 in case of rejection; Di = 1 in case of acceptance, with i = 1 or 2. The joint decision for the incoming call is a Boolean operation, i.e. D joint = D1 AND D2. In the typical scenario shown in Figure 10.13, all entities involved in admission control functions are ranked according to the system context. A token is introduced that determines the sequence of the AC. The token holder sets a flag in order to allow follow-up procedures. As shown in Figure 10.13, the current load of the RAN is low, and the BS has relatively high capacity left. At the same time, the GW identifies a higher probability of congestion in the backbone or a limitation from other sub-networks which the expected traffic has to pass. In

Figure 10.13 Token setting for sequential flag.

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that case, the GW holds the token to perform the congestion prediction first before the radio admission control is performed. In addition, the token assignment can be service-dependent. For example, incoming high rate data services (such as high data rate FTP) need different token assignment from voice services. For instance, high traffic load services require an early check at the GW; low data rate real-time services require an early check at the BS. If an incoming session is expected to add too much load on data rate in the backbone network or to other sub-networks, the GW sends the cng flag to the BS on which the UT is camped. The BS then immediately rejects the session. When the GW identifies the capacity in the backbone is sufficient, it issues an adm flag to indicate the capacity potential. After receiving the adm flag, the BS checks if the available resources are sufficient for the incoming call; if this is the case, an ‘admission command’ is issued. The RRM server and CoopRRM server are the entities that provide enhanced information to the system, which helps to create more intelligent ranking of the decision making sequence in order to assign the token. The RRM server coordinates radio resource for neighbouring BSs, whereas the CoopRRM server coordinates resources between future systems and legacy systems. In that case, the CoopRRM and RRMserver may serve as ‘token setters’ that assign the token to the right entity. Multi-Hop and Relayed Network A deployment with multiple relay nodes (RNs) supporting coverage extension and throughput enhancement also needs such a protocol. The BS cannot perform admission control only based on its available resource or only based on the available resources of the RN that should directly serve the UT. The intermediate relaying capacity between RNs and between RNs and the BS has to be taken into account. In this case, the distributed mechanisms explained for the GW and BS can be applied to the BS and the RN. The available capacities of all segments for all possible routing paths between the UT and the BS have to be checked before an admission command is generated. The segments are defined according to the direct connections among the RNs and the BS. The token holder is given with respect to DL/UL and the final relay node to the to-be-admitted UT. In general, the capacities of UL and DL are different, which also makes token holder assignment different. For instance, as shown in Figure 10.14, the poorest link in the UL from RN2 is S12U, therefore the token is assigned to RN2 ; however, in the DL, the poorest link is S01D for a path to RN2 , therefore the token is assigned to the BS. As an example, when RN2 identifies a lack of resource for the incoming calls (bottleneck identified), it immediately sends a soft flag with its marker (header) to the central resource control unit in the cell (typically the BS) as depicted in Figure 10.15 (Step 2). The BS checks with the GW about the core network resource, re-partitions the resources and at the same time confirms to RN2 by sending the adm flag. RN2 then admits the UT after having confirmed that the re-partitioning led to sufficient availability of resources. This process is different to the BS–GW case, since the resources amongst the involved entities are shared. The rationale is that a potential trunking gain may be exploited. For each RN, the optimal routing paths have to be restored for any potential incoming sessions. Throughout the path, the bottleneck has to be identified with respect to the QoS expected from the incoming calls.

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Figure 10.14 Segments and token holders.

Figure 10.15 Soft flag and piggy-packed adm flag along with resource partitioning.

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For systems with multiple segments, comprised of sub-networks and relays, the number of stages can be higher than two, however, the approach and principle can easily be generalised. The cng or adm flag is sent always to the next decision maker. However, the soft flag is sent amongst the coupled entities that may perform a resource repartitioning in order to admit the incoming calls (typically the BSs). Other Controlling Functions The default token can be set during the network planning and maintenance phase. When the load and capacity changes in the system, the default token can be re-allocated to the most severe entity following the principles and mechanisms described earlier. Advantages Two-stage admission control has the following advantages:

r high overall system performance in terms of QoS (less congestion) and grade of service (GoS), i.e. less dropping rate;

r balanced decision load of the involved entities; r no unnecessary inter-GW–BS context transfer; r potential reduction of the air interface signalling load (RRC messages); r negotiation of BS–GW signalling is not needed, since the GW holds the QoS information for idle UTs and the policy enforcement information;

r much faster AC decisions than in conventional systems (because of the balanced decision load and because a red flag from the token holder can reject the call immediately). The decision load is defined as the admission request intensity per time unit. Due to the balance, each entity has a lower load and, therefore, the response time of the network entity may be decreased (for instance, 10 requests per second in the classic solution can be reduced to 5 requests per second for one entity). If the GW is the limiting factor, simple admission control only in the BS normally results in a biased or wrong decision. This can lead to the UT context having to be re-allocated from the GW to the BS. After dropping, the flow context has to be completely deleted from the BS and user NAS context has to be re-allocated back to the GW. If the GW has already decided on a rejection, the BS does not need to check the RAN capacity and so there are fewer RRC messages. Checking the RAN capacity process sometimes needs to trigger measurement reports. If the GW rejects a network-triggered connection, the paging channel and the random access handshake are saved as well as layer 1 and 2 signalling. 10.3.5.2 Flow Control There are two interfaces that need flow control: the GW–BS interface and the BS–RN interface, in cases where the UT is connected via the RN. On both interfaces, the flow control mainly refers to the downlink [KML+08]. GW–BS Flow Control Due to the capacity difference between the GW and BS, overflow of user-plane data at the BS might happen for a single UT or a group of UTs. If the BS detects that the buffered data of a

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particular flow is rapidly increasing and approaching the buffer limit, it can issue an explicit signal to the GW to suspend forwarding to the BS, in addition to invoking preventive buffer management policies, such as random early dropping (RED). This explicit signalling conveys two fields, the identifier of the IPCL data flow (or of the UT) that is approaching the buffering limit and a command that requests the GW to suspend or resume data forwarding. If the IPCL layer at the GW receives such a command, it temporarily holds its PDUs in a buffer, instead of forwarding them immediately to the RLC at the BS after the processing of the SDUs. When the congestion state at the BS is alleviated, the BS can send a recovery message that informs the GW to increase the forwarding rate. Instead of having a simple flag to stop or resume the GW–BS data forwarding, the BS can also advertise a receiving window size to the GW, so that the IPCL layer on the GW only forwards as much data as allowed by the receiving window. The indication of the receiving window size by the BS to the GW can be updated periodically or can be event driven, e.g. in the case of handover or other situations that suddenly decrease the air interface data rate for a particular UT. During the handover process, BS–GW signalling could be utilised to help maintain the in-sequence delivery of IPCL PDUs from the GW to the BS. The context transfer has to be executed between the source BS and the target BS. The buffered RLC SDUs and preferably also the RLC PDUs are forwarded from the source BS to the target BS. The source BS also has to notify the GW about switching the data-forwarding path after the handover to the target BS is completed. Before the notification of path switching, the GW forwards the IPCL PDUs to the source BS and they are then tunnelled by the source BS to the target BS. If the forwarding of the data is not finished before the path switching, new IPCL PDUs from the GW might arrive at the target BS before some of the previous IPCL PDUs from the source BS. The RLC layer on the target BS could only recover the original order of the IPCL PDUs if it could probe into the IPCL header to obtain the sequence number information, which violates the layering paradigm. A possible solution is to use GW–BS flow control signalling. The source BS can request the GW to stop forwarding further IPCL data to it, to avoid unnecessary tunnelling of the PDUs to the target BS. In addition, the source BS should notify the GW about path switching after it has forwarded all the buffered RLC SDUs and PDUs to the target BS. As a result, the forwarded data from the source BS always arrives earlier than IPCL data from the GW to the target BS, so that in-sequence delivery is preserved. The whole handover process with downlink GW–BS flow control is shown in Figure 10.16, where XON and XOFF represent the commands to suspend and resume data forwarding from the GW to the BS. BS–RN Flow Control With a fixed RN and line-of-sight connection between the BS and the RN, the data transmission between BS and RN should be stable and of high data rate. In contrast, due to the fading condition, the RN–UT link is unstable and usually of lower data rate than the BS–RN link. Although the downlink transmission is controlled by scheduling and resource allocation on the BS, an overflow or underflow situation at the RN buffer might arise without specific flow control. The principle of BS–RN flow control is to keep an appropriate level of buffering status on the RN. The flow control mechanism should operate on a per-flow basis, which controls the BS sending rate for the RN with the awareness of the RN buffer status. It is suggested that the RLC layer should also be implemented on the RN, so that two separate RLC connections would be in place for the BS–RN and RN–UT links (see

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Figure 10.16 GW–BS flow control signalling during the handover process.

Figure 10.17). The two RLC connections could conduct independent ARQ (if the RLC AM mode is configured) on each individual radio link, which makes the error recovery more efficient than in the case of single RLC over the BS–RN–UT path, (see also Section 8.5.4). Nevertheless, the two RLC connections could share the same sequence number of the RLC PDUs, so that RLC SDUs don’t have to be restored on the RN and again segmented for the RN–UT link.

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Figure 10.17 Status report forwarding by RN to the BS for the downlink transmission.

As shown in Figure 10.17, for the RLC AM mode with shared sequence number of the two RLC connections, the RN could forward the status report from the UT back to the BS, in addition to its own RLC receiver status report. With awareness of both the RN status and the UT status at the RLC layer of the BS, the BS can implicitly calculate the buffering status on the RN. The BS then adapts its sending rate for the RN according to the calculated RN buffering status, by adjusting the scheduling and resource allocation at the MAC layer. The RN can control the pace of sending both status reports to the BS, since it can poll the UT to obtain its status whenever needed. In case of transmission by RLC UM mode, no status report is defined and hence the buffering status of RN is unknown at the RLC layer of the BS. In this case, a specific flow control signalling might have to be defined at other layers. It can be a special RRC message that asks the BS to increase or decrease its sending rate to the RN for a particular logical channel. Alternatively a special MAC layer message can be used, which requests the MAC scheduler at the BS to adjust the scheduled data rate for the particular logical channel. Nevertheless, we predict that the RLC AM mode would be the main operating mode, due to its ARQ functionality and the efficiency enabled by ARQ/HARQ interworking.2

10.3.6 Load and Congestion Control Admission control algorithms are designed to make decisions about new session requests, in order to maintain the load of the network below a certain threshold. If the load of the network exceeds that threshold, the network experiences an overload or congestion situation. Network congestion control is a critical issue, especially given the growing size, demand, and speed (bandwidth) of the increasingly integrated services networks. Congestion situations can be caused by saturation of network resources such as communication links, channels, throughput, etc. For example, if a communication link delivers packets to a queue at a higher

2 The given flow control protocol is considered as one realisation of the inter BS-RN flow control. Alternative approaches of flow control under this context can be found at [WIN2D6114].

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rate than the service rate of the queue, then the size of the queue will grow. Network resources are limited and must be managed for sharing among the competing users. The basic result of a congested network is degradation of the network performance. Users experience long delays in the delivery of packets and packet loss caused by buffer overflows and retransmission of lost packets, which in turn results in a waste of the available throughput. There are many ways to detect or sense a congestion situation in the network:

r packet loss sensed by the queue as an overflow, by the destination (through sequence numbers) or by the sender due to a lack of acknowledgment (timeout mechanism);

r packet delay, which can be inferred by the queue size or observed by the destination (e.g. using time stamps in the packet headers) or the sender (e.g. by a packet probe to measure round-trip time (RTT)); r loss of throughput, which can be observed by the sender queue size (waiting time in queue); r other events, such as increased network queue length and its growth or queue inflow and its effect on future queue behaviour. In Figure 10.18 a proposal for an algorithm for load/congestion control in WINNER is presented. This algorithm is split into three phases: the congestion detection phase, the congestion resolution phase, and the congestion recovery phase. In the congestion resolution phase, there are five steps for decongesting the network (prioritised from left to right in Figure 10.18). The first action is to reject any non-emergency new request. Then the network requests additional radio resources. Then other techniques, such as handovers, resource re-negotiation and dropping high-load flows are applied. The priorities are assigned to these steps taking into account their effectiveness on load reduction and on the user’s perception. In Figure 10.18, the interworking between load and congestion control functions is also shown. According to the value of the load in the network, the functions that are used and their priority can be defined.

Figure 10.18 Load control function interactions.

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10.4 Centralised, Distributed and Hybrid RRM Architecture 10.4.1 Distributed RRM Under the category of distributed cooperation architecture, the radio resource management scheme is limited to a decentralised level. The system excludes the RRM server (see Section 4.3.6.5), i.e. no centralised entity is available to support load sharing or control, micro mobility, admission control, and resource partitioning. With a distributed cooperation architecture, we can obtain the following advantages:

r faster decisions in some cases (e.g. low network load, decisions based on few variables): since decisions are made locally (i.e. between two BSs), no delay limit is required for communication with the RRM server; r greater flexibility: adaptations and changes can be made locally for each cell without evoking signalling exchange with other cells; r BSs don’t have to consult the RRM server in cases of low network load, and intramode (and in some cases of intermode) handover.

10.4.2 Centralised RRM In the centralised RRM approach (including the RRMserver), the radio resource management scheme is centralised by one or a set of central units, which controls the resources of the entities under its supervision. In order to support advanced RRM functions and in cases of higher network load, decisions should be taken in an efficient way by a central RRM entity, avoiding multiple queries and their associated delay to neighbouring BSs. To operate efficiently with high network load, congestion avoidance functionality is of benefit; this functionality could use other system functionalities, such as handover (forcing some UTs to hand over) and admission control (not admitting new UTs or flows). These functionalities need a central RRM entity able to orchestrate the overall network. Centralised RRM enables better decisions in cases where intercell, intermode and intersystem information is required. In centralised RRM, more information is available in one place. The following specific advantages and functionalities are provided by the use of a centralised RRM in the WINNER system:

r efficient coordination of radio resources between multiple modes (and RATs) using effective interference coordination or mitigation;

r spectrum re-farming (between WINNER and legacy networks) and assignment (interWINNER modes);

r improved load balancing (resource partitioning between modes and RATs); r congestion avoidance gain (using handover and admission control); r additional multi-user diversity gain with more potential resources available from different deployment modes;

r bridging between the WINNER RAN and the operations and maintenance sub-system.

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10.4.3 Hybrid RRM A proposed solution to the dilemma of distributed or centralised RRM is hybrid RRM (HRRM), adopted by the WINNER system. The system performance is optimised as much as possible and by default with decentralised RRM but, when necessary, RRM algorithms should be prepared to consult a centralised RRM server, when it exists. The RRM server provides centralised RRM gains to the baseline distribute architecture. As this node only includes control plane functionality, its complexity is much lower than for current centralised nodes (e.g. RNC in UMTS). The assumption is that if the server doesn’t exist, some system functionalities are not supported and some decisions could be sub-optimal due to the absence of central intelligence provided by the RRM sever. The RRM is activated only in case of medium to high congestion, when the advantages of centralised coordination are noticeable or when sophisticated functionalities are given. The RRM server can provide network scalability from initial deployments with low load and without advanced system functions (in these deployments the RRM sever could be absent) to networks with medium to high load and a complete set of network services (coordinated by the RRM server). To enable scalability and future network upgrade with RRM servers, the corresponding interface should be mandatory in BSs. Figure 10.19 depicts the overall RRM architecture, centralised for cooperation with other networks using the WWI HRRM or cooperative RRM (CoopRRM) entity, and HRRM for cooperation inside the WINNER RAN.

10.5 System-Level Performance Results In this section, we evaluate the performance of the WINNER handover mechanisms. We consider both radio and IP handover, by evaluating intra-WINNER handovers (both intramode and intermode) as well as intersystem handovers (considering the case of a UT connecting to the legacy UMTS when losing the WINNER coverage). Among the different possible WINNER RRM architectures, we focus on a distributed solution. In the following sections, we describe the proposed handover triggers and then we evaluate the performance with the OPNET system-level simulation tool for intersystem and intermode handover.

10.5.1 Intersystem Handover For intersystem handover, we propose to base UT decisions on fuzzy logic. We assume that, if no WINNER BS is available to guarantee wireless connectivity or good user performance, a handover to UMTS should be performed. Fuzzy logic is used to compare quantities from heterogeneous systems using simple rules. Fuzzy logic is well-adapted to radio resource management because of radio environment fluctuations and uncertainty (measurements averaging, shadowing, traffic model,. . .). In complex systems, fuzzy models based on simple IF–THEN rules give more easily assimilated information than precise models. Learning techniques improve the fuzzy system’s parameters and enhance its performance accordingly.

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Figure 10.19 Hybrid and scalable cooperation architecture and mapping of system functions into logical nodes.

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Fuzzy logic can control systems that could not be totally modelled mathematically [DP80]. The fuzzy system uses a list of rules to control the system [Glo99; Kay93]. System behaviour can be tuned simply by modifying the appropriate rules. The ‘fuzzification’ function converts an input value into linguistic variables by means of membership functions. The inference function applies the fuzzy rules and the decision choice function [Zha04]. The fuzzy rules application function applies a number of rules to the outputs resulting from the ‘fuzzification’ operation. Rules with an IF–THEN structure decide whether or not the mobile should hand over to another system and choose the target entity in the affirmative case. An example of a simple fuzzy rule could be: IF (Current system = WINNER FDD) AND (Mobile terminal velocity = LOW) AND (WINNER TDD coverage = MEDIUM OR HIGH) AND (WINNER TDD load = LOW OR MEDIUM) THEN (handover to WINNER TDD) Simulations show that a fuzzy-logic-based handover algorithm improves handover quality indicators, such as FTP download response time and TCP throughput. Detailed results can be found at [WIN2D483], where the fuzzy adaptive intersystem handover is compared to an algorithm based on fixed coverage and load thresholds.

10.5.2 Intermode Handover 10.5.2.1 Simulation Setup The proposed algorithms for intermode handover were tested with IEEE 802.16e representing WINNER FDD and IEEE 802.11g representing WINNER TDD, as implementations of these protocols were readily available and focus is on the handover process, not on the details of the individual modes. The 802.16e cell addresses LOS and NLOS operations within the 2 to 11 GHz frequency range and provides some mobility support capabilities. It has around 400 m of cell radius with 20 Mbps of theoretical throughput. The 802.11g cell operates in the 2.4 GHz licensed band and offers coverage of 70–80 m with a theoretical throughput of 54 Mbps. The UT implementation includes also the mobile IP process and the common RRM entity, needed to perform intermode handover. The Common RRM entity is the key element of the multi-mode UT: it periodically receives measurement reports and load information from the interfaces and controls continuously the performance of each mode, triggering handover if needed. WINNER user terminals can have several radio interfaces with different physical and data link layers corresponding to the FDD and TDD modes and also other legacy networks such as UMTS. Moreover, different interfaces have different IP addresses. These interfaces constantly monitor the link quality of each mode and forward this information to a common RRM process implemented on the mobile device, which controls continuously the performance of each wireless connection and triggers the intermode handover if needed. Each multi-mode terminal able to transmit and receive using the FDD and TDD WINNER modes, as well as using UMTS, is characterised by different PHY, MAC and RLC layers in the protocol stack. The UMTS system is represented as a tri-sectored cellular system with hexagonal cell sites. Perfect power control and RRM algorithms such as soft handover, admission control and load control are modelled for the three central cells.

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The simulation architecture of the upper layers is shown in Figure 10.20. On top of the IP layer, we find all possible routing protocols and resource reservation protocols:

r Resource ReSerVation Protocol (rsvp): developed for supporting different QoS classes in IP applications;

r (Open Shortest Path First (ospf ): a routing protocol that determines the best path for routing IP traffic over a TCP/IP network;

r Enhanced Interior Gateway Routing Protocol (eigrp); r Routing Interchange Protocol (rip); r Intermediate System–Intermediate System (isis); r Internet Gateway Routing Protocol (igrp). The transport layer is either TCP or UDP, and a process model called TPAL connects the transport layer to the application layer. We assume that the level of inter-working between UMTS and WINNER is tight coupling [3GPP07]. A handover between UMTS and WINNER is seamless: the common RRM entity takes the handover decision and requests the UT to attach to the new RAN. After a successful attachment request, the common RRM entity sends a detachment request to the UT in order to release the connection from the previous RAN. If the common RRM entity decides to hand over a UT from UMTS to WINNER TDD, it requests the UT to connect to the selected local area base station. When attachment to WINNER TDD is accomplished, the common RRM entity has to ask the UMTS radio resource control (RRC) layer to detach the mobile station if it is not in idle mode. If the mobile is originally on WINNER TDD, an attachment request is sent to the UMTS RRC layer by the cooperation entity if the mobile needs to be in CELL DCH state. When connection to UMTS is established, the mobile receives a WINNER TDD detachment request from the WINNER BS. Regarding the different WINNER modes, it must be highlighted that:

r in any one instant only one interface is used for data transmission; r MAC and PHY are continuously able to receive and send control packets (e.g., to probe the channel quality);

r each interface (FDD and TDD) has its own IP address, thus an IP handover is performed when switching from FDD to TDD modes. 10.5.2.2 Intramode and Intermode Handover Algorithms In this section, we propose two algorithms that should be jointly implemented to efficiently trigger a handover in the WINNER system. The first one, composed of two triggers, can be used both for intramode and intermode handover. The second one is specific for handover from local area to wide area. The first algorithm combines all the available parameters characterising the quality of the communication (SINR, estimated instantaneous throughput, and network load) and consists of two independent triggers, one for maintaining the wireless connection and one for maximising the user and network performance. The first one is a wireless connection trigger, which aims at guaranteeing an available wireless connection for the mobile station and only takes place when the actual connection degrades and is likely to be lost.

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Figure 10.20 User terminal with three interfaces: FDD, TDD and UMTS.

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A SINR target is defined, to obtain a packet error rate PER < 0.01 with packets of 1500 bytes on the most robust modulation scheme. By default, the period of evaluation for the SINR is 0.2 s, but, when the SINR becomes lower than the target, it can be decreased to 0.02 s for an intensive evaluation. From the collected values, an average SINR is determined. If the average SINR is lower than the target, the handover is triggered. The network performance trigger instead combines the measured SINR and the network load in order to maximise the MAC layer performance. The metric used to activate the trigger is called ‘residual throughput’ TPRes and is defined as: TP Res = R · (1 − PER) · (1 − CO)

(10.1)

where R is the data rate, PER the packet error rate, and CO the channel occupation in the cell. The word ‘residual’ means in this context, that if other users already occupy a part of network resource, then the handover decision is only based on the remaining bandwidth at the user disposal. The handover trigger is based on a comparison between the estimation of the residual throughput on the current cell TPRes,cur with the one that could be achieved on another cell (TPRes,target ). If the ratio between TPRes,cur and TPRes,target is bigger than a threshold TPthres , a handover is triggered. Extensive simulations identified TPthres = 1.1 being a value that avoids ping-pong effects and at the same time does not limit the gain that can be achieved with handovers. Compared to the wireless connectivity trigger, which periodically evaluates the link quality for maintaining the connection, evaluations of the network performance trigger are less frequent. Indeed, a time average of more samples can better express the network performance in ‘long term’. To derive the data rate, the channel occupation and the PER, the UT performs measurements in the used cells and on broadcast messages sent by the BSs of neighbouring cells. For additional information please refer to [WIN2D482; WIN2D483]. The second algorithm aims at avoiding frequent intramode handovers in case of high user mobility (and small local area cells). A specific trigger for intermode handover has been introduced: if the velocity of the user terminal using a local area mode grows over a threshold vthres , then a trigger for a handover from local area to wide area cells is activated. A good selection for vthres is 20 km/h – it avoids frequent interruptions of the communication and allows exploitation of the higher data rate of local area mode.

10.5.3 Intermode Handover Results 10.5.3.1 Intermode Handover Triggered by Residual Throughput In this section, we evaluate the effect of two combined WINNER handover triggers (i.e., the connectivity-based and the throughput-based trigger for intermode handover) on UT mobility across the coverage of different modes. Complementary simulation results can be found in [FV07]. Figure 10.21 presents the simulation scenario, in which a mobile station crosses the wide area (FDD) cell (BS FDD) passing through two different local area (TDD) cells (BS TDD 1 and BS TDD 2). The mobile station activates the wireless interfaces in the following order: TDD – FDD – TDD – FDD. The simulation results (Figure 10.22) show that the UT, initially in the coverage of BS TDD 1, performs a handover to FDD mode when the throughput estimate on the FDD

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Figure 10.21 Handover scenario with residual throughput criterion.

mode exceeds the one in the TDD mode. Then, it enters the coverage of the second TDD BS BS TDD 2. When the TDD throughput exceeds the one of the serving cell (BS FDD), the second handover triggered by the network performance is performed. Finally, the third handover from TDD to FDD is triggered by the wireless connectivity criterion. Indeed, the FDD throughput is lower than the local area throughput because the station is far away from the FDD BS. For this reason the network performance trigger is not activated and the connection is maintained due to the wireless connectivity trigger and packet loss is avoided.

Figure 10.22 Estimated throughput of the UT with intermode handover.

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Figure 10.23 Cumulative Distribution Function of the Packet Error Rate at different speeds.

10.5.3.2 Intermode Handover Triggered by UT Velocity Now we analyse the gain achieved implementing the specific intermode handover trigger based on UT velocity. We consider the following scenario: a road is covered by several local area BSs (BSTDD ) at inter-site distance of 100 m and a wide area deployment (BSFDD ); the user moves along this road. Two cases are considered. In the first case, the speed-based trigger is not used: the UT, moving along the road, hands continuously over from one BSTDD cell to another, since the wireless connection trigger is continuously activated. In the second case, the speed-based trigger to FDD mode is implemented. Figure 10.23 shows the cumulative distribution function (CDF) of the experienced packet error rate (PER) for two speeds (50 km/h and 25 km/h), both above the threshold that activate the specific trigger. The two flat distributions correspond to the algorithm without specific trigger, and the other two curves correspond to the algorithm with velocity-specific trigger. There is a noticeable reduction of the probability of high PER in the cell by the use of the velocity-specific trigger, which avoids the excessive number of handovers between local area BSs (at which high PER occur), and keeps the UT connected to the wide area BSs.

10.6 Conclusion A description of the features and innovations of the WINNER IP Convergence Layer (IPCL), the radio resource control (RRC) layer, and the radio resource management (RRM) has been given. Some RRM functions (such as interference mitigation and flexible spectrum use) are treated in Chapters 9 and 11, respectively. The main innovative developments described in this chapter are:

r scalable and hybrid RRM architecture provided by the RRM server; r coordinated radio and IP handover; r coordinated intersystem, intermode and intramode handover; r intramode and intermode handover triggered by residual throughput and UT velocity; r fuzzy logic applied to intersystem handover; r admission control based on backbone and radio load.

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IPCL functions and the principal functions of the RRC have been described. The needed information to support each RRC state (disconnected, idle and active) was identified. The services, functions and protocols of the IPCL protocol layer have been described: header compression and decompression of the different classes of Internet transport and network protocols (IP, UDP, TCP), transfer of user data between IPCL peer entities (typically the UT and the GW) using the RLC layer transport modes, in-sequence delivery of upper layer PDUs, and duplicate detection of lower layer SDUs and ciphering of UP information. We propose load balancing between GWs in the same pool using the existing IP handover protocol. The following RRC functions have been analysed:

r Idle UT mobility management: The paging protocol supports UT mobility in idle mode and the process for cell selection and re-selection as well as the information to be exchanged between GWs in the case of change of the controlling GW (context transfer in idle mode). r Active mode micro mobility: The process for selecting the best radio cell was presented and also the processes associated with intramode, intermode and intersystem handover. Integrated radio and IP handover was discussed. r Radio and IP handover: Coordination of these two types of handover allows fast handover by default when two BSs belong to the same service pool without the need to use IP handover. However the network owner may deploy the existing IP handover protocol without adding protocols to realise the load balancing among GWs that potentially reduces the network cost. r Flow control: A description of flow admission interactions and protocols was given, as well as the flow establishment, maintenance and release of flow classes. r Admission control: An innovative approach to two-level admission control was described. The motivation for adopting such an AC mechanism was that due to the high traffic capability of the future radio interfaces, congestion could be located at the radio or at the backhaul, and the status of these two interfaces should be known, before the admission of new calls or sessions. r Load/congestion control: A description of flow control phases, load monitoring, congestion resolution and congestion recovery, was given. r Hybrid and scalable RRM architecture: An RRM architecture allowing traffic-oriented network dimensioning is proposed. Through on-demand centralised RRM, rush-hour traffic intensity can be dissolved by trunking gain given by resource sharing among collaborating network entities. On the other hand, due to inter-connections among the GWs and BSs in the same service area, the network can easily be scaled and updated. Intermode and intersystem handover simulation results have been presented, using algorithms based on the signal strength, residual cell data throughput and fuzzy logic, using a multi-mode/multi-RAN terminal and a common RRM entity that coordinates the different networks. It justifies that the RRC protocol designed for the WINNER system introduces performance improvement for future networks and service requirements.

Acknowledgements The authors would like to thank all their colleagues of the WINNER II mobility, interworking and cooperation task, Annika Klockar, Xiayoun Xue, Sana Horrich, and Alfonso Tierno, for the constructive and fruitful discussions. Special thanks go to Roberta Fracchia, who coordinated

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in-task simulation, and also many others for their valuable work in the context of radio resource management. Finally, special thanks to other colleagues involved in the cross-task system architecture that have provided the needed coordination through the overall project.

References [3GPP06]

3GPP (2006) Specification of the 3GPP Confidentiality and Integrity Algorithms UEA2 & UIA2, Document 4: Design conformance test data (Release 7), Technical Specification TS 35.218 Version 8.0.1, 3GPP, Sophia Antipolis. [3GPP07] 3GPP (2007) Requirements on 3GPP system on the Wireless Local Area (WLAN) Interworking, Technical Specification TS 22.234 (Release 7), 3GPP, Sophia Antipolis. [3GPP08a] 3GPP (2008) Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN), Stage 2, TR 36.300, V8.0.1, Technical Specification Group Radio Access Network, 3GPP. [3GPP08b] 3GPP (2008) Radio Resource Control (RRC); Protocol Specification (Release 7), Technical Specification TS 25.331, Version 8.0.3, 3GPP, Sophia Antipolis. [BHK+07] Bajzik, L., Horvath, P., Korossy, L. and Vulkan, C. (2007) ‘Impact of Intra-LTE Handover with Forwarding on the User Connections’, Proc.16th IST Mobile & Wireless Communications Summit 2007, Budapest, Hungary. [DP80] Dubois, D. and Prade, H. (1980) Fuzzy Sets and Systems: Theory and Applications, Academic Press, Amsterdam. [FV07] Fracchia, R. and Vivier, G. (2007) ‘An efficient trigger to improve inter-WiFi handover performance’, 1st Home Networking Conference, Paris, France. [Glo99] Glorennec, P. (1999) Algorithmes d’apprentissage pour syst`emes d’inf´erence floue, Herm`es Science Publications. [IETF01] IETF (2001) Robust Header Compression (ROHC): Framework and four profiles: RTP, UDP, ESP, and uncompressed, Technical Specification RFC 3095, IETF, www.faqs.org/rfcs/ rfc3095.html. [Kay93] Kay, S. (1993) Fundamentals of Statistical Signal Processing: Estimation Theory, Prentice Hall, New Jersey. [KML+08] Klockar, A., Mihovska, A., Luo, J., Mino, E. and Tragos, E. (2008) ‘Network Controlled Mobility Management with Policy Enforcement towards IMT-A’, International Conference on Communications, Circuits and Systems, Xiamen, China. [MAL+08] Mihovska, A., Anggorojati, B., Luo, J., Kyriazakos, S. and N.R. Prasad, N. (2008) ‘Multi-Stage Admission Control for Load Balancing in Next Generation Systems’, Wireless Personal Multimedia Conference, Ivalo, Finland, September 2008. [MJT07] Mino, E., Luo, J., Tragos, E., Mihovska, A. (2007) ‘Scalable and Hybrid Radio Resource Management for Future Wireless Networks’, Proc. 16th IST Mobile & Wireless Communications Summit 2007, Budapest, Hungary. [MLT07] Mihovska, A., Luo, J., Tragos, E. and Mino, E. (2007) ‘Policy-Based Mobility Management for Heterogeneous Networks’, Proc. 16th IST Mobile & Wireless Communications Summit 2007, Budapest, Hungary. [RTR07] Racz, A., Temesvary, A. and Reider, N. (2007) ‘Handover Performance in 3GPP Long Term Evolution (LTE) Systems’, Proc. 16th IST Mobile & Wireless Communications Summit 2007, Budapest, Hungary. [TMM+07a] Tragos, E., Mihovska, A., Mino, E., Karamolegkos, P. and Panagiotis, T. (2007) ‘Access selection and mobility management in a beyond 3G RAN: the WINNER approach’, ACM International Workshop on Mobility Management and Wireless Access, Crete. [TMM+07b] Tragos, E., Mihovska, A., Mino, E., Luo, J. and Fracchia, R. (2007) ‘Hybrid RRM architecture for future wireless networks’, International Symposium on Personal, Indoor, and Mobile Radio Communications, Athens. [WIN2D351] WINNER II (2006) IST-4-027756 Relaying concepts and supporting actions in the context of CGs, Deliverable D3.5.1, October 2006, viewed 20 June 2009, http://projects.celticinitiative.org/winner+.

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WINNER II (2006) IST-4-027756 WINNER II Intramode and Intermode Cooperation Schemes Definition, Deliverable D4.8.1, June 2006, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D482] WINNER II (2007) IST-4-027756 Cooperation Schemes Validation, Deliverable D4.8.2, June 2007, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+. [WIN2D483] WINNER II (2007) IST-4-027756 Integration of cooperation on WINNER II system Concept, Deliverable D4.8.3, November 2007, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D6114] WINNER II (2007) IST-4-027756 Final WINNER II System Requirements, Deliverable D6.11.4, June 2007, viewed 20 June 2009, http://projects.celtic-initiative.org/ winner+. [Zha04] Zhang, W. (2004) ‘Handover Decision Using Fuzzy MADM in Heterogeneous Networks’, Wireless Communication and Networking Conference 2004, Atlanta, USA.

11 Sharing and Flexible Spectrum Use Capabilities Carl Wijting,1 Mehdi Bennis,2 Saied Abedi,3 Shyamalie Thilakawardana,4 and Roufia Yahi Serpelloni5 1

Nokia Research Center University of Oulu 3 Fujitsu Laboratories of Europe 4 University of Surrey 5 Orange Labs 2

11.1 Introduction The WINNER system [WIN2D61314; WIN1D76] aims at fulfilling the requirements for IMT-Advanced wireless communication systems defined in [ITU03]. Additionally, in [ITU06], an increasing spectrum demand was identified based on the growing use of wireless communication networks in the future. These trends make it essential that future radio systems have high spectrum efficiency and are able to share and coexist in an efficient manner. World Radio Conference 2007 (WRC-07) recognised this increasing need for spectrum for wireless systems and identified additional spectrum for cellular networks (see Chapter 12). The amount of identified spectrum is less than that needed [ITU06] and the spectrum blocks are not wide enough to support large bandwidths for a substantial number of operators. Therefore dynamic spectrum technologies can be seen as key enablers to gain access to additional spectrum and the deployment of large bandwidth required to offer increased data rates. Two categories of mechanisms for dynamic spectrum management are considered in this chapter: spectrum sharing and coexistence (SSC) and flexible spectrum use (FSU). SSC aims at facilitating the coexistence of the proposed system with other systems in the same frequency band. FSU considers the spectrum usage of different radio access networks (RAN) within the same radio access technology (RAT) [WDK+08].

Radio Technologies and Concepts for IMT-Advanced Martin D¨ottling, Werner Mohr and Afif Osseiran C 2009 Martin D¨ ottling, Werner Mohr, Afif Osseiran

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This chapter is organised as follows: Section 11.2 presents an overview of the spectrum functions and focuses on spectrum assignment in different deployment scenarios. A more detailed description of the spectrum assignment functions is presented in Section 11.3. Section 11.4 discusses various technologies that enable dynamic spectrum use, including multi-band scheduling and base station to base station communication. Sharing between WINNER and fixed satellite services (FSS) is investigated in Section 11.5. Section 11.6 presents performance evaluations of different approaches to spectrum assignment. Finally conclusions are drawn in Section 11.7.

11.2 Spectrum Technologies Framework Dynamic spectrum management functionalities that address both sharing between WINNER RANs and other radio technologies and within WINNER RANs have been designed. Many interactions between these two categories of functionalities are required and therefore the definition of clear interfaces between them is paramount. The two categories are defined as follows:

r Spectrum sharing considers sharing the spectrum on a flexible basis between WINNER and non-WINNER systems. These mechanisms allow the WINNER system to operate in a frequency band that is shared with other technologies, resulting in the accessibility of a larger amount of total radio spectrum. r Spectrum assignment controls the flexible spectrum usage between WINNER RANs and for WINNER RAT only. These mechanisms allow WINNER operators to access larger bandwidths than would be obtainable when no flexible spectrum use mechanisms were in place. Furthermore, more capable, faster services and more user satisfaction in terms of quality of service (QoS) are guaranteed for the upcoming future services. Under the spectrum sharing umbrella, two different classes of schemes are investigated: vertical sharing and horizontal sharing. The classification is based on the access rights of each system to the shared spectrum. If one system has priority access to the spectrum over the other, then vertical sharing schemes are applied. In such a case, if the WINNER system is the primary system then this results in Vertical Sharing 1 scheme (VS1 ). If the WINNER system is a secondary system and has higher priority access rights than any other secondary systems, then Vertical Sharing 2 scheme (VS2 ) is defined. If both systems, i.e. WINNER and the other radio technology in the frequency, have the same access rights to the spectrum then this results in horizontal sharing schemes. The horizontal sharing situation maps into two different sharing schemes. One assumes that the systems contending for spectrum can coordinate with each other to enable efficient spectrum allocation. This is called horizontal sharing with coordination (HwC). The second scheme is considered when both systems do not coordinate with each other in the framework of spectrum sharing functionalities. This scheme is called horizontal sharing without coordination (HwoC). The coordinated horizontal sharing introduces QoS agreement to the users compared to the uncoordinated horizontal sharing case. The close interaction between the different functionalities is illustrated in Figure 11.1. Additional to the mechanisms previously introduced, generic spectrum functionalities have also been defined. These functions support the exchange of information between the different functional entities, enabling access to spectrum availability information, spectrum etiquette rules and measuring metrics that provide important inputs to the dynamic spectrum management

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Figure 11.1 Interactivity of the spectrum sharing and spectrum assignment functions in the WINNER concept, from [BKO+07]. (Reproduced with kind permission of Springer Science and Business Media © 2008).

functions (such as load and resource partitioning information). The functions from Figure 11.1 are detailed in the following sections.

11.2.1 Sharing and Co-existence Functions This section describes the spectrum sharing and co-existence sub-functions that are included in the spectrum sharing concept. 11.2.1.1 Vertical Sharing 1: WINNER is the Primary System This function is considered when WINNER is the primary system. WINNER may assist a secondary system by allowing it to share primary spectrum resources. This is feasible by signalling unused spectrum resources via its (WINNER) broadcast channel (BCH) or by means of a universal or customised broadcast radio beacon. The WINNER RAN can actively optimise its spectrum assignments so that unused spectrum becomes available in blocks that can be easily shared with other systems. The willingness to do so depends on available incentives to share spectrum resources. The responsibility for creating unused spectrum belongs to the overall resource optimisation which involves the spectrum assignment functions. Since the spectrum resources leased to the secondary systems are operator-wise, each WINNER operator can use part of its own prioritised spectrum resources or its assigned common pool resources. 11.2.1.2 Vertical Sharing 2: WINNER is the Secondary System When the WINNER RAN is the secondary system, it has to control its emissions from the base station (BS) and all the user terminals (UT) in order to avoid interfering with the primary

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system. More generally, the secondary system may adopt a dynamic, opportunistic use of the unused part of the spectrum. For that purpose, considerable knowledge about the deployed primary system may be required. Besides the information acceded through the WINNER spectrum manager, spectrum related measurements done at physical layer (BSs and UTs) might be compiled. More description of the WINNER spectrum manager can be found later in Section 11.2.3.1. These measurements are facilitated when the primary system emits a standard beacon periodically. The compiled information (i.e. measurements, site information, location, protection requirements of the primary system downloaded from databases, etc.) is transformed into a set of transmission constraints defining and characterising the shared spectrum, e.g. exclusion zones in the context of sharing with FSS as the primary system. In non-loaded conditions, and depending on incentives for the WINNER RAN, the WINNER system may lease its identified unused spectrum to other secondary systems since WINNER may have better capabilities in accessing the spectrum compared to other secondary or sub-secondary systems (secondary to WINNER). In this case of spectrum ‘re-sharing’, additional mechanisms may be required, for example for regulatory or independent third-party supervision of the spectrum trades.

11.2.1.3 Horizontal Sharing with Coordination In horizontal sharing with coordination (HwC), the involved systems (i.e. WINNER and a non-WINNER RAT) have equal access rights to the spectrum and coordinate their spectrum access based on a set of predefined (spectrum sharing) rules to which all the involved systems submit. Each system adapts its transmission to mitigate interference to others by applying constraints issued from common policies shared by all the candidate systems or determined on the basis of the previous coordination phase. Location services and measurements of the other system’s radio activity might be useful for a better coverage estimation of the other system, resulting in better coordination in terms of actual mutual interference. These measurements are facilitated by using special purpose mutual beacon signals; therefore access to the BCH at the MAC layer is required for this function. Similarly to the VS2 function, the obtained shared spectrum indicators are conveyed to longterm (LT) assignment functions via the spectrum register. In the case that the spectrum register entity is rather centralised and slow direct communication from the horizontal sharing function to the short term (ST) function might be implemented for the more dynamic opportunistic local area approach.

11.2.1.4 Horizontal Sharing Without Coordination The horizontal sharing without coordination (HwoC) scheme is targeted towards the radio local access network (RLAN) licensed exempt band around 5 GHz. It is known that the current unlicensed bands are limited from the point of view of interference protection. They are also likely to be congested with regard to the number of services operating on them. Therefore, there is a need for the spectrum sharing functions to bring discovery mechanisms in order to reach for an opportunistic use of the spectrum. Efficient detection of white spaces, i.e. unused spectrum, is not an easy task due to problems of the discovery of hidden nodes or energy-based

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detection mechanisms see [Hay05]. Since QoS cannot be guaranteed for systems deployed in the RLAN band, it is important that the WINNER concept can perform satisfactorily within these scenarios as well.

11.2.2 Spectrum Assignment Functions The goal of flexible spectrum use is two-fold: to increase the overall system efficiency when using the radio spectrum and the flexibility and scalability of the system concept [WIN1D63]. Spectrum sharing is complementary to this, in a sense that it allows accessing of new radio spectrum in a shared manner that otherwise would be used exclusively by another technology and would therefore remain inaccessible. Significant benefits can be expected from intra-system spectrum sharing. It may provide more efficient use of available spectrum through the trunking gain. Even more importantly, it significantly increases the spectral scalability of the WINNER system concept [WIN1D63; HTL+06]. This allows for:

r gradual long-term spectral deployment of the WINNER system: individual bands do not need to be available for all WINNER RANs at the launch of the WINNER system and additional bands can be made available to the RANs along the gradual growth in the number of WINNER subscribers and traffic in each RAN; r flexibility towards geographical differences in regulatory spectrum assignments; r versatile operation of networks, e.g. due to differences in business models: some WINNER RANs are likely to be deployed nationwide, while others may provide focused services covering only selected areas; r adaptation of the spectrum available to a network according to network load changes: these changes may be caused by changes in market share or by daily variations in traffic. 11.2.2.1 Long-term Assignment The long-term (LT) assignment function partly coordinates the usage of the spectrum between WINNER RANs. This function coordinates and negotiates the spectrum assignments between multiple WINNER RANs for large geographical areas with spatial granularity of cluster of cells. The LT assignment entity is located in the SpectrumServerLN with an interface to the GW and BSs (see Section 4.3). The spectrum assignments are updated periodically and at a slow rate, that is, in a time frame of several tens of minutes. It can be used also to provide dynamic spectrum assignments between WINNER RANs. This entity is also responsible for the coordination between the WINNER TDD and FDD modes. Inter-RAN coordination is achieved through direct negotiation between peer LT assignment functions in different WINNER RANs. The LT assignment function could be extended to support the coordination of (static) spectrum assignments over country borders. This requires signalling between the WINNER spectrum managers in the neighbouring countries, providing sufficient information to establish signalling between the related LT assignments over the IP network and over the country border. However, another natural option is that over-the-border coordination is included fully in the WINNER spectrum manager.

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11.2.2.2 Short-term Assignment This function controls the short-term and local, i.e. cell-specific, variations of the large-scale spectrum assignments. Hence, it enables faster adaptation to the local traffic load variations and geographically more accurate spectrum assignments than the LT assignment. The assignments are performed in the timescale of several MAC super-frames, i.e. 100 ms to several minutes. Due to the above cell-wise functionalities, the ST assignment is located at the BS. The short term (ST) assignment function requests spectrum resources from other WINNER RANs after being triggered by the LT assignment or by preventive load control. The fundamental reason for developing ST assignment is that in the case of two RANs providing the exact same services, traditional handover between RANs to support the load condition could be envisaged, but operators may be reluctant for such a handover of users, i.e. operators would prefer to keep the UTs connected to their own RAN. Further, in the case that increasing the spectrum of one RAN would result in enhancing the service capabilities, e.g. in terms of capacity provided to the UTs, spectrum assignment becomes a necessity for creating new services with better user QoS capabilities.

11.2.3 Generic Spectrum Functions In this section, a description of the general spectrum entities and functions is presented. These functions can be accessed by all the spectrum functions when needed.

11.2.3.1 WINNER Spectrum Manager The spectrum manager manages the usage of the spectrum within the WINNER RANs. It is a policy rule maker so that peer-to-peer negotiation between WINNER RANs follows the same rules. It contains relevant information on spectrum priorities as well as on fairness or cost metrics and, thus, establishes a common control point on the spectrum assignment. It enables also simple introduction of new RANs by specifying the rules and limits. Further it may maintain logs of the parameters necessary for recovery, etc. It may be, for instance, a server accessible over the IP network maintained by a trusted party. It is located outside the WINNER RANs.

11.2.3.2 Spectrum Register Each RAN maintains its own spectrum register in the SpectrumServerLN . The spectrum register conveys information on exclusion zones and available spectrum from spectrum sharing functions to the spectrum assignment functions within a WINNER RAN. The information in the register is dynamically updated. The introduction of the register is motivated by sharing the spectrum with FSS for example, where related exclusion zones may be large. The purpose of the register is to provide an alternative for a centralised control entity while allowing for self-organising deployment of LA cells. The spectrum register could be made accessible via the gateway (allowing access either through the RAN’s own core network or even through the IP network in the case of small networks). The information flow between spectrum sharing and spectrum assignment functions may cause increased delays. For this reason, the spectrum

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register logical entity can be bypassed in the case of spectrum sharing schemes that require fast dynamic behaviour.

11.3 Detailed Design of a Spectrum Assignment Negotiation Mechanism 11.3.1 Long-term Spectrum Assignment LT assignment consists of three main functionalities: spectrum assignment negotiation, measurement, and triggering functionalities. The spectrum assignments are updated periodically and at a slow rate, that is, in a time frame of several tens of minutes. It can also be used to provide dynamic spectrum assignments between WINNER RANs. This entity is responsible for the coordination between the WINNER TDD and FDD modes. Inter-RAN coordination is achieved through direct negotiation between peer LT assignment functions in different WINNER RANs. The main sub-functions for LT spectrum assignment are:

r resource request calculation; r resource negotiation between WINNER RANs; r resource re-arrangement calculation; r re-arrangement negotiation between WINNER RANs; r resource update and spectrum availability. A possible conceptual structure for LT spectrum assignment was developed in WINNER I [WIN1D63] and is illustrated in Figure 11.2. From the figure, one can see that LT spectrum assignment is a periodical, or scheduled, function. One can also notice that although inter-RAN communications occur in several component functions, recovery from possible communication failure (not resolved by normal communication protocols) between the RANs is carried out in the resource update function. The resource request calculation decides the requested spectral resources for the next assignment period based on the inputs from load prediction, MAC control feedback, and spectrum sharing. Due to the slow adaptation rate of LT spectrum assignment, relatively longterm estimates on the network load are required. Hence the load prediction is not only based on the current network load, but also on longer-term traffic patterns, such as daily or weekly averages. Resource negotiations between WINNER RANs are carried out based on the resource requests and by utilising appropriate fairness or cost metrics. The total amount of spectral resources assigned for each RAN is fixed during the resource negotiations with tentative explicit resource assignments. After the negotiations, the spectrum assignment function looks

Figure 11.2

Main components of long-term spectrum assignment.

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for possibilities of improving the tentative spectral resource assignments (e.g., by minimising inter-RAN guard bands) during the resource re-arrangement calculation. It is followed by re-arrangement negotiation during which exchange requests are sent and negotiated with other WINNER RANs. The purpose of this second negotiation phase is to remove any inefficient solutions that might have been established during the negotiation phase. During the resource update phase, the negotiated resources are updated to the system settings and error checking is done [WIN1D63]. During the resource update, the following processes are performed: 1. It compiles necessary spectral resource and constraint information messages for load control and resource partitioning located at the base stations (BSs), with timing signal for the next assignment period. 2. If necessary, it may trigger short-term spectrum assignment with requests. 3. It updates logs on the spectrum use, fairness metrics, etc. 4. At certain time intervals, it sends log updates to the WINNER spectrum manager and downloads updated parameters from the database. 5. It performs recovery from any possible communication failures between the WINNER RANs. During the recovery, networks fall back to the use of predefined basic spectrum assignments. From this high-level functional overview, a further functional break-down was made. Figure 11.3 is a flowchart of the spectrum assignment sub-function. The function takes several input signals depending on the type of spectrum assignment; these signals include traffic load, information from the spectrum register, the spectrum manager, and sharing and coexistence schemes. Based on these inputs, the logical node determines its spectral resource needs, both in terms of current need and projected future needs. In order to incorporate the future needs in the analysis, the node may add a margin to its prediction. This margin can be a fixed percentage, e.g. 10 % or 20 % of the current load depending on the amount of conservatism build into the mechanism. After determining the current and future spectral needs, this prediction is mapped to the available spectral resources. The available resources can consist of resources dedicated to the network as well as resources that have already been exchanged between networks. If the current spectral resources are more than sufficient to meet the predicted load, the wireless node may identify resource units for release (if it is currently borrowing spectrum), or it could decide to lend out some of its current resources. If the current amount of resources is insufficient, then a procedure should be started to obtain additional resources, including getting back resources that were previously lent to other networks. Figure 11.4 shows a vertical-time sequence diagram showing messages and processes performed by a logical node of RAN1 and another logical node of RANn . The logical node can be either a BS or a GW or SpectrumServer. These messages may be sent as part of either long-term or short-term spectrum assignment. Both logical nodes first determine their spectral resource need, i.e. whether there is a surplus or a shortage of resources. Based on this determination, both nodes construct a ‘spectrum resource change’ (SRC) message and these messages are exchanged between the nodes. These SRC can be advertisements of available resources, specific requests for resources and so on. Based upon the exchanged SRC messages each logical node constructs a resource unit MAP (RU MAP) in which the current spectrum availability is stored, based on its own information collected by the logical node and SRC messages received from other nodes.

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Figure 11.3 units).

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Spectrum assignment sub-function (SR: spectrum resource; x, y: thresholds; RU: resource

This process may be followed by an optional process in which the constructed RU MAPs are exchanged between the nodes and each logical node has the opportunity to compile its own global RU MAP. After constructing the global RU MAPs, or based only on the local RU MAPs, the two networks may engage in an auctioning and billing procedure. During this phase, spectrum resources can be traded between the two nodes, this can be a real-time process with varying prices or based on bulk traffic agreements, for example. Based on the outcome of this process, the nodes may accept or decline a trade-off spectrum between the two networks.

11.3.2 Short-term Spectrum Assignment This function controls the short-term and local, i.e. cell-specific, variations of the large-scale spectrum assignments. The ST assignment requests spectrum resources from other WINNER

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Figure 11.4

Signalling flow for FSU.

RANs after being triggered by the LT assignment or by preventive load control. It is triggered by LT assignment to adjust to the new, updated, LT spectrum assignments and (if the solution of LT assignment has not been fully satisfactory) to improve the solution further, at the cell level. Preventive load control may trigger ST assignment to obtain the spectrum needed for the current load or services. In cases where common pool spectrum resources are not fully used by the LT assignment, the ST assignment may reserve free common pool resources for local use, e.g. as a hot spot, in addition to the normal way of requesting resources from the neighbouring BSs in the neighbouring WINNER RANs. This is mainly in the case where neighbouring RANs do not have any spectral resources to share amongst each other since they all are saturated in terms of spectrum resources. Periodic updates of spectrum assignments by the LT assignment functions result in varying the amount of common pool resources that may be available to the ST assignment functions. The main sub-functions for ST spectrum assignment are: requests for additional spectrum to other networks, requests for spectrum from other networks, and spectrum availability determination.

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Figure 11.5 Signalling flow for short-term FSU: (a) a more complex mechanism and (b) a mechanism with special constraints for swift operation.

Figure 11.5 shows a further refinement of the signalling flow specifically addressing shortterm spectrum sharing. Figure 11.5(a) is very similar to Figure 11.4 but includes a phase where the feasibility of the RU exchange is harmonised with the neighbouring nodes. This availability information is stored in the RU MAP. Available resources do not have to be made available right away but can be exchanged without further checking when another network makes a request for them. This exchange is done following the procedure from Figure 11.5(b), which may run with a much higher frequency, enabling fast spectrum trading for shortterm spectrum assignment without the need to cross-check each trade with the neighbouring cells.

11.3.3 Interactions between Long-term and Short-term Spectrum Assignment There is a close interaction between the long-term and short-term spectrum assignment algorithms. Long-term spectrum assignment is done based on long-term statistics and the allocations are passed on to the short-term assignment function in the BS. Local optimisation of

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the allocation based on long-term assignment is then performed by the short-term assignment function. If structurally additional resources are required, the short-term assignment can provide feedback to the long-term assignment function to obtain additional resources. Feedback is also provided via the resource partitioning and constraint processor in terms of resources that are blocked due to the short-term assignment process (see Chapter 4). Figure 11.1 presents a conceptual diagram of the interactions between the different spectrum functions that have been developed. The top part of the diagram focuses on sharing and coexistence and the lower part focuses on spectrum assignment. The information exchanged between LT and ST assignment functions includes: the results of the spectrum negotiations at each level, further indicators about spectrum assignments in the coming period (spectrum assignment indicators, cell-wise chunk assignments, transmit power, and direction constraints), and LT provides the policy rules that apply to assigning resources.

11.3.4 Registration of Nodes with Spectrum Manager When determining which neighbours should be contacted for the negotiation, the received signal from neighbouring base stations can be used as well as neighbour lists already available in the operation, administration, and maintenance (OAM) system or the RAP (e.g. in order to set up IBB interfaces, for handover, etc.). Also, the spectrum manager can provide information about expected neighbouring RAPs. Upon switch on, the RAPs can register with a database registry. The database stores, among other things, the position of all the registered RAPs that participate in the short-term spectrum assignment. The database may be a common entity shared between different networks or each network may have its own spectrum manager. The information stored in the spectrum manager could, for example, be the geographical coordinates of the RAP, the transmit power, the class or type of the RAP (e.g. indoor, outdoor, relay), or the standard antenna configuration of the RAP. Based on the registration in the database, a unique identifier can be assigned to the RAP and it can be sent a list of its neighbours. The list of neighbouring RAPs is used by the newly registered RAP to scan its environment. After scanning and discovering the neighbours, it can then start participating in short-term flexible spectrum sharing with them. This enables the newly installed RAP to quickly become operational. Further, it provides a tool to check how the different RAPs interfere with each other, which is relevant when selecting the resources that can be traded between cells and networks.

11.3.5 Specific Short-term Spectrum Assignment Algorithms Different types of short-term spectrum assignment algorithms have been considered. They roughly fall into two categories: negotiation-based and interference-measure-based. In the first category, the exact number of required resources is exchanged, in the latter category an interference metric is determined upon which resources are requested to meet the required performance on average and the resources are scheduled after the interference metrics are obtained. Both mechanisms rely on cooperation between the networks.

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11.3.5.1 Negotiated Amount of Resources Exchanged First it is determined which resources should be exchanged so that the required performance can be achieved. It is taken into account how neighbouring nodes are affected and which resources can be obtained interference-free, or which resources can be lent out without significantly decreasing the overall performance. 11.3.5.2 Matching Amount of Resources Exchanged In this algorithm, the amount of additionally required resources is determined and, rather than negotiating directly with the neighbouring base stations, the resources are assigned based on a determined interference metric. This means that the algorithm is more flexible and requires less negotiation, however it is less efficient. 11.3.5.3 Surplus of Resources Exchanged Rather than assigning the exact amount of resources in this algorithm, an additional fraction of resources is assigned. This fraction takes into account the additional loss due to interference and additionally provides the borrowing network the possibility of further exploiting flexibility since it can schedule its transmissions on less interfered resources. The disadvantage of this scheme is two-fold: the impact on the lending network is larger since more resources are exchanged and overall fewer resources are available for exchange since more resources are fixed per transaction. This means that the scheme is less flexible. However, in more static cases, performance is improved by this scheme.

11.4 Spectrum Assignment Enabling Mechanisms This section describes several technologies that enable the implementation of dynamic spectrum management. Solutions for multi-band operation and communication between base stations is particularly considered. The multi-band scheduler (MBS) accounts for the variable and elastic traffic flows over multiple spectrum bands; in this way, spectrum can be managed in a more efficient manner and multiple bands (with different access characteristics) can be used by the technology. Communication between base stations (BS–BS) has been identified as one of the key enabling technologies for spectrum negotiations, especially for ST spectrum assignment where fast negotiations and decisions have to be made between negotiating parties for an efficient spectrum exchange.

11.4.1 Multi-band Scheduler Introduction of the new spectrum functionality in the WINNER architecture makes it possible to create a balance between the data pipe and the available spectrum. This phenomenon is depicted in Figure 11.6, illustrating the flexible bandwidth use of WINNER. It can be seen in Figure 11.6 that in the traditional system, the overall available spectrum can not be varied in response to the increasing traffic demand; for the WINNER system, extra sources of spectrum can be provided on a flexible basis. MBS is introduced to the WINNER architecture to realise the flexibility and elastic behaviour.

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Figure 11.6 Concept of matching the spectrum pipe to the data pipe; elasticity leads to flexible utilisation of the spectrum.

The distinctive feature shown in Figure 11.6 is the changing amount of available spectrum in both shared band and dedicated bands. A mechanism is required to match the available data in the queues to the bandwidth. For example, assume that currently no shared band is available. The WINNER system responds positively to a spectrum demand and shared band suddenly becomes available. The scheduler must be capable of realising such a change in the spectrum pipe and shift some of traffic load from the dedicated band to the shared band. Preferably this should be data with a lower priority. The scheduler must also be capable of further load balancing by actively monitoring the forthcoming changes in the spectrum and traffic data in order to shift the load from the shared band to the dedicated and vice versa. It is worth noting that MBS has another distinctive feature with regard to the spectrum manager and ST spectrum assignment. MBS has active interaction with these entities and is well aware of their decisions. For example, as a short-term spectrum assignment process is completed, it informs the MBS about the current fine tuning of spectrum either in the shared or the dedicated band. MBS subsequently employs the information to take suitable action in response to the spectrum changes. The general framework for the MBS is presented in Figure 11.7. Network packets arriving at the RLC layer may be fragmented into smaller units or concatenated into bigger units by the segmenting and reassembly (SAR) unit [DWK07]. The resulting data units are referred to as RLC PDUs. If RLC-acknowledged operation is used, then an outer end-to-end ARQ is performed at RLC level. A user terminal (UT) ID, a transmission sequence number, and optionally a CRC checksum are added. If RLC-acknowledged operation is used then an outer end-to-end ARQ is performed at the RLC level. The MBS schedules the resulting retransmission units (RTUs) for transmission in the correct band. The resource scheduler (RS) per band reads the RTUs from the buffer which are scheduled for transmission. The RS for each band is done independently and the coordination of the operation of the different bands is done exclusively by the MBS.

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Figure 11.7 Main functions of the Multi-band scheduler, including E2E ARQ and HARQ, from [DWK07]. (Reproduced by Permission of IEEE © 2009).

Different hybrid ARQ (HARQ) protocols exist and two possible approaches could be used: chase combining (retransmission of the whole PDU and combining at the receiver) and incremental redundancy (retransmission of additional redundancy bits, providing the receiver with more information about the PDU). When the RTUs are ready for transmission they can be additionally (and optionally) encoded by an outer code, which can be punctured and interleaved; the resulting units are called FEC blocks. HARQ can be performed on these FEC blocks. The MAC adds a retransmission sequence number to FEC blocks that use HARQ. The FEC blocks are inserted in the resource scheduling buffer, one buffer for uplink and for downlink if required (e.g. a UT has only an uplink buffer). From the resource scheduling buffer the data is processed: the data is drained bit-by-bit and mapped according to the scheduling criteria (e.g. mapped on optimal chunks or on a dispersed set of chunks to achieve frequency diversity). If a certain band is no longer available, the MBS provides a mechanism for transferring the context of the users from that particular band to another band that is still being serviced (context transfer unit). This transfer occurs in real-time and is seamless to the end user. During the context transfer, parameters essential for the transmission are transferred. Adaptation and prioritisation of the user flows may be needed as the new band might not be able to accommodate all traffic from the band that is no longer available. The timing of the transfer is an important issue; real-time changes when a band becomes unavailable are supported, as well as preventive context transfers when information is obtained that the availability of a band will change in the (near) future. This is done by band monitoring functionality in the MBS. In the multi-band architecture considered here, many common functions for the operation on the different bands can be identified. These functions may be shared between the different bands. Therefore, for example, the same flow ID, UT ID, etc. can be used on both bands,

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which simplifies the context transfer between the bands. However some problems related to ARQ and synchronisation remain to allow fast switching and are addressed in the following sections. In the conceptual discussion above, we distinguished two spectrum bands, but a more generalised approach can also be used for further fragmented spectrum. 11.4.1.1 Hybrid ARQ Context Transfer In order to allow a fast context transfer between two bands, the ARQ procedure is very important. If ARQ retransmissions have to be finished before switching to the other band then the switching process will be slow depending on the ARQ settings, e.g. a delay of up to 20 ms is quite possible. In order to allow a fast context transfer, the following ARQ operation has been proposed [DWK07]. Outer-ARQ (E2E ARQ) The end-to-end (E2E) ARQ is placed above the MBS and thus the context transfer from one band to the other does not change the E2E ARQ process (see Figure 11.7). After a context transfer, the multi-band scheduler should take into account that a band is (temporarily) not in use. Inner-ARQ (HARQ) The HARQ process is independent of the two bands, and thus a fast context transfer option is required. To allow the HARQ process to continue, we propose to transfer the HARQ buffer between the two bands. The context transfer unit coordinates the transfer from the MBS, and the HARQ units of each band exchange the data and parameters. When UTA moves from the extension or E band to the basic dedicated or B band, the HARQ buffer is also transferred and the HARQ retransmissions can be continued on the B band. This requires that:

r the buffers for the B and the E queue are pooled; r the HARQ processes in the B queue and the E queue use a common numbering scheme; r in case of (fast) context transfer, the data is either copied or read from the other queue, i.e. schedulers should be able to read both buffer types (B and E);

r arriving data is scheduled to the new band queue and data remaining in the buffers is transferred to the queues of the other band. During the context transfer, the data that is exchanged could include with the data in the buffers, the number of performed retransmissions and the already shared redundancy bits (for incremental redundancy HARQ). Preparation of Band Switch In many situations, the band switch is already known beforehand and preparations can be started. For example, if the BS knows that the E band will not be available in 5 ms then it can command the UT to synchronise with the B band. The BS can assist the synchronisation, for example by sending the time shift to the beginning of the next frame on the B band, the frequency shift between the two bands and other system information such as the position of the resource allocation table. Further, the UT can estimate the path loss and an initial channel quality indicator and report it before switching to the B band. When the UT switches the band this information is forwarded from the E band to the B band scheduler.

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11.4.1.2 MBS and Spectrum Sharing The MBS can be deployed to enable operation in the basic and extension band to abstract the different physical layers used from the higher network layers. The MBS can use priority levels of the traffic for assigning to traffic to either band and, in the case of changes in availability of the extension band, the MBS can relocate traffic or decide to drop or reduce the bandwidth of less important flows. For this, the MBS takes an input from the sharing negotiation functionalities in the network. When the extension or shared band is unavailable, a fast transfer to the basic band should be provided to maintain connectivity and provide basic and priority services. In addition, there can be various reasons to initiate a band transfer; for example, the load in the other system with whom the resources are shared may change or a user terminal might move into an area where it would interfere with the other system’s transmissions like FSS. Anticipation of band change improves the system performance, so that contexts can be exchanged before the connection on the band is actually lost (band monitoring). The information about this can be obtained by scanning the other network or from spectrum negotiation functionality. The MBS can be used to coordinate switching between bands by providing seamless continuous operations (smooth context transfer) or by completion of transmission before context transfer. In conclusion, from the spectrum management point of view the proposed MAC layer opens the way for a novel mechanism to efficiently balance the traffic load in response to the changes in the spectrum pipe. This matches the data pipe and spectrum pipe in the best possible efficient way. This feature would ultimately translate into a combined, joint and fast process of spectrum-packet scheduling which is unique to the WINNER system.

11.4.2 Communication Between Base Stations Inter-base-station communication (IBSC) between can be applied in the three WINNER deployments, metropolitan area, wide area and local area, using e.g. the IBB interface (see Chapter 4). IBSC between operators is needed for ST spectrum assignment communication. ST may require very frequent and fast (low-delay) communication between BSs. One particular scenario for using BS-to-BS communication between operators is when a fast, distributed resource assignment is performed amongst cells that relay into direct communication between BSs, which implies resource partitioning as well as interaction from the spectrum sharing among RANs. The ST spectrum negotiation protocols impose requirements on the system design. The spectrum negotiation protocol among BSs works within a time-span of several hundreds of milliseconds. Within this time-span, an agreement is to be met between operators. Usually, negotiation protocols among interested parties (i.e. operators) meet a final agreement after a process of several offers and counter-offers. This section provides a comparison between the wired, over-the-air (OTA) and core network (IP)-based base station communications methods and their suitability for ST spectrum assignment. 11.4.2.1 Trends in BS-to-BS Communication and Site Sharing Site-sharing is the current trend among operators, including sharing of physical premises (i.e. a rooftop, a mast in the countryside, etc.) and even the facilities to some extent. In the simplest

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case of site-sharing, one operator lends to another operator a physical placement at a given rent. The most important drivers for such agreements are cost. Operators owning a site are pleased to reduce their operational expenditures (OPEX) by renting part of their premises. The OPEX of a site is heavy: cooling, electricity supply, maintenance, taxes, etc. Site acquisition cost has increased constantly in recent years and is not expected to decrease. More stringent local regulation, public opinion acceptance and other factors have led in the long term to sites suitable for placing a BS becoming a valuable and scarce asset. Coexistence studies show that site sharing is beneficial from a technical point of view. Indeed, it avoids the near–far effect, in which a UT belonging to operator A transmits towards a BS far away and consequently induces a larger blocking probability into the reception of a nearby BS belonging to operator B. The operator’s strategy in this case is a non-aggression agreement. The number of shared sites can be up to 75 % for new operators in the arena and significantly less for long-established operators, which already had a volume of sites before the idea of site sharing came into play. Therefore, some of these sites were not intended for sharing, and lack enough space or are restricted by contractual and regulatory issues. A more technically demanding possibility is ‘network-sharing’. In this modality, the whole radio access system (from the fibre connection to the antenna) is shared among several operators. A scenario for this sharing is large, lightly populated areas that do not provide individually enough revenue to cover the deployment cost. Operators may share a BS and feed the BS with own fibre connections to their respective core networks. In other situations, depending on the agreement, operators may split the coverage areas, being responsible for the SLA and QoS in their area and providing virtual coverage to the other one by means of roaming services transparent to the end user. Operators have already constructed test-beds and set up operative pilots for some of these concepts. With respect to the current use of OTA BS-to-BS communication, in the context of spectrum sharing the main conclusion might be that its application among operators is low. For the large number of co-sited BSs (up to 75 % in some cases and increasing), currently the most widely used solution is direct connection among BSs of different operators, either via a fibre or coaxial cable. In the case of site sharing between network operators, the BSs may be located on shared sites, but the cell planning is not necessarily identical. This means that the coverage provided by cells from one operator may be different from the area covered by other operators. It may still happen that a BS of one operator needs to communicate with a BS from another operator located on another site. In this case, a direct cable solution is not sufficient. 11.4.2.2 Requirements for BS-to-BS Communication The main requirements for BS-to-BS communication are derived from the short-term spectrum assignment mechanisms. Herein, we distinguish requirements on the message content and size and delivery mechanisms. Sharing Information Content and Size The base stations involved in the ST spectrum assignment process may share information depending on the proposed algorithm for the spectrum sharing assignment process. Possible information fields in the message include:

r bandwidth: size of the shared bandwidth;

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r carrier: available carrier; r channel: channel number (optionally, if the carrier information is not available); r triggering flags: informing of the transaction or the need for transactions for new spectrum; r status: accept or reject; r timing flags: for how long the spectrum is available from the other operator. An increased amount of information shared between the BSs leads to extra load in the network. The size of payload depends on the amount of information being exchanged between the involved BSs. A larger required payload increases the cost of the BS-to-BS communication since a higher link capacity is required between the BSs. Once the sharing scheme is selected, the actual amount of information to be exchanged can be determined, and thus the incurred overhead. Of course, for any sharing scheme, a lower bound for the amount of information has to be met. In the case of sharing the spectrum between several systems other than WINNER, i.e. another IMT-Advanced technology, it may be necessary to have BSs of the same or different operators exchange information on the availability of the spectrum. This allows better utilisation of the spectrum since its usage will be more dynamic between technologies. This implies that the IMT-Advanced technologies need to include a similar communication protocol (i.e. same messages) as for WINNER. Sharing Information Delivery Short-term spectrum assignment mechanisms operate at a time scale of 200 to 500 ms. Therefore the maximum acceptable delay of signalling messages between BSs is the maximum in the same order of magnitude. If a handshake of messages is required, the delay constraint is therefore stricter. Two neighbouring BSs belonging to the same operator may not be in each other’s transmission range, for example, due to strong shadowing or a large inter-base station distance. There are also cases where a direct wired connection does not exist. This might (especially) be the case when the BSs belong to different operators. Hence, robust and efficient routing algorithms (such as ‘message-passing’) must be specified for these scenarios. 11.4.2.3 Possibilities for Inter-BS Communication There are numerous possibilities for BS-to-BS communications. This section presents a brief comparison of the possible options for BS-to-BS communication:

r over-the-air (OTA) communication; r IP-based BS-to-BS communication via core network nodes; r wired connection; r wired inter-connection of BSs via shared equipment. When checking the suitability of a BS-to-BS communication proposal, the following guidelines must be respected:

r The channels must have enough power (and, thus, range) to reach their objectives. r The latencies incurred by the proposed schemes must be checked against latency needs. r The information rate (in bps) needed by the spectrum negotiation protocols distributed over several BSs must be supported.

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Figure 11.8

Over-the-air communication between BSs of different operators.

Over-the-Air Communication Various options for over-the-air (OTA) communication can be considered. Figure 11.8 depicts two BSs of different operators communicating through a direct link over the air. One option is to use the burst within the random access channel (RACH) of the super-frame preamble. This would allow an information handshake protocol between two BSs every 6 ms. However, due to the contention-based nature of the RACH, delays are to be expected when a cell of an operator wants to trade its spectrum. For this reason, the RACH channel should be properly dimensioned to accommodate BS-to-BS communication. The BS transmitting a RACH message has to act as a UT. It is assumed that BSs of different operators transmit in different, but adjacent frequency bands. In WINNER FDD mode, no time synchronisation is assumed between operators, but a BS has to get the frame synchronisation of the peer BS to transmit the RACH at the proper time where it is expecting this message. This renders the BS hardware more complicated, hence more expensive. From the radio frequency point of view, it has to have an RF transmitter in a band close to its normal receiver, but also an RF receiver to get the preamble of the BS and synchronisation close to its normal transmitter. The same problem arises in the WINNER TDD case. In this mode, time synchronisation, even though not mandatory, is recommended for avoiding mutual interference. Again, a BS must transmit a RACH message in an adjacent band to where it is receiving from the UT. Solutions to overcome these disadvantages include forwarding the signalling messages by a relay node, using additional hardware (an extra transceiver) and using different re-use patterns for different BSs, in other words, not allocating resources in a cell when transmission from a neighbouring base station should be received. Another option is to use the Broadcast Channel (BCH) instead of the RACH. BCH is not a contention-based channel therefore this would allow an information exchange protocol among BSs with a 6 ms delay. But this alternative may prevent useful cell control information being conveyed through the BCH. Therefore, proper scheduling of the small amount of extra dedicated resources within BCH is required. Moreover, the problem with BCH is that it is common to all involved radio entities and cannot carry specific information for a single BS or radio entity. A third option is to use arbitrary chunks embedded within the super-frame. The benefits are allowing an exchange protocol among BSs up to every 0.35 ms (half-frame duration). Nevertheless, the information exchange signalling can have an impact on the carrier traffic and trigger an additional degree of complexity for the resource partitioning algorithms.

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Figure 11.9

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IP communication between BSs of different operators through GWs.

The fourth option is to use a second transceiver. This would remove both RACH and BCH constraints. However, additional hardware complexity is created for manufacturers and operators and the spectrum is not efficient because it is using out-of-band spectrum. Communication through IP If communication is done entirely via IP there will be a need to classify BSs based on the employed IP address and specifically to identify the assigned IP address of all the involved BSs. Using the IP backhaul for this purpose could introduce too much delay which would result in no gain at all in ST assignment because the spectrum trading opportunities would be lost. An alternative would be to use the IP connection through GWs (Figure 11.9). This has the advantage of being spectrum-efficient at the cost of higher latency increasing RAN signalling as well as the requirement for the classification and localisation of the BSs. One should note that the delay introduced by core network communication is larger than that of OTA communication. However, the target of future IMT-Advanced systems (as well as WINNER) is having core latency less than 5 ms which might be acceptable for several ST sharing algorithms. Wired Connection In cases where BSs of different operators are collocated (e.g. on the same mast) then a simple wired connection would be the most suitable option as illustrated in Figure 11.10(a). In this

(a)

Figure 11.10 shared BS.

(b)

BS-to-BS communication between different operators using (a) a shared mast or (b) a

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case, a possible alternative is to use a direct wired cable connection with a low-complexity solution (which is easy to implement). However, this solution is only envisaged for collocated equipment. Communication via Shared Equipment If operators are using shared equipment (especially shared BSs), these BSs could be used as a ‘bridge’ for spectrum information. Within one operator, an interface can be used between the BSs where the amount of spectrum and resources available are identified and the shared BSs are used for negotiations of the spectrum between the operators. This is illustrated in Figure 11.1(b). An alternative is to use a BS that is commonly shared between two operators relying on an existing inter-operator BS-to-BS communication link (IBB ). The disadvantage of this option is that it requires a sufficient amount of the shared BS to avoid bottlenecks.

11.4.2.4 Summary of BS-to-BS Communication Technologies OTA communications provide a faster means of communication (less latency) between BSs compared to an IP connection while consuming the available radio spectrum. IP connection on the other hand is slow but does not consume the allocated spectrum although it increases the required network signalling overhead and the required processing power in the GW. There is a trade-off between the achievable gain of employing OTA communication and the spectrum allocated for these communications between the BSs and between the network signalling overhead allocated for IP-based communication and the achievable performance improvements. For faster localised negotiations, wired connections between the BSs are suggested. For centralised (i.e. through GW) and not delay-critical communications, IP connection is suggested. For long-distance, delay-critical, one-to-one BS-to-BS communications OTA communications are suggested. In conclusion, a hybrid and flexible solution of all the possible communications methods between the BSs is suggested according to the different situations as suggested in Table 11.1.

11.5 WINNER Sharing with FSS During the preparatory work for WRC-07 performed within ITU-R, the frequency bands 3400–4200 MHz and 4400–4990 MHz were considered as two candidates for the future development of the terrestrial component of the IMT 2000 and IMT-Advanced systems, with the understanding that the use of these bands will be limited to the terrestrial component of IMT-Advanced. As the bands 3400–4200 MHz and 4500–4800 MHz are allocated worldwide on a primary basis to the fixed-satellite service (FSS), there is a need to consider issues of sharing between FSS and WINNER to guarantee that no harmful interference would be caused to FSS systems. The sharing studies have shown that FSS and WINNER systems could co-exist under conditions of minimum required geographical or frequency separation around the FSS earth station (ES) to guarantee full protection of FSS systems operation. Frequency separation allows

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Table 11.1 Comparison of different BS-to-BS technologies. Technology Over-the-air communication

Communication through IP

Wired communication or microwave link

Hybrid of above methods (recommended)

Advantages Faster communication Low latency No cable-related infrastructure Suitable for medium-distance communication and both in-band and out-band communication Best for long-distance, delay-critical, one-to-one, BS-to-BS communication Low data rate communication No consumption of available radio spectrum Less costly than OTA communication Suitable for centralised (i.e. through GW) communication that is not delay-critical High data rate communication Very fast communication Low cost Low complexity No IP overhead No consumption of spectrum Suitable for short-distance communication or co-site spectrum negotiations All data rates (high, low and medium) Low cost Low complexity High flexibility Suitable for all sorts of communication

Disadvantages Consumes the available radio spectrum to a degree More expensive transceiver structure Less flexibility Might require special communications channel

Slower communication Higher signalling overhead Extra processing required on gateway and network IP backbone Less flexibility Lack of availability of wired infrastructure (wired connections) Not suitable for longer distance communication Not suitable when BS are located far away

More effort required to harmonise or design the selection and combination of the involved techniques

operation inside the minimum separation distance. Outside the minimum separation distance from the FSS earth stations, the full band could be made available for IMT-Advanced. Different sharing cases are illustrated in Figure 11.11, where the use of overlapping or adjacent channels within an exclusion zone is prohibited or limited, cells or frequencies located further away may still be used. The magnitude of the required protection distance is very dependent on operational conditions of each FSS site: co-channel or adjacent channel operations, environment characteristics and local conditions, such as the terrain profile, the parameters of the networks and the deployment of the two services. Without any mitigation technique and based on the worst case of system parameters and terrain profile, these distances can range from less than a kilometre to more than 100 kilometres. When employing mitigation techniques, studies have shown that these distances can be significantly minimised.

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Figure 11.11 Frequency and distance separation between IMT-Advanced and FSS can be utilised to facilitate sharing.

11.5.1 Dimensioning of Hard Exclusion Zones This section summarises the results of spectrum-sharing studies performed in [WIN2D5103] and the improvements in [WIN2D5101], between WINNER and FSS systems in the candidate bands 3400–4200 MHz and 4500–4800 MHz. The studies cover both co-channel and adjacent band scenarios. The work is based on WINNER system characteristics and sharing related technical parameters. ITU-R agreed sharing parameters for a generic IMT-Advanced technology and FSS parameters contained in ITU-R Recommendations [ITU05]. The simulations have been conducted with a CEPT tool called SEAMCAT 3 [ERO08]. 11.5.1.1 Typical FSS Parameters Considered for the Simulation Table 11.2 summarises the assumed FSS earth station parameters and Table 11.3 presents the parameters used during the evaluation. Two interference limits to FSS have been considered [ITU05]: I /N = −12.2 dB and I /N = −20 dB, where N is the noise received by the FSS earth station. For propagation path loss, the Recommendation ITU-R P.452 [ITU07] has been used in a flat terrain profile. This profile comes down to a free space model below 40 km (for ranges above 40 km, earth radius impacts the path loss). A terrain model reduces the protection distances due to the presence of obstacles. 11.5.1.2 Results Table 11.4 shows the exclusion distances calculated for a WINNER system. These have been calculated to comply with the interference criteria defined in [ITU05]. For the simulation, a single BS was first targeted, with different FSS ES elevation angles. The tables show

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Table 11.2 Typical downlink FSS parameters considered in the 4 GHz band. Parameter Range of operating frequencies Earth station off-axis gain towards the local horizon (dBi)a Reception bandwidth Receiving system noise temperature/ Noise floor Antenna height/ diameter Antenna peak gain Antenna reference pattern a

Typical value 3400–4200 MHz, 4500–4800 MHz Elev. Angleb Off-axis gain

5◦ 14.5

10◦ 7.0

20◦ −0.5

30◦ −4.9

48◦ −10

85◦ 0

50 MHz 100 K/ − 101.6 dBm

15 m / 5.5 m 44.4 dBi Recommendation ITU−R S.465 [ITU93] (up to 85◦ )

The values were derived by assuming a local horizon at 0◦ of elevation. 5 is considered the minimum operational elevation angle.

b ◦

the minimum and maximum distances obtained for these angles. In the aggregate case, the effect of all the BSs is taken into account. A certain number of BSs have been uniformly (equi-spaced) located on a circle around the FSS earth station. The radius is the result of the required protection distance meeting the interference criterion. The number of BSs is assessed according to the protection distance and the BS inter-site distance range. Finally the simulation was carried out considering that BS and FSS ES are working in adjacent channels.

Table 11.3 Parameters used during the evaluation of the suburban macro cell (50 MHz bandwidth). Parameter

Base station (BS)

User Terminal (UT)

Antenna type (Tx/Rx) a Thermal noise floor including noise figure (dBm) Tx output power(dBm) Antenna gain (dBi) Antenna down tilt (◦ ) Antenna height (m) Intersite distanceb (km)

Tri-sector −92

omnidirectional −88

43 20 2 30 5

24 0 0 1.5 Uniform distributed in a circle of 5 km diameter

a b

The gain is assumed to be flat within one sector. This is the separation between BSs.

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Table 11.4 Exclusion distances between BS and FSS ES to avoid harmful interference. Protection distance (km) −12.2 dB criterion

Macro (WA for WINNER, urban for FSS) Co-channel Adjacent channel

Single BS Aggregate BS Single BS Aggregate BS

Min

Max

46 52 3 8

59 61 31 32

Protection distance (km) −20 dB criterion Min 50 56 6 18

Max 66 74 41 41

11.5.2 Mitigated Exclusion Zone Calculation A mitigation technique is an engineering technique used to reduce harmful interference effects on a victim system. They may be implemented within a victim system or directly within an interfering system. In this section, specifically disabling transmission in certain sectors targeted at the FSS earth station is considered as a mitigation technique. WINNER technologies are assumed to be able to switch the carrier of the base station antenna sector from the FSS band to another band already allocated to the IMT-2000 systems, to prevent the WINNER base stations from transmitting signals in a direction that would create a source of interference potential (see Figure 11.12). The coverage area of the disabled sector of WINNER antenna base station will be covered through the use of other frequency bands already allocated to IMT-2000 systems or within cooperation mechanisms between heterogeneous mobile systems. This technique and its resulting protection zones are developed in [WIN2D5101]. Figure 11.13 shows the differences in the protection distance when considering disabling one of the three sectors in the tri-sectored configuration that points towards the FSS earth

Figure 11.12

Sector disabling as a mitigation technique.

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61 one BS station disabled up to 3rd ring one sector disabled up to 10th ring full active BS sectors

60

59

Protection distance [km]

58

57

56

55

54

53

52

51

5

10

15

20 25 30 Elevation angle earth station [°]

35

40

45

Figure 11.13 Protection distances for fully active BS sections and BS sectors where one of the trisectored nodes is disabled up to a certain tier.

station, in the BS closer to the FSS ES, with the –12.2 dB criterion. The protection distance is reduced quite a lot in the worst cases of elevation angle. Compared to normal full active sector mode, the application of disabling sectors has shown that the separation distance ranges are reduced by up to 49 % in generic studies (without terrain horizon profile) and up to 83 % for a specific site (with terrain horizon profile) depending on the access mode and the elevation angle of FSS earth station [WIN2D5101]. The conclusion is that the WINNER system can share the FSS frequency band using adjacent channels or exclusion distances that have been calculated for flat terrain profiles. When taking into account the terrain profiles in a concrete deployment, the exclusion distances are much shorter.

11.5.3 Advanced Mitigation Techniques This section presents several additional mitigation techniques that can be used to enhance the performance of the system and enable sharing between the WINNER system and other technologies in a more flexible manner. Specifically three techniques are considered: the use of

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a database storing shared information and accessible by all parties, the introduction of specific beaconing mechanisms and the use of advanced antenna techniques. 11.5.3.1 Utilisation of Information Describing the FSS Usage in a Database A database with information about the usage of different radio technologies in an area enables sharing between radio technologies in an efficient manner. In the WINNER system, the information from this database would be managed by a spectrum manager and could contain information about the FSS ES, such as the location, the frequency usage, channel bandwidth, the operation times, the direction and the movement (in case of non geostationary satellites) of the antennas. It is very important that the information contained in the database is accurate and up to date, therefore open interfaces and updating of the information is needed. The database may need to be operated by a trusted third party, not involved in the sharing. On the basis of this up to date information from the database shared services could be introduced and used in specific geographical areas. Depending on local conditions, the available radio resources for shared services may vary. The interference can be limited by the application of appropriate spectrum mask and guard bands which would avoid causing adjacent channel interference. The protection distance range can then be reduced drastically in the case of such a coordinated approach. 11.5.3.2 Utilisation of Spectrum Beacon Channel Another possibility is the use of a beacon or control information broadcast in a dedicated band (i.e. outside the FSS spectrum band). The IMT-Advanced operator operates outside the protection distance and this may be used as a powerful mitigation technique. The beacon is operated by either WINNER system operators or FSS operators. The radio access point broadcasting the beacon is co-located with the earth station. The information contained in this beacon or control information can be measured by any WINNER BS. At reception of the beacon, the BS adapts its operation in the shared spectrum. This in turn provides a good and active protection to the FSS ES since appropriate spectrum mask and guard bands can be generated. The broadcast beacon or control information may include one or more of the following information elements:

r protection distance, e.g. a WINNER Station is not allowed to transmit in a cell or sector; r power limit, e.g. a maximum power limit that can be accepted by the FSS earth station; r gradual power limit, e.g. RAPs ensure that the transmitting power from a BS close to the earth station is low, while it may be higher for BSs further away;

r indication of an alternative spectrum band; r reduction in the available bandwidth; r location information. 11.5.3.3 Multi-antenna Technologies

Multi-antenna technologies may be helpful to prevent the BS from transmitting signals in a direction which would create a source of potential interference. [WIN2D341] describes the

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spatial-temporal processing in WINNER. The envisaged multi-antenna solutions rely either on sector-wise power adjustment (sectorised cells) or beamforming capabilities. In countries where FSS usage is extensive and their locations or characteristics cannot be determined by a reasonable effort, band segmentation could be an alternative solution. In many cases FSS earth stations use only part of the band. So the remaining frequency band can be used for WINNER, which (due to the FSU capabilities) can transmit only in the non-harmful band in the surroundings of the FSS ES.

11.6 Performance Evaluation of Spectrum Assignment Mechanisms This section presents performance evaluation results on long-term and short-term spectrum sharing approaches. The evaluations are done by means of system simulations.

11.6.1 Performance Assessment of Long-term Spectrum Assignment The performance of LT assignment in WINNER deployment scenarios was investigated during WINNER Phase I [WIN1D63]. The spectral resources assigned to the networks are defined in terms of chunks and hence separation between networks can be done either in frequency (FDD) or both in time and frequency (TDD). By maintaining the chunks orthogonal and without excessive guard bands, the inter-RAN signalling is simplified. The following principles are assumed about long-term spectrum assignment:

r Spectrum re-assignments are negotiated directly between the networks. r Spectral resources are divided into two categories: resources assigned with a priority and common pool resources.

r Fairness and cost metrics can be used in the assignment negotiation to achieve fair and efficient assignment solutions. The purpose of introducing the priority and common pool resources is to combine both approaches. The existence of both prioritised and common pool categories increases the flexibility of the system concept by facilitating a wide range of spectrum-trading policies. From an RRM viewpoint, it may be preferable that resources in the common pool are assigned to a network for several spectrum assignment periods at a time. In other words, not all common pool resources are re-assigned at each period. This results in a smoother spectrum assignment adaptation, reducing undesirable abrupt changes on the available resources. A possible solution is that assignments have a common duration of multiple assignment periods and a resource can be released during the assignment. In this section, a simplified assessment model is introduced. After that, LT assignment between RANs is considered in three scenarios addressing various aspects of spectral scalability, and preliminary results on the achievable gains are presented. 11.6.1.1 Considered Scenarios In the first scenario, the gradual spectral deployment of WINNER system is analysed. All networks are assumed to have similar, slowly increasing spectrum demand. LT assignment

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in which networks or operators, sharing first one, then two, and finally three carriers is investigated. This is compared to the conventional spectrum use, where all networks have their own carriers from the beginning. In the second scenario, the impact of unequal average spectrum demands on RANs is studied. In the third scenario, the impact of the spectral resource categorisation on the LT assignment is investigated. For the first two scenarios, the spectral demands on all the networks are aggregated and available spectral resources are treated as common pool resources. In the third scenario, all spectral resources are treated as prioritised resources assigned to a certain RAN and the different ways of using the prioritised resources are investigated. In all the scenarios, three WINNER RANs in TDD mode are considered. The spectral resources available for RANs are represented in terms of time–frequency chunks; in this case the super-frame of TDD mode is partitioned into 4992 chunks. The total number of chunks is denoted by S. Similar spectrum demand patterns, related to the network load, are assumed for all RANs. Spectrum demand is expressed in terms of the number of chunks requested per super frame. This is referred to as a spectral resource request di of the operator i. Both deterministic and random spectral resource requests are considered. Random requests are samples from a cyclostationary process with a 24-hour period and a truncated Gaussian distribution (i.e. negative values were discarded), sampled at intervals of 15 minutes. The standard deviation σ of di is proportional to the mean of di by σ = p ∗ E[di (τ )], where τ is the sampling instant. Notation p = 0 is used for the deterministic requests. In the evaluations, p = 0.5 is used with random requests. In the first and third scenarios, E[di (τ )] follows a Gaussian bell curve E[di (τ )] = Di exp(−((τ − τd )/a)2 ), where parameter a defines the width of the curve and Di is the maximum of E[di (τ )] over the period of 24 hours. LT assignment is assumed to cause some loss in spectral efficiency due to inter-RAN interference as well as the need for additional guard chunks. This is modelled by excluding a portion g from the total available chunks S. In the case of conventional fixed spectrum assignments, g equals zero. The performance is measured with two metrics: the number of used chunks and the number of excess spectral resource requests (i.e. the requests that cannot be satisfied for each network, due to the insufficient amount of spectrum). The number of used chunks, for LT assignment, is defined as r (τ ) = min[(1 − g)S, i di (τ )], where the summation is taken over the RANs, whereas in the case of fixed spectrum assignments, the number of used chunks in each carrier is defined as ri (τ ) = min[S, di (τ )]. The number of excess spectral resource requests is defined as b(τ ) = i di (τ ) − r (τ ) for FSU and as bi (τ ) = di (τ ) − ri (τ ) for the fixed spectrum assignments. The results are gathered for a scenario with 24 hours of 1000 simulated days. The average r(τ ) is normalised to the total number of available chunks, S. The value 1 corresponds to the situation where all the chunks are used constantly over the day. For the FSU, the maximum is equal to 1 − g. In the figures showing the results, the spectrum use intensity (i.e. the ratio between the average r(τ ) and the total number of chunks, S) is presented against the increasing Di , normalised with the number of chunks per carrier (note that the index i of Di is omitted). The excess spectral resource request b(τ ) is measured over the simulated days separately for each sampling instant of the day. From the sampling instances, the most congested moment of the day, τ g , is considered – the 90th percentile of b(τ g ) is presented against the increasing Di , normalised with the number of chunks per carrier.

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0.9

Average value of [(average chunks used) / (total chunks)]

0.8 1 carrier 0.7 2 carriers 0.6 3 carriers 0.5

0.4

0.3

0.2

0.1

0

0

Figure 11.14

0.2

0.4 0.6 0.8 1 The maximum average spectral resource request (D)

1.2

1.4

Spectrum use intensity vs normalised maximum average spectral resource request.

11.6.1.2 Gradual Spectral Deployment of WINNER RANs The performance of FSU between WINNER RANs is presented for the whole range of spectrum demand separately for each number of shared carriers (from one to three carriers). This is compared with the fixed assignment of a single carrier per network. The spectral resource requests are considered to be both deterministic (solid lines) as well as random variables (dotted lines). The overall spectral efficiency loss due to the guard chunks and inter-network interference is shown for two values: g = 10 % (square marker) and g = 20 % (diamond marker). In Figure 11.14 and Figure 11.15, the amount of spectral resources available to the networks varies with the number of carriers. The ratio between the total available resources and requested resources varies for the same value of D. To illustrate this further, all available chunks, S, are needed to satisfy (on average) the aggregate peak (moment) spectral resource requests at the normalised spectral resource request values of 1/3, 2/3 and 1 for FSU with 1, 2 and 3 carriers, respectively; and 1 for fixed spectrum assignment. For the case of a single carrier shared between three RANs, the spectrum use intensity is about 45 % already for D = 0.2, as seen from Figure 11.15. The impact of the guard chunk ratio g or randomness on the spectral resource requests (p = 0 and 0.5) to the spectrum use intensity is also negligible for values of D below 0.3. The randomness on the resource requests

90th percentile at max [E(Excess Request) / E(Spectral Request)]

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0.5

0.4

0.3

0.2

0.1

0

0

0.2

0.4 0.6 0.8 1 The maximum average spectral resource request (D)

1.2

1.4

Figure 11.15 90th percentile of maximum excess request portion (over the day) vs normalised maximum average spectral resource request.

has clear impact on the excess resource requests as seen from Figure 11.15. Nevertheless, even for the case of g = 0.2 and p = 0.5, the 90th percentile of the excess requests is well below 5 % of the spectral requests during the peak hour of the day for D = 0.2, which is considered to be acceptable. In other words, a single carrier is sufficient for three RANs up to the region of D of 0.2. When the spectrum use intensity of FSU with a single carrier is compared to the one of the fixed spectrum assignment requiring three carriers in total (D = 0.2), the fixed assignment achieves only 15 % spectrum use intensity. FSU achieves a spectrum use intensity that is three times the spectrum use intensity of the conventional fixed spectrum assignment at D = 0.2. Similarly, LT assignment results with 2 carriers indicate that the networks can operate safely up to the region of normalised D of 0.4. At this point the spectrum use intensity for FSU is about 42 % and for the conventional fixed spectrum assignment around 30 %. In this case, the 90th percentile of the excess request is well below 5 % of the spectral requests during the peak hour of the day, except for g = 0.2 in the case of random requests. For the case of three carriers, LT assignment and the fixed spectrum assignment require the same amount of spectrum and have similar spectrum use intensities. For random resource requests, LT assignment can support normalised D values up to the region of 0.6–0.8, depending on the value of the g parameter, while the fixed spectrum assignment can support normalised

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D values only up to 0.6. In the case of deterministic resource requests, the fixed spectrum assignment can support normalised D values up to 1 and slightly over, while FSU suffers from the guard chunk g parameter. Therefore in the gradual spectral deployment of the WINNER system, one carrier is sufficient for three RANs for average peak hour spectral resource requests per network, normalised with the number of chunks per carrier below 20 %. Similarly, two carriers are sufficient for normalised resource requests per network below 40 %. When three carriers are available to WINNER RANs having similar spectral resource request patterns, the gain or loss from LT assignment depends on the amount of randomness in spectral resource requests and on the amount of excess guard chunks needed by LT assignment. The results illustrate that LT assignment facilitates a smooth gradual spectral deployment for the WINNER system, achieving higher intensity in the spectrum use than in the conventional fixed spectrum assignment.

11.6.2 Performance Assessment of Short-term Spectrum Assignment For the performance analysis of short-term spectrum assignment the main focus is on inter-cell interference issues and the impact of different cell pair selection algorithms between which the negotiations take place. The evaluations are done using a network-level simulation tool modelling a cellular topology consisting of 16 cells. A realistic traffic pattern with a day and night variation is used. The WINNER system parameters have been used to model the air interface, see Table 13.2 (Section 13.4.2). Interference mitigation between cells is based on interference avoidance using orthogonal sequences based on Costas arrays. 11.6.2.1 Evaluation of Inter-cell Interference Issues for ST Spectrum Assignment This section presents the simulation studies describing the issue of inter-cell interference in ST assignment in the above scenario. In this case, WINNER MA deployment with TDD mode of operation is considered. The ST assignment period is considered as a super-frame duration of 230 chunks in the frequency direction and 16 chunks in the time direction. Therefore, a truncated Costas array representation of 230 by 16 is considered for resource allocation patterns in each cell. At the same time, in order to avoid intra-cell interference in a single cell only one user is assigned to a frequency chunk at one time. Also it is assumed that in neighbouring cells, the same chunk is allocated to different users who are separated by distance. For each cell, the average amount of chunk usage is considered as a constant value. In the first case, only 50 average frequency chunks (from the available 230 frequency chunks) or around 20 % of the available frequency chunks are used in the generous cell (here assumed to be Cx ) in each chunk duration (time slot). In the second case, 80 average frequency chunks (around 35 %) are used in cell Cx . In both cases, the generous cell (Cx ) opens chunk negotiation with the greedy cell (here assumed to be Cy ) of RAN2 . The two cases (where the chunk usage in the generous cell is 20 % and 35 % of the available chunk frequency) are investigated against an interference threshold (Pthreshold ) that is the maximum allowable interference within ST negotiation. Also the generous cell can consider the rather selfish approach of not agreeing with neighbouring cells about interference levels that the negotiated chunks can impose on the first tier of cells. At the same time, with the increase of agreement between neighbouring cells, the number of negotiated chunks decrease due to interference with neighbouring cells. This is investigated starting from the selfish approach

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ST negotiation compared to number of negotiating cells (N) 1

0.9

0.8

Interference Threshold

0.7

0.6

0.5 N=1 N=2 N=3 N=4 N=5 N=6 N=7

0.4

0.3

0.2

0.1

0

0

10

20

30 40 50 60 70 Percentage of chunks available for negotiation

80

90

100

Figure 11.16 Probability of interference threshold vs percentage of chunks for negotiation (average chunk usage 20 %).

(where no neighbouring cells are considered for interference agreement) and increasing step by step the number of neighbouring cells considered in interference agreement. Figure 11.16 presents the available percentage of chunks for negotiation for different interference threshold values and with different numbers of neighbouring cells in agreement for average chunk usage 20 % of the available chunks. In the case of the selfish approach, 100 % of the unused chunks are available for negotiation between Cx and Cy irrespective of the interference caused to the first tier. When neighbouring cells are taken into account, within a given maximum interference value, the number of chunks available for negotiation decreases. For example, it can be seen from Figure 11.16, for the maximum interference threshold value of 0.82, when two cells are agreed on interference only 67 % of available chunks are given for negotiation. When the number of cells in agreement for interference increases to 3, 4, 5, 6 and 7 the percentage of available chunks for negotiation between Cx and Cy decreases from 67 % to 50 %, 38 %, 28 %, 21 % and 15 % respectively. Therefore in the case of the selfish approach, where only Cx makes the decision for negotiation irrespective of the interference it causes to the first tier, 100 % of the unused chunks are available for negotiation. When Cx involves all the cells in the first tier in decision making, this results in only 15 % of the unused chunks being available for negotiation.

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11.6.2.2 Cell-Pair Selection Algorithms for ST Assignment For each RAN, we define greedy cells and generous cells depending on the additional or insufficient resources; Na are the resources available, Nd the desired resources. When (Na > Nd ) then the cell is categorised as a generous cell – extra resources are available for negotiation. If (Na < Nd ), the cell is a greedy cell since it is starving for more resources. Na is the number of resources allocated to each cell during LT assignment and Nd is the actual number of resources needed from the traffic demand curves. During the ST assignment period, only generous and greedy cells negotiate with each other. To avoid conflicts between more than one cell pair entering into the ST negotiation, within this study we limit the number of cell pairs involved in a negotiation to one. A cell-pair selection algorithm defines the unique set of generous and greedy cells for the ST negotiation process between two WINNER RANs. The impact of cell-pair selection algorithms on the performance of the ST assignment are investigated in this section. The cell-pair selection algorithms for ST assignment can be categorised as selecting the cell pair based on:

r minimum request chunks: the cell pair which includes the greedy cell with the minimum request chunks is considered;

r maximum requested chunks: the cell pair which includes the generous cell with the maximum extra chunks is considered;

r least difference: the cell pair with the least difference of chunks between the generous cell and the greedy cell is considered (this is referred to as the least satisfaction algorithm);

r highest difference: the cell pair with the highest difference of chunks between the generous cell and the greedy cell is considered (this is referred to as the maximum flexibility algorithm);

r random selection.

The selection of cell pairs in this study is based on the least satisfaction algorithm and the maximum flexibility algorithm. In the least satisfaction algorithm, the generous cell satisfies the minimum requirement of the greedy cell with a matching amount of resources. In the maximum flexibility algorithm, the cell pair with the most flexibility in allocating chunks is selected: the generous cell provides the maximum possible satisfaction, that is more than the required resources, to the greedy cell. Having more resources than required allows flexibility in resource allocation for the greedy cell thus avoiding interference with neighbouring cells of the generous cell. 11.6.2.3 Impact of Cell-Selection Algorithms on ST Performance Assignment This section presents the comparison between the least satisfaction and maximum flexibility algorithms once applied to the ST assignment between two RANs. When cell pairs are selected according to the least satisfaction criteria, the amount of offered frequency chunks in the generous cell is similar to the number of extra frequency chunks required by the greedy cell. When cell pairs are selected according to the maximum flexibility criteria, the generous cell offers much more than required by the greedy cell. For successful ST assignment performance it is necessary to reduce extra interference caused by the offered frequency chunks to the neighbouring cells of the generous cell. With plentiful resources, the greedy cell has the

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Comparison of Average Gain (frequency chunks) 25

Least Satisfaction Algorithm (0.1) Maximum Flexibility Algorithm (0.1) Maximum Flexibility Algorithm (0.05) Least Satisfaction Algorithm (0.05)

Average Gain in Extra Frequency Chunks

20

15

10

5

0

0

20

40

Figure 11.17

60

80 100 120 Average Offered Load

140

160

180

200

Algorithm comparison for average gain in ST assignment.

flexibility of deploying chunks to minimise interference with neighbouring cells in the first tier of the generous cell. In Figure 11.17, the x axis represents the average offered load in each RAN. This is given in terms of frequency units. The y axis represents the average extra frequency chunks that can be gained with ST assignment. In each ST assignment period, the negotiation is limited to a single cell pair. The impact of variance of the traffic pattern is also investigated. In the simulation scenario, the standard deviation from the average traffic demand at each time instance is assumed as 10 % of the average traffic demand (i.e. σ = 0.1∗ µ). In Figure 11.17, this is investigated for the value of 10 % and 5 % (i.e. σ = 0.05∗ µ). It can be seen clearly that, in both cases, the amount of negotiation chunks or gain in terms of frequency units is higher with higher variation independent of the cell-selection algorithm. Therefore more bursty traffic demand patterns in the cell contribute positively to the performance gains of ST assignment. Taking into account the scheduling of extra spectrum chunks gain from ST assignment, the maximum flexibility algorithm has more freedom than the least satisfaction algorithm. In the case of the least satisfaction algorithm, the output of the average spectrum resources gain from the ST assignment is equal to the minimum requirement of the greedy cell, therefore the scheduling of these newly gained chunks must not cause extra interference to the neighbouring

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cells of the generous RAN. In the case of maximum flexibility algorithm, the average spectrum resources gain from the ST assignment is more than the minimum requirement of the greedy cell, therefore scheduling has more freedom for allocating the resources.

11.7 Conclusion Future mobile systems will need to improve on today’s already efficient use of the spectrum since the use of wireless networks both in quantity and quality continues to increase. Additionally the available spectrum for mobile systems remains a scarce resource. Capabilities for flexible spectrum use and spectrum sharing are widely seen to be a novel and very important feature in future wireless systems. The achievable gains depend on the efficient implementation and application of these capabilities. This chapter has investigated spectrum assignment mechanisms for spectrum sharing between wireless networks of the same technology. Using spectrum assignment functions, the spectrum can be exchanged between different RANs of the same technology. Two levels of operation are distinguished: long-term spectrum assignment and short-term spectrum assignment. Long-term spectrum assignment assigns the resources on a long timescale (in the order of minutes or more) over a larger geographical area. Short-term spectrum assignment assigns the resources in a short timescale (in the order of several 100 ms) at a cell level. The long-term assignment follows slowly varying average usage patterns over longer periods; the short-term assignment optimises for short variations of the load in a cell. The chapter presents a detailed breakdown of spectrum assignment functions. Further it addresses the interaction between different spectrum functions (sharing and coexistence and spectrum assignment), and also the embedding of the spectrum assignment functions into the WINNER concept. Medium access control enhancements have been considered, mainly targeting the enabling operation in multiple bands. Multi-band operation is paramount to the introduction of spectrum sharing mechanisms since the availability of a dedicated band for guaranteed service and a shared band for extended services is a strong candidate solution. In order to enable operation in multi-band environment, a multi-band scheduler was developed that enables operation on multiple bands and allows for fast switches between bands when they are no longer available for a particular reason (for example, due to changed sharing conditions or changed load conditions). BS-to-BS communications have been identified as one of the key enabling technologies for spectrum negotiations (i.e. spectrum assignment and sharing). A comparative study of different BS-to-BS communication schemes concluded that the best solution is to have a hybrid of OTA, IP and wired connections. The OTA method is generally more expensive than other methods although it is the best mean of communications for long-distance, delay-critical, one-to-one BS-to-BS communications. For faster, localised negotiations and when co-site BSs are available, wired connections between the BSs are suggested. For centralised (i.e. through GW) and not delay-critical communications, IP connection is suggested. Sharing studies between the WINNER system and satellite systems have been performed, and coexistence was shown to be feasible. To this end, exclusion zones around the satellite earth stations are introduced. In these exclusion zones, transmissions are either prohibited or subject to specific constraints. In practical scenarios, it was shown to be possible to have exclusion

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zones with a radius of a few tens of kilometres. Additional advanced antenna concepts can be used to further reduce the exclusion zone size. Algorithms for spectrum assignment have been investigated by means of system simulations. Two main approaches to short-term spectrum assignment are applied. The maximum flexibility algorithm has more degrees of freedom than the least satisfaction algorithm, especially after taking into account the scheduling of extra spectrum chunks exchanged when using this algorithm. Both approaches were seen to obtain better performance with increased variability of the traffic loads in the network. Long-term spectrum assignment was shown to improve the overall spectrum use since operators can share the frequency bands, resulting in higher spectrum efficiency.

Acknowledgements The authors would like to thank all their colleagues from the WINNER spectrum technologies tasks for the constructive and fruitful discussions resulting in the WINNER spectrum concept. Special thanks go to Jean-Philippe Kermoal for his work towards developing the WINNER spectrum technologies and Pekka Ojanen for his inspiring leadership of the WINNER spectrum work. Thanks also go to Marja Matinmikko and Afif Osseiran for proofreading the manuscript.

References [BKO+07]

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[DWK07]

[ERO08] [Hay05] [HLP+05]

[HTL+06]

[ITU02]

[ITU03]

[ITU04]

Bennis, M., Kermoal, J.-P., Ojanen, P., Lara, J., Abedi, S., Pintenet, R., Thilakawardana S. and Tafazolli, R. (2007) ‘Advanced spectrum functionalities for future radio network’, Wireless Personal Communication Journal, Springer. Cave, M. and Webb, W. (2004) ‘Spectrum Licensing and Spectrum Commons: Where to draw the line’, Research report prepared for the International Workshop on Wireless Communication Policies and Prospects: A Global Perspective, Annenberg School for Communication, University of Southern California. Doppler, K., Wijting, C.S., and Kermoal, J.-P. (2007) ‘Multi-band Scheduler for Future Communication Systems’, Proc. International Conference on Wireless Communications Networking and Mobile Computing (WICOM), pp. 6744–8. European Radiocommunications Office (2008) SEAMCAT (Spectrum Engineering Advanced Monte Carlo Analysis Tool), viewed 20 June 2009, www.ero.dk. Haykin, S. (2005) ‘Cognitive Radio: Brain Empowered Wireless Communication’, IEEE Journal on Selected Areas, 23(2). Hooli, K., Lara, J., Pfletschinger, S., Sternad, M. and Thilakawardana, S. (2005) ‘Radio resource management architecture for spectrum sharing in B3G systems’, Proc. Wireless World Research Forum (WWRF) meeting 15, Paris, France. Hooli, K., Thilakawardana, S., Lara, J., Kermoal, J.-P and Pfletschinger, S. (2006) ‘Flexible Spectrum Use between WINNER Radio Access Networks’, Proc. IST Mobile and Wireless Summit, Mykonos, Greece. ITU (2002) Coexistence between IMT-2000 time division duplex and frequency division duplex terrestrial radio interface technologies around 2600 MHz operating in adjacent bands and in the same geographical area, Report ITU-R M.2030, International Telecommunication Union, Geneva. ITU (2003) Framework and overall objectives of the future development of IMT-2000 and systems beyond IMT-2000, Recommendation ITU-R M.1645, International Telecommunication Union, Geneva. ITU (2004) Mitigating techniques to address coexistence between IMT-2000 time division duplex and frequency division duplex radio interface technologies within the frequency range 2500–2690 MHz operating in adjacent bands and in the same geographical area, Report ITU-R M.2045, International Telecommunication Union, Geneva.

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[ITU05]

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ITU (2005) Apportionment of the allowable error performance degradations to fixed satellite services (FSS) hypothetical reference digital paths arising from time invariant interference for systems operating below 15 GHz, Recommendation ITU-R S.1432, International Telecommunication Union, Geneva. [ITU06] ITU (2006) Spectrum Requirements for the future development of the IMT-2000 and IMT-Advanced, Report ITU-R M.2078, International Telecommunication Union, Geneva. [ITU07] ITU (2007) Prediction procedure for the evaluation of microwave interference between stations on the surface of the Earth at frequencies above about 0.7 GHz, Recommendation ITU-R P.452, International Telecommunication Union, Geneva. [ITU93] ITU (1993) Reference Earth-Station Radiation Pattern for Use in Coordination and Interference Assessment in the Frequency Range from 2 to About 30 GHz, Recommendation ITU-R S.465, International Telecommunication Union, Geneva. [JLL+05] Javaudin, J.-P, Lain´e, J., Lacroix, D. and Seller, O. (2005) ‘On Inter-cell Interference in OFDMA Wireless Systems’, Proc. of the EUSIPCO conference, Antalya, Turkey. [LMT+04] Leaves, P., Moessner, K., Tafazolli, R., Grandblaise, D., Bourse, D., Tonjes, R. and Breveglieri, M. (2004) ‘Dynamic spectrum allocation in composite reconfigurable wireless networks’, IEEE Communication Magazine, 42(5) 72–81. [WDK+08] Wijting, C.S., Doppler, K., Kallioj¨arvi, K., Johansson, N., Nystr¨om, J., Olsson, M., Osseiran, A., D¨ottling, M., Luo, J., Svensson, T., Sternad, M., Auer, G., Lestable, T. and Pfletchinger, S. (2008) ‘WINNER II System Concept: Advanced Radio Technologies for Future Wireless Systems’, Proc. ICT Mobile Summit 2008, Stockholm, Sweden. [WIN1D63] WINNER I (2005) IST-2003-507581 WINNER Spectrum Aspects: Assessment reports, Deliverable D6. 3, December 2005, viewed 20 June 2009, http://projects.celtic-initiative .org/winner+. [WIN1D76] WINNER I (2005) IST-2003-507581 WINNER System Concept Description, Deliverable D7.6, September 2005, viewed 20 June 2009, http://projects.celtic-initiative .org/winner+. [WIN2D341] WINNER II (2006) IST-4-027756 The WINNER II Air Interface: Refined Spatial-Temporal Processing Solutions, Deliverable D3.4.1, November 2006, viewed 20 June 2009, http://projects .celtic-initiative.org/winner+. [WIN2D5101] WINNER II (2007) IST-4-027756 The WINNER Role in the ITU Process Towards IMT-Advanced and Newly Identified Spectrum, Deliverable D5.10.1, November 2007, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+. [WIN2D5103] WINNER II (2006) IST-4-027756 WINNER II Spectrum Sharing Study, Deliverable D5.10.3, November 2006, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+. [WIN2D61314] WINNER II (2008) IST-4-027756 WINNER II System Concept Description, Deliverable D6.13.14, January 2008, viewed 20 June 2009, http://projects.celtic-initiative.org/ winner+.

12 ITU-R Spectrum Demand Calculation for IMT-Advanced Marja Matinmikko,1 J¨org Huschke,2 Tim Irnich,3 Jussi Ojala,4 and Pekka Ojanen4 1

VTT Technical Research Centre of Finland Ericsson 3 RWTH Aachen University 4 Nokia 2

12.1 Introduction The determination of spectrum demand of future wireless systems represents an important step in the process of defining and standardizing the systems. Spectrum-related issues will play a key role in the development of future wireless systems due to the increasing bandwidth demand to meet the requirements of future services in the current spectrum regulatory framework where most of the spectrum is already allocated to different services. The radio spectrum is globally administered by the International Telecommunication Union (ITU) whereas the use of radio spectrum in each country is nationally regulated by the corresponding government agencies that have the freedom to make the spectrum available for particular uses in their operational area. The ITU Radiocommunication sector (ITU-R) arranges the World Radiocommunication Conference (WRC) every three to four years to review and revise the Radio Regulations, which are the international treaty governing the use of the radio spectrum and satellite orbits. Recent decisions on spectrum use, including spectrum for the mobile service, were made at WRC-07 with new spectrum identifications for International Mobile Telecommunications (IMT) systems. The demand for additional spectrum for IMT systems was justified by a spectrum requirement calculation methodology that was used to estimate the total spectrum demand of IMT systems in the time span 2010–20. This chapter gives background information on the ITU-R activities that led to the decisions on new spectrum identifications for IMT systems taken at WRC-07. The chapter introduces the Radio Technologies and Concepts for IMT-Advanced Martin D¨ottling, Werner Mohr and Afif Osseiran C 2009 Martin D¨ ottling, Werner Mohr, Afif Osseiran

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role of the ITU-R in the spectrum regulatory framework and reviews the ITU-R preparatory work for WRC-07 on the frequency-related matters of IMT systems. The main emphasis is on the ITU-R spectrum requirement calculation methodology that predicted the total spectrum demand of IMT systems in 2010 to 2020. The methodology is a result of truly international cooperation and the major parts of the methodology originate from the WINNER project. This chapter reviews the spectrum requirement calculation methodology including the input parameters, processing steps and the software implementation of the methodology that was used to perform the actual calculations for deriving the spectrum demand. Finally, the ITU-R results of the spectrum requirement calculation studies are reviewed together with the final decisions on the spectrum identification for IMT-Advanced made at WRC-07.

12.2 ITU-R Work on Spectrum Requirements of IMT-Advanced 12.2.1 Background and Role of ITU-R The ITU is an international organisation within the United Nations where governments and the private sector coordinate global telecommunication networks and services. The ITU Radiocommunication sector (ITU-R) plays a vital role in the management of finite natural resources: radio-frequency spectrum and satellite orbits. Until 2007, study group 8 of ITU-R (ITU-R SG 8) was responsible for mobile, radiodetermination, amateur, and satellite services. Working party 8F in ITU-R SG 8 (ITU-R WP 8F) was responsible for the terrestrial component of IMT-2000 and IMT-Advanced. Currently, study group 5 of ITU-R (ITU-R SG 5) Terrestrial Services is responsible for systems and networks for fixed, mobile, radiodetermination, amateur and amateur-satellite services; the working party 5D (ITU-R WP 5D) is responsible for IMT systems. ITU-R is the international body which has sole responsibility for defining and recommending the standards for IMT systems. IMT-2000 encompasses a number of mobile communication technologies and their enhancements that fulfil the requirements set by the ITU-R. IMTAdvanced is the ITU-R name defined in [ITU07a] to denote systems beyond IMT-2000. IMT is the root name that encompasses both IMT-2000 and IMT-Advanced systems. The ITU-R is not responsible for the detailed definition of IMT systems – it will be undertaken by external organisations. ITU-R arranges the WRC every three to four years to review and revise the radio regulations that form the international treaty governing the use of the radio-frequency spectrum and satellite orbits. The radio regulations represent the frequency allocations to different services and the rules of using the frequency bands. The WRC is the only authority to decide on frequency allocations and identifications in the radio regulations. For an issue to be considered at the WRC, it needs to be included on the WRC agenda, which is approved at the preceding conference. Proposals for an item to be included on the WRC agenda need to be discussed well before the preceding conference. A conference preparatory meeting (CPM) is organised before the WRC to prepare a consolidated report to support the work of the WRC. Due to the above procedure, the identification of frequency bands takes at least five to six years from the beginning of the discussions until the identification itself. From the allocation of frequency bands for some services, it usually takes several years until the actual frequency bands can be used for the service. Frequency-related matters must, therefore, be considered well in advance of the actual need for the spectrum bands. Prior to WRC-07, previous conferences (the World Administrative Radio Conference (WARC) in 1992 and WRC-2000 in year 2000) made the following global spectrum

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identifications for IMT-2000: 806/862–960 MHz, 1710–1885 MHz, 1885–2025 MHz, 2110–2200 MHz and 2500–2690 MHz. The need for additional spectrum for IMT-2000 was justified for WRC-2000 with a methodology for estimating the spectrum requirements of the terrestrial component IMT-2000 by the year 2010. The ITU-R methodology developed for the WRC-2000 is presented in [ITU99]. Actual calculations showing the spectrum requirements for IMT-2000 using the methodology, its MS Excel implementation, and the required input parameters are presented in [ITU00]. After the decision on the spectrum identification made at the WRC-2000, the ITU-R continued its work on the spectrum-related topics of IMT systems. [ITU03] defined the framework and objectives of the future development of IMT-2000 and IMT-Advanced, stating that additional spectrum may be needed. The peak aggregate data rate targets for IMT-Advanced were identified in [ITU03] to be 1 Gbps and 100 Mbps for new nomadic/local area wireless access and for new mobile access, respectively. To accommodate the increased data rate requirements, the spectrum bands for IMT-2000 systems are not wide enough. To initiate the process on the spectrum issues for IMT-Advanced, WRC-2000 decided that WRC-2003 should review progress and WRC-07 should make the necessary decisions. The WRC-2003 did review the progress and confirmed that the WRC-07 should have the following Agenda Item 1.4: to consider frequency-related matters for the future development of IMT-2000 and systems beyond IMT-2000 taking into account the results of ITU-R studies in accordance with Resolution 228 (Rev. WRC-03).

12.2.2 ITU-R Preparations for WRC-07 In preparation for Agenda Item 1.4 of WRC-07, the ITU-R WP 8F, which was responsible for the terrestrial component of IMT-2000 and systems beyond at that time, conducted several studies and developed the following documents:

r Report ITU-R M.2072, World mobile telecommunication market forecast [ITU06a]; r Report ITU-R M.2074, Radio aspects for the terrestrial component of IMT-2000 and systems beyond IMT-2000 [ITU06b];

r Recommendation ITU-R M.1768, Methodology for calculation of spectrum requirements for the future development of the terrestrial component of IMT-2000 and systems beyond IMT-2000 [ITU06c]; r Report ITU-R M.2078, Spectrum Requirements for the future development of the IMT-2000 and IMT-Advanced [ITU06d]; r Report ITU-R M.2079, Technical and operational information for identifying spectrum for the terrestrial component of future development of IMT-2000 and IMT-Advanced [ITU06e]. The WINNER project developed a tool [WIN06] that implemented the spectrum requirement calculation methodology [ITU06c]. The tool is publicly available on the ITU web site and was used to perform the calculations for [ITU06d]. The relationships between the ITU-R documents are shown in Figure 12.1. The results of the ITU-R studies were collected into the CPM report [ITU07b] that was the major input to WRC-07. The spectrum requirement calculation methodology was developed in the Future Service & Market Aspects (WG SERV) and Spectrum (WG SPEC) working groups of ITU-R WP 8F and presented in [ITU06c]. It is described in detail in [TW08].

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WG SERV Rep. ITU-R M.2072 World mobile telecommunication market forecast

Rec. ITU-R M.1768 Methodology for calculation of spectrum requirements for IMT-Advanced

WG TECH Rep. ITU-R M.2078 Estimated spectrum requirements for IMT-Advanced

Rep. ITU-R M.2074 Radio aspects for IMT-Advanced

Rep. ITU-R M.2079 Technical and operational information for identifying bands for IMT-Advanced

‘SPECULATOR’ Tool for estimating spectrum requirements for IMT-Advanced

CPM Report to WRC-07

WG SPEC Figure 12.1 Relationships between ITU-R documents.

The starting point for all spectrum considerations for the future development of IMT-2000 and IMT-Advanced are the market expectations for wireless communications services between 2010 and 2020. ITU-R WP 8F sent out a questionnaire on services and the market for IMT systems and other mobile systems in 2004. The responses to the market questionnaire were collected into [ITU06a] which was developed by the WG SERV of ITU-R WP 8F. The report provides a summary of the market analysis and forecasts of the evolution of the mobile market and services for the future development of IMT-2000, IMT-Advanced, and other wireless systems in 2010–20. The report includes market study results provided by approximately 30 administrations and organisations, and shows strong growth in the wireless market. The report provides examples of potential services and applications for the future development of IMT2000 and IMT-Advanced. One section of the report provides calculated traffic values in 2010, 2015, and 2020 to be used as input to the spectrum requirement calculation methodology. The spectrum requirement calculation methodology uses a limited set of radio-related parameters as input to the calculations. It takes a technology-neutral approach in the technical studies of radio access techniques, and uses the classification of radio access technique (RAT) groups defined in [ITU06b] developed by the Working Group Technology (WG TECH) of ITU-R WP 8F. The technical input parameters to the spectrum calculations are described in [ITU06b] and their meaning and role is further explained in [TW08]. Through the RAT group approach, the technical considerations for spectrum requirement calculation can be conducted without referring to the detailed specification of individual radio interfaces of existing and future mobile systems.

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The requirements set for the spectrum calculation methodology demand that the software implementation is suitable for use during the meetings of ITU-R WP 8F and other groups, in terms of the computing facilities needed and the time required to perform the analysis. The methodology is, therefore, implemented in MS Excel to allow wide usability of the tool and transparency in the calculations [WIN06]. Results of the spectrum calculation studies conducted by ITU-R WP 8F using the calculation methodology and MS Excel tool have been collected in [ITU06d]. The report defines values for all the input parameters needed in the calculations and provides the final estimates of the spectrum requirements of IMT systems in 2010–2020. The report considers two sets of market scenarios: the lower user density market setting and the higher user density market setting. The predicted total spectrum requirement for both existing mobile cellular systems, including pre-IMT-2000 and IMT-2000 and its enhancements, and IMT-Advanced for the year 2020 were calculated for low and high user density settings to be 1280 MHz and 1720 MHz, respectively. Therefore, the report recognised a need to identify additional spectrum for the future development of IMT-2000 and IMT-Advanced at WRC-07, in addition to the bands identified at WARC-1992 and WRC-2000. Possible frequency bands to accommodate the spectrum demand of IMT-Advanced are considered in [ITU06e]. The report evaluates suitable frequency ranges to fulfil the vision for the future development of IMT-2000 and IMT-Advanced, and provides a summary of the investigation on current band usage and sharing study results. The report indicates that the candidate bands should be considered between 400 MHz and 5 GHz. Finally based on the above studies, WRC-07 made global spectrum identifications for IMT systems on the bands 450–470 MHz and 2.3–2.4 GHz. In addition, the bands 698/790– 862 MHz and 3.4–3.6 GHz were identified for IMT systems in some parts of the world.

12.2.3 WINNER Contributions to ITU-R The process of preparation for the WRC in Europe is coordinated by the Conference Preparatory Group (CPG) of the Conference of European Post and Telecommunications (CEPT), where the European decisions on the proposed work for the WRC are taken by national administrations. The national administrations and industries can contribute their inputs to the CPG or to the CEPT groups working on the individual WRC agenda items. However, it is preferable to have common national positions agreed between industry and administrations that are then converted into contributions to the European process to achieve a common European position for the ITU-R and the WRC. In Europe, the work on IMT-2000 and IMT-Advanced is currently carried out in Project Team 1 of the Electronic Communication Committee (ECC PT1). The proposals intended for ITU-R WP 5D (previously, until 2007, ITU-R WP 8F) are contributed to the ECC PT1 with a view to possibly forming common European contributions to the ITU-R. The WINNER project members participated actively in the ITU-R and CEPT preparations for WRC-07 by providing dozens of contributions to ITU-R WP 8F and ECC PT1 in 2004–6. The early contributions in 2004 were mainly targeted at the development of the spectrum calculation methodology [ITU06c] and defining the input parameters for the spectrum calculation methodology [ITU06a; ITU06b]. In essence, most parts of the spectrum calculation methodology originate from the WINNER project. In 2005, the methodology recommendation [ITU06c] was finalised and the WINNER project participants delivered the first version

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of the software tool [WIN06] implementing it. At the same time, discussions on the input parameter values to estimate the spectrum requirements were started and the final results on the spectrum estimation studies [ITU06d] were concluded in May 2006. Finally, the results of the spectrum requirement calculation studies were collected into the CPM report [ITU07b] that was prepared and approved in February/March 2007.

12.3 ITU-R Spectrum Calculation Methodology 12.3.1 Methodology Flow and Definitions The ITU-R spectrum requirement calculation methodology for IMT-2000 [ITU99] developed for WRC-2000 was voice-based, whereas packet-based service delivery will dominate future IMT-Advanced systems. Therefore, the ITU-R methodology for IMT-2000 in [ITU99] is not directly applicable to calculating the spectrum requirements of future developments of IMT2000 and IMT-Advanced. To properly model the future services with packet-based service delivery, the ITU-R developed a new methodology [ITU06c] for calculating the spectrum requirements for the future development of IMT-2000 and IMT-Advanced in the time span 2010–20. The methodology is presented in [ITU06c; WIN1D62] and a thorough treatment of the methodology and related topics is given in [TW08]. In addition, [M07; I09; IW04; IW05; MIH+05; IWT05] are theses and publications on the topic. The ITU-R methodology considers similar scenario elements as discussed in Chapter 2, such as environments, services and traffic parameters, but the definitions are somewhat different due to the adaptation to the ITU framework, scope and terminology. The methodology considers the whole market of wireless services and models both voicebased and packet-based service deliveries. The flow chart of the ITU-R spectrum calculation methodology is shown in Figure 12.2. The methodology follows a deterministic approach starting from the market expectations of wireless communication services and ending in the final spectrum requirements of pre-IMT, IMT-2000, future development of IMT-2000 and IMT-Advanced. The methodology uses a number of input parameters which are listed in Figure 12.3. In the following discussion, the definitions used in the methodology, i.e., services, environments and radio access techniques, are described together with the actual calculation steps.

12.3.1.1 Services The methodology defines service categories (SC) which are combinations of service types and traffic classes. The service types are characterised by the service bit rates and the traffic classes by their sensitivity to delay. The methodology considers five service types, i.e., very low rate data, low rate data and low multimedia, medium multimedia, high multimedia, and super high multimedia, and four traffic classes, i.e., conversational, streaming, interactive, and background. The combinations of service types and traffic classes lead to 20 service categories considered in the methodology. Service categories are delivered using either a circuit-switched (reservation based) or a packet-switched transmission scheme. The traffic classes conversational and streaming (SC 1–10) are assumed to be circuit-switched, while the traffic classes interactive and

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Step 1: Define A) service categories B) service environments C) radio environments D) radio access technique (RAT) groups

Step 2: Analyse collected market data

Step 3: Calculate traffic demand by service categories and service environments Step 4: Distribute traffic between radio environments and RAT groups and calculate offered traffic RAT group 1

RAT group 2

Step 5: Calculate required system capacity to carry offered traffic

RAT groups 3 and 4 Spectrum requirements are not calculated for RAT groups 3 and 4

Step 6: Combine capacity requirements and calculate initial spectrum requirements Step 7: Apply necessary adjustments on spectrum requirements (multiple operators, minimum spectrum) Step 8: Calculate aggregate spectrum requirements of all operators

Step 9: Calculate final spectrum requirements over teledensities

Figure 12.2 Flow chart of spectrum calculation methodology.

background (SC 11–20) are packet-switched. Each service category can include two types of traffic, unicast and multicast traffic. Unicast traffic is communicated between a transmitter and a single receiver, whereas multicast traffic is transmitted to multiple users simultaneously. The methodology considers both traffic types.

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Service categories are characterised with parameters which are obtained either from market studies or from other sources. The following parameters describing the traffic volumes and usage scenarios are obtained from the market study in [ITU06a]:

r user density; r session arrival rate per user; r mean service bit rate; r average session duration; r mobility ratio (i.e., a proportion of users for the classes stationary, low, high, and super high). Other service category parameters describing the packet structure and quality of service (QoS) criteria include mean packet size, second moment of packet size, maximum allowable mean packet delay, and maximum allowable blocking probability. The service category parameters are listed in the first two columns in Figure 12.3.

Market study parameters

Other service category parameters

User density ) (users/

Mean packet size (bit/packet)

Session arrival rate per user (sessions/s/user)

Radio-related parameters

Other parameters

Cell area /cell)

Population coverage percentage (%)

Second moment of packet size ) (

Spectral efficiency (bit/s/Hz/cell)

Traffic distribution ratio among available RATGs

Average session duration (s/session)

Maximum allowable mean packet delay (s/packet)

Number of overlapping network deployments

Mean service bit rate (bit/s)

Maximum allowable blocking probability (%)

Minimum spectrum deployment (MHz)

Mobility ratio (stationary, low, high, and super-high) Traffic volume calculation

Capacity calculation

(

Guard band between operators (MHz)

Radio environment definition

RATG 1 and RATG 2

Application data rate (bit/s) Support for multicast (yes/no)

RATG 3 and RATG 4

Maximum supported velocity (km/h)

Figure 12.3 Input parameters for spectrum calculation methodology, from [TW08]. (Takagi & Walke (Eds.) Spectrum Requirement Planning in Wireless Communications: Model and methodology for IMTAdvanced. Reproduced by Permission of John Wiley and Sons, © 2008).

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Teledensity Service usage pattern

Dense urban

Suburban

Home

SE 1

SE 4

Office

SE 2

Rural

SE 6 SE 5

Public area

SE 3

Figure 12.4 Service environments and teledensities.

12.3.1.2 Environments A service environment (SE) is a combination of teledensity and service usage pattern. Teledensities describe the population density in different areas while service usage patterns describe the user behaviour. The methodology considers three teledensities (dense urban, suburban and rural) and three service usage patterns (home, office, and public area). Teledensities are geographically non-overlapping areas but several service usage patterns can coexist in a given teledensity resulting in possibly several service environments in a given teledensity. The methodology considers six service environments as shown in Figure 12.4. The market data is given per service environment while the spectrum requirements are calculated per teledensity. Radio environments (RE) are characterised as the cell layers of a hierarchical network. The methodology considers four radio environments, i.e., macro cell, micro cell, pico cell, and hot spot. Radio environments are areas exhibiting common propagation and deployment conditions. The deployment of radio environments depends on the service environments so that only certain radio environments are available in a given service environment. In practise, the total area of a service environment is covered by the radio environment only up to a certain percentage denoted as the population coverage percentage. The population coverage percentage represents the ratio of population that is in the service area of the given radio environment in the given service environment. The radio environments are characterised by the cell area (km2 /cell) and population coverage percentage (%) parameters shown in Figure 12.3. 12.3.1.3 Radio Access Technique Groups The methodology takes into account the total mobile telecommunication market provided by various communication means according to [ITU03]. A key requirement for the methodology was to be technology neutral and generic, and therefore the individual radio access techniques (RAT) are grouped into four RAT groups presented in [ITU06b]:

r Group 1: Pre-IMT systems, IMT-2000 and its enhancements; r Group 2: IMT-Advanced (e.g., new mobile access and new nomadic or local area wireless access);

r Group 3: Existing radio LANs and their enhancements; r Group 4: Digital mobile broadcasting systems and their enhancements.

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The RAT group approach allows the conduct of spectrum calculation studies without considering the detailed specifications of the individual existing and future radio access techniques. The RAT groups are characterised by the limited sets of radio parameters needed in the calculations. All four RAT groups are considered in the first four steps of the methodology flow, i.e., up to and including the distribution of traffic among radio environments and RAT groups. From step 5 onwards, only RAT groups 1 and 2 are considered in the calculation of capacity and spectrum requirements. The required radio parameters to characterise the RAT groups are shown in the third column in Figure 12.3. RAT groups 1 and 2 require more radio parameters as the final spectrum requirements are only calculated for these two RAT groups. The ranges of values for radio parameters developed by ITU-R WP 8F are given in [ITU06b] and the ITU-R agreed values eventually used in the spectrum requirement calculation studies are shown in [ITU06d].

12.3.2 Traffic Calculation and Distribution The second and third steps in the methodology flow are to analyse the collected market data and to calculate the traffic demand. The collected market data in [ITU06a] presents traffic forecasts of future mobile communications in different parts of the world in 2010, 2015, and 2020. The market parameters listed in the second column in Figure 12.3 are needed for different service categories in different service environments, and forecast years. Separate values are provided for uplink (UL) and downlink (DL) directions. The market study parameter values are further processed to obtain suitable input traffic parameter values used in the methodology. The market study traffic volumes in the individual market studies, which were obtained as responses to a questionnaire on services and markets sent out by the ITU-R in 2004, are given for different services inside the service categories. Therefore, the traffic values per service must be processed to obtain traffic values per service category, which is done in [ITU06a]. Moreover, the individual market studies include different values for the market study parameters and the combined market study [ITU06a] gives the traffic values as ranges of values to represent all the responses to the questionnaire on services and markets, while the actual spectrum requirement calculations require single values within the ranges. In addition, the mobility ratio parameters from [ITU06a] need to be further processed to obtain suitable input parameters for the spectrum calculation methodology [ITU06c]. The fourth step in the methodology flow is to distribute the traffic of each service category in each service environment and forecast year to different radio environments and RAT groups by computing the traffic distribution ratios. The availability of different radio environments and RAT groups in different service environments and forecast years is first determined by comparing the mean service bit rate requirement of the service category in the service environment with the application data rate of the RAT group in the given radio environment. Moreover, the fact that not all radio environments are available in all RAT groups or all service environments is taken into account. After identification of possible combinations of service categories, service environments, radio environments, and RAT groups, the traffic is distributed among the radio environments based on the population coverage percentage in the service environment and the mobility ratios of the service category in the given service environment. The mobility ratios are taken into account in the traffic distribution among radio environments to represent the inability of small cells to support very high velocities. The population coverage percentages are taken into

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account to characterise the possibly limited coverage of the different cell types in different environments. After distribution between the radio environments, the traffic is further distributed among available RAT groups inside the radio environments, with predetermined distribution ratios among available RAT groups (see the fourth column in Figure 12.3) which are major input parameters to the methodology. Traffic distribution is performed separately for UL and DL directions. The above traffic distribution considers unicast traffic, while separate distribution ratios are derived for multicast traffic without considering mobility or population coverage percentages. In each available RAT group, all multicast traffic in a service category is carried by the available radio environment that uses the largest cells to provide coverage. After determining the traffic distribution ratios, the traffic values of the different service categories in different service environments can be calculated for the different radio environments and RAT groups. The traffic values are calculated separately for circuit-switched and packet-switched service categories, which are modelled with different parameters due to their different characteristics. Moreover, the traffic volumes in different service environments that belong to the same teledensity as shown in Figure 12.4 are aggregated at this point. For circuitswitched service categories, the traffic calculation gives the offered traffic, in Erlang/cell, and the mean service bit rate, in bps, of each service category in different radio environments, teledensities, RAT groups and forecast years. For packet-switched service categories, the traffic calculation provides the offered traffic per cell in bps/cell for each service category in different radio environments, teledensities, RAT groups and forecast years.

12.3.3 Capacity Requirement Calculation Step 5 in the methodology flow is to determine the system capacity in bps/cell required to carry the offered traffic of the given RAT group in the given radio environment, teledensity, and forecast year. Separate algorithms are used for circuit-switched and packet-switched service categories in order to properly model the services with different characteristics and QoS requirements. Some service categories require guaranteed bit rates and they are treated in the network more in circuit-switched manner. Therefore, they are modelled using Erlang-based formula. Other service categories have more truly packet-oriented nature in the network and thus a queuing model is applied. The capacity requirements are calculated separately for unicast and multicast traffic, and for UL and DL directions. The major aspect in the capacity calculation algorithms is that they take into account the gain in multiplexing among different circuitswitched SCs, as well as among packet-switched SCs with different QoS characteristics. The capacity requirement for circuit-switched service categories is calculated based on the multi-dimensional Erlang-B theory. The capacity calculation uses the following input parameters of the different service categories:

r offered traffic per cell in Erlang/cell; r mean service bit rate in bps; r maximum allowable blocking probability. The multi-dimensional Erlang-B theory is an extension of the traditional Erlang-B formula which takes into account the trunking gain and allows simultaneous occupation of several

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channels by each call. The required system capacity of all circuit-switched service categories is calculated based on the number of required channels to meet the blocking probability criteria with the given traffic loads and the mean service bit rate per channel. The capacity requirement for packet-switched service categories is calculated based on M/G/1 nonpreemptive priority queue [K76] using the following input parameters of the service categories:

r offered traffic per cell in bps/cell; r maximum allowable mean packet delay; r mean packet size; r second moment of packet size. In the M/G/1 model, a single server is used to serve the packets and the next packet to be served from the queue is the one with the highest priority. Inside a priority level, the firstcome, first-served (FCFS) principle is used. Nonpreemptive service means that the service is not disrupted before completion even if a higher priority packet arrives during the service. The capacity requirement is calculated with Cobham’s formula [C54], which is used to present the mean delay, i.e. time from packet arrival until service completion, in terms of arrival rate, mean packet size, second moment of packet size, and system capacity. The capacity requirement is calculated separately for each service category using its QoS criterion taking into account the total traffic from all service categories. Each calculation derives the capacity that is required to carry the traffic from all service categories subject to the mean delay requirement of the current service category. The calculation finds the service category that requires the highest capacity to fulfil its own delay requirements considering the whole aggregate traffic and uses this capacity requirement to represent the capacity requirement of all packet-switched service categories.

12.3.4 Spectrum Requirement Calculation In Step 6 of the methodology flow, the capacity requirements of packet-switched and circuitswitched service categories for unicast and multicast traffic in RAT groups 1 and 2 are further processed to obtain spectrum requirements. First, the capacity requirements of the UL and DL directions are summed. After that the capacity requirements of packet-switched and circuitswitched service categories are summed. The corresponding capacity requirements of unicast and multicast traffic are divided by their spectral efficiency values to get initial spectrum requirements which are then summed. Step 7 applies adjustments to the initial spectrum requirements with the number of overlapping network deployments and the minimum spectrum deployment per operator per radio environment. First, the spectrum requirement per operator is calculated by dividing the spectrum requirement by the number of overlapping network deployments. The spectrum requirement per operator in each radio environment is then adjusted to be an integer multiple of the minimum deployment per operator per radio environment required to build a practical network deployment. In Step 8 of the methodology flow, the spectrum requirements are aggregated over the radio environments and operators, taking into account the guard bands between operators. The

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spectrum requirements per operator are aggregated over the radio environments assuming that the pico cells and hot spots are spatially non-coexisting and can use the same frequencies, while the macro cells and micro cells are spatially coexisting with the pico cells and hot spots and use different frequencies. The total required spectrum for all operators is obtained by multiplying the spectrum requirement per operator with the number of overlapping network deployments and adding the necessary guard bands between operators. Finally, in Step 9, the spectrum requirements of RAT groups 1 and 2 in the three forecast years are taken as maximum over the teledensities, i.e. dense urban, suburban and rural. The spectrum requirements are given separately for the two RAT groups in the three forecast years 2010, 2015 and 2020.

12.3.5 Summary of Methodology The spectrum requirement calculation methodology described above calculates the spectrum requirements for RAT group 1 (i.e., pre-IMT, IMT-2000, and further development of IMT2000) and RAT group 2 (i.e., IMT-Advanced) in 2010, 2015, and 2020. The methodology accommodates a complex mixture of services from market studies using service categories with different traffic volumes and QoS constraints. The methodology is technology neutral and handles emerging, established and future systems using a RAT group approach with a limited set of radio parameters. The four RAT groups considered cover all relevant radio access technologies supporting the future mobile telecommunication market without considering the detailed specifications of the individual radio access technologies in the RAT groups. The methodology distributes traffic from the market studies to four RAT groups and four radio environments using technical and market related information that characterise the user requirements as well as the capabilities of the technologies. For the traffic distributed to RAT groups 1 and 2, the methodology transforms the traffic volumes into capacity requirements, using separate algorithms for packet-switched and circuit-switched service categories taking into account the gain in multiplexing among circuit-switched service categories, as well as among packet-switched service categories with differing QoS characteristics. The capacity requirements are processed with spectral efficiency values to get initial spectrum requirements. The methodology considers factors relevant for practical network deployments to adjust the spectrum requirements, and calculates the final spectrum requirements of RAT groups 1 and 2 in the three forecast years. Figure 12.5 shows an example of the spectrum calculation with six service environments, three radio environments, and two RAT groups. The first row shows the traffic in each service environment. In the second row the traffic in each service environment is divided among the radio environments. In the third row, the traffic in each radio environment is further distributed among the RAT groups. The fourth row shows the resulting traffic in different service environments, radio environments, and RAT groups. The distributed traffic for the service environments belonging to the same teledensity is accumulated in the fifth row of Figure 12.5. The spectrum requirements are then calculated in the sixth row by using the capacity requirements which are derived from the traffic volumes, and the spectral efficiencies. The rectangles shown in the sixth row represent the spectrum requirements of RAT groups in different radio environments and teledensities. The final spectrum requirement of the RAT group is the maximum among the teledensities which is shown in the seventh row.

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Traffic in service environments

Dense urban home SE 1

Dense urban office SE 2

Dense urban public area SE 3

Sub-urban home SE 4

Sub-urban office/public area SE 5

Rural SE 6

Traffic distribution among radio environments

Traffic distribution among RATGs

RATG 1 RATG 2

Traffic aggregation over service environments RATG 1

RATG 2

Spectrum requirement per radio environment, RATG and teledensity

Micro cell RATG 2

Pico cell

Macro cell RATG 1

Maximum over teledensities

Spectrum requirement of RATG 1

Spectrum requirement of RATG 2

Figure 12.5 Traffic distribution and spectrum requirement calculation, from [TW08]. (Takagi & Walke (Eds.) Spectrum Requirement Planning in Wireless Communications: Model and methodology for IMTAdvanced. Reproduced by Permission of John Wiley and Sons, © 2008).

12.4 Software Implementation of Methodology A software implementation of the spectrum requirement calculation methodology presented in Section 12.3 has been produced in the WINNER project with valuable help from the mobile IT Forum (mITF) from Japan. The tool has been implemented in MS Excel to allow wide usability and transparency in calculations. The tool is publicly available on the ITU-R web site [WIN06]. The following subsections describe the structure and use of the software calculation tool.

12.4.1 Description and Use of Software Tool The MS Excel tool for calculating the spectrum requirements consists of 27 worksheets and seven modules of macros. The worksheets include input parameter values, intermediate calculation results from spreadsheet calculations and macro calculations, and final spectrum

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Figure 12.6 Front sheet of the software implementation of the ITU-R methodology, from [TW08]. (Takagi & Walke (Eds.) Spectrum Requirement Planning in Wireless Communications: Model and methodology for IMT-Advanced. Reproduced by Permission of John Wiley and Sons, © 2008).

requirements with some charts. The core of the tool is the front sheet which is shown in Figure 12.6. The front sheet, called ‘Main’, consists of six parts: ‘Major parameters’, ‘Calculate the spectrum requirement’, ‘Output’, ‘Warnings’, ‘Time-shifted spectrum requirement’, and ‘Unadjusted spectrum requirement’. The major parameters section includes two parameters that have a large effect on the spectrum requirements, i.e., the number of network deployments and the cell area. The calculation section includes seven buttons for performing different calculation steps, and a button for performing all the calculations steps. The processing status is also

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shown. The output section shows a table of the final spectrum requirements of RAT groups 1 and 2 in 2010, 2015, and 2020. The warnings section shows possible errors in the calculations. The time-shifted spectrum requirement section presents the final spectrum requirements of RAT groups 1 and 2 in the ‘time-shifted’ format, which defines three development scenarios (fast, medium and slow deployment) that reflect regional differences in market development and the deployment of new systems. The unadjusted spectrum requirement section gives the intermediate spectrum requirement results for RAT groups 1 and 2 in different teledensities, radio environments, and forecast years before any adjustments are made. The tool is executed from the front sheet. Each of the seven buttons in the front sheet executes different macros or the Run All button can be used to perform the calculations at once. The individual buttons for performing the calculations are ‘Reset’, ‘Read Input Values’, ‘Distribute Traffic’, ‘PS-Capacity’, ‘CS-Capacity’, ‘Apply Adjust.’, and ‘Spectrum Req.’. The macros associated with the buttons are collected in the module ‘MainModule’. The Reset button resets the values in the worksheets to initialise the calculations. The Read Input Values button calls the macros in the ReadInput module to read the input market parameter values from the market studies. The Distribute Traffic button calls the macros in the DistributionModule and the OutputTrafficModule to distribute and calculate the traffic volumes. The PS-Capacity button calls the macros in the PSCapacityModule and calculates the capacity requirements for packet-switched service categories. The CS-Capacity button calls the macros in the CSCapacityModule and calculates the capacity requirements for circuitswitched SCs. The Apply Adjust. button applies adjustments to the spectrum requirements on the worksheet without macros. Finally, the Spectrum Req. button calculates the final spectrum requirements and displays them on the front sheet.

12.4.2 Inputs to Software Tool The input parameters for the calculations are given in worksheets 2–13 of the MS Excel tool. The tool includes the input parameter values from [ITU06d] where two market scenarios are considered: higher and lower user density settings. The higher user density setting is used to characterise countries where IMT-2000 has already been widely deployed and broadband services are popular. The lower user density setting is used for countries where the deployment of IMT-2000 has just started. The default scenario in the tool is the higher user density setting while the lower user density setting can be applied by changing the values of the market parameter ‘user density’ and the radio parameter ‘spectral efficiency’ that are provided in worksheets two and five, respectively. In this section, the worksheets including the different input parameters are explained in more detail. The ‘Market-Setting’ worksheet contains the year selector and the market attribute settings which choose the considered forecast years and the actual values for the market parameters within the ranges given in [ITU06a]. The individual values within the ranges are chosen by defining percentages for each service category and forecast year so that 0 % represents the lower limit and 100 % the upper limit of the range. The percentage is given to the user density, session arrival rate per user, mean service bit rate, and average session duration while the mobility ratios are selected from three sets of mobility scenarios including the lowest, middle and highest mobility. The ‘RATG-DistRatio-Input’ worksheet includes the values for the distribution ratios among available RAT groups which are used to distribute the traffic among RAT groups inside the radio environments.

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The ‘SE-Input’ worksheet gives the mapping of radio environments and service environments, i.e. it shows the availability of different radio environments in different service environments in three forecast years. The worksheet also contains the values for the population coverage percentage parameter. The ‘RATG1&RATG2Def-Input’ worksheet includes the radio parameters for RAT groups 1 and 2. The application data rate parameter can be used to turn off certain RAT groups in certain forecast years to model the availability of the mobile communications systems. The ‘RATGEFF-Input’ worksheet includes the spectral efficiency values for RAT groups 1 and 2 in different radio environments and teledensities. Separate tables are given for the three forecast years and for unicast and multicast traffic to model the evolution of mobile systems. The ‘RATG3&4Def-Input’ worksheet includes the radio parameters for RAT groups 3 and 4, which are fewer than those for RAT groups 1 and 2 since the spectrum requirements are eventually calculated only for RAT groups 1 and 2. The ‘RATGBearer-Input’ worksheet shows the fixed mapping of service categories to circuit or packet-switched transmission schemes. The ‘SCategory-Input’ worksheet gives the parameters needed for the service categories which are not provided by the market study, i.e., the mean packet size, the second moment of packet size, the maximum allowable mean packet delay, and the maximum allowable blocking probability. The results of the market studies from [ITU06a] for the three different forecast years are collected in the worksheets ‘Market-Input 2010’, ‘Market-Input 2015’, and ‘MarketInput 2020’ (sheets 10 to 12). The worksheets show the upper and lower limits from the market studies, and the actual values within the range which are selected by defining the percentages in the worksheet ‘Market-Setting’. The values are given separately for the UL and DL directions, and unicast and multicast traffic.

12.4.3 Intermediate Calculations and Outputs from Software Tool The intermediate calculation steps are performed in worksheets 14–26 using the input parameter values from worksheets 2–13. Worksheets 14–16 perform the intermediate calculations on the market parameters, worksheets 17–19 calculate the traffic distribution ratios, worksheets 20–23 distribute the traffic among the radio environments and RAT groups according to distribution ratios, worksheets 24–25 perform the capacity requirement calculations with the calculated traffic values and QoS criteria, and finally worksheets 26–27 calculate the spectrum requirements. The ‘AreaArrivalRate’ worksheet (sheet 14) shows the session arrival rate per area in session arrivals/s/km2 as the product of the user density and the session arrival rate per user from the ‘Market-Studies’ worksheet. The values are given for unicast traffic for the 20 service categories in six service environments in three forecast years and separately for UL and DL. The ‘SessionVolume’ worksheet (sheet 15) gives the traffic volume of one session in kbit/session as the product of the average session duration and the mean service bit rate from the ‘MarketStudies’ worksheet. The ‘AreaTrafficVolume’ worksheet (sheet 16) gives the traffic volume per area in kbps/km2 as the product of the session arrival rate per area from the ‘AreaArrivalRate’ worksheet and the volume of one session from the ‘SessionVolume’ worksheet. The ‘Dist-Ratio-Input’ (sheet 17) worksheet starts the traffic distribution functionality, i.e., Step 4 of the methodology flow. The actual calculations for deriving the traffic distribution ratios are done with the macros given in the module ‘DistributionModule’. The ‘Dist-RatioInput’ worksheet gives the supported radio environments in different service environments in

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different RAT groups and forecast years, as well as the supported service categories in different radio environments in different RAT groups, service environments, and forecast years. The ‘Dist-comb’ worksheet (sheet 18) shows the possible combinations of service categories, radio environments, and RAT groups in different service environments and forecast years obtained from the ‘Dist-Ratio-Input’ worksheet. The ‘Dist-Ratio-Matrix’ worksheet (sheet 19) gives the final distribution ratios which are used to split the traffic among the different radio environments and RAT groups. A checking function is also performed to find out if all traffic can be distributed. The calculation of the traffic values of different service categories in different service environments, radio environments and RAT groups using the traffic distribution ratios is performed with the macros given in the module ‘OutputTrafficModule’. The traffic values after the distribution step are written to the ‘PSTraffic-op’ and ‘CSTraffic-op’ worksheets for the packet-switched and circuit-switched service categories, respectively. The ‘PSTrafficop’ worksheet (sheet 20) gives the traffic per cell in kbps/cell for packet-switched service categories for RAT groups 1 and 2 in different service environments, radio environments and forecast years while the ‘CSTraffic-op’ worksheet (sheet 21) gives the traffic per cell for circuit-switched service categories in session arrivals/s/cell. The accumulation of traffic over the service environments belonging to the same teledensity is done with the macros from ‘OutputTrafficModule’ in the ‘PSTraffic-op-teledensity’ and ‘CSTraffic-op-teledensity’ worksheets for packet-switched and circuit-switched service categories, respectively. The ‘PSTraffic-op-teledensity’ worksheet (sheet 22) gives the traffic in kbps/cell for packet-switched service categories in RAT groups 1 and 2 in different teledensities, radio environments and forecast years while the ‘CSTraffic-op-teledensity’ worksheet (sheet 23) gives the traffic for circuit-switched service categories in Erlang/cell as well as the mean service bit rate in kbps. The ‘PSCapacity calculation’ worksheet (sheet 24) performs the capacity calculations for packet-switched service categories using the M/G/1 nonpreemptive queuing model with the macros given in the module ‘PSCapacityModule’. The ‘CS-CapacityCalc’ worksheet (sheet 25) performs the capacity calculations for circuit-switched service categories using the multidimensional Erlang-B formula with the macros given in the module ‘CSCapacityModule’. The capacity calculations derive the required system capacities in kbps/cell for packet-switched and circuit-switched service categories for RAT groups 1 and 2 in different teledensities, radio environments and forecast years. The ‘Spectrum requirement’ worksheet (sheet 26) calculates the spectrum requirements of RAT groups 1 and 2 in different radio environments, teledensities, and forecast years. The capacity requirements from ‘PS-CapacityCalc’ and ‘CS-CapacityCalc’ worksheets are first combined and divided by the corresponding spectral efficiency values from the ‘RATGEffInput’ worksheet. The resulting spectrum requirements of unicast and multicast transmission modes are then combined. As output, the MS Excel tool calculates the final spectrum requirements of RAT groups 1 and 2 in 2010, 2015, and 2020. The final spectrum requirement results are calculated on the last worksheet ‘Adjs&AggSpectrum’ (sheet 27) and shown also on the front sheet ‘Main’. The spectrum requirements are adjusted for the number of network deployments from the front sheet and combined over the radio environments. Also the minimum deployments and guard bands from the ‘RATG1&RATG2Def-Input’ worksheet are taken into account. The final spectrum requirements for RAT groups 1 and 2 are taken as maximum over the teledensities.

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The spectrum requirement calculation tool also provides the spectrum requirements of the two RAT groups in time-shifted format. The time-shifted format defines three development scenarios, i.e., fast, medium, and slow deployment, to characterise the differences in the market development and the deployment of RAT groups in different countries. The medium deployment scenario represents the average global common market situation and is directly obtained as output from the calculations performed in the tool. The fast deployment scenario illustrates countries where the new systems are introduced quickly and the usage of the new services develops rapidly. The spectrum requirements of the fast deployment scenario in 2010 and 2015 correspond to the spectrum requirements of the medium scenario in 2015 and 2020. The slow deployment scenario illustrates countries which employ the new systems when they are at mature state. The spectrum requirements of the slow deployment scenario in 2010 and 2015 correspond to the spectrum requirements of the medium scenario in 2015 and 2020.

12.5 Estimated Spectrum Requirements of IMT-Advanced The WRC is the only forum for identifying global spectrum for IMT systems. Internationally agreed frequency bands encourage the wide adoption of future IMT-Advanced systems and provide the premises to start the standardisation activities to define the systems in more detail. To obtain global harmonisation in spectrum use, it is important to align all the world regions1 along with national interests. The goal of globally harmonised spectrum ensures global roaming and reduction in equipment cost. The ITU-R estimated the spectrum requirements of pre-IMT, IMT-2000 and IMT-Advanced in [ITU06d] using the spectrum requirement calculation methodology from [ITU06c] and the tool from [WIN06]. The calculations were done for two market setting scenarios representing lower and higher user densities based on the market data presented in [ITU06a]. The estimated total spectrum requirements for RAT groups 1 and 2 for the year 2020 were calculated to be 1280 MHz and 1720 MHz for the lower and higher user density market settings, respectively. The values included the spectrum already in use, or planned to be used, for RAT group 1, i.e., 693 MHz in Region 1, 723 MHz in Region 2, and 749 MHz in Region 3. The spectrum requirements were calculated assuming one network deployment. If there are several parallel network deployments in the country that do not share the spectrum, the spectrum requirements are higher. There are regional differences in the estimated spectrum requirements indicating that some countries predicted their spectrum demand to be lower than the lower user density market setting value or higher than the higher user density market setting value. The spectrum requirements of IMT-Advanced arise from two aspects. Firstly, the market studies of future mobile telecommunication services shown in [ITU06a] predict a strong growth in the per user and aggregate traffic volumes of wireless services in the time span 2010–20. To accommodate the growing traffic demands, the spectrum previously identified for IMT2000 will become scarce. Secondly, the data rate requirements of IMT-Advanced described in [ITU03] result in the need for larger bandwidths, even with significant improvement in the systems’ spectral efficiency. Therefore, new spectrum identifications are needed to realise the envisaged capabilities of IMT-Advanced.

1 The ITU divides the world into three regions that are roughly defined as Region 1: Europe, Africa and the Middle East; Region 2: Americas and Pacific Islands; and Region 3: Asia and Oceania.

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The candidate spectrum bands to accommodate the spectrum demand for the terrestrial component of IMT-2000 and IMT-Advanced were identified in [ITU06e] to include 410–430 MHz, 450–470 MHz, 470–806/862 MHz, 2.3–2.4 GHz, 2.7–2.9 GHz, 3.4–4.2 GHz, and 4.4–4.99 GHz. Currently, these bands accommodate various systems and services as described in [ITU06e] and therefore are not readily available for worldwide deployment of IMT-2000 and IMT-Advanced. In November 2007, WRC-07 made a decision on the spectrum identification for IMT systems. It identified the following new spectrum bands for IMT systems:

r 450–470 MHz globally r 698–806 MHz in Region 2 and nine countries in Region 3 r 790–862 MHz in Regions 1 and 3 r 2.3–2.4 GHz globally r 3.4–3.6 GHz in a large number of countries in Regions 1 and 3. The spectrum identifications made at WRC-07 do not provide the amount of spectrum that was estimated by the ITU-R in preparation for WRC-07, which implies a need for intelligent frequency management schemes that enable flexible spectrum use and spectrum sharing as discussed in Chapter 11.

12.6 Conclusion The spectrum considerations represent an important step in the process of defining and standardising the future systems. Due to the current spectrum regulatory framework where most of the spectrum is already allocated to different services, the spectrum functionalities will play a key role in the development of future wireless systems. The ITU-R identified additional spectrum for IMT systems at the WRC-07 in November 2007 to supplement the spectrum identifications made for IMT-2000 at WARC-92 and WRC-2000. The spectrum identification for IMT systems at WRC-07 was based on extensive research efforts that calculated the total spectrum demand of pre-IMT, IMT-2000 and IMT-Advanced systems in the time span 2010–20. This chapter reviewed the ITU-R preparatory work for WRC-07 on the frequency matters of IMT systems with an emphasis on the developed ITU-R spectrum requirement calculation methodology for IMT-Advanced systems. The ITU-R spectrum requirement calculation methodology for IMT-Advanced is technology neutral and generic and uses the notion of RAT groups to consider the individual radio access technologies with limited sets of radio parameters. The methodology considers market studies predicting the traffic volumes of the future mobile telecommunication market in 2010–20 and divides the predicted traffic among different RAT groups and cell layers. The methodology calculates system capacity requirements based on the traffic volumes and QoS criteria and handles packet-based and circuit-based traffic separately due to their different characteristics. The spectrum requirements of IMT systems in 2010–20 are derived from the capacity requirements by using spectral efficiency values and adjustments with practical network deployments. The spectrum requirements of the IMT systems in 2020 including pre-IMT, IMT-2000 and IMT-Advanced estimated by the ITU-R ranged from 1280 MHz to 1720 MHz while the actual spectrum identifications for IMT systems including the outcome of WRC-07 were lower than

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that. Therefore, the spectrum identifications made at WRC-07 imply that flexible spectrum use and spectrum sharing mechanisms will be important elements in future wireless systems, as discussed in Chapter 11.

Acknowledgements The spectrum requirement calculation activities described in this chapter were conducted in close cooperation between the WINNER spectrum team and the mobile IT Forum (mITF) from Japan. The authors are deeply grateful to Hitoshi Yoshino, Naoto Matoba, Hideaki Takagi, Mitsuhiro Azuma, Kohei Satoh, Motoi Shiraishi, Hajime Nakamura, and Takefumi Yamada from mITF, Euntaek Lim from Korea and Volker Reitz from Germany for the successful cooperation at various stages of the spectrum-related work on IMT-Advanced. The authors would also like to acknowledge the continuous support from their colleagues in the WINNER project, in particular Bernhard H. Walke, Werner Mohr, Shyamalie Thilakawardana, Martin D¨ottling, Claes Eriksson, Miia Mustonen, Johan Axn¨as, Antti Lappetel¨ainen, Stephan Pfletschinger, and Carl Wijting.

References [C54] [I09] [ITU00] [ITU03] [ITU06a] [ITU06b] [ITU06c]

[ITU06d] [ITU06e]

[ITU07a] [ITU07b] [ITU99] [IW04]

[IW05]

Cobham, A. (1954) ‘Priority assignments in waiting line problems’, Operations Research, 2(1): 70–76. Irnich, T. (2009) ‘A new methodology for radio spectrum requirement prediction of wireless communication systems’, Ph.D. thesis, RWTH Aachen University, Germany. ITU-R (2000) Spectrum requirements for International Mobile Telecommunications-2000 (IMT-2000), Report ITU-R M.2023, International Telecommunication Union, Geneva. ITU (2003) Framework and overall objectives of the future development of IMT-2000 and systems beyond IMT-2000, Recommendation ITU-R M.1645, International Telecommunication Union, Geneva. ITU-R (2006) World mobile telecommunication market forecast, Report ITU-R M.2072, International Telecommunication Union, Geneva. ITU-R (2006) Radio aspects for the terrestrial component of IMT-2000 and systems beyond IMT-2000, Report ITU-R M.2074, International Telecommunication Union, Geneva. ITU-R (2006) Methodology for calculation of spectrum requirements for the future development of the terrestrial component of IMT-2000 and systems beyond IMT-2000, Recommendation ITU-R M.1768, International Telecommunication Union, Geneva. ITU (2006) Spectrum requirements for the future development of the IMT-2000 and IMT-Advanced, Report ITU-R M.2078, International Telecommunication Union, Geneva. ITU-R (2006) Technical and operational information for identifying spectrum for the terrestrial component of future development of IMT-2000 and IMT-Advanced, Report ITU-R M.2079, International Telecommunication Union, Geneva. ITU-R (2007) Naming for International Mobile Telecommunications, Resolution ITU-R 56, International Telecommunication Union, Geneva. ITU-R (2007) Report of the CPM to WRC-07, ITU-R CPM07-2, International Telecommunication Union, Geneva, www.itu.int/md/R07-CPM-R-0001/en. ITU-R (1999) Methodology for the calculation of IMT-2000 terrestrial spectrum requirements, Recommendation ITU-R M.1390, International Telecommunication Union, Geneva. Irnich, T. and Walke, B.H. (2004) ‘Spectrum estimation methodology for next generation wireless systems’, Proceedings of the 15th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communication (PIMRC 2004), Barcelona, Spain, pp. 1957–62. Irnich, T. and Walke, B.H. (2005) ‘Spectrum estimation methodology for next generation wireless systems: Introduction and results of application to IMT-2000’, Proceedings of the 16th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communication (PIMRC 2005), Berlin, Germany, pp. 2801–8.

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Irnich, T., Walke, B.H. and Takagi, H. (2005) ‘System capacity calculation for packet-switched traffic in the next generation wireless systems, Part I: nonpreemptive priority queueing model for IP packet transmission’, Proceedings of the 19th International Teletraffic Congress (ITC-19), Beijing, China, pp. 13–23. [K76] Kleinrock, L. (1976) Queueing Systems, vol 2: Computer Applications, John Wiley & Sons, New York. [M07] Matinmikko, M. (2007) ‘Estimation of spectrum requirements of IMT-Advanced systems’, Licentiate thesis, University of Oulu, Finland. [MIH+05] Matinmikko, M., Irnich, T., Huschke, J., Lappetel¨ainen, A. and Ojala, J. (2005) ‘WINNER methodology for calculating the spectrum requirements for systems beyond IMT-2000’, Proceedings of the 14th IST Mobile & Wireless Communications Summit, Dresden, Germany. [TW08] Takagi, H. and Walke, B.H. (eds) (2008) Spectrum requirement planning in wireless communications, John Wiley & Sons, Ltd, Chichester. [WIN06] ITU-R (2006) IST-2003-507581 WINNER I and IST-4-027756 WINNER II ‘SPECULATOR’ Tool for estimating the spectrum requirements for the future development of IMT-2000 and IMT-Advanced, viewed 20 June 2009, www.itu.int/ITU-R/study-groups/docs/speculator.doc. [WIN1D62] WINNER I (2005) IST-2003-507581 Methodology for estimating the spectrum requirements for ‘further developments of IMT-2000 and systems beyond IMT-2000’, Deliverable D6.2, December 2005, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+.

13 System Model, Test Scenarios, and Performance Evaluation Martin D¨ottling,1 Ralf Irmer,2 Kari Kallioj¨arvi,3 and St´ephanie Rouquette-L´eveil4 1

Nokia Siemens Networks Vodafone Group Services 3 Nokia 4 Motorola 2

13.1 Introduction This chapter provides an overview of the system-level investigations related to the performance assessment of the WINNER system concept. While investigations of a particular feature or technical component can be found in the appropriate chapter, this text focuses on the interworking and interrelation of multiple WINNER features at system level. Performance evaluation of future wireless systems faces various challenges, in particular due to the fact that the employed sophisticated multi-user techniques require the investigations to be carried out at the system-level. Furthermore, multi-dimensional adaptation to fast varying properties of the channel and data traffic is performed, which necessitates system-level simulations with high temporal resolution. Additional complexity increase can be attributed to the fact that advanced multi-user MIMO techniques are used, i.e. MIMO channel models with realistic correlation properties need to be considered. After a brief overview of simulators used for evaluation of wireless systems, this chapter explains a novel link-to-system interface developed and applied within the WINNER project. Next evaluation methodology and three test scenarios are described; they are used to investigate the performance of example WINNER designs and deployments. Spectral efficiency and the number of satisfied users under QoS constraints are defined and system-level performance evaluations addressing key features of the WINNER system are presented, including spatial processing, relaying, resource allocation strategies, multicast and broadcast transmissions, and the impact of traffic models. Radio Technologies and Concepts for IMT-Advanced Martin D¨ottling, Werner Mohr and Afif Osseiran C 2009 Martin D¨ ottling, Werner Mohr, Afif Osseiran

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13.2 Performance Assessment of Wireless Networks In order to reduce the computational complexity of performance assessment of wireless networks, various levels of abstractions of functions have always been used in simulators. Depending on the intended investigations these abstractions are performed in different areas. Since significantly different investigations were intended to be carried out within the WINNER project, a ‘one size fits all’ simulator is not feasible. In addition, the comparison of simulation results is significantly easier, when the simulators use similar abstractions and modelling methodologies. Hence several classes of simulators have been defined. The protocol level oriented simulator is for the purpose of studying the performance of layer 3 and above in a multi-cell and multi-user set-up. Thus, traffic models and network protocols are considered throughout the simulations and performance is often measured in terms of the network throughput, e.g., TCP throughput, and packet delay statistics. The system time to be simulated is of the order of several second or even minutes due to the long ‘response time’ of the network and higher layer protocols. Consequently, lower layer and especially physical layer behaviour must be largely abstracted in order to keep protocol simulations feasible. For instance, the propagation channel is approximated by means of path loss and shadowing. Typical functionalities investigated include routing, congestion control, end-toend ARQ (in multi-hop deployments), and interworking with TCP/IP. The dynamic system level simulator acts on a shorter time scale in the order of seconds allowing a more detailed modelling of layer 1 and layer 2 characteristics. Dynamic system behaviour in terms of traffic models, user movement as well as ‘birth and death’ of new and old users is taken into account. System functions such as handover, admission control, dynamic channel allocation, power control and scheduling can be evaluated with dynamic system level simulators. Typically a higher level of accuracy in channel modelling is used compared to protocol level system simulations. Typical performance measures include system and user throughput, delay statistics, fairness, and other similar system level metrics. Quasi-static system level simulators act on a shorter time scale of several milliseconds. During each simulation run, a user is assumed to stay at a fixed position, even though the user experiences a fast fading channel with high velocity. Such a simulator is basically suitable for investigating system functions such as adaptive resource scheduling and fast power control. Furthermore it allows using the full WINNER spatial channel model and is therefore well-suited for in-depth investigations of spatial processing techniques and their adaptation. Although traffic models can also be used in these simulators, mostly full buffer simulations are performed, since deriving sufficient statistics of processes in the order of seconds is challenging given the complexity and run-time of such simulators. Static system level simulators are used for basic investigation of deployment, coverage and system parameterisation. They consider only the long-term average behaviour, i.e. they exclude fast fading. Typically the results are average SINR distributions, which can be mapped to cell throughput using a modified and truncated Shannon curve. The results correspond to the long-term average obtained with quasi-static or dynamic simulators for the case of round-robin scheduling and full buffer traffic. Link level simulations are used to assess the performance of a point-to-point link. Here ultimate details of physical layer techniques and corresponding impairments can be investigated. The results in terms of mapping curves can be used to feed system level simulators to avoid detailed modelling of the physical layer especially the computationally complex channel

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decoding part. Simulation results are generally provided in terms of BER, BLER, FER or PER vs E b /N0 or SNR performance curves. Within WINNER all of these simulator classes have been used for various purposes. In particular, an interface between link-level and system-level simulations suitable for fast adaptation in multi-state channels, has been developed, as described in the following section.

13.3 Interface between Link and System Simulations To understand system and end user performance in modern wireless networks, system level evaluations are crucial. For complexity reasons such system level evaluations have to rely on simplified link level models that must be accurate enough to capture the essential behaviour at the physical layer. Traditionally, the performance of radio communication links have been evaluated in terms of packet error rate as a function of signal-to-interference plus noise ratio averaged over a sufficient number of channel realisations of one specific channel model. Packet error rate versus average signal-to-interference plus noise ratio performance has therefore been widely used as the interface between the link- and system-level simulators. This may be adequate as long as every transmitted packet encounters similar channel statistics, which implies very large packet sizes/coding blocks with respect to the channel coherence time. However, this is generally not the case. As shown in [LRZ02], the specific channel realisation encountered may result in performance which is significantly different compared to the one predicted from the average curve. Consequently, the performance assessment of fast resource scheduling and fast link adaptation in system level simulations requires a more accurate link performance model that takes into account the instantaneous channel and interference conditions. Naturally, this includes the effects of multiple antennas at transmitter and/or receiver in combination with the applied spatial processing technique such as e.g. beamforming or spatial multiplexing. Let us consider a transceiver structure employing bit-interleaved coded modulation (BICM), as depicted in Figure 13.1. Since different bits can be transmitted on different spatial layers, in different OFDM symbols, and on different OFDM subcarriers (i.e., coding/interleaving across space, time, and frequency) they may have different quality. Thus, we have a multi-state

source

coding

Π

STF Mapping

(adaptive) modulation

STF preprocessing

MIMO channel

bit sequence (with corresponding ‘reliability information’)

sink

decoding

Π−1

STF Demapping

noisy data symbol carrying m bits

noise + interference

^ Ikqi (Soft output) demodulation

linear STF equalization

SINR kqi

Figure 13.1 Transceiver block diagram employing BICM and adaptive transmission in space, time, and frequency (STF), reproduced from [BAS+05]. (Reproduced by Permission of IEEE © 2009).

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channel and the decoder model has to map the set of soft bit quality values {θ 1 , . . ., θ P }, such as SINRs, to a block error probability. The total number of resource elements in frequency (i.e., subcarriers or blocks of subcarriers), time (i.e., subsequent OFDM symbols or HARQ retransmissions), and space (i.e., layers for MIMO vector or general matrix modulation) is typically far too large for direct use of the associated quality measures {θ 1 . . . θ P } in the mapping to PER. Therefore the basic idea is to find a compression function that maps the sequence of varying SINRs to an effective SINR value that is strongly correlated with the actual BLER. The concept of an effective E b /N0 was introduced in [NR98] for a CDMA system and further developed and evaluated in [Eri03a, Eri03b, Nor03, Nor04]. The use of mutual information as a compression function was proposed and investigated in [Eri03a]. This approach is attractive since the modulation alphabet is accounted for and it is therefore also applicable to the WINNER frequency-adaptive transmission concept, where bits of a code word are mapped onto symbols from different modulation alphabets. The bit-interleaved coded modulation (BICM) mutual information can be written as [CTB96]: ⎫ ⎧ √ 2 ⎪ ⎪ ⎪ ⎪ ˆ exp(−|Y − x( x − z)| ) mp 1 ⎬ ⎨ 1 ˆ x∈X log , (13.1) Im p (x) = m p − E Y √ ⎪ 2m p i=1 b=0 exp(−|Y − x(x˜ − z)|2 ) ⎪ ⎪ ⎪ ⎭ ⎩ z∈X bi i ˜ b x∈X

where m p accounts for the bits per symbol, X is the set of 2m p symbols, X bi is the set of symbols for which bit i equals b. Further, Y is a zero mean unit variance complex Gaussian variable. Note that the use of the BICM capacity expression depends on the demodulator used. If an approximate demodulator is used to calculate log likelihood ratios, another capacity expression would apply. Based on (13.1) the mutual information effective SINR mapping (MIESM ) is given by ⎞ ⎛

Pu SINR 1 p ⎠ , (13.2) Im SINReff = β · Im ref −1 ⎝ Pu p=1 p β where β is an additional scaling parameter and Im p is the mutual information function of the applied modulation alphabet of size 2m p at the pth data symbol. A random interleaver is assumed and averaging is performed over all resource elements p of one codeword. It has been shown that the MIESM link-to-system interface is applicable to all considered modulation and coding schemes and gives excellent results that outperform previous link-tosystem interface proposals. Moreover, MIESM also achieves high packet error rate prediction accuracy in case of resource specific adaptive modulation and in case of different spatial processing techniques [BAS+05; WIN1D27]. It was therefore chosen as the link-to-system interface model within the WINNER project.

13.4 Test Scenarios While the WINNER radio interface provides a versatile concept and architecture that is able to support a wide range of deployment and application scenarios, the benefits of WINNER

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are demonstrated by means of three concrete design examples and the performance evaluation results associated with them. The test scenarios focus on different environments, deployments, physical layer modes, and parameterisations, and they highlight different key aspects for future radio interfaces:

r The base coverage urban test scenario is an urban macro-cellular deployment using the FDD physical layer mode, a carrier frequency of 3.7/3.95 GHz and 2 × 50 MHz bandwidth. Self-contained ubiquitous coverage for populated areas including the full range of mobility is the focal point of investigations here. r The microcellular test scenario is an urban micro-cellular scenario using the TDD physical layer mode and 100 MHz bandwidth at 3.95 GHz. This test scenario addresses higher user densities with lower mobility and highlights adaptivity and flexibility of the WINNER system. r The indoor test scenario investigates the in-home and hotspot scenario, such as hotels, shopping centres, small offices, etc., using the TDD physical layer mode and 100 MHz bandwidth at 5 GHz. Here investigations are centred on questions of self-organisation and self-adaptation.

13.4.1 Test Environments Table 13.1 summarises details about the operating environment and its characteristics. In the base coverage urban test scenario, no specific topographic details are taken into account, while in the microcellular and indoor test scenarios they are incorporated to the scenario. Both the indoor environment and the microcellular (i.e. Manhattan grid) environment are specified in [ETSI98]. Illustrations of the test environments are provided in the following section along with the deployment assumptions. Unless otherwise indicated a full buffer traffic model is used.

13.4.2 Deployment Assumptions Table 13.2 summarises assumptions about the deployment-specific parameters and assumptions. The parameterisation of base station (BS), user terminal (UT), and relay nodes (RN) are described in the following. These parameterisations were selected for the performance assessment work, and do not necessarily represent their final solutions in the WINNER system concept. The FDD physical layer mode was selected to be assessed in the base coverage urban test scenario, whereas the other test scenarios focus on the assessment of TDD. FDD is beneficial from an inter-cell interference point of view, especially in case of frequency re-use 1 as considered in all cellular test scenarios. On the other hand, FDD limits channel identification through reverse link measurements to long-term channel parameters only. TDD overcomes this limitation allowing for full channel identification by exploitation of channel reciprocity. Since interference plays a less critical role in the microcellular scenario due to shadowing effects of buildings, TDD is the preferred duplexing technique for that scenario. It supports a high degree of adaptivity to actual propagation conditions with reasonable signalling overhead and is thus a key technique to reach the targets for the WINNER system design. Obviously, the same argument applies to the indoor scenario.

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Table 13.1 Environment-specific simulation parameters. Base coverage urban

Microcellular

Indoor

Environment characteristics

Two-dimensional without topographic details

Two-dimensional regular grid of buildings (‘Manhattan grid’) Number of building blocks: 11 × 11 Building block size: 200 m × 200 m Street width: 30 m

User distribution model (at simulation start-up)

Number of users is variable Users are uniformly distributed in the entire area

Number of users is variable Users are uniformly distributed in the streets (outdoor UT simulations) Users are uniformly distributed in the buildings (indoor UT simulations)

User mobility model

Fixed and identical speed |v| of all UTs |v| ∈ {3, 50, 120 km/h} v = θ v ∼ U(0◦ ,360◦ )

Fixed and identical speed |v| of all UTs |v| ∈ {3, 50 km/h} ∠v: UTs only move along the streets they are in. Direction is random and both directions are equally probable

One floor of a building with a regular grid of rooms, walls and corridors Number of rooms: 40 Room size: 10 m × 10 m × 3 m Number of corridors: 2 Corridor size: 100 m × 5m × 3m Number of users is variable 90 % of users are uniformly distributed in rooms and 10 % of users are uniformly distributed in corridors Fixed and identical speed |v| of all UTs |v| ∈ {0, 5 km/h} ∠v = θv ∼ U(0◦ , 360◦ )

Another major difference between the base coverage urban and the microcellular scenario is the deployment of BS antennas. In base coverage urban deployments, the BS antennas were placed above rooftop level, while in microcellular deployments they were placed below rooftop level. This difference in antenna placement results in significantly different propagation conditions in these two deployments. The number of BS antennas is limited to four (base coverage urban) or eight antennas (microcellular, indoor). This limitation takes operator constraints into account by demanding the size of the radomes not exceed 450 mm per sector. Additional cross polarisation has a negligible effect on the array size but potentially increases the channel rank which may be exploited by advanced spatial processing techniques. In order to provide proof-of-concept under challenging assumptions, all test scenarios use a relatively wide bandwidth of 100 MHz. The carrier frequency used for base coverage urban and microcellular scenarios is around 4 GHz, and for the indoor scenario 5 GHz. It should be noted that the WINNER system concept is not restricted to these frequency bands and bandwidths, but it can address all the existing bands currently used by GSM and 3G, and bands identified by WRC07 for IMT below and above 1 GHz. The OFDMA technology used in WINNER can make use of whatever bandwidth is available by leveraging the fine granularity of the utilisation of frequency resources.

487

∗

0.5 λ = 0.5 c/fc (fc : DL carrier frequency, c: speed of light)

∗ 0.5λ

4 Cross polarised linear array X X

100 MHz cellular Manhattan grid lay-out [UMTS 30.03] Below rooftop, 10 m 37 dBm = 5.012 W Follows from Figure 13.4 and Table 13.1 2

TDD (1:1) 3.95 GHz

Microcellular

Am = 20, θ 3 dB = 70o (Am = 23, θ 3 dB = 35◦ for 6-sector site) 8 dBi 5 dB

, Am [dB]

Antenna gain Receiver noise figure

θ3 d B

Am = 20, θ 3 dB = 70o (Am = 23, θ 3 dB = 35◦ for 6-sector site) 14 dBi (17 dBi for 6-sector site) 5 dB

θ

2

A(θ) = − min 12

Azimuth antenna element pattern

0.5λ∗

4 Linear array | | | |

3

Above rooftop, 25 m 46 dBm = 39.81 W 1 km

FDD (1:1) 3.95 GHz DL 3.7 GHz UL 2 × 50 MHz cellular hexagonal layout

Number of antennas per sector Antenna configuration (per sector) Antenna element spacing

Channel bandwidth Deployment (see Figures 13.3–13.6) Location/height Max. Transmit power per sector Inter-site distance (only BS layout) Number of sectors per BS

Duplexing (asymmetry) Carrier frequency f c

Base coverage urban

Table 13.2 Deployment-specific simulation parameters.

General

Base station

5 dBi 5 dB

Am = 12, θ 3 dB = 70◦

(continued)

4 arrays operated as remote radio heads 8 elements per array Cross polarised linear array XXXX 0.5λ∗

100 MHz isolated site regular room layout [UMTS 30.03] 3m 21 dBm = 125.9 mW N/A

TDD (1:1) 5.0 GHz

Indoor

1.5 m 24 dBm = 251.2 mW 2 Dual cross polarised antennas: X A(θ) = 0 dB 0 dBi 7 dB Below rooftop, 5 m 37 dBm = 5 W 1 1 Single antenna with omnidirectional pattern N/A omnidirectional 9 dBi 5 dB

Location/height Max. Transmit power per sector Number of sectors per RN Number of antennas per sector Antenna configuration (per sector) Antenna element spacing Azimuth antenna element pattern Antenna gain Receiver noise figure

Base coverage urban

Height Transmit power Number of antennas Antenna configuration Azimuth antenna element pattern Antenna gain Receiver noise figure

Table 13.2 (Continued)

User terminal

Relay node

488 Below rooftop, 10 m 30 dBm = 1 W 1 1 Single antenna with omnidirectional pattern N/A omnidirectional 7 dBi 5 dB

1.5 m 24 dBm = 251.2 mW 2 Dual cross polarised antennas: X A(θ ) = 0 dB 0 dBi 7 dB

Microcellular

1.5 m 21 dBm = 125.9 mW 2 Dual cross polarised antennas: X A(θ) = 0 dB 0 dBi 7 dB

Indoor

System Model, Test Scenarios, and Performance Evaluation

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13.4.2.1 Base Station The parameters for the base station follow typical assumptions for state-of-the art wireless standards and products. It is important to note that the back-to-front ratio of the sector antenna elements in the base coverage urban and microcellular test case is 20 dB, i.e. a maximum of 20 dB SINR can be achieved if resources are re-used. The values of the deployment parameters, i.e. location/antenna height, transmit powers, inter-site distances, etc. are chosen as typical values in these kinds of deployments. Four antennas per sector are assumed in the base coverage urban and microcellular scenarios, while eight antennas are proposed for the indoor base station, all with λ/2 antenna element spacing.

13.4.2.2 User Terminal Dual-polarised transmit and receive antennas are assumed using 24 dBm transmit power in the base coverage urban and microcellular test scenarios, and 21 dBm indoors. An ideal omnidirectional antenna characteristic is assumed and the noise figure of 7 dB accounts for mass-market devices.

13.4.2.3 Relay Node Relay nodes have different constraints related to deployment, size, and cost compared to base stations. To reflect these constraints, an RN uses lower maximum transmit power, and lower number of sectors and antennas. The requirement to have small RNs suited for e.g. lamppost mounting, makes it impossible to use the same large vertical antenna aperture (panel antennas) as are used at BSs. However, an antenna aperture of 10 cm × 4 cm, showing omnidirectional antenna pattern and providing an elevation gain of 9 dBi appears to be feasible using three radiating elements. Therefore for the RN in the base coverage urban test scenario a single antenna with such characteristics is assumed. For the microcellular test case, a single omnidirectional antenna with 7 dBi is assumed (the lower elevation gain accounts for the requirement of a larger beamwidth in elevation). These antenna parameter estimates are based on readily available antenna elements [Kat08]. It has been assumed that identical gains can be obtained when scaling the aperture size with the carrier wavelength. The antenna configurations above are considered cost efficient and feasible for lamppost mounting and are therefore proposed as basic assumptions. Note, in total the effective isotropic radiated power (EIRP) of an RN is reduced by 14 dB in the base coverage urban and by 8 dB in the microcellular test cases. Under the requirement of the same QoS at the cell border (approximated initially by the same average SINR in the link budget), the relay cells will have significantly reduced cell ranges compared to the BS.

13.4.2.4 Network Layout In the base coverage urban case, no specific topographical details are taken into account. Base stations and relays are placed in a regular grid, following hexagonal layout. A basic hexagon layout for a deployment consisting only of base stations is shown in Figure 13.2, where basic geometry (antenna bore-sight, cell range, and inter-site distance ISD) is also defined.

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Radio Technologies and Concepts for IMT-Advanced

t

gh

na

-si ore

b

n nte

a

ll ce ran ge

Figure 13.2 Base coverage urban cell layout without RNs.

In the base coverage urban deployment option including relays, they are placed along the antenna bore-sight of each sector at 2/3 of the cell hexagon diameter, i.e. 444 m from the BS, see Figure 13.3. The antenna array orientation of the RN is such that it has the same bore-sight direction as the serving BS sector. Each antenna element at the RN has an omnidirectional antenna pattern. Please note, that for the relay-enhanced cell (REC) deployment, the cell shape will deviate from the hexagon form and the actual shape of the RECs will depend on the interference situation, i.e. the details of resource partitioning and re-use. It is important to note that the placement of radio access points (RAPs) does not consider propagation conditions, such as shadowing. Users are distributed uniformly over the whole area. In the microcellular test case, known as a Manhattan grid, a two-dimensional regular grid of streets and buildings is considered (Figure 13.4). Base stations are placed in the middle of the streets and in the middle between two cross-roads. Two sectors are formed with array bore-sight

Figure 13.3 Base coverage urban cell layout with RNs for coverage extension.

System Model, Test Scenarios, and Performance Evaluation

491

T T T

T

T T

Figure 13.4 Microcellular layout without RNs.

along the street direction, but with 180◦ coverage each. The corresponding relay-enhanced cell deployment is shown in Figure 13.5. The indoor scenario consists of one floor (height 3 m) of a building containing two corridors of 5 m × 100 m and 40 rooms of 10 m × 10 m, as depicted in Figure 13.6. To highlight innovative deployment concepts, a distributed antenna array in the form of remote radio head deployment is investigated here. Four antenna arrays, each containing eight antennas,

Figure 13.5 Microcellular layout with RNs.

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Radio Technologies and Concepts for IMT-Advanced

8-element antenna array

Figure 13.6 Indoor environment (one floor). (Reproduced by Permission of IEEE © 2009).

are placed in the middle of the corridor at 25 m and 75 m (with respect to the left side of the building). The antenna array orientation is rotated by 45◦ as shown in Figure 13.6. It is assumed that all antenna arrays are operated by one central BS.

13.4.2.5 Channel Modelling The channel model parameters to be used in the test scenarios are provided in Table 13.3. The channel models are described in detail in Chapter 3, where the WINNER channel models are introduced. Different channel models are used for the links between BS and UT, BS and RN, as well as RN and UT. Note, that all BS–RN links are assumed to be line-of-sight (LOS).

13.4.3 Basic OFDM Parameters and Frame Dimensions Basic OFDM parameters, dimensions of chunks, super-frame and frames are explained in detail in Chapter 4. The total number of chunks per frame duration of 0.6912 ms is 2 × 144 in FDD and 2 × 230 in TDD. Duplex guard times of 2 × 8.4 µs are taken into account for TDD providing sufficient time to switch between transmission and reception. Details on control channel and pilot overhead can be found in Chapter 6. A summary of basic assumptions for the downlink is provided in Table 13.4. For frequency-adaptive transmission, a downlink control overhead of six bits per allocated downlink chunk layer and six bits per allocated uplink chunk layer was assumed. For instance, with QPSK, R = 1/2 coding (appropriate for frequency-adaptive transmission at SINR 5 dB or higher, see [WIN2D461,

Table 13.3 Channel modelling parameters.

channel model BS↔outdoor UT channel model BS↔indoor UT channel model BS↔RN channel model RN↔outdoor UT channel model RN↔indoor UT

Base coverage urban

Microcellular

Indoor

C2/(C1) B4 C1 B1 NLOS / (B1 LOS) B4

B1 B4 B5c B1 B4

– A1 A1LOS – A1

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Table 13.4 Downlink frame parameters.

Overall frame length Number of OFDM symbols per frame Chunk layer dimension in symbols × subcarriers Dedicated pilot + control symbols per chunk layer in frequency-adaptive transmission Dedicated pilot + control symbols per chunk layer in non-frequency-adaptive transmission Number of chunks per frame in time and frequency direction Duplex guard time

Base coverage urban

Microcellular

Indoor

0.6912 ms 24

0.6912 ms 30

0.6912 ms 30

12 × 8 = 96

15 × 8 = 120

15 × 8 = 120

4 + 12 = 16

4 + 12 = 16

4 + 12 = 16

8 + 18 = 26

10 + 18 = 28

10 + 18 = 28

2 × 144

2 × 230

2 × 230

0 µs

2 × 8.4 µs

2 × 8.4 µs

Section 5.2.1]), then 12 control symbols are required for controlling a downlink chunk and a subsequent uplink chunk. For non-frequency-adaptive transmission, users with low SINR need to be supported. Although less information is required for non-frequency-adaptive transmission (identical MCS for all chunks of one FEC block), it requires stronger channel coding, to reliably reach users with SINR <5 dB. As an approximate value 50 % more control symbols are required, i.e. 18 control symbols are assumed.

13.5 Spectral Efficiency and Number of Satisfied Users under QoS Constraints Although several key performance indicators will be discussed in this chapter, particular focus is on spectral efficiency under QoS constraints. The complex matter of QoS is modelled in a way suitable for MAC- and PHY-oriented system-level simulations by a so-called satisfied user criterion (SUC). For full buffer simulations, the SUC is modelled by a requirement on the 5th percentile of the average user throughput [WDK+08]. In wide area, this requirement is set to 2 Mbps in downlink and to 1.3 Mbps in uplink, i.e. satisfactory service provision is assumed if 95 % of the users have an average user throughput greater than these values. This 5th percentile of average user throughput is also referred to as cell edge user throughput in 3GPP. User throughput is defined as the number of correctly received information bits including all effects of packet loss and retransmissions, and taking into account the overhead due to guard bands, guard times, preambles, pilots, headers, and control signalling. The impact of functions related to header compression, encryption, ciphering, and transport delays between base station and gateway is however not considered. In simulations, the number of users per sector is increased up to a load where this SUC is still met. At this maximum number of supported users, the cell spectral efficiency η is

Radio Technologies and Concepts for IMT-Advanced

5th percentile average user throughput [Mbps]

494

SUC

technique 1 increasing number of users (load)

2 2 technique 2

4 4

maximum number of supported users

8 8 16 16

32 32

η1 η2

spectral efficiency η [bps/Hz/sector]

Figure 13.7 Evaluation methodology for spectral efficiency under QoS constraints modelled by a satisfied user criterion.

determined, as illustrated in Figure 13.7. In this example, technique 2 would provide higher spectral efficiency. Moreover the maximum number of supported users (32 in this example) is another key performance indicator, which is closely related to the economics of a network operator. This methodology was utilised to obtain many of the results presented later in this chapter. This concept was also extended for simulations using traffic models. In this case, user satisfaction is only assumed if a requirement for average user throughput and user packet delay statistics are simultaneously fulfilled. The user packet delay is defined as a one-way transit time between a time instance when a packet is available at the IP layer in the transmitter and the moment when it becomes available at the IP layer of the receiver. Thus, user packet delay includes delay introduced by associated protocols and control signalling assuming the user terminal is in the active state. The impacts of functions related to header compression, encryption, ciphering, and transport delays between base station and a gateway are not considered. The cumulative distribution function (CDF) of the user packet delay is calculated for each user (and called user packet delay CDF). The system packet delay CDF is calculated by extracting the 95th percentiles of all user packet delay CDFs. The packet delay SUC is then defined based on the value of the system packet delay CDF that is only exceeded by 5 % of the users. The actual values of the SUC are different for different services. The WINNER performance evaluation methodology is very similar to the one applied by standardisation bodies, such as 3GPP, 3GPP2 and IEEE 802.16. The Next Generation Mobile Networks (NGMN) alliance has published another methodology which is used for all the above standards [NGMN07]. The fairness problem – ensuring that cell edge users get reasonably good performance – is addressed with a normalised fairness bound, i.e. 90 % of all users should at least get 10 % of the average sector user throughput. This fairness criterion scales with frequency bandwidth, and is comparable to the satisfied user criterion applied here.

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13.6 End-to-End Performance Evaluation In previous chapters, performance evaluation results focusing on particular features and technical components have been presented. This section covers system-level results, which mainly give insight into overall system performance by applying the methodology described in Section 13.5, focusing on user experience and the interrelation of different features of the WINNER system concept. Section 13.6.1 investigates the base coverage urban test scenario outlined above; Section 13.6.2 is based on the microcellular area and the indoor test scenario is used in Section 13.6.3.

13.6.1 Base Coverage Urban Scenario 13.6.1.1 Frequency-domain Link Adaptation Gains In the WINNER system concept, a novel frequency-domain link adaptation scheme is used for frequency-adaptive transmission. The mutual-information, adaptive coding and modulation (MI-ACM) method performs bit loading within the chunks of one codeword using shortterm adaptive M-QAM mapping per chunk and a common weighted average code rate, as described in Chapter 5. At link level, it has proven to provide near-optimum performance with significantly less complexity than the optimal Hughes–Hartogs bit and power loading algorithm [SBC07; PPS07]. However, at a system level, other functions also make opportunistic use of frequency selectivity. Therefore we investigate the gains of MI-ACM compared to standard approaches using dynamic system-level simulations in the downlink of the base coverage urban test scenario. The downlink of the wide area test scenario as described in Section 13.4 has been evaluated using a dynamic system-level simulator. The following MIMO schemes (see Chapter 7) are compared to SISO performance:

r per-user switching between Alamouti space–time block coding (STBC) and selective per antenna rate control (S-PARC) based on average SINR using two antennas at the UT and at the BS (called ‘2x2 adaptive MIMO’), using either vertically polarised antennas with 2λ element spacing or cross-polarised antennas; r GoB baseline design, i.e. four antenna elements, vertically polarised at λ/2 element spacing, generating eight beams; r GoB using SDMA based on the above GoB set-up (GoB+SDMA). For the GoB, eight fixed beam directions are defined for each sector. The SINR from each beam is measured and upon this information the optimal beam for a given user is selected. For GoB+SDMA it was assumed that in one sector the same resources can be used at most by two spatially separated users with a minimum of two beams separation in order to control inter-beam interference even in the baseline NLOS case. For simplicity, no coordination of the used beams between sectors is assumed. Simulation assumptions were compliant to the baseline design (see Section 13.4), but further elaborated in the following main aspects:

r additional (and also adaptive) spatial schemes as described above were investigated; r the impact of chunk-wise adaptive modulation within one codeword was studied (i.e. the additional gain when using identical chunks) but we either adapted all of them to the average

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Radio Technologies and Concepts for IMT-Advanced

SINR (basic link adaptation, or BLA) or used the reference algorithm for frequency-adaptive transmission MI-ACM, see Chapter 5 and [SBC07; WIN2D222]; r different schedulers, i.e. proportional fair (PF), round-robin (RR), and Maximum C/I (MaxCoI) were compared; r an explicit modelling of a four-channel stop-and-wait protocol and Chase combining for HARQ was used. A spectral efficiency comparison of MIMO schemes and frequency-domain link adaptation algorithms is provided in Figure 13.8. Proportional fair scheduling is used and the overhead taken into account was 12 symbols per chunk for general control information (see Section 13.4.3). In the case of MI-ACM, an additional symbol per chunk is assumed to be used to

GoB BLA GoB MI−ACM 3.5

GoB+SDMA BLA GoB+SDMA MI−ACM MIMO 2x2 BLA MIMO 2x2 MI−ACM

3

SISO BLA

spectral efficiency [bps/Hz/sector]

SISO MI−ACM

2.5

2

1.5

1

0.5 5

10

15

20 25 30 35 number of users per sector

40

45

50

Figure 13.8 Impact of chunk-wise adaptive modulation within a codeword (MI-ACM) on spectral efficiency for different MIMO schemes, from [SOD+08]. (Reproduced by Permission of IEEE © 2009).

System Model, Test Scenarios, and Performance Evaluation

497

probability

transmit the additional modulation information. Four common pilot symbols per antenna and chunk are assumed in order to allow fair comparison of different antenna configurations. The 2x2 MIMO results are based on cross-polarised antennas at the BS and the UT. The additional gain of MI-ACM compared to state-of-the art adaptation to average SINR (shown as ‘BLA’ in the figures) decreases with the increasing number of users, as in this case the number of chunks allocated to one user diminishes and hence less variability of SINR exists within the chunks of one codeword. It can also be observed that the relative gain over the baseline case is different for each MIMO scheme. For 16 users, around 10 % gain is observed for GoB+SDMA, 40 % for the 2x2 adaptive MIMO, and 25 % in the SISO case. However, almost no improvement is obtained for the GoB processing. The reason for this behaviour is that GoB results in relatively high received SINR. The usage probability of different MCS is depicted in Figure 13.9. The GoB scheme very often reaches the upper limits of the MCS scheme (64-QAM, R = 3/4). A proper adaptation of the individual chunks is then no longer possible and the gain of MI-ACM is small. Further simulations with an extended MCS scheme (including up to 256-QAM) show that in this case spectral efficiency for the GoB processing is increased to 2.3 bps/Hz/sector and 2.4 bps/Hz/sector for BLA and MI-ACM, respectively [SOD+08]. For the 2 × 2 adaptive MIMO scheme (which switches between Alamouti and S-PARC transmission based on an SINR threshold of 3 dB) the interrelation of MI-ACM gains with different scheduling strategies is further investigated in Figure 13.10. For 16 users per sector,

0.5

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1

2

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6 7 MCS index

Figure 13.9 MCS distribution for different MIMO schemes, from [SOD+08]. (Reproduced by Permission of IEEE © 2009).

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5 MaxCoI BLA 4.5

MaxCoI MI−ACM PF BLA PF MI−ACM

spectral efficiency [bps/Hz/sector]

4

RR BLA RR MI−ACM

3.5

3

2.5

2

1.5

1

0.5 5

10

15

20 25 30 35 number of users per sector

40

45

50

Figure 13.10 Interrelation between gain of chunk-wise adaptive modulation and scheduling strategy, from [SOD+08]. (Reproduced by Permission of IEEE © 2009).

the MI-ACM gain is around 40 % for proportional fair (PF) and MaxCoI scheduling, and 100 % gain is achieved in the case of round-robin scheduling. PF scheduling using MI-ACM even outperforms the Max CoI scheduler adapting to average SINR for up to 12 users per sector. It needs to be noted that details of the implemented frequency-dependent scheduling strategy significantly impact the observed gains from MIACM, as both techniques basically exploit frequency selectivity. In particular, for large bandwidth and few high-rate users, chunk-wise adaptive modulation is of major interest. Moreover, under real-world operational conditions, it can be assumed that the scheduler would have several additional constraints (e.g. from QoS per flow, interference coordination, etc.). Therefore, the relevance of performing an advanced link adaptation per chunk is even more important.

13.6.1.2 Spectral Efficiency and Maximum Number of Satisfied Users Key system-level performance indicators are spectral efficiency and the maximum number of users supported under QoS constraints, as outlined in Section 13.5. Figure 13.11 shows the 5th percentile of the average user throughput vs spectral efficiency. The figures along the curves indicate the associated load in terms of the number of users per sector.

System Model, Test Scenarios, and Performance Evaluation

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7 GoB BLA 5th percentile user average throughput [Mbps]

GoB MI−ACM 6

8

GoB+SDMA BLA

8

8

GoB+SDMA MI−ACM 8

MIMO 2x2 BLA

5

MIMO 2x2 MI−ACM SISO BLA

4

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16

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8 12

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8

SUC

48

48 1

24 32

32 32 40 40

24 32 40

40 16 24

24 48

48

1.5 2 spectral efficiency [bps/Hz/sector]

2.5

3

Figure 13.11 5th percentile average user throughput vs spectral efficiency for different spatial processing schemes, from [SOD+08]. (Reproduced by Permission of IEEE © 2009).

A clear benefit of using the MI-ACM scheme is evident for SISO and MIMO: a higher number of users can be supported and higher spectral efficiency is achieved. It can be seen that the 4x2 GoB-based schemes in particular boost the maximum number of satisfied users: from seven users per sector for SISO, to nine users for 2x2 adaptive MIMO, to 28 users for GoB, and 30 users for GoB+SDMA. The spectral efficiency achieved for this maximum supported load is 1.0 bps/Hz/sector, 1.9 bps/Hz/sector, 2.2 bps/Hz/sector, and 2.9 bps/Hz/sector for SISO, 2x2 MIMO, 4x2 GoB, and 4x2 GoB+SDMA, respectively. The relative gains compared to SISO for these schemes are summarised in Table 13.5. While 2x2 MIMO already improves spectral efficiency by 90 %, there is only a small increase (30 %) in the supported number of users, since the considered schemes do not improve the performance for low SINR users, which determine the number of supported users under the satisfied user criterion. However, the maximum number of supported satisfied users is the most relevant end-to-end performance criterion, since it directly relates to potential revenues of an operator. The 4x2 GoB-based schemes show impressive performance advantages in this respect due to improved cell-edge performance (300 % increase of satisfied users); see also [WIN2D473]. A complete overview of spectral efficiency under the satisfied user criterion constraint and the associated maximum number of supported users per sector is given in Table 13.6. The spectral efficiency requirements of WINNER (2 bits/s/Hz/sector, see Section 2.6.5) are met for all considered 4x2 schemes and almost for the 2x2 adaptive MIMO scheme using MI-ACM.

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Table 13.5 Relative gains of spectral efficiency and satisfied users for spatial processing schemes using MI-ACM and PF scheduler compared to SISO. MIMO config 2x2

4x2 4x2

Scheme

Spectral efficiency increase compared to SISO

Increase of satisfied users compared to SISO

+90 %

+30 %

+120 % +190 %

+300 % +330 %

Adaptation between STBC, S-PARC, x-pol Tx GoB GoB+SDMA

Table 13.6 Spectral efficiency and supported number of users based on 2 Mbps satisfied user criterion for different spatial processing schemes, from [SOD+08]. (Reproduced by Permission of IEEE © 2009)

MIMO config. 1x1 1x1 2x2

2x2

2x2

2x2

2x2

2x2

2x2

2x2

4x2 4x2 4x2 4x2

Scheme SISO SISO Adaptation between STBC, S-PARC, v-pol Tx, 2λ Adaptation between STBC, S-PARC, v-pol Tx, 2λ Adaptation between STBC, S-PARC, x-pol Tx Adaptation between STBC, S-PARC, x-pol Tx Adaptation between STBC, S-PARC, x-pol Tx Adaptation between STBC, S-PARC, x-pol Tx Adaptation between STBC, S-PARC, x-pol Tx Adaptation between STBC, S-PARC, x-pol Tx GoB GoB GoB+SDMA GoB+SDMA

Link Adaptation Scheduler

Receiver

Spectral efficiency Max. number (bit/s/Hz/ of satisfied sector) users

BLA MI-ACM BLA

PF PF PF

MRC MRC MRC/MMSE

0.8 1.0 1.0

5 7 6

MI-ACM

PF

MRC/MMSE

1.4

8

BLA

PF

MMSE

1.3

7

MI-ACM

PF

MMSE

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9

BLA

MaxCoI

MMSE

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3

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MaxCoI

MMSE

1.5

3

BLA

RR

MMSE

1.1

3

MI-ACM

RR

MMSE

1.4

4

BLA MI-ACM BLA MI-ACM

PF PF PF PF

MMSE MMSE MMSE MMSE

2.2 2.2 2.7 2.9

28 28 25 30

System Model, Test Scenarios, and Performance Evaluation

501

13.6.1.3 Improving Cell Edge Performance and Indoor Coverage by Relaying Future radio systems require higher bandwidth which can typically be allocated only at higher frequencies. Due to the increased path loss, shadowing and wall penetration loss, however, the radio network operation at higher frequencies imposes severe challenges in cell edge performance, and in particular in indoor coverage by outdoor base stations. Indoor coverage in the base coverage urban test scenario is investigated with and without relaying using static system-level simulations that model the long-term average SINR distributions considering path loss, shadowing, beam patterns, spatial processing, and interference. The baseline GoB scheme is used at the BS while MRC is used at the UT. Throughput calculations are based on a SINR-to-throughput mapping using a modified Shannon approximation considering Chase combining and the limitation due to the maximum modulation and coding scheme, as described in [WIN2D353, Section 2.2.2]. It has been shown [Ham07] that such a simulation approach is a good approximation of the results for dynamic system-level simulations with round-robin scheduling and full buffer traffic. A fixed resource partitioning is used based on the requirement of equal user throughput in the areas covered by the base station and RN, as well as ensuring that the throughput of the BS–RN link is greater than or equal to the aggregation of the RN-to-user links (see [WIN2D353, Section 2.2.2]). Simulation assumptions follow the base coverage urban case as described in Section 13.4.2. For a particular shadowing situation, a comparison between BS-only and relay-enhanced deployment is shown in Figure 13.12. It can be seen that with the current parameterisation of the RNs, focusing on small low-cost devices, the improvement is limited to the close environment of the RN and multiple RNs are required to cover all the shadowed areas in one cell. Since additionally the location of the RN is fixed and not deployed at e.g. areas shadowed from the BS, the following results can be considered lower bounds on performance.

SINR [dB] 30

1500 1500 1000

y [m]

y [m]

500 0 −500

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20

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10

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0 x [m]

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0 x [m]

500

1000

1500

−30

(b)

Figure 13.12 Long-term average SINR including shadowing: (a) BS-only deployment and (b) relayenhanced deployment.

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Radio Technologies and Concepts for IMT-Advanced

1

Probability

0.8

0.6

0.4

BCU outdoor BCU indoor REC outdoor REC indoor REC 2 RN outdoor REC 2 RN indoor

0.2

0 −10

0

10

20 SINR [dB]

30

40

50

Figure 13.13 Impact of penetration loss for indoor coverage by outdoor RAPs.

SINR distributions are given in Figure 13.13 for three different deployments: base coverage urban (BCU) without relays, one relay per cell (REC), and two relays per cell (REC 2 RN). Outdoor users are indicated as well as indoor users, who experience a 20 dB wall penetration loss. In general, the SINR is improved if more RNs per sector are used. A slightly larger improvement is seen for the first RN compared to the additional gain of the second RN. Furthermore relaying ameliorates, in particular, the SINR of indoor users. We observe that indoor coverage by a traditional BS-only deployment is a challenge at 4 GHz and inter-site distance of 1 km. Simulations have shown that noise limitation starts at around 10 dB penetration loss. For the BCU indoor case, the 5th percentile of the SINR CDF is decreased by 8 dB to −5 dB, as listed in Table 13.7. Already two retransmissions of the lowest MCS are required. Relaying allows improving the 5th percentile by 3 dB and cell edge user throughput is increased by 50 % with only one RN per cell since a higher MCS can be used. For two RNs per cell, the SINR improvement is 5 dB and the cell edge throughput is increased by 200 %. Relaying is therefore very useful to improve indoor coverage in particular at the cell edge. The average cell throughput is improved by 10 % and 22 % for one RN or two RNs per cell, respectively, see Table 13.7. Table 13.7 Performance comparison for baseline MCS (max. 64-QAM, Rc = 0.75).

Scenario BCU REC REC 2 RN BCU REC REC 2 RN

Penetration loss (dB) 0 0 0 20 20 20

5 % SINR (dB)

Cell throughput (Mbps)

Normalised cell throughput (b/s/Hz/cell)

3 4 5 −5 −2 0

96 94 96 63 68 77

1.9 1.9 1.9 1.3 1.4 1.5

System Model, Test Scenarios, and Performance Evaluation

503

From Figure 13.13 it can be seen that the SINR CDF includes relatively high values, where the maximum MCS of the WINNER baseline design (64-QAM, Rc = 0.75) limits the throughput. Therefore the simulations have been repeated using higher MCS including up to 256-QAM and Rc = 0.92. In particular, relay-enhanced cells benefit from the extended MCS and the cell throughput outperforms the base station deployment by 6 % in the outdoor case and by 16 % for indoor users for a single RN per cell. For two RNs per cell, the cell throughput improvement is 13 % and 32 % for outdoor and indoor users, respectively. However, this can be considered as the upper limit of the achievable gain, since real-world imperfections, such as error vector magnitude (EVM), will in practice restrict the maximum SINR. 13.6.1.4 Dynamic Resource Allocation in Relay-Enhanced Cells While results in the previous section were using a simple and fixed resource partitioning between the relays and the base station, significant performance increase can be leveraged using advanced schemes – in particular, if they are combined with scheduling and spatial processing. In this section, we provide more insight of performance evaluation for relay-based deployment in a wide area test case of a dynamic approach for the resource partitioning. In particular, performance evaluations of the dynamic resource sharing (DRS) introduced in Section 8.4.4.2 are shown. Results are obtained considering only the cell in the centre and 18 cells around, C2 NLOS channel model for BS-UT links, C1 NLOS for BS–RN links and B1 NLOS for RN–UT links. Both BS and RN are equipped with an array of four transmit antennas having total transmit power of 46 dBm and 40 dBm, respectively. We have assumed an ideal approach for packet retransmissions due to errors: if a packet is received with errors, it will be enqueued in the source. This assumption consists of having ideal feedback that is not affected by transmission errors. We have assumed a larger packet size split into segments of 1200 bytes. Users are considered fixed in position. The full buffer model and round-robin resource scheduling have been adopted. The idea behind the dynamic resource sharing (DRS) approach consists of grouping users served by a single RAP and which are unable to share the same resources, according to their mutual spatial correlation. In the following discussion, each group of users is called a ‘logical beam’. Details can be found in [CFR+07; FRC+07; WIN2D352]. Logical beams that might be grouped together and share the same resources can be selected using a simplified estimate of the transmission rate of each beam. The computation of this term makes use of estimates of the average inter-beam interference, i.e. a measure the interference experienced by the users of a beam when a transmission is occurring in another one. In [WIN2D352; FRC+07], the Chunk-by-Chunk Balancing (CCB) of the relay and access link resource partitioning scheme is presented. The CCB algorithm tries to achieve the maximum possible cell throughput by always allocating the groups of beams having the highest total rate. It further keeps the balance between the first and second hop allocation by making sure that enough resources are assigned for the first hop for each allocation on the last hop. The chosen approach consists of having a stepwise alternating allocation between the two links of the same connection. At each step, a chunk is assigned on the access link to the group of beams which achieves the highest average throughput. The allocation on the relay links is then checked to see whether it is sufficient to forward all the data which is scheduled to be

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Radio Technologies and Concepts for IMT-Advanced

Table 13.8 Spectral efficiency comparison of BS-only and RN deployment with DRS. Scenario BS only BS and RN (1 RN per sector)

Resource partitioning

Spectral efficiency in b/s/Hz/cell

– DRS

2 2.5

transmitted on the access link, and resources are assigned to them when needed. When one of the beams belonging to the selected group has been completely served, a new beam group is calculated and the whole procedure is repeated. The main advantage of this algorithm is that at each step every allocation on the second hop corresponds at least an equivalent allocation on the first one in order to guarantee enough resources for the end-to-end connection. The resulting resource partitioning consequently causes lower losses due to unbalanced allocations with respect to a fixed resource partitioning case. Table 13.8 shows the spectral efficiency for BS-only and relay-based deployments using DRS resource partitioning. The relaying solution provides 25 % increase in spectral efficiency compared to BS-only deployment for a high number of users. Since no gain in spectral efficiency was observed for the similar case in Section 13.6.1.3 for a fixed resource partitioning scheme, it can therefore be concluded that advanced dynamic resource partitioning is essential to leverage the full gains of relaying. 13.6.1.5 Cooperative Relaying Cooperative relaying can further enhance the performance of a relay deployment. To evaluate the potential benefits, we compare the cooperative multi-antenna relaying scheme using distributed LQ pre-coding as described in [WIN2D352] with single-path relaying and a BS-only deployment that uses only direct links between BSs and UTs. Three RNs per sector were assumed. In the user throughput CDF shown in Figure 13.14, we can clearly observe that the number of users with low throughput is significantly reduced by introducing single-path relaying. Additional improvement at the cell edge (i.e. the 5th percentile of the CDF) can be seen in the case of cooperative relaying. This is of course at the cost of additional signalling and control overhead. Nonetheless, the coordinated and joint transmission of BSs and RNs seems to be a viable option especially in those areas where a UT experiences similar channel conditions to both RAPs. MIMO cooperative relaying is more affected by a limited BS–RN link capacity than by single path relaying, due to the higher amount of data which has to be communicated to the RNs. Hence, the proposed MIMO cooperative relaying solution requires a high capacity BS–RN link. 13.6.1.6 Multicast/Broadcast Services in Relay-Enhanced Cells Delivery of multimedia services is a key ingredient of WINNER. Many multimedia services, such as video streaming need to be received by many users who might also be distributed over a large area. Therefore multicast and broadcast (MCBC) services require point-to-multipoint wireless connections.

System Model, Test Scenarios, and Performance Evaluation

505

100

Pr(θ(.,.) < θ)

Direct Transmission

10−1 Single-Path Relaying

Cooperative Relaying

10−2 10−1

100 Throughtput θ in Mbps

101

Figure 13.14 Throughput CDF comparison for three RNs per sector.

In the multicast case, the network has at least partial knowledge about the served users, whereas broadcasting is performed without any information about the recipients. These limitations severely restrict possibilities for dynamic resource allocation, link adaptation and spatial processing and therefore low link spectral efficiency is obtained. However, since the same radio resources are used to serve many users, the system spectral efficiency can outperform those of point-to-point connections starting from a certain number of users in the MCBC group. We address this trade-off and estimate the average MCBC group size required to benefit from this effect. MCBC transmission is based on the non-frequency-adaptive transmission mode which uses B-EFDMA to obtain frequency diversity. In the base coverage urban test scenario a fixed GoB is used for unicast transmission. For broadcast transmission, it is not reasonable to use beamforming because coverage has to be provided for all users in the cell at the same time. Instead diversity techniques such as the Alamouti space–time block code should be used. A fixed number of retransmissions together with Chase combining is used to enable MCBC service for low SINR users. To be compatible with half-duplex RNs either one or three retransmissions must be used, as depicted in Figure 13.15. We focus our investigations on the broadcast case, where no channel quality indicator (CQI) is available. Therefore coding and modulation needs to be adapted to ensure proper reception in most of the coverage area. We assume that 95 % of the area needs to be served, i.e. the modulation and coding scheme is chosen that corresponds to the 5th percentile of the long-term average SINR (after Chase Combining) observed in the base coverage urban test scenario with one relay per cell. Link adaptation to the low SINR (e.g. cell edge) users results in poor link efficiency. Exploiting macro diversity offered by local single frequency networks (SFN) is a technique

506

Radio Technologies and Concepts for IMT-Advanced

Figure 13.15 Fixed retransmission scheme for MCBC in relay-enhanced cells.

to improve cell edge SINR. In SFNs, data is sent by different radio access points on the same resources and combined at the receiver. Achieving such co-ordinated transmission (and the required synchronisation) between the radio access points (i.e. the BS and relays) of one site is straightforward since they are controlled by one BS. SFNs extending over several sites offer potential additional gain, however, at the expense of co-ordination overhead and synchronisation requirements between different BSs. Unless otherwise indicated, the simulation assumptions are as described in Section 13.4.2. Figure 13.16 shows the SINR distributions with (REC) and without relaying (BCU) and different configurations of the SFN assuming outdoor users. At the 5th percentile, an SFN per site with RECs already improves the SINR by 2 dB, whereas a three-site SFN provides an additional 1.8 dB gain. It is important to note, that for a single-site SFN no macro diversity gain is obtained at low SINRs if relaying is not used. For BS-only deployments notable gains are only seen for an SFN extending over three sites, which means only if inter-BS co-ordination and synchronisation is ensured. The combination of relaying and SFN, however, already provides significant gain and the simple single-site SFN in REC significantly outperforms the more complex three-site and even seven-site SFN in a deployment without relays. The combination of relaying and SFN is therefore an interesting option to improve link efficiency of MCBC transmissions. Table 13.9 compares the resulting link efficiencies for point-to-point (p-t-p) transmission (using GoB at the BS) and point-to-multipoint (p-t-m) transmission using the Alamouti and SFN variants. Using p-t-m transmission results in extremely poor indoor performance since link adaptation needs to cater for very poor SINR users. For the single-site SFN, relaying allows link efficiency to improve by 45 % for outdoor users and by 100 % for indoor users. Extending the SFN over three sites improves link efficiencies additionally between 15 % and 30 %. The link efficiencies can be used to estimate the required number of users in the MCBC transmission until equal or better system efficiency is achieved. Table 13.10 shows that relaying

System Model, Test Scenarios, and Performance Evaluation

507

1 0.9 0.8 0.7

Probability

0.6 0.5 BCU, no SFN BCU, SFN per site BCU, SFN of 3 sites BCU, SFN of 7 sites REC, no SFN REC, SFN per site REC, SFN of 3 sites REC, SFN of 7 sites

0.4 0.3 0.2 0.1 0 −5

0

5

10

15

20 25 SINR per chunk [dB]

30

35

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45

50

Figure 13.16 Comparison of long-term SINR distribution for the base coverage urban (BCU) deployment without relays, for relay-enhanced cells (REC) and using different sizes of the local single-frequency network (SFN).

allows MCBC to be used efficiently already at lower MCBC group sizes, especially if indoor users need to be served. Based on the WINNER channel models and propagation delays between RAPs, the impact of synchronisation errors has been investigated. Due to the differences in path loss involved, delay spread and relative propagation delay have shown minor impact. Also the design is robust with respect to Doppler spread. On the other hand, time and frequency synchronisation errors might occur between different sites (perfect synchronisation of all RAPs on one site, as well as of the UTs, is assumed). Figure 13.17 shows the gain at the 5th percentile of the SINR CDFs for an SFN in relay-enhanced cells of three sites as compared to a deployment Table 13.9 Link efficiencies of point-to-point and point-to-multipoint links in different deployments. p-t-m

Outdoor Indoor

BCU REC BCU REC

p-t-p

No SFN

SFN 1 site

SFN 3 sites

1.9 1.9 1.3 1.4

0.32 0.37 0.03 0.05

0.33 0.48 0.04 0.08

0.43 0.60 0.05 0.12

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Radio Technologies and Concepts for IMT-Advanced

Table 13.10 Required MCBC group size to outperform p-t-p performance SFN 3 sites

Outdoor Indoor

SFN 1 site

BCU

REC

BCU

REC

5 26

4 12

6 33

5 18

without SFN. The subcarrier spacing is 39 kHz and symbol duration is 28.8 µs. The residual gain of around 1.8 dB is the gain that remains due to the SFN per site (which has always perfect synchronisation). From this plot, the requirements for inter-sitesynchronisation can be extracted for a given target SINR gain at the 5th percentile. 13.6.1.7 Impact of Traffic and Packet Modelling on Spectral Efficiency Full buffer simulations provide optimistic results, since neither the impact of the traffic model (reduced queue length, reduced multi-user diversity, reduced link adaptation accuracy, etc.) nor the impact of packet handling, such as segmentation, padding loss, etc., are considered. In order to understand the resulting degradation, dynamic system-level simulations are performed comparing full buffer simulations with a constant bit rate model (CBR). A CBR traffic model with constant packet size was used in order to eliminate effects due to varying packet size and reading times. The CBR traffic parameters are comparable to a low-resolution, live streaming

5% SINR gain [dB]

3.5

3

2.5

2

1.5 20

40 30

0

20 10

−20 ∆t [µs]

0

+/− ∆f [kHz]

Figure 13.17 Gain at the 5th percentile of the SINR CDF for an SFN of three sites of relay-enhanced cells as a function of time and frequency synchronisation errors between the sites.

System Model, Test Scenarios, and Performance Evaluation

509

service class according to the service classification of ITU-R (see Chapter 12) and to the interactive, high-quality multimedia service class described in Section 2.3. The traffic load is 2.06 Mbps assuming one packet (of 712 information bits) arrival per slot per user. The serviceclass-related satisfied user criterion (SUC) requires that both of the following conditions are true:

r 95 % of the users have an average user throughput greater than or equal to 2 Mbps (i.e. a maximum of 3 % packet loss is allowed on average);

r 95 % of the packets arrive with a delay of less than TSUC = 100 ms. Packets are discarded if they are older than 110 ms in order to prevent congestion in the transmit queue. Evaluations are performed for the downlink of a frequency-adaptive transmission in the base coverage urban test scenario. Simulation assumptions are according to Section 13.4 and [WIN2D6137]. Results are shown from dynamic system-level simulations using dedicated modelling of an N-channel-stop-and-wait protocol with Chase combining of packets. Overhead is considered by including 13 symbols for control signalling and four pilot symbols per antenna, resulting in a total overhead of 29 out of 96 symbols. Besides a standard proportional fair scheduler, simple delay-aware schedulers are used, where the standard metric m is multiplied by a factor Md that considers packet delay. Two factors are investigated: Md,lin =

TSUC , TSUC − min (tHOL , TSUC − 1)

(13.3)

and

Md,ex = e

w·TSUC TSUC −min(tHOL ,TSUC −1)

.

(13.4)

where TSUC = 100 ms is the delay-related satisfied user criterion of the service and tHOL is the age of the head-of-line packet (oldest packet) in the transmit queues of the particular user. In the subsequent figures, a scheduler using the additional factor according to Equation (13.3) is denoted DS (delay-sensitive), DSexp indicates a scheduler according to Equation (13.4). The CDFs of user packet delay and user packet throughput are shown in Figure 13.18 for a load of 40 users and the baseline spatial processing technique of a fixed grid of eight beams (4x2 MIMO). Both figures contain a zoom-in for the area of the SUC requirements on delay and throughput, respectively. It can be seen that all scheduler variants still fulfil the packet delay requirement. Unlike the standard PF scheduler, the DS and certain parameterisations of the DSexp scheduler (w = 3, 0.3) meet the average throughput requirement. For a load of 44 users all investigated schedulers already infringe upon the throughput requirement. It can therefore be concluded that the maximum load using a delay-aware scheduler and GoB is around 40 users. The same investigation has been performed for GoB with SDMA. Comparing both schemes (i.e. Figure 13.18 against Figure 13.19) at equal load of 40 users per sector, it is clearly visible that GoB+SDMA improves user throughput and packet delay compared to GoB. For GoB+SDMA and delay-aware scheduling, the load can be even further increased up to 56 users, as shown in Figure 13.20.

Radio Technologies and Concepts for IMT-Advanced

Cumulative distribution function values

Cumulative distribution function values

510

1 SUC 0.8

DS

1

DSexp w=0.003

0.6

DSexp w=0.03

0.98

DSexp w=0.3

0.4

DSexp w=3

0.96

PF

0.2

SUC 100 105

95 0 0

20

40

60 Packet delay [ms] (a)

80

110

100

120

1 0.1

DS

0.8

DSexp w=0.003

0.08

DSexp w=0.03

0.6

DSexp w=0.3 DSexp w=3

0.4

0.06 SUC 0.04

PF 1.9

0.2 0

1.95

2

2.05 SUC

0.2

0.4

0.6

0.8 1 1.2 1.4 User throughput [Mbps] (b)

1.6

1.8

2

2.2

Figure 13.18 CDFs of (a) user packet delay and (b) user packet throughput for a 4x2 GoB configuration and scheduler variants (40 users per sector), from [OSD+08]. (Reproduced by Permission of IEEE © 2009).

A comparison of the spectral efficiency obtained for full buffer simulations and CBR with scheduler variants is given in Figure 13.21 for both GoB and GoB+SDMA. For the full buffer model, simply the successfully transmitted bits are counted, whereas packets, segmentation and padding are modelled for the CBR service. In the full buffer simulations, all users have permanently an infinite amount of data to transmit and therefore can be allocated many resources. Results with the same simulator and simulation parameters have shown that in this case the maximum load for GoB is 28 users per sector in order to fulfil the throughput SUC (95 % of users having 2 Mbps user throughput or more). For CBR traffic only a few or just one packet might reside in the transmit buffer. Therefore, as stated above, 40 users can be served using GoB due to the reduced offered traffic load of 2.06 Mbps per user. While the spectral efficiency vs number of users curve is already in saturation for the full buffer assumption, multi-user scheduling gain is still considerable for the CBR traffic in Figure 13.21. It is therefore important to compare the loss in spectral efficiency not at an equal number of users,

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Figure 13.19 CDFs of (a) user packet delay and (b) user packet throughput for a 4x2 GoB + SDMA configuration and scheduler variants (40 users per sector), from [OSD+08]. (Reproduced by Permission of IEEE © 2009).

but at the maximum load supported for this particular service. In this operational point, the degradation in spectral efficiency is around 23 % (from 2.2 bps/Hz/sector for full buffer and 28 users to 1.7 bps/Hz/sector for CBR and 40 users). For GoB+SDMA similar trends are observed: spectral efficiency is decreased by 21 % for CBR traffic and delay-aware scheduler with respect to full buffer (from 2.9 bps/Hz/sector for full buffer and 30 users to 2.3 bps/Hz/sector for CBR and 56 users). It can be concluded, therefore, that full buffer simulations overestimate spectral efficiency but underestimate the maximum number of users that can be served given the same throughput SUC for all simulations. The SUC, i.e. QoS constraints, prevent full exploitation of the multiuser scheduling gain for the CBR traffic investigations. Amongst the different variants of the delay-aware schedulers, virtually no difference is visible in the overall spectral efficiency in Figure 13.21. Figure 13.18, Figure 13.19, and Figure 13.20 show that, related to delay and average user throughput, the DSexp, w = 3

Radio Technologies and Concepts for IMT-Advanced

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Figure 13.20 CDFs of (a) user packet delay and (b) user packet throughput for a 4x2 GoB + SDMA configuration and scheduler variants (56 users per sector), from [OSD+08]. (Reproduced by Permission of IEEE © 2009).

performs best. However, the DS metric (see Equation (13.4)) performs almost as well and avoids the use of the tuning parameters w. It can therefore be regarded as an efficient and simple implementation of a delay-aware scheduler. If a standard PF scheduler is used instead of a delay-aware scheduler as outlined above, the maximum number of satisfied users decrease from 40 to 32 in case of GoB and from 56 to 48 for GoB+SDMA. The spectral efficiency obtained at the maximum supported number of users decreases accordingly from 1.7 bps/Hz/sector to 1.45 bps/Hz/sector for GoB and from 2.3 bps/Hz/sector to 2.1 bps/Hz/sector for GoB+SDMA. The simple delay-aware scheduling algorithms used here can improve the maximum number of supported users and spectral efficiency notably. A comparison of the performance at the maximum number of satisfied users is provided in Table 13.11.

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Figure 13.21 Comparison of spectral efficiency of full buffer and CBR simulations for (a) GoB and (b) GoB+SDMA, from [OSD+08]. (Reproduced by Permission of IEEE © 2009).

13.6.2 Microcellular Scenario 13.6.2.1 Indoor Coverage Improvement by Relay Deployments In this section we investigate the coverage improvement by deploying RNs in a microcellular scenario. The BS-only and relay deployment are illustrated in Figure 13.22. The BS-only deployment includes 144 BS. The relay deployment consists of 96 BS sectors, each having an additional RN. The cost of both scenarios is similar for an RN–BS cost ratio of one fourth. Table 13.11 Maximum number of supported users and spectral efficiency comparison, from [OSD+08]. (Reproduced by Permission of IEEE © 2009) MIMO scheme GoB

GoB+SDMA

Traffic model – scheduler

Max. number of supported users

Spectral efficiency (bps/Hz/sector)

Full Buffer – PF CBR – PF CBR – DS Full Buffer – PF CBR – PF CBR – DS

28 32 (+15 %) 40 (+43 %) 30 48 (+60 %) 56 (+87 %)

2.2 1.45 (−34 %) 1.7 (−23 %) 2.9 2.1 (−28 %) 2.3 (−21 %)

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Radio Technologies and Concepts for IMT-Advanced

REC 2 sector BS RN

Figure 13.22 BS-only (left) and relay (right) deployment (zoom of center cells), from [DWV08]. (Reproduced by Permission of IEEE © 2009).

The coverage has been calculated using the WINNER path loss and channel models as presented in Chapter 3 and in [WIN2D112]. The urban micro-cell path loss model B1 was used for UT located in the streets and the outdoor-to-indoor model (B4) for UT located inside buildings. For the outdoor-to-indoor model, the path loss consists of three components: the outdoor path loss P L B1 as defined by the B1 channel model, the penetration loss into the building P L tw and the indoor path loss P L I n . The path loss equation is given as PL[in dB] = min{PLn,B1 + PLtw + PLn,in },

n = {1, 2, 3, 4}

(13.5)

where the lowest path loss using the four points of the outer walls of the building blocks that are closest to the indoor UT is selected. The outdoor path loss calculation is based on the distance travelled in the streets. Furthermore, we distinguish whether the two nodes are in the same street or not, i.e. the line-of-sight (LOS) or non-LOS path loss model is used. Figure 13.23 illustrates the coverage area of the BS-only and the relay deployment. We define the coverage area as the area where the signal-to-noise ratio (SNR) provided by the radio access points is higher than 7 dB and thus provides a spectral efficiency of 1 b/s/Hz. Intuitively, it can be seen that the coverage area of the RN deployment is higher than the coverage area of the BS-only deployment. In fact, the relay deployment can increase the covered area from 83.3 % to 88.5 % compared to a BS-only deployment of similar cost. Both deployments cover all the streets but the relay deployment provides much better outdoor to indoor coverage, where it increases the indoor area coverage by 9 %. 13.6.2.2 Soft Frequency Re-use We further compare the throughput of indoor users for the relay deployment (shown in Figure 13.5) with the same deployment but without RNs (denoted by BS) for different resource assignment strategies. Soft frequency re-use is utilised as an interference coordination strategy. It uses different transmit powers for different chunks and coordinates their use across RAPs in order to reduce inter-cell interference, as described in Section 9.3.3.1. To leverage its potential benefits, a

System Model, Test Scenarios, and Performance Evaluation

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myPropag 18 1800

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Figure 13.23 Downlink coverage plot (SNR) for (a) relay deployment and (b) BS-only deployment, from [DWV08]. (Reproduced by Permission of IEEE © 2009).

suitable scheduling strategy is required. We use a two-stage scheduling process. A timedomain scheduler guarantees fairness between the users. In our simulations, it selects the six users from approximately 20 users (served on average by a RAP) with the lowest average throughput in the last 50 ms. A slightly modified version of the phased scheduling approach [WIN2D352] is used as a frequency domain scheduler. It schedules low SINR user preferably in high power phases of the power mask. We compare different resource partitioning options: where BS and RN serve UTs at the same time and where they don’t serve UTs at the same time. In both options, each RAP

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Table 13.12 Relative user throughput comparison of BS only and relay deployment. Scenario BS only RN does not serve UTs at same time, RN serves UT in 4 frames RN serves UT at same time, RN serves UT in 4 frames RN serves UT at same time, Optimal number of frames when RN serves UT RN serves UT at same time, Optimal number of frames when RN serves UT, Soft frequency re-use

Relative average user throughput 1 0.65 1.02 1.19 1.28

occupies the whole bandwidth using the power masks as defined in Table 13.12. By allowing the RN to serve its UT at the same time as the BS does, we obtain an increase of 27 % in the average user throughput. The amount of frames within a super-frame where the RN is serving UTs depends on the capacity of the BS–RN link and the RN–UT links. As the capacity of the BS–RN link is very high, the best result was achieved when the RN can serve its UTs in six out of eight frames. Thus, two out of eight frames are sufficient for BS–RN communication. Selecting the optimal number of frames for the RN transmission improves the user throughput by an additional 17 %. This indicates that next to interference coordination in the frequency domain, the performance of relay deployments strongly depends on the proper balance between the resources spent on the first hop, between BS and RN, and on the second hop, between RN and UT. Finally, the best average user throughput was achieved when using soft frequency re-use combined with the phased scheduling strategy. Overall, the relay deployment could outperform the BS-only deployment by 28 %. However, without proper radio resource management, the potential benefits of the relay deployment are lost. The results also show that the relaying concept in Chapter 8 can adapt very well to the environment. 13.6.2.3 Soft and Fractional Frequency Re-use and Re-use One The deployment pattern of the BS and the power mask assignment for the soft frequency re-use (SFR) and fractional frequency re-use (FFR) case is illustrated in Figure 13.24. For SFR, we subdivide the available OFDMA resource units (chunks) in the frequency domain into equal-sized groups and assign one of the power levels, P = {1, 0.5, 0.25} to each group. The power levels have not been optimised but a power mask step of 3 dB was found to be the best choice when comparing several options (ranging from 1 dB to 5 dB). In the FFR case, two-thirds of the chunks use re-use one and the rest is subdivided between the three groups of BS (see Section 9.3.3.1 for a general description of FFR and further results in the base coverage urban scenario). We assume that the frequency domain scheduler has channel state information (SINR) for each chunk. The user throughput statistics have been collected every 400 ms. To avoid edge effects, the simulation results are only obtained from four monitored cells in the centre. One tier of cells around these centre cells is fully modelled, including user mobility, handovers, scheduling, etc. The residual cells are modelled as interfering cells, i.e. the user traffic and the scheduling are not modelled but they are included in the interference calculation.

System Model, Test Scenarios, and Performance Evaluation

517

2 sector BS Power Masks SFR: [0.5 0.25 1] [0.25 1 0.5] [0.0.5 0.25] Power Masks FFR [1 1 1 1 1 1 0 0 1] [1 1 1 1 1 1 1 0 0] [1 1 1 1 1 1 0 1 0]

Figure 13.24 BS deployment pattern and assigned power masks for soft frequency re-use and fractional frequency re-use.

We randomly placed 2000 UTs, moving along streets in areas served by the active cells at a pedestrian speed of 3 km/h. The corresponding channel and path loss models for all links are urban micro-cell (B1) with LOS for nodes in the same street and non-LOS for nodes in different streets, see Chapter 3 and [WIN2D112]. We utilise an interference-aware phased scheduling scheme for SFR that exploits the interference variations introduced by SFR. The phased scheduler aims to increase the throughput of cell edge users while keeping at least a similar cell throughput than the proportional fair scheduling algorithm. Similar to [PPM+07], we introduce a two-phase scheduling process. In a first phase, a time-domain scheduler groups users into groups with high and low SINR. It then selects users of each group for frequency domain scheduling. The proportional fair frequency domain scheduling algorithm then allocates the physical resources using the SINR variations introduced by SFR. No performance increase was obtained for FFR when applying the phased scheduler. Thus, for re-use one and FFR we utilised a proportional fair scheduler. Figure 13.25 compares the user throughput and Figure 13.26 the SINR CDF for SFR, FFR and re-use one. Both SFR and FFR increase the SINR CDF of the received packets significantly compared to re-use one. At the 5th percentile of the CDF, the increase is 2.6 dB for SFR and 5.1 dB for FFR. The higher increase in SINR for FFR comes at the expense of reduced availability of resources for scheduling which reduces the average cell throughput by 17 % compared to SFR. On the contrary, SFR has the whole resources available for scheduling and the scheduler can utilise the introduced SINR variations to support users with low SINR. The split in high and low SINR user groups and selecting UTs from both groups for frequency domain scheduling makes optimal use of the SINR variations which also increases the average cell throughput by 8 % compared to re-use one.

13.6.3 Local Area Scenarios Relaying and cooperative relaying are investigated in the local area scenario to guarantee the envisaged coverage under the assumption of cost efficiency. System-level results for different MIMO schemes employing distributed antennas in the indoor test scenario described

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Radio Technologies and Concepts for IMT-Advanced

Figure 13.25 User throughput CDF comparison for soft frequency re-use, fractional frequency re-use and re-use one, from [DWV08]. (Reproduced by Permission of IEEE © 2009).

in Section 13.4 are presented in Sections 7.4.5 and 7.7.2. There the assumption was that the signal distributions to the antenna arrays located in the corridors is done via backhaul. In this section, an alternate solution is investigated, where relays are placed at the same positions in the corridor. As no access or change to the building infrastructure is required, this is a solution with ultimate flexibility, e.g. for temporal and changing network deployment. 1 0.9 0.8 0.7

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Figure 13.26 SINR CDF comparison for soft frequency re-use, fractional frequency re-use and re-use one. (Reproduced by Permission of IEEE © 2009).

System Model, Test Scenarios, and Performance Evaluation

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Figure 13.27 Baseline relay deployment in indoor scenario.

We compare three schemes: direct transmission (single-hop), single path (conventional) relaying and RN-to-RN cooperation (cooperative relaying) using the baseline relay deployment presented in Figure 13.27 and the simulation parameters presented in [WIN2D61312]. Four RNs (dark grey, 1–4) are coordinated by the base station located in the centre (very dark grey, 0). RNs are placed in the corridors at the same positions as the remote antenna heads in the no-relaying case and are located 25 m and 75 m away from the left and right sides of the building, respectively. Due to the symmetries in the deployment, the simulation area was limited to a set of 10 rooms located in the bottom right of Figure 13.27, i.e. around RN3. Two RNs (2 and 3) are used and they are coordinated by the BS (0) located in the centre. Each user is assigned one chunk, i.e. 8 subcarriers and 15 OFDM symbols and resources are partitioned according to the scheme presented in Figure 13.28. The structure of the superframe is defined according to Chapter 4 and radio resources are partitioned in both temporal and spectral domains. A similar pattern is repeated twice within the duration of a super-frame.

PREAMBLE

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Figure 13.28 Radio resource partitioning.

520

Radio Technologies and Concepts for IMT-Advanced

(b)

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Figure 13.29 (a) Deployment of RNs and relative user throughput for (b) direct transmission, (c) single path relaying via RN3 and (d) RN2–RN3 cooperation.

First, the resources are assigned to the base station and then to different combinations of RNs. As a result, a maximum of three RNs can be active at the same time. The performance results presented below compare the following strategies: direct transmission, single path relaying, and RN-to-RN cooperation using the static REACT algorithm [Wod07] where the BS selects the RAP or cooperating RAPs, and is responsible for resource partitioning and scheduling. The results presented in Figure 13.29 show significant throughput degradation in most of the area of interest when only the direct link (Figure 13.29(b)) is considered. Moreover we can restore an almost homogenous throughput (fixed modulation and coding rate is simulated

System Model, Test Scenarios, and Performance Evaluation

521

here) when we apply single path relaying (Figure 13.29(c)). Unfortunately the gain provided by RN–RN cooperation compared to the single path relaying case is diminishing (see Figure 13.29(d)). The reason is that the signal coming to the destination (UT) from the cooperating relay node RN2 is usually strongly attenuated by a higher number of walls than the direct link. According to the A1 NLOS propagation model, each wall is modelled by 5 dB attenuation of the transmitted signal, which results in a gap between the power levels of the signals received by the destination from both cooperating RNs (e.g. 15 dB). As a result, the signal coming from the other RN (i.e. RN2 in Figure 13.29(a)) is usually not strong enough to provide a significant improvement in throughput using cooperating RNs [Wod08]. For this reason, other RN deployments were evaluated in [WIN2D61312] and show that the gain provided by single path and cooperating relaying is rather sensitive to the RN placement. While interpreting these results, one should keep in mind two important aspects of the simulation assumptions. First, it was assumed that the RNs take part in the transmission even if the packet to be retransmitted is in error. However, after having received a packet in error, such a wrongly positioned RN should in general remain silent. This way the cooperation between the RNs would only be activated when beneficial and not spoil the performance in other cases. As a result the other RN does not penalise the system performance and the overall achievable throughput increases. Secondly, a fixed modulation and coding scheme was used in the simulations and effects of link adaptation are not considered. The main outcome is that the local area deployment is a very specific one. Single path relaying provides a significant increase in throughput, but we observe almost no gain from RN–RN cooperation due to the existence of many walls providing a strong attenuation.

13.7 Conclusion To conquer the immense system-level simulation requirements of the highly adaptive future wireless systems, different classes of simulators need to be used depending on the investigated matter. WINNER has provided a novel link-to-system interface suitable for multi-state channels of advanced OFDMA systems. Exemplary system-level simulations are shown for different test scenarios highlighting the interrelation of different WINNER features that exploit the frequency selectivity of the channel, e.g. frequency-domain link adaptation and scheduling. Relaying is proven to be a powerful deployment scenario to improve indoor coverage by outdoor base stations, especially at the cell edge, both in the base coverage urban and microcellular scenario. In combination with different MIMO and resource allocation schemes further performance gain can be leveraged. The use of single-frequency networks for MCBC is particularly promising for relay-enhanced cells. System-level evaluation employing a constant bit rate traffic model with satisfied user criteria on user throughput and packet delay show that full buffer simulations significantly overestimate the spectral efficiency, but underestimate the number of users that can be accommodated for a given target user throughput. Even simple delay-sensitive schedulers can improve the performance by around 20 %.

Acknowledgements The authors would like to thank all people involved in the system-level evaluation work in the WINNER project for their contribution and inspiring discussions. In particular we would like to acknowledge the contributions of Klaus Doppler, Laurits Hamm, Jakub Oszmia´nski,

522

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Simone Redana, Peter Rost, Krystian Safjan, Carl Wijting, and Michał W´odczak, who were directly involved in the generation of the results presented in this chapter.

References [BAS+05]

[CFR+07] [CTB96] [DWV08]

[Eri03a] [Eri03b] [ETSI98] [FRC+07]

[Ham07]

[Kat08] [LRZ02] [NGMN07]

[Nor03] [Nor04] [NR98] [OSD+08]

[PPM+07]

[PPS07]

[SBC07] [SOD+08]

Br¨uninghaus, K., Ast´ely, D., S¨alzer, T., Visuri, S., Alexiou, A., Karger, S. and Seraji, G.A. (2005) ‘Link performance models for system level simulations of broadband radio access systems’, Proc. of IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 4: 2306–11, Berlin, Germany. Costa, E., Frediani, A., Redana, S. and Zhang, Y. (2007) ‘Dynamic Resource Sharing in Relay Enhanced Cells’, Proc. of European Wireless Conference, Paris, France. Caire, G., Taricco, G. and Biglieri, E. (1996) ‘Capacity of bit-interleaved channels’, Electronic Letters, 32(12): 1060–61. Doppler, K., Wijting, C. and Valkealahti, K. (2008) ‘On the Benefits of Relays in a Metropolitan Area Network’, Proc. of IEEE 67th Vehicular Technology Conference (VTC 2008-Spring), Singapore, pp. 2301–5. Ericsson (2003) ‘Effective SNR mapping for modelling frame error rates in multiple-state channels’, 3GPP2 Document 3GPP2-C30-20030429-010. Ericsson (2003) ‘System-level evaluation of OFDM: further considerations’, 3GPP TSG RAN WG1 Meeting #35, Document R1-031303, Lisbon, Portugal. ETSI (1998) Selection procedures for the choice of radio transmission technologies of the UMTS (UMTS 30.03 version 3.2.0), Technical Report TR 101 112, Version 3.2.0, ETSI, Sophia Antipolis. Frediani, A., Redana, S., Costa, E., Capone, A. and Zhang, Y. (2007) ‘Dynamic Resource Allocation in Relay Enhanced Cells based on WINNER System’, Proc. of IST Mobile Summit, Budapest, Hungary. Hamm, L. (2007) ‘Spectral Efficiency of the Next-Generation MIMO-OFDM Wireless System WINNER under Deployment and QoS Constraints’, Diploma Thesis, TU Ilmenau, Institut f¨ur Informationstechnik. Kathrein (2008) Antenna Line Products for Mobile Communications, Product Catalogue 2008, viewed 20 June 2009, http://www.kathrein.de. Lampe, M., Rohling, H. and Zirwas, W. (2002) ‘Misunderstandings about link adaptation for frequency selective fading channels’, Proc. PIMRC 2002, Lisbon, Portugal. Next Generation Mobile Networks (2007) ‘Next Generation Mobile Networks Radio Access Performance Evaluation Methodology’, NGMN White Paper, Version 1.2, viewed 20 June 2009, http://www.ngmn.org/nc/downloads/techdownloads.html. Nortel Networks (2003) ‘Effective SIR Computation for OFDM System-Level Simulations’, 3GPP TSG RAN WG1 Meeting #35, Document R1-031370, Lisbon, Portugal. Nortel Networks (2004) ‘OFDM Exponential Effective SIR Mapping Validation, EESM Simulation Results’, 3GPP TSG RAN WG 1 Ad hoc Meeting, Document R1-04-0089. Nanda, S. and Rege, K.M. (1998) ‘Frame Error Rates for Convolutional Codes on Fading Channels and the Concept of Effective Eb/N0’, IEEE Trans. on Veh. Techn., 47(4): 1245–50. Osmia´nski, J., Safjan, K., D¨ottling, M. and Bohdanowicz, A. (2008) ‘Impact of Traffic Modeling and Scheduling on Delay and Spectral Efficiency of the WINNER System’, Proc. IEEE VTC 2008 Spring, Singapore. Pokhariyal A., Pedersen, K.I., Monghal, G., Kovacs, I.Z., Rosa, C., Kolding, T.E. and Mogensen, P.E. (2007) ‘HARQ Aware Frequency Domain Packet Scheduler with Different Degrees of Fairness for the UTRAN Long Term Evolution’, Proc. IEEE VTC 2007 Spring, Dublin, Ireland. Pfletschinger, S., Piatyszek, A., and Stiglmayr, S. (2007), ‘Frequency-selective Link Adaptation using Duo-Binary Turbo Codes in OFDM Systems’, Proc. IST Mobile and Wireless Communications Summit, Budapest. Stiglmayr, S., Bossert, M. and Costa, E. (2007) ‘Adaptive coding and modulation in OFDM systems using BICM and rate-compatible punctured codes’, Proc. European Wireless, Paris. Safjan, S., Osmia´nski, J., D¨ottling, M. and Bohdanowicz, A. (2008) ‘Frequency-Domain Link Adaptation for Wideband OFDMA Systems’, Proc. IEEE WCNC 2008, Las Vegas.

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Wijting, C.S., Doppler, K., Kallioj¨arvi, K., Johansson, N., Nystr¨om, J., Olsson, M., Osseiran, A., D¨ottling, M., Luo, J., Svensson, T., Sternad, M., Auer, G., Lestable, T. and Pfletchinger, S. (2008) ‘WINNER II System Concept: Advanced Radio Technologies for Future Wireless Systems’, Proc. ICT Mobile Summit 2008, Stockholm, Sweden. [WIN2D112] WINNER II (2007) IST-4-027756 WINNER II Channel Models, Deliverable D1.1.2, September 2007, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+. [WIN2D222] WINNER II (2006) IST-4-027756 Link Adaptation Procedures, Deliverable D2.2.2, October 2006, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+. [WIN2D233] WINNER II (2007) IST-4-027756 Link Level Procedures for the WINNER System, Deliverable D2.3.3, November 2007, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN1D24] WINNER I (2005) IST-2003-507581 Assessment of adaptive transmission technologies, Deliverable D2.4, February 2005, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN1D27] WINNER I (2005) IST-2003-507581 Assessment of advanced beamforming and MIMO technologies, Deliverable D2.7, February 2005, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D351] WINNER II (2006) IST-4-027756 Relaying concepts and supporting actions in the context of CGs, Deliverable D3.5.1, October 2006, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D352] WINNER II (2007) IST-4-027756 Assessment of relay based deployment concepts and detailed description of multi-hop capable RAN protocols as input for the concept group work, Deliverable D3.5.2, June 2007, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D353] WINNER II (2007) IST-4-027756 Final assessment of relaying concepts for all CGs scenarios under consideration of related WINNER L1 and L2 protocol functions, Deliverable D3.5.3, September 2007, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+. [WIN2D461] WINNER II (2006) IST-4-027756 The WINNER II Air Interface: Refined multiple access concepts, Deliverable D4.6.1, November 2006, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D473] WINNER II (2007) IST-4-027756 Smart antenna based interference mitigation, Deliverable D4.7.3, June 2007, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D6137] WINNER II (2006) IST-4-027756 WINNER II Test Scenarios and Calibration Cases Issue 2, Deliverable D6.13.7, November 2006, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D61312] WINNER II (2007) IST-4-027756 Final CG ‘local area’ description for integration into overall System Concept and assessment of key technologies, Deliverable D6.13.12, October 2007, viewed 20 June 2009, http://projects.celtic-initiative.org/winner+. [Wod07] W´odczak, M. (2007) ‘Extended REACT: Routing information Enhanced Algorithm for Cooperative Transmission’, Proc. 16th IST Mobile & Wireless Communications Summit 2007, Budapest, Hungary. [Wod08] W´odczak, M. (2008) ‘Cooperative Relaying in an Indoor Environment’, Paul Cunningham and Miriam Cunningham (Eds), Proc. ICT-Mobile Summit 2008 Conference, Sweden.

14 Cost Assessment and Optimisation for WINNER Deployments Marc Werner1 and Peter Moberg2 1 2

Qualcomm CDMA Technologies GmbH Ericsson

14.1 Introduction To prove the cost effectiveness of future mobile communications systems based on the WINNER concept, suitable deployment cost modelling and analysis methodologies have to be identified. The procedures developed in this chapter allow an analysis of the performance versus costs trade-off for different WINNER deployments utilising different technology options, and enable a comparison with evolved legacy systems as well as other systems under development. With the introduction of advanced radio technologies, mobile services and new deployment strategies (e.g. infrastructure sharing and coexistence with legacy systems), the deployment cost assessment for cellular networks has gained more importance for network operators. Today, cost assessment is a mandatory design guideline for systems engineering, and it will require increased attention in future development processes. Investment decisions in a changing wireless ecosystem become increasingly complex, and all cost models need to make some simplifying assumptions that may only hold for specific deployment environments. The initial section of this chapter has the purpose of giving an overview of the cost modelling and assessment methodology. Additionally, the challenges related to the estimation of radio access network (RAN) cost in the changing wireless ecosystem are elaborated upon, and the impact of network technology options on the resulting costs is discussed. Following this general section on how the cost of a RAN is structured, some more detailed discussion of the cost components of a RAN is given. Also, some cost estimates of different cost components are presented. A WINNER system has the possibility of exploiting new or advanced wireless technologies such as multi-hop relaying and advanced spectrum techniques such as flexible spectrum use. Radio Technologies and Concepts for IMT-Advanced Martin D¨ottling, Werner Mohr and Afif Osseiran C 2009 Martin D¨ ottling, Werner Mohr, Afif Osseiran

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It is expected that these technologies – at least in certain deployment scenarios – will result in significant system performance gains. It is however also understood that they might not always and not by all operators be deployed and that their implementation can result in additional costs. If there are relevant deployment cases where the new technologies do not provide a significant gain, these costs might be prohibitive. It is therefore important to analyse whether new technologies are essential for the WINNER concept and, if they are optional, which costs result from their implementation. Two technology components on which the analysis of this chapter focuses are multi-hop relaying and multiple antenna solutions, both central components of the WINNER system. Since different technology alternatives usually have different cost structures and performance characteristics, one of the main contributions of this chapter is to develop a framework that can be used for evaluating the trade-off between additional cost and increased performance. One of the cornerstones of the developed methodology is the use of what can be referred to as ‘iso-performance analysis’. The term tries to capture that the analysis framework is based on the fact that a predetermined performance level can be achieved by means of different technology or deployment alternatives. When performance is kept constant, the most favourable technology option would be the one with least cost. It should be pointed out that, obviously, comparisons are very dependent on the performance metrics and deployment scenario under investigation. Furthermore, this chapter dedicates a section to introducing and discussing the cost comparison methodology and cost assessment framework relying on the iso-performance approach. Finally, the cost assessment framework is applied to some specific deployment scenarios and numerical results are obtained and evaluated. To summarise, the outline of this chapter is as follows: in Section 14.2, an initial overview of the cost modelling and assessment methodology is provided. Additionally, there is a discussion on the challenges regarding the estimation of the cost of a RAN. Section 14.3 provides a detailed classification of all relevant CAPEX and OPEX cost components for the RAN, as well as example cost figures for these components. Section 14.4 describes the developed models for traffic demand, radio propagation, radio access point (RAP) deployment, and the final cost assessment and optimisation using iso-performance curves. In Section 14.5, these models are applied to example deployment scenarios for illustration. Section 14.6 summarises the main conclusions that can be drawn.

14.2 Cost Assessment Framework and Assumptions To perform a cost assessment framework of a RAN is a challenging task. This section brings up some of the assumptions made and discusses some difficulties related to cost estimation and evaluation. Initially, an overview of the cost assessment procedure is outlined.

14.2.1 General Cost Assessment Procedure The overall methodology for cost assessments of RAN deployments consists of several elements. Together, they form a framework that can be used for a cost estimation of different future WINNER deployments, but is also applicable to other systems if the assumptions that were made still hold. The elements of the cost assessment framework are summarised below and further explained in the indicated sections.

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r The type of cost assessment to be carried out must be defined. Furthermore, an analysis of the wireless ecosystem in which the system is to be deployed should be performed as the network costs may be carried by different entities. Additionally, a differentiation between ‘incumbent’ and ‘greenfield’ operator scenarios is necessary. ‘Incumbent’ operators already own a RAN from legacy systems and ‘greenfielders’ do not own any network and will deploy WINNER in wide, metropolitan or local area scenarios. See Sections 14.2.2 and 14.2.3. r The baseline technology assumptions for the RAN deployment must be selected, with respect to consequences on cost and network performance. At this point of the cost analysis, it is typically unknown if a certain technology option is worthwhile. Therefore, multiple cost assessments should be performed for different sets of technology options which represent variations within the selected baseline technology assumptions. See Section 14.2.4. r The relevant cost components must be identified and classified. An analysis must be made of the deployments and economic scenarios for which each cost component is relevant and the absolute or differential cost figures for each cost component at the time of deployment must be estimated. See Section 14.3. r The types of services offered by the RAN, the service usage, and the spatial distribution of user density must be characterised in a traffic demand map. Typically, the traffic demand is modelled in a simplified way, in the sense that temporal traffic variations are not considered. The constant traffic demand then reflects a certain network or cell outage probability used in network planning. See Section 14.4.3.1. r The RAP deployment model must be defined in terms of the order of priority for deployment of different RAP types, as well as candidate locations and site selection rules. See Section 14.4.3.2. r Appropriate radio propagation and cell capacity models, noise and interference calculation methods, and criteria for capacity and coverage checks must be selected for the deployment scenario under study. See Sections 14.4.3.2 and 14.4.3.3. r As a result of multiple deployment simulations with different densities of RAP types, an optimum RAP deployment analysis can be carried out using multi-dimensional isoperformance functions. See Section 14.4.4. r The total deployment cost and cost benefits due to RAP density optimisation can be calculated directly from the above cost optimisation. See Section 14.4.4. The remainder of this section deals with the underlying assumptions regarding the general economic background and the WINNER system and deployment characteristics for which the cost assessments are carried out.

14.2.2 Types of Cost Assessment Accounting for the challenges in cost analysis mentioned in Section 14.2.3, the following approach is used to perform deployment cost assessments for the WINNER system. In a case study, absolute total cost figures are derived for exemplary WINNER deployments. It is not the primary ambition of this approach to be representative or to provide exact cost figures but, on the one hand, to demonstrate the fundamental cost modelling methodology that allows analysis of the full deployment costs and demonstrates cost optimisation strategies, and on the other hand, to perform cost comparisons for different WINNER deployment scenarios or technology options, e.g., with or without relay nodes (RNs). Absolute cost figures that are not

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available are estimated. Assumptions for service portfolios, traffic characteristics, etc., are kept as simple as possible and are not necessarily representative. Apart from proving the applicability of the cost-modelling approach, the case studies can help identify the major cost components for a particular RAN deployment and often reveal the sensitivity of cost studies to certain deployment or design assumptions.

14.2.3 Challenges in Cost Assessment For various reasons, a general and full quantification of the deployment costs of radio access systems is impossible to perform. Certain cost components are operator-specific as they depend, e.g., on the contractual relationship between the hardware manufacturer and the operator. Others depend on the regulatory and legislative environment in the country of deployment. Beyond this, a prediction of the developments of the traffic demand and the cost structure over a period of five or more years – assuming a WINNER deployment in 2011–15 – is even more difficult since the wireless ecosystem will likely undergo significant changes in the future. Another challenge of a cost assessment for future radio access systems is the increased diversity of mobile radio access systems and of the supported service portfolio that will make it difficult to define a reference performance scenario or service portfolio for a fair and meaningful comparison. Technical figures such as the spectral efficiency will have to be used with care as they are only valid in connection with the definition of a target quality per radio service. These target qualities typically differ for each employed radio service. Furthermore, the different characteristics of deployment regions make it difficult to find a set of deployment cost assumptions that are valid for all parts of the world. In this chapter, we assume a highly developed deployment region where, e.g., broadband fixed line connections are available with only a small effort at each base station site. Despite these limitations, the cost analysis in this chapter aims to provide, on the one hand, some insight into the trade-off between costs and performance for different deployment options and, on the other hand, some exemplary quantitative cost figures for WINNER systems in certain deployment areas. Cost assessments based on comparisons of different systems implicitly assume that the operator has to provide the financing for all the capital expenditures (CAPEX) and operational expenditures (OPEX) with exception of the part of the terminal costs paid by the subscriber. The network operator pays for exclusive spectrum access, network deployment, operation and maintenance as well as subscriber acquisition, marketing and terminal subsidies. On the other hand, the operator gets all income from the offered services. It can be questioned whether future and all existing systems are correctly described by this model and whether a cost assessment relying on this model can be applied to other business models as well. Therefore, it is necessary to discuss the evolution of the wireless ecosystems and the implications of the changing business models on the cost assessment. 14.2.3.1 Spectrum Sharing There are technical and economic reasons for a future change in the wireless ecosystem. One reason is the increasing demand for spectrum that is necessary to provide high-rate data services

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to customers. Suitable spectrum is scarce so that the concept of flexible spectrum sharing (e.g., between different WINNER deployments or between WINNER and other systems) might be introduced, which will change the traditional procedures of licensing the operator for exclusive spectrum use. The cost of spectrum access may be significantly reduced by these techniques.

14.2.3.2 Roaming Agreements If the government-issued licence to operate a mobile communication network allows, operators might choose not to deploy a network in all possible areas but to set up a roaming agreement for their customers instead. For example, different specialised operators might concentrate on different deployment scenarios (wide area, metropolitan or local area) and allow mutual roaming to become more cost-efficient in network rollout and operation.

14.2.3.3 Infrastructure Sharing Another trend in network deployment is the sharing of infrastructure or base station sites among operators. Site sharing significantly reduces the cost of site acquisition, while the joint use of base station hardware (possibly combined with adaptive allocation of spectrum between operators) facilitates better utilisation of these resources. This reduces the costs for the infrastructure significantly. Parts of the infrastructure costs are now allocated to different operators that might provide different systems and services.

14.2.3.4 Third-party Network Ownership, Operation and Maintenance Recently, a trend can be observed that the operators that own systems outsource the operation of the networks (e.g., to vendors, who in turn often outsource the network maintenance to third parties). Since these ‘contracted operators’ can operate more than one network, the costs of network operation and maintenance can be significantly reduced. In this case, the fee paid by the traditional operator to the entity operating the network does not need to be directly related to the OPEX of the system. Here, the CAPEX and OPEX of the wireless system are allocated to different entities and a new kind of OPEX (i.e., the amount charged by the contracted operator) becomes applicable in the cost calculation for the network owner. This has to be taken into account when applying a conversion of OPEX into CAPEX (see Section 14.3.4.2). In addition, the CAPEX and OPEX can be shared between separate entities that provide different services.

14.2.3.5 New Business Entities As a new entity in the wireless ecosystem, mobile virtual network operators (MVNOs) that do not own spectrum or a network were established. They pay subsidies for the terminals for their subscribers and receive the subscription fee and the service fee from the subscribers. Instead of paying for network deployment and operation, they only pay for access to a network. The owner of the network however generates additional income by providing the network.

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14.2.3.6 Summary Taking the above developments into account, the following example of a future wireless ecosystem can be imagined:

r Several entities (infrastructure owners) own wireless communication infrastructure that can be a mix of several systems (WLAN, UMTS, broadcast systems, WINNER systems, etc.).

r Technical network operators rent capacity in this infrastructure that is then combined into packages so that bit pipes fulfilling certain QoS requirements can be offered to virtual network operators. r Virtual network operators sell the service to subscribers. Spectrum access rights can also be owned by additional partners (as we already see in mobile broadcast ecosystems). It is possible for different cost classes to be allocated to several entities and that the cost figures do not directly become input to a profit-and-loss calculation. As a conclusion of this example, different cost classes are handled separately, considering the dependence of the interpretation of the cost comparison on the evolution of the wireless ecosystem. The cost assessment work in this chapter focuses on the total deployment costs for RANs, irrespective of who actually pays for various cost components. The total deployment cost is regarded as the relevant key figure in addition to key RAN performance indicators on which deployment decisions are based.

14.2.4 WINNER: Assumptions and Technology Options A WINNER system is characterised by new or advanced wireless technologies, such as multi-hop relaying, and advanced spectrum techniques, such as flexible spectrum use. These technologies are expected to bring significant system performance gains. It is, however, understood that they might also result in additional costs. It is therefore important to analyse whether specific new technologies are essential for the WINNER concept and, if they are optional, which costs result from their implementation. Technologies to be potentially treated in the cost assessment of specific WINNER deployments are:

r relaying or multi-hop; r centralised or distributed radio resource management (RRM); r adaptive duplex schemes and uplink/downlink asymmetry in FDD and TDD; r beamforming, MIMO and SDMA (different numbers of antennas in mobile terminals); r fractional frequency reuse and other interference mitigation and avoidance techniques; r adaptive multiple access: frequency-adaptive and non- frequency-adaptive chunk assignment. The application of a certain selection of these technologies is reflected in the technical parameter settings for the deployment and radio propagation models, described in Section 14.5.1.

14.3 Cost Components The aim of this section is to identify, define and classify the WINNER RAN cost components. First the concepts of CAPEX and OPEX are introduced and a discussion is given on how to

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classify RAN-related costs into these categories. Section 14.3.4 estimates and presents some example cost figures. With the absolute cost figure estimations from this section available, it is possible to estimate the cost of different RAN deployments, derived from corresponding user scenarios.

14.3.1 Classification of Cost Components The WINNER RAN costs can be broken down into cost classes, equal to those of any other public communications RAN. From this basis, its cost elements can, in principle, be classified according to the standard financial categories CAPEX and OPEX. However, and in order to simplify the WINNER system cost analysis, some indirect cost elements are removed from the CAPEX–OPEX classification and given a category of their own. This applies mainly to associated cost components which are not directly attributable to the network deployment itself. These costs may be highly variable between different systems, do not depend on the employed technology and are nearly impossible to predict, such as costs of terminals, frequency spectrum, marketing, standardisation, etc. CAPEX are the once-off costs that have to be paid at the time of deployment. OPEX are costs that occur continually, operational costs. For financing and accounting reasons, CAPEX is usually annualised, i.e., it is distributed equally over a number of years equal to its amortisation, so that on a yearly basis the total network cost is OPEX plus annualised CAPEX. As a rule of thumb, investment is targeted in such a way that OPEX is close to the annualised CAPEX. For the calculation of a total deployment cost figure, the transformation of OPEX into CAPEX is carried out by calculating the net present value of OPEX investments (see Section 14.3.4.2). In this chapter, only CAPEX and OPEX elements directly linked to the network are addressed in detail. Indirect or singular costs are only briefly mentioned for sake of completeness. Another criterion for the classification of cost components is the scaling criterion. In order to simplify the cost modelling, we try to classify the cost components with respect to figures in which they scale linearly. Five cost groups (CGs) are considered with respect to the scaling criterion:

r CG1: costs that are proportional to the number of base station sites, such as base station equipment, site acquisition costs, and site operation costs. (Note: If different types of base station are used in one deployment, each type constitutes its own cost group.) r CG2: costs that are proportional to the number of relay nodes (if considered), such as relay node device costs, relay node operation and maintenance costs. These costs may, in turn, be proportional to the number of cells. (Note: If different types of relay node are used in one deployment, each type constitutes its own cost group.) r CG3: costs that are only considered once per network, such as spectrum licence, costs for RRM server, and gateway costs. r CG4: costs that are system specific, such as costs for research, standardisation, and interoperability tests. r CG5: costs that are proportional to the number of subscribers, such as terminal device costs, subscriber acquisition and care. As a main result of the cost assessment work within WINNER, a cost components table (see Table 14.1) has been created which sorts the relevant cost components according to the

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Table 14.1 Classification of cost components according to the scaling and CAPEX–OPEX criteria. CAPEX costs CG1

Base station site acquisition

Costs proportional to the number of base stations

Base station equipment

CG2 Costs proportional to the number of relay nodes CG3 Costs considered once per network CG4 System-specific costs CG5 Costs proportional to the number of subscribersa a

Base station deployment

OPEX costs

Indirect costs

BS power supply costs Rent for RAN connectivity Base station site rent and maintenance

RAN connectivity (wireline or wireless connections) Relay site acquisition RN power supply costs Relay equipment Relay deployment Relay site rent and maintenance Centralised RRM servers Network operation Gateways Software updates Initial network optimisation

Spectrum licences Marketing Billing system Research Standardisation Subscriber acquisition Subscriber care Terminal device costs Cost of sales

Economies of scale could apply.

scaling and CAPEX–OPEX classification criteria described above. The costs components that are considered in deployment cost assessments are discussed in more detail in the remainder of this section, where assumptions about each cost component are given.

14.3.2 RAN CAPEX Costs This section lists and comments on the infrastructure components resulting in CAPEX.

14.3.2.1 Base Station Equipment This cost component comprises all costs related to the active and passive parts of base stations, such as the power amplifiers, combiners, antennas, feeder cable, baseband processing, cabinet, cooling, etc. IPR costs per hardware unit are included in the equipment cost. It does not include costs for site acquisition or transmission towers that are not related to the hardware deployed or costs of operation (power consumption, maintenance, etc.), which are covered in other sections. It is important to highlight the fact that the cost of the BS technology, i.e., that of the actual BS network element, is less important than that of its auxiliary equipment and site acquisition. Not only that, the cost of network equipment is expected to diminish with time, while the cost

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of elements not related to the WINNER technology, i.e., powering, cooling, site acquisition, etc., are expected to increase. This fact is a driver for site sharing between competing operators. We make the following assumptions in our discussion:

r The costs of the air-interface-specific part are determined by the maximum TX power (power amplifier), the multi-antenna configurations (feeder and antenna subsystem) and the required baseband processing capabilities (baseband processing boards). r Advanced antenna techniques result in more complex antenna configurations where the costs depend on the number of antennas and their configuration, as well as on the RF processing and filters. r The output power of the base station affects the sites that can be selected. Legislative and regulatory rules can also restrict the possible deployment sites. r Macro, micro and pico base stations have different hardware costs that need to be considered. r Macro base stations are assumed to offer relatively high output power, a very high cell capacity and increased reliability (e.g. meeting emergency call requirements). Therefore, the cost for macro BS equipment is assumed to be much higher than for micro or pico BSs (or RNs).

14.3.2.2 Relay Equipment The costs of RNs depend on the functionalities provided by the RN and additional requirements they have to fulfil. Parts of the RN that might determine the device costs include the power amplifier and RF chain, the memory and the antenna configuration. We make the following assumptions in our discussion:

r The cost for the power amplifier depends on the maximum output power. Low-cost power amplifiers can be assumed for an output power significantly lower than that of macro base stations. r Memory requirements depend on the scheduling and MAC functionality supported by the RN. Since scheduling and retransmission are supported by RNs, the memory costs will be significantly higher than the memory cost related to the radio interface in terminals. r Advanced antenna techniques in RNs will result in more complex antenna configurations where the costs depend on the number of antennas and their configuration as well the RF processing and filters. r The output power of the RN affects the sites that can be selected. Legislative and regulatory rules can also restrict possible deployment sites. r It is assumed that RNs are deployed at the time of network roll-out for those WINNER network designs that rely on RNs. They are therefore classified as CAPEX.

14.3.2.3 Base Station Deployment These costs cover the installation of base station hardware at the prepared site, including installation of backhaul and power connections, initial testing and optimisation of, e.g., antenna tilt. Macro, micro and pico base stations have different deployment costs.

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14.3.2.4 Relay Deployment Deployment costs for RNs include the installation, power connection and initial optimisation costs. They are much lower than base station deployment costs due to, e.g., smaller and less complex hardware and lack of a wired backhaul connection. 14.3.2.5 Base Station Site Acquisition These costs cover the comprehensive set of CAPEX fees to be paid for acquiring a new base station site and preparing it for the installation of radio equipment. This includes the cost of identifying a suitable site, initial fees to the site owner, initial siting taxes, if applicable, and structural changes necessary for the installation (e.g. construction of an equipment shelter etc.). It does not include the cost of the radio equipment and backhaul connection itself or running costs, such as site rent or electricity bills. We make the following assumptions in our discussion:

r Macro and micro base station site acquisition costs are similar to those of UMTS. r Home node site acquisition costs are negligible. r No base station site acquisition costs are considered for incumbent operators who deploy WINNER base stations on top of existing 2G or 3G sites. However, the base station site rent and maintenance OPEX cost still apply at 100% (see Section 14.3.3.3). 14.3.2.6 Relay Site Acquisition The comprehensive set of CAPEX fees to be paid for acquiring an RN site and preparing it for the installation of radio equipment. A similar definition as for base station site acquisition costs applies. We make the following assumptions in our discussion:

r The radio RN site acquisition cost is estimated depending on the dimensions of the relay node.

r No base station site acquisition costs are considered for incumbent operators who deploy WINNER RNs at existing 2G or 3G sites. 14.3.2.7 Gateways Gateways are WINNER RAN entities that provide the interface to the core network. It is assumed that the number of gateways will be small compared to the number of base stations. Therefore, the costs of all gateways are considered as ‘once per network’ costs. This cost component does not comprise the RAN connectivity between base stations and gateways. We make the following assumptions in our discussion:

r The costs of gateways depend on the functionalities supported. r Costs for the siting and deployment of gateways are not considered separately as they are often co-located with a base station.

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14.3.2.8 Centralised RRM Servers RRM Servers are separate WINNER RAN entities that provide centralised radio resource control functionalities. There are very few or only one RRM server per WINNER RAN. Therefore, the costs of all RRM servers are considered as ‘once per network’ costs. The costs for the fixed-line connections between the RRM servers and other WINNER RAN entities, as well as deployment costs, are not considered in this cost component. 14.3.2.9 RAN Connectivity These are initial costs for providing wireline or wireless connectivity in the WINNER radio access network (connectivity between base stations, RRM servers, and gateways (GW)). The connectivity will be provided by connections which in general do not include synchronisation capabilities. A relay node uses in-band, radio-link connectivity to the base stations and is hence not associated with any fixed-line connection cost. The cost for RAN connectivity strongly depends on the region where the network is deployed. The cost results obtained for one region are not necessarily applicable to another. Two different scenarios are possible regarding RAN connectivity:

r wireless connectivity through microwave links; This solution has high CAPEX and low OPEX. The network elements are linked (mainly between BS and GW and RRM servers) through high-capacity LOS radio links, to avoid the cost of leased lines. Satellite backhaul is also an option. Time division multiplexing (TDM), synchronous digital hierarchy (SDH) and Ethernet interfaces are possible for backhaul connections. r leased lines. CAPEX investment for network connectivity is lower than in the radio connectivity case but the OPEX costs are significant and spread over the network operation, resulting in the payment of a periodic fee according to the total bandwidth required. The deployment cost assessment examples in this chapter assume that leased backhaul lines are available in the deployment area. This might not be the case in all regions, and then the cost for supplying the necessary infrastructure must be additionally taken into account. 14.3.2.10 Initial Radio Planning and Network Optimisation Costs for the initial optimisation of configuration parameters, such as maximum output powers, antenna tilts, handover conditions, RRM parameters, etc. cannot be determined during the planning phase but have to be established during network operation. In our discussion, we make the assumption that this cost component arises once per macro or micro base station, as they form part of the cellular network. Pico base stations are assumed to be located mainly indoors and are exempt from this cost.

14.3.3 RAN OPEX Costs Network infrastructure OPEX costs are defined in this section as the set of costs the operator has to defray to sustain fault-free operation and updating of the network elements. When

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addressing these costs, there are items which clearly fall under the category of radio access network elements OPEX, while others are linked to the core network OPEX. Irrespective of this, only the radio network costs are analysed, though this can mean in some cases an artificial division of cost items. 14.3.3.1 Base Station Site Rent and Maintenance This cost component represents the comprehensive set of running fees to be paid for siting a base station. It includes the rent to the site owner, insurance and running taxes, if applicable. In general, it is expected to be higher in urban than in rural environments. We make the following assumptions in our discussion:

r Macro and micro base station siting costs are similar to those of UMTS. r Co-siting with other network operators reduce costs by a factor based on the number of operators involved.

r Home-node siting costs are negligible. 14.3.3.2 Relay Site Rent and Maintenance This cost component represents the comprehensive set of running fees to be paid for siting a relay node. It includes the rent to the site owner, insurance and running taxes, if applicable. In general, it is expected to be higher in urban than in rural environments. We assume that the RN siting cost is a fee payable to the owner of the wall or mast on which it is hung. 14.3.3.3 Rent for RAN Connectivity If connectivity between the elements in the radio access network is provided by fixed-line connections, these lines are often rented from wireline network operators. In current cellular systems, the radio network elements are connected to leased lines that carry synchronisation information traceable to national time reference master clocks. In particular, UMTS fixed lines usually provide E1 connectivity (a full duplex link with a data rate of two times 2.048 Mbps). However, if the interface between the core network and radio access elements can be designed in future networks to be asynchronous (e.g., Ethernet interfaces), the fixed-lineconnection OPEX stand to be much cheaper per bit rate covered than current UMTS ones, though this statement requires some precision. Connectivity OPEX depends not only on whether the interface is synchronous or asynchronous, but also, or especially, in the connection quality, i.e., guaranteed throughput in opposition to best effort flows, reliability, availability and guaranteed delay and jitter, among others. In WINNER outdoor scenarios, two options can be envisaged:

r micro cells, where quality requirements can be lax; r macro cells, where the traffic volume requires a significant quality level.

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Micro BSs can be fed via a residential access line and could hence be cheap. However, for macro cells, probably with duplicate connections to the core network to ensure minimum outage time in case of individual network element failures, OPEX can still be expected to be significant.

14.3.3.4 Power This cost item is quite easy to evaluate once the number and type of network elements per geographical area are determined in a scenario analysis. The expected power consumption for different deployment equipment can be estimated as well as the expected future energy price. With assumptions made of these parameters, a value for the total current cost of power should be possible to determine. We make the following assumptions in our discussion:

r For a given radiated power there should not be a significant power consumption difference between a UMTS/HSPA site and a WINNER site.

r For ease of cost modelling, it is reasonable to classify the outdoor network elements from a power consumption point of view in different categories such as macro BS, micro BS cellular, or RN. r The power consumption comprises an offset (50 %) and a part that is a function of the carried network traffic (0–50 %). The maximum power consumption is given for the maximum system throughput for which the system is designed.

14.3.3.5 Network Operation and Maintenance This cost item includes generic maintenance activities for the whole radio network.

14.3.3.6 Software and Firmware Updates The costs for software updates of the RAN elements are often agreed between the hardware manufacturer and the network operator to be independent of the number of RAPs.

14.3.4 Example Cost Figures The assumed CAPEX and OPEX cost figures are summarised in Table 14.2 and Table 14.3, respectively. It must be stated that these figures are only examples, based partly on available literature [JFK+04] [TONIC11] and partly on estimations. They will definitely vary depending on the specific deployment scenario. In the cost assessments of this chapter, only the applicable listed cost components are considered, reflecting the RAP types contained in the different RAN deployment scenarios. As a general rule, considering a 10 year depreciation value, OPEX should be higher than annualised CAPEX, i.e., CAPEX divided by 10 years. The OPEX costs are represented by their net present value, assuming a lifetime of 10 years and a discount rate of 6%.

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Table 14.2 CAPEX cost elements for urban outdoor scenarios. Cost element Macro BS equipmenta

Unitary cost (k€) 50

Micro BS equipmentb 4 × 4 macro BS equipmentb

5 102.5

2 × 2 micro BS equipmentb Pico BS equipmentb Relay equipmentb

7.5 2 7

Macro BS site acquisition and deploymentc Micro BS site acquisition and deploymentc Pico BS site acquisition and deploymenta Relay site acquisition and deploymentc Gatewaysb RRM serversb Macro BS fixed-line connectionsb Micro BS fixed-line connectionsb Pico BS fixed-line connectionsb Initial radio planning and network optimisationa

Assumptions and remarks Three sectors, includes air conditioning, batteries, container, antennas, cables, etc. (see Section 14.3.4.1) Three sectors, includes air conditioning, batteries, container, antennas, cables, etc.

Assuming deployment in 2011–15, cost will be higher than micro BS due to technological innovation required (this might change, if relay nodes become mass-market products)

58 6

Small footprint

0.1 4 100 100 0.15 0.05

Small footprint, no backhaul

Connection to fibre-optic access network

0.05

Lower capacity than macro BS, connection to mass-market ADSL line available in all sites (highly developed region) Same as for micro BS

1.6

Per macro or micro BS

a

Cost is dominated by factors which are expected to remain roughly constant over time. Cost is dominated by factors which are expected to fall significantly over time. c Cost is dominated by factors which are expected to increase over time. b

14.3.4.1 Breakdown of Macro BS Equipment Costs In Table 14.2, a value of 50 k€ is assigned to a three-sector macro BS with its associated auxiliary equipment. That figure is in turn derived from a cost of 10 k€ for each BS sector, plus 20 k€ for cooling and powering equipment, passive RF components, urban tower, planning costs, etc. In particular, some representative values of these auxiliary, or non-network element, items are:

r tower or mast (depends on wind load): r self-supporting tubular tower, 6 m high – 4500 € r self-supporting tubular tower, 9 m high – 6200 €

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Table 14.3 OPEX cost elements for urban outdoor scenarios.

Cost element Macro BS site rent, maintenance, power and operationa Micro BS site rent, maintenance and powerb Pico BS site rent, maintenance and powerb Relay site rent, maintenance and powerb Macro BS fixed-line connectionsa Micro BS fixed-line connectionsa

Pico BS fixed-line connectionsa a b

Unitary cost (k€/yr)

Net Present Value for 10 years (k€)

17

132

3

23.2

0.5

Assumptions and remarks Includes software upgradesb Small footprint, no back-up batteries

3.9

2

15.6

20

156

0.8

6.3

0.8

6.3

33 dBm relay node, no back-up batteries Professional high-capacity backhaul line, failsafe Lower capacity than macro BS, connection to mass-market ADSL line available in all sites (highly developed region) Same as for Micro BS

Cost is dominated by factors which are expected to fall significantly over time. Cost is dominated by factors which are expected to increase over time.

r antenna support structure – 550 € r cooling system – 300 € r backup batteries 180Ah, +48V DC – 2100 € r feeder cable – 1.3 €/m r lightning (surge arrestor) protection system – 85 €/kit The 20 k€ bundled cost associated with these elements is typical for urban environments. In rural areas, the cost can be different. For example, if the tower is taller than 9 m, extra cost is associated with it. A self-supporting lattice tower would cost: 18 m high – 5300 €; 24 m high – 6900 €, 30 m high – 9700 €. To the BS equipment cost, the site acquisition and the annualised site rent and maintenance costs have to be added. This brings out an outstanding consideration of actual network deployment: the technology cost is lower than that of auxiliaries, and this in turn constitutes a pressure or driver on network operators to share sites. This chapter does not address the savings obtained by eventual infrastructure sharing. 14.3.4.2 Transformation of OPEX into CAPEX Costs OPEX costs ck per time period can be treated as discounted cash flows and represented by their present value (CAPEX) c at the beginning of network deployment: c=

K −1 k=0

ck (1 + d)k

(14.1)

540

Radio Technologies and Concepts for IMT-Advanced Table 14.4 Total cost (CAPEX + OPEX) for different RAP types. RAP type

Total Cost (k€)

Macro BS Micro BS Pico BS RN

397.8 42.2 12.4 26.6

The planned life time K of the network (the number of OPEX time periods) as well as a discount or interest rate d have to be determined beforehand. The total cost (CAPEX plus OPEX) per RAP type, calculated from Tables 14.2 and 14.3, is given in Table 14.4.

14.4 Cost Assessment Models In this section, the cost analysis framework is developed. The aim is a methodology that enables an assessment of the trade-off between cost and performance for different deployments and technologies. The cost assessment relies on the core idea that the cost of a RAN scales linearly with the number of BSs deployed in the network. Furthermore, this section provides a description of how the underlying traffic demand is generated and how the RAN deployment procedure is performed. This is followed by an introduction of the iso-performance analysis, which gives a theoretical framework for the comparison of different deployment alternatives regarding cost and performance.

14.4.1 Previous Work Early work on cost analysis of wireless systems appears e.g. in [Sta96; Zan97]. In [Sta96], a model for the total wireless system (infrastructure) cost is proposed that accounts for switching costs, interconnection costs, and radio equipment costs. Different base station output power levels, which affect the coverage as well as the cost of a base station, are studied and both star and bus network architectures are considered. The cost structure of wideband wireless networks is further analysed in [Zan97] accounting for infrastructure and spectrum costs. The work in [Zan97] indicates that the cost of high data rate wireless networks with universal coverage increases virtually in proportion to the bandwidth. The work in [Zan97] was later extended in [Zan02], which discusses the problem and potential solutions of providing high data rates with good coverage to users in wireless networks. The cost structure of cellular systems is further discussed in [JFK+04]. The paper proposes a model that may be used to estimate the cost of running a cellular business. Based on UMTS cost estimates presented in [TONIC11], [JFK+04] provides insight into the cost structures of cellular networks. Operational costs, such as site rental, transmission, employee costs, and marketing, make up roughly 75 % of the total costs in a highly developed, rather densely populated large country. The dominant parts among the capital expenditure (those that make up the remaining 25 % of the total cost) comprise investments in radio networks, sites, and core networks for the same large country case study. Moreover, [JFK+04] provides some

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numerical figures for typical UMTS equipment costs. However, the figures for hardware cost of a macro base station, site build out and installation cost may now be outdated. The base station cost estimate is originally from the Gartner Group while the site and installation cost estimates are from [TONIC11], in which a cost estimate of a UMTS macro base station is given based on the prices in the year 2001. However, the hardware costs might have declined significantly since then. In [JFK+04] the cost analysis is focused on the radio access network, which includes base station equipment costs, site costs, and transmission costs (‘last-mile’ transmission). Administrative and marketing costs, labour costs, and core network infrastructure costs are not considered. For an analysis that is focused on the comparison of different radio access networks with dissimilar architecture, topology, or performance this seems to be a wellmotivated approach. An analysis of the cost efficiency of relay-enhanced deployments can be found in [SW07]. It derives efficiency gains introduced by relaying, using a similar set of assumptions and the same methodology (based on iso-performance curves) as in this chapter.

14.4.2 Background and Principles This section describes the cost modelling approach and gives an introduction to the key ideas of the RAN cost assessment. The approach is based on the assumption that all dominant cost components scale approximately linearly with the number of deployed RAPs of different types, for a constant performance per RAP type. This is possible since many of the network deployment costs can be mapped to a RAP type. Regarding the backhaul connection to the fixed network, it is assumed that the associated cost is approximately constant for each deployed RAP of a certain type (this might not be strictly valid, e.g. in developing countries). The basic idea is to study a system area with a given user population, which may be uniformly or non-uniformly distributed. We assume that the given user density of a certain area element directly translates into a traffic demand in bits per second (e.g., during the busy hour). With a network area and an estimated user demand as the starting point, the next step is to deploy RAPs to serve the users. Different RAPs, such as macro BSs, micro BSs and wireless RNs, are considered. The deployment procedure (detailed in Section 14.4.3) continues until the entire network area is covered, i.e. all the users in the network are satisfied according to their desired data rates. It is clear that the network can be covered in alternative ways, i.e. with different combinations of RAP types. The overall ambition of the cost assessment framework in this chapter is to determine the best deployment alternative for each scenario from an economic perspective.

14.4.3 Network Deployment This section focuses on the network deployment procedure and the radio propagation model. For radio-resource related aspects only downlink traffic is considered. The starting point of the network deployment evaluations throughout this chapter is an underlying network area, or traffic map, and the following section describes how this is generated.

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14.4.3.1 Traffic Modelling Both uniform and non-uniform traffic demand are interesting to consider in cost assessment studies. Since a uniform traffic distribution basically involves only one parameter, i.e. the mean traffic density, this section describes how a heterogeneous traffic map is derived. The starting point is the user density distribution and the granularity of this is considered to be squares that are, e.g., 40 × 40 m and the values of the elements are log-normally distributed around a large scale mean value. On a smaller scale the standard deviation is set so that peak values occur with reasonable probability. Another important parameter is the total network area which is chosen in each scenario so that a meaningful range of RAP deployment densities can be studied. An example of the heterogeneous traffic distribution for a network area of 10 × 10 km is shown in Figure 14.1. The model is further described in [Tim05] and [FAJ05]. For deployment simulations, urban and rural scenarios are considered. The user densities and traffic assumptions in these two cases differ. In [WIND6112], not only estimates of the user densities for urban and rural areas are made, but also how many of the users are expected to use voice and data services, respectively. Furthermore, the traffic is determined with assumptions regarding user behaviour, operator market share and service level. Parts of this work have been reproduced in Chapter 2 (see Table 2.4). From the analysis of traffic distribution, mean traffic densities of 7260 kbps/km2 for the urban area and 312 kbps/km2 for the rural area were derived. These densities can be regarded as rather low for a beyond-3G system. However, it was

5

10000 9000 8000

4

7000 3.5 6000 3 5000 2.5

4000 3000

2

2000

1.5

1000 0

Traffic Density [log10(Mbps/km2)]

4.5

1 0

1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

Figure 14.1 Traffic map realisation, from [W+08]. (Reproduced by Permission of IEEE © 2009).

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demonstrated [WIN2D61313] that the total deployment costs scale approximately linearly with the traffic demand, and that therefore the main conclusions in comparing network alternatives remain valid for different traffic demand assumptions. The linear scaling assumption is based on the concept that a heterogeneous traffic demand is covered by a related density of RAPs. Within each RAP type, the employed technologies (e.g., backhaul connectivity) are constant. For a concept in which existing RAPs are upgraded (e.g. from ADSL to fibre backhaul links) to cover an increasing traffic demand, different cost models may apply.

14.4.3.2 RAP Deployment Strategies The starting point of the deployment is the underlying traffic map and the initial step is to deploy a number of macro BSs. Both uniform and non-uniform deployments are possible. For the uniform case, BSs are deployed hexagonally according to a predetermined inter-site distance (ISD). Users (or rather area units) are then connected to the RAP with the most favourable radio conditions, accounting for path loss and shadow fading. Clearly, if the traffic density is low in a BS cell, it is likely that this particular BS can serve all the users within that cell. However, if the traffic density in an area is large, then the probability increases that the serving BS lacks resources, thus indicating that this cell is capacity limited. The handling of resources in the network is described in more detail in Section 14.4.3.4. Briefly, every RAP serves a number of traffic elements all requiring a share of the RAP’s resources. This share can be calculated and clearly depends on the radio propagation model, further described in the next section, and the amount of traffic in that element. Summing the contribution from all of the traffic elements within a BS cell gives a measure of how much of the BS’s total resources are necessary to serve the users in this particular cell. This measure is referred to as ρ. Clearly, if ρ is less than one, the macro BS can handle all the traffic within its cell. If, however, ρ is greater than one, the macro BS cell is capacity limited. It is clear that an initially sparse BS deployment will imply that many or all of the BS cells are initially capacity limited (ρ > 1), hence requiring additional RAPs to satisfy the users in the network. These additional RAPs can be of different types, and both wireless RNs and micro BSs will be considered besides macro BSs in the analysis. The decision regarding where to deploy the second round of RAPs could be made on several different premises, e.g. the site could be chosen where the traffic demand peaks. Another strategy is to choose a site as far from the initially deployed BS as possible. The network area is covered and the deployment is finished, if the constraint ρ < 1 is fulfilled for all RAPs in the network. It should be noted that the deployment cost calculations strongly depend on the deployment strategy and only assessments based on the same strategy can fairly be compared to each other. Non-uniform Access Point Deployment It has already been mentioned that the starting point of all the network deployments considered here is an initial macro BS deployment. If macro BSs are supposed to support the entire traffic demand in the network, the simplest deployment strategy is to deploy them densely enough following a hexagonal topology. The main drawback with this strategy is evident when dealing with non-uniform traffic density, since the homogeneous hexagonal structure of the BS’s positions does not correspond to the traffic distribution in the network.

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A better approach, however not at all trying to be optimal, is to strive towards a non-uniform macro BS density. This can be realised according to the following procedure: 1. Deploy a number of initial BSs. These can e.g. be deployed according to a sparse hexagonal grid with a predetermined (far) ISD. Users are connected to the BS with the highest signal strength. 2. Calculate ρ for all the BS cells. A sparse deployment will result in many cells where ρ BS > 1 and thus additional deployment is needed. 3. For a certain cell with ρ BS > 1, choose a new BS coordinate in the vicinity according to some metric, e.g. farthest distance, highest traffic, highest load, etc. 4. Deploy the BS. Recalculate ρ for the entire network to capture the new RAP topology. 5. Check if ρ BS < 1 for all cells. If not, repeat from step 3 and deploy another BS. The process continues until ρ BS < 1 is satisfied for all BS cells in the network. The outcome of this procedure is a non-uniform BS deployment. It is important to stress that the deployment is in no way optimal or aiming for optimality. However, it enables a comparison with alternative deployments including other RAP types, using the same overall deployment strategy. Deployments of different RAP types are further described below. Special Issues for Deployments of Secondary RAP Types In many aspects, a relay-enhanced deployment follows the same methodology as described above, i.e. an initial BS deployment as a starting point and then RNs are sequentially added until coverage is reached. The initial BS deployment can be either uniform and non-uniform. A relay-based deployment is a little bit more complicated when it comes to the radio resource control. The reason is that the macro BS does not have to support the traffic within the relay cell directly, but the multi-hop characteristics of a relay-based deployment requires that the macro BS still serves all the traffic supported by the RN, through a wireless link between the BS and RN. The radio resource control of a relay-enhanced deployment is further described in Section 14.4.3.4. A micro BS enhanced deployment is also considered. Such a deployment is performed similarly to the RN deployment. The main difference is of course that in the micro BS case there is no need for a supporting macro BS. This implies that the deployment of an RN will decrease ρ for the serving macro BS, but not as much as when deploying a micro BS (since in the RN case, the BS must spend resources on the RN). Further, this means that in some cells it will be necessary to deploy more RNs compared to micro BSs to reach a ρ less than one for the macro BS cell.

14.4.3.3 Radio Propagation Models In the evaluations, radio propagation and channel models for three different types of links (BS–UT, BS–RN, and RN–UT) are needed. The employed radio propagation model includes distance-dependent path loss and shadow fading (SF). Antenna gains at the transmitter and the receiver side are also accounted for but are not described here. Short-term multipath fading is not considered.

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Figure 14.2 Attenuation versus distance for the specified path loss models.

The path loss (Lp ) between two nodes as a function of the distance (d) in meters is modelled according to: L p (d) = β + 10 · α · log10 (d) + Xσ [dB]

(14.2)

where β is the (average) attenuation at a distance of one meter, α is the attenuation factor, and Xσ is a log-normally distributed random variable (shadowing). Seen from a base station or a relay node site, the shadow fading (Xσ ) is correlated in space while the shadow fading from different sites is assumed to be uncorrelated. Serving and interfering links are modelled according to the same principle but possibly using different models or parameterisations. The path loss models are based on WINNER results [WIN2D111], and the following sections provide details and parameter values for the considered scenarios. Figure 14.2 depicts the (average) attenuation versus the distance for the respective path-loss models. Wide Area Urban Scenario (at 3.95 GHz) In the wide area urban scenario, a carrier frequency of 3.95 GHz is employed and the C2 NLOS path loss model is used for the BS–UT and RN–UT links. For the BS–RN link, the B5a LOS model is used for serving links while the C2 NLOS model is used for interfering links. That is, a LOS probability of 100 % is assumed for serving BS–RN links (in line with WINNER baseline scenarios [WIN2D6137]) while a LOS probability of 0 % is assumed for interfering BS–RN links. For the C2 NLOS model, a base station or relay node antenna height of 25 m is assumed. Table 14.5 summarises the employed wide area urban radio propagation parameters.

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Table 14.5 Radio propagation model parameters: WA urban scenario. Type of link Name Carrier frequency Attenuation factor (α) Attenuation at 1 m (β) SF standard deviation (σ ) SF correlation distance

BS–UT

RN–UT

BS–RN (serving/interfering)

C2 NLOS 3.95 GHz 3.57 40.5 8 dB 50 m

C2 NLOS 3.95 GHz 3.57 40.5 8 dB 50 m

B5a LOSa /C2 NLOS 3.95 GHz 2.35/3.57 40.5/40.5 4 dB 0

a

For the B5a and D1 models, we ignore that the path loss is lower bounded by the free-space attenuation.

Wide Area Rural Scenario (at 2 GHz) In the wide area rural scenario, a carrier frequency of 2 GHz is used. A modified version of the D1 path loss model is used for the BS–UT, the RN–UT and for interfering BS–RN links. The base station and relay node antenna heights are 25 m while the user terminal antenna height is 1.5 m. For simplicity, an NLOS probability of 100 % is employed. For serving BS–RN links, the B5a model at 2 GHz is used (in comparison to the wide area urban scenario where the same model is used at a carrier frequency of 3.95 GHz and the path loss is lowered by around 6 dB). A LOS probability of 100 % is assumed for the serving link between the BS and the RN [WIN2D6137]. Table 14.6 summarises the employed wide area rural radio propagation parameters.

14.4.3.4 Radio and Resource Assignment Model The material presented here is closely related to the model introduced in [MSJ+07] and explains and motivates the use of a simple but still valid radio resource model. Only downlink traffic is considered, however an uplink study would not imply any main differences in the approach. First, consider a RAP cell (Figure 4.3), where a RAP can refer to either a BS or an RN.

Table 14.6 Radio propagation model parameters: WA rural scenario. Type of link Name Carrier frequency Attenuation factor (α) Attenuation at 1 m (β) SF standard deviation (σ ) SF correlation distance a

BS–UT

RN–UT

BS–RN (serving/interfering)

D1 NLOSa 2 GHz 2.52 46.9 8 dB 120 m

D1 NLOSa 2 GHz 2.52 46.9 8 dB 120 m

B5a LOSa /D1 NLOSa 2 GHz 2.35/2.52 34.5/46.9 4 dB 0

For the B5a and D1 models, we ignore that the path loss is lower bounded by the free-space attenuation.

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The evaluations are not dynamic, i.e. only snapshot studies are considered, and the rate that can be provided to a certain point in the cell is given by the Shannon formula according to: R(d) = W · γ · log(1 + SINR)

(14.3)

Here, R is the wireless link capacity, W is the spectrum bandwidth, γ is a parameter describing how close the system can approach the theoretical Shannon capacity, and SINR is the signal to interference and noise ratio at the receiver. The SINR depends on the path loss (pl) between the transmitter and receiver (which, in turn, depends on the distance d in meters; pl = pl(d)), the transmitter output power (Ptx ), the transmitter and receiver antenna gains (GA,tx and GA,rx , respectively), and the noise and interference power experienced at the receiver (I(d)). Expressed in a logarithmic scale: SINR = Pt x + G A,t x − pl(d) + G A,r x − I (d)

(14.4)

The path loss can be written as: pl(d) = β + 10 · α · log10 (d) + Xσ

(14.5)

where β is the (average) attenuation at a distance of one meter, α is the attenuation factor, and Xσ is a log-normally distributed random variable (shadowing). Seen from a base station or RN site, the shadow fading (Xσ ) is correlated in space while the shadow fading from different sites is assumed to be uncorrelated. Further, the noise may be estimated based on the thermal noise (−174 dBm/Hz), the transmission bandwidth (bw) and the receiver noise figure (NF). Additionally, all other simultaneous RAP transmissions, forming the set , cause unwanted interference at the receiving UT. Pt x · plAPk (d) (14.6) I (d) = −174 + 10 log10 (bw) + NF + APk ∈

To control the resources in a cell, assuming traffic density ωdA bits/s in a certain area unit dA, we introduce the relative measure ρ dA , describing what share of the RAP’s total resource in time that each area unit dA will demand, according to: ρdA =

ωdA R(d)

(14.7)

To exemplify, if ρ dA = 0.01, then the BS must dedicate 1 % of its time to serve this user (or area unit). A similar definition of ρ was introduced in [JF07]. Considering the single-cell situation described in Figure 14.3, it can be determined whether the RAP cell lacks resources or not. This is done by integrating over the cell area to determine the total share of resources

Figure 14.3 A RAP cell.

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Radio Technologies and Concepts for IMT-Advanced

available in the cell that is required (note that to have a clearer notation, we integrate over the variable r instead of d): 2π ρtot =

RAP dθ

0

ω rdr = 2π R(d)

0

RAP

ω rdr R(d)

(14.8)

0

Clearly, the cell is capacity limited if ρ tot > 1. The first observation is that the integral cannot be solved analytically, but a numerical approximation is sufficient for this analysis. Using the model introduced here, different types of RAPs, both wired base stations and relays, can be evaluated following the same procedure, but with different assumptions regarding the antenna gains and output power. Resources in Relay-Enhanced Cells The previous section has provided a method of determining if a RAP can handle the offered traffic in its cell, from a radio resource perspective. It was general for any type of RAP, and is therefore valid also for relay nodes (RNs). However, deploying an RN is different in one important aspect compared to other types of RAPs, namely that the RN requires a feeding RAP since it is not connected to a backhaul transmission network. Hence, when deploying an RN, consideration has to be taken not only of the RN itself, but also the resource situation of the serving RAP, from here on referred to as a BS. In order for an RN deployment to be beneficial in general, the BS to RN link should be more favourable from a path-loss perspective than the BS to UT link. In the propagation model used, line of sight (LOS) transmissions are assumed between the RNs and the feeding BS while other transmissions between RAP and UT handle non-LOS conditions. Further, the propagation between a BS and the RNs served by other BSs, are also assumed to follow nonLOS conditions. Now, resource control for an RN-based deployment involves three parameters: ρ BS controls the resources spent on BS–UT transmissions; ρ RN , for the RNs that are served by the BS in question, controls the resources spent on RN–UT transmissions; ρ BS,RN controls the (LOS) transmissions between the BS and the RNs. To guarantee that a relay-enhanced cell can serve its users, the following two constraints must be satisfied: ρBS + ρBS,RN ≤ 1

and

ρRN + ρBS,RN ≤ 1

(14.9)

The ρ BS,RN is specific per BS cell, i.e. each cell includes a phase where only BS to RN transmissions are performed. It is clear that the length of this phase is shorter if the BS to RN transmissions are favourable, i.e. LOS, and longer if the BS to RN conditions are unfavourable. Equations (14.9) secure that both the BS and the RN can serve its own users and at the same time also manage to transmit (BS) and receive (RN) traffic on the LOS link between BS and RN. Scheduling and Interference Model Introducing a heterogeneous traffic model has the consequence that different BS cells will have different magnitudes of traffic to serve, making ρ BS differ significantly between BSs. This in turn implies that an interference model based on all other RAPs transmitting with equal power all the time is no longer applicable, and the need for some kind of scheduling mechanism is called upon. Therefore, we divide the snapshot into a number (e.g. 100) of time

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frames. Now, if a BS has a ρ BS equal to 0.5, based on all RAPs transmitting with equal power all the time, it will on average transmit only in 50 out of the 100 frames, whereas a BS that is capacity-limited will transmit in all of the frames. It seems reasonable to account for this in the interference model. The approach here is that, for every frame, a random number between 0 and 1 will determine if a certain BS is to transmit or not, by comparing the random number to ρ BS for the BS in question. If the random number is lower than ρ BS , this implies that the BS will transmit in that particular frame. This enables a new average rate to be calculated between each BS and its users, based on the different rates achieved in the phases of active transmission. Since the interference is reduced, the new rate is higher, which in turn results in a lower ρ for all RAPs. The problem is that since ρ has changed, so have the interference levels based on ρ and it is clear that the process is iterative. To reach a stable ρ (level of interference), we therefore perform consecutive recalculations, until the value has converged. In the simulations, 100 frames and five iterations are used, since this has been shown to give reliable results. Note that deploying an additional RAP in the network requires that the above procedure must be carried out to adapt both the new RAP measure and the already deployed ones to the new conditions.

14.4.4 Cost Calculation One possibility for evaluating the deployment cost is to estimate the total cost related to every type of RAP, including the costs for equipment, site acquisition, site rental, transmission, power, operation and maintenance, etc. (see discussion in Section 14.3). Thereafter, the total deployment cost can be determined by considering only the number of different RAPs. An approach based on the assumption that the total deployment cost scales close to linearly with the number of RAPs is frequently used in related research on the subject [JFK+04], [Zan97], but should always be checked with respect to the chosen deployment environment. The main advantage with the methodology described in this chapter is its general applicability to all scenarios where the mentioned assumptions apply. With necessary model parameters, regarding cost values as well as radio model assumptions, many different RAPs and combinations can be included in the deployment procedure. This implies that the WINNER system deployment cost not only can be analysed individually, but also compared to other systems. Further, it should be pointed out that the mean traffic density can be varied, which implies that we can study rural as well as metropolitan and high traffic density deployment areas.

14.4.4.1 Relay-Specific Cost Evaluation Issues An important aspect of the cost study is to compare a relay-based deployment (comprising macro and perhaps micro base stations together with RNs) to a benchmark system (using a macro base station single access but also the more interesting alternative including macro, micro and pico base stations, all connected to the wired infrastructure). Relay-based deployment has one main cost benefit compared to a wired BS with equal performance properties, namely that it is not associated with a transmission cost, i.e. the cost of a fixed line connecting the site to the backhaul IP network. The trade-off for the decrease in transmission cost is based on the fact that the traffic covered by an RN has to be transmitted

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via the feeding base station, implying a decrease in the BS’s maximum capacity. If the BS is not fully loaded, excess capacity can be used to serve the associated RNs. From the model description above, it is clear that a relay-based deployment benefits from a large improvement in signal conditions between the BS and RN, i.e. typically α is significantly lower for line of sight compared to presumably shadowed transmissions. If the same α was valid for both LOS and other transmissions, a relay-based deployment could only be favourable in special coverage-enhancing situations for shadowed areas, since the feeding of the RN would then require almost as much resource as supporting the user on a single-hop link, making a twohop transmission less advantageous. Providing coverage to shadowed areas, i.e. covering spots where the signal conditions are very unfavourable, but not due to distance path loss, is not the focus of this analysis, and for such specific situations a comparison between RNs and repeaters is probably more appropriate. The outcome of the approach introduced here only concerns the other two WINNER relay-based deployment strategies: capacity and coverage-enhancing deployments, further explained in e.g. [WIN2D351]. 14.4.4.2 Deployment Representation by Indifference Maps Using the deployment model introduced in this chapter, the output provides information regarding the positions and numbers of necessary RAPs to cover the traffic in a predefined area. Estimating the total cost per RAP then makes it possible to calculate the total deployment cost. The optimum deployment densities of different types of RAP can also be represented by a theoretical framework. The deployment strategy of the modelling tool described above should closely match the mathematical optimum. A proposed mathematical cost analysis framework has been developed in WINNER II and was mostly designed for a cost comparison between a regular (single access) and a relay-based deployment, similarly to earlier cost evaluation work in the literature, see e.g. [Tim05]. The following sections review the cost analysis framework originally developed for the cost assessment of relay-enhanced cells and its extension to include different types of RAP. Note that this cost analysis framework provides optimum deployment densities for different RAPs, but no specific RAP locations are calculated. The introduced deployment model, on the other hand, tries to match these optimum densities by explicitly placing RAPs in a traffic density map. This placement procedure imposes certain restrictions on the deployment results regarding mathematical optimality. Furthermore, the methodology relies on the assumption that the mean cost per RAP type is constant, irrespective of deployment densities and locations, which might not be valid in certain areas (e.g., developing countries where the necessary infrastructure may not be available in significant parts of the deployment area). Indifference Map for Two Types of RAP (Base Stations and Relay Nodes) The performance of a multi-hop network, in terms of coverage and capacity density (total information rate per unit geographical area), improves with both RN density (the number of RNs per unit geographical area) and BS density (the number of BSs per unit geographical area). As a consequence, a decrease in BS density may be compensated for by an increase in RN density, in order to maintain constant performance. This trade-off is illustrated in the form of an indifference map in Figure 14.4, where an iso-performance or indifference curve is plotted. An iso-performance (indifference) curve is hence characterised by the fact that each

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Figure 14.4 An indifference map showing the trade-off between BS density and RN density required to maintain constant capacity density and coverage.

point on the line represents a different multi-hop system, each having identical performance (see e.g. [Tim05] and [WIN2D351]). In Figure 14.4, one such system is shown having BS density B and RN density A. A tangent is also shown at this point on the indifference curve. Its gradient represents the change in RN density required to compensate for a change in BS density for this system. If the gradient of the tangent is equal to –r, where r is the ratio of the cost of a BS and the cost of an RN, then the tangent is an ‘equal-cost’ line, such that any point on the line represents the same total cost of BSs plus RNs. Then, r=

B

BS cost A = . −B RN cost

(14.10)

The significance of the system represented by the point at which the equal-cost line is tangent to the indifference curve is that it is the least-cost combination of BSs and RNs capable of providing this performance with this BS–RN cost ratio. If the BS–RN cost ratio were to vary, the corresponding equal-cost line would be tangent to the indifference curve at a different point, corresponding to a system with a different combination of BSs and RNs. The cost benefit of the multi-hop system relative to the conventional cellular system can also be obtained from the indifference curve: Cost benefit =

B0 × BS cost B0 B0 = = . B × BS cost + A × RN cost B + A/r B

(14.11)

Inspection of the indifference map in Figure 14.4 shows that the cost benefit increases with the BS–RN cost ratio, r, and with the curvature of the indifference curve. Note that the cost ratio r is a very important parameter and has to be carefully verified. For a network operator, it is also desirable to minimise the total amount of spectrum used by the system to deliver a specified service. This is equivalent to maximising the spectral efficiency of the system, which is defined as the ratio of the system capacity to the total amount of spectrum used by the system; that is, the system capacity per unit bandwidth. For the purposes of comparing conventional and relay-based systems, however, it is more useful

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to define ‘effective spectral efficiency’ ηeff . Both spectral efficiency and effective spectral efficiency have units of bps/Hz/site.1 The recommended metric for the cost-efficiency comparison of different multi-hop systems is the effective spectral efficiency for a specified service at a particular level of coverage. It is described mathematically as follows: ηeff =

C(m) , S (1 + m /r )

(14.12)

where S = total spectrum used by the system, C(m) = capacity or total information rate per BS, including its RNs, m = A/B = ratio of the number of RNs to number of BSs in the network, and r = ratio of the cost of a BS and the cost of an RN In the absence of RNs (m = 0), this metric becomes equal to spectral efficiency, as used for conventional cellular systems, and is therefore equally applicable to multi-hop and conventional cellular systems. The cost benefit of a multi-hop system relative to a conventional cellular system is readily derived using the combined metric: 1/Cost benefit = With

B × BS cost + A × RN cost A × RN cost B + = . B0 × BS cost B0 B0 × BS cost

B C(0) = for equal capacity per unit area in both points, B0 C(m) 1/Cost benefit =

C(0) + C(m)

ηeff (0) C(0) A = (1 + m/r ) = C(m) C(m) ηeff (m) B× ×r C(0)

⇔ Cost benefit =

ηeff (m) B0 = . B ηeff (0)

(14.13)

Here, ηeff (0) represents the conventional cellular system with no RNs and ηeff (m) is the metric for the multi-hop system with m RNs per base station. Cost benefit > 1 if the relaybased network provides comparable spectral efficiency for a lower infrastructure cost. Indifference Map for Three or More Types of RAP The representation of optimum deployment by indifference maps can be extended to three or more dimensions (i.e., three or more types of RAP, such as macro BSs and two types of relay node). We illustrate the extension to three dimensions explicitly via the three-dimensional indifference map. The addition of more dimensions becomes obvious with the extension of the relevant equations. The trade-off between deployment densities of macro base stations (b), Type 1 relay nodes (a), and Type 2 relay nodes (c) is illustrated in Figure 14.5 by a three-dimensional indifference map in the positive subspace {a ≥ 0 ∩ b ≥ 0 ∩ c ≥ 0}. The convex iso-performance that for m = 0, the term ηeff does not express the spectral efficiency (cell capacity divided by radio bandwidth) but serves as a derived metric to compare different BS–RN deployments regarding cost effectiveness. These comparisons are only valid within a specific radio configuration (reuse factor, SDMA deployment, etc.). 1 Note

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553

a

A’ ~–rba ~–rca

A

~–rca da

db

C/rbc B

A/rba B1

B’

B0

b

C C’

~–rbc

c

Figure 14.5 Indifference map for the three-dimensional case, from [W+08]. (Reproduced by Permission of IEEE © 2009).

or indifference surface curve is indicated by its intersections with the planes a = 0 and c = 0. Each point on the indifference surface now represents different deployment densities of the three RAP types, which exhibit identical performance. The optimum deployment is given by the BS density B, RN1 density A and RN2 density C. Note that the surface does not intersect with the plane b = 0 because neither RN1 nor RN2 nodes can operate without a supporting BS.2 The tangent ‘equal-cost’ plane at the point (A,B,C) of the convex indifference surface exhibits gradients representing the change in RN1 or RN2 density required to compensate for a change in BS density for this system. The A/B gradient of the tangent plane is equal to −rba and the C/B gradient is equal to −rbc , where rba and rbc are the ratios of the BS cost to the RN1 cost and RN2 cost, respectively, and any point on the plane represents the same total cost of BSs plus RN1s plus RN2s. Then, rba =

B

A BS cost = − B1 RN1 cost

and rbc =

B

C BS cost = . − B1 RN2 cost

(14.14)

The cost benefit of the multi-RAP-type system relative to the conventional cellular system with only one type of RAP can now be obtained from the indifference map, similar to the 2 The cost-optimal deployment representation by indifference maps can also be applied to other types of RAP, e.g. micro or pico BS within a mixed deployment, i.e. as long as there is at least one reference (macro) BS in the deployment. This constraint is necessary in the derivation of the cost benefit as a function of the effective spectral efficiency.

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two-dimensional case: Cost benefit =

B0 × BS cost A × RN1 cost + B × BS cost + C × RN2 cost

=

A/rba

B0 B0 = + B + C/rbc B

(14.15)

The relation B = A/rba + B + C/rbc can also be geometrically derived from Figure 14.5: B − B1 = A/rba and B1 − B = db = da/rba = C/rbc because da/db = rba and da/C = rca with rca = rba /rbc . The cost benefit can be described in terms of the effective spectral efficiency in a similar way as for the two-dimensional case. The definition of ηeff is adapted accordingly: ηeff =

C(m, n) , S(1 + m/rba + n/rbc )

(14.16)

where S = total spectrum used by the system; C(m, n) = total information rate per BS, including its associated RNs (RN1 and RN2); m = A/B = ratio of the number of RN1s to the number of BSs in the network; n = C/B = ratio of the number of RN2s to the number of BSs in the network; rba = ratio of the cost of a BS to the cost of an RN1; rbc = ratio of the cost of a BS to the cost of an RN2. The cost benefit of a three-dimensional optimum deployment relative to a conventional cellular system is described using the combined metric: 1/Cost benefit = = With

B × BS cost + A × RN1 cost + C × RN2 cost B0 × BS cost B A × RN1 cost C × RN2 cost + + . B0 B0 × BS cost B0 × BS cost

C(0, 0) B = for equal capacity per unit area in both points, B0 C(m, n) C A + C(m, n) C(m, n) B× B× × rba × rbc C(0, 0) C(0, 0) ηeff (0, 0) C(0, 0) = (1 + m/rba + n/rbc ) = C(m, n) ηeff (m, n)

1/Cost benefit =

C(0, 0) + C(m, n)

⇔ Cost benefit =

ηeff (m, n) B0 = B ηeff (0, 0)

(14.17)

Here, ηeff (0, 0) represents the conventional cellular system with no RNs and ηeff (m, n) is the metric for the multi-hop system with m RNs of type 1 and n RNs of type 2 per base station. The extension from the two-dimensional to the three-dimensional case can be used similarly to describe deployments with even more dimensions (types of RAP). The equations in the Ndimensional case can be derived by the same extension method as described before.

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14.5 Reference Deployment Scenarios and Cost Assessments In this section, deployment simulation results are presented for different example scenarios. Based on these results, cost optimisations are performed regarding the deployment densities of different types of RAP. Furthermore, some complementary cost assessments are given for MIMO base station deployment scenarios that are not covered by deployment simulations. The results in this section should be regarded as examples that illustrate the cost optimisation procedure based on iso-performance curves. The presented cost assessment results depend strongly on the assumptions about cost figures, deployment strategy, and scenario. It is not the intention of this section to provide general statements about the cost efficiency of certain deployment options. The specific gains of certain deployment options should always be assessed after the underlying assumptions have been carefully determined. It should be strongly emphasised that the results in this section are only valid in the example scenario and for the related assumptions explained in the text.

14.5.1 Deployment Simulations and Assumptions Deployment simulations were carried out for two scenarios that used a channel model of B5a LOS for the BS–RN link:

r S1 is WA urban, FDD, 3.95 GHz, 2 × 50 MHz with a channel model of C2 NLOS for the BS–UT link;

r S2 is WA rural, FDD, 2 GHz, 2 × 10 MHz with a channel model of D1 NLOS for the BS–UT link. The simulations used two deployment strategies:

r D1 models a WA greenfield operator with non-uniform flexible (according to traffic) deployment of a macro BS;

r D2 models a WA incumbent operator with fixed hexagonal positions for the macro BS. Both strategies used intelligent deployment of other RAPs according to traffic. Table 14.7 contains a list of the case studies for which cost assessments are presented in the remainder of this section. Table 14.8 lists the simulation parameters that depend on the scenario and Table 14.9 contains the parameters that depend on the RAP type.

14.5.2 Case Studies 1 and 2: WA Urban, Relay Nodes vs Micro BS 14.5.2.1 Simulation Scenario Description The results in this section are based on eight macro BS radii and a network area of 20 × 20 km. Parameter values are found in Section 14.5.1. Further, the values in Table 14.10 are obtained as the mean values of 10 simulations based on randomly generated traffic maps. Another important parameter is the cell radius for the RNs and micro BSs. The choice of 300 m here guarantees that the RN and micro BS cells are not capacity limited, since that could imply that not all the users are supported.

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Table 14.7 Case studies for cost assessments. Case study

Scenario

Deployment strategy

1 2

S1 S1

3 4

RAP types

Assessment subject

D2 D2

Macro BS, RN Macro BS, micro BS

Gain of RNs compared to micro BSs, urban, hexagonal macro BS position

S2 S2

D2 D2

Macro BS, RN Macro BS, micro BS

Gain of RNs compared to Micro BSs, rural, hexagonal macro BS position

5 6

S1 S1

D1 D1

Macro BS, RN Macro BS, micro BS

Gain of RNs compared to Micro BSs, urban, nonuniform macro BS position

7 8

S1 S1 MIMO

D1 D1

Macro BS Macro BS with MIMO

Cost gain due to sparser MIMO deployment

It is clear that when the macro BS density decreases, the number of RNs or micro BSs increases. This is expected, since more macro BS cells become capacity limited. Further, and also in line with the discussion in Section 14.4.3.4, the number of RNs always exceeds the number of micro BSs and the difference seems to be stable at around 10 %. Figure 14.6 shows an indifference map for a deployment consisting of macro base stations with RNs or micro BSs. The curves follow each other closely, but it is clear that the relay-based deployment requires a higher density of RAPs compared to the micro BS deployment. Table 14.8 Scenario-dependent simulation parameters. Parameter

S1 (urban)

S2 (rural)

3.95 2 × 50 1

2 2 × 10 1

BS–RN link Percentage of NLOS/LOS conditions Path loss : offset β Attenuation exponent alpha per link type Noise power spectral density (dBm/Hz) Noise figure (RN receiver) (dB)

100 % LOS B5a 40.5 2.35 −174 5

100 % LOS B5a 34.5 2.35 −174 5

BS–UT or RN–UT link Percentage of NLOS/LOS conditions Path loss: offset β Attenuation exponent alpha per link type Noise power spectral density (dBm/Hz) Noise figure (UT receiver) (dB)

100 % NLOS C2 40.5 3.57 −174 7

100 % NLOS D1 46.9 2.52 −174 7

Radio Carrier frequency (GHz) Radio bandwidth (MHz) Re-use or fractional re-use

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Table 14.9 RAP-dependent simulation parameters. Parameter Max. transmit power (dBm) Antenna gain, omnidirectional (dBi)

Macro BS

Micro BS

Pico BS

RN

46 10

37 2

30 0

37 2

14.5.2.2 Cost-optimal Deployment and Total Deployment Cost of RNs Using the iso-performance approach described in Section 14.4.4.2, an analysis is carried out of the cost-optimal deployment densities for the two RAP types in this example scenario. Figure 14.7 shows the simulated deployment densities (indicated by an ‘×’ symbol), as well as an MMSE fitting curve representing an exponential function of a third-order polynomial through these points. The line of constant cost is defined by its gradient, which is equal to the ratio of total cost per macro BS and RN (see Table 14.11). The cost-optimal deployment is determined by the position at which the line of constant cost meets the iso-performance curve. The optimum numbers or densities of RAPs are given in Table 14.11 and form the basis of the calculation of the total network cost. The total deployment cost is calculated by adding the CAPEX and transformed OPEX cost components for each network element, multiplied by the number of elements for the scenario. The equivalent cost of a deployment with macro BS as the only RAP type would be significantly higher (in excess of 100 million € ), indicating a dramatic efficiency gain by the introduction of RNs (or micro BSs) as supporting nodes. However, this gain can to some extent be attributed to the deployment strategy D2 for macro BSs on a fixed hexagonal grid, which may lead to unfavourable macro-only deployments. The non-uniform BS deployment model D1 addresses this issue (see Section 14.4.3.2). From the calculated cost figures, the cost per area of the example scenario is easily derived (see Table 14.11). For the urban, non-uniform macro BS plus RN deployment, it amounts to 84.20 k€/km2 (without gateway and RRM server). The underlying traffic map exhibits a mean traffic demand of 7260 kbps/km2 . Dividing the cost per area by this figure, the cost per served Mbps is 11.60 k€/Mbps) for an operational period of the network of 10 years. This value can also be used as basis for comparison with systems or deployments serving a different traffic demand. Table 14.10 Number of RNs and micro BSs for deployments based on different macro BS inter-site distances. Macro BS radius 800 900 1000 1100 1200 1300 1400 1500

No. of macro BSs

No. of RNs

No. of micro BSs

RN/micro ratio

224 175 154 120 114 85 80 60

2.9 10.9 15.1 36.9 46.2 120.0 157.4 356.0

2.6 9.6 13.6 33.4 42.5 109.0 142.0 329.0

1.10 1.13 1.11 1.10 1.09 1.10 1.11 1.08

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Figure 14.6 Indifference map for WA urban, RNs vs micro BSs, densities per km2 .

Figure 14.7 Cost-optimal deployment for WA urban with RNs.

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Table 14.11 Deployment cost calculation for WA urban, RN deployment.

Number of elements Cost per element (k€) Total cost of all elements (k€) Total cost per area (k€/km2 ) Total cost per Mbps (k€/Mbps)

Macro BS

RN

64 397.8 25 459.2

309 26.6 8219.4

Total

33 678.6 84.20 11.60

14.5.2.3 Cost-optimal Deployment and Total Deployment Cost of Micro BSs The focus of the results presented in Case Studies 1 and 2 is on an example cost comparison between the optimum deployments of different types of supporting RAP, for a deployment of macro BSs on hexagonal site positions. We now look at a scenario where micro BSs are deployed instead of RNs. The cost-optimal deployment for micro BSs is presented in the iso-performance curve in Figure 14.8 and in the cost calculation in Table 14.12. The ratio of the total cost of deployment with micro BSs compared to that with RNs is 1.096, indicating a small cost–performance benefit from RN deployment. It should be noted that once-per-network costs, such as gateways and RRM servers, have not been included in this comparison and that the results strongly depend on the assumptions made.

Figure 14.8 Cost-optimal deployment for WA urban, micro BSs.

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Table 14.12 Deployment cost calculation for WA urban, micro BS deployment. Macro BS Number of elements Cost per element (k€) Total cost of all elements (k€) Total cost per area (k€/km2 ) Total cost per Mbps (k€/Mbps) Cost ratio compared to RN supported deployment

RN

70 397.8 27 846.0

Total

215 42.2 9073.0

36 919.0 92.30 12.71 1.096

14.5.2.4 Incumbent vs Greenfield Deployment Costs To facilitate comparison of costs between a deployment with fixed BS positions co-sited with existing 2G or 3G equipment (thus saving the cost of site acquisition) and a deployment with full cost of site acquisition, a price discount can be applied to the existing sites. The deployment can be optimised using an iso-performance curve and slightly different RAP densities result. It has been shown [WIN2D61313] that 5.7 % of the total RAP cost can be saved in the hexagonal scenario if the BS site acquisition cost is dropped. However, a ‘greenfield’ deployment might additionally benefit from more flexible site selection.

14.5.3 Case Studies 3 and 4: WA Rural, Relay Nodes vs Micro BS 14.5.3.1 Simulation Scenario Description The results in this section are based on seven different macro BS radii spanning from 4000 to 7000 m and a network area of 100 ×100 km. Parameter values are found in Section 14.5.1. Further, the values in Table 14.13 are obtained as the mean values of 10 simulations based on randomly generated traffic maps. The cell radii for the RNs and micro BSs are set to 1000 m. This choice guarantees that the RN and micro BS cells are not capacity limited. An indifference map that shows a deployment consisting of macro base stations and either RNs or micro BSs is provided in Figure 14.9. The relay-based deployment requires a higher density of RAPs compared to the micro BS deployment. The reason for this difference is that the radio channel between the macro BS and RN is not as beneficial in a rural scenario (since the other transmissions also have favourable conditions). As stated in Section 14.4.4.1, Table 14.13 Number of RNs and micro BSs for deployments based on different macro BS inter-site distances. Macro BS radius 4000 4500 5000 5500 6000 6500 7000

No. of macro BSs

No. of RNs

No. of micro BSs

RN/micro ratio

224 175 161 120 114 85 80

0.7 9.3 7.7 71.7 101.0 499.5 724.0

0.3 6.0 5.3 39.7 61.7 235.5 325.5

2.00 1.56 1.44 1.81 1.64 2.12 2.22

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Figure 14.9 Indifference map for WA rural, RNs vs micro BSs.

if the link quality between the macro BS and RN is not so favourable compared to other transmissions, the relay-based deployment is less beneficial. The radio propagation models in Figure 14.2 show that, in the rural scenario, the path-loss difference between a macro BS to RN transmission and a RAP to UT transmission is about 20 dB at 1000 m. The corresponding path-loss difference in an urban scenario is about 40 dB at 1000 m.

14.5.3.2 Cost-optimal Deployment and Total Deployment Cost Again, the deployment of RNs is compared with the deployment of micro BSs. After an optimisation using iso-performance curves, the total cost calculations, are presented in Tables 14.14 and 14.15.

Table 14.14 Deployment cost calculation for WA rural, RN deployment.

Number of elements Cost per element (k€) Total cost of all elements (k€) Total cost per area (k€/km2 ) Total cost per Mbps (k€/Mbps)

Macro BS

RN

97 398.81 38 586.6

247 26.60 6570.2

Total

45 156.8 4.52 14.47

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Table 14.15 Deployment cost calculation for WA rural, micro BS deployment.

Number of elements Cost per element (k€) Total cost of all elements (k€) Total cost per area (k€/km2 ) Total cost per Mbps (k€/Mbps) Cost ratio compared to RN supported deployment

Macro BS

RN

91 397.8 3 6199.8

173 42.2 7300.6

Total

43 500.4 4.35 13.94 0.963

As in the urban case, a large number of RNs per macro BS is favourable. Due to a decreased underlying traffic density of 312 kbps/km2 , the total cost per area is much lower than in the urban case, but the cost per Mbps is higher. In this example scenario, the micro BS deployment obtains a slightly better result in terms of the cost for performance. The total cost ratio of the deployment with micro BSs compared to that with RNs in the rural case is 0.963, compared to 1.096 in the urban case.

14.5.4 Case Studies 5 and 6: WA Urban, Relay Nodes vs Micro BS, Intelligent BS Deployment Simulation Scenario Description Since the above case studies did not facilitate a fair comparison between multi-access deployment and a single macro BS deployment, this section introduces ‘intelligent’ macro BS deployment that uses the non-uniform BS deployment strategy described in Section 14.4.3.2. Another motivation is to determine the cost gain of increased site flexibility, e.g., if there are no existing sites that an operator wants to re-use. The iso-performance curve depicted in Figure 14.10 is achieved as follows: 1. A traffic map with size 5 × 5 km is created. 2. An initial deployment of 10 macro BSs is performed. 3. RNs are deployed until the users are satisfied (see Section 14.4.3.2 and the RN-related constraints in Equation 14.9). The result is the leftmost point in the curve, i.e. 10 BSs and 42.1 RNs (a mean of 10 maps). 4. Two additional BSs are added according to the procedure in Section 14.4.3.2. 5. RNs are deployed until the users are satisfied. The result is a combined deployment of 12 BSs and 37 RNs (a mean of 10 maps). 6. Steps 4 and 5 are repeated to generate successives point in the iso-performance curve until the users are satisfied with only BSs. In this scenario it is achieved by 28 BSs (a mean of 10 maps). This procedure is repeated for a number (in this case, 10) of randomly generated traffic maps. For every macro BS added to the initial deployment, the number of required RNs to achieve full coverage is decreased. The deployment continues until the network is covered by only macro BSs (see Table 14.16).

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Figure 14.10 Indifference map for WA urban, non-uniform position macro BSs and RNs.

Cost-optimal Deployment and Total Deployment Cost It appears that the result is a line with approximately constant slope. This rather surprising result means that the economic trade-off between adding RNs or micro BSs is independent of the existing macro BS density. The slope is around 2.3 for RNs, thus implying that 2.3 RNs can be traded for 1 BS while still achieving equal performance. It also implies that if the cost of 2.3 RNs is lower than the cost of 1 BS, it is economically advantageous to deploy RNs

Table 14.16 Number of RNs and micro BSs for deployments based on different macro BS inter-site distances. No. of macro BSs 28 26 24 22 20 18 16 14 12 10

No. of RNs

No. of micro BSs

RN/mic ratio

0 1.9 4.0 8.4 12.6 17.5 25.4 31.5 37.0 42.1

0 1.6 3.1 6.5 10.5 15.2 17.6 21.8 25.6 28.1

— 1.19 1.29 1.29 1.20 1.15 1.44 1.44 1.45 1.50

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and this is independent of the existing BS density in the network. The same reasoning can be applied to micro BSs, where the constant slope is 1.55, implying that 1 macro BS can be substituted by 1.55 micro BS and still give equal service. It is also possible to compare the RNs directly with the micro BSs. Here the difference is that the number of RNs is about 50 % higher than the number of micro BSs (the leftmost points contain 28 micro BSs and 42 RNs). This implies that RNs are economically beneficial in this example scenario if micro BSs are 50 % more expensive than RNs. Considering the cost of RNs and micro BSs presented in Tables 14.14 and 14.15, the cost of a micro BS was estimated to be around 50 % more expensive than an RN. Consequently, it is hard to draw conclusions on the most cost-efficient deployment alternative in this scenario.

14.5.5 Case Studies 7 and 8: MIMO Assessment In the WINNER project, the performance of multi-antenna technology in the WINNER RAN has been evaluated using performance figures such as spectral efficiency or total cell throughput [WIN2D341]. While the performance gain due to the deployment of multiple antennas at the base station or terminal is significant, so is the cost increase caused by these enhancements, as has been shown in [WIN2D61313]. In this section, an example MIMO BS deployment is compared to a conventional macro BS deployment model. The cost difference is then compared under the assumption of constant network performance.

14.5.5.1 Deployment Cost Comparison between SISO and MIMO Systems The chosen procedure is to assess the deployment of multi-antenna enhanced RAPs, in this case only macro BSs, and to compare such a deployment with a corresponding SISO deployment. Both SISO and MIMO systems follow the deployment procedure described in Section 14.4.3.2. Differences are accounted for at the radio link level, namely on the SINR. Based on multi-cell, system-level simulations of an interference limited network according to the WINNER simulation assumptions in [WIN2D6137], some performance differences have been established. Four technologies are evaluated: besides the SISO alternative, 2 × 2 MIMO and 4 × 1 MISO are considered. In the case of 2 × 2 MIMO, the base station transmits two separately encoded streams for two different antennas using the scheme referred to as per antenna rate control (PARC). The mobile receiver uses IRC to suppress interference; successive interference cancellation (SIC) after channel decoding is used to remove interference caused by already decoded streams. Such a receiver is here denoted an IRC–SIC receiver. For the 4 × 1 MISO scheme, four antennas at the base station are used for transmit beamforming and a single receive antenna is used at the mobile receiver. Since the more advanced antenna configurations translate to better radio transmission performance, it is clear that more users can be served by a specific BS and therefore a sparser BS deployment can be enabled. The more sophisticated equipment also results in a higher cost, a fact that has been discussed exhaustively earlier in this chapter. The purpose of this section is thus to evaluate the trade-off between a sparser BS deployment and a higher BS hardware cost.

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Figure 14.11 Post-receiver SINR distributions.

14.5.5.2 Performance Improvement from the Use of Multiple Antennas The improvements stemming from more advanced antenna configurations translate to a higher SINR level compared to the baseline SISO performance. The SINR distributions in Figure 14.11 depict the performance of the MIMO technologies. Note that for 2 × 2 MIMO, two streams are transmitted but the SINR is measured per stream. The difference between the two streams is due to the SIC receiver. When decoding the first stream, the second stream acts as interferer. When decoding the second stream, the signal associated with the first stream is regenerated and subtracted from the input by the SIC receiver and hence the first stream does not cause any interference to the second stream.

14.5.5.3 Deployment Evaluation The deployment strategy is the same as that described in earlier sections, i.e. an initial round of BSs is deployed and additional BSs are added in capacity-limited cells (areas). The simulation assumptions are listed in Table 14.8 and the urban traffic model is used. The simulated network area is 5 × 5 km. As an average of 10 deployment realisations with different traffic maps, 26.9 MIMO-enabled macro BSs are required for SISO, 16 for 2 × 2 MIMO (PARC–SIC) and 11.9 for 4 × 1 MISO. As expected, deploying more advanced antenna equipment enables a sparser BS density.

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Table 14.17 Total network deployment cost for the antenna configurations.

Hardware cost (k€) Total cost per BS (k€) No. of BSs Total network cost (k€) Performance vs cost gain (relative to SISO)

SISO

PARC–SIC (2 × 2)

MISO–GoB (4 × 1)

30 397.75 26.9 10 699 1.00

44.1 411.85 16 6590 1.62

82.5 450.25 11.9 5358 2.00

14.5.5.4 Cost Assessment of the Multi-antenna Configurations Table 14.17 presents the cost-related values of the antenna configurations. It should be pointed out that the simulations consider only the downlink and that the potential increase in cost for the terminal, e.g. for the SIMO scheme, is not included. However, using this simulation setup and with the assumptions made here, it is clear that the increase in hardware cost due to a more sophisticated antenna configuration is justified by the economic gain stemming from the ability to reduce the number of required BSs and still support the same user traffic demand.

14.6 Conclusion In this chapter, a full cost assessment framework was developed and applied to a range of example WINNER deployments. Although the absolute cost results depend significantly on the estimated figures per cost component and on the deployment strategy and environment, consistent results were obtained that demonstrate the derivation of meaningful statements in a relative comparison of the analysed deployment options. The deployment cost optimisation method based on iso-performance curves was derived under the assumption that the total RAN deployment cost can be approximately expressed as a linear combination of the numbers of different RAP types. In this case, a selection of valid deployment RAP densities, meeting the underlying traffic demand, is made by applying a line of constant cost to the iso-performance curve. The presented deployment models are based on heterogeneous traffic maps, propagation based on WINNER channel models, and RAP capacity calculation using finite traffic elements. The deployment model can be applied to WINNER and other cellular RAN systems employing different RAP types. In an urban area WINNER scenario example, it was observed that a deployment of RNs supporting macro BSs requires a higher density of RAPs than micro BSs supporting macro BSs, due to the in-band BS–RN link. The total cost of the deployment with micro BSs compared to that with RNs, however, was shown to be slightly higher because RNs also offer a substantial cost saving due to the missing leased-line backhaul. The optimum deployments are characterised by a very large number of supporting RAPs (either micro BSs or RNs). In another deployment example considering a rural area case with reduced traffic demand, the cost advantage of RNs compared to micro BSs no longer applied. Furthermore, the total deployment cost per area is much lower compared to the urban case, but the cost of providing a Mbps to the user is higher.

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567

For MIMO deployments, a performance versus cost gain could be clearly observed in dedicated optimised-deployment simulations. In the MIMO deployment scenario under consideration, the 4 × 1 MISO–GoB configuration exhibited a gain factor of 2.00 and the 2 × 2 PARC–SIC configuration resulted in a gain factor of 1.62 over the SISO case. It should be noted that all cost gains strongly depend on the assumptions made and that separate cost estimations and optimisations are necessary for each specific deployment scenario to be assessed.

Acknowledgements The work described in this chapter has been carried out within Task 11 (Standardisation, Requirements, and Deployment) of the WINNER II project. The editors would like to thank all colleagues who were active in the research work for the cost assessment activities, and provided material for this chapter, especially:

r Per Skillermark, Ericsson Research, Sweden; r Wsewolod Warzanskyj and Ignacio Berberana, Telef´onica I + D, Spain; r Mark Naden, Nortel, UK; r Paulo Jesus and Carlos Silva, Portugal Telecom Inovac¸a˜ o, Portugal; r Hans Schotten, Qualcomm, Germany (now at the University of Kaiserslautern, Germany). The reviewing efforts of Bernhard Walke, RWTH Aachen University, are acknowledged. They contributed to a further clarification of the methodologies and statements presented in this chapter.

References [FAJ05]

[JF07] [JFK+04] [MSJ+07] [Sta96] [SW07]

[Tim05] [TONIC11] [W+08]

Furusk¨ar A., Almgren M. and Johansson, K. (2005) ‘An infrastructure cost evaluation of singleand multi-access networks with heterogeneous user behavior’, Proc. of IEEE VTC Spring 2005, Stockholm. Johansson, K. and Furusk¨ar, A. (2007) ‘Cost efficient deployment strategies for heterogeneous mobile data systems’, Proc. of IEEE VTC Spring 2007, Dublin. Johansson, K., Furusk¨ar, A., Karlsson, P. and Zander, J. (2004) ‘Relation between base station characteristics and cost structure in cellular system’, Proc. of IEEE PIMRC 2004, Barcelona. Moberg, P., Skillermark, P., Johansson, N. and Furusk¨ar, A. (2007) ‘Performance and cost evaluation of fixed relay nodes in future wide area cellular networks’, Proc. of IEEE PIMRC 2007, Athens. Stanley, R.A. (1996) ‘A methodology for evaluating and optimizing wireless system infrastructure costs’, Proc. of IEEE PIMRC 1996, Athens. Schultz, D.C. and Walke, B.H. (2007) ‘Fixed Relays for Cost Efficient 4G Network Deployments: An Evaluation’, Proc. of IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2007), Athens, Greece. Timus, B. (2005) ‘Cost analysis issues in a wireless multihop architecture with fixed relays’, Proc. of IEEE VTC Spring 2005, Stockholm. TONIC (2002) IST-2000-25172 Final results on seamless mobile IP service provision economics, Deliverable 11. Werner, M. et al. (2008) ‘Cost Assessment and Optimization Methods for Multi-Node Radio Access Networks’, IEEE VTC Spring 2008, Singapore.

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[WIN2D111]

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WINNER II (2006) IST-4-027756 WINNER II Interim Channel Models, Deliverable D1.1.1, November 2006, viewed 20 June 2009, http://projects.celtic-initiative.org/ winner+. [WIN2D341] WINNER II (2006) IST-4-027756 The WINNER II Air Interface: Refined Spatial-Temporal Processing Solutions, Deliverable D3.4.1, November 2006, viewed 20 June 2009, http://projects. celtic-initiative.org/winner+. [WIN2D351] WINNER II (2006) IST-4-027756 Relaying concepts and supporting actions in the context of CGs, Deliverable D3.5.1, October 2006, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D6112] WINNER II (2006) IST-4-027756 Key Scenarios and Implications for WINNER II, Deliverable D6.11.2, September 2006, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D6137] WINNER II (2006) IST-4-027756 WINNER II Test Scenarios and Calibration Cases Issue 2, Deliverable D6.13.7, November 2006, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [WIN2D61313] WINNER II (2007) IST-4-027756 Deployment cost assessment for the WINNER II system concept, Deliverable D6.13.13, November 2007, viewed 20 June 2009, http://projects.celticinitiative.org/winner+. [Zan02] Zander, J. (2002) ‘Affordable multiservice wireless networks – research challenges for the next decade’, Proc. of IEEE PIMRC 2002, Lisbon. [Zan97] Zander, J. (1997) ‘On the Cost Structure of Future Wideband Wireless Access’, Proc. of IEEE VTC 1997, Phoenix.

Index 3GPP, 2, 5, 45, 93 3GPP2, 2, 5, 45 AAA server, 105 Active mode, 383–5, 386 Adaptive multi-user transceiver, 243 Admission control, 112, 394–6, Alamouti, 227, 495, 505–6 AMPS, 2 Angle of arrival, 45, 59, 64, 78–80, 87, 233 Angle of departure, 45, 59, 64, 78–80, 87, Antenna bore-sight, 489–90 AoA see Angle of arrival AoD see Angle of departure ARIB, 7 ARQ outer, 311, 434 inner see Hybrid ARQ see also Relay ARQ Array gain, 220 Average session duration, 466, 474–5 Azimuth spread, 58, 61 Base coverage urban (test scenario), 486–93, 495–513 Base station, 96–7, 106–7 parameter, 487, 489 Radio Technologies and Concepts for IMT-Advanced C 2009 John Wiley & Sons, Ltd

Beamforming, 232–43 Beams fixed, 233, 366 grid of, 233, 366, 495–500, 509–513 Bit-interleaved coded modulation (BICM), 136, 484 Block Low-Density Parity Check (BLDPC) encoding, 139 codes (BLDPCC), 138 decoding, 141 Broadcast, 102, 111, 117 see also Multicast Candidate spectrum bands, 440, 442, 478 Capacity requirement calculation, 465, 469–70, 474–6 CAPEX, 531–5 CBR see Constant bit rate model CDL model see Clustered delay line model cdma2000, 2 Cell edge performance, 283, 493, 499, 501, 504–6 user throughput, 241, 243, 270, 493, 502 Cell range, 490 CEPT, 2 Channel allocation dynamic channel allocation (DCA), 357 fixed channel allocation (FCA), 357

Martin D¨ottling, Werner Mohr and Afif Osseiran

570

Channel estimation, 179 cellular interference, 182 degradation, 184, 186, 190 genetic algorithm (GA) aided, 185 iterative (ICE), 180, 182 MIMO, 185 pilot-aided (PACE), 180, 181 prediction, 190 Channel impulse response, 72, 84, 87 Channel measurement, 47, 55–9 Channel prediction see Channel estimation, prediction Channel quality indicator (CQI), 169, 228, 257, 259, 332–5 delay, 252–3 Channel state information (CSI), 170, 228, 259 effective (ECSI), 170 feedback, 200 long-term, 170, 366 quantisation errors, 200 short-term, 170 transmitter (CSIT), 170 Channel symbol, 118, 333 Chase combining, 163–5, 496, 501, 505, 509 Chunk, 118, 224–5 layer, 119, 224–5, 237 Clustered Array, 243 Clustered delay line model, 86–7 Code Division Multiple Access (CDMA), 2, 328 Multi-carrier CDMA (MC-CDMA), 329 Common phase error (CPE), 196–198 Competition band, 177, 269 Concatenation, 114, 119–20, 122 Congestion control, 404–5 Constant bit rate model, 16, 508 Control plane, 111–12, 307 Control signalling, 95, 125–6, 228 Cooperative diversity see Diversity, cooperative Cooperative relaying, 258, 260, 304–6 Coordinated Multipoint (CoMP) Transmission see Distributed antenna systems

Index

Correlation, 44–7, 86 of large-scale parameters, 64–68 distance, 68, 79, 546 parameters, 78–80 Cost assessment framework, 526 components, 530 figures, 537 of relays, 293 optimisation, 555 Cross-correlation of parameters, 64–8, 78, 80 Cross-polarisation (XPR), 79 Cyclic delay diversity (CDD), 369–72 DARPA, 7 DAS see Distributed antenna systems Data rate peak, 2, 31 sustainable, 31 Dedicated band, 434, 446 Delay spread, 45, 61, 64, 78, 81, 85–6, 327 Delay, propagation, 209 HARQ, 164 multi-hop, 289, 306 requirement of service classes, 21–2 see also Packet delay transmission, 348 user, achievable, 32 definition, 31 Deployment scenario, 18, 19 local area, 19, 517 metropolitan area, 19 wide area, 18 Distributed antenna systems (DAS), 258, 261 Distributed MIMO see Distributed antenna systems Diversity cooperative, 260–2, 271 distributed, 261 gain, 221 macro, 105, 304, 368, 505 receive, 231, 370 transmit, 370

Index

Doppler, 44, 62–3, 87 181 Double directional, 40, 47 Drop, 68–9 DRS see Dynamic Resource Sharing Duo-Binary Turbo Codes (DBTC), 151–60 Dynamic Resource Sharing (DRS), 301, 503–4 E2E ARQ see ARQ, outer EDGE, 2 Elevation spread, 80–1 Equipment sharing, 101, 440 Erlang-B formula, 469, 476 ETRI, 7 European Commission, 1, 3 Exclusion zone, 422 424, 442–4 Exponential distribution, 65, 67–8, 72 Extension band, 435 Fast fading, 45, 69 351, 482 FDD, 117–19, 341–2 Feedback, 125, 182, 255–8, 335 Firefly synchronisation, 205 compensating propagation delays, 210 pulse-coupled oscillators, 206 rules, 207 Fixed satellite services (FSS), 420 Flashlight effect, 251 Flexible spectrum use (FSU), 419 Flow, 105 class, 105–6, 119–121, 347 control, 106, 401–4 Forward error correction (FEC) code, 137, 222–4, 433 blocks, 120, 129 Frame, 117–9, 330, 335, 492–3 Frequency Division Multiple Access (FDMA), 326 Block Equidistant FDMA (B-EFDMA), 331 Block Interleaved FDMA (B-IFDMA), 331, 337 Interleaved FDMA (IFDMA), 337 Localised FDMA (LFDMA), 337

571

Frequency re-use, 188, 360 fractional (FFR), 361, 364, 516–8 soft (SFR), 302; 360, 514–8 FuTURE Forum, 6 FuTURE project, 6 Fuzzy Logic, 407 Gateway, 96, 98–9, 397, 398, 401 logical node, 96 control gateway, 96, 104–6 IP anchor gateway, 96, 104–6 Generalised multi-carrier modulation (GMC), 95, 337 Generic (channel) model, 47, 63, 70, 78, 80, 86 Genetic Algorithm see Channel estimation, genetic algorithm aided Geometry-based (stochastic) channel model, 40, 46, 47, 59 Global time reference, 210 GoB see Beams, grid of GPRS, 2 Grid-based scenario, 41, 42, 80 GSM, 2 Handover, 99–100, 106 horizontal, 4 intermode, 390–2, 409, 410–4 intramode, 389–90, 410–2 intersystem, 392–3, 407 IP handover, 381, 388–9, 393 vertical, 4 Hardware sharing see Equipment sharing High power amplifier (HPA), 192 backoff, 193, 194 Higher order sectorisation (HOS), 333 Horizontal sharing, 422 HSPA, 2 Hybrid automatic repeat request (H-ARQ), 113–15, 126–7, 162–4 context transfer, 434 segment 120–2 process, 120

572

Hybrid Information System (HIS), 385 Idle mode, 383–5 IEEE standardisation, 6 IEEE802.xx series, 3, 7, 279 Impulse response, 44, 48, 59, 72, 85 see also Channel impulse response IMT-2000, 2, 5, 460–1, 464, 478 IMT-Advanced, 1, 3, 460–4, 477–8 Incremental redundancy, 120 cyclic, 163–5 Indifference curve, 294, 295, 550–1 Indoor scenario, 486–93, 517–521 coverage, 501–3, 506–8, 513–4 Inter-GW handover, 393 Inter-base-station communication (IBSC), 435–40 Inter-carrier interference (ICI), 196 Interference cancellation (IC), 352–7 direct IC, 354 Indirect IC, 354 inter-cell IC (IIC), 353 Parallel IC (PIC), 353 Interference coordination, 360, 514 Interference mitigation, 250, 326, 349 Interference rejection combining (IRC), 232, 370 International Telecommunication Union, 1, 3, 4, 459–61 Interpolation, 179–80, 181 Inter-site distance, 487, 489–90 IP convergence layer (IPCL), 109–110, 112–14, 378, 381 IPCL see IP convergence layer IS–95 cdma, 2 ISD see Inter-site distance Iso-performance, 550 Iterated block decision feedback equaliser (IBDFE), 184, 187 Iterative channel estimation (ICE) see Channel estimation, Iterative (ICE)

Index

ITU see International Telecommunication Union Kalman Predictor, 191 Laplacian distribution, 45, 87 Large-scale parameter, 48, 64–70 LDC see Linear dispersion code Least satisfaction algorithm, 453 Line of Sight (LoS), 41–3, 54, 71, 75–9 model parameters, 78–81 probability, 80 Linear dispersion code (LDC), 221, 270 adaptive, 237 Link level procedures, 169 Link-to-system interface, 351, 483–4 Load balancing, 100, 106 between gateways, 394 between RAPs, Load control see Congestion control Logical channel, 109–10, 114–16, 123–4, 396 LBCCH, 123 LCCCH, 124 LDCCH, 124 LDTCH, 124 LMCCH, 124 LMTCH, 124 LPCCH, 124, 385 Logical node, 93, 96, 104–110 architecture, 93, 96–109 Low Density Parity Check Code (LDPCC) see Block Low Density Parity Check code LSP see Large-scale parameters LTE, 2 M/G/1 nonpreemptive priority queue, 470, 476 MAC see Medium access control Manhattan grid, 486–7, 490 Market study, 462, 466, 468

Index

Maximum flexibility algorithm, 453 Maximum number of supported users, 493–4 Maximum ratio combining (MRC), 232, 357, 370 MCBC, see Multicast Mean service bit rate, 466, 468–9 Measurements, 200, 257 metrics, 200 signalling, 200 Medium access control (MAC), 115–117, 119–21, 223, 308relay, 308 transmission control, 346–8 MI-ACM (mutual interference based adaptive coding and modulation), 161, 495–500 Microcellular (test scenario), 486–93, 513–517 MIESM, 484 MIMO see Multiple Input Multiple Output Minimum Mean Square Error (MMSE), 191, 232 linear, 197 successive, see Sucessive MMSE MISO see Multiple Input Single Output mITF, 7 Mobility ratio, 17, 466, 468 Mobility, 16, 25, 98–100, 106–110 MRC see Maximum ratio combining Multi-band scheduler, 122–3, 431–3 Multi-band transmission, 102–4 Multicast, 465, 469, 470, 504–8 see also Multimedia broadcast multicast service Multi-hop network, 281 Multi-user MIMO, concept, 244,247, 248 uplink performance, 269 Multimedia broadcast Multicast Service (MBMS), 102–3, 105, 124–5 Multiple access, 325

573

Multiple input multiple output (MIMO), 219, 220 distributed, 31 model, 44–9, 59, 63 see also Multi-user MIMO Multiple Input Single Output (MISO), 220 MultiSphere level concept, 4 Multi-state channel, 483 Mutual information based adaptive coding and modulation see MI-ACM Mutual information effective SINR mapping see MIESM Network layout, 489–92 NGMC, 7 NGMN, 3 Non Line of Sight (NLoS), 41–2, 56, 57, 72, 77 model parameters, 76, 78–81 NSF, 7 Operational expenditures (OPEX), 294, 436, 531 Opportunity Driven Multiple Access (ODMA), 281 Optimum combining, 232 Orthogonal Frequency Division Multiple Access (OFDMA), 328 Orthogonal pilot set see Pilot, Set Over-the-air (OTA), 435, 438 Packet delay, 31–2, 318, 466, 494, 509–12 Paging, 105, 385, 386 channel, 124,125 indication (PI), 125 message (PM), 125 PARC see Per Antenna Rate Control PAS see Power azimuth spectrum Path loss, 45, 71, 75–7, 278–9 PDC, 2 PDU see Protocol data unit

574

Peak-to-average power ratio (PAPR), 193, 202 Per stream rate control (PSRC), 226 Phase locked loop (PLL), 198 Phase noise, 195 linear minimum mean squared error (LMMSE), 197 model, 196 OFDM, 196 serial modulation, 198 Phase response curve (PRC), 206 Physical channel, 126, 131 PADC, 129 PBCH, 127 PDCFC, 128 PMBC, 129 PNDC, 128–30 PRACH, 117-0, 130 PUCH, 130–1 Physical layer, 125–31 Physical node, 96 Pilot design, 169, 253–6 capacity, 179 downlink, 174 FDD, 174, 176 preamble, 177 TDD, 175, 177 uplink, 175, 179 Pilot preamble, 117–8 synchronisation, 130–1 boost, 174 common, 171 dedicated, 172 grid, 169, 171, 172, 174 overhead, 175, 184 pattern 170, 172 re-use, 173, 182 sequence, 174 set, 171 spatial multiplexing see Pilot, re-use type, 171 Pilot-aided channel estimation (PACE) see Channel estimation, Pilot aided

Index

Point-to-multipoint see Multicast Pool area see Pool concept, Pool concept, 98–102 Power azimuth spectrum, 87 Power spectrum sidelobe, 193 Propagation scenario, 41, 71 Protection distance, 442, 444–6 Protocol architecture, 94, 109 data unit (PDU), 110, 114, 119, 378 Pulse-coupled oscillator, 205 see also Firefly Synchronisation QC-BLDPC see BLDPC Radio access technique group, 462, 467–8 Radio environment, 467, 468, 475 Radio handover, 387–9 see also Handover Radio link control (RLC), 114–15, 311, 382, 432 Radio regulations, 459–61 Radio resource control (RRC), 110–12, 381, 383–5 Radio resource management, 406centralised, 406 common, 409 distributed, 406 hybrid, 407 in relay-enhanced cells, 297 Random vector quantisation (RVQ), 201 Rapp model, 193 RAT group see Radio access technique group Reference BS, 210 Reference design, 93 Refractory period, 207 Regularised block diagonalisation (RBD), 249–51, 266–9 Relay Alamouti diversity, 261 Relay coherent combining (RCC), 261 Relay cyclic delay diversity (RCDD), 261 Relay node, 96–7, 107, 277, 399

Index

Relay ARQ, 311 deployment, 282–92 deployment cost, 293 parameter, 488–9 relay-enhanced cell (REC), 279 Relaying amplify-and-forward, 261, 281 cooperative Relaying (CR), 304 decode-and-forward, 261, 296 MIMO, 392 performance assessment, 312–19, 501–8, 513–21 Requirement coverage, 30 delay, 31–2 measurements, 26 performance, 29 spectrum fragmentation, 34 range, 34 system, 24 Resource allocation, 121–2, 299, 308–10, 336–8, 503 Resource negotiation, 425 Resource partitioning, 117, 310, 490, 519 load-based, 299 dynamic, 503 fixed, 501 with soft frequency, 516 Retransmission delay, 348–9 unit, 114, 119 Ricean K-factor, 64, 71 RLC see Radio link control RRC see Radio resource control RRM server, 96–8, 108, 378, 395, 396, 399 Satisfied user criterion, 246, 493–4 Scheduler, 115–7, 120–3, 496–8, 501 delay-aware, 509–3 Maximum C/I, 496 phased approach, 515, 517

575

proportional fair, 496, 498, 509–13 round robin, 482, 496–8, 501, 503 SDMA see Space division multiple access SDU see Service data unit Segmentation, 114–15, 119–20 Service access points, 110, 223 Service category, 466 Service data unit (SDU), 110, 378 Service environment, 467 Session arrival rate per user, 466, 475 SFN see Single frequency network Shadow fading, 58, 60, 64–8, 75–76, 78–9 SIMO see Single Input Multiple Output Simulation parameters deployment-specific, 487–91 environment-specific, 486 Simulation dynamic system level, 482 link level, 482 protocol level, 482 quasi-static system level, 48 static system level, 482 Simulator see Simulation Single frequency network (SFN), 103, 505–8 Single Input Multiple Output, 219 SISO see Single Input Single Output Site-sharing, 436, 529 Slot, 117–19 Small-scale parameter, 59, 72 Snapshot, 45 Soft frequency re-use see Frequency re-use Space time Coding 221 Space-time block coding, 495, 500 S-PARC, 497, 500 Spatial adaptation, 229 Spatial division multiple access, 228, 233, 496–500, 511–3 Spatial multiplexing gain, 221

576

Spatial user selection, 230 Spectral efficiency, 32, 493–4, 496, 498–500, 504, 513 Spectrum assignment, 420, 423 long term, 423, 425–7, 447–51 short term, 424, 427–9, 430, 451–4 Spectrum beacon channel, 446 Spectrum calculation methodology, 461–72, tool, 461, 472–7 Spectrum demand, 459–60, 477–8 see also Spectrum calculation Spectrum identification for IMT, 459–60, 463, 478 Spectrum manager, 430 Spectrum mask, 193 Spectrum register, 424 Spectrum resource change (SRC), 426 Spectrum server, 96–8, 108–9 Spectrum sharing, 420 and coexistence (SSC), 35, 419 Spread Spectrum Multi-Carrier Multiple Access (SS-MC-MA), 329 STBC see Space time block coding STC see Space time coding SUC see Satisfied user criterion Successive MMSE, 248–51, 268–70 Super-frame, 117–8, 297–8, 308–10 Synchronisation frequency, 201, 203 link, 201 narrowband interference (NBI), 204 network see Firefly synchronisation of BS, 506–8 preamble, 201, 204 time, 201, 202 System concept, 93 System information, 113, 387, 400 System packet delay, 494

Index

TCP, 378–80, 410 TDD, 117–9 Teledensity, 3, 467, 476 Test scenario, 485 Time Division Multiple Access (TDMA), 327 Timing advance procedure, 209 T-Pilot, 201 Traffic calculation, 468 Traffic distribution, 469, 472, 542 Traffic map heterogeneous, 542 Traffic FTP, 23 interactive applications, 24 internet and multimedia, 22 model, 20 streaming, 23 video telephony, 23 Voice over Internet Protocol (VoIP), 23 Transmission mode frequency-adaptive, 331 non-frequency-adaptive, 331 Transmitter generic, 221, 222 Transport block, 114, 119, 223–5, 347 Transport channel, 110, 123–5, 131 TBCH, 124 TMCH, 125 TPCH, 125 TRAC, 125 TSCH, 125 Turbo equalisation, 190 Two-dimensional cyclic prefix (2D-CP), 262 UMTS, 2, 5 Unicast, 102–3 traffic, 465, 469 User density, 16, 466, 474–5, 542 User grouping, 229 User mobility see Mobility User packet delay see Packet delay

Index

User plane, 109–10, 381, 384 User terminal, 108, 498–9 User throughput, 30, 493–4 UT Active see Active mode UT Detached, 383 UT Idle see Idle mode Van diagram, 5 Vertical sharing, 420–1

577

Wall loss, 41 Wiener filter, 181 model mismatch, 181 Wiener process, 196 WINNER Radio Access Network (WRAN), 93, 96 WINNER, 1, 3, 7, 8 World Radiocommunication Conference (WRC), 1, 3, 419 WWI, 9 WWRF, 3, 4