Smart Antenna Engineering

Smart Antenna Engineering

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Smart Antenna Engineering Ahmed El Zooghby

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Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the U.S. Library of Congress.

British Library Cataloguing in Publication Data El Zooghby, Ahmed Smart antenna engineering.—(Artech House mobile communications series) 1. Antennas (Electronics) 2. Software radio I. Title 621.3’824 ISBN-10: 1-58053-515-1 Cover design by Yekaterina Ratner

The material covered in this book represents the views of the author, and does not necessarily reflect those of QUALCOMM Incorporated unless it is so indicated. © 2005 ARTECH HOUSE, INC. 685 Canton Street Norwood, MA 02062 All rights reserved. Printed and bound in the United States of America. No part of this book may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without permission in writing from the publisher. All terms mentioned in this book that are known to be trademarks or service marks have been appropriately capitalized. Artech House cannot attest to the accuracy of this information. Use of a term in this book should not be regarded as affecting the validity of any trademark or service mark. International Standard Book Number: 1-58053-515-1 10 9 8 7 6 5 4 3 2 1

Contents Preface

xiii

Acknowledgments

xvii

1

Introduction

1

1.1

Wireless Mobile Communications Systems

1

1.2

Global Mobile Market Growth

3

1.3

Alternatives for Meeting Data Demand

4

1.4

Technology Peak Rates and Throughput

6

1.5

Why Smart Antennas?

7

1.6

Benefits of Smart Antennas

7

1.7

Types of Smart Antennas

8

1.8

Switched and Fixed Beam Antennas

9

1.9

Adaptive Arrays References

10 11

2

Multiple Access Techniques for 2G and 3G Systems

13

2.1

Introduction

13

2.2 2.2.1 2.2.2

Multiple Access Wireless Communications FDMA Systems TDMA Systems

14 14 15

v

vi

Smart Antenna Engineering

2.2.3 2.2.4

Frequency Reuse Cochannel Interference

16 18

2.2.5

CDMA Systems

20

2.3 2.3.1 2.3.2

Fundamentals of CDMA Isolated Cell Capacity CDMA Codes

21 24 25

2.3.3

IS-95 CDMA Systems

29

2.4 2.4.1

Third Generation Systems CDMA2000

36 37

2.4.2 2.4.3

WCDMA HSDPA

43 45

2.5 2.5.1 2.5.2 2.5.3 2.5.4

Basic CDMA Procedures Acquisition State Idle State Access State and Call Setup Traffic or Dedicated State

49 49 52 52 53

2.6 2.6.1

CDMA Embedded Cell Capacity Multipath Fading

53 55

2.7 2.7.1

Coverage Versus Capacity Trade-Off Coverage-Capacity Trade-Off in the Uplink

55 56

2.8

Conclusion References Selected Bibliography

57 57 59

3

Spatial Channel Modeling

61

3.1

Introduction

61

3.2

Radio Environments and Cell Types

63

3.3

The Multipath Channel

64

3.4

Channel Characterization

65

3.5 3.5.1

Path Loss Models Okumura-Hata Propagation Models

66 66

3.6 3.6.1

Spatial Channel Modeling Spatial Channel Model Parameters

67 68

Contents

vii

3.6.2 3.6.3

Number of Clusters Spatial Distribution of Clusters and Scatterers

69 69

3.6.4 3.6.5

Base Station Azimuth Power Spectrum and Angle Spread Mobile Station Azimuth Power Spectrum and Angle Spread

69

3.7

74

Spatial Channel Model Application in System Simulations

74

3.8

Angle Spread Impact References Selected Bibliography

77 80 81

4

Fixed Beam Smart Antenna Systems

83

4.1

Introduction

83

4.2

Conventional Sectorization

83

4.3

Limitations of Conventional Sectorization

88

4.4 4.4.1 4.4.2 4.4.3 4.4.4 4.4.5 4.4.6 4.4.7 4.4.8 4.4.9 4.4.10

Antenna Arrays Fundamentals Broadside and End-Fire Arrays Impact of Number of Elements Impact of Element Spacing First Null Beamwidth Half-Power Beamwidth Array Directivity Array Gain Trade-Off Analysis Impact of Element Pattern Planar Arrays

89 91 92 93 96 97 99 100 100 101 101

4.5

Beamforming

105

4.6

The Butler Matrix

107

4.7

Spatial Filtering with Beamformers

110

4.8

Switched Beam Systems

111

4.9

Multiple Fixed Beam Systems

113

4.10

Adaptive Cell Sectorization in CDMA Systems References

114 116

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Smart Antenna Engineering

5

Adaptive Array Systems

117

5.1 5.1.1

Uplink Processing Diversity Techniques

117 117

5.1.2 5.1.3 5.1.4

Angle Diversity Maximum Ratio Combining Adaptive Beamforming

118 121 122

5.1.5

Fixed Multiple Beams Versus Adaptive Beamforming

130

5.2 5.2.1 5.2.2

Downlink Processing Transmit Diversity Concepts Transmit Diversity in 3G CDMA Standards

132 134 134

5.3

Downlink Beamforming

142

5.3.1 5.3.2 5.3.3

Spatial Signature-Based Beamforming DOA-Based Beamforming Maximum SNR

145 146 147

5.4

Conclusion References Selected Bibliography

149 151 152

6

Smart Antenna Receivers and Algorithms for Radio Base Stations

159

6.1 6.1.1 6.1.2

Reference Signal Methods The Least Mean Square Algorithm The Recursive Least Squares Algorithm

159 159 161

6.1.3 6.1.4 6.1.5 6.1.6 6.1.7 6.1.8 6.1.9 6.1.10

Blind Adaptive Beamforming Least Squares Constant Modulus Algorithm Decision-Directed Algorithm Cyclostationary Algorithms Conjugate Gradient Algorithm Lagrange Multiplier Method Comparison of Adaptive Algorithms

161 161 162 162 163 164 167 169

6.2 6.2.1

Neural Network DOA-Based Beamforming Generation of Training Data

170 174

6.2.2

Performance Phase of the RBFNN

174

Contents

ix

6.3

Angle Spread Impact on Optimum Beamforming

175

6.4

Downlink Beamforming

181

6.5

Vector Rake Receivers

182

6.6

Channel Estimation

183

6.7

Beamforming

184

6.8

Conclusion References

185 186

7

Coverage and Capacity Improvements in 3G Networks 191

7.1

Introduction

191

7.2 7.2.1 7.2.2 7.2.3 7.2.4 7.2.5 7.2.6 7.2.7

Link Budgets and Coverage Mobile Station Parameters Base Station Parameters System Parameters Margins Other Parameters Fade Margin Confidence (Cell Area)

192 192 193 193 193 193 194 195

7.2.8

CDMA Traffic Loading

196

7.3 7.3.1 7.3.2

Voice Services Uplink Budgets Downlink Budgets

197 198 198

7.4

Data Applications

203

7.5 7.5.1 7.5.2

Limiting Links for Coverage and Capacity Coverage Limited Scenarios Capacity Limited Scenarios

209 210 211

7.6

Smart Antennas Impact on Uplink Coverage and Capacity

211

7.6.1

Smart Antenna Impact on Downlink Capacity

216

7.7

Conclusions References

226 227

x

Smart Antenna Engineering

8

Smart Antennas System Aspects

231

8.1

Introduction

231

8.2

Third Generation Air Interfaces and Protocol Stacks

232

8.3 8.3.1

Physical Layer Data Multiplexing

233 233

8.3.2 8.3.3

Transmit Chain UL/RL PN Scrambling/Spreading DL/FL Physical Channel Formatting

235 235

8.4

Mobile Call States

237

8.4.1

WCDMA

237

8.4.2 8.5

237

8.5.1 8.5.2

CDMA2000 Mobility Procedures to Support High-Speed Data Transfer Cell_FACH State or Control Hold Mode Idle, Cell_PCH, or URA_PCH States

240 240

8.6 8.6.1 8.6.2

Procedures to Reestablish High-Speed Data Transfer Cell_FACH State or Control Hold Mode Idle Mode, Cell_PCH, or URA_PCH States

240 240 240

8.7 8.7.1 8.7.2

Packet Data Services WCDMA Approach CDMA2000 Approach

240 241 241

8.8 8.8.1 8.8.2

Pilot Channels CDMA2000 WCDMA

241 241 243

8.9

Channels Applicable for Downlink Beamforming

243

8.10 8.10.1 8.10.2 8.10.3 8.10.4

Overview of Major Radio Network Algorithms Power Control Initial Power Setting Admission Control Congestion Control

244 244 245 246 246

8.10.5 8.10.6

Soft/Softer Handoff Hard Handoff

246 247

8.11 8.11.1

System Impact of Advanced Spatial Techniques Transmit Diversity

247 247

238

Contents

xi

8.11.2

Fixed Beam Approach

248

8.12 8.12.1

Beam Steering/Adaptive Beamforming Channel Estimation at the Mobile

258 259

8.12.2 8.12.3

Advantages and Disadvantages Uplink Beamforming

260 260

8.13

Conclusion

261

References

262

9

Mobile Stations’ Smart Antennas

265

9.1

Introduction

265

9.2

Multiple-Antenna MS Design

268

9.3 9.3.1 9.3.2

Combining Techniques Selection (Switched) Diversity Maximal Ratio Combining

272 272 272

9.4

Adaptive Beamforming or Optimum Combining

272

9.5

RAKE Receiver Size

278

9.6

Mutual Coupling Effects

279

9.7

Dual-Antenna Performance Improvements

280

9.8

Downlink Capacity Gains

284

9.9

Conclusions References

286 287

10

MIMO Systems

289

10.1

Introduction

289

10.2 10.2.1 10.2.2 10.2.3

Principles of MIMO Systems SISO SIMO MISO

290 291 291 292

10.2.4

MIMO

293

10.3 10.3.1

Transmission Strategies Water Filling

295 296

10.3.2

Uniform Power Allocation

296

xii

Smart Antenna Engineering

10.3.3 10.3.4

Beamforming Beam Steering

297 297

10.4

MIMO Approaches

297

10.5

MIMO Advantages and Key Performance Issues

298

10.6

RF Propagation Characterization

299

10.7

SINR Environment

299

10.8

Spatial Multiplexing

300

10.9

Conclusion

302

References

303

List of Acronyms

305

About the Author

311

Index

313

Preface Mobile and wireless communications systems are becoming increasingly more complex in an effort to cope with the growing demand for more supportable peak data rates, coverage requirements, and capacity objectives, as well as exciting new applications such as wireless multimedia and anywhere-anytime mobile Internet access. Although new air interface standards and access technologies such as code division multiple access (CDMA2000), wideband code division multiple access (WCDMA), and their evolutions, including evolution data optimized (EV-DO) and high-speed downlink packet access (HSDPA), promise to meet these requirements with data rates up to several megabits per second, this is often achievable only under ideal channel conditions—assumptions are rarely encountered in real systems deployment. Smart antennas have great potential in overcoming the impairments of these systems by exploiting the spatial domain to reduce the effects of interference, extend the range and coverage of wireless networks, increase system capacity, and achievable data throughout. The area of smart antennas application in wireless communications has received increased attention both in the wireless industry and academia for the past few years. It is an interdisciplinary topic that requires knowledge and skills in areas such as antenna arrays, signal processing, digital communications, radio frequency (RF) engineering, and wave propagation. Today, a large body of literature about the topic exists, although much of this is in the form of complex research papers published across a multitude of technical journals, magazines, and conference proceedings, making it very difficult for a practicing engineer to develop the skills required for a successful design in a reasonable amount of time. With that in mind, this book attempts to close the gap by consolidating and presenting the principles of smart antennas along with the issues associated

xiii

xiv

Smart Antenna Engineering

with their application in modern communications systems in an easy-to-follow format. The book’s purpose is to explain the principles and techniques of smart antennas application in wireless and mobile communications systems. It presents topics and issues in the design of advanced antennas systems in an easy-to-follow methodology. The book is intended for graduate students in electrical engineering, practicing communications engineers, engineering and product managers, and wireless systems designers. It is intended to provide a useful and needed reference in one place and cover a collection of topics necessary for successful application of smart antennas in wireless systems. The book begins in Chapter 1 with a brief history of wireless communications systems and their drive to achieve increasing demands in terms of coverage and capacity. In Chapter 2, the effects of cochannel interference, multiple access interference, and other impairments affecting existing and future multiple access techniques of 2.5 and third generation (3G) wireless systems are discussed to show how they prevent these systems from achieving their full potential of range and system capacity. Models for the mobile radio propagation channel are integral tools that allow system designers to evaluate the benefits of different measures for enhancing system performance. The coverage of smart antennas would not be complete without addressing models that take the spatial domain into account. In Chapter 3, shortcomings of conventional models will be outlined, along with a description of spatial directional channel models adopted by the industry’s standards bodies. Interference reduction with smart antennas offers an efficient way to reduce the interference in mobile communications systems through the use of narrow beams directed to a cluster of users or an individual user while, at the same time, steering nulls toward interfering users. Smart antennas could be divided into two major types, fixed multiple beams and adaptive array (AA) systems. A detailed explanation of these two approaches, along with their advantages and drawbacks, will be covered in Chapters 4 and 5. First, we will provide an overview of the fundamentals of antenna arrays and then show how these concepts tie into schemes like the Butler matrix and adaptive beamforming. We will also discuss diversity techniques and other methods applicable to both the uplink and downlink of wireless mobile communications systems. A daunting task facing any smart antennas developer is selecting the receiver structure and adaptive algorithms most suitable for the application in hand. Today, a large number of proposed methods and technical solutions exist. A comprehensive classification of smart antennas algorithms along with the main implementation issues and trade-offs is presented in Chapter 6, as well as some comparison between the different techniques. In Chapter 7, a section on system performance improvements demonstrates the impact of using smart antennas at the radio base station and potential improvements in terms of coverage and capacity of mobile communications networks. In Chapter 8, we will address the systems aspects of smart antennas and their interaction with various

Preface

xv

network control algorithms such as admission control, power control, and radio resource management. The application of antenna arrays in handsets is discussed in Chapter 9. Finally, the book concludes with a brief overview of multiple input multiple output (MIMO) systems, which combine antenna arrays at both the receive and transmit side to create parallel spatial channels that dramatically increase spectral efficiency and system capacity. Although practicing engineers and designers as well as engineering and product managers are the primary audience for this book, it can be easily adopted as a graduate course textbook in smart antenna applications in mobile communications systems.

Acknowledgments First, I would like to thank God for the knowledge and strength that made this project possible. I would also like to acknowledge and thank my family and friends for their support throughout this book. In addition, I would like to thank Bo Hagerman, Soren Andersson from Ericsson Research Corporate Unit, and Patrick Lundqvist from Ericsson Wireless Communications, Inc. for their valuable insights and numerous discussions in adaptive antennas for wireless mobile communications. Special thanks go to Professor Christos Christodoulou, chair of the electrical and computer engineering department at the University of New Mexico, for his encouragement and inspiration, which made this work possible. I would also like to thank Dr. Said El Khamy and Dr. Hassan El Kamshoushi from the University of Alexandria in Egypt for their guidance in my early work in adaptive antennas. In addition, I would like to express thanks to Qualcomm Inc. for permission to use some illustrations in this book. I would also like to acknowledge the publishing team at Artech House for their guidance and assistance, as well as the reviewer of this project. I welcome any comments and suggestions for improvement or changes that could be implemented in possible future editions and can be reached at [email protected].

xvii

1 Introduction Adaptive antennas have been used for decades in areas such as radars, satellite communications, remote sensing, and direction finding, to name a few. For instance, radar and secure communications systems take advantage of the ability of these antennas to adapt to the operating environment to combat jamming. Satellite communications systems have used multiple beam and spot beam antennas for years to tailor their coverage to specific geographic locations. Each of these applications is associated with its own unique set of challenges, such as the channel in which the system operates, the propagation environment, sources of interference, and noise or jamming. In addition, the end goal for which the adaptive antenna is used affects the selection of the type of array, size, adaptive algorithms, and integration with other system components. In this chapter we provide a summary of the status of current mobile cellular communications systems, their various evolution paths, mobile systems growth potentials, as well as an introductory discussion of the benefits and use of smart antennas in 3G cellular communications systems.

1.1 Wireless Mobile Communications Systems In the 1980s and 1990s, wireless cellular and personal communications systems (PCS) began to flourish with the advent of second generation mobile communications systems, or simply 2G, to cope with increasing demands. Early mobile communications systems were based on analog technologies that used frequency division multiple access (FDMA). In multiple access, a number of users access or share the resources of a common source. In FDMA systems, the available spectrum is divided into channels of specific bandwidth [30 kHz in the case of 1

2

Smart Antenna Engineering

advanced mobile phone service (AMPS), the North American analog standard] and users are assigned a pair of these channels for bidirectional communications with a base station (BS). In other words, the resource shared by all users is the bandwidth. Since the available spectrum is finite, there is a fundamental limit on the capacity or number of users that can be served by a cell. It is possible to reuse the whole available spectrum in each cell to maximize the capacity—this is called reuse factor of one. However, the base station transmit power required to communicate with all these users plus additional margins to overcome fading caused by multipath creates so much cochannel interference to users in neighboring cells that the signal quality is significantly degraded. To reduce this interference to acceptable levels that support a given signal quality, the number of channels assigned to each cell must be decreased—in other words, the reuse factor must be increased. This, of course, will lower the overall system capacity. Engineers then turned to technologies based on digital techniques to solve this trade-off between capacity and interference. In time division multiple access (TDMA), each user is assigned the entire resource at specific time slots. In this case, the shared resource is time. Global systems for mobiles (GSM) are based on this technology and it uses channels with bandwidth of 200 kHz. In TDMA-based systems, frequency planning plays an important role in balancing system capacity versus cochannel interference. Another multiple access technique based on spread spectrum technology is CDMA, in which the code domain is shared among users as defined in the IS-95 standard. One major difference between CDMA systems and other multiple access technologies is their re-use factor of one, which enables them to offer higher capacities. This is possible because of the unique way in which CDMA handles interference. A combination of pseudonoise (PN) sequences and orthogonal codes are used to spread and channelize the base station and user’s data. Spreading the signal to a much wider bandwidth helps reduce the power levels and makes each signal appear as background noise to other users. This scheme allows a large number of users to simultaneously share the same 1.25-MHz carrier. In addition to spreading, CDMA systems use power control techniques to maintain the interference in the system at the acceptable levels required to satisfy the signal or radio link quality. Furthermore, CDMA systems take advantage of multipath through the use of RAKE receivers to combat fading. Due to the explosion of mobile communications demand and the increasing shift to offer new and advanced services based on high-speed data rates, third generation technologies were developed. The main goals of 3G systems are to increase the voice capacity, improve mixed voice and data services, and offer peak data rates of up to 2 Mbps. There are currently two major 3G technologies, both based on CDMA. These are wideband CDMA or WCDMA, also known as universal mobile telecommunications system (UMTS) [1], and CDMA2000 [2]. Peak data rates of 384 Kbps are being achieved in commercially deployed WCDMA networks, whereas the WCDMA

Introduction

3

evolution path with HSDPA and high-speed uplink packet access (HSUPA) extends the peak rate to 14.4 Mbps on the downlink and more than 4 Mbps on the uplink, respectively, in a 5-MHz carrier. Similarly, peak data rates of 153.6 Kbps in a 1.25-MHz carrier are being achieved on the currently deployed CDMA2000 1x networks. The CDMA2000 1xEV-DO standard further extends the peak rates to 3.1 Mbps and 1.8 Mbps on the downlink and uplink, respectively. Both 1xEV-DO and HSDPA technologies were developed to significantly increase the peak data rates to meet the rapidly growing demand for high-speed data applications. The basic concept behind both technologies is the same, namely the introduction of new features such as adaptive modulation and coding (AMC), short frames, multicode operation, fast L1 hybrid automatic repeat request (HARQ), and base station scheduling. In fact, these features replace the two basic CDMA features, namely variable spreading factor (VSF) codes and fast power control by adaptive rate control based on channel conditions. AMC is a fundamental feature of HSDPA and 1xEV-DO. It consists of continuously optimizing the code rate, the modulation scheme, the number of codes employed, and the transmit power per code based on the channel quality reported [channel quality indicator (CQI) feedback] by the mobile station. To achieve very high data rates, higher order modulation schemes such as 16 QAM is added to the existing quadrature phase shift keying (QPSK) modulation used for R’99 WCDMA and CDMA20001x channels. Different combinations of modulation and the channel coding-rate can be used to provide different peak data rates. Essentially, when targeting a given level of reliability, users experiencing more favorable channel conditions (e.g., closer to the base station) will be allocated higher data rates. According to industry bodies, at the beginning of 2005, global subscriptions to 3G/UMTS networks reached 16 million on more than 60 networks, whereas more than 180 million subscribers are using CDMA2000 on approximately 120 networks.

1.2 Global Mobile Market Growth At the end of 2004, worldwide cellular subscriptions passed the 1.4 billion mark and the rapid growth is expected to last for many years. The chart in Figure 1.1 shows that the number of worldwide cellular users is expected to reach nearly 2.5 billion by 2010 [3], while Figure 1.2 provides a breakdown of this forecast for CDMA technologies. Note that this breakdown does not include GSM and EDGE subscribers. This continued growth and evolution in mobile usage is driven by data services such as short message service (SMS), multimedia messaging service (MMS), downloadable ring-tones, images and games, news and information

4

Smart Antenna Engineering Wordwide cellular users (millions)

3000 2500

Rest of world

2000

Central and Eastern Europe Central and Latin America

1500

Asia Pacific

1000

Western Europe

500

North America

0

2004

Figure 1.1

2005

2006

2007

2008

2009

2010

Worldwide cellular users forecast [3].

CDMA worldwide cellular users (millions) 1400 1200 1000 800

WCDMA CDMA2000 1xEV

600

CDMA2000 CDMAOne

400 200 0 2004

Figure 1.2

2005

2006

2007

2008

2009

2010

Worldwide CDMA cellular users forecast [3].

sources, mobile chat sites, and Web portals. It is anticipated that voice services will still significantly contribute to revenue streams along with new 3G enabled services, including personalized access to information and entertainment services, mobile access to the Internet and corporate networks, location based services, and rich voice, which is the simultaneous transmission of photos, graphics, video, maps, documents, and other forms of data with pure voice. The chart in Figure 1.3 shows how the worldwide mobile voice traffic is expected to increase during the next few years to nearly three times the current levels by 2010.

1.3 Alternatives for Meeting Data Demand Different wireless service providers have different evolution paths with different technology choices to upgrade their 2G networks to third generation systems defined in the IMT-2000 standard of the International Telecommunications

Introduction

5

Voice traffic growth

350% 300% 250% 200% 150% 100% 50% 0% 2004

Figure 1.3

2005

2006

2007

2008

2009

2010

Projected voice traffic growth [3].

Union (ITU). The main evolution paths for GSM and CDMAOne operators are shown in Figure 1.4. A number of GSM operators have chosen a migration path that involves upgrading their networks to GPRS and EDGE as an interim step before a full WCDMA migration while others have chosen to evolve their networks directly to WCDMA. CDMAOne operators have a somewhat smoother migration path with CDMA2000. Eventually, to meet the growing demand for voice and data capacity, most current 2G networks will be upgraded to use CDMA. Figure 1.5 shows the global 3G cellular users forecast by technology until 2010. 2G

2.5G

GSM

3G WCDMA R'99

GPRS

Voice centric 9.6 Kbps

Data 40 Kbps

EDGE

Voice + data 384 Kbps

Evolved 3G

HSDPA Data DL: 14.4 Mbps/ UL: 384Kbps

HSUPA Data DL:14.4/ UL: 4.3 Mbps

Data 120 Kbps Voice + data 153.6 Kbps cdmaOne

CDMA2000 1xRTT

Data Data DL: 2.4 Mbps/ DL: 3.1 Mbps/ UL: 153.6 Kbps UL: 1.8 Mbps 1xEV-DO Rev. 0

Voice centric 9.6/14.4 Kbps 1xEV-DV

Figure 1.4

2G evolution paths toward 3G.

1xEV-DO Rev. A

6

Smart Antenna Engineering 3G worldwide users (millions)

1400 1200 1000

WCDMA

800

CDMA2000 1x

600

CDMA2000 1xEV

400 200 0

2004

Figure 1.5

2005

2006

2007

2008

2009

2010

3G cellular users forecast by technology.

1.4 Technology Peak Rates and Throughput As we can see from Figure 1.4, different technologies support different peak data rates. The peak rate is the maximum transmission speed an individual user may experience under ideal conditions (i.e., it only affects the user experience). Data throughput, on the other hand, is a far more important metric for performance. Sector throughput is the average total capacity available to multiple users, whereas user throughput is the average data rate a user may experience. As the sector throughput increases, each sector can handle higher volumes of data, the network requires fewer sites, and, consequently, the capital and operational expenses are also reduced. Table 1.1 compares the peak data rates and throughput for different 3G technologies.

Table 1.1 3G Technology Comparisons

Technology

Carrier Bandwidth/ Spectrum (MHz)

Downlink Peak Data Rate (Kbps)

Average User Throughput (Kbps)

CDMA2000 1x

1.25/1.25

153.6

60–80

CDMA2000 1xEV-DO

1.25/1.25

2,458

300–500

WCDMA

3.84/5

384

220–320

HSDPA

3.84 -/- 5

14,400

550–1100

Rev.0

Introduction

7

For CDMA2000 1x and CDMA2000 1xEV-DO, user throughputs listed in Table 1.1 are based on promotional material from North American operators and on real network deployments. WCDMA and HSDPA user throughputs are based on results from [4-8]. Unlike EV-DO systems, there are no commercial deployments of HSDPA systems yet; these systems are expected to deploy in late 2005 and into 2006. The user throughput for HSDPA is based on simulation data [5]. Moreover, the choice of scheduler significantly affects the throughput of both 1xEV-DO and HSDPA because of the adaptive modulation and coding nature of the technologies. For instance, one popular scheduler called the proportional fairness (PF) schedules users according to the ratio between their instantaneous achievable data rate and their average served data rate. This results in all users having equal probability of being served even though they may experience very different average channel quality. This scheme provides a good balance between system throughput and fairness. Other schedulers will be discussed in Chapter 2.

1.5 Why Smart Antennas? Achieving the peak data rates specified in each standard in a real system remains very unlikely because it would require an unloaded system serving a single user to be extremely close to the base station. This leads to two questions: why the increased interest in smart antennas—a more attractive name for adaptive antennas—and how are they being considered as a viable technology for applications such as mobile communications? As we have seen, operators are faced with increasing capacity demands for both voice and data services. Although various 3G technologies offer higher data rates and double voice capacity compared with their 2G counterparts, their actual performance is still susceptible to interference, and adverse channel conditions created by multipath propagation and system loading. As such, smart antennas techniques can complement 3G systems and improve their performance by alleviating and reducing the degradation caused by the aforementioned factors. In fact, because of their nature, technologies such as HSDPA and 1xEV-DO can greatly benefit from smart antennas since any improvement in the SNR experienced by the users would directly translate to better throughput for individual users as well as increased sector throughput that can support higher capacities.

1.6 Benefits of Smart Antennas It is a fact that current technologies have nearly maximized the use of temporal and spectral techniques to improve capacity and data transfer speeds. This leaves

8

Smart Antenna Engineering

an additional parameter that has not been fully tapped yet, namely space. In space division multiple access (SDMA), a user or cluster of users are assigned a dedicated narrow beam that tracks their movement across the cell, adapting to the constantly changing radio environment. The obvious advantage of this approach is its applicability to any multiple access technique. Wireless system design and planning involve the optimization of two major components, coverage and capacity through the manipulation and control of power, interference, and noise. To that extent, smart antennas offer substantial benefits to the design of wireless mobile communications systems, which can be summarized as follows: • Increased antenna gain: this helps increase the base station range and

coverage, extends battery life, and allows for smaller and lighter handset designs. • Interference rejection: antenna pattern nulls can be generated toward

interference sources. On the reverse link or uplink this reduces the interference seen by the base station. It also reduces the amount of interference spread in the system on the forward link or downlink. Such improvements in the carrier to interference ratio C/I lead to increased capacity. • Diversity: composite information from the array can be used to mini-

mize fading and other undesirable effects of multipath propagation. In addition to spatial and polarization diversity, antenna arrays also allow the use of angular diversity. As with any other adaptive antennas application, the nature of the system in which they are employed, the conditions under which they operate, and the results they are intended to achieve all have to be considered when a smart antenna system design is incorporated in a specific wireless system. Figure 1.6 shows a system overview that describes some of the involved factors when we consider a smart antenna design for mobile communications systems. Subsequent chapters will provide more details and analysis regarding these areas and how they affect the selection, design, and performance of a smart antenna system.

1.7 Types of Smart Antennas Sectorization schemes, which attempt to reduce interference and increase capacity, are the most commonly used spatial technique that have been used in current mobile communications systems for years. Cells are broken into three or six

Introduction

9

Network planning Structure and algorithms

Radio network protocols

Propagation environment spatial channel modeling interference environment

Channel

Transmitter

. . .

Air interface parameters

. . .

Network dependent parameters

Structure and algorithms Network dependent parameters Air interface parameters Radio network protocols Receiver Radio network control

Figure 1.6

Smart antenna system overview.

sectors with dedicated antennas and RF paths. Increasing the amount of sectorization reduces the interference seen by the desired signal. One drawback of current sectorization techniques is that their efficiency decreases as the number of sectors increases due to antenna pattern overlap. Furthermore, increasing the number of sectors increases the handoffs the mobile experiences while moving across the cell. Compare this technique to that of a narrow beam being directed towards a desired user. It is clear that some interference that would have been seen by the existing 120° sector antenna will be outside the beamwidth of the array. Any reduction in the interference level translates into system capacity improvements. Smart antennas could be divided into two major types, fixed multiple beams and AA systems. Both systems attempt to increase gain in the direction of the user. This could be achieved by directing the main lobe, with increased gain, in the direction of the user, and nulls in the directions of the interference [9, 10].

1.8 Switched and Fixed Beam Antennas The switched beam method is considered an extension of the current cellular sectorization scheme. The switched beam approach further subdivides the macro-sectors into several micro-sectors. Each micro-sector contains a predetermined fixed beam pattern with the greatest gain placed in the center of the

10

Smart Antenna Engineering

beam. When a mobile user is in the vicinity of a micro-sector, the switched beam system selects the beam containing the strongest signal. During the call, the system monitors the signal strength and switches to other fixed beams if required. Better performance can be achieved with integrated embedded systems of fixed multibeam antennas, which can enhance signal detection on the uplink by making use of the signals from all the available paths in the beams followed by maximum ratio combining (MRC) [11]. The beam receiving the most power in the uplink can be used to transmit to the desired mobile on the downlink.

1.9 Adaptive Arrays The main advantage of adaptive antenna arrays compared with switched beam antennas is their ability to steer beams towards desired users and nulls toward interfering signals as they move around a sector. Several beamforming approaches exist with varying degrees of complexity. A conventional beamformer or delay-and-sum beamformer has all the weights of equal magnitudes. To steer the array in a particular direction, the phases are selected appropriately. In order to be able to null an interfering signal, the null-steering beamformer can be used to cancel a plane wave arriving from a known direction producing a null in the response pattern at this direction. When the number of interferers becomes large, such as in the case of IS-95 based systems, this beamformer might not be a practical approach. The well-known minimum variance distortionless response (MVDR) beamformer attempts to minimize the total output noise while keeping the output signal constant in the direction of the desired user. This is the same as maximizing the output SNR. For an M-element array with M degrees of freedom, the number of interferers must be less than or equal to M – 2, since one has been used by the constraint in the look direction. This may not be true in a mobile communications environment with multipath arrivals, and the array beamformer may not be able to achieve the maximization of the output SNR by suppressing every interference source. Some a priori knowledge of the desired signal such as the direction of arrival (DOA) is required by the MVDR beamformer. Since in the MVDR approach the weight vector that minimizes the output power is a function of the spatial correlation matrix, some degree of coherency between the uplink and downlink is needed to provide an estimate of the correlation matrix for transmission. In the minimum mean square error (MMSE) approach a minimization of the square of the difference between the array output and a reference signal results in the weight vector that maximizes the signal quality. Since this approach relies on the inversion of the covariance matrix, its complexity is very high. The maximum likelihood (ML) principle attempts to estimate the data sequence that was most likely sent based on the received or observed data. Other spatial techniques

Introduction

11

include transmit diversity and MIMO systems. In MIMO systems, antenna arrays are used in the transmitter as well as in the receiver, and the system creates multiple parallel channels that significantly increase the supportable data rates. Figure 1.7 compares the performance improvement expected from major smart antenna techniques with their complexity.

MIMO systems

Performance improvements

Beam steering (beam shaping, adaptive nulling)

Fixed multi-beam antennas

Transmit diversity Higher order sectorization Basic sectorization System complexity

Figure 1.7

Comparison of major spatial techniques.

References [1]

Third Generation Partnership Project, http://www.3gpp.org.

[2]

Third Generation Partnership Project2, http://www.3gpp2.org.

[3]

“Worldwide Cellular User Forecasts (2004–2010),” Strategy Analytics, December 2004.

[4]

“The Economics of Wireless Mobile Data,” Qualcomm Inc, http://www.qualcomm.com.

[5]

“Data Capabilities: GPRS to HSDPA,” Rysavy Research, September 2004, http:// www.rysavy.com.

[6]

Holma, H., and A. Toskala, WCDMA for UMTS: Radio Access for Third Generation Mobile Communications, 3rd ed., New York: John Wiley & Sons, 2004.

[7]

“HSDPA for Improved Downlink Data Transfer,” Qualcomm CDMA Technologies, October 2004, http://www.cdmatech.com.

[8]

“Nokia High Speed Packet Access Solution,” ZD Net UK, http://whitepapers.zdnet.co. uk/.

12

Smart Antenna Engineering

[9] Rappaport, T. S., (ed.), Smart Antennas: Adaptive Arrays, Algorithms and Wireless Position Location, New York: IEEE Press, 1998. [10] Tsoulos, G.V., (ed.), “Adaptive Antennas for Wireless Communications,” IEEE Press, 2001. [11] Göransson, B., B. Hagerman, and J. Barta, “Adaptive Antennas in WCDMA Systems—Link Level Simulation Results Based on Typical User Scenarios,” IEEE Vehicular Technology Conference, Boston, MA, September 2000.

2 Multiple Access Techniques for 2G and 3G Systems 2.1 Introduction Evaluating the various design choices of different smart antennas architectures, algorithms, and performance trade-offs when applied to modern mobile cellular communications systems requires knowledge and understanding of core access technologies as well as the impairments facing different systems. This chapter presents the concepts of FDMA, TDMA, and CDMA and describes the main differences between these access technologies. An overview of the frequency reuse concept and cochannel interference, critical to the network design of some second generation mobile communications systems, is provided. Since all 3G technologies are based on CDMA, there will be greater emphasis on this technology. When evaluating performance issues, two main components are usually considered, the link level performance and system level performance. In link level performance, we are mainly concerned with a single link between a mobile station and the base station; this link is typically based on the physical layer structure of the air interface. The physical layer is the layer that carries the actual RF transmissions. On the other hand, in system level performance the impact of the upper layers and their interactions with the physical layer has to be taken into consideration. Functions performed by the upper layers include radio resource management, admission control, and so on. This chapter has been divided as follows. First, in Section 2.2 we discuss the concepts of FDMA and TDMA systems and how frequency reuse is applied in the design of mobile networks also briefly cover cochannel interference. The fundamentals of CDMA technologies are discussed in Sections 2.3 and 2.4, 13

14

Smart Antenna Engineering

along with systems aspects such as RAKE receiver, power control, and soft handoff, as well as an overview of the IS-95 air interface. In Section 2.5, we introduce third generation systems and summarize the CDMA2000 and WCDMA standards. An overview of how a CDMA phone works and the different procedures employed to acquire the system and complete a mobile call are introduced in Section 2.6. Since the main motivation for using smart antennas with 3G systems is to improve their coverage and capacity performance, the chapter concludes with Section 2.7, in which the factors affecting CDMA capacity are presented and the coverage versus capacity trade-off is discussed using simple models. In a later chapter, a more complex and specific discussion involving this trade-off will be presented to provide tools to evaluate the different smart antennas gains.

2.2 Multiple Access Wireless Communications In cellular and PCS wireless communications systems a multitude of users access and share network resources (frequency bandwidth) to obtain different types of services, including voice, messaging, and data. The goals of these multiple access communications systems are to provide communications services in a near-universal geographical coverage while minimizing both subscriber stations and network equipment, deployment, and operational costs. Because regulatory agencies have allocated limited bandwidth to these services, a crucial goal of these solutions is to achieve high spectral efficiency, traditionally measured in Erlangs/megahertz/unit service area for voice applications and in bits/second/megahertz/unit service area for data applications. The cellular concept pioneered by Bell Labs in the 1970s makes use of multiple fixed stations, or cells that each serve a number of mobile subscribers within a limited geographical area. When a subscriber moves between cells, over-the-air messaging is used to handoff the call between cells, ensuring its continuity. The first such system in North America was called AMPS. Similar analog systems were also deployed in different parts of the world, including the Nordic Mobile Telephone (NMT) in Scandinavia, and the Total Access Communications System (TACS) used in the United Kingdom, China, and other countries. The spectrum chosen for these systems was in the 800–900-MHz band. The frequency band allotted to each system was then divided according to a scheme called FDMA. 2.2.1

FDMA Systems

In wireless mobile communications systems subscribers share a common resource such as time, frequency spectrum, power, or code. This is referred to as access technology or channelization. This leads to the generation of interference in the system, which affects signal quality. The degree to which system

Multiple Access Techniques for 2G and 3G Systems

15

performance is affected by interference actually depends on the access technology used to separate the users in the network. In FDMA, the available spectrum is divided among users by assigning different frequencies to various users, as shown in Figure 2.1. With FDMA systems, a user is assigned a 30-kHz or a 25-kHz pair of frequencies for the forward link (downlink) and the reverse link (uplink) throughout a call. To maintain the interference between the two links at a minimum, the frequency pair is separated by, for example, 45 MHz and 80 MHz in North American cellular and PCS systems, respectively. The FDMA scheme could be equally applied to analog and digital communications systems. 2.2.2

TDMA Systems

TDMA is a digital transmission technology that allows a number of users to access a single RF channel while reducing interference by allocating unique time slots to each user within each channel. In TDMA systems channelization is provided first by dividing the frequency among the users, just like in FDMA, and then again by dividing users in time by assigning users different time slots. This transmission scheme multiplexes three signals over a single channel. The TDMA standard for cellular divides a single channel into six time slots, with each signal using two slots, providing a 3 to 1 gain in capacity over AMPS. Each caller is assigned a specific time slot for transmission, shown in Figure 2.2. In US TDMA (IS-54), a 30-kHz channel is further divided into three time slots,

Time

Power

f1

f2

f3

f4

fn

Frequency

Figure 2.1

The FDMA concept.

16

Smart Antenna Engineering

Time Power

Frequency

Figure 2.2

The TDMA concept.

which increases the number of simultaneous users per channel to three. In the European TDMA version, or GSM, a 200-kHz channel is divided among eight users. TDMA relies on the fact that the audio signal has been digitized; that is, divided into a number of milliseconds-long packets. It allocates a single frequency channel for a short time and then moves to another channel. The digital samples from a single transmitter occupy different time slots in several bands at the same time. One of the disadvantages of TDMA is that each user has a predefined time slot and users handing off from one cell to another are not allotted a time slot. Thus, if all the time slots in a cell are already occupied, no additional calls are allowed. This represents a hard limit on the cell capacity. Another problem with TDMA is that it is subjected to multipath distortion. 2.2.3

Frequency Reuse

In cellular and PCS systems, a cell’s coverage is typically represented by a hexagon when omnidirectional antennas with constant transmit power are used at the base station. As we have seen with FDMA and TDMA systems, the available frequency spectrum is divided among the users in the network. Now, let us assume two adjacent cells with two users assigned frequency f1. As these mobile stations move closer together, their use of a frequency f1 will begin to create interference.To overcome this problem, a process called frequency planning is implemented, where a group of frequencies are reused in cells that are separated from one another by distances large enough to maintain the interference at

Multiple Access Techniques for 2G and 3G Systems

17

acceptable levels. Frequency reuse is the term that describes how frequencies are allocated throughout the system as a result of frequency planning. Assume a cellular system has F total frequency pairs or duplex channels available for users. By allocating each cell a group of k channels and dividing the F channels among N cells, we get F = kN

(2.1)

It follows that the cluster of N cells use the complete available band of frequencies. By replicating this cluster several times across the whole system, we can see that the system capacity will be proportional to N, which is also referred to as cluster size. Since each cell is assigned 1/N of the total channels, this factor is called the frequency reuse factor. Since the available spectrum is finite, there is a fundamental limit on the capacity or number of users that can be served by a cell. It is possible to reuse the whole available spectrum in each cell to maximize the capacity; this is called reuse factor of one. However, the base station transmit power required to communicate with all these users plus additional margins to overcome fading caused by multipath creates so much cochannel interference to users in neighboring cells that the signal quality is significantly degraded. To reduce this interference to acceptable levels that support a given signal quality, the number of channels assigned to each cell must be decreased; in other words, the reuse factor must be increased. This, of course, will lower the overall system capacity. Typical cellular reuse assumes N = 7 sets of channels are used, one set in each cell. This seven-cell building block is then repeated over the service area, as shown in Figure 2.3. The design ensures that there are no adjacent cells using the same channel (frequency). Several N-way reuse patterns have been deployed in different networks, including the above seven-way reuse. To calculate the capacity of an N-way reuse pattern, let us consider a 12.5-MHz band in which we need to deploy a cellular AMPS system. The total number of available channels with K = 7 becomes Capacity =

12.5MHz = 57 channels 30KHz ∗ 7

That is, there are approximately 57 AMPS channels available per cell. TDMA systems use the same frequency reuse concept as well but their capacity is higher than that provided by analog systems. The capacity derived above assumes that the cells are using omnidirectional antennas. In practice, cell sites are sectorized, usually into three sectors (i.e., each site is equipped with three sets of directional antennas, with their azimuths separated by 120°). In practice, sectorization does not lead to an

18

Smart Antenna Engineering

3 2

3

4 2

1 R 7

4

D

7

6

4

7

7

4 1

5 6

5 6

3 2

1

4 1

5 6

3 2

2

1

5

3

7

5 6

Figure 2.3

N = 7 frequency reuse plan.

increase in a sector’s capacity in AMPS. This is because the sector isolation, often no more than a few decibels, is insufficient to guarantee acceptably low interference. However, an increase in coverage is possible with sectorization because of the increased gain of the directional antenna but there is no gain in the reuse. The total cell capacity remains at 57 and the sector capacity becomes 19 channels. With this scheme the overall reuse factor (sector-based) becomes K = 7 *3 = 21. 2.2.4

Cochannel Interference

In FDMA and TDMA-based systems, when signals from cells using the same frequency group interfere with each other they create cochannel interference, which affects the signal quality and system performance. Therefore, these cells must be separated by some distance, which is referred to as cochannel separation D and is given by [1] D = 3NR

(2.2)

Under the assumption that the cell sizes and cell transmit powers are the same, cochannel interference becomes a function of the ratio of the separation distance to the cell’s coverage distance or D/R, where R is the cell radius [2, 3].

Multiple Access Techniques for 2G and 3G Systems

19

This shows that reducing the cochannel interference requires larger cochannel separations. Let K be the number of cochannel interfering cells, then the signal to interference ratio (SIR) could be approximated as SIR =

S = I

1 K

∑ (D i R )

−n

(2.3)

i =1

where n is the path loss exponent. It can also be shown that most of the cochannel interference results from cells in the first tier. Based on the hexagonal cell shape, we get K = 6, assuming that the cochannel separations are the same, and using (2.2) we can rewrite (2.3) as follows S 1 = I 6( 3N

)

−n

=

1 6(3N )

−n 2

(2.4)

From (2.4) we can clearly see the trade-off that exists between the system capacity and cochannel interference. To illustrate this trade-off let us assume that we have a 12.5-MHz spectrum available and a 30-kHz channel bandwidth. Figure 2.4 shows the relation between the cell’s capacity in terms of the number of voice channels and the SIR versus N for n = 4. We can clearly see the trade-off between achieving a high-capacity design versus maintaining an acceptable SIR. Thus, smart antennas become a crucial tool in dealing with such issues, as we will see in subsequent chapters.

Figure 2.4

Capacity and SIR versus cluster size.

20

2.2.5

Smart Antenna Engineering

CDMA Systems

As we have seen in previous sections, the most fundamental issue in wireless mobile systems design is how to deal with interference between users. One approach to mitigating interference is using the concept of slotting, in which each mobile user is assigned a frequency or time slot that he, and he alone, uses while he is active, such as in FDMA- and TDMA-based systems. The drawback of this approach is the reduced spectral efficiency inherent in the frequency reuse approach because only a portion of the available spectrum can be used in a given cell at any given time. Another drawback is the need to change the frequency plan when new base stations are added to cope with increased capacity demands. In CDMA, users are divided by the assignment of a unique code to each. Because users can be identified by their unique code, there is no need to divide the spectrum in either frequency or time and all users in a CDMA system are given access to the system at the same time and on the same frequency. This is shown in Figure 2.5, where a number of users share the same RF band using different codes. One major difference between CDMA systems and other multiple access technologies is their reuse factor of one, which enables them to offer higher capacities. This is possible because of the unique way by which CDMA handles interference. A combination of PN sequences and orthogonal codes are used to spread and channelize the base station’s and user’s data. Radio receivers based on other digital technologies separate channels by filtering in the frequency domain. CDMA receivers separate channels by means of the pseudo-random modulation that is applied and removed in the digital domain, not on the basis of frequency. Spreading the signal to a much wider bandwidth helps reduce the

t

Cn

C3 C2 C1 f

Figure 2.5

CDMA access technology.

Multiple Access Techniques for 2G and 3G Systems

21

power levels and makes each signal appear as background noise to other users. This scheme allows a large number of users to simultaneously share the same 1.25-MHz carrier. In addition to spreading, CDMA systems use power control techniques to maintain the interference in the system at the acceptable levels required to satisfy the signal or radio link quality. Furthermore, CDMA systems take advantage of multipath through the use of RAKE receivers to combat fading. There are currently two major 3G technologies, both based on CDMA, namely, WCDMA and CDMA2000. Let us consider the link between a mobile station and a base station in a CDMA system communicating using a unique code. Because of the characteristics of these codes, namely, orthogonality, the communication is successful despite the interference generated in the system from other mobiles. This is possible because of the way CDMA is designed, where the signals from the other links are filtered out as background noise. So in a way, CDMA mitigates interference between users by accepting the fact that interference is present and optimizing the system to operate in an environment of interference. To achieve this goal, CDMA uses spread spectrum technology. One form of spread spectrum is direct sequence spread spectrum, in which special spreading codes are used to spread out the signal over a wide bandwidth while reducing its power at the same time, as shown in Figure 2.6. A spreading code is applied to the narrowband data at the transmitter, resulting in a signal with a much wider bandwidth. Since the total signal power remains the same, the signal level drops to the noise floor level. After passing through the channel, the signal at the receiver will consist of the wanted signal, multiple access interference, and noise. By applying the same spreading code used in the transmitter to the combined signal, a pulse-like peak results for the wanted signal and a small residual signal level for all interferers. The major advantage of CDMA technology is the potential of extraordinary capacity increase over narrowband multiple access wireless technologies. Idealized models show that the capacity improvement may be as high as 20 times that of the narrowband cellular standards, such as AMPS in North America, NMT in Scandinavia, TACS in the United Kingdom, and 13 times that of TDMA. However, in practice coverage areas are highly irregular, the load is not spatially uniform and is time variant throughout the day, leading to less but still significant capacity improvements.

2.3 Fundamentals of CDMA The key to CDMA high capacity is the use of noise-like carrier waves. Instead of assigning frequency or time slots, different users are assigned different nearly orthogonal instances of the noise carrier. This alters the system sensitivity to

22

Smart Antenna Engineering

Interference + noise + signal

Spreading code

I C

Spreading

Channel

Despreading

Data

C C

I

C Spread signal

Figure 2.6

Direct sequence spread spectrum fundamentals.

interference, from having to design a system based on the worst-case interference to the average interference. Traditional time or frequency slotted systems must be designed with a reuse ratio that satisfies the worst-case interference scenario, which is experienced by only a small fraction of users. Use of pseudonoise carriers, with all users occupying the same spectrum, makes the effective noise the sum of all other-user signals. The CDMA receiver correlates its input with the desired noise carrier, enhancing the signal-to-noise ratio at the detector and overcoming the summed noise enough to provide an adequate SNR at the detector. Because the interference is summed, the system is sensitive to the average interference instead of the worst-case interference. Frequency reuse is universal, that is, multiple users use the same CDMA carrier frequency. Capacity is determined by the balance between the required SNR for each user, and the spread spectrum processing gain, defined as the ratio between the carrier chip rate to the user’s data rate. The figure of merit of a well-designed digital receiver is the dimensionless Eb/Nt, defined as

Multiple Access Techniques for 2G and 3G Systems

Eb = Nt

23

Energy per bit Noise Power Spectral Density + Interference Power Spectral Density (2.5)

The noise part of Eb/Nt, in a spread spectrum system is the sum of thermal noise and all the other-user interference. Assuming the spectrum of the signals is rectangular, with a bandwidth W, then the noise + interference power spectral density is Nt =N o +

∑P

i

otherusers

(2.6)

W

where the first term represents the thermal noise level of the receiver. We can then rewrite Eb/Nt in terms of the data rate and the spread-spectrum bandwidth as: Eb = Nt j

Pj R No +

∑P

(2.7)

i

otherusers

W

The interference in this equation is the sum of the signals from all users other than the one of interest. This equation is the key to understanding how and why CDMA works. Early arguments against CDMA were centered on what is termed the near-far problem. In the mobile radio environment some users may be located near the base station while others may be located at the cell edge. The propagation path loss difference between those extreme users can be on the order of several tens of decibels. Consequently, the difference in the received power and the SNR at the base station from users in those two extreme cases could be as high as 50 or 60 dB, if the users are all transmitting at the same constant power. Hence, for the base station to accommodate users at the cell edge, the spreading bandwidth would have to be on the order of 40 dB or so, that is 10,000 times the data rate. Using a bandwidth of 100 MHz to support a data rate of 10 Kbps would lead to a much worse spectral efficiency than compared with a narrowband system. Choosing a more reasonable bandwidth would deny service to remote users. The key to the high capacity of commercial CDMA was a simple solution; instead of using constant power, the transmitter’s power can be controlled in such a way that the received powers from all users are roughly equal. This works because by controlling the received power, the total interference seen at the base station cannot be dominated by any single user as long as all users have similar data rates. Assuming perfect power control, the interference can be

24

Smart Antenna Engineering

given by Io = (N – 1)P where N is the total number of users and P is the received signal power from each user. The uplink Eb/Nt now becomes P R W R Eb = = N t N o + (N − 1) P W N oW P + (N − 1) N =

N

pole

W R No − +1 Eb P Nt

=

W R as P → ∞ Eb Nt

(2.8)

(2.9)

(2.10)

Equation (2.10) shows the fundamental dependence of CDMA capacity not only on power control but also on interference reduction techniques such as smart antennas. Capacity can be maximized if we adjust the power control, or more broadly P, so that the SNR is exactly what is needed for an acceptable error rate.

2.3.1

Isolated Cell Capacity

Using (2.10) to solve for N with the assumption that power in unlimited P → ∞ and a nominal SNR target of 4.5 to 5 dB for IS-95 CDMA with 9.6-Kbps data rate, we obtain an uplink pole capacity of 46 to 42, respectively. The pole capacity of a cell is defined as the maximum number of users a cell can support if there is no constraint on the peak received power. In practice, the pole capacity cannot be reached since it implies that the interference is allowed to grow to such high levels that the coverage shrinks to zero. Typically CDMA networks are designed and planned to operate at uplink loads of 50%–60%, levels considered to provide good coverage versus capacity trade-off. Ideally, that leads to 21–23 users on the uplink with IS-95A CDMA. The actual number of subscribers that 50% or 60% translates to in real networks may vary depending on the data rate selected and fade margin expected, among other factors. Note that since capacity and SNR are reciprocal, a reduction in the required SNR or Eb/Nt leads to improvement in capacity, and vice versa. CDMA capacity will be discussed in more details in the next sections, along with additional factors that contribute to the actual performance, where we will see that overall there is major improvement over narrowband technologies. Recall that in the same

Multiple Access Techniques for 2G and 3G Systems

25

1.25-MHz bandwidth, a single sector of a single AMPS cell has only two channels available. 2.3.2

CDMA Codes

Since in CDMA systems all mobiles need to share the same frequency carrier, orthogonal codes called Walsh codes are used to separate between users and different communications channels within a cell; that is, they provide channelization on the forward link. This is essential in CDMA to avoid or at least minimize multiple access interference in the forward link. Walsh codes are orthogonal binary sequences generated using the Hadamard matrix as follows [4, 5]: W N W 2N =  W N

WN  W N 

(2.11)

Figure 2.7 shows how Walsh codes are generated based on (2.11). Similarly, Walsh codes of any length 2N where N is an integer can be generated. By changing 0s to -1s, Walsh codes can be rewritten as W 12 = [ −1 − 1], W 22 = [ −1 1] where W mn denotes the mth Walsh code of length n. To illustrate how Walsh codes are used in CDMA, let us consider three users with messages given by

W11 = 0

W12 = 0 0

W 14 = 0 0 0 0

Figure 2.7

W 24 = 0 1 0 1

Walsh code generation.

W 22 = 0 1

W 34 = 0 0 1 1

W 44 = 0 1 1 0

26

Smart Antenna Engineering

m 1 = [1 − 1 1] m 2 = [ −1 1 1]

(2.12)

m 3 = [1 1 − 1] Now let us assign each of the users a Walsh code of length eight, respectively, W 28 = [ −1 1 − 1 1 − 1 1 − 1 1 ] W 48 = [ −1 1 1 − 1 − 1 1 1 − 1 ] W

8 6

(2.13)

= [ −1 1 − 1 1 1 − 1 1 − 1 ]

Since the chip rate for the Walsh code in this case is eight times the message bit rate, spreading each signal with its assigned code will result in widening the band from 1/Tb to 1/Tc where Tb and Tc are the bit and chip periods, respectively. The spread spectrum signals of the three users Sn(t) and the combined signal C(t) are then given by, respectively, S 1 (t ) = m 1 (t )W 28 S 2 (t ) = m 2 (t )W 48 S 3 (t ) = m 3 (t )W 68 C (t ) = S 1 (t ) + S 2 (t ) + S 3 (t ) The resultant signals are shown in Figures 2.8 through 2.11, respectively. Now, in order to recover a user’s original message, the receiver spreads the

Figure 2.8

User 1 spread spectrum signal.

Multiple Access Techniques for 2G and 3G Systems

Figure 2.9

Figure 2.10

27

User 2 spread spectrum signal.

User 3 spread spectrum signal.

received composite signal with the Walsh code assigned to that user. This operation is shown in Figure 2.12 for user 1, where the receiver integrates all the values over a bit period. The original message is reconstructed using the following decision criterion m$ (t ) = 1 if C (t ) ⋅W mn > 0 m$ (t ) = −1 if C (t ) ⋅W mn < 0

(2.14)

28

Figure 2.11

Smart Antenna Engineering

Composite spread spectrum signal.

(a)

(b)

Figure 2.12 (Tb = 8Tc).

(a) Effect of spreading received signal with first user’s code; (b) User 1 recovered signal

Multiple Access Techniques for 2G and 3G Systems

29

If the receiver attempts to spread the composite signal with a code that was not assigned to the user, (e.g., withW 88 = [ −1 1 1 − 1 1 − 1 − 1 1 ], we get all zeros after the integration, as we can see in Figure 2.13, which means the signal cannot be recovered. In addition to using Walsh codes to separate different users and different channels on the forward link within a sector, a CDMA system needs to separate transmissions from different sectors within a network. This is accomplished using PN codes, as described in Figure 2.14. Some important key differences between Walsh codes and PN codes, which greatly impact the interference level in a CDMA system, are illustrated in Table 2.1.

2.3.3

IS-95 CDMA Systems

The TIA IS-95 CDMA system is a 2G mobile wireless system that operates in the cellular 800-MHz band [6, 7]. Another version of this system that operates in the PCS 1,900-MHz band is defined in J-STD-008 [8]. Both systems use a 1.25-MHz wide carrier and a chip rate of 1.2288 Mcps. On the forward link, a family of 64 Walsh codes is used to separate the different channels and different users. Short PN codes of length 215-1chips with a period of 32768 chips or 26.67 ms are used to separate transmissions from different sectors. This is accomplished by using the same PN sequence for all sectors and then identifying each sector by a unique time offset in increments of 64 chips, resulting in 512 possible PN sequences. On the reverse link, long PN codes of length 242-1chips are used for channelization, that is, to distinguish different users. In addition,

Figure 2.13

Effect of spreading received signal with wrong Walsh code.

30

Smart Antenna Engineering Walsh 1

Data stream 1

Sector specific PN code

Walsh 2 Data stream 2

Filtering

Modulator

Walsh n Data stream n

CDMA transmitter

Sector specific PN code Walsh 1

MS1

Data stream 1 Demodulator

Filtering

Sector specific Walsh n PN code

Demodulator

Filtering

MS2

Data stream n

CDMA receiver

Figure 2.14

CDMA transmitter and receiver block diagrams.

the reverse link signal is further spread by short PN codes of length 215-1chips to identify the sector to which the transmission is intended. 2.3.3.1 Forward Link Channels

The IS-95 and J-STD-008 standards define two types of forward link channels, namely common channels broadcast to all mobiles in a sector and dedicated channels to specific mobiles. Note that in addition to assigning different Walsh

Multiple Access Techniques for 2G and 3G Systems

31

Table 2.1 Comparison Between Walsh and PN Codes

Transmit and Receive

Walsh Codes PN Codes Correlation Correlation

Same codes, same time offsets

100%

100%

Different codes

0%

Low noise-like

Same codes, different time offsets

> 0%

Low noise-like

< 100%

codes to each channel, all channels are spread with the same PN sequence associated with the transmitting sector. The set of channels defined in the standard are listed here: • Pilot channel: The pilot channel is continuously transmitted sector-wide

to provide timing and phase references to all users to aid in system acquisition, signal strength comparison, and demodulation operations. The pilot channel is assigned Walsh code 0 or W0, which is a sequence of 64 zeros with 1.2288 Mcps chip rate. Note that no baseband information is carried by the pilot channel. • Sync channel: The sync channel is also continuously transmitted sector

wide to provide timing information to the mobile during system acquisition and power up. Baseband information contained in the sync channel message is used to inform mobiles of system synchronization information and other system parameters. The sync channel is assigned Walsh code W32 and it is transmitted in groups of superframes at the bit level. Each superframe lasts for 80 ms and consists of three 26.67 ms sync channel frames that are synchronized with each period of the short PN sequence. Hence, once the mobile acquires synchronization with the pilot channel, the sync channel frame boundaries are immediately known. • Paging channel: The paging channel is used to transmit overhead infor-

mation to a mobile such as pages and other commands. Call setup commands and traffic channel assignments are also sent over the paging channel. Based on the standard specifications, there can be up to seven paging channels but there must be at least one. At the bit level, each paging channel frame lasts for 20 ms, four of which are combined into an 80-ms paging channel slot.

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Smart Antenna Engineering

• Forward traffic channels: Forward traffic channels carry voice, data, and

signaling once a call has been established. There are two rate sets defined in the standard. Rate set 1, or RS1, with data rates 1.2, 2.4, 4.8, and 9.6 Kbps, and RS2 with data rates 1.8, 3.6, 7.2, and 14.4 Kbps. In systems with only one paging channels, there are 61 available Walsh codes that could be assigned to traffic channels. Traffic channel frames last for 20 ms. 2.3.3.2 Reverse Link Channels

In IS-95 and J-STD-008 standards there are only two reverse link channels: • Access channel: This channel is used by the mobiles to access the system

for registration, call origination, page responses, and overhead transmission to the base stations. • Reverse traffic channels: Similar to the forward link, the reverse traffic channels carry voice, data, and signaling once a call has been established. 2.3.3.3 RAKE Receiver

The presence of buildings, trees, hills, and other objects in the areas served by mobile systems cause signal reflection, diffraction, and scattering. This creates multiple replicas of the transmitted signal with different attenuations and time delays at the receiver. The interaction of the incoming waves at the receiver antenna results in deep and rapid fading or fluctuations in the signal strength. This significantly degrades the system performance. IS-95 based CDMA systems actually take advantage of the multipath components through the use of RAKE receivers. Multiple correlators are used to detect the strongest multipath components using a searcher finger designed to compare the incoming signals with the PN code used. This operation detects multipath arrivals by producing a series of correlation peaks at different times. The magnitude of each peak is proportional to the envelope of the signal in a particular path, whereas the time of each peak relative to the time of arrival of the first path gives that path’s delay. With the amplitudes and time delays of the strongest multipath components known, a RAKE receiver compensates for the delays and combines the signals based on their strengths. This produces a diversity gain at the CDMA receiver, which helps combat fading. The block diagram of a CDMA RAKE receiver is shown in Figure 2.15. 2.3.3.4 Power Control

Recall that in CDMA systems all users share the same RF carrier through the use of PN codes, therefore each user appears like random noise to other users and

33

Path 3

Path 1

Path 2

Multiple Access Techniques for 2G and 3G Systems

Strongest multipath components

Delay element (chips)

Path 1+ interference Correlator

A1

Delay element (chips) Path 2+ interference Correlator

A2

Sum

Delay t element (chips) Path 3+ interference Correlator

Figure 2.15

A3

RAKE receiver block diagram.

contributes to the system noise. If the power of each user is not properly controlled and allowed to increase unnecessarily, other users would suffer from interference that could severely degrade system performance. Consider a CDMA system where all users transmit at the same power. A user close to the base station will result in a high SNR1 at the receiver, whereas another user further away from the base station would yield a lower SNR2. Obviously, this disparity results in different signal quality between users. This is the classical near-far problem. Assume that the required SNR necessary to maintain the desired signal quality is given by SNRreq. When new users are added to the cell, the interference level in the cell increases, thus reducing the SNRs of existing and new users up to the point at which the SNR of a new user would not be able to reach SNRreq. Therefore, no more users can be added to the cell and the capacity is reached with only a few users. Hence, power control is essential to overcome the near-far problem and maximize the capacity. Power control is the

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Smart Antenna Engineering

process by which the transmit power of each user is controlled such that the received powers at the base station are equal. The capacity is then maximized by only allowing each user to transmit just enough power to achieve SNRreq. 2.3.3.5 Reverse Link Open Loop Power Control

As in any communications system, there is always a propagation loss that impairs the signal on the forward and reverse links. In addition to the regular distance-dependent path loss, other factors such as shadowing and multipath produce fading in mobile communications systems. Basically, there are two types of fading, slow fading and fast fading. Slow fading is modeled by a lognormal distribution and it manifests itself by slow power variation over several wavelengths, as shown in Figure 2.16. This type of fading is typically caused by the signal being partially blocked by buildings, trees, and other obstacles. On the other hand, when multipath components with different amplitudes, phases, and arrival times add up at the receiver, they combine constructively and destructively, forming a standing wave pattern with a half wavelength period. As the mobile moves through this pattern, the received power will experience fast fading with an envelope distribution characterized by a Rayleigh distribution. When a mobile is in idle state, that is, a state where it monitors the overhead channels but no call has been established yet, the base station cannot control the power of the mobile. To solve this problem, the IS-95 standard defines the open loop power control process, which ensures that each mobile starts its initial transmissions, also called access probes, with a power level that depends on the received power from the base station pr or Slow fading

Signal strength in dB

Fast fading

Time

Figure 2.16

Fading as function of time.

Multiple Access Techniques for 2G and 3G Systems

Pt ,initial = − p r − K + NOM _ PWR + INIT _ PWR

35

(2.15)

where K is a constant equal to 73 in the cellular band and 76 in the PCS band. NOM_PWR and INI _PWR are system parameters that are broadcast from the base station to all mobiles. The reason this is called open loop power control is that if the mobile does not receive an acknowledgement from the base station after sending an access probe, it waits for a random time period before sending the next access probe with a slightly higher power. The mobile repeats this process until an acknowledgement is received but there is no feedback from the base station about the signal quality. Since this process is slow, it only compensates for the slow lognormal fading. 2.3.3.6 Reverse Link Closed Loop Power Control

The closed loop power control attempts to balance losses between the link due to Rayleigh fading or fast fading at slow mobile speeds and interference variations due to loading once the mobile is on a traffic state. It also improves the performance of mobiles at the cell edge where the signal is weak and the interfering signals from other cells are strong. As briefly described previously, power control adjusts the transmit power of each mobile to maintain the required SNR given a specific signal quality. To achieve that, the power control process must be able to determine the value of the SNRreq to maintain the signal quality. The outer loop power control performs this function by adjusting the target SNR according to the prevailing environment to achieve the desired end-user quality of service. Let us define Eb as the energy per bit and No as the interference plus noise power spectral density, then we get P Eb W P W R = = ⋅ = ⋅ SNR N o (N + I ) R N +I R W

(2.16)

where W is the RF carrier bandwidth, R is the signal data rate, and W/R is defined as the processing gain. It is clear from (2.16) that adjusting Eb/No is equivalent to adjusting the SNR. The closed loop power control is summarized in Figure 2.17. Based on the target Eb/No, the base station controls the mobile transmit power. The power control commands are sent from the base station on the forward link in the form of power control bit (PCB); each power control group lasts for 1.25 ms in IS-95 based systems. Hence, the power of the mobile can be adjusted up to 800 times per second. This is performed using the inner loop power control as follows: The base station monitors the reverse link Eb/No and compares it to (Eb/No)Target. If Eb/No > (Eb/No)Target, the base station commands the mobile to decrease the transmit power by sending a power down command,

36

Smart Antenna Engineering

Decrease ( E b No ) Target

Figure 2.17

Yes

Is received signal quality better than required quality

No

Increase ( E b No ) Target

Closed loop power control mechanism.

⇒PCB = 0. If Eb/No < (Eb/No)Target, the base station commands the mobile to increase the transmit power by sending a power up command, ⇒PCB = 1. 2.3.3.7 Soft Handoff

One major advantage of having all users in a CDMA system on the same RF carrier is the ability of maintaining simultaneous connections. When a mobile maintains simultaneous traffic channels with sectors belonging to different base stations, it is said to be in soft handoff. On the forward link, the mobile’s RAKE receiver demodulates the signals received from separate sectors and combines them to produce a signal with a better quality. On the reverse link, multiple base stations demodulate the mobile’s signal and the demodulated frames are sent back to the base station controller (BSC) to select the best frame. This operation provides some diversity since the signals on different links are typically uncorrelated and do not fade at the same time with the same depth. This results in a soft handoff gain, which improves the air interface capacity. As the mobile moves around the system, it keeps a list of all active pilots from the soft handoff links in a set called the active set. Other pilots with raw SNR Ec/Io strong enough to be candidates for soft handoff are kept in a set called the candidate set. Another important set kept by the mobile is the neighbor set, which contains those pilots that are neighbors to the current serving sector. When the sectors in the active set belong to the same base station, the mobile is said to be in softer handoff. The procedure by which these sets are maintained and the pilots are processed is defined in the IS-95 standard.

2.4 Third Generation Systems As second generation systems started to reach their limits in terms of spectral efficiency along with the increasing demands for higher data rate services, a need

Multiple Access Techniques for 2G and 3G Systems

37

emerged for improved networks that can provide these future requirements. This led to the development of 3G systems [5, 9], with the following main objectives: • Provide data rates from 144 Kbps up to 384 Kbps for mobility

scenarios; • Provide data rates up to 2 Mbps for limited mobility and fixed wireless scenarios; • Provide higher spectral efficiency compared with 2G systems; • Support multiple simultaneous services (e.g., speech, high-speed data). There are currently two major standards adopted for 3G systems, both of which are based on CDMA, namely CDMA2000 and WCDMA. Another emerging technology also based on CDMA is the time division synchronous CDMA (TD-SCDMA). 2.4.1

CDMA2000

The CDMA2000 family of standards is a wideband spread spectrum radio interface that uses CDMA technology to meet the objectives of 3G systems while maintaining backward compatibility with IS-95 based systems. This means that mobile handsets designed according to the IS-95 standard are capable of operating in a CDMA2000 system and vice versa. The first component of the CDMA2000 standard is called 1X radio transmission technology (1X RTT) because it uses an RF carrier of 1.25 MHz just like IS-95 based systems, hence the 1X, which is also referred to as spreading rate (SR)1. The key benefits of the 1XRTT technology standardized under the name of IS-2000 [10, 11] compared with IS-95A/B standards [7] can be summarized as follows: • Better forward error correction (FEC). This is achieved through the use of

higher convolutional coding rates as well as turbo codes for high data rates. The coding rate refers to the number of symbols produced by the encoder for every bit of input data. The greater this number is, the more protection we get against errors because of the increased correction power. A direct impact of the improved coding is a reduction in the required Eb/No, which directly translates into higher capacity or higher data rates. A coding gain of up to 2dB can be achieved with IS-2000 systems compared with the IS-95 standard. • Fast forward link power control mechanism. As described earlier, a power control mechanism is defined for the reverse link of the IS-95 standard, whereby the transmit power of the mobile is controlled up to 800 times

38

Smart Antenna Engineering

per second. IS-2000 extends the use of this power control process to the forward link as well, where the mobile station can control the power transmitted by the base station with speeds of 400–800 times per second through the use of the forward link closed loop power control mechanism. This allows the power resources on the forward link to be optimized and used much more efficiently than in IS-95 systems, yielding significant improvements in capacity. • Multimedia services and improved data services support. In addition to the

improvements listed above, the IS-2000 standard introduces new dedicated channel and common channels to support high data rate applications as well as improved diversity techniques. Moreover, the battery life is extended through the use of a new quick paging channel. The combination of these improvements results in a voice capacity increase of 1.6 to 2 times compared with IS-95A/B as well as data rates of up to 307 Kbps. 2.4.1.1 Overview of IS-2000 Forward Link Physical Channels

As we have seen in the IS-95 standards, there are two rate sets, RS1and RS2, with data rates of up to 9.6 Kbps and 14.4 Kbps, respectively. In IS-2000, a wider range of data rates are available and are defined in terms of radio configurations (RC), which can be summarized as: • RC1, which supports IS-95A/B backward compatibility for all rate set 1

(RS1) based services up to 9.6 Kbps; • RC2, which supports IS-95A/B backward compatibility for all rate set

2 (RS2) based services, up to 14.4 Kbps; • RC3, which supports data rates from 1,500 bps up to 153.6 Kbps,

using rate 1/4 FEC encoding; • RC4, which supports data rates from 1,500 bps up to 307.2 Kbps,

using rate 1/2 FEC encoding; • RC5, which supports data rates from 1,800 bps up to 230.4 Kbps,

using rate 1/4 FEC encoding. Table 2.2 provides a summary of the forward link physical channels of the IS-2000 standard and a brief description of their functions. 2.4.1.2 Overview of IS-2000 Reverse Link Physical Channels

Since CDMA networks based on the IS-95 standard have been launched in the 1990s it became apparent over the years that there are certain inefficiencies in

Multiple Access Techniques for 2G and 3G Systems

39

Table 2.2 IS-2000 Forward Link Physical Channels

Channel

Function

Common pilot channel (F-CPICH)

Used to broadcast pilot for the entire cell/sector, for channel and phase estimation (coherent demodulation), initial acquisition, and handoffs.

Common auxiliary pilot channel (F-CAPICH)

Used for beamforming applications for a group of mobiles.

Dedicated auxiliary pilot channel (F-DAPICH)

Used for beam steering and beamforming applications for a single mobile.

Sync channel

This is the same channel as in IS-95A/B, containing system synchronization information.

Fundamental channel (F-FCH)

This is identical to the 95A/B traffic channel. Used for voice, data, and control. There can be 0-1 channels.

Supplemental channel (F-SCH), (RC 3-9)

This channel was introduced for supporting high data rates. There can be 0–2 channels.

Supplemental code channel (F-SCCH), (RC1-2)

This channel was introduced in IS-95B for medium data rate service option. There can be 0–7 channels.

Dedicated control channel (F-DCCH)

Introduced for MAC, data, and signaling. The power control subchannel can also be punctured here when F-FCH is absent.

Common assignment channel (F-CACH)

Designed to provide fast response reverse link channel assignments to support transmission of random access packets on the reverse link.

Common power control channel (F-CPCCH)

Used by the base station for transmitting common power control subchannels.

Paging channel IS 95 A/B

One common control paging channel (PCH) where broadcast and mobile station directed messages are transmitted.

Broadcast control channel (F-BCCH)

Broadcasts only cell-specific overhead messages (e.g., CDMA channel list, extended systems parameters message and neighbor list) at (4.8, 9.6, or 19.2 Kbps). The sync channel is used to let mobiles know if F-BCCH is supported.

Common control channel (F-CCCH)

Broadcasts mobile station specific messages (e.g., extended channel assignment message, general page message, order message) at 9.6, 19.2, and 38.4 Kbps in discontinuous transmit mode.

Quick paging channel (F-QPCH)

Helps decrease the “wake” time of a mobile station, that is the time the mobile has to periodically demodulate the PCH or F-CCCH. This improves MS standby time and reduces battery consumption. The support of this channel is optional.

40

Smart Antenna Engineering

the design of the reverse link. This led to an IS-2000 physical layer design that adds several enhancements to improve the performance of the reverse link. Table 2.3 provides a summary of the reverse link physical channels of the IS-2000 standard and a brief description of their functions. One of the major drawbacks of the IS-95 reverse link design is the noncoherent demodulation by rake receiver, which requires a high received signal-to-noise ratio for good performance. The IS-2000 standard solves this problem by introducing the reverse link pilot or R-PICH, which allows the base station to estimate the carrier phase and makes coherent demodulation possible. This improves both searching and tracking of mobiles. Another major improvement is the introduction of the forward link power control by which the mobile can adjust the forward link power. This is performed by time-multiplexing forward power control (PC) information on the reverse pilot channel, as shown in the frame structure in Figure 2.18. 2.4.1.3 CDMA2000 1x EV-DO

Data services are expected to have a significant growth over the next few years and will likely become the dominant source of 3G traffic and revenue. The Table 2.3 IS-2000 Reverse Link Physical Channels

Channel

Function

Reverse pilot channel (R-PICH)

Used for searching, tracking, and coherent demodulation. Also used by the forward link channels to adjust forward link power and maintain the quality of the link.

Access channel (R-ACH) and enhanced access channel (R-EACH)

Used by mobiles to access the system for registration, call origination, and page responses.

Reverse common control channel (R-CCCH)

Used to support efficient access procedures of packet data services.

Reverse dedicated control channel (R-DCCH)

Used for the discontinuous transmission of user traffic, control, and signaling information to the base station while the mobile is in the traffic state.

Reverse fundamental channel (R-FCH)

Used to carry user traffic for RC1 and RC2.

Reverse supplemental code channel

Used to support medium data rates based on services for RC1 and RC2. There can be up to seven such channels.

Reverse supplemental channel (R-SCH)

Used to support high data rates for RC3 and RC4. There can be zero to two channels.

Multiple Access Techniques for 2G and 3G Systems

41

1 Frame (20ms)

0

1

2

15

Reverse pilot channel 1 PCG (1.25ms)

Figure 2.18

Reverse power control subchannel

IS-2000 reverse link pilot frame structure.

current 3G operators in Japan and Korea as well as in the United States are already experiencing great success with their data services. KTK in Korea has reported that 34% of its ARPU was related to data usage for the third quarter of 2003, particularly after the deployment of 1xEV-DO. The 1xEV-DO standard [12–20] is optimized for wireless high-speed packet data services. Because of the typical asymmetric characteristics of IP traffic, the downlink is the more critical of the two links [12]. Thus, several techniques were introduced in 1xEV-DO to optimize the downlink throughput. The 1xEV-DO downlink uses time-division-multiplexed (TDM) waveform, which eliminates power sharing among active users by allocating full sector power and all code channels to a single user at any instant. This is in contrast to code-division-multiplexed (CDM) waveform on the IS-95 downlink, where there is always an unused margin of transmit power depending on the number of active users and power allocated to each user. Through power control, this margin is used to account for large variations of the required mobile station transmit power in fading channels to guarantee a given target frame error rate. Figure 2.19 shows the sector power usage of the IS-95 and 1xEV-DO downlinks. Each channel in IS-95 is transmitted the entire time with a certain fraction of the total sector power, while the equivalent channel in 1xEV-DO is transmitted, at full power, only during a certain fraction of time. The efficient usage of sector power resource in 1xEV-DO improves cell coverage as well as signal-to-interference and noise ratio (SINR) for noise-limited users. Similar to the IS-95 concepts, every mobile station reports to the network the strongest downlink pilots it can measure. In turn, the network selects an active set for each terminal. Each sector in the terminal’s active set maintains a connection with the terminal. The active set of sectors for any given terminal is also the set of power controlling sectors for its uplink. However, instead of transmitting equal power on all downlink traffic channels in the active set as adopted

42

Smart Antenna Engineering

Unused power margin

Control channel

Sync channel

Pilot channel

Traffic channels

Max transmit power

Sector transmit power

Sector transmit power

Max transmit power

Total data traffic channels

Paging channel

Pilot channel Time

Figure 2.19

Time

Sector power usage comparison between 1xRTT(left) and 1xEV-DO (Right).

in IS-95, the 1xEV-DO network only transmits on the best link and allocates no power on the others. To accomplish this procedure, a mobile terminal monitors the SINR of all the sectors in its active set and informs the network, via a feedback channel, of the identity of the selected serving sector. Due to the TDM waveform of the 1xEV-DO downlink, a terminal is allocated the full sector power whenever it is served, thus no power adaptation is needed. Instead, rate adaptation is used on the downlink. The highest data rate that can be transmitted to each terminal is a function of the received SINR from the serving sector, which is typically a time-varying quantity. To achieve the highest data rate at each time of transmission, each terminal predicts the channel condition over the next packet for its serving sector based on the correlation of the channel states. It selects the highest data rate that can be reliably decoded based on the predicted SINR, and then informs the serving sector of its selected rate over an uplink feedback channel. Whenever the network decides to serve a terminal, it transmits at the most recent selected rate fed back from the terminal. Since a sector transmits traffic data to a single user at any instant of time, a scheduling algorithm is implemented in each sector to fairly allocate the available time slots among the active users, thus maximizing capacity by exploiting the channel dynamics. Because different users experience independent fading processes, it is unlikely that all users’ SINR will fall into deep fades at the same time. In other words, when some users experience a deep fade, others reach peaks of their received signal strength. As a mobile user goes through periods of varying fades, the data rate allocated to it by the network will vary. However, since Internet

Multiple Access Techniques for 2G and 3G Systems

43

protocol (IP) traffic can tolerate relatively longer and variable time delays, unlike voice services, this can be tolerated. The standard does not specify the type of scheduler to be used. A smart scheduler will attempt to serve an active user near its peak SINR while maintaining a certain degree of fairness. For instance, the PF scheduling algorithm represents a good balance because it incorporates the two important features of a capacity enhancing scheduler: multiuser diversity gain and fairness. The algorithm selects the terminal based on a metric equal to the ratio of the instantaneous channel state to the long-term average of the served throughput. Thus, it attempts to serve each terminal at their local peaks of channel conditions and maintain higher average served throughput when the terminal is in better coverage. Another critical concept in the design of the 1xEV-DO standard is link adaptation. Link adaptation is achieved by combining several mechanisms designed to improve spectral efficiency while achieving the required simplicity and robustness for effective operation in a wireless cellular environment. The idea behind link adaptation is to optimize spectral efficiency by matching the transmit data rate, modulation, and coding to the time varying received SINR at the terminal. A variety of modulation schemes, including QPSK, 8 PSK, and 16 QAM as well as coding rates that best matches the fading channel, are defined in what is commonly called adaptive modulation and coding techniques. To fully exploit these concepts, the system includes a collection of techniques that consist of incremental redundancy and repetition coding, time diversity adaptation, and HARQ [15–18]. 2.4.2

WCDMA

WCDMA is another 3G air interface based on direct-sequence CDMA (DS-CDMA). WCDMA uses a chip rate of 3.84 Mcps, compared with 1.2288 Mcps in both IS-95 and IS-2000 standards and requires an RF carrier with 5-MHz bandwidth [21–25]. There are two modes of operation in the WCDMA air interface, a frequency division duplex (FDD) mode, where a pair of 5-MHz carriers are used, and a time division duplex (TDD), where only one carrier is used. Similar to IS-95 and IS-2000 systems, channelization is achieved through the use of orthogonal codes referred to as orthogonal spreading factor codes (OVSF), which are Walsh codes of variable lengths, whereas source separation is achieved using gold codes. Table 2.4 shows a comparative summary of the WCDMA, IS-95, and IS-2000 air interfaces. 2.4.2.1 FDD-WCDMA Forward Link (Downlink) Physical Channels

In this section we will summarize the physical channels associated with the WCDMA downlink. The physical channels are those channels that perform the actual transmission of data bits and are distinguished by an RF carrier, a channelization code, a spreading code, and modulation parameters. WCDMA

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Smart Antenna Engineering

Table 2.4 Main Differences Between IS-95, IS-2000, and WCDMA

Link

Function

IS-95 A/B

IS-2000

WCDMA

Forward link (Downlink)

Channelization

64-chip Walsh codes

4~256-chip Walsh codes

4~512-chip OVSF codes

Source separation

(215–1)-chip short PN codes

(215–1)-chip short PN codes

38400 chips of 218 Gold code

Power control rate

Slow

800 Hz

1,500 Hz

Channelization

None

4~256-chip Walsh codes

4~256-chip OVSF codes

Source separation

(242–1)-chip long PN codes

(242–1)-chip long PN codes

38,400 chips of 225 Gold code

Power control rate

800 Hz

800 Hz

1,500 Hz

Reverse link (Uplink)

physical channels can be grouped into four categories: common channels broadcast to all mobiles in the cell or sector, channels that carry paging information, channels used for random- and packet-access, and dedicated connection channels. • Common pilot channel (CPICH): This channel is a cell-wide channel,

which provides a coherent phase reference for the downlink channels, and it uses the gold code specific for that cell. It also aids channel estimation for cell selection and reselection as well as handoff procedures for the mobiles. The CPICH uses orthogonal codeC 0256 . • Primary common control physical channel (P-CCPCH): This channel is used to broadcast cell information; that is, cell system frame number (SFN) and timing reference for all downlink channels necessary for synchronization operations. That is why the P-CCPCH is continuously transmitted over the entire cell and it always uses the same channelization codeC 1256 . • Secondary common control physical channel (S-CCPCH): This channel is used to transmit information related to the forward access channel (FACH) and the PCH and is mainly monitored by the mobiles in idle mode.

Multiple Access Techniques for 2G and 3G Systems

45

• Paging indication channel (PICH): This channel is used in conjunction

with the PCH to provide mobiles with a sleep mode operation, which saves the battery in idle mode. Basically, the PICH is used to alert mobiles of an incoming page. • Dedicated physical channel (DPCH): The DPCH consists of two separate channels, the dedicated physical data channel (DPDCH) and the dedicated physical control channel (DPCCH), which are time multiplexed onto one time slot. The DPCCH carries control bits such as pilot bits, which are used by the receiver to measure the channel quality, and transmission power control (TPC) bits, which are used to adjust the power of the mobile. The DPDCH is mainly used to carry user traffic as well as some overhead and signaling data. 2.4.2.2 FDD-WCDMA Reverse Link (Uplink) Physical Channels

As with the downlink, there are two categories of channels on the WCDMA uplink, common uplink physical channels and dedicated uplink physical channels. • Physical random access channel (PRACH): This channel is used to carry

access requests (i.e., control information and short data bursts) and does not contain any pilot or TPC bits since it uses only open loop power control. • Physical common packet channel (PCPCH): This channel is used to carry connectionless packet data. • Dedicated physical data channel (DPDCH): The uplink DPDCH is used to carry dedicated user traffic data generated at an upper layer. There may be zero, one, or up to six uplink DPDCHs. • Dedicated physical control channel (DPCCH): The uplink DPCCH is used to carry control information consisting of pilot bits to support channel estimation for coherent detection, TPC commands, and some feedback information. Unlike the downlink case, the DPDCH and DPCCH are not time multiplexed; instead, they are fed into the I and Q inputs of a complex spreader. 2.4.3

HSDPA

To meet the increasing demand for high data rates in multimedia services over networks supporting WCDMA, the Third Generation Partnership Project (3GPP) has released a new high-speed data transfer protocol named HSDPA [26–29]. HSDPA is expected to provide significant improvements over the basic

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WCDMA R’99 for downlink asymmetrical and bursty packet data services. HSDPA will offer a peak data rate up to and in excess of 10 Mbps (as opposed to the currently deployed 384 Kbps), as well as at least threefold sector throughput. For end users, HSDPA will mean lower delays and faster connection and response times, particularly for high data rate applications in loaded systems. The substantial increase in data rate and throughput is achieved by implementing a fast and complex channel control mechanism based on a short and fixed packet transmission time interval (TTI, fast HARQ, and fast scheduling performed at the Node B (base station) instead of the radio network controller (RNC), the equivalent of the BSC. The TTI indicates how often data arrives from higher layers to the physical layer and could take any of the values of 10, 20, 40, or 80 ms in R’99 WCDMA. R’99 WCDMA already includes three different channels for downlink packet data transmission: dedicated channel (DCH), downlink shared channel (DSCH), and FACH. The FACH is a common channel offering low latency. However, it is not spectraly efficient since it does not support fast closed loop power control. It is therefore limited to carrying only small data traffic. The DCH is the primary data channel and can be used for any traffic class. The DCH is allocated a certain orthogonal variable spreading factor (OVSF: 4-512) according to the connection peak data rate, whereas the block error rate (BLER) is controlled by inner and outer loop power control. The DCH code and power allocation are therefore inefficient for bursty and low duty cycle data applications since channel reconfiguration can be very slow (in the range of 500 ms) [21]. The DSCH provides the possibility to time-multiplex different users and improve the channel reconfiguration time and packet scheduling procedure (in the order of 10 ms) [5]. The HSDPA concept can be seen as an extension of the DSCH with the introduction of new features such as AMC, short packet size, multicode operation, and fast L1HARQ. In fact, these features replace the two basic WCDMA features, namely variable spreading factor VSF and fast power control [21]. Next, we will provide an explanation of key HSDPA features. A new transport channel named high-speed downlink shared channel (HS-DSCH) has been introduced as the primary radio bearer. Similarly to the DSCH, the HS-DSCH is shared between all users in a particular sector. The primary channel multiplexing occurs in the time domain, where each TTI consists of three time slots (or 2 ms). The TTI is also referred to as a subframe. The TTI has been significantly reduced from the 10, 20, 40, or 80 ms TTI sizes supported in R’99 to better achieve short round-trip delay between the mobile station and the Node B and improve the link adaptation rate and efficiency of the AMC. Within each 2 ms TTI, a constant spreading factor (SF) of 16 is used for code multiplexing with a maximum of 15 parallel codes for the HS-DSCH. These codes may all be assigned to one user during the TTI, or may be split amongst several users. Note that the more codes allocated to a user, the higher peak data rate it can achieve. The number of parallel codes allocated to each user

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depends on cell loading, quality of service (QoS) requirements, and the mobile station code capabilities (5, 10, or 15 codes). To support the HS-DSCH operation, two control channels have been added: the high-speed shared control channel (HS-SCCH) and the high-speed dedicated physical control channel (HS-DPCCH). The HS-SCCH is a fixed rate channel used for carrying downlink signaling between the Node B and the mobile station before the beginning of each scheduled TTI. This includes the mobile station identity, HARQ-related information, and the parameters of the HS-DSCH transport format selected by the link-adaptation mechanism. The HS-DPCCH carries uplink cyclic redundancy check (CRC)-based ACK/NACK signaling for the physical layer retransmission as well as CQI to be used in the link adaptation mechanism. The CQI is based on the CPICH and is used to estimate the transport block size, modulation type, and number of channelization codes for downlink transmission. The feedback cycle of the CQI can be set as a network parameter in predefined steps of 2 ms. 2.4.3.1 Adaptive Modulation and Coding

Adaptive modulation and coding is the fundamental feature of HSDPA. It consists of continuously optimizing the code rate, the modulation scheme, and the number of multicodes employed as well as the transmit power per code according to the channel quality experienced (CQI feedback) by the mobile station. To achieve very high data rates, HSDPA adds a higher order modulation (16 QAM) to the existing QPSK modulation in R’99. Different combinations of modulation and channel encoding can be used to provide data rates ranging from 119 Kbps/code with QPSK and 1/4 code rate to 712 Kbps/code with 16 QAM and 3/4 code rate (SF = 16). Users with the most favorable channel condition (close to the Node B) will get the highest data rates, whereas users with the least favorable channel condition will get the lowest data rates (located at the cell edge). HSDPA supports the use of 5, 10, and 15 multicodes. A single user can receive up to 15 multicodes, resulting in a peak data rate of 10.8 Mbps. However, the maximum specified peak data rate with HSDPA is 14.4 Mbps (or 960 Kbps/code) when 16 QAM modulation is used with no coding (effective code rate of 1) and 15 multicodes. This rate remains very unlikely to achieve since it corresponds to an unloaded system where the served user is extremely close to the node B. Another benefit of AMC is better utilization of the Node B power. If no power constraints are specified, the leftover power from the dedicated channels (R’99) can be allocated to HS-DSCH, resulting in near-maximum power utilization. 2.4.3.2 Hybrid-ARQ with Soft Combining

The retransmission mechanism selected for HSDPA is HARQ with Stop and Wait protocol (SAW). HARQ allows the mobile station to rapidly request

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retransmission of erroneous transport blocs until they are successfully received. HARQ functionality is implemented at the medium access control (MAC) layer, as opposed to the radio link control (RLC) layer, as is the case in R’99 WCDMA. Therefore, the retransmission delay of HSDPA is much lower than for R’99 because it does not involve the RNC. In normal circumstances, a NACK may require less than 10 ms at the MAC layer, while it can take up to 100 ms at the RLC layer when Iub signaling is involved [9]. This reduces significantly the delay jittering for Transmission Control Protocol/Internet Protocol (TCP/IP) and delay-sensitive applications. During retransmission, the mobile station does not discard the original transmission but rather combines it with the following retransmission(s) to increase the probability of successful decoding. This is called soft combining. HSDPA supports both chase combining (CC) and incremental redundancy (IR). CC is the basic combining scheme. It consists of the Node B simply retransmitting the exact same copy of the original packet. With IR, additional redundant information is incrementally retransmitted, providing additional coding gain. This can result in fewer retransmissions than for CC. However, the disadvantage of IR over CC is the much higher memory requirement for the phone. 2.4.3.3 Fast Scheduling

The scheduler is a key element of HSDPA that determines the overall behavior of the system and, to a certain extent, its performance. For each TTI, it decides which terminal (or terminals) the HS-DSCH should be transmitted to and, in conjunction with the AMC, at which data rate. One important change from the R’99 implementation is that the scheduler is located at the Node B as opposed to the RNC. This, with the short TTI (2 ms), enables the scheduler to quickly track the user equipment (UE) channel condition and adapt the data rate allocation accordingly. Three main scheduler algorithms have been proposed for HSDPA: round robin (RR), maximum C/I, and PF. RR schedules users according to a first-in first-out approach. It allows achieving a high degree of fairness between the users at the expense of the overall system throughput (and therefore spectral efficiency) because some users can be served even when they are experiencing a destructive fading. The maximum C/I schedules users with the highest C/I during the current TTI. This naturally leads to the highest system throughput because most of the served users will likely sustain a high peak data rate with a low probability of error. However, the fairness between the users is minimal. In fact, users at the cell edge will be largely penalized by experiencing excessive service delays and significant outage. The PF offers a good trade-off between RR and the maximum C/I. The PF schedules users according to the ratio of their instantaneous data rate to average served data rate. This results in all users getting an equal probability of being served even though they may experience very

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different channel quality. This allows having a good balance between the system throughput and the user fairness.

2.5 Basic CDMA Procedures As previously described, each CDMA base station transmits a different PN sequence (gold code). In WCDMA, there are 512 primary scrambling codes available. Each scrambling code has low cross-correlation with any other scrambling code regardless of the timing offset between the two scrambling codes. This allows the base stations to be deployed asynchronously. In CDMA2000, base stations transmit the same PN sequence (M-sequence) offset by different amounts of time. There are 512 PN offsets available. An M-sequence transmitted with a given PN offset has low autocorrelation with any other PN offset. To guarantee that each base station transmits a PN offset that is distinct from those in its vicinity, base stations need to be synchronized so that they have a common sense of timing. Figure 2.20 shows a typical CDMA cell layout where the color of each cell indicates a different PN offset. This approach is necessary so users could identify the different base stations or sectors and to enable a CDMA phone to acquire the system. This acquisition process is one of several possible states for a mobile phone or UE. Other states shown in Figure 2.21 include idle, access, and dedicated. 2.5.1

Acquisition State

Acquisition means acquiring the system. It is done upon power up or loss of service. For example, in WCDMA acquisition consists of a three-step process

Figure 2.20

CDMA cell layout.

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Power up/initialization state Phone acquires system. CPICH, SYNC .

Idle state Phone receives overhead information on the paging channel.

Traffic/dedicated state A dedicated channel is allocated to the phone.

PCH,P-CCPCH,S-CCPCH,PICH

FCH,SCH,DPCH

Access state Phone accesses the network for call origination. RACH,PRACH

Figure 2.21

CDMA call states.

designed to simplify the 512 x 38,400 search space, where there are 512 primary scrambling codes (PSCs) and 38,400 possible chip offsets of the PSC. The UE acquisition begins by searching and finding the synchronization channel (SCH). On the SCH, the primary SCH (P-SCH) and the secondary SCH (S-SCH) are sent simultaneously, as shown in Figure 2.22. These two channels are coded differently. The P-SCH is coded with the primary synchronization code, which is the same for every cell in the system. The S-SCH is coded with the secondary synchronization code, comprising 64 different code groups. When the code group is determined (step 2), the PSC is determined on the CPICH. There are eight PSCs per code group; the UE tries these eight combinations.

256 chips PSC SSC

One slot (2560 chips) PSC SSC

PSC SSC Common pilot channel One frame (38400 chips, Tf =10 ms)

Figure 2.22

WCDMA cell search signals.

PSC SSC

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When the PSC is found, the UE resolves a 10 ms ambiguity on the broadcast channel (BCH). Finally, the UE can start to demodulate, or read, the transport BCH on the PCCPCH. This process, also know as cell search, is summarized as follows. Step 1: Slot synchronization

During the first step of the cell search procedure, the UE uses the SCH’s primary synchroniszation code to acquire slot synchronization to a cell. This is typically done with a single matched filter matched to the primary synchronization code, which is common to all cells. The slot timing of the cell can be obtained by detecting peaks in the matched filter output. Figure 2.23 shows a depiction of this step. Step 2: Frame synchronization and code-group identification

During the second step of the cell search procedure, the UE uses the SCH’s secondary synchronization code to find frame synchronization and identify the code group of the cell found in the first step. This is done by correlating the received signal with all possible secondary synchronization code sequences, and identifying the maximum correlation value. Since the cyclic shifts of the sequences are unique, the code group as well as the frame synchronization is determined. Step 3: Scrambling-code identification

During the third and last step of the cell search procedure, the UE determines the exact primary scrambling code used by the found cell. The primary scrambling code is typically identified through symbol-by-symbol correlation over the CPICH with all codes within the code group identified in the second step. After the primary scrambling code has been identified, the primary CCPCH can be detected and the system- and cell-specific BCH information can be read. This operation is shown in Figure 2.24. If the UE has received information about which scrambling codes to search for, steps 2 and 3 above can be simplified. Indicates timing of PSC

Matched filter 2560 chips

Figure 2.23

WCDMA acquisition step 1.

2560 chips

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Smart Antenna Engineering Common pilot Correlator bank

Correct code 10 ms

10 ms

Figure 2.24

WCDMA acquisition step 3.

In CDMA2000, the mobile first gains some idea of system timing by searching for usable pilot signals. The mobile can first align its own timing by correlating with the pilot. When this correlation is found, the mobile synchronizes with the synchronization channel. It reads the sync channel message, and changes to absolute time. When the mobile is synchronized to absolute time, it can then read the broadcast messages on the F-PCH or F-BCCH. 2.5.2

Idle State

Once the phone has acquired the system upon power up or loss of service, it enters into an idle state except if a call needs to be placed. During this state, the phones monitors the PCH or the P-CCPCH to get updated system information or to receive pages. To save power, the mobile periodically enters a low-power or “sleep” state, during which it cannot receive data from the network. To page a mobile while it is in this state, the network assigns times when the mobile should “wake up” and receive any paging messages. Both WCDMA and CDMA2000 use a paging indication protocol. A paging indicator indicates to the mobile whether it has a paging channel message. If the paging indicator indicates that the mobile has a paging message, the mobile demodulates the entire paging channel message. Otherwise, it returns to sleep until the next paging occasion. 2.5.3

Access State and Call Setup

The mobile enters the access state whenever it needs to get dedicated network resources, respond to a page, or establish a voice or data call. An access channel (ACH) is used by the mobile to send requests to the network to set up a dedicated connection. The same procedure is used in both CDMA2000 and WCDMA, although the channel structure and messages contents and sequence are different. Let us assume that the mobile phone needs to place a call; the following summarizes what happens to establish this call: • The first step in call setup is called origination, in which the phone

sends a message to the network over the ACH or PRACH to request a connection.

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• The network then responds to the phone to acknowledge the message

receipt and inform the mobile about the connection setup and code channels to be assigned. This is done over the PCH or S-CCPCH. • Once the network authenticates the mobile, both the phone and the

network start negotiating the type of service and data rate required. • When the service has been negotiated, the network assigns RF and

hardware resources to the call and this information is exchanged with the mobile so that a dedicated channel [fundamental channel (FCH), SCH, or DPCH] could be established. 2.5.4

Traffic or Dedicated State

Call setup is concluded by assigning a dedicated traffic channel to the mobile. In CDMA2000 this could be the FCH, the SCH, or a combination of both, depending on the type of call and data rate. In WCDMA, the network assigns a DPCH for the same purposes. Although the procedures used for call setup are similar, the GSM layering structure (RRC, MM, CC) of WCDMA requires layer 3 signaling to be exchanged at many different layers, resulting in more signaling messages. In CDMA2000, the messaging is more streamlined.

2.6 CDMA Embedded Cell Capacity The isolated CDMA cell capacity derived in Chapter 1 assumes only a single cell and ignores the interference from users in neighboring cells. The capacity of an isolated cell in a narrowband system would also be very high since a reuse factor of one can be employed and all channels can be assigned in the 1.25-MHz bandwidth. In fact, CDMA makes a big difference when the impact of neighboring cells is taken into account. Let us rewrite the isolated cell pole capacity given by N

pole

=

W R as P → ∞ Eb Nt

(2.17)

in which the interference was averaged only over the users in the same cell. To quantify the potential improvements of smart antennas in CDMA systems the characteristics of the interference must be understood. On the downlink, several base stations are radiating and a mobile unit suffers from interference from other cells as well as from its home cell. On the uplink of a direct sequence CDMA system, the capacity is related to the Eb/Nt, as was shown in Chapter 1. If Eb/Nt is

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too low, the frame error rate (FER) or BLER will be high and the system performance will degrade. If Eb/Nt is too high, the interference level will increase and this will decrease the reverse link capacity. In TDMA and GSM systems, for every time slot there is one desired signal and a very small number of cochannel interferers, which makes the application of adaptive interference cancellation practical. On the other hand, in low-rate CDMA systems (i.e., for voice-dominated services), due to the large number of users sharing the channel, the interference in these systems is typically assumed to be statistically close to white Gaussian noise. And since in most cases the users can be assumed to be uniformly distributed across the cell, interference can also be considered spatially white in most operating scenarios. This means that isolating individual users requires arrays of large size. In CDMA2000 and WCDMA systems, a large number of voice users are mixed with a smaller number of high-speed data users. Since high data rate users have a lower processing gain, in order for them to maintain the same required Eb/Nt as voice users, their transmit power must be much stronger than voice users. As a result, on the reverse link high data rate users will present strong directional interference in the reception of voice users and the interference observed by voice users will be colored by data users and is no longer approximated as white Gaussian. In these mixed voice and high data rate systems an interference cancellation/reduction algorithm can be effectively used to null/reduce the impact of the limited number of high bit rate users, thereby increasing the overall system capacity. In CDMA systems, the uplink pole capacity of an embedded cell is given by [4] N

pole

=

W R Eb (1 + f )ν Nt

Gs

(2.18)

where v is the voice activity factor, Gs is the sectorization gain, Nt is the total noise + interference power spectral density, and f is the reuse efficiency defined as f =

I oc I sc

(2.19)

In (2.19), Ioc denotes the other cell interference power and Isc is the same cell interference power. From (2.18) it can be readily seen that reducing the interference level increases the number of maximum supportable users in a sector. It turns out that the fraction of the uplink interference that comes from the neighbor cells is about 60% of the own-cell interference and this ratio is not very sensitive to the parameters of the model, provided the assumption that the

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mobiles are power-controlled in a sensible way still holds. The effective frequency reuse factor in CDMA can be calculated as F = 1+ f

(2.20)

F plays the same role in the CDMA capacity equation as that of the narrowband frequency reuse factor K in TDMA and GSM systems. 2.6.1

Multipath Fading

Just as system capacity is affected by interference, it is also affected by propagation phenomena. Fading in a moving vehicle is more rapid than for pedestrians being caused by motion of the vehicle through stationary interference patterns, where the spatial scale of the interference pattern is the wavelength. We can address the impact of multipath fading on the performance of CDMA by first understanding under what circumstances will fading affect CDMA and what is that effect on the CDMA channel. When the multipath components’ delays separated by at least the decorrelation time of the spreading, they can be resolved by the CDMA waveform and can be separated by the despreader in the receiver because each component correlates at a different delay. When the multipath components are separated by less than the decorrelation time, then they cannot be separated in the receiver, and they interfere with one another, leading to flat fading. The duration of one spreading chip is 1/1.2288 Mcps = 814 ns in CDMA2000 and 1/3.84 Mcps = 260 ns in WCDMA. Multipath differences less than those will lead to flat fading, whereas greater separations will lead to resolvable multipath, which will be diversity combined by the receiver. The effects of fading depend mainly on the fading rate, which in turn depends on the velocity of the mobile station. Fading increases the average Eb/Nt needed for a particular error rate, which in turns causes capacity degradation. Coverage is also affected because a certain fading margin has to be built into the link budget. Power control mitigates the effects of fading at low speed and, to a less degree, at high speed. At high speed, the forward error correction coding and interleaving becomes more effective in combating fading as the characteristic fade time becomes less than the interleaver span.

2.7 Coverage Versus Capacity Trade-Off There is an inherit trade-off in CDMA between the capacity and coverage because of the way interference affects performance. On the uplink, interference increases as the load is increased and follows the expression of 1/(1–η), where η is fractional loading defined as N/Npole. Figure 2.25 shows how the interference

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rises over thermal noise as the fractional loading increases. To reach the pole capacity, the power that the mobiles are required to transmit goes to infinity to overcome this interference. As the required power increases, mobiles at the edge of coverage will begin to run out of transmitter power. That is, they will be asked to transmit more than their capability allows. It then follows that the system load should be controlled so that the planned service area never experiences coverage failures because of this phenomenon. This trade-off is not so much a problem or a limitation of CDMA systems as it is a system design consideration, which implies that maximum capacity and maximum coverage cannot be simultaneously achieved. 2.7.1

Coverage-Capacity Trade-Off in the Uplink

Simple capacity models of the reverse link show that RF power rises with loading to overcome interference, as previously discussed. Real systems must operate below the pole capacity because real user stations have an upper bound to their transmitter power. As the load is increased, the average interference level also increases. Because mobile stations are transmitting at their maximum power, S, their corresponding received power at the base station is fixed and N is growing rapidly; that is, SNR degrades. This in turns means that those users would need to move closer to the base station to a point where a smaller path loss allows their received power and, consequently, SNR to be restored to the target set point necessary to achieve the required quality of service. In effect, the cell coverage shrinks by the same range. The effect of traffic loading on the range from a cell site is referred to as cell breathing. For example, for a loading of 50% of the pole capacity η = 0.5, the loss of coverage on the uplink is 1/(1–0.5) = 2, or 3 dB loss. The impact of the interference rise over thermal noise on the CDMA coverage is illustrated in Figure 2.25. CDMA networks are typically planned with this

Figure 2.25

CDMA cell breathing.

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fractional loading level. That is, the network is designed with the appropriate margins, and sufficient number of sites and the site locations are optimized so that there is no performance degradation when the system is loaded. In a poorly planned CDMA system, increasing this loading would lead to range reductions and open coverage holes under high loads. The pole capacity of a cell depends only on the average Eb/Nt target, the processing gain, and voice activity factor. The coverage area of a cell defined as the area over which all users obtain the target Eb/Nt, depends on the fractional loading relative to the pole capacity. Detailed analysis of the interaction of coverage and capacity is a complex process involving power control, soft handoff, fading, the mobility mix of subscribers, and other factors, as well as the differences between downlink and uplink.

2.8 Conclusion Third generation mobile communications systems are already offering significant improvements over their 2G counterparts in peak data rates and throughput. However, impairments caused by the propagation channel such as multipath and interference caused by other users in the system still represent challenges for the network and system design. As we have seen, in CDMA systems the performance of most users except those at the cell edge is interference limited. Techniques to reduce the average interference would therefore significantly improve performance in terms of capacity and coverage. As such, the technology of smart antennas can be considered as complimentary to existing multiple access techniques and an extra tool at the disposal of the system designer.

References [1]

Faruque, S., Cellular Mobile Systems Engineering, Norwood, MA: Artech House, 1996.

[2]

Garg, V. K., and J. E. Wilkes, “Principles and Applications of GSM, Upper Saddle River, NJ: Prentice Hall, 1999.

[3]

Rappaport, T., Wireless Communications, Principles and Practices, New York: IEEE Press and Prentice Hall, 1996.

[4]

Yang, S. C., CDMA RF System Engineering, Norwood, MA: Artech House, 1998.

[5]

Garg, V. K., IS-95 CDMA and CDMA2000 Cellular/PCS Systems Implementation, Upper Saddle River, NJ: Prentice Hall, 2000.

[6]

TIA/EIA IS-95A, “Mobile Station-Base Station Compatibility Standard for Dual-Mode Wideband Spread Spectrum Cellular System,” 1995.

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[7] TIA/EIA-95B, “Mobile Station-Base Station Compatibility Standard for Wideband Spread Spectrum Cellular Systems,” 1999. [8] ANSI J-STD-008, “Personal Station-Base Station Compatibility Requirements for 1.8 to 2 GHz CDMA Personal Communications Systems,” 1995. [9] Ojanpera, T., and R. Prasad, Wideband CDMA for Third Generation Mobile Communications, Norwood, MA: Artech House, 1998. [10] TIA/EIA/IS-2000.1-A, “Introduction to CDMA2000 Standard for Spread Spectrum Systems,” 2000. [11] TIA/EIA/IS-2000.2-A, “Physical Layer Standard for CDMA2000 Spread Spectrum Systems,” 2000. [12] Esteves, E., M. Gurelli, and M. Fan, “Performance of Fixed Wireless Access with CDMA2000 1xEV-DO,” IEEE 58th Vehicular Technology Conference, Vol. 2, October 6–9, 2003, pp. 836-840. [13] Kuenyoung, K., K. Hoon, and H. Youngnam, “A Proportionally Fair Scheduling Algorithm with QoS and Priority in 1xEV-DO, Personal, Indoor, and Mobile Radio Communications,” 13th IEEE Intl. Symp., Vol. 5 , September 15–18, 2002, pp. 2239–2243. [14] Huang, C. Y., et al., “Schedulers for 1xEV-DO: Third Generation Wireless High-Speed Data Systems,” 57th IEEE Semiannual Vehicular Technology Conference, Vol. 3, April 22–25, 2003, pp. 1710-1714. [15] Yavuz, M., and Paranchych, D. W., “Adaptive Rate Control in High Data Rate Wireless Networks,” IEEE Wireless Communications and Networking, Vol. 2, March 16–20, 2003, pp. 866-871. [16] Sindhushayana, N. T., and P. J. Black, “Forward Link Coding and Modulation for CDMA2000 1XEV-DO (IS-856),” 13th IEEE Intl. Symp. on Personal, Indoor, and Mobile Radio Communications, Vol. 4, September 15–18, 2002, pp. 1839–1846. [17] Yonghoon, C., and H. Youngnam Han, “A Channel-Based Scheduling Algorithm for CDMA2000 1xEV-DO System,” 5th Intl. Symp. on Wireless Personal Multimedia Communications, Vol. 2, October 27–30, 2002, pp. 621–625. [18] Yavuz, M., et al., “Performance Improvement of the HDR System Due to Hybrid ARQ,” IEEE VTS 54th Vehicular Technology Conference, Vol. 4, October 7–11, 2001, pp. 2192–2196. [19] Chung, W., W. L. Hong, and M. Jungbae, “Downlink Capacity of CDMA/HDR,” IEEE VTS 53rd Vehicular Technology Conference, Vol. 3, May 6–9, 2001, pp. 1937–1941. [20] Qualcomm, Inc., http://www.qualcomm.com/technology/1xev-do/whitepapers.html. [21] Holma, H., and A. Toscala, WCDMA for UMTS, New York: John Wiley & Sons, 2004. [22] 3GPP Technical Specification 25.211, Physical Channels and Mapping of Transport Channels onto Physical Channels (FDD). [23] 3GPP Technical Specification 25.212, Multiplexing and Channel Coding (FDD). [24] 3GPP Technical Specification 25.213, Spreading and Modulation (FDD). [25] 3GPP Technical Specification 25.214, Physical Layer Procedures (FDD). [26] Kolding, T. E., et al., “High Speed Downlink Packet Access: WCDMA Evolution,” IEEE Vehicular Technology Society News, February 2003, pp. 4–10.

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[27] Parkvall, S., et al., “WCDMA Evolved–High Speed Packet Data Services,” Ericsson Review, No. 2, 2003, pp. 56–65. [28] Helmersson, K. W., and G. Bark, “Performance of Downlink Shard Channels in WCDMA Radio Networks,” Proc. IEEE Vehicular Technology Conference, Vol. 4, Spring 2001, pp. 2690–2694. [29] 3GPP TS25.855, “High Speed Downlink Packet Access; Overall UTRAN Description.” 2001.

Selected Bibliography CDMA Andersen, N. P., M. Pecen, and I. Gonorovsky, “GSM/EDGE Evolution, Based on 8-PSK Circuits and Systems,” Proc. of the 2003 International Symposium on ISCAS, Vol. 3, May 25–28, 2003 pp. III-598–III-601. Cai, J., and D. J. Goodman, “General Packet Radio Service,” GSM Communications Magazine, IEEE, Vol. 35, No. 10, October 1997, pp. 122–131. Christensen, G., et al., Wireless Intelligent Networking, Norwood, MA: Artech House, 2000. “The Evolution of Digital Wireless Technology from Space Exploration to Personal Communication Services,” IEEE Trans. on Vehicular Technology, Vol. 43, No. 3, August 1994. “Four Laws of Nature and Society: The Governing Principles of Digital Wireless Communication Networks,” Wireless Communications: Signal Processing Perspective, H. V. Poor and G. W. Wornell, (eds.), Upper Saddle River, NJ: Prentice Hall, 1998, pp. 380–392. Gilhousen, K. S., et al., “On the Capacity of a Cellular CDMA System,” IEEE Trans. Veh. Tech., Vol. 40, No. 2, 1991, pp. 303–312. Glisic, S., and V. Branka, Spread Spectrum CDMA Systems for Wireless Communications, Norwood, MA: Artech House, 1997. Hallmann, E., and R. Helmchen, “Investigations on the Throughput in EDGE and GPRS Radio Networks,” IEEE VTS 53rd Vehicular Technology Conference, Vol. 4, May 6–9, 2001, pp. 2823–2827. Jakes, W. C. Jr., Microwave Mobile Communications, New York: John Wiley & Sons, 1974; reprinted by IEEE Press, 1994. Lee, J. S., and L. E. Miller, CDMA Systems Engineering Handbook, Norwood, MA: Artech House, 1998. Lee, W. C. Y., Lee’s Essentials of Wireless Communications, New York: McGraw-Hill, 2000. Lee, W. C. Y., Mobile Cellular Telecommunications, 2nd ed., New York: McGraw-Hill, 1995. Molkdar, D., W. Featherstone, and S. Larnbotharan, “An Overview of EGPRS: The Packet Data Component of EDGE,” Electronics & Communication Engineering Journal, Vol. 14, No. 1, February 2002, pp. 21–38. Parsons, D., The Mobile Radio Propagation Channel, New York: John Wiley & Sons, 1992. Peterson, R. L., R. E. Ziemer, and D. E. Borth, Introduction to Spread Spectrum Communications, Englewood Cliffs, NJ: Prentice Hall, 1995. Prasad, R., CDMA for Wireless Personal Communications, Norwood, MA: Artech House, 1996.

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Pribylov, V. P., and I. I. Rezvan, “On the Way to 3G Networks: The GPRS/EDGE Concept,” Proc. of the 4th IEEE-Russia Conference on MEMIA, December 23–26, 2003, pp. 87–98. Proakis, J. G., Digital Communications, 2nd ed., New York: McGraw-Hill, 1989. Simon, M. K., et al., Spread Spectrum Communication Handbook, New York: McGraw-Hill, 1994. Ross, A. H. M., and K. S. Gilhousen, “CDMA Technology and the IS-95 North American Standard,” in The Mobile Communications Handbook, Boca Raton, FL: CRC Press and IEEE Press, 1996, pp. 430–448. Scholtz, R. A., “The Origins of Spread Spectrum Communications,” IEEE Trans. Commun., COM-30, May 1982 (Part I), pp. 822–854. Shannon, C. E., “Communication in the Presence of Noise,” Proc. IRE 37, January 1949, pp. 10–21. Turin, G. L., “Introduction to Spread Spectrum Antimultipath Techniques and Their Application to Urban Digital Radio,” Proc. IEEE 68, 1980, pp. 328–354. Viterbi, A., CDMA: Principles of Spread Spectrum Communication, Reading, MA: Addison-Wesley, 1995. Viterbi, A. J., “Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm,” IEEE Trans. Inform. Th. IT-13, 1967, pp. 260–269. Viterbi, A. J., A. M. Viterbi, and E. Zehavi, “Performance of Power-Controlled Wideband Terrestrial Digital Communications,” IEEE Trans. on Comm., Vol. 41, No. 4, 1993, pp. 559–569. Viterbi, A. J., et al., “Soft Handoff Extends CDMA Cell Coverage and Increases Reverse Link Capacity,” IEEE J. Selected Areas in Communications, Vol. 12, No. 8, 1994, pp. 1281–1288. Viterbi, A. M., and A. J. Viterbi, “Erlang Capacity of a Power Controlled CDMA System,” IEEE J. on Selected Areas in Communication, Vol. 11, No. 6, 1993, pp. 892–900. Yallapragada, R., V. Kripalani, and A. Kripalani, “EDGE: A Technology Assessment,” IEEE International Conference on Personal Wireless Communications, December 15–17, 2002, pp. 35–40. Yang, S. C., CDMA RF System Engineering, Norwood, MA: Artech House, 1998.

GSM/GPRS/EDGE Bates, R. J., GPRS: General Packet Radio Service, New York: McGraw-Hill, 2001. Mehrotra, A., GSM System Engineering, Norwood, MA: Artech House, 1997. Redl, S. M., et al., GSM and Personal Communications Handbook, Norwood, MA: Artech House, 1998. Redl, S. M., et al., Introduction to GSM, Norwood, MA: Artech House, 1996. Seurre, E., et al., EDGE for Mobile Internet, Norwood, MA: Artech House, 2003. Steele, R., et al., GSM, cdmaOne and 3G Systems, New York: John Wiley and Sons, 2001. Timo, H., GSM, GPRS, and EDGE Performance, 2nd ed., New York: John Wiley & Sons, 2003.

3 Spatial Channel Modeling 3.1 Introduction The detailed knowledge of radio propagation characteristics is essential to develop a successful wireless system. Measurement studies have been carried out to identify propagation loss, spatial distribution of power, wideband and narrowband statistics concerning the random variables of received signals at a fixed location due to any surrounding movement, and delay spread. A radio channel is a generally hostile medium in nature. Transmitted signals undergo several propagation phenomena such as reflection, diffraction, and scattering before they reach the receiver. This is mainly caused by the existence of objects in the physical channel between a transmitter and a receiver such as buildings, trees, mountains, hills, and moving objects. Therefore it is rather difficult to predict the channel behavior. Traditionally, radio channels are modeled in a statistical way using real propagation measurement data. Classical propagation models, commonly used for narrowband transmission systems, represent a signal in the radio environment using a large-scale path loss component together with a medium-scale slow varying component having a lognormal distribution, and a small-scale fast varying component with a Rician or Rayleigh distribution, depending on the presence or absence of the line-of-sight (LOS) component between the transmitter and receiver [1, 2]. Accordingly, conventional radio propagation models describing a wireless cellular environment have focused on: • Area mean power depending on the path loss characteristics between

the transmitter and receiver. • Local mean power within that area, which is slow varying. This can be represented by a lognormal distribution. 61

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• Superimposed fast fading instantaneous power, which follows a Ray-

leigh or nonline-of-sight (NLOS) or Rician distribution (LOS). Figure 3.1 illustrates a typical propagation environment. Large variations in the transmission path between the transmitter and receiver can be found, ranging from direct LOS to severely obstructed paths due to buildings, mountains, or foliage. The phenomenon of decreasing received power with distance due to reflection, diffraction around structures, and refraction within them is known as path loss. Propagation models have been developed to determine this path loss and are known as large-scale propagation models because they characterize the received signal strength by averaging the power level over large transmitter-receiver separation distances, in the range of hundreds or thousands of meters. On the other hand, medium-scale propagation models determine the gradual changes in the local-mean power if the receiving antenna is moved over distances larger then a few tens or hundreds of meters. The medium-scale variation of the received signal power is called shadowing, and it is caused by obstruction by trees and foliage. The term local-mean power is used to denote the power level averaged over a few tens of wavelengths, typically 40λ. Finally, small-scale propagation models characterize the fast variation of the signal strength over a short distance on the order of a few wavelengths or over short time duration on the order of seconds. Small-scale fast fading, also known as short-term fading or multipath fading, is due to multipath reflections of a transmitted wave by local scatterers such as houses, buildings, and man-made

Multipath Direct path

Multipath

Multipath

Figure 3.1

Mobile radio propagation channel.

Spatial Channel Modeling

63

structures, or natural objects such as forests surrounding a mobile unit. Typically, detailed models are needed for a complete coverage and capacity analysis in a certain region. On the other hand, if only rough capacity and range calculations are needed, simple and easy-to-use models are sufficient. In addition to the modeling of the propagation environment, the mobility of the wireless terminals also needs to be understood in each radio environment. Mobility modeling has a significant impact on the analysis of radio resource management, channel allocation, and handoff performance.

3.2 Radio Environments and Cell Types There are a large number of environments where mobile radio systems can operate. These include large and small cities, with variations in building construction, as well as tropical, rural, desert, and mountainous areas. Moreover, antenna design and height impacts the radio environments. Since it is impossible to consider all possible radio environments in the design of a mobile radio system, more general models that will consider the essence of different radio environments are required. Therefore, the large number of possible radio environments has to be condensed into a finite set of generic radio environments [3–6]. One approach is to classify a radio environment based on the typical cell size, which leads to: • Macrocells: In a macrocell the base station antenna is placed above the

rooftops and is much higher than the mobile users. Usually macrocells have a radius of more than 1 km and can be found in rural as well as urban areas. • Minicells: A minicell can be considered as a small macrocell where the base station antenna is placed at the same height as the rooftops. This type of cell is only used in urban environments with cell radius ranging from 700m to 1 km. • Microcell: In a microcell the base station antenna is placed in street level with a typical antenna height in the range of 5m. It has a cell radius of typically 200 to 500m and is mainly used for increasing the coverage and capacity in a dense urban environment. • Picocell: A picocell is mainly for indoor usage. The cell radius is about 10 to 20m and is limited by the building itself due to high penetration losses in the walls, number of floors, and their compositions. Another approach is to classify radio environments based on the nature of the mobile users being served by the system. This leads to the following classification:

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• Vehicular radio environment: The vehicular environment is character-

ized by large macrocells and large transmit powers as well as high mobile speeds (fast-moving vehicles). There is typically no LOS component, and the received signal is mostly composed of reflections. In these environments, the average power of the received signal decreases with distance raised to some exponent, referred to as the path loss exponent, which varies depending on the environment but is typically between three and five. In addition, shadowing is caused by obstruction from trees and foliage, and the resulting medium-scale variation in the received signal power can be modeled with a lognormal distribution. The standard deviation of that power varies considerably; for example, 8 to 10 dB is generally used in urban and suburban areas, whereas a lower value is used in rural and mountainous areas. In addition, small-scale fading is characterized by Rayleigh distribution. Typical delay spreads in this case are on the order of 0.8 µs but can be as high as tens of microseconds. • Outdoor to indoor and pedestrian radio environment: This radio environment is characterized by small microcells and low transmit powers with the antennas usually located below rooftops. Both LOS and NLOS multipath components exist. Indoor coverage can also be provided from this outdoor base station. The path loss exponent varies quite a bit and can be anywhere from two in areas with LOS up to six with NLOS cases due to trees and other obstructions along the path. Furthermore, a mobile station can experience a sudden drop of 15 to 25 dB when it moves around a corner. The standard deviation of the shadowing in these environments varies from 10 to 12 dB and the small-scale fading is either Rayleigh (NLOS) or Rician (LOS), with typical delay spreads on the order of 0.2 µs. • Indoor office radio environment: In the indoor office radio environment transmit powers are small and base stations and users are located indoors. Path loss attenuation exponent varies from two to five depending on the scattering and attenuation by walls, floors, and metallic structures. Note that each one of these environments has individual characteristics with respect to path loss attenuation, shadowing, and small-scale fading.

3.3 The Multipath Channel The short-term fluctuations of the received signal caused by multipath propagation are called small-scale fading. The different propagation path lengths of the multipath signal give rise to different propagation time delays. A multipath

Spatial Channel Modeling

65

channel can be represented by a power-delay profile consisting of different sets of distinct paths, which are also called multipath taps. Depending on the phase of each multipath signal when arriving at the receiver, they sum either constructively or destructively. Consequently, the power of each multipath tap is time varying, resulting in fading dips. The depth of the fading dips depends on the channel type. Using a baseband complex envelope representation and modeling the RF channel as a time-variant channel, we can represent the classical channel impulse response as a sum of L multipath components given by [1] L

h (t , τ ) = ∑ A l (t )e j ϕ l

(t )

l =0

δ(t − τ l (t ))

(3.1)

where Al(t) is the amplitude, ϕl(t) is the phase, and τl(t) is the time delay of the signal component. The distribution of the instantaneous power of the channel taps can be described by a distribution function, which depends on the radio environment. A so-called Rayleigh fading channel is the most severe mobile radio channel, with deep fading dips every λ/2. In a Rayleigh fading channel all multipath taps are independent and there is no dominant path. The envelope of individual multipath components in this case can be characterized by a Rayleigh distribution given by:  r ( − r 2 e p (r ) =  σ 2  0

2σ 2

)

0≤r ≤ ∞ r <0

(3.2)

In a Rician fading channel, the fading dips are shallower due to a dominant path in addition to the scattered paths. This is usually the case in microcell and picocell environments due to the existence of LOS.

3.4 Channel Characterization The multipath fading channel can be characterized based on delay spread, coherence bandwidth, Doppler spread, and coherence time. The root-meansquare (rms) value of the delay spread is a statistical measure that describes the spread of the multipath components around the mean delay of the channel. The maximum delay spread tells the delay difference between the first and last multipath components in the power-delay profile. The coherence bandwidth is the maximum frequency difference for which the signals are still strongly correlated. The coherence bandwidth is inversely proportional to the delay spread (i.e., the smaller the delay spread the larger the coherence bandwidth). If the transmission bandwidth of the signal is larger than the coherence

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bandwidth, the signal will undergo frequency selective fading. On the other hand, a flat fading channel results if the transmission bandwidth of the signal is smaller than the coherence bandwidth. The coherence bandwidth can be thought of as being a measure of the diversity available to a RAKE or equalized receiver. A smaller coherence bandwidth means a higher order diversity. If the coherence bandwidth is as large as the transmission bandwidth, then the entire received spectrum would be observed to fade. The maximum delay spread can be used to calculate how many resolvable paths exist in the channel that could be used in the RAKE receiver. In addition, the movement of the mobile station gives rise to a Doppler spread, which is the width of the observed spectrum when an unmodulated carrier is transmitted. If there is only one path from the mobile to base station, the base station will observe a zero Doppler spread combined with a simple shift of the carrier frequency (Doppler frequency shift). The Doppler frequency varies depending on the angle of the mobile station movement relative to the base station. The range of values when the Doppler power spectrum is nonzero is called the Doppler spread. The reciprocal of the Doppler shift is a measure of the coherence time of the channel. The coherence time is the duration over which the channel characteristics do not change significantly.

3.5 Path Loss Models Typically, path loss models are derived using a combination of analytical and empirical methods. In the empirical approach, the measured data is modeled using curve fitting or analytical expressions. The validity of empirical models in other environments and frequencies can only be validated by comparing the model to data measured from the specific area and for the specific frequency. It should be noted that these models present only a snapshot of the real radio environment. 3.5.1

Okumura-Hata Propagation Models

The empirical Okumura method [2] is based on exhaustive measurements that were performed in the Tokyo metropolitan area. The results were a series of curves, plotting recorded field strength as a function of distance from the transmitting antenna. The model is valid for distance ranges of 1 km to 100 km, frequency bands from 150 MHz to 2,000 MHz, and base station effective antenna heights from 30m to 1,000m. 3.5.1.1 Hata’s Model

Since Okumura’s curves and tables were intended to be used manually as a look-up resource, Hata was able to derive equations from Okumura’s work. This allowed for accurate computation of path loss without having to peer

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67

through any set of graphs. The standard Okumura-Hata model for an urban city is given by: Max. Path Loss = 69.55 + (44.9 − 6.55 log Hb ) log R − 13.82 log Hb + 26.16 log f −a( Hm )

(3.3)

where Hb is the radio base station (RBS) effective antenna height, Hm is the mobile subscriber antenna height, f is the operating frequency, and R is the distance between the RBS and the mobile station (MS), or the “radius” to RBS from the measurement point. The propagation model is valid under the following conditions: 150 < f < 1,500 MHz, 1 < R < 20 km, 30 < Hb < 200m and 1 < Hm < 10m. The mobile height correction factor a(Hm) can be computed as follows: a(Hm) = 3.2 ( log (11.75 Hm) )1/2 – 4.97, when f ≥ 400 MHz for urban environments a(Hm) = (1.1 log f – 0.7) Hm – 1.56 log f + 0.8 for suburban or rural areas. 3.5.1.2 The COST-231 Model (Suburban)

This model has been developed by the European Union’s Forum for Co-operative Scientific and Technical Research (COST). Since the traditional Okumura-Hata model is restricted to application in the frequency band below 1,500 MHz, it is not applicable in either the PCS or IMT-2000 spectrum regions. The COST-231 model [2] was developed based on analysis of Okumura’s propagation curves in the higher frequency regions with the aim of implementing a suitable formula that characterizes radio wave propagation in the PCS and IMT-2000 bands. The results led to the following adaptation of the Okumura-Hata equation: Max. Path Loss = 46.3 + (44.9 – 6.55 log Hb) log R – 13.82 log Hb + 33.9 log f – a(Hm) (3.4) where: a(Hm) = 3.2 ( log (11.75 Hm) )1/2, when f ≥ 400 MHz for large cities a(Hm) = (1.1 log f – 0.7) Hm – 1.56 log f + 0.8 for medium small cities or rural areas.

3.6 Spatial Channel Modeling As we can see from previous sections, classical propagation models focus on the power delay profile without taking into account the angular distribution of the

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multipath components. To analyze the performance impact of smart antennas at the link and system level, the spatial domain must also be considered. Channel models that characterize the DOA of multipath components are referred to as spatial channel models. Taking into account the angular dependence of the channel, the directional channel impulse response can be written as [7]: L

h (t , τ, θ, φ) = ∑ A l (t )e j ϕ l t ) a ( θ l (t ), φ l (t ))δ(t − τ l (t )) (

(3.5)

l =0

where α(θ, φ) is the array response vector given by: a ( θ, φ) = [1 e − jψ K e − j

( M −1 ) ψ

]

T

(3.6)

As can be seen from (3.5) the spatial channel can be characterized by a number of multipath components L, each with a complex amplitude Al, elevation angle Al, azimuth angle θl, and delay τl. We have previously seen how classical channel models define envelope probability distribution functions, delay and Doppler spread ranges for different radio environments, and cell types. The same approach can also be adopted for spatial channel modeling to define the characteristics of the parameters that make up the directional channel impulse response. In order for (3.5) to be used in link level or any other type of simulation intended to study the performance of smart antennas, we either have to define the amplitude, time delay, and angular spread distributions of the different multipath components or the spatial distribution of the different scatterers in the channel that can then be used to generate different multipath components. 3.6.1

Spatial Channel Model Parameters

It has been known that multipath components tend to cluster in groups that could be exploited in modeling the structure of the directional or angular impulse response. For instance, a cluster can be viewed as a collection of multipath components that experience the same small-scale variations since small–scale variations such as fast fading, caused by the instructive or destructive interactions of multipath components, occur on the scale of a wavelength. This clustering is mainly caused by the fact that the physical structures that cause scattering, reflections, and shadowing of the radio signals can be grouped into those in the vicinity of the mobile, those located near or around the base station, and a third group of distant objects that might exist in the channel. This spatial distribution of objects in the channel will cluster the multipath components into groups of signals with similar time delay and angular properties.

Spatial Channel Modeling 3.6.2

69

Number of Clusters

Clearly, there must be at least one cluster present in the channel that occurs due to local scattering around the mobile or base station. The existence of more clusters will depend on whether there is any scattering due to other distant objects in the channel, such as buildings, hills, and so on. Therefore, the number of clusters, NC, has to be determined based on measurements. In [8] the appropriate values for the number of clusters for different radio environments for the COST 259 model has been identified based on power delay profile measurements throughout Europe. It was found that in macrocell urban environments, NC ranges from one to two. This is due to the presence of scattering from local as well as far objects. In suburban and rural environments, NC is typically around one, implying that only those objects in the vicinity of the receiver contribute to the channel. On the other hand, [9] considers the number of clusters in any urban environment to be six. 3.6.3

Spatial Distribution of Clusters and Scatterers

Since the local objects in the vicinity of the MS or BS will significantly contribute to the multipath, it is reasonable to seek a model for their spatial distributions from which the components of the channel impulse response could be derived. Let us consider the macrocell case where the BS antenna is mounted on a rooftop higher than any of the local scatterers; as a result, the scattering is dominated by those objects around the MS. In fact, most of the relevant scatterers are those located closer to the MS since they will have the greatest impact on the channel. In addition, the signals received at the BS will mainly arrive from a certain angular region, as shown in Figure 3.2. A distribution function that approximates this physical behavior is the Gaussian distribution given by ζ f (r ) = e− 2 πR 2

2 rr − rrMS

2R2

(3.7)

r r where r is the position vector, rMS is the MS location vector, assuming the BS as the origin, ζ is a normalization constant, and R is the radius of the scattering circle, shown in Figure 3.2. Recall that with this Gaussian shape, f (r) will be larger for objects closer to the mobile (i.e., for small r – rMS ) and will decrease for increasing r – rMS or for objects further away from the MS. 3.6.4

Base Station Azimuth Power Spectrum and Angle Spread

Once the spatial distribution of the scatterers is known, it is possible to compute the azimuth power spectrum, that is, the distribution of the received power versus the azimuth angle. Various PDFs have been proposed in the literature for the

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Scatterers MS

BS

Figure 3.2

Macrocell base station model.

azimuth distribution, including a cosn (φ) distribution, a uniform distribution, and a normal distribution [10]. However, based on field measurements, it was found that Laplacian distribution is a better representation for the azimuth power spectrum [9, 11]. From the azimuth power spectrum and angle of arrival (AOA) distribution, we can derive an expression for the azimuth angle spread. Let us consider the geometry shown in Figure 3.3. Here we assume that each cluster will contribute Np paths to the channel. To characterize each of these paths, we need to define their AOA as the mean angle with which an arriving path’s power is received by the BS array with respect to the bore site and their angle spread defined as the rms of angles with which an arriving path’s power is received by the base station array and azimuth power spectrum. The Laplacian distribution was adopted by the COST 259 model [8] and the spatial channel models jointly developed by the third generation partnership programs Third Generation Partnership Project 2 (3GPP2) and 3GPP to model the azimuth power spectrum of both paths and clusters. On a cluster basis, the azimuth power spectrum can then be written as Np

P φ ( φ) =

∑ P (φ )

−

l

l =1

2σ φ

e

2 φ l −φ o σφ

(3.8)

Spatial Channel Modeling

71

Cluster

γ

MS

σφ φc

LOS Path

φo

BS

Figure 3.3

Spacial channel model parameters.

Np

where ∑ P ( φ l ) is the total power received from the cluster. On a path basis, we l =1

can write the azimuth power spectrum as P φp ( φ l ) = N norm e

−

2 φ l −φ p σp

G ( φ)

(3.9)

where Nnorm is a normalization constant, φp is the mean path AOA, σp is the angle spread, and G(φ) is the BS antenna gain at angle φ. The normalized azimuth power spectrum is shown in Figure 3.4 for various spread angles for a mean path AOA of 45°. Now, let us look at the angle spread given by [12] S φ = E {φ 2 } − [ E {φ} ]

2

(3.10)

Using (3.8) we can write

E (φ

2

)=

Nc

Np

i =1

l

∑∑φ Nc

Np

i =1

l

2 i,l

P φ ( φi , l )

∑ ∑ P (φ ) φ

i ,l

(3.11)

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PDF

0.7 0.6

Angle spread:10

0.5

Angle spread:20

0.4

Angle spread:30

0.3 0.2 0.1 0 0

Figure 3.4

20

40 60 Azimuth Angle

80

100

Azimuth power spectrum as a function of φ and σp for φp of 10°, 20°, and 30°.

From Figure 3.3 we can see that the path AOA is given by φi = φ o + φ c + γ

(3.12)

Substituting (3.12) in (3.11) we then get Nc

E (φ 2 ) =

Np

∑ ∑ (φ i =1

+ φ c,i + γ i,l ) P φ ( φi , l ) 2

ο

l

Nc

Np

i =1

l

∑ ∑ P (φ ) φ

i ,l

Np  Nc 2 φ + φ ∑ ( o c ,i ) ∑ P φ ( φ i , l ) + l  i =1 Nc

=

Np

Nc

(3.13)

Np

∑ (γ ) ∑ P (φ ) + ∑ ∑ 2(φ i =1

Similarly

2

φ

i ,l

i ,l

i =1

l

Nc

Np

i =1

l

o

l

∑ ∑ P (φ ) φ

i ,l

 + φ c ,i )P φ ( φi , l )γ i , l  

Spatial Channel Modeling

[ E ( φ)]

2

73

 Nc Np   ∑ ∑ ( φ o + φ c ,i + γ i,l )P φ ( φi , l )   =  i =l l Nc Np   P φ ( φi , l ) ∑ ∑     i =1 l

2

   ∑ ( φ o + φ c ,i ) ∑ P φ ( φ i , l ) + ∑ ∑ γ i , l P φ ( φ i , l )  l i =1 l  =  i =l Nc Np   P φ ( φi , l ) ∑ ∑     i =1 l Np

Nc

Np

Nc

2

(3.14)

We can see from (3.13) that Sφ is a random variable that is a function of the LOS angle φo, the clusters angles φc, the path angles within a cluster γ, the number of clusters, the number of paths Np, and the azimuth power spectrum. Note that the AOA of the clusters and paths have PDFs that could, for example, be given by [13]: f (φc ) =

1 2 πσ c

e

φ  − c   σc 

2

(3.15)

and f (γ ) =

1 2 πσ p

e

 γ   −   σp 

2

(3.16)

where σc is the cluster’s AOA spread and σp is the path AOA spread. Let us consider the special case where all clusters have the same AOA spreads σc and all paths have the same power. It follows that the expected value of Sφ is given by [11]

E (S φ

)

  2 2  = σ c + σ p 1−   

  i =1  Nc Np  P φ ( φ)  ∑ ∑  i =1 l =1 Nc

∑P

2

i

(3.17)

In the spatial channel model adopted by the 3GPP and 3GPP2 [9], two values are considered for the path angle spread at the BS, 2° and 5° corresponding to path AOAs of 50° and 20°, respectively.

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Mobile Station Azimuth Power Spectrum and Angle Spread

Due to the nature of the scatterers that are mostly located around the mobile station, it is reasonable to assume that the azimuth power spectrum has a uniform distribution [–180°, 180°] for which the angle spread is given by [10] S φ = 180°

3 = 104 °

However, in some cases where the scatterers are not uniformly distributed around the mobile, a Laplacian power azimuth spectrum can be a better approximation for the path’s power as follows: 2 φ l −φ p

P φp ( φ l ) = N norm e

σp

(3.18)

where the MS is assumed to have an omnidirectional antenna. In this case, the angle spread is be given by [10]  S φ = N norm  2 σ 3p − 14 e 

−π 2σ p

(π

2

 + 4 σ 2p + 8 πσ p  

)

(3.19)

In such a case, the path angle spread is expected to be lower compared with the uniform azimuth power spectrum.

3.7 Spatial Channel Model Application in System Simulations To evaluate the performance of a given smart antenna algorithm or implementation, a system level simulation needs to be carried out. In this approach, multiple base stations or sectors and mobile stations are considered in addition to mobility modeling to account for the fast fading resulting from a user’s motion. A general procedure to generate a spatial channel model for this type of system level simulation may consist of the following steps: 1. Select a radio environment such as urban, suburban or rural. 2. Select a cell type, such as macrocell, microcell, or picocell. 3. Define the path loss model based on the radio environment and cell type selected above. For instance, the COST 231 model can be used for suburban macrocells.

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75

4. Define the antenna pattern and gain both at the base station and mobile station. This will directly impact the azimuth power spectra for the multipath components. 5. Determine the locations and orientations of all sectors and users in the system. Based on this, LOS AOAs as well as distances between the users and sectors can be computed. 6. Determine the delay spread, angle spread, and lognormal shadow fading parameters. The distributions of these parameters are assumed to have the form σ DS = 10

( αεDS + µDS )

for the delay spread

(3.20)

where µDS is the logarithmic mean of the distribution of the delay spread and εDS is the logarithmic standard deviation of the distribution of the delay spread. Similarly, for the angle spread we assume the distribution σ AS = 10

( βε AS + µ AS )

(3.21)

where µAS is the logarithmic mean of the distribution of the angle spread and εAS is the logarithmic standard deviation of the distribution of angle spread. The shadow fading distribution is given by σ LN = 10 ( σ SF γ 10

)

(3.22)

The parameters α, β and γ are defined in [9, 14] and σSF is the well-known lognormal shadow fading standard deviation. 7. Determine the number of clusters NC and their random delays τn such that τ N c > τ N c –1 >...> τ 1 . These delays are assumed to follow the classical exponentially decaying profile based on the Laplacian distribution: 1 − P τ (τ ) = e στ

2 τ− τm στ

(3.23)

where στ is the well-known rms delay spread. 8. Determine the random average power for each cluster. It is expected that the cluster power is a function of the distance between that cluster and the BS or MS. Furthermore, the longer the cluster’s delay is

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(implying additional path loss), the more attenuation there is. A simple model similar to that adopted by the COST 259 would then set the power of the first cluster equal to the transmit power attenuated by the path loss computed from the appropriate Hata or COST 231 empirical formulae, taking into account shadow fading as P1 = PPL σ LN n and additional cluster powers as P n = P PL σ LN n ⋅10

k τ min ( τ n − τ 1 , τ max ) 10

(3.24)

where k is shown in Figure 3.5 and the Sn is the shadow fading gain defined above. This implies that we have an exponentially decaying power profile up to a maximum delay after which the power received is almost negligible. 9. Determine the powers and phases of the Np paths within each cluster. Here we can assume that all paths have identical powers (Ppath = Pn/Nc). The phases can be drawn from a uniform distribution [0° to 360°]. 10. Determine the AOA of the clusters and paths. This is achieved by first locating the clusters according to their spatial distribution and then computing their AOA relative to either the BS or MS. The paths AOA are calculated based on offsets from the clusters AOA such that the desired angle spread is obtained. 11. Associate the clusters and paths with the BS and MS. This will allow the calculation of the antenna gains at the respective AOAs of each of the multipath components.

Pτ

− kτ

− k τ τmax

τmax

Figure 3.5

Power delay profile.

τ

Spatial Channel Modeling

77

3.8 Angle Spread Impact Due to multipath, shadowing, and mobile speed the wireless propagation channel causes the transmitted signal to appear spread in time, frequency, and angle at both the base station and mobile user sides. As we discussed earlier, this dispersion can be characterized in terms of delay spread, Doppler spread, and angle spread [15]. These three parameters determine frequency fading, temporal fading, and spatial fading, respectively. The impact of the angle spread in terms of spatial fading is illustrated in Figures 3.6 through 3.9. In a channel with a

(a)

(b)

Figure 3.6

(a, b) Fading envelope, Laplacian Azimuth power spectrum, AS = 10°, angle of arrival 90°.

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(a)

(b)

Figure 3.7

(a, b) Fading envelope, Laplacian Azimuth power spectrum, AS = 60°, angle of arrival 0°.

narrow angle spread the fading envelope across an antenna array’s elements is relatively constant in space, as can be seen in Figure 3.6 (AS = 10°). This is due to the fact that the signals across the antennas are correlated. This scenario is beneficial to the performance of beamforming techniques. A narrow angle spread helps maintain a focused and narrow beam for better interference reduction. On the other hand, in Figures 3.7, 3.8, and 3.9 we see the impact of

Spatial Channel Modeling

79

(a)

(b)

Figure 3.8 90°.

(a, b) Fading envelope, Laplacian Azimuth power spectrum, AS = 360°, angle of arrival

wide angle spread (60° and 360°). In these cases, the signal experiences fading in space, where we can clearly see peaks and valleys in the fading envelope. This results from paths with low cross-correlation and is beneficial for spatial diversity applications that result in higher diversity gain as the signals become uncorrelated, but it will degrade the performance of transmit beamforming.

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Figure 3.9

Fading envelope, AS = 360°, angle of arrival 0°.

References [1]

Rappaport, T., Wireless Communications, Principles and Practices, NJ: IEEE Press and Prentice Hall, 1996.

[2]

Siwiak, K., Radiowave Propagation and Antennas for Personal Communications, Norwood, MA: Artech House, 1995.

[3]

Lee, W. C. Y., Mobile Communications Engineering, New York: McGraw-Hill, 1982.

[4]

Parsons, J. D., The Mobile Radio Propagation Channel, New York: John Wiley & Sons, 1992.

[5]

Lee, W. C. Y., Mobile Cellular Telecommunications Systems, New York: McGraw-Hill, 1989.

[6]

Fleury, B. H, and P. E. Leuthold, “Radiowave Propagation in Mobile Communications: An Overview of European Research,” IEEE Communications Magazine, February 1996.

[7]

Ertel, R. B., et al., “Overview of Spatial Channel Models for Antenna Array Communication Systems,” IEEE Personal Communications, February 1998.

[8]

Correia, L. M., Wireless Flexible Personalized Communications, COST 259: European Co-operation in Mobile Radio Research, New York: John Wiley & Sons, 2001.

[9]

3GPP-3GPP2 SCM-121, “Spatial Channel Model Text Description,” March 14, 2003.

Spatial Channel Modeling

81

[10] Fuhl, J., A. F. Molisch, and E. Bonek, “Unified Channel Model for Mobile Radio Systems with Smart Antennas,” IEE Proc. Radar, Sonar Navigation, Vol. 145, No. 1, February 1998. [11] 3GPP-3GPP2 SCM-027, “Note on the Angle Spread Distribution,” Motorola, May 22, 2002. [12] Fleury, B. H., “Direction Dispersion and Space Selectivity in the Mobile Radio Channel,” IEEE VTS Fall 52nd Vehicular Technology Conference, 2000, Vol. 2, 2000. [13] 3GPP-3GPP2 SCM-025, “RMS Angle Spread,” Motorola, May 3, 2002. [14] 3GPP-3GPP2 SCM-029, “Correlated System Level Spatial Channel Model,” June 5, 2002. [15] Buehrer, R. M., “Generalized Equations for Spatial Correlation for Low to Moderate Angle Spread,” Proc. 10th Va. Tech. Symp. Wireless Communication, Blacksburg, VA, June 2000, pp. 123–130.

Selected Bibliography Beach, M., B. Allen, and P. Karlsonn, “Correlation of Power Azimuth Spectrum for Varying Frequency Division Duplex Spacings,” EPMCC 2001, Centre for Communication Research, University of Bristol, Vienna, February 20–22, 2001. Bertoni H. L., et al., “Sources and Statistics of Multipath Arrival at Elevated Base Station Antenna, IEEE 49th Vehicular Technology Conference, Vol. 1, 1999. Chen, M, and Asplund, H., “Measurements and Models for Direction of Arrival of Radio Waves in LOS in Urban Microcells,” 12th IEEE Int. Symp. on Personal, Indoor, and Mobile Radio Communications, Vol. 1, September 2001. Greenstein, L. J., et al., “A New Path-Gain/Delay-Spread Propagation Model for Digital Cellular Channels,” IEEE Trans. on Vehicular Technology, Vol. 46, No. 2, May 1997. Hata, M., “Empirical Formula for Propagation Loss in Land Mobile Radio Services,” IEEE Trans. on Vehicular Technology, Vol. VT-29, No. 3, August 1980. Kuchar, A., J. P. Rossi, and E. Bonek, “Directional Macrocell Channel Characterization from Urban Measurements,” IEEE Trans. on Antennas and Propagation, Vol. 48, No. 2, February 2000. Liberti, J. C., and T. S. Rappaport, “A Geometrically Based Model for Line-of-Sight Multipath Radio Channels,” Proc. IEEE VTC’96, Atlanta, GA, May 1996. Lu, M., T. Lo, and J. Litva, “A Physical Spatio-temporal Model of Multipath Propagation Channels,” Proc. IEEE VTC´97, Phoenix, AZ, May 1997. Pajusco, P., “Experimental Characterization of D.O.A at the Base Station in Rural and Urban Area,” Proc. IEEE VTC’98, Ottawa, Canada, May 1998. Pedersen, K. I., P. E. Mogensen, and B. H. Fleury, “A Stochastic Model of Temporal and Azimuthal Dispersion Seen at the Base Station in Outdoor Propagation Environments,” IEEE Trans. on Vehicular Technology, Vol. 49, No. 2, March 2000. Pedersen, K. I., P. E. Mogensen, and B. H. Fleury, “Spatial Channel Characteristics in Outdoor Environments and Their Impact on BS Antenna System Performance,” 48th IEEE Vehicular Technology Conference, Vol. 2, 1998.

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Pedersen, K. I., P. E. Mogensen, and B. H. Fleury, “Spatial Channel Characteristics in Outdoor Environments and Their Impact on BS Antenna System Performance,” Proc. IEEE VTC’98, Ottawa, Canada, May 1998. Petrus, P., J. H. Reed, and T. S. Rappaport, “Geometrically Based Statistical Model for Macrocellular Mobile Environments,” Proc. IEEE Globecom’96, London, November 1996. Saleh, A. A. M., and R. A. Valenzuela, “A Statistical Model for Indoor Multipath Propagation,” IEEE Journal on Selected Areas in Communications, Vol. SAC-5, No. 2, February 1987. Turin, G. L., et al., “A Statistical Model of Urban Multipath Propagation,” IEEE Trans. on Vehicular Technology, Vol. VT-21, No. 1, February 1972.

4 Fixed Beam Smart Antenna Systems 4.1 Introduction In wireless system design and planning, one has to deal with two main problems, coverage and capacity. Coverage designs include, among other parameters, the selection of the number of base station sites, locations, heights, antenna orientation, and transmit power required to provide service in a given geographic area. When the system is first deployed, the number of subscribers is low and the aim is to maximize the coverage area of each site. As the number of network users grows, the number of users per site and sector also grows. As more users are added to a sector, the base station transmit power as well as the total power transmitted by those users increases, which effectively increases the interference on both the forward and reverse links. The system then becomes capacity or interference limited. One of the most common ways to deal with this interference problem is sectorization.

4.2 Conventional Sectorization In base stations employing omnidirectional antennas, the transmit power is equally radiated in all directions. The equal distribution leads to a portion of the power being transmitted throughout the cell but not received by the user. This wasted power then becomes forward link interference to other base stations or users in other cells. Similarly, each new user added to a cell increases the interference and noise levels on the reverse link. This results in a reduction in the signal-to-noise ratio, which in turn degrades the performance of the detection and demodulation operations. One way to reduce interference is to divide the cell

83

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into a number of smaller sectors using directional antennas. The most common scheme is the three 120°-sectors; however, two and six-sector cells also have some practical applications. As we can see from Figure 4.1, in a three-sector site the radiation pattern of the directional antenna allows it to receive substantially higher power levels from its own sector compared with that received from the other two sectors. It is obvious that sectorization is an effective technique that can increase capacity. In fact, since CDMA capacity is noise or interference limited, assuming ideal antenna radiation patterns, the capacity of an Ns-sector cell will be Ns times that of an omnidirectional cell. This capacity gain is often referred to as sectorization gain (SG). In practice, overlapping sector coverage areas due to nonideal antenna radiation patterns will increase the multiuser interference, reducing SG [1–3]. It is well known that CDMA has a soft capacity, which is determined by the balance between the required SNR for each user and the spread spectrum processing gain (PG) given by PG =

W R

(4.1)

where W is the bandwidth of the spreading signal and R is the user’s data rate. The figure of merit of the digital receiver is the dimensionless SNR given by 1

90

60

120 0.8 0.6

30

150 0.4 0.2

0

180

210

330

240

300 270

Figure 4.1

Three-sector patterns.

Fixed Beam Smart Antenna Systems

Eb = N0

Energy per bit Noise plus Interference power spectral density

85

(4.2)

where the energy per bit is given by Eb =

P R

(4.3)

To derive an approximate SG for a CDMA system, let us consider the reverse link capacity of an omni system given by N omni =

W R Eb (1 + f omni )ν Io

(4.4)

where v is the voice activity factor, Nomni is the number of users, Io is the total interference density, and f is the reuse efficiency f =

I oc I sc

(4.5)

In (4.5) Ioc denotes the other cell interference power and Isc is the same cell interference power. From (4.4) it can be readily seen that reducing the interference level increases the number of maximum supportable users in a cell. Now, let us consider the capacity of a single sector given by: N sec t =

W R Eb (1 + f sec t )ν Io

(4.6)

Hence the sectorization gain for an N-sector cell becomes SG = N s ⋅

 1 + f omni  N sec t =N s ⋅  N omni  1 + f sec t 

(4.7)

It is clear that the sectorization gain is highly dependent on the amount of reduction in interference provided by the antenna, which in turn is a function of the antenna beamwidth and the size of the overlap region. Based on simulation results for reuse efficiency provided in [4], Figure 4.2 shows SG as a function of

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Smart Antenna Engineering 8

7

Sectorization gain

6

5

4

3

2

1 2

Figure 4.2

3

4

5 6 Number of sectors

7

8

9

Sectorization gain.

NS. For three-sector sites, SG becomes 2.4, which is comparable to 2.55, the sectorization gain measured in actual CDMA network deployments. To show the effect of the overlap area on the sectorization efficiency, consider Figure 4.3, where a three-sector site is shown with the areas of overlap or softer handoff determined by the angle θSH. Recall that in CDMA systems, there are mainly two types of handoffs, namely hard handoffs and soft handoffs, as discussed in Chapter 2. Soft handoff can be further divided into handoff between two or more sectors of different cells and handoff between two sectors of the same cell, which is referred to as softer handoff. As a result of the three overlapping sectors, instead of reducing the interference by three times, as in the ideal case, only (120° + θSH)/360° of the interference is blocked. Hence, we can define the sectorization efficiency by E sect =

120 120 + θ SH

(4.8)

Figure 4.4 shows the relation between the sectorization efficiency and θSH. Sectorization and soft/softer handoffs also affect the capacity of the forward link of a CDMA system. On the forward link, a simplified form for capacity is given by N FL =

1 − P overhead Ptraffic ⋅ H ⋅ ν

(4.9)

Fixed Beam Smart Antenna Systems

87

Softer handoff Region

θSH

Figure 4.3

Softer handoff overlap areas.

0.98

0.96

Efficiency

0.94

0.92

0.9

0.88

0.86

0.84

Figure 4.4

4

6

8

Sectorization efficiency.

10

12 14 Overlap angle

16

18

20

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Smart Antenna Engineering

where Poverhead denotes the total power in the common channels, Ptraffic is the average traffic channel power that depends on the forward link SNR or Eb/Nt, and H denotes the handoff reduction factor. When mobile users are in soft or softer handoff, additional power is required on the forward link, hence the forward link capacity is reduced. However, the Eb/Nt required by those mobiles to achieve a given FER will be lower than that required without soft/softer handoff, so sectorization will also provide some capacity gain on the forward link. [2–6] provide detailed analysis of the impact of sectorization and soft/softer handoff on CDMA systems.

4.3 Limitations of Conventional Sectorization The most common form of sectorization uses spatial diversity antennas for signal reception and a single antenna for transmission, as shown in Figure 4.5, where all antennas use vertical polarization. Another form of diversity, called polarization diversity, uses either 0°/90° antennas, otherwise known as vertical/horizontal polarization or ±45°, also known as cross polarization. In either case, two replicas of the received signal are available to the receiver for diversity combining to combat fading. One drawback of conventional sectorization is that the signals cannot be separated in the spatial domain, which makes spatial interference cancellation or reduction impossible to carry out. Another fundamental problem at the heart of network optimization is that of traffic loading imbalance when cellular traffic is distributed unevenly among different geographical areas of the network or among the sectors of a site leading to increased blocking. This imbalance is often time dependent, for instance during rush hour traffic on highways, business districts, or sport avenues. Tx

Rx

Rx

Duplexers and LNAs

Conventional sectorization scheme.

Tx

Rx

Rx

Figure 4.5

Fixed Beam Smart Antenna Systems

89

Alleviating such imbalance would require sectors with flexible orientations or beamwidths, which are not available with conventional sectorization. As a result, unused capacity on other sectors/sites is locked and wasted. As discussed earlier, on the forward link handoff zones have an immediate impact on capacity. Reducing the size of handoff zones and shifting those zones from high- to low-traffic areas can minimize the negative impact of soft/softer handoff on capacity. However, with conventional sectorization both the size and orientation of these handoff zones are fixed. One way to deal with these issues and provide means for spatial signal separation for further processing is to replace the conventional base station antennas with antenna arrays. The additional degrees of freedom provided by antenna arrays can offer more effective techniques to deal with multipath and interference and improve signal quality, leading to improved coverage and/or capacity. The fundamental advantage of arrays is their ability to generate one or more main beams with tailored beamwidths, with radiation pattern nulls and increased gain. Two main approaches exist —fixed multiple beam antennas and fully adaptive antennas. The remainder of this chapter is devoted to the first approach.

4.4 Antenna Arrays Fundamentals Assume that we have a linear array composed of M identical antenna elements arranged along some axis, with interelement spacing d, as shown in Figure 4.6. In the simplest form, the array elements are fed with equal amplitudes Am and constant phase delay β. The radiation pattern of the array excluding the element pattern is referred to as the array factor. A general form for the array factor is given by AF = A ⋅ (1 + e j where k =

(kd cos γ + β )

(

+ e j 2 kd cos γ + β ) + L + e j

( M −1 ) (kd cos γ + β )

)

(4.10)

2π . Without loss of generality, we can assume the signal amplitudes λ

as follows: A m = 1 m = 1, 2,K M

(4.11)

Hence, the array factor can be rewritten as M

AF = ∑ e j m =1

( m −1 ) ψ

(4.12)

90

Smart Antenna Engineering z M

3 d 2

1

ar θ y

φ

x

Figure 4.6

Coordinate system for linear antenna arrays.

where ψ = kd cos γ + β

(4.13)

The total radiation pattern of an antenna array in the far field E(θ, ϕ) is represented by a product between two factors, the array factor AF (θ, ϕ) and the element factor EF (θ, ϕ). The element factor depends on physical dimensions and electromagnetic characteristics of the radiating element, whereas the array factor depends on the amplitude, phase, and position of each of the elements in the array antenna. In (4.13) γ is the angle between the array axis and the vector from the origin to the observation point. For an array along the z-axis, we have:

)

cos γ = a$ z ⋅ a$ r = a$ z ⋅ (a$ x sin θ cos φ + a$ y sin θ sin φ + a$ z cos θ = cos θ (4.14) It follows that ψ = kd cos θ + β When the array is placed along the x-axis, we get:

(4.15)

Fixed Beam Smart Antenna Systems

91

)

cos γ = a$ x ⋅ a$ r = a$ x ⋅ (a$ x sin θ cos φ + a$ y sin θ sin φ + a$ z cos θ = sin θ cos φ

(4.16)

and ψ = kd sin θ cos φ + β

(4.17)

Finally, for an array along the y-axis, we get:

)

cos γ = a$ y ⋅ a$ r = a$ x ⋅ (a$ x sin θ cos φ + a$ y sin θ sin φ + a$ z cos θ = sin θ sin φ

(4.18)

and ψ = kd sin θ sin φ + β

(4.19)

When the reference point or origin is chosen at the physical center of the array, it can be shown [7, 8] that the normalized array factor of a uniformly excited, equally spaced linear array is reduced to 1 sin( M2 ψ ) AF = ⋅ ψ M sin( 2 ) 4.4.1

(4.20)

Broadside and End-Fire Arrays

Note that the AF in (4.20) has a maximum at ψ = 0. To find the conditions under which the maximum radiation occurs, let us assume we have a linear array placed along the z-axis; it follows that ψ = kd cos θ o + β = 0

(4.21)

therefore β = –kdcosθo, where θo is the direction of the maximum radiation. When θo = 90°, the array is called broadside, and it follows that β = 0. The maximum of the radiation pattern of broadside arrays is always directed normal to the array axis. From the above we can see that a broadside array requires equal magnitude and phase excitation. For θo = 0° or θo = 180° the resulting array is called an end-fire array. The maximum of the radiation pattern in this case is directed along the array axis. For θo = 0° we get ψ = kdcosθ + β = kdcos(0) + β = kd + β = 0. Hence, the progressive phase shift required for an end-fire array with maximum radiation directed at 0° is

92

Smart Antenna Engineering

β = −kd

(4.22)

As can be seen, the array factor given by (4.20) is a function of the number of elements M, element spacing d, and the phase shift β. Therefore, it is important to investigate the impact of these parameters on the radiation pattern of an antenna array. 4.4.2

Impact of Number of Elements

Figures 4.7 and 4.8 show the effect of increasing the number of elements M on the radiation pattern of a broadside array along the z-axis.

θ

Figure 4.7

Radiation patterns of broadside array along the z-axis, d = λ/2.

θ

Figure 4.8

Radiation patterns of broadside array along the z-axis, d = λ/2.

Fixed Beam Smart Antenna Systems

93

We can observe that increasing M has the following effects on the radiation pattern: • The width of the main lobe decreases; in other words, it becomes nar-

rower. This is crucial for the applications of smart antennas when a single narrow beam is required to track a mobile or cluster of mobiles. • The number of sidelobes increases. In addition, the level of the first and

subsequent sidelobes decreases compared with the main lobe. Sidelobes represent power radiated or received in potentially unwanted directions. So in a wireless communications system, sidelobes will contribute to the level of interference spread in the cell or sector by a transmitter as well as the level of interference seen by a receiver when antenna arrays are used. • The number of nulls in the pattern increases. In interference cancella-

tion applications, the directions of these nulls as well as the null depths have to be optimized. 4.4.3

Impact of Element Spacing

The element spacing d also has a significant impact on the shape of the radiation pattern. It is evident that the more elements an array has or alternatively the larger the array gets, the better the characteristics of the radiation pattern as far as its shape and degrees of freedom. Another way of achieving a larger array would be by increasing d. The major drawback of this approach lies in the behavior of the array factor function in (4.20), namely the appearance of replicas of the main lobe in undesired directions, referred to as grating lobes. Figure 4.9 shows the polar radiation pattern of a broadside six-element array along the z-axis with element spacing of d = λ/2. We can see that for this element separation, aside from a few sidelobes, we only have a main lobe directed toward 90°. When we increase the spacing to d = λ, we get the radiation pattern shown in Figure 4.10. Notice the appearance of a grating lobe at 0°. Not only have we wasted power in the grating lobe, we also spread or receive more interference from the broader lobe. In practice, the optimum element spacing for beamforming and adaptive interference cancellation applications is d = λ/2. However, in specific applications such as transmit diversity, we intentionally design an array with much larger spacing to combat fading effects, as will be described in detail in a later chapter. A typical transmit or receive diversity antenna array has two elements separated by up to 10λ. The radiation patterns of a two-element array with element spacing of λ/2, 5λ, and 10λ are shown in Figures 4.11, 4.12, and 4.13, respectively. The

94

Smart Antenna Engineering 90

1 60

120 0.8 0.6

30

150 0.4 0.2

0

180

210

330

240

300 270

Figure 4.9

Polar pattern, broadside array, M = 6, d = λ/2.

90

1 60

120 0.8 0.6

30

150 0.4 0.2

0

180

210

330

240

300 270

Figure 4.10

Polar pattern, broadside array M = 6, d = λ.

Fixed Beam Smart Antenna Systems 90

95

1 60

120 0.8 0.6

30

150 0.4 0.2

0

180

210

330

240

300 270

Figure 4.11

Polar pattern, broadside array, M = 2, d = λ/2.

90

1 60

120 0.8 0.6

30

150 0.4 0.2

0

180

210

330

240

300 270

Figure 4.12

Polar pattern, broadside array, M = 2, d = 5λ.

96

Smart Antenna Engineering 90

1 60

120 0.8 0.6

30

150 0.4 0.2

0

180

210

330

240

300 270

Figure 4.13

Polar pattern, broadside array, M = 2, d = 10λ.

sidelobes and grating lobes generated in Figures 4.11 and 4.12 makes this design unsuitable for applications seeking to improve system performance when the degradation is mainly caused by interference or jamming. 4.4.4

First Null Beamwidth

The null-to-null beamwidth (NNBW) of the array has a significant impact on the performance of a smart antenna system and is considered one of the important parameters that need to be considered in the antenna design. For a broadside array on the z-axis, the null-to-null beamwidth is given by [7] π  λ  θ N = 2  − cos −1    Md   2

(4.23)

The behavior of the NNBW is shown in Figures 4.14 and 4.15 as a function of d and M, respectively. Note that the larger the array, the smaller the NNBW becomes and the narrower the main lobe gets.

Fixed Beam Smart Antenna Systems

θ

97

λ

Figure 4.14

NNBW as a function of element spacing d.

λ

θ

λ

Figure 4.15

4.4.5

λ λ

NNBW as a function of number of elements M.

Half-Power Beamwidth

Another very important beamwidth measure to consider is the half-power or 3-dB beamwidth. The 3-dB beamwidth of a broadside array on the z-axis is given by [7] . λ  π  1391 θ H = 2  − cos −1  for πd λ << 1   πMd   2

(4.24)

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Smart Antenna Engineering

A more general formula for the 3-dB beamwidth of a linear-phased array antenna is λ  θ H = cos −1  cos θ o − 0.443 Md 

λ   −1   − cos  cos θ o + 0.443 Md  (4.25)

θ

This is valid for a range of scanning angles but not for end-fire arrays. In Figures 4.16 and 4.17, we notice the same behavior for the 3-dB beamwidth when we increase M or d. Figure 4.18 demonstrates that the 3-dB beamwidth of a linear-phased array of a given size is not constant but rather it depends on the scanning angle.

λ Figure 4.16

3-dB bandwidth as a function of element spacing d.

λ λ

θ

λ

Figure 4.17

3-dB bandwidth as a function of number of element spacing M.

λ

99

θ

Fixed Beam Smart Antenna Systems

θ

Figure 4.18

4.4.6

Effect of the scanning angle on the 3-dB beamwidth.

Array Directivity

The radiation intensity of an antenna array can be defined as: U ( θ ) = [ AF ]

2

(4.26)

Antenna arrays have the ability to direct or concentrate the radiated power in a particular angular direction in space. This ability is measured by what is called the directive gain, defined as [9]: D ( θ, ϕ) =

4 π power radiated per unit solid angle in the direction ( θ, ϕ) (4.27) Total power radiated by the antenna

The directive gain in the direction of the maximum radiation density is referred to as the directivity and is given by Do =

4 πU max P rad

(4.28)

For a broadside array and small element spacing (d < λ), the directivity can be approximated by [7] D o = 2M

d λ

(4.29)

100

4.4.7

Smart Antenna Engineering

Array Gain

The gain of an antenna array is the ratio of the radiation density in a particular angular direction in space to the total input power to the array or G ( θ, ϕ) =

4 π power radiated per unit solid angle in the direction ( θ, ϕ) (4.30) Total input power to the antenna

Note that we can define the antenna array efficiency as P rad Pin

(4.31)

G = Dη

(4.32)

η= It follows that

4.4.8

Trade-Off Analysis

We have seen from previous discussions the effect of different parameters on the characteristics of the array and potential system performance impacts. Table 4.1 presents a trade-off analysis summarizing the impact of increasing each of these parameters. Table 4.1 Impact of Array Parameters on System Performance

Parameter

Pros

Cons

Number of elements M

Lower sidelobe levels

More sidelobes

More and deeper nulls

Larger arrays may be more costly

Narrower beams

Smart Antenna Performance Impact Better interference cancellation capabilities Improved performance because of higher gain and narrower beams

Higher gain

Physical limitations on installation

Element spacing d

Narrower beams

Grating lobes

Grating lobes have negative impact on interference nulling

Scanning angle θo

Smaller 3-dB beamwidths.

—

Improved performance because of narrower beams

Higher gain

Fixed Beam Smart Antenna Systems 4.4.9

101

Impact of Element Pattern

As we indicated earlier, the total radiation pattern of an antenna array in the far field E(θ, ϕ) is represented by a product between two factors, the array factor AF(θ, ϕ) and the element factor EF(θ, ϕ). Consider two of the most widely used antennas, namely, the short dipole and a half-wave patch antenna, with element patterns given by E ( θ, ϕ) =

cos( π 2 cos( θ ))

half wave dipole

sin 2 ( θ )

  πL E ( θ, ϕ) = cos sin ( θ )   λ

E plane, half wave patch

λ

where L ≈ 0.49

is the resonant length of a half-wave patch [10]. εr The complete radiation pattern of the array is obtained by pattern multiplication, as shown in Figures 4.19 through 4.22, where we have considered two- and four-element arrays of half-wavelength dipoles as well as half-wave patches. It is obvious how the element pattern can affect the total array pattern by changing the shape of the pattern and the direction of the main beam, as in Figure 4.19, by changing the beamwidth, as in Figure 4.21, and by changing the maximum gain, as in Figure 4.22. 4.4.10 Planar Arrays

In planar arrays, elements are placed in a planar or rectangular grid. Let us consider a general planar array antenna with elements located at arbitrary positions (yn, zn) in the yz-plane, as shown in Figure 4.23. 90 1 120

60

0.5

150

120 30

150

90 1 60 0.8 0.6 0.4

30

150

0.2 180

0 180

210

330 240

Figure 4.19

270 Array factor

300

1

90 120

0.8 0.6 0.4

60 30

0.2 0 180

210

0

330 210 300 270 Element pattern

240

330 240

270

Total pattern

Total pattern, two-element array of half-wave dipoles, β = 180°, d = λ/2.

300

102

Smart Antenna Engineering 90 1 120

60

0.5

150

90 1 60 0.8 0.6 0.4

120 30

150

90 1

120 30

0.8 0.6 0.4

150

0 180

210

330 240

Figure 4.20

270 Array factor

90 1

210

300

0

330 210 300 270 Element pattern

240

60

0.5

150

210 240

270 Array factor

90 1

120 30

180

330 300 270 Total pattern

240

0.5

150

90 1

60

120 30

60

0.5

30

150

0 180

0 180

0

330 210

330 210

330

300

300 270 Element pattern

240

240

270 Total pattern

300

Total pattern, four-element array of half-wave patch, β = 180°, d = λ/2.

90 1 120

60

0.5

150

210 240

270 Array factor

90 1

120 30

180

Figure 4.22

0 180

Total pattern, two-element array of half-wave dipoles, β = 0°, d = λ/2.

120

Figure 4.21

30

0.2

0.2 180

60

0.5

150

90 1

60

120 30

60

0.2 150

30

0 180

0 180

0

330 210

330 210

330

300

300 270 Element pattern

240

240

300 270 Total pattern

Total pattern, four-element array of half-wave patch, β = 0°, d = λ/2.

The array factor for this array antenna is given by M

AF = ( θ, φ) = ∑ a n e

(

jk y n sin θ sin φ + β y + z n cos θ + βz

)

(4.33)

m =1

where an is the complex excitation of element n. Assuming we have M elements in the y-direction and N elements in the z-direction, we can rewrite (4.33 ) as

Fixed Beam Smart Antenna Systems

103

z N . . .

dy

3 dz 2 θ 1

y 2

3

M

φ

x

Figure 4.23

Planar array geometry. M

N

jk ( m −1 ) ( d y sin θ sin φ + β y ) + ( n −1 ) ( d z cos θ + βz ) ] AF ( θ, φ) = ∑ ∑ a mn e [

(4.34)

m =1 n =1

The above array factor can then be separated into the product of two terms as follows: N

AF ( θ, φ) = ∑ e jk

( n −1 ) ( d z cos θ + βz )

n =1

M

∑a m =1

mn

e

(

jk ( m −1 ) d y sin θ sin φ + β y

)

(4.35)

where dy and dz are the element separations in the y and z directions, respectively, and amn is the complex excitation of the element at position (mdy; ndz). For simplicity, let us assume the excitation distribution over the array to be uniform and equal to ao, we can then rewrite (4.35) as a product of two sums as N

AF ( θ, φ) = a o ∑ e jk n =1

( n −1 ) ( d z cos θ + βz )

M

∑e

(

jk ( m −1 ) d y sin θ sin φ + β y

)

m =1

Comparing (4.36) and (4.12) and using (4.20), it follows that

(4.36)

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  Nψ z    sin  Nψ y     sin     2    1  2   1 AF ( θ, φ) =  N  ⋅ M   sin  ψ z    sin  ψ y     2     2   

(4.37)

To get an expression for the radiation pattern of the planar linear array, we multiply the array factor by the element factor: E ( θ, φ) = AF ( θ, φ)EF ( θ, φ)

(4.38)

An example of the 3D array factor of a planar array of 16 elements is plotted in Figure 4.24. 4.4.10.1 Directivity of Planar Arrays

Using the expression in (4.27) we can write the directivity as D ( θ, φ) =

4 πE ( θ, φ) π π

∫ ∫ E ( θ, φ)

2

2

(4.39)

sin θ d θ d φ

−π 0

1 0.8 0.6 0.4 0.2 0 200 200

150 150

100 100

50

50 0

Figure 4.24

0

4 x 4 planar array pattern, dz = dy = λ/2

Fixed Beam Smart Antenna Systems

105

Notice that the directivity is defined for arbitrary angles θ and φ. We are often interested in the maximum directivity Do of a pattern. For large planar broadside arrays, the elevation half-power beamwidth is small. We can then use the approximation sin(θ) = 1 and rewrite the directivity as Do =

4 π AF ( θ )EF ( θ ) AF ( φ)EF ( φ) π π

∫ ∫ AF ( θ )EF ( θ ) AF ( φ)EF ( φ)

2

2

(4.40)

sin θd θd ϕ

−π 0

which can be further separated into Do =

2 AF ( θ )EF ( θ ) π

∫ AF ( θ )EF ( θ )

2

2 π AF ( φ)EF ( φ)

2

π

sin θd θ

∫ AF ( φ)EF ( φ)

2

2

= D θ D φ (4.41)

d ϕ

−π

0

From (4.41) we can see that with large planar arrays, the directivity can be calculated as the product of the directivities of two linear arrays.

4.5 Beamforming As we have previously seen, an array’s main beam direction depends on the element spacing as well as the phase difference between adjacent elements. From (4.12) the phase difference between element m and the first element considered as the array reference would be ψ m1 = (m − 1)ψ = (m − 1)( β + kd cos γ )

(4.42)

For half-wavelength spacing, the phase difference between two adjacent elements can then be written as ψ = β + kd cos θ = β +

2π λ ⋅ ⋅ cos θ = β + π cos θ λ 2

(4.43)

It then follows that for a broadside array with θo = 90°, ψ becomes zero. Now, if we wish to direct the array main beam toward some other angle θb, where 0° ≤ θb ≤ 180°, then ψ b = βb + π cos θ b

(4.44)

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Since the direction of the main beam occurs when ψb = 0, we obtain the following relation βb = − π cos θ b

(4.45)

Consider a linear antenna array connected to a signal generator (or a receiver); as we have seen earlier, this will produce a main beam at a specific angle and nulls at other directions. Therefore, in order to produce multiple beams at different directions, we need to feed the array with multiple signal generators (or connect it to multiple receivers) simultaneously. This can be accomplished using a feed network referred to as a beamformer, as shown in Figure 4.25. In an M x M beamformer, M input ports are connected to M antenna elements, whereas M output ports are connected to signal generators or receivers. The presence of a signal at one of the output ports will induce a phase shift between adjacent input ports and array elements, resulting in a radiation pattern with a main beam and nulls along specific directions. Now, when different signals are fed to or applied at all output ports, corresponding radiation patterns will be produced, the superposition of which will result in multiple simultaneous beams along different angles. When the peak of a radiation pattern is directed along the nulls of other patterns, the beamformer is called orthogonal. Note that an M x M beamformer produces M beams. In a

45°

1

Figure 4.25

2

4 × 4 Butler matrix beamformer.

45°

3

4

Fixed Beam Smart Antenna Systems

107

symmetric beamformer, M/2 beams are produced on each side of the array’s boresight. In an asymmetric beamformer, a broadside beam and M/2-1 beams are formed on one side of the array’s boresight, whereas M/2 beams are produced on the other side. The resulting phase difference between adjacent array elements will be given by [20] βb = (bπ M )( 2 − 1 b ), where b = − M 2,K1,K M 2

(4.46)

for symmetric beamformers and βb = ( 2b π M ), where − M 2 ≤ b ≤ M 2

(4.47)

for asymmetric beamformers. Using (4.47), we can determine the angle θb off the array’s axis at which the bth beam is pointing as θ b = cos −1 ( ψ b π )

(4.48)

4.6 The Butler Matrix The Butler matrix is the most commonly used beamforming network that, in conventional form, is capable of producing M beams, where M is any integral power of 2. The Butler matrix uses passive hybrid power dividers and fixed phase shifters to produce the desired progressive phase shifts at the elements of an antenna array necessary to form simultaneous multiple beams [11, 12]. A four-element Butler matrix is shown in Figure 4.25, where phase-lag directional couplers and fixed phase shifters are used to produce four orthogonal beams. The directional couplers have outputs that are equal in power but are 90° out of phase, as shown in Figure 4.26. Then, from Figure 4.25, we can see that when the input signal e j0 is applied at each of the matrix ports, the resulting phases at the array elements are given as shown in Table 4.2. Now let a four-element array be connected to the input ports of the 4 x 4 Butler matrix. Then for a symmetric beamformer, the locations of the four beams and the corresponding adjacent element phase shifts would be given in Table 4.3. Let us assume that a signal source is located along one of the main beams. The array factor, which in this case could also be thought of as the transfer function between the signal source and the corresponding port, is given by: G (θ ) =

sin ( M2 ψ ) sin (

ψ 2

)

⋅e j

( M −1 ) ψ 2

(4.49)

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0°

0° –90°

Figure 4.26

–90°

Hybrid coupler.

Table 4.2 Phases at Array Elements in a 4 × 4 Butler Matrix

Input Port Output Port

A1

A2

A3

A4

P1

0°

–45°

–90°

–135°

P2

–90°

45°

–180°

–45°

P3

–45°

–180°

45°

–90°

P4

–135°

–90°

-45°

0°

Table 4.3 Beam Locations

Beam Index b

Phase Shift b

Beam Location

–2

–135°

138.6°

–1

–45°

104.5°

1

45°

75.6°

2

135°

41.4°

b

Fixed Beam Smart Antenna Systems

109

Using (4.44), the magnitude of the set of beam patterns are then given by AF1 =

1 sin[ M2 ( β1 + kd cos θ )] ⋅ sin ( β1 + kd cos θ ) M

(4.50)

AF 2 =

1 sin[ M2 ( β 2 + kd cos θ )] ⋅ sin ( β 2 + kd cos θ ) M

(4.51)

AF3 =

M 1 sin 2 ( β3 + kd cos θ ) ⋅ M sin ( β3 + kd cos θ )

[

]

(4.52)

[

]

(4.53)

M 1 sin 2 ( β 4 + kd cos θ ) AF 4 = ⋅ M sin ( β 4 + kd cos θ )

Figure 4.27 shows the beam patterns of a 4 × 4 beamformer.

0 −2 −4

Array Factor (dB)

−6 −8 −10 −12 −14 −16 −18 −20 0

Figure 4.27

20

40

60

80 100 AOA Degrees

120

Radiation pattern of 4 × 4 Butler matrix beamformer.

140

160

180

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4.7 Spatial Filtering with Beamformers As we have previously seen, in an orthogonal beamformer, the peak of any beam coincides with nulls of all other beams. Considering the 4 × 4 beamformer already discussed, it then follows that if only port 1 was fed by a signal generator while all other ports were match terminated, a pattern will be formed with a main beam pointing at 104.5° off the array axis and nulls at 138.6°, 75.6°, and 41.4°. Similarly, if a signal source was located at 104.5° off the array axis, then a signal will appear only at port 1. Hence, the beamformer network allows for the spatial separation of the signal sources. Even though all signals at the beamformer input ports are transported to all output ports, the signal from a source located along one of the main beams will only appear at the corresponding output port. Let us assume we have four signal sources, s1(t), s2(t), s3(t), s4(t) located along the directions of the four main beams in addition to L interfering signal sources Il (t) located at arbitrary angles θl. Let the transfer functions between the signal sources along the main beams and their corresponding output ports be denoted by Gi and the transfer function between interference signal l and port i be denoted by Gli. Moreover, assume all the signals are uncorrelated. It follows that the total signal appearing at port i is given by L

y i (t ) = s i (t )G i ∑ I l (t ) ⋅G li ( θ l )

(4.54)

l =1

The total output power at port i will then be given by y i (t )

L   = s i (t )G i + ∑ I l (t ) ⋅G li ( θ l )   l =1

2

= s i (t )G i

2

2

 L +  ∑ I l (t ) ⋅G li ( θ l )   l =1

2

(4.55)

  L  + 2 Re (s i (t )G i )  ∑ I l (t ) ⋅G li ( θ l )    l =1  However, since all the signals are uncorrelated, the total output power reduces to y i (t ) = s i (t ) G i 2

2

2

L

+ ∑ I l (t ) G li ( θ l ) 2

2

(4.56)

l =1

Now let us consider the more general case where we have L signal sources located at arbitrary angles throughout the array field of view represented by the

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111

vector s(t) = [s1(t) s2(t) … sL(t)]T. Let the signals induced at the array elements be denoted by the vector x (t ) = [ x 1 (t ) x 2 (t ) L x M (t )]

T

(4.57)

As we have seen earlier, the signals at the array elements (or beamforming network input ports) will be transported to all output ports being modified in the process with the transfer functions between the input and output ports. Let the matrix T denote the transfer functions between the input and output ports of the beamforming network and be given by T = [w 1

w2

L wM

]

T

(4.58)

Note that since these transfer functions or weights are fixed, this is often referred to as fixed beamforming. It follows that the beamforming network output can be written as y (t ) = T H x (t )

(4.59)

where the superscripts T and H denote the transpose and complex conjugate transpose of a vector or matrix, respectively. It follows that the output power is given by P = E [ y (t )

y H (t )]

= T H E [ x (t ) x H (t )]T

(4.60)

= T RT H

where E[.] denotes the expectation operator and R is the array correlation matrix whose elements are the correlation between various elements. Once the signals are transported from the array elements to the output ports of the beamforming network, a scheme must be developed to extract the desired signal and deal with interfering sources, either by reducing or canceling their signals. With this type of fixed beamforming, there are two general approaches that can be used, namely switched beam systems and multiple fixed beam systems.

4.8 Switched Beam Systems The switched beam method is considered an extension of the current cellular sectorization scheme. In the switched beam approach, the sector coverage is

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achieved by multiple predetermined fixed beam patterns with the greatest gain placed in the center of a beam. When a mobile user is in the vicinity of a beam, then the signals at the output ports will be given as in (4.54). This enables the switched beam system to select the signal from the output port corresponding to that beam [13, 14]. As the mobile moves to the coverage of another beam during the call, the system monitors the signal strength and switches to other output ports as required. A basic switched multiple beam antenna architecture is shown in Figure 4.28. Switched beam systems offer several advantages, including: • Low complexity and cost. Since switched beam systems only require a

beamforming network, RF switches, and simple control logic, they are relatively easy and cheap to implement.

MxM beamforming network

Duplexer Tx

Duplexer

Switch Duplexer Duplexer

Rcvr

Rcvr

Rcvr

Rcvr

Beam selection

Combining and demodulation

Figure 4.28

Switched beam system architecture.

Fixed Beam Smart Antenna Systems

113

• Moderate interaction with base station receivers. In practice, switched

beam systems can simply replace conventional sector antennas without requiring significant modifications to the radio base station antenna interface or the baseband algorithms implemented at the receiver. • Coverage extension. The antenna array aperture gain will boost the link

budget, which could be translated to a coverage extension. However, there are several limitations to switched beam antennas that can be summarized as follows: • Susceptibility to interfering signals or multipaths arriving from angles

near that of the desired signal since, in that case, those signals would also appear at the same output port as the desired signal, making it hard to separate them. • Scalloping. From Figure 4.27, we can see that the antenna pattern

drops or rolls off as a function of AOA as we move away from the center of a beam to the intersection of two beams by as much as 3.9 dB. For clarity, this is shown in Figure 4.29. As a result, the mobile’s signal strength will fluctuate by the same amount as it moves across the different beams’ coverage. • Lack of path diversity. Since the switched beam system will only select

the signal from one port, it will be unable to combine coherent multipath components that might be arriving from directions of beams other than the main beam.

4.9 Multiple Fixed Beam Systems In multiple fixed beam antennas, rather than selecting the signal from a specific port, the signals from all ports are combined, making use of path diversity. This approach, which we can refer to as an integrated embedded system of fixed multibeams, can achieve better performance since it enhances the received signal detection on the uplink by making use of the signals from all the available paths in the beams followed by some kind of diversity-combining technique such as maximum ratio combining. On the reverse link, or uplink, the beam receiving the most power can be used to transmit to the desired mobile on the downlink.

114

Smart Antenna Engineering 0 –0.5

–1

Array gain (dB)

–1.5

–2

–2.5

–3

–3.5 –4 40

Figure 4.29

50

60

70

80 90 100 AOA (Degrees)

110

120

130

140

Scalloping.

4.10 Adaptive Cell Sectorization in CDMA Systems As we have seen earlier, traffic imbalance can lead to locking unused capacity on some sectors or sites, prompting operators to deploy additional carriers before they are truly needed networkwide. Hence, balancing the traffic load among the sectors of the cell can help reduce peak loading and increase the cell’s capacity. We have also seen how the forward link capacity is reduced due to soft handoff. Reducing the size of handoff zones and shifting those zones from high- to low-traffic areas can minimize this negative impact of soft/softer handoff on capacity. Another common problem in CDMA networks is that of pilot pollution, where multiple pilot signals of approximately equal strength exist at some locations, resulting in unreliable call originations and terminations, unreliable handoffs, and decreased capacity. Reducing the size of the coverage footprint of such pilots by decreasing the elevation of offending antennas, introducing down tilt, or reducing the transmit power can help overcome pilot pollution. Adaptive cell sectorization allows an operator to control cell site sectorization for increased CDMA capacity and improved network performance. It can provide CDMA service providers with flexible tuning options for controlling interference sources, creating dominant servers or pilots, managing handoff activity and handoff zones, and dealing effectively with nonuniform and time-varying traffic distributions. In this sense, a phased array antenna at the base station can be considered as an advanced optimization tool that uses beamforming technology to manage and control a specific network’s hot spots and help reduce blocking, rather than

Fixed Beam Smart Antenna Systems

115

an actual smart antenna system that attempts to improve capacity and coverage on a systemwide basis. The basic idea is to replace the conventional base station antenna with a multiple beam antenna system of M beams of θBW beamwidth. It then becomes possible to use the individual beams or a group of beams to adapt the size and position or orientation of sectors to the load distribution. There are several ways to combine the beams; for instance, one can combine M/3 beams to form the equivalent of three 120° sectors. Alternatively, load balancing can be achieved by combining different number of beams per sector to customize sector azimuth pointing angle and beamwidth in θBW increments to balance traffic loading across sectors. Average busy hour traffic distribution based on BSC or switch data such as channel assignments, successful calls, blocking, and forward/reverse power overload control duration can be used to study the network performance over time. Based on this evaluation, sectors or sites near or at the blocking thresholds that are contributing to load imbalances can be identified [15]. By reducing peak loading in those most heavily used sectors or sites, the system can create capacity headroom for traffic growth in the cell and reduce the access failure rate. It is important to note that the actual system capacity is not increased, rather the traffic is merely redistributed in a more balanced way, as illustrated in Figure 4.30. One advantage of this approach is the ability to create any number of sectors and tailor that to the network traffic demand; however, Sector capacity limit

Traffic / Sector

Beta Gamma Alpha

(a)

Traffic / Sector

Sector capacity limit Beta Gamma Alpha

(b)

Figure 4.30 (a) Load distribution with conventional sectorization; and (b) load balancing with adaptive cell sectorization.

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excessive sectorization is inefficient. The only way to increase the system capacity with this approach is to eventually transform it into a switched beam system. Furthermore, beam gains and phase settings can be continuously adjusted to create sharper sector rolloff, thus reducing the size of softer handoff zones and reducing the impact of the handoff reduction factor on capacity.

References [1] Yang, X., S. Ghaheri, and N. R. Tafazolli, “Sectorization Gain in CDMA Cellular Systems,” First International Conference on 3G Mobile Communication Technologies, 2000, pp. 70–75. [2] Uc-Rios, C. E., and D. Lara-Rodriguez, “On the Effect of Directional Antennas on the Reverse Link Capacity of CDMA Cellular Systems,” IEEE 54th Vehicular Technology Conference, Vol. 1, 2001, pp. 217–221. [3] Lee, C. C., and R. Steele, “Effect of Soft and Softer Handoffs on CDMA System Capacity,” IEEE Trans. on Vehicular Technology, Vol. 47, No. 3, August 1998. [4] Chan, G. K., “Effects of Sectorization on the Spectrum Efficiency of Cellular Radio Systems,” IEEE Trans. on Vehicular Technology, Vol. 41, No. 3, August 1992, pp. 217–225. [5] Lee, T. S., and Lee, Z. S., “A Sectorized Beamspace Adaptive Diversity Combiner for Multipath Environments,” IEEE Trans. on Vehicular Technology, Vol. 48, No. 5, September 1999. [6] Wong, T. W., and Prabhu, V. K., “Optimum Sectorization for CDMA 1900 Base Stations,” IEEE 47th Vehicular Technology Conference, Vol. 2, May 4–7, 1997, pp. 1177–1181. [7] Balanis, C.A., “Antenna Theory: Analysis and Design,” John Wiley & Sons, New York, N.Y., 1982 [8] Kraus, J.D., “Antennas,” Second edition, McGraw Hill, New York, N.Y., 1988. [9] Litva, J., K.Y. Lo, Titus, “Digital Beamforming in WIreless Communications, Artech House, Norwood, MA, 1996. [10] Stutzman, W. L., and G. A. Thiele, “Antenna Theory and Design,” John Wiley and Sons, 1998. [11] Shelton, J., K Kelleher, “Multiple Beams from Linear Arrays,” IRE Transcriptions on Antennas and Propogation, Vol. 9, Issue 12, 1961, pp. 154–161. [12] Foster, H. E., R. E. Hiatt, “Butler Network Extension to any number of antenna ports,” IEEE Transaction Antennas and Propogation, Vol. 18, Issue 6, pp. 818–820. [13] Dong, L., M. A. Ingram, “Beam Selection Algorithum based on PTR metric and its Synchronization Performance,” Radio and Wireless Conference, August 10–13, 2003, pp. 115–118. [14] Matumoto T., S. Nishioka, D. J. Hodder, “Beam Selection Performance Analysis of a Switched Multi-beam Antenna System in Mobile Communications Environments,” IEEE Transactions on Vehicular Technology, Vol. 46, Issue 1, 1997, pp. 10–20. [15] Mahmoudi, M., E. S. Sousa, and H. Alavi, “Adaptive Sector Size Control in a CDMA System Using Butler Matrix,” IEEE 49th Vehicular Technology Conference, Vol.2, No., July 1999, pp. 1355–1359.

5 Adaptive Array Systems Chapter 3 showed how the multipath channel causes significant impairments to the signal quality in mobile radio communication systems. As signals travel between the transmitter and receiver, they get reflected, scattered, diffracted, and shadowed. In addition, user’s mobility gives rise to Doppler shift in the carrier frequency. As a result, those signals experience fading because of the attenuation and phase shifts when they are combined at the receiver. Another source that degrades the performance of signal reception in a mobile environment is interference, especially in interference-limited systems such as those based on CDMA. Techniques that overcome these impairments and improve system performance are examined in this chapter, namely, diversity and adaptive beamforming, both of which are spatial techniques. Diversity techniques provide a diversity gain or a reduction in the margin required to overcome fading. In a digital communication system, this results in an improvement in the required SNR or Eb/No necessary to achieve a given quality of service in terms of bit error rate (BER) or FER. Similarly, beamforming provides several types of improvements in terms of array gain, interference reduction, and spatial filtering, which have the cumulative effects of improving Eb/No as well. We will show how these techniques are applied on both the uplink and downlink and discuss the limitations of each approach.

5.1 Uplink Processing 5.1.1

Diversity Techniques

As we have shown in Chapter 3, a mobile user can experience both slow and fast fading. The effects of slow fading can easily be overcome using closed loop 117

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power control, as is the case in all CDMA systems. Fast power control is used on both the uplink and downlink at speeds of 800 Hz in CDMA2000 and 1,500 Hz in WCDMA. However, even at these speeds, power control may not be fast enough to deal with fast fading, especially at high mobile speeds. Diversity reception is a more effective solution to increase the receiver’s immunity against fast fading, especially in Raleigh fading channels. The diversity concept relies on the fact that when multiple replicas or multipaths of the transmitted signal fade independently as they go through distinct channels, the probability of a deep fade occurring in all channels at the same time is significantly reduced. Assume the probability of deep fade in one channel to be given by p, then for L channels the probability becomes pL. Improvements in signal statistics depend on two main conditions, namely, uncorrelated signals with comparable local mean powers or signal strengths. If the signals from the different multipaths are highly correlated then they will fade together and no diversity is provided. Cross-correlation coefficients of 0.7 or lower between the multipaths have been shown to provide reasonable improvements [1]. If the mean signal powers of the multipaths are widely unequal or unbalanced, that would affect the performance of the diversity- combining techniques. Diversity gain is generally computed by comparing the cumulative distribution function (CDF) of the Rayleigh fading on a single antenna to the CDF of the fading signal at the output of the diversity receiver at a specified probability. An empirical formula for this diversity gain was derived in [2] for various combining techniques. For maximum ratio combining, discussed shortly, the two-branch diversity gain at 90% signal reliability is given by G div = 714 . e

( −0 .59 ρ − 0 .11 ∆ )

(5.1)

where ρ is the cross-correlation coefficient between signals and ∆ is the mean signal level difference. Figure 5.1 shows the effect of ρ and ∆ on the diversity gain. It is also possible to define the diversity gain at other percentiles, such as 95% and 99%. Obviously, the diversity gain is strongly dependent on the definition. 5.1.2

Angle Diversity

Several antenna schemes exist to create the diversity channels necessary to achieve the diversity gain above, including spatial and polarization diversity. In spatial diversity, a popular technique used in many wireless communication systems, the system uses antennas with vertical polarization horizontally spaced by at least 10λ in order to achieve the required low cross correlation between the diversity branches. Spatial diversity systems are designed such that the signals at the different antennas of the receiver have low cross correlation with the

Adaptive Array Systems

Figure 5.1

119

Diversity gain as a function of correlation coefficient.

maximum gain achieved for uncorrelated signals. The correlation between the signal replicas is a function of both the antenna spacing and the angle spread of the wireless propagation channel. For example, on the base station side, the angle spread is typically narrow—in the order of 10°–20°. Therefore, high decorrelation between the signals requires large antenna separation, as much as 10 wavelengths in order to achieve a high diversity gain. On the other hand, at the mobile station side, the angle spread is very wide, typically close to 360°, in which case the decorrelation of the signals can be achieved with separations on the order of a quarter wavelength. In the case of multiple beam systems, the array is typically designed with half wavelengths interelement separation, thus it might not provide any significant improvements based on spatial diversity unless the angular spread of the received multipaths is such that the low cross-correlation condition is still met. Polarization diversity uses antennas with orthogonal polarizations to achieve the same conditions necessary to obtain a good diversity gain. The very nature of multiple beam systems offers another scheme, referred to as angle diversity, in which the beams are used to create the diversity channels as shown in Figure 5.2, where the different paths are received through different beams. Note that angle diversity would be expected to provide significant diversity gains in urban and dense urban environments where rich multipaths always exist but would be of little use in rural environments characterized more with line-of-sight Rician channels. Recall that in urban and dense urban environments a large number of scatterers typically exist and the mobile station’s signal is received at the base station

120

Figure 5.2

Smart Antenna Engineering

Angular diversity concept.

from several DOAs. Therefore, these types of environments tend to have large angular spread. On the other hand, in rural environments the received signal at the base station is dominated by an LOS component and very few multipath components. This yields a small angular spread. Therefore, using narrow beams in the base stations allows the receiver to capture different replicas of the signal from the multitude of DOAs. The narrower the beam, the better the spatial resolution becomes and in rich multipath environments, angular diversity has been shown to yield diversity gain comparable with space diversity. This is because in rich scattering channels, all beams are expected to receive signal paths with low cross correlation and comparable mean powers. By contrast, those same narrow beams will yield unbalanced mean signal powers in the diversity branches in Rician fading LOS channels, causing a degradation to the diversity gain. In [2], multiple beam antennas with 24 15° beams and 12 30° beams with selection combining were used to compare the performance of angular diversity and space diversity. The reported results show an angular diversity gain of 8.5 dB with the 15° multibeam antenna and 7.5 dB with the 30° multibeam antenna versus 8 dB and 7 dB, respectively, of space diversity gain at 99% reliability level in an urban environment. In a rural environment, space diversity outperformed angular diversity by 2.5 dB with 15° multibeam antennas and 4.5 dB with 30° multibeam antenna at a 99% reliability level due to imbalance in the mean received signal strength between the diversity branches.

Adaptive Array Systems 5.1.3

121

Maximum Ratio Combining

As discussed in Chapter 4, multiple fixed beam antennas systems generate a number of signals in all ports, making use of the signals from all the available paths in the beams. Diversity schemes can then be used to combine these signals. Since classical switched and selective diversity only selects one signal out of all the possible diversity branches, they are not suitable for the application at hand. On the other hand, the equal-gain combining technique applies different phase shifts to all signals (cophasing) and then combines them with equal magnitudes. The drawback of this technique is that overall system SNR is reduced when one of the branches has a significantly lower SNR than the remaining branches. The most optimal diversity scheme is the MRC. In MRC the received signals are first co-phased (aligned in phase), weighted by their instantaneous SNRs, and then combined. Assume that we have L diversity branches, then the signal received by the ith branch will be given by x i = shi + n i where s is the transmitted signal, αi is the complex channel attenuation, and ni is additive noise. The optimum weight for each branch is given by [1, 3]. wi =

hi2

(5.2)

σ i2

where σ i2 is the noise power at the branch. It follows that the SNR at the combiner output is 2

L

SNR MRC =

∑w h i =1 L

∑w i =1

i

i

2 i

σ

(5.3) 2 i

When the noise power at individual branches are all equal to σ i2 , it then follows that L

SNR MRC =

∑ i =1

hi2 hi σ n2 2

2

 hi2  2  2  σn ∑ i =1  σ n  L

2

=

L 1 L hi = SNR i ∑ ∑ 2 i =1 σ n2 i =1

(5.4)

122

5.1.4

Smart Antenna Engineering

Adaptive Beamforming

Consider a linear array composed of M elements and let L be the number of narrowband plane waves, centered at frequency ω0 impinging on the array from directions {θ1 θ2 … θL}, as shown in Figure 5.3. Using complex signal representation, the received signal at the mth element can be written as L

x m (t ) = ∑ s i (t )e − j

( i −1 ) k i

i =1

+ n m (t )

(5.5)

where si(t) is the signal of the ith source at the mth element, nm(t) is the noise signal received at the mth element, and ki =

2πd sin ( θ i ) λ

(5.6)

Using vector notation, we can write the array output (M elements) in the matrix form X (t ) = AS(t )N (t )

(5.7)

where X (t ) = [ x 1 (t ) x 2 (t ) L x M (t )]

T

S(t ) = [s 1 (t ) s 2 (t ) L s M (t )]

T

N (t ) = [n 1 (t ) n 2 (t ) L n M (t )]

T

lth source

θl

d

Figure 5.3

Uniform linear array.

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123

The superscript “T” indicates the transpose of the matrix. The steering matrix whose columns are the steering vectors representing the array response to a signal at angle θi is given by A = [ a( θ 1 ) a( θ 2 ) L a( θ L )]

(5.8)

and the steering vector of a linear array a( θ i ) = [1 e − jk i

e − j 2k i

L e −j

( M −1 ) k i

]

T

(5.9)

In wireless mobile communications systems the array usually receives many multipath components of the transmitted signal with different DOAs in addition to the signal received along the direct path. Hence, we can rewrite the total signal vector (without the noise and interference components) as [4] L

a( θ 1 )s 1 (t ) + ∑ α l a( θ l )s 1 (t ) = a 1 ( θ 1 )s 1 (t )

(5.10)

l =2

where a1 is called the spatial signature and αl is the phase and magnitude difference between the lth component and the direct path. In adaptive arrays, complex weights are applied to the element outputs represented by the M-dimensional vector: W = [w 1

w2

L wM]

T

(5.11)

Then the array output can be written as: M

y (t ) = ∑ w i ∗ x i (t ) = W H X (t )

(5.12)

i =1

The mean output power is thus given by: P ( w ) = E y (t ) y ∗ (t ) = W H R s W + W H R N W

(5.13)

where * denotes the conjugate and E [.] denotes the expectation operator and R is the array correlation matrix given by R = E  X (t ) X H (t ) = R s + R N

(5.14)

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where H is the complex conjugate transpose of a vector or a matrix and RS and RN represent the signal and noise plus interference correlation matrices, respectively. Several techniques exist to obtain the weight vector, which vary in complexity and limitations, as discussed next. Those can be classified as suboptimal, such as beam steering and null steering, and optimal, which seek to optimize a selected performance criterion. 5.1.4.1 Beam Steering

Beam steering is the simplest form of beamforming that can be achieved by a delay-and-sum beamformer. The weights of the beamformer are all made equal in magnitude, whereas the phases are selected to steer the main beam of the array in a particular direction θ0 [5]. The array weights are given by W BS =

1 a( θ 0 ) M

(5.15)

where M is the number of elements and a(θ0) is the steering vector. In other words, the array main beam is steered toward the DOA of the desired source. Thus, beam steering requires the knowledge of the desired signal location. The SNR at the output of the beamformer is given by: SNR BS

W HRsW = W HRN W

(5.16)

In the special case where the system is dominated by uncorrelated noise (RN = σ2I ) and no dominant interference exists, the output SNR then becomes

SNR BS

1 H a ( θ 0 )a( θ 0 )APA H a( θ 0 )a H ( θ 0 ) 2 P MP = M = 2 = 2 (5.17) 2 σn H σn σn a ( θ 0 )a( θ 0 ) 2 M M

5.1.4.2 Null Steering Beamforming

In a null steering beamformer, the signal arriving from a known direction can be cancelled by placing nulls in the array response at the DOA of that signal [5]. This can be accomplished by choosing the beamformer weights so that a beam with unity gain is created toward the DOA of the desired signal while nulls are created at the directions of interference. This can be formulated as follows

Adaptive Array Systems

W H A = W H [ a( θ 0 ) a ( θ 1 )

1  0  L a( θ k )] =   M  0   

125

(5.18)

where a(θ0) is the steering vector associated with the desired signal and a(θ1),… a(θk) are those associated with the interfering signals. To null the M – 1 interfering signals we then get H W NS = [1 0 L 0 ] A −1 T

(5.19)

One disadvantage of null steering techniques is that it requires the knowledge of the DOA of all interference sources. In addition, beam steering and null steering do not result in the maximum output SNR except in special cases. To obtain the optimal performance, several criteria can be used to derive the optimal weight vector. These include maximum SINR, minimum mean square error, minimum variance, and maximum likelihood [6–8]. 5.1.4.3 Maximum Signal-to-Interference and Noise Ratio

As we have seen earlier, the SINR at the beamformer output is given by: W HRS W SINR = W HRN W where R S = E [SS H ] is the desired signal’s correlation matrix and R N = E

 NN  is the noise and interference correlation matrix. If we maximize the H

quantity WHRSW subject to the constraint that WHRNW = 1, then we can achieve the maximum SINR. Using the method of Lagrange multipliers and setting the gradient with respect to W to zero we then get ∇[ W H R s W + λ(1 − W H R N W )] = R s W − λR N W = 0

(5.20)

This results in the eigenvalue problem R s W = λR N W

(5.21)

This can be further written as R −N1 R s W = λW

(5.22)

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Smart Antenna Engineering

The solution of (5.22) requires that W be an eigenvector of R −1 N R s . Hence, the eigenvector corresponding to the maximum eigenvalue (λmax) would maximize the SINR and we get SINRmax = λmax. Therefore R s W = λmax R N W = SINR ⋅ R N W

(5.23)

Solving for the optimum weight and using the definition of the signal correlation matrix we then get: W opt = K SINR R N−1 A

(5.24)

5.1.4.4 Minimum Mean Square Error

In the MMSE criterion, the error between the desired signal or a reference signal d(t) closely representing the desired signal and the output of the beamformer is minimized. The mean square error is given by: E [e 2 (t )] = E

{[d (t ) − W

H

X (t )][d (t ) − W H X (t )]

H

}

(5.25)

which can be expanded as d (t )d H (t ) − d (t ) X H (t ) W −  E e 2 (t ) = E  H  H H H  W X (t )d (t ) + W X (t ) X (t ) W  The gradient of the MMSE with respect to W can then be written as ∇E e 2 (t ) = E {−2d (t ) X H (t ) + 2 X (t ) X H (t ) W }

(5.26)

Setting this gradient to zero, we get the optimum weight given by W opt = R −1 r

(5.27)

where r is E d H (t ) X (t ). Using (5.25) and letting d(t) = s(t), then r = E S H ( AS(t ) + N (t ))  = σ s2 A

(5.28)

When the interference is orthogonal to the desired signal, the correlation matrix in (5.14) can be expressed as

Adaptive Array Systems

127

R = σ 2s AA H + R N

(5.29)

using the matrix inversion lemma If F = B −1 + CD −1 C H then F −1 = B − BC(D + C H BC) C H B −1

Hence using F = R , B = R −N1 , C = A , D = 1 σ s2 , we get R

−1

=R

R −1

−1 N

 1  − A  2 + A H R −N1 A  σs 

−1

A H R −N1

  −1 H  A RN A  = R −N1 I −  1 −1 H   + A R A N   σ 2s

(5.30)

(5.31)

which could be simplified to   1 R −1 = R N−1  2 −1 H   1+ σ s A R N A 

(5.32)

Hence, W opt

σ s2  −1  = R N A = K MMSE R N−1 A 2 −1 H  1 + σ s A R N A 

(5.33)

One disadvantage of the MMSE scheme is that it requires the knowledge of the desired signal or a closely correlated replica to use as the reference signal. 5.1.4.5 Minimum Variance Distortionless Response

Another optimum performance criterion involves minimizing the array output so that the desired signals are passed with specific gain while minimizing the contributions due to noise and interference. In other words: min W H RW subject to W H A d = r

(5.34)

In (5.34) Ad is the steering matrix pointing to the desired signals and r is the V × 1 constraint vector, where V is the number of desired signals. When the

128

Smart Antenna Engineering

elements of r are all 1s, the criterion is known as the MVDR. From (5.34) we can see that minimizing the variance of the beamformer output power is equivalent to minimizing WHRNW. The method of Lagrange multipliers can be used to solve this constrained minimization problem by taking the gradient with respect to W and setting it to zero as follows ∇  W H R N W + λ(1 − W H A d ) = R N W − λA d = 0

(5.35)

Hence, W opt = λR N−1 A d

(5.36)

Multiplying through by A Hd and making use of the fact that WHAd = 1, we get A Hd W = λA Hd R −N1 A d = 1

(5.37)

yielding λ=

1 A R −N1 A d H d

(5.38)

Therefore, the optimum weight is given by W opt =

R −N1 A d = K MVDR R −N1 A d A Hd R −N1 A d

(5.39)

One advantage of the MVDR beamformer is that it does not require any knowledge of the directions of the interference, rather only those of the desired signal(s). 5.1.4.6 Optimum SINR

We have shown in the previous sections that the optimum weight has the form W opt = K R −1 N A

(5.40)

Note that in (5.40), A refers to the steering vector/matrix or it could also be substituted for by the spatial signature. It follows that the optimum SINR

Adaptive Array Systems

129

can be obtained by substituting the optimum weight into the SINR equation, which results in

(K R N−1 A ) R −s 1 (K R −N1 A ) 2 H −1 = = σs A RN A H (K R −N1 A ) R −N1 (K R −N1 A ) H

SINR opt

(5.41)

For simplicity let us rewrite the array output assuming we have a desired signal Sd, an interference signal Sl, and thermal background noise [9]. This could, for instance, represent a simplified multirate model for the CDMA uplink where we have a low data rate voice user, a high data rate user, and a white noise term that includes the thermal background noise plus all other low data CDMA multiple access interference terms. In other words, we are assuming that enough low data CDMA users are present, who each are independently Rayleigh fading that they can be modeled as white spatial noise. The equivalent white noise term on each antenna element is complex Gaussian and the noise on all antenna elements has zero mean and variance equal to σ2. X (t ) = A d S d (t ) + A I S I (t ) + N (t )

(5.42)

We further assume that the equivalent white noise term is uncorrelated with Sd, Sl, Ad, and AI. The power in signal Sd is E S d S dH  = σ 2s and the power

in signal Sl is E S I S IH  = σ I2 . It then follows that noise and interference correlation matrix is given by R NI = σ I2 A I A HI + R N

(5.43)

where RN = σ2I. Using the matrix inversion lemma we can write R −NI1 = R −N1 − R −N1 σ I A I [1 + σ I A IH R −N1 σ I A I

R −NI1

]

−1

σ I AIH R N−1

    2 σ 1 1  = 2 I − I2 A I A HI σ  σ  σ I2 H   1 + 2 A I A I     σ  

which could be reduced to

(5.44)

(5.45)

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Smart Antenna Engineering

    A I A HI 1   = 2 I − σ  σ2 H    2 AI AI      σI 

R −NI1

(5.46)

Substituting into the expression for SINR we get

SINR opt

SINR opt

    H A  AI AI A = σ s2 I − d σ  σ2  H  2 + AI AI     σI  

(5.47)

    H H A d AI AI A d  σ  H = A Ad − σ  σ2  H  2 + AI AI     σI  

(5.48)

H d 2

2 s 2

Since A HI A d = A IH A d cos( θ S ), where θS is the angle between the signal and interference steering vectors, we can then write

SINR opt =

2

σ Ad 2 s

σ2

  1 −   

  A I cos ( θ s )  σ2 2   2 + AI    σI   2

2

(5.49)

which can be rearranged as

SINR opt =

5.1.5

σ Ad 2 s

σ2

2

 σ2 2 2 A sin ( + θ  I s) σ I2   σ2 2   2 + AI     σI

     

(5.50)

Fixed Multiple Beams Versus Adaptive Beamforming

To compare the performance of adaptive beamforming to that of the fixed multiple beams followed by the MRC approach, we must first derive a generalized

Adaptive Array Systems

131

form for the SINR in the MRC case. Let us rewrite the general form of the MRC weight as W MRC =

Ad σ2

(5.51)

Substituting into the SINR expression we get: SINR MRC =

( A d )H R s ( A d ) A Hd σ s2 A d A Hd A d = A Hd R NI A d ( A d )H R NI ( A d )

SINR MRC =

σ 2s ( A Hd A d )

(5.52)

2

A Hd (R N+ σ T2 A I A IH )A d

Since RN = σ2I, it then follows that SINR MRC =

σ s2 ( A Hd A d )

2

σ 2 A Hd A d + σ I2 A Hd A I A HI A d

=

σ s2 A d σ 2 + σ I2 A I

2

2

cos 2 ( θ s ) (5.53)

Now that we have derived expressions for the SINR achievable using multiple beam antennas with MRC and optimum adaptive beamforming, general comparisons can be drawn regarding the performance of both approaches and the environments for which they are most suitable. • Noise-dominated systems: In systems where background noise is domi-

nant, such as the case of no or very little directional interference in the channel, we then end up with SINR opt = SINR MRC =

σ s2 A d σ2

2

(5.54)

This is also the case when the interference is spatially white, such as in 2G CDMA (IS-95A) based systems, where all users have low data rates and no interference spatial coloring is experienced. This would also be the case in 3G CDMA systems (based on either IS-2000 or WCDMA) when only low data rate users are present in the sector. Hence, in such systems, optimum adaptive beamforming will perform

132

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as well as multiple fixed beam antennas followed by MRC since there is no interference to cancel. • Spatially collocated desired and interference signals: In this case the angle between the desired and the interference signals steering vectors or spatial signatures θs = 0°. It then follows that SINR opt = SINR MRC =

σ s2 A d

2

σ 2 + σ I2 A I

2

(5.55)

In this case, since the desired and interference signal have the same DOA, both approaches would perform similarly. • Interference-limited systems: When we have one or more low data rate users along with one or more high data rate users, then the interference experienced by the lower data rate users will be directional or spatially colored. Such is the case in multirate services, where both low data rate speech service and high data rate applications are simultaneously supported. It becomes apparent by comparing the SINR for both approaches that optimum adaptive beamforming will provide a gain over multiple fixed beam antennas with MRC because of the ability to deal with the directional interference more effectively. The amount of this gain will depend on the ratio of the interference-to-noise variance, θs, and the spatial signature or steering vector of the interference signal.

5.2 Downlink Processing Just as fading and interference degrade the performance of mobile communications systems on the reverse link (uplink) they also impair the performance of the forward link or downlink, reducing the capacity and affecting the signal quality. In Chapter 4, we presented expressions for the reverse link capacity of a CDMA system that showed that reducing the Eb/Nt that denotes the energy per bit to noise and interference ratio necessary to maintain the required signal quality measured for instance by the FER would increase the capacity. To understand the impact of fading and interference on the performance of the downlink, we need to calculate the capacity of that link. Unlike the uplink case, interference on the downlink of a CDMA system has many different sources, including overhead channels such as the pilot, sync, and paging. Since the base station power is limited, the downlink capacity will depend on the strength of the overhead or common channels, as well as all the supported active traffic channels. Recall that the traffic channels are power controlled; that

Adaptive Array Systems

133

is, the transmit power allocated to each traffic channel is adjusted by the outer and inner loop power control to achieve the required Eb/No in the presence of fading, interference, and other channel impairments. We can then write the following for the total base station power of an IS-2000 CDMA system: Ptot = Total Overhead Power (paging, pilot, and sync channels) + Number of Traffic Channels * Traffic Channel Power. It is then straightforward to obtain the downlink pole capacity of an IS-2000 system as [10]

N MAX =

E  E E  1 −  Pilot c + Paging c + Sync c  I or  I or I or  E   h ⋅ E Traffic c  I or  

(5.56)

In general, the downlink CDMA capacity is given by

N MAX =

E   1 − Common Channels c  I or   E   h ⋅ E Traffic c  I or  

(5.57)

where NMAX is the maximum number of supportable users, h is the handoff reduction factor, and Ec /Ior is the average channel transmit energy, relative to the total CDMA channel transmit energy. The handoff reduction factor accounts for the soft handoff links that consume additional downlink power for a given user. The traffic channel Ec /Ior is a function of the voice activity factor, chip rate, data rate, and traffic Eb /Nt, which is the required traffic channel received SNR corresponding to rate Rb at nominal FER. Examining (5.57) one might argue that the CDMA downlink pole capacity can be improved simply by increasing the total transmitter power. However, this argument ignores the fact that when using conventional sectorized antennas, increasing the transmit power would simply raise the interference seen on the downlink, and the traffic channel Ec /Ior required to maintain the same FER or BLER would also increase to combat the higher interference level. The only and most effective way to improve the capacity would then be to improve the traffic channel Ec /Ior, that is, adopt techniques that would reduce the required Ec /Ior for a given FER or BLER.

134

5.2.1

Smart Antenna Engineering

Transmit Diversity Concepts

As we have shown in the previous sections, receive diversity helps combat fading and is widely used in 2G systems to improve the base station receiver performance. Similarly, transmit diversity can improve the MS receiver performance by providing it with multiple independent copies of the transmitted signal [11, 12]. Note that at the mobile side, receive diversity can be used to achieve performance gains similar to those obtainable with transmit diversity, as will be described in detail in Chapter 9. There are two categories of transmit diversity (TD) schemes, open loop techniques, which do not require any channel information at the transmitter, and closed loop techniques, which rely on feedback channels from the MS or UE that provide necessary channel information. Common methods for TD employ two spatially separated antennas (several wavelengths apart) and use delay or frequency diversity. In delay diversity, copies of the same symbols are transmitted from multiple antennas at different times (i.e., with delays). This has the effect of producing multipath-like distortion at the receiver that could be exploited to obtain some diversity gain. A major drawback of delay diversity is the reduced throughput because we have to send the same symbols multiple times. Let us assume that the signal received at the MS without TD is given by x = sh + n

(5.58)

where s is the transmitted signal, h is the complex channel attenuation, and n is additive noise whose variance is σ2. Let the signal variance be given by σ 2s so the SNR at the mobile station is given by: SNR =

σ 2s ×h σ2

2

(5.59)

Bad channel conditions mean that |h|2 is small, in which case the base station has to increase the transmit power to achieve the same quality of service (same SNR) at the mobile station. This in turn increases the interference level in the system since the transmit power increases and, consequently, the forward link (downlink) capacity decreases. To reduce this fading impact, the following transmit diversity schemes were proposed for the 3G CDMA standards. 5.2.2

Transmit Diversity in 3G CDMA Standards

5.2.2.1 Open Loop Transmit Diversity

Since open loop TD (OLTD) techniques do not require any feedback from the MS, there is no additional signaling overhead and the receiver complexity is not

Adaptive Array Systems

135

increased by much. Three OLTD techniques were adopted in the 3G standards, orthogonal TD (OTD), space-time spreading (STS) for the IS-2000 standard [13], and space-time TD (STTD) for the WCDMA standard [14]. To improve diversity gain in a multiple access environment, mutual interference between the transmitting antennas at the receiver is minimized through the use of orthogonal signals on each antenna. The orthogonal signals are constructed using a pair of complex symbols every two-symbol (2T) period. Orthogonal Transmit Diversity

The principle of OTD is illustrated in Figure 5.4. OTD is a transmit diversity technique using two spatially separated antennas. After encoding and interleaving, the symbols are split into two different streams, even and odd. Even symbols are transmitted on the first antenna, whereas odd symbols are transmitted on the second antenna simultaneously each at half power using two orthogonal Walsh codes W1 and W2. Let the transmission matrix be defined by X that represents the set of two transmitted signals over two antennas over 2T duration. The transmission matrix X will then represent the output of the antenna pair every 2T time interval. The rows of X indicate the transmitted signal every 2T time interval per antenna. In the OTD case, the matrix is  S eW X =  −S o w

Se w  S o w 

(5.60)

se

Encoder

Demux

W1

pilot 1

W2

pilot 2

so

Figure 5.4

Orthogonal transmit diversity.

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Smart Antenna Engineering

where Se,So denote the even and odd symbol with 2T length and w is an orthogonal Walsh code with length T. We can also write X in terms of effective orthogonal sequences of period 2T as S e W 1  X =  S oW 2 

(5.61)

where W1 = [w, w] and W1 = [w, w ] and w, w are complementary Walsh codes. The received SNR at the mobile station is: For even symbols: SNR e =

σ 2s × h1 σ2

SNR o =

σ 2s × h2 σ2

2

(5.62)

and for odd symbols: 2

(5.63)

Space-Time Spreading (STS)

The STS technique is another OLTD scheme based on Alamouti’s idea [15]. In STS, both symbols are transmitted on both antennas over a 2T time interval by using different orthogonal Walsh codes, as shown in Figure 5.5. The transmission matrix is given by S W − S o*W 2  X =  e* 1  S e W 2 + S oW 1 

(5.64)

where W1,W2 are two orthogonal codes with 2T length. Since this scheme achieves intra- antenna orthogonality using Walsh codes (i.e., in the spreading code domain), we can denote it as a CDM technique [16]. Space-Time Transmit Diversity

STTD is another OLTD technique that implements Alamouti’s space-time block code. In STTD, both symbols are transmitted on both antennas over two consecutive T duration time slots, as shown in Figure 5.6. The output matrix becomes  Sow X = *  −S e w

Se w  S o* w 

(5.65)

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W1

W2

Encoder

--------

Demux

W2

W1

Figure 5.5

Space-time spreading.

where w is an orthogonal code with length T. Therefore, the total transmission time remains 2T. The same Walsh code is used in STTD as opposed to the STS case since the intra-antenna orthogonality is achieved in the time domain (i.e., by TDM signaling). Both STTD and STS schemes are similar and they achieve the same performance in terms of diversity gain. The received SNR at the mobile station in the case of STS and STTD is given by [17] SNR =

σ 2s × h1 2σ 2

(

2

+ h2

2

)

(5.66)

As seen earlier, OTD showed a diversity gain compared with the nontransmit diversity case. But with OTD, if one of the channels experiences deep fading, half the symbols are received with a bad SNR since they are only transmitted from one antenna. This problem is solved with STS or STTD since each symbol is received with the same SNR that depends on the two channels. 5.2.2.2 Closed Loop Transmit Diversity

In closed loop transmit diversity (CLTD), the base station uses the channel knowledge it receives from a feedback channel from the MS to calculate transmit weights for the signals on the different antennas such that the SNR at the MS receiver is maximized. In CLTD the same symbol is transmitted from the two antennas with the same spreading sequences or Walsh codes. Since with this technique the weights adapt with the MS movement, this CLTD scheme is adaptive in nature and is often referred to in the literature as transmit adaptive

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s o, s e

Encoder

Demux

W

pilot 1

W

pilot2

–s * , s o* e

Figure 5.6

Space-time transmit diversity.

antennas (TXAA), with the goal to enhance the forward link capacity of CDMA systems [18]. Besides being adaptive, another difference between CLTD and OLTD schemes discussed earlier is that whereas STS and STTD cannot be easily extended to a higher number of antennas, TXAA can theoretically be used for any number of antennas, however, certain implementation and performance issues bound the maximum number of antennas. The block diagram of TXAA for two antennas is shown in Figure 5.7. Each antenna at the base station is weighted by a complex coefficient so as to maximize the SNR received at the mobile station. The data on the forward traffic channel or dedicated channels are transmitted simultaneously with the same code on each antenna but with antenna-specific amplitude and phase weighting. The complex weight coefficients depend on the fading channels corresponding to each antenna. So each mobile station estimates the fading channels using the pilots transmitted on each antenna by the base station. The MS then computes the optimal weights that the base station must use to maximize the power received at the mobile station. Once it has computed the coefficients, the mobile station feeds them back to the base station, which applies them to transmit data to the mobile. In other words, each user computes a set of weights for the antenna array and feeds them back to the base station. To aid the MS with the demodulation, the base station can transmit an additional pilot: a dedicated per-user pilot. In the presence of errors in the transmission of the optimal weights computed by the mobile station, the base station applies corrupted coefficients and the mobile station can use this dedicated pilot to estimate the weights that were used and demodulate the data received on the traffic channel. Now let hi be the channel coefficients for each antenna i, i ∈ [1 … M], where M is the number of transmit antennas. Let wi be the weight for a given user. To keep the same transmit power, we impose the following constraint:

Adaptive Array Systems User specific pilot

139

Pu Sector pilot P 0 h 1( t )

Common channels RX

w1(t )

Traffic channel

h 2( t )

Matched filter for P0

w2( t ) Auxiliary pilot P 1

Matched filter for P1

Decode weights

RX

Figure 5.7

hˆ1 ( t ) Compute/ encode weights hˆ 2 ( t )

TX

TXAA architecture. M

∑w i =1

2 i

=1

The received signal at the mobile station is then given by: M  x i = s ⋅  ∑ w i hi  + n  i =1 

(5.67)

where s is the transmitted signal. Let’s define H = [h1 h2 … hM] and W = [w1 w2 … wM]T. We can then rewrite the received signal as X = HWs + n

(5.68)

The SNR at the mobile station is: SNR =

σ 2s W H H H HW ) 2 ( σ

(5.69)

The optimal weights are those that maximize the received SNR or WHHHHW subject to the constraint W = 1. It follows that the solution to this is

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the eigenvector corresponding to the largest eigenvalue of the matrix HHH. When H is a vector, the optimal weight becomes: W opt =

HH HH H

(5.70)

which means that the weights are the complex conjugates of the channel coefficients. Substituting the expression for the optimal weight into the SNR we then get for the TXAA SNR TXAA

σ2 = 2s σ

  H H  H H  H H   σ 2s H H H   = H   HH H   σ 2   HH 

2

(5.71)

For the two antenna cases, the above equation reduces to SNR TXAA =

σ 2s σ2

M

∑ hi i =1

2

=

σ 2s h1 σ2

(

2

+ h2

2

)

(5.72)

Comparing (5.72) and (5.66) we can see that TXAA increases the received SNR by a factor of two. This means that the transmit power required to achieve a given FER or BLER is halved, which corresponds to a gain of 3 dB in the transmit power. Note that with TXAA, just like the STS and STTD schemes, the diversity gain is a function of both channels. Furthermore, by examining the SNR in (5.72) we can conclude that with TXAA, the performance gain will increase as the number of channels and antennas increase. This extra gain compared with OLTD schemes can be explained by the fact that in TXAA the weights are chosen in such a way that the signals transmitted on each antenna add constructively at the mobile station because the optimal weights are the conjugates of the channel coefficients. So by applying those weights, we compensate for the phase differences between the channels corresponding to each antenna, and then each is weighted in amplitude as a function of the quality of the channels. TXAA Operation

The first step in the TXAA operation is to estimate the channel from each antenna to the mobile. Matched filters to the orthogonal user-specific pilots are used to perform this channel estimation. Based on this estimate, the MS can compute the weights, which are up to a scaling factor the conjugate of the channel. Now, since we can only transmit a limited number of bits per update on the feedback channel—otherwise the overhead is increased—we need

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141

to quantize the weights that the mobile station feeds back to the base station. Let’s define the following notations for the weights corresponding to the two antennas: w 1 = w 1 e jφ 1 w 2 = w 2 e jφ 2 The following quantization is adopted in the 3G standards, one bit is used for the power ratio of the two antennas w 1 / w 2 and three bits for the phase difference between the two antennas: φ2 – φ1. For instance, in WCDMA there are two modes of operation defined for CLTD, closed loop mode 1 (CL1) and closed loop mode 2 (CL2) [14]. In CL1 the amplitude ratio between the weights is kept at one and only the phases are changed. In odd-numbered slots, the mobile can choose from a phase difference of 0 or π between the antennas, whereas in even-numbered slots the phases are either π/2 or –π/2. In CL2 the weights are chosen based on Tables 5.1 and 5.2. Once the MS have transmitted the weights back to the base station they are applied, after transmission and reception delays, to the antennas. Note that in fast fading conditions, the channel may change faster than the weights are applied because of those delays and this would result in outdated weights.

Table 5.1 Gain Ratios in CL2 Mode

Index

0

1

w1

0.8

0.2

w2

0.2

0.8

Gain ratio (dB)

6

–6

Table 5.2 Phase Ratios in CL2 Mode Index

000

001

010

011

100

101

110

111

Phase Ratio

180°

–135°

–45°

–90°

135°

90°

0°

45°

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5.3 Downlink Beamforming While CLTD or TXAA techniques can improve the downlink performance by reducing the impact of fading they are not capable of addressing interference issues. Moreover, while TXAA performance gains can be improved by increasing the number of antennas, there are some practical implementation issues that would limit their performance. Recall that TXAA require wide antenna spacing (∼5–10 λ) to achieve low cross correlation between the elements and consequently provide independent channels at the MS. Obviously, increasing the number of antennas would be impractical due to space and tower limitations and would consume more of the overhead signaling capacity because of the increased feedback information requirements. Furthermore, increasing the number of elements at these spacings would generate grating lobes in the antenna array radiation patterns, which would spread more interference in the downlink, as shown in Figures 5.8 and 5.9. Hence, the most promising 1

90

90 1

60

120

120

0.8 0.6

150

30

0.4

0.6

150

0.2 0

210

180

0

210

330 300

330 300

240

270 (a)

270 (b)

90 120

1 0.8 0.6

150

60 30

0.4 0.2

180

0

210

330 300

240 270 (c)

Figure 5.8

30

0.4

0.2 180

240

60

0.8

Two-element array radiation pattern. (a) d = λ/2, (b) d = 5λ, (c) d = 10λ.

Adaptive Array Systems

143

1

90

90 1

60

120

120

0.8 0.6

150

30

0.4

0.6

150

30

0.4

0.2

0.2

180

0

210

180

0

210

330 300

240

60

0.8

330 300

240

270 (a)

270 (b) 90 120

1 0.8 0.6

150

60 30

0.4 0.2

180

0

210

330 300

240 270 (c)

Figure 5.9

Four-element array radiation pattern. (a) d = λ/2, (b) d = 5λ, (c) d = 10λ.

technique to combat interference on the downlink would be to use narrow beams that would be transmitted to a desired user or cluster of desired users and nulls in directions of interference. For beamforming applications, the interelement spacing required is λ/2 since they require high correlation between the antenna elements. The spatial correlation between antenna elements for small angular spreads (below 25°) can be approximated as [19, 20] ρ(d ) = e

j

2 πd sin θ λ

e

2  2 πdσ p cos θ  −  2 λ  

(5.73)

where σp is the angular spread AS. Equation (5.73) is shown to be valid for Gaussian, uniform, and Laplacian power azimuth distributions in [20]. Furthermore, it is shown to provide very accurate approximation for high correlation

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values but introduces some errors for low correlation values below about 0.4. Since good diversity performance is achievable for correlation values below about 0.7, as described earlier, this approximation can be used to evaluate the impact of angular spread on the spatial correlation between a pair of antenna elements regardless of the angular distribution function. Figure 5.10 illustrates the effect of the AS on the antenna elements spatial correlation. We can see that for narrow AS, high correlation values result with small separations, whereas low correlation values require larger separations from which the well-known rule of thumb of 10λ at the base station is derived. On the other hand, for large AS, low correlation values can still be obtained even with small separations. This has a significant impact on the performance of spatial techniques such as transmit diversity TD and beamforming. In the case of TD, low correlation is necessary to achieve large diversity gains. This can be easily achieved in environments with large angular spreads without the need for large separations, whereas it would require separations on the order of 10λ with narrow angular spreads. This implies that TD techniques are more suitable for

1

AS=3 deg AS=5 deg AS=10 deg AS=15 deg

0.9

Spatial correlation

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

0

Figure 5.10

1

2

3

5 6 7 4 Element separation (wavelength)

8

9

Spatial correlation versus element spacing at the base station side for various AS.

10

Adaptive Array Systems

145

channels with large AS. Similarly, since beamforming performance depends on having high correlation values between the antenna elements, we can conclude from Figure 5.10 that their performance will outperform that of TD in narrow AS environments and show some degradation as the AS is increased. On the mobile side, the AS is much higher than that at the base station side and, as we see from the correlation coefficient plot in Figure 5.11, low correlation values can be achieved even for very small separations. This implies that diversity techniques can provide good diversity gain even with the physical constraints of mobile stations, as we will see in more detail in Chapter 9. 5.3.1

Spatial Signature-Based Beamforming

Let us assume that we have K sources each represented by their spatial signature SS given by

Angle spread = 360 degrees 1 0.9

AOA=0 deg AOA=10 deg AOA=30 deg AOA=60 deg

Spatial correlation coefficient

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

0

Figure 5.11 AS of 360°.

0.05

0.1

0.15 0.25 0.3 0.35 0.2 Element separation (wavelength)

0.4

0.45

0.5

Spatial correlation versus element spacing at the mobile side with various AOAs and

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Smart Antenna Engineering

L

x (t ) = a( θ 1 )s 1 (t ) + ∑ α l a( θ l )s 1 (t ) = a 1 ( θ 1 )s 1 (t )

(5.74)

l =2

then the total received signal at the base station antenna array from all sources is given by K

X (t ) = ∑ a k s k (t ) + n(t )

(5.75)

k =1

One approach for the beamforming weight vector design [4, 21] is to capture the uplink SS and transmit its complex conjugate back on the downlink. This is equivalent to maximizing the SNR at the MS. Note that this technique does not attempt to cancel any interference on the downlink. This technique is highly effective and suitable for TDD systems in which the uplink and downlink share the same carrier frequency. However, in FDD systems like those based on CDMA the uplink and downlink carrier frequencies are different and the SS captured on the uplink is not the same as the downlink SS. Spatial signatures change as a function of time, physical displacement, and frequency. For a stationary MS, variation of the spatial signature is primarily a result of changes in the local scatterers distribution. This would be vehicle movement, surrounding pedestrian movement, or any other physical changes of the scattering objects in the vicinity of the MS. SS variations versus frequency for an FDD system reported in test results [4, 22] show that from 874 to 924 MHz the relative amplitude change of the spatial signature was on the order of 14 dB peak to peak and the relative angle change was close to 100%. The spatial signature variation is thus quite significant for small changes in frequency, mainly due to the wavelength differences relative to the paths traveled by the received signal. Additional test results [23] at 1.6 GHz also confirm significant changes in spatial signature as a result of change in frequency. Given the experimental results available in the literature, it can be concluded that the spatial aspects of the RF channel (urban, suburban) change significantly with the frequency differences on the order of CDMA FDD systems (IS-2000, WCDMA), so an adaptive antenna system that bases downlink beamforming weights solely on uplink spatial signatures will likely be suboptimal. 5.3.2

DOA-Based Beamforming

In the DOA approach the uplink channel correlation matrix or the uplink SS are first captured and then some DOA estimation technique is applied to

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147

determine the signal’s DOAs and their associated amplitudes. The DOA θmax with the maximum amplitude, which would indicate the strongest path, is selected and its array response vector a(θmax) is chosen as the downlink beamforming weight. Note that this is equivalent to the beam steering technique used earlier on the uplink. Recall that the spatial signature is a linear combination of the array response vectors from the direct and multipath components of the signal, which are dependent upon the DOA of the signal and the angular spread (AS). Let us assume that the main signal’s DOA is the peak of the power azimuth spectrum (PAS), which is valid for sufficient averaging of the PAS in urban and suburban outdoor RF environments as the shape tends toward a Laplacian distribution. DOA variation versus frequency was tested in [24]. The results show negligible variation in DOA for this frequency change compared with the spatial signature variations in the 899 to 924 MHz range. In [25] the averaged channel characteristics of uplink and downlink duplexes under fast fading conditions is investigated. The test bed was a base station uniform linear array of eight elements with half wavelength spacing at 1,780 MHz and a single MS transmitting antenna. Results show that the main lobes of the PAS are almost identical (within a few degrees) for uplink and downlink frequencies. The major differences between the two are in the tails of the PAS. We can then conclude based on the published measurements results that, provided we apply sufficient averaging, the MS DOA and AS characteristics over the uplink and downlink duplexes show a high degree of correlation. These results show that in an adaptive antenna system (where no MS channel feedback is available), pointing beams in the general direction of the received DOAs has the potential to be successful. 5.3.3

Maximum SNR

Let us assume that we have L signal sources, each with K multipath components. The downlink channel vector can then be written as h DL (t ) =

P LK

LK

∑e i =1

j ( ϕ i + 2 πf i t )

a( θ i )

(5.76)

where P is the channel power, ϕi is the random phase of the ith component, fi is the Doppler frequency shift of the ith component, and θi is the angle of departure of the ith component distributed over  θ − σ p / 2, θ + σ p / 2 . One beamforming criterion is to select the weights that optimize the array gain so that the SNR or SINR at the MS is maximized subject to the constraint that the

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Smart Antenna Engineering

total transmitted power is equal to that of a single antenna. Let the power received by the kth MS be denoted by Pk = w Hk R SUM DL w k

(5.77)

where the sum of all the mean channel correlation matrices corresponding to all taps given by LK

LK

i =1

i =1

R SUM = ∑ R i = ∑ E [ h i (t )h Hi (t )] DL

(5.78)

Hence, the maximization problem can be formulated as follows: Maximize SNR =

w Hk R SUM DL w k subject to w k σ2

2

=1

(5.79)

As we have seen in a previous section, the solution to this problem is the unit norm principal eigenvector (corresponding to the maximum eigenvalue) of R SUM DL w opt = u DL ,max

(5.80)

The issue now is that the base station needs information about the downlink correlation matrix to calculate the eigenvectors. One possible solution is to capture the uplink correlation matrix, calculate its principal eigenvector uDL, max, and use it as the weight vector. As long as the FDD gap is small enough there is a strong correlation between the uplink and downlink average statistical properties. Results reported in [26] show that for two-, four-, and eight-element arrays with σp ∼ 10° the correlation between the uplink and downlink principal eigenvectors ρul, dl ≥ 0.9 for FDD gaps up to 500 MHz with a performance loss of 0 to -1 dB. For larger FDD gaps, there will be a performance loss that can be as high as -6 dB and increases with the number of elements. This implies that current CDMA systems with FDD gaps of 45 MHz or 80 MHz in the North American IS-2000 bands and 190 MHz in the European WCDMA bands would be able to apply this technique without sacrificing significant performance. To avoid or at least minimize the performance loss SUM caused by using the principal eigenvector of R UL instead of R SUM DL , we must find a way to estimate the downlink correlation matrix with reasonable accuracy.

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One such approach described in [26] uses the uplink correlation matrix to estimate the PAS as SUM PAS ( θ, f UL ) = a H ( θ, f UL )R UL a( θ, f UL )

(5.81)

From the estimated PAS, the directions and amplitudes Pi of the main peaks, peaks within 10 dB from the maximum peak, can be determined and used in constructing the downlink spatial correlation matrix by transforming the array response vectors from the uplink to the downlink frequency as N

R SUM = ∑ Pi [ a( θ i , f DL ) a H ( θ i , f DL )] DL

(5.82)

i =1

N

where ∑ Pi = 1 (i.e., the amplitudes) are normalized. i =1

5.4 Conclusion Smart antenna technology uses sophisticated signal processing techniques to manipulate signals at the base station or the mobile side and dynamically control transmission and reception. Conventional radio systems indiscriminately broadcast energy, creating interference for other users. Using adaptive beamforming, radio transmission and reception is optimized by selectively amplifying signals to and from users of interest and rejecting unwanted and interfering signals. This substantially increases the signal quality and suppresses and mitigates interference on both the uplink and downlink radio channels, resulting in increased coverage and spectral efficiency or system capacity. A comparison of the different spatial techniques discussed in this chapter is provided in Table 5.3. Many approaches are equally applicable to both the uplink and downlink, whereas others are only designed for a specific link. Some approaches like TD perform well in large angular spread environments, which make them good candidates for microcells, whereas the performance of beamforming tends to degrade in such environments. On the other hand, beamforming tends to perform better in small angular spread situations such as in macrocells.

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Table 5.3 Comparison of Beamforming Approaches Spatial Technique Receive Diversity

Aspects

Pros

Cons

Requires low correlation and comparable signal strength between antenna pair.

Provides diversity gain and fading reduction.

Spatial diversity requires large antenna separation (10λ) but in large AS environments λ/2 separation could still provide some decorrelation.

Does not require any feedback from the MS, so there is no additional signaling overhead.

Since channel is unknown to the transmitter, diversity gain is low.

Open Loop TD Improves the mobile station’s receiver performance by providing it with multiple independent copies of the transmitted signal.

Provides the most gain for users at cell edge (low geometry).

Closed Loop TD

Improves the mobile Provides higher gains station’s receiver than open loop TD techperformance by provid- niques. ing it with multiple independent copies of the transmitted signal.

Requires feedback from the MS, so there is additional signaling overhead, which limits performance at high speed.

Beam Steering

Simplest form of beamforming.

Weight is the same as steering vector.

Requires direction of arrival estimation of the desired user.

Main beam is directed toward desired user.

Provides gain of M for spatially white interference environments.

Suboptimal performance in terms of SNR or SIR.

Pattern nulls are directed toward interference sources.

Provides gain in SIR as a result of interference reduction.

Requires direction of arrival estimation of all interfering sources.

The most adaptive scheme.

Optimal weight vector and optimal performance.

Computationally intensive and may require adaptive algorithms for implementation.

Performs as well as optimum beamforming when interference is spatially white (when all users have low/ similar data rates).

Underperforms in systems with multirate services where both low data rate speech service and high data rate applications are simultaneously supported since beams cannot track users.

Null Steering

Optimum Beamforming

SNR and SINR are optimized based on some given criterion. Fixed Multiple Set of predefined fixed Beams multiple beams.

Suboptimal performance in terms of SNR or SIR.

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References [1] Saunders, S. R., Antennas and Propagation for Wireless Communication Systems, John Wiley & Sons, 1999. [2] Perini, P. L., and C. L. Holloway, “Angle and Space Diversity Comparisons in Different Mobile Radio Environments,” IEEE Trans. on Antennas and Propagation, Vol. 46, No. 6, June 1998, pp. 764–775. [3] Jakes, W. C., Microwave Mobile Communications, New York: IEEE Press, 1974, p. 316. [4] Jeng, S. S., et al., “Experimental Studies of Spatial Signature Variation at 900 MHz for Smart Antenna Systems,” IEEE Trans. on Antennas and Propagation, Vol. 46, No. 7, July 1998, pp. 953–962. [5] Godara, L. C., “Application of Antenna Arrays to Mobile Communications, Part II: Beamforming and Direction-of-Arrival Considerations,” Proc. of the IEEE, Vol. 85, No. 8, August 1997, pp. 1195–1245. [6] Litva, J., and T. K. Lo, Digital Beamforming in Wireless Communications, Norwood, MA: Artech House, 1996. [7] Ertel, R., Ph.D., Dissertation Thesis Submittal, “Antenna Array Systems: Propagation and Performance,” Virginia Polytechnic Institute and State University, July 1999, pp. 16–21. [8] Liberti, J. C., and T. S. Rappaport, Smart Antennas for Wireless Communications: IS95 and Third Generation CDMA Applications, Chapter 6, Upper Saddle River, NJ: Prentice Hall, 1999. [9] Burke, J. P., and J. R. Zeidler, “CDMA Reverse Link Spatial Combining Gains: Optimal vs. MRC in a Faded Voice-data System Having a Single Dominant High Data User,” IEEE Global Telecommunications Conference, Vol. 1, No., 2001, pp. 264–268. [10] Lee, J. S., CDMA Systems Engineering Handbook, Norwood, MA: Artech House, September 1998. [11] Derryberry, R. T., et al., “Transmit Diversity in 3G CDMA Systems,” IEEE Communications Magazine, April 2002, pp. 68–75. [12] Parkvall, R. T. et al., “Transmit Diversity in WCDMA: Link and System Level Results,” IEEE 51st Vehicular Technology Conference Proceedings, 2000. VTC 2000–Spring Tokyo. Volume 2, May 15–18 2000 pp. 864–868. [13] TIA/EIA IS-2000 Physical Layer Specification for CDMA Spread Spectrum Communications Systems, June 2000. [14] TS 25.214 3GPP TSG RAN WG4, v.3.2.0, Physical Layer Procedures (FDD), 2000-03. [15] Alamouti, S. M., “A Simple Transmit Diversity Technique for Wireless Communications,” IEEE Trans. on Selected Areas in Communications, Vol. 16, October 1998, pp. 1451–1458. [16] Kogiantis, A., et al., Downlink Improvement Through Space-Time Spreading, 3GPP2-C3019990817-014. [17] Vucetic, B., and J. Yuan, Space-Time Coding, John Wiley & Sons, 2003. [18] Harrison, M., Transmit Adaptive Array Description, New Results and Feedback Channel, 3GPP2/TSG-C3 #1, 1999. [19] Bae, K., J. Jiang, and W. H. Tranter, “Downlink WCDMA Performance Analysis with Diversity Techniques Combined with Beamforming,” IEEE

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Wireless Communications and Networking, 2003, Vol. 1, March 16–20, 2003, pp. 202–206. [20] Buehrer, R. M., “The Impact of Anular Energy Distribution on Spatial Correlation,” Proc. of the Fall 2002 Vehicular Technology Conference, Vancouver, Canada, September 2002. [21] Winters, J. H., J. Salz, and R. D. Gitlin, “The Impact of Antenna Diversity on the Capacity of Wireless Communications Systems,” IEEE Trans. on Communications, February 1994, pp. 1740–1751. [22] Lin, H. P., et al., “Experimental Studies of SDMA Schemes for Wireless Communications,” 1995 International Conference on Acoustics, Speech, and Signal Processing, Vol. 3, 1995, pp. 1760–1763. [23] Xu, G., et al., “Experimental Studies of Space-Division-Multiple-Access Schemes for Spectral Efficient Wireless Communications,” IEEE International Conference on Communications, Conference Record, Serving Humanity Through Communications, 1994, pp. 800–804. [24] Bigler, L., et al., “Experimental Direction of Arrival and Spatial Signature Measurements at 900 MHz for Smart Antenna Systems,” IEEE 45th Vehicular Technology Conference, Vol. 1, 1995, pp. 55–58. [25] Pedersen, K. I., P. E. Mogensen, and F. Frederiksen, “Joint-Directional Properties of Uplink and Downlink Channel in Mobile Communications,” Electronics Letters, Vol. 35, No. 16, August 5, 1999, pp. 1311–1312. [26] Koutalous, A. C., and J. S. Thompson, “Effect of Frequency Division Duplex on Open Loop Downlink Beamforming in WCDMA Systems,” Proceedings, 56tj VTC-2002, Vol. 2, 24–28 Sept. 2002, pp. 686–690.

Selected Bibliography Abu-Dayya, A., and N. C. Beaulieu, “Outage Probability of Diverse Cellular Systems with Co-Channel Interference in Nakagami Fading,” IEEE Trans. Veh. Technol., Vol. 41, 1992, pp. 343–355. Adachi, F., et al., “Cross Correlation Between the Envelopes of 900 MHz Signals Received at a Mobile Radio Base Station Site,” Proc. Inst. Elect. Eng., Vol. 133, Pt. F, 1986, pp. 506–512. Agee, B. G., “Blind Separation and Capture of Communication Signals Using a Multitarget Constant Modulus Beamformer,” Proc. IEEE MILCOM, Bedford, MA, 1989, pp. 340–346. Agee, B. G., S. V. Schell, and W. A. Gardner, “Spectral Self Coherence Restoral: A New Approach to Blind Adaptive Signal Extraction Using Antenna Arrays,” Proc. IEEE, Vol. 78, 1990, pp. 753–767. Alamouti, S. M., “A Simple Transmit Diversity Technique for Wireless Communications,” IEEE Journal on Select Areas in Communications, Vol. 16, No. 8, October 1998, pp. 1451–1458. Alouini, M-S., and A. J. Goldsmith, “Capacity of Rayleigh Fading Channels Under Different Adaptive Transmission and Diversity-Combining Techniques,” IEEE Trans. on Vehicular Technology, Vol. 48, No. 4, July 1999, pp. 1165–1181. Anderson, S., et al., “An Adaptive Array for Mobile Communication Systems,” IEEE Trans. Veh. Technol., Vol. 40, 1991, pp. 230–236.

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6 Smart Antenna Receivers and Algorithms for Radio Base Stations In Chapter 5, several adaptive beamforming criteria were discussed and a general form for the optimum array weight vector was derived that would require large amounts of computational load. Hence, it is necessary to find techniques that can find this optimum solution in real time, adapting to the time variant channel while keeping the computational load to a reasonable level. A classification of such adaptive array algorithms is shown in Figure 6.1. A brief description of these algorithms can be found in [1]. In the remainder of this chapter we will describe some of these algorithms in detail and investigate issues related to their performance.

6.1 Reference Signal Methods 6.1.1

The Least Mean Square Algorithm

As we can see from Figure 6.1, the least mean square (LMS) algorithm belongs to the trained algorithms category in which a reference signal is used to update the weights at each iteration as follows w (n + 1) = w (n ) − µ∇ w ( MSE )

(6.1)

where ∇w is the gradient of the MSE, which is the mean square error between the reference signal r(n) and the array output given by

159

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Adapative array algorithms

Trained

Least mean square LMS

Blind

Recursive least squares RLS

Conjugate gradient

Property restoral

Constant modulus

Decision directed

Figure 6.1

Least squares

Lagrange multiplier

Spectral coherence

Classification of adaptive array algorithms.

[ = E [ r(n + 1) ] + w

MSE ( w (n )) = E r(n + 1) − w H (n )x (n + 1) 2

H

2

]

(n )Rw (n ) − 2 w H (n )E [x (n + 1)r(n + 1)] (6.2)

In the LMS algorithm, we are searching for the optimal weight that would make the array output either equal or as close as possible to the reference signal, which is the weight that minimizes the MSE. Since the MSE has a quadratic form, moving the weights in the negative direction of the gradient of the MSE should lead us to the minimum of the error surface. The gradient can be calculated as ∇ w MSE ( w (n )) = 2Rw (n ) − 2 E [ x (n + 1)r(n + 1)]

= 2 x (n + 1)x H (n + 1)w (n ) − 2 x (n + 1)r(n + 1) (6.3) = 2 x (n + 1)ε ∗

where ε is the error given by ε = w H (n )x (n + 1) − r(n + 1)

(6.4)

Smart Antenna Receivers and Algorithms for Radio Base Stations

w (n + 1) = w (n ) − µx (n + 1)ε ∗

161

(6.5)

The constant µ, also called the step size, determines how close the weights approach the optimum value after each iteration and it controls the convergence speed of the algorithm. Typical values for the step size are 0 < µ < Trace(R). 6.1.2

The Recursive Least Squares Algorithm

One of the drawbacks of the LMS algorithm is its slow convergence speed under certain conditions, for example when the eigenvalue spread of R is large. This leads to the development of the recursive least squares (RLS) algorithm, which replaces the step size µ with the inverse of R. The algorithm is initialized by first setting R −1 (0 ) =

1 I , δ> 0 δ

(6.6)

The weights are then updated using w (n + 1) = w (n ) − R −1 (n + 1)x (n + 1)ε ∗

(6.7)

where the update of the inverse of the correlation matrix is given by R −1 (n + 1) =

6.1.3

R −1 (n )x (n + 1)x H (n + 1)R −1 (n )  1  −1 n R ) − ( ξ  ξ + x H (n + 1)R −1 (n )x (n + 1) 

(6.8)

Blind Adaptive Beamforming

Self-adaptive or blind beamforming algorithms have been gaining a lot of attention, especially with the introduction of digital technology in wireless cellular and PCS systems. The term blind refers to the fact that no array calibration is necessary (i.e., the array manifold knowledge is not required). Some blind algorithms can be classified as property restoral techniques; that is, they rely on reference signals that satisfy known properties of the desired signal, whereas others, like the least squares, are based on using the temporal characteristics of the desired digital signals to determine the array response and transmitted sequence [1]. 6.1.4

Least Squares

Using the standard array model, we can write the received signal at the array output as

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X (t ) = AS(t ) + N (t )

(6.9)

where X (t ) = [ x 1 (t ) x 2 (t ) L x M (t )]

T

S(t ) = [s 1 (t ) s 2 (t ) L s M (t )] is the signal vector T

N (t ) = [n 1 (t ) n 2 (t ) L n M (t )] is the noise vector. T

The LS algorithm minimizes the ML criterion min S ∈Ω X − AS

(6.10)

A flowchart of the LS algorithm is shown in Figure 6.2. 6.1.5

Constant Modulus Algorithm

Some modulation techniques used in modern cellular communications systems such as frequency modulation (FM), phase-shift keying (PSK), frequency-shift keying (FSK), or quadrature amplitude modulation (QAM) produce signals with constant or low modulus variation. Assuming that the transmitted signals have a constant envelope, the array output should also have a constant envelope. However, due to multipath fading effects, the array output will not have a constant envelope. The constant modulus algorithm (CMA) can therefore be used to restore the array output to a constant envelope signal on average. A cost function, which measures the signal modulus variation, is minimized to adjust the array weights. Although a simple search algorithm such as the steepest-descent method can easily implement the CMA, a major drawback is that the convergence is not guaranteed because the cost function is nonconvex and may have false minima. Because CDMA systems use power control, that is, every user’s signal power is adjusted to meet the quality of service criteria, this algorithm is not suitable for CDMA signals. 6.1.6

Decision-Directed Algorithm

In this algorithm, a reference signal is generated based on the outputs of a threshold decision device. The beamformer output y(n) is demodulated to obtain the signal q(n). The decision device then compares q(n) to the known alphabet of the transmitted data sequence and makes a decision in favor of the closest value to q(n) denoted by r(n). The reference signal is obtained by modulating r(n), then the cost function for the beamformer is established. The decision-directed algorithm convergence depends on the ability of the receiver to

Smart Antenna Receivers and Algorithms for Radio Base Stations

163

Set initial random guess A 0H

S(n) =

A H (n −1)X A H (n −1)A (n −1)

Project elements of S(n) onto the nearest value in the alphabet

A (n) =

NO

Figure 6.2

XS H (n) S(n)S H (n)

Has symbol sequence converged

YES

END

Flowchart of the least squares method.

lock onto the desired signal. Since it may not always be able to do that, the convergence is not guaranteed. 6.1.7

Cyclostationary Algorithms

Some commonly used communications signals such as amplitude modulation (AM), binary phase-shift keying (BPSK), binary frequency-shift keying (BFSK),

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and Gaussian minimum-shift keying (GMSK) possess some unique nontrivial cycle frequency. A signal is said to be cyclostationary if its cyclic autocorrelation R(τ, α) is nonzero at some time delay τ or at some frequency shift α, that is, R ( τ, α) = E {s (n )s ∗ (n + τ )e − j 2 παk }

(6.11)

Therefore, based on its cycle frequency, a particular signal can be extracted from a mixture of signals in time or frequency domain. The reference signal is generated based on the knowledge of both the time delay τ and the cycle frequency α of the wanted signal. The least squares spectral self-coherence restoral (SCORE) algorithm is one of the beamforming techniques that makes use of this cyclostationarity property. The minimization leads to a solution for the weights vector that is similar to the Wiener solution. Another class of blind algorithms takes the approach of iteratively solving for the optimum beamforming weight based on maximizing the SINR or SNR [1–12]. For convenience, let us rewrite the expression for the SNR at the beamformer output as: SNR =

wHRsw σ n2 w H w

(6.12)

where σ 2N is the noise variance. The maximization leads to the eigenvalue problem R s w = λw where the optimal weight is given by the eigenvector corresponding to the maximum eigenvalue. However, this solution requires large amounts of computations and is often impractical. One technique that can be used to solve this eigen decomposition iteratively, the conjugate gradient (CG) algorithm, is described next. 6.1.8

Conjugate Gradient Algorithm

In some CDMA systems, the interference can become spatially white (e.g., in 2G CDMA (IS-95A) based systems where all users have low data rates and no interference spatial coloring is experienced). This would also be the case in 3G CDMA systems (based on either IS-2000 or WCDMA) when only low data rate users are present in the sector. In that case, the correlation matrix can take the form R = ASA H + σ 2n I

(6.13)

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165

Let the eigenvectors and eigenvalues of R be denoted by {e1 e2 … eM} and {λ1 λ2 … λM}, respectively. Assuming we have L signals received at the array, it then follows that the set of eigenvectors {e1 e2 … eL} spans the signal subspace, whereas the set of eigenvectors {eL+1 e2 … eM} spans the noise subspace. Note that since the two subspaces are orthogonal, each signal eigenvector can be written as a linear combination of the signal direction vectors. We can then write the eigenvector corresponding to the maximum eigenvalue as L

e 1 = ∑ ζ l a( θ 1 )

(6.14)

l =1

where ζl is a constant that depends on the magnitude and angle distribution of every signal component. When the optimal weight is set to e1, which is a function of all the signals incident on the array, the resulting beam pattern produces main lobes in the directions of {θ1 θ2 … θL}, that is, in the direction of the desired and interfering signals. The adaptive algorithm introduced in [2, 9] to solve the eigenvalue problem makes use of a unique CDMA characteristic, namely, the fact that the desired signal power becomes much larger than that of the interference after the correlation of the desired and interfering signals with the chip sequence corresponding to the desired user. More specifically, the power of the desired signal becomes PG times greater than that of the interference where PG is the processing gain, which could be quite high for low data rate users. We can then use the approximation e 1 ≈ ζ 1 a( θ 1 )

(6.15)

This would lead to a main lobe in the direction of the desired user with much smaller gain in the direction of the interfering signals. To estimate the correlation matrix, the CG algorithm uses the approximation 1 R$ (n ) = N

N

∑ x (n )x n =0

H

(n )

(6.16)

In the CG algorithm [2] subsequent updates to R are computed as R (n ) = f R (n − 1) + x (n )x H (n )

(6.17)

where f is called a forgetting factor. The weights are updated using w (i + 1) = w (i ) + z (i )u (i )

(6.18)

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where the adaptive gain or step size z(i) and the search direction vector u(i) are determined by maximizing the Raleigh quotient J ( w (i )) =

w H (i )R (n )w (i ) w H (i )w (i )

(6.19)

This maximization leads to [9] z (i ) =

−B − B 2 − 4 AC 2A

(6.20)

and u (i + 1) = r(i + 1) −

r(i + 1) r(i )

2

2

u (i )

(6.21)

where a (i ) = w H (i )R (n )u (i ) b (i ) = u H (i )R (n )u (i ) c (i ) = w H (i )u (i ) d (i ) = u H (i )u (i )

A = b (i ) Re[c (i )] − d (i ) Re[a (i )]

λ(i ) = w H (i )R (n )w (i )

B = b (i ) − λ(i )d (i )

C = Re[a (i )] − λ(i )d (i ) r(i + 1) = λ(i + 1)w (i + 1) − R (n )w (i + 1) is the instantaneous error. The procedure is started by setting the following initial conditions: 1 x (0 ) , λ(0 ) = w H (0 )R (0 )w (0 ), u (0 ) = λ(0 )w (0 ) − R (0 ) , w (0 ) = M x (0 )

w (0 ), and r(0 ) = u (0 ). At the end of each iteration, the weight vector is normalw ized as follows w = . Although this algorithm eliminates the need for the 2 w direction solution of the eigenvalue problem, it still requires lots of matrix

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multiplications with a computational load of O(3N2 + 12N), where O (N) denotes the order of computational load required for a scalar product of two N 1 complex-valued vectors. This load can be significantly reduced if the forgetting factor f is set to zero. It then follows that R (i ) = x (i )x H (i )

(6.22)

and λ(i ) = w H (i )x (i )x H (i )w (i ) = y (i )

2

a (i ) = w H (i )x (i )x H (i )u (i ) = y (i )x H (i )u (i ) b (i ) = u H (i )x (i )x H (i )u (i ) = u H (i )x (i )

2

r(i + 1) = λ(i + 1)w (i + 1) − x (i )x H (i )w (i + 1) = λ(i + 1)w (i + 1) − x (i ) y ∗ (i ) The computational load is thus reduced to O(11N). This reduction in complexity is mainly because the correlation matrix is computed based on the instantaneous received signal rather than averaging the matrix over a period of time using previous instances of R, as shown in (6.17). This added simplicity comes at the expense of less accurate estimates of the correlation matrix and its eigenvalues and eigenvectors resulting in less accurate weights. In practice, one has to consider this trade-off between the need for faster convergence along with reduced computational complexity and therefore implementation costs versus accuracy of the optimal weights and any possible performance degradation resulting from using less accurate weights. 6.1.9

Lagrange Multiplier Method

When we have one or more low data rate users along with one or more high data rate users, then the assumption of spatially white interference breaks down since the interference experienced by the lower data rate users will be directional or spatially colored. Such is the case in multirate services, where both low data rate speech service and high data rate applications are simultaneously supported. Let x(n) denote the input signal to the correlator and let the despread signal vector at the output of the correlator be given by y (n ) = s d (n ) + s I (n )

(6.23)

where sd(n) and sI(n) are the desired and interfering signal vectors. It then follows that the SINR can be written as

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SINR =

wHRd w w H RI w

(6.24)

where R d = E  s d (n )s Hd (n ) and R I = E  s I (n )s HI (n ). Note that the signals sd(n) and sl(n) cannot be separately obtained from x(n), however, [8, 13] demonstrate that Rd and Rl can be obtained from x(n) and y(n), as shown next R x = R d + RI

(6.25)

R y = PG R d + R I

(6.26)

We can then use this information to write wHR y w wHRx w

=

wHR y w wHRx w

w H (PG R d + R I ) w

=

w H (R d + R I )w

 wHR w PG  H d  + 1  w RI w   wHRd w  H  +1  w RI w 

(6.27)

(6.28)

From (6.28) we can see that for PG > 1 the weight vector that maximizes wHR y w wHRd w also maximizes or the SINR, which leads to w H RI w wHRx w R yy w = λR xx w

(6.29)

In [8, 10], the method of the Lagrange multiplier is used to solve this eigenvalue problem using the following procedure: First, the multiplier f ( w ) = w H R y w + γ (1 − w H R x w )

(6.30)

is introduced for the constraint w H R x w = 1. The weight updates are carried out based on w (n + 1) = w (n ) + 0.5 µ∇ w ( f ( w ))

(6.31)

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where ∇ w ( f ( w ) ) = 2  R y w − γR x w  It then follows that w (n + 1) = w (n ) + µ(R y w − γR x w

)

(6.32)

The multiplier is then found as [8] γ=

b − b 2 − ac a

(6.33)

where a = µδ b = wH x c = µ xH y 6.1.10

2

2

(x H x)

2

(x H x ) + µ(x H x ) Re[x H y ⋅ w H x (w H y )

2

∗

(x H x ) + µ(x H x ) Re[x H y ⋅ w H x (w H y )

]

∗

]

Comparison of Adaptive Algorithms

As we indicated earlier, adaptive algorithms were developed to overcome the computationally intensive task of finding optimal weights in real time. Many of these techniques trace their roots to equalization schemes and vary in their performance. Performance criteria most relevant to the mobile communications applications include convergence rate, mean error between optimal and derived weights, computational complexity in terms of the number of complex multiplications, additions that directly contribute to the receiver complexity, and cost. Most of these techniques use some iterative implementation to find the solution to the cost function, which minimizes some error between the desired signal, its replica, and the received signal. Hence, convergence becomes highly dependent on the step size as well as the initial conditions of the algorithm. A small step size leads to good estimates of the optimal weights at the expense of a slower convergence rate. A large step size, on the other hand, would speed up the convergence rate but the price paid for this is less accurate estimates of the weights. The characteristics of the correlation matrix also may affect the convergence rate. For instance, in the LMS algorithm the rate is a function of the eigenvalue spread of

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the correlation matrix, whereas the convergence rate of the RLS is independent of the eigenvalue spread. Convergence speed is critical for mobile applications because the signals are fast-changing in time due to fading and mobile speed. A comparison of the various algorithms described in this section is shown in Table 6.1.

6.2 Neural Network DOA-Based Beamforming Some efforts have been made to use neural networks in adaptive beamforming. In [48], J. Litva and T. Lo propose the use of a recurrent neural network, namely, the Hopfield network. They demonstrated that Lyapunov functions, Table 6.1 Comparison of Weight Adaptation Algorithms

Adaptive Algorithm LMS

RLS

CMA

Approach

Convergence

References

Minimizes the mean square error between the received signal and a reference signal.

Could be slow.

[14–27]

Minimizes the mean square error between the received signal and a reference signal.

Faster than LMS.

Restores signal envelope by minimizing interference impact on the modulus.

Nonconvex cost function may have false minima.

[34–37]

Depends on the correlation matrix eigenvalue spread. [28–33]

Independent of the correlation matrix eigenvalue spread.

Suitable for constant envelope modulations. DecisionDirected

Minimizes the error between the received signal and the closet member of a known alphabet.

Depends on receiver ability to lock on the desired signal.

[38–42]

Cyclostationary

Exploits cyclic nature of the desired signal to extract it from interference and noise.

Solution requires eigenvalue decomposition.

[43–47]

Conjugate Gradient

Iterative solution to the eigenvalue decomposition problem.

[1–12]

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which are functions that become smaller for any change in the state of the network until a stable state is reached, exist for the Hopfield network. This makes them suitable for solving optimization problems. To minimize the minimum square error (MSE) in adaptive beamforming, the MSE is mapped into the energy function of the network. This is a DOA-based beamforming technique that does not require a reference signal to construct an error function to minimize. The only a priori knowledge about the desired signals are their angles of arrivals. The optimum weight vector is a nonlinear function of the correlation matrix and the constraint matrix. Therefore, it can be approximated using a suitable architecture such as a radial basis function neural network (RBFNN) [49-58]. Note that a radial basis function neural network can approximate an arbitrary function from an input space of arbitrary dimensionality to an output space of arbitrary dimensionality. The block diagram of an RBFNN used for beamforming is shown in Figure 6.3. RBFNNs [59, 60] are a member of a class of general-purpose methods for approximating nonlinear functions. The RBFNN can be considered as designing neural networks as a curve fitting (or interpolation) problem in a high-dimensional space. The mapping from the input space to the output space may be thought of as a hypersurface Γ representing a multidimensional function of the input. During the training phase, the input-output patterns presented to the network are used to perform a fitting for Γ. The generalization phase represents an interpolation of the input data points along the surface built as an approximation for Γ. The architecture considered in this chapter consists of three layers—the input layer (sensory nodes), a hidden layer of high dimension, and an output layer, as shown in Figure 6.3. Z1

Z2

Z 2M(M−1)

...

Input layer

Hidden layer h1

h2

hL

...

Output layer ...

W1

Figure 6.3

W2

WK

Radial basis function neural network for adaptive beamforming.

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The transformation from the input space to the hidden-unit space is nonlinear, whereas the transformation from the hidden layer to the output space is linear. The network represents a mapping from the p-dimensional input space to the m-dimensional output space: p→ m . The radial-basis functions (RBF) technique consists of choosing a function F that has the following form: N

F ( x ) = ∑ w i ϕ( x − x i 1

where

)

(6.34)

denotes the norm, N is a set of arbitrary functions, and xi are the cen-

ters of the radial-basis functions. One of the common and most useful forms for ϕ is the Gaussian function defined by: ϕ( x ) = e

−x 2 2σ 2

for σ > 0, and x ≥ 0

(6.35)

Different learning strategies exist for training RBF networks: 1. Fixed centers selected at random: The locations of the centers are selected randomly from the training set. The standard deviation of the Gaussian functions is fixed according to the spread of the centers. If Nc is the number of centers and d is the maximum distance between the chosen centers, then σ=

d 2N c

This choice avoids too peaked or too flat functions. Thus, only the linear weights w of the output layer need to be learned. 2. Self-organized selection of centers: In this approach, the radial-basis functions can move the locations of their centers using a standard rule such as the k-nearest neighbor [61]. Then, a supervised learning rule such as the LMS algorithm can be used to compute the linear weights of the output layer. 3. Supervised selection of the centers: This is the most generalized form of RBF networks. A gradient-descent procedure is used to find the weights, the centers, and their spreads, as described in [60]. The benefit of such an approach is a minimal network configuration; in other words, the same generalization performance can be achieved with a smaller network. As can be seen from Figure 6.3, the RBFNN consists

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of three layers of nodes, the input layer, the output layer, and the hidden layer. The input layer is the layer where the inputs are applied, whereas the output layer is the layer where the outputs are produced. In our application, the input layer consists of J = 2M nodes for an M-element array to accommodate both the real and imaginary part of the input vector [i.e., X(t)]. The output layer consists of 2M nodes to accommodate the output vector (i.e., Wopt). As is the case with most neural networks, the RBFNN is designed to perform an input-output mapping trained with examples (Xl(t); Wlopt) ;l = 1,2,…,NT, where NT stands for the number of examples contained in the training set. The purpose of the hidden layer in a RBFNN is to transform the input data X(t) from an input space of dimensionality J to a space of higher dimensionality L (see Figure 6.3). The rationale behind this transformation is based on Cover’s theorem [62], which states that an input/output mapping problem cast in a high-dimensionality space nonlinearly is easier to solve. The nonlinear functions (the h’s in Figure 6.3) that perform this transformation are usually taken to be Gaussian functions of appropriately chosen means and variances. There are a lot of learning strategies that have appeared in the literature to train an RBFNN. We use the learning strategies where an unsupervised learning algorithm (such as the K-means) is initially used to identify the centers of the Gaussian functions comprising the hidden layer. Then, an ad hoc procedure is used to determine the widths (standard deviations) of these Gaussian functions. According to this procedure, the standard deviation of a Gaussian function of a certain mean is the average distance to the first few nearest neighbors of the means of the other Gaussian functions. The aforementioned unsupervised learning procedure allows us to identify the weights (means and standard deviations of the Gaussian functions) from the input layer to the hidden layer. The weights from the hidden layer to the output layer are identified by following a supervised learning procedure, applied to a single layer network (the network from hidden to output layer). This supervised rule is referred to as the delta rule. The delta rule is essentially a gradient decent procedure applied to an appropriately defined optimization problem. Once training of the RBFNN is accomplished, the training phase is complete and the trained neural network can operate in the performance mode (phase). In the performance phase, the neural network is supposed to generalize, that is, respond to inputs (X(t)’s) that it has never seen before but are drawn from the same distribution as the inputs used in the training set. One way of explaining the generalization exhibited by the network during the performance phase is by remembering that after the training phase is complete, the RBFNN

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has established an approximation of the desired input/output mapping. Hence, during the performance phase the RBFNN produces outputs to previously unseen inputs by interpolating between the inputs used (seen) in the training phase.

6.2.1

Generation of Training Data

It is quite clear that the performance and accuracy of the weight vectors generated using neural networks depends on the type and amount of data used during the training phase. One approach for the training data design is to simulate a large number of scenarios in terms of the number of desired users, relative power levels, azimuth distributions, angular separations, and so forth. The step-by-step procedure to produce the training data {Zl(t); Wlopt ;l = 1,2,…,NT} for the RBFNN in this application is provided next. 1. Generate array output vectors {Xl(t); l = 1,2,…,NT}. 2. Normalize each one of the above array output vectors by its norm. For simplicity of notation we still refer to these vectors by Z(t)’s. 3. Evaluate the correlation matrix Rl (l = 1,2,…, NT) for each of the array output vectors generated in step 1. Using the calculated Rl ’s, calculate the vectors {Wlopt; l = 1,2,…, NT } based on the Wiener solution. 4. Produce the required training input/output pairs of the training set, that is {(Zl(t); Wlopt) ;l = 1,2,…,NT}. Another approach to the selection of the training data is to use real network data collected from field measurements, for example during the spatial channel modeling. This data collection could be conducted at different times of the day, corresponding to periods of various traffic demands. Recall that this training phase is performed such that once the RBFNN is trained with a representative set of training input/output pairs it is ready to function in the performance phase or the real-time operating mode. In the performance phase, the RBFNN produces estimates of the optimum weights for the array outputs through a simple, computationally inexpensive, two-step process, described next.

6.2.2

Performance Phase of the RBFNN

Once the training phase of the RBFNN is completed offline, the neural network is ready for online processing of the array data in real time using the following simple steps:

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$ (t ) and perform the 1. Collect the normalized array output vector X normalization. 2. Present the normalized array output vector at the input layer of the trained RBFNN. The output layer of the trained RBFNN will produce as an output the estimates of optimum weights for the array out$ ). puts (i.e., W opt Unlike the LMS, RLS, or SMI algorithms, where the optimization is carried out whenever the directions of the desired or interfering signals change, in the RBFNN approach the weights of the trained network can be used to produce the optimum weights needed to steer the narrow beams of the adaptive array to the direction of desired users. Knowing that the response time for neural networks (i.e., the time that it takes a trained neural network to produce an output if it is excited by an input) is very small, this leads us to believe that the proposed adaptive beamforming technique will track the mobile users as they move.

6.3 Angle Spread Impact on Optimum Beamforming A number of adaptive algorithms discussed earlier use a fundamental characteristic of CDMA systems with low data rate users, namely the fact that under these assumptions the processing gain is large, therefore resulting in a much stronger desired signal compared with other interfering signals after despreading. This enables the algorithm to approximate the optimum vector solution given by the principal eigenvector of the spatial correlation matrix, which corresponds to the maximum eigenvalue, as shown in (6.15). This approximation holds when the magnitude of the maximum eigenvalue is much larger than the remaining eigenvalues. One of the most important factors affecting the performance of any adaptive solution is the impact of the channel on the structure of the spatial correlation matrix. It is therefore important to understand the relation between the eigenvalues under different channel conditions. In this section, we analyze the effect of the angular spread on the eigenvalues of R to gain insight on the performance of some of the algorithms discussed in this chapter. Let us consider an antenna array with M elements transmitting to L users on the downlink of a CDMA system and assume that the azimuth power spectrum follows a Gaussian probability density function. Figure 6.4 plots the relative eigenvalue amplitudes for M = 6, L = 4, angle of departure (AOD) = 0°, and AS of 0° with relative powers of 0, –6, –10, and –13 dB. It is clear that for small AS, the principal eigenvalue is dominant and the approximation can hold. However, as the AS starts to increase, the amplitudes of the remaining eigenvalues also increase and become comparable to that of the principal eigenvalue. Figure 6.5 shows the same behavior for AOD of 30°.

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Relative Eigenvalue amplitude (dB)

–4 –6 –8

–10 –12

Eigenvalue 1 Eigenvalue 2 Eigenvalue 3 Eigenvalue 4

–14 –16 –18 0

10

20

30

40

50

60

AS (Degrees)

Figure 6.4

Eigenvalues versus AS, M = 6, AOD = 0°, unequal signal powers.

0 –2

Relative Eigenvalue amplitude (dB)

–4 –6 –8

–10 –12

Eigenvalue 1 Eigenvalue 2 Eigenvalue 3 Eigenvalue 4

–14 –16 –18 0

10

20

30

40

50

AS (Degrees)

Figure 6.5

Eigenvalues versus AS, M = 6, AOD = 30°, unequal signal powers.

60

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177

In Figure 6.6 we plot the eigenvalues for equipower signals with AOD of 0°. We can observe that although the curves tend to shift up or down across the three figures, they hold the same behavior as far as the impact of the AS on the relative eigenvalue amplitudes. We can then conclude that the solutions that depend on approximating the weight vector by (6.15) are valid for large PG and low AS. The question then becomes how would these solutions behave for large AS and low PG. To investigate this we compare the beampattern corresponding to the maximum SINR criterion for an array of eight elements transmitting to one desired and one interfering user at 30° and 60°, respectively, with AS of 0°. In Figure 6.7, the signals are assumed to have equal powers, whereas in Figure 6.8 the desired signal is assumed to be 128 times larger than the interfering signal (i.e., PG = 128 or about 21 dB). We can see that when the signals have comparable powers, the beampattern results in two main lobes at both the desired and interfering user (Figure 6.7). This introduces unnecessary interference on the forward link or the downlink, limiting that link’s capacity. In contrast, Figure 6.8 shows that when the PG is large enough only one main lobe is generated toward the desired user, whereas the gain toward the interference is significantly reduced. Recall that the weight vector in these cases was approximated by the principal eigenvector of the correlation matrix. 0 –2

Relative Eigenvalue amplitude (dB)

–4 –6 –8

–10 –12

Eigenvalue 1 Eigenvalue 2 Eigenvalue 3 Eigenvalue 4

–14 –16 –18 0

10

20

30

40

AS (Degrees)

Figure 6.6

Eigenvalues versus AS, M = 6, AOD = 0°, equal signal powers.

50

60

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Array pattern (dB)

–5

–10

–15

–20

–25 –100

Figure 6.7

–80

–60

–40

–20

0 20 AOD (Degrees)

40

60

80

100

Array beampattern, M = 8, equal signals at 30° and 60°, AS = 0°.

0

Array pattern (dB)

–5

–10

–15

–20

–25 –100

Figure 6.8 = 0°.

–80

–60

–40

–20

0 20 AOD (Degrees)

40

60

80

100

Array beampattern, M = 8, desired signal (30°) 21 dB higher than interference (60°), AS

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Figure 6.9 shows the beampattern for the same array with the desired signal power being 21 dB higher than the interfering signal power and AS of 10°, whereas Figure 6.10 assumes the signals have equal powers and AS of 15°. We can then observe that as the AS is increased the beampatterns show degradation in performance. For example, in Figure 6.9 the main lobe is slightly off the desired user, whereas in Figure 6.10 we can observe a peak at 80° almost equal in gain to that at the desired user. As we have seen earlier, the difference between the amplitude of the largest eigenvalue corresponding to the principal eigenvector and those of the remaining eigenvalues diminishes as the AS increases. To investigate how this affects the beamwidth of the main lobe (and consequently the array’s ability to focus energy in a narrow angular section), we plot the radiation pattern of a six-element array in Figure 6.11, where we consider one desired signal at 30° and one interferer at 60° that is 21 dB weaker than the desired signal, assuming the azimuth power spectrum follows a uniform probability density function with various angular spreads. For zero AS, we observe a narrow beam at the direction of the desired user. As we start increasing the AS we observe that the main beam starts to become wider; that is, more power is received from directions other

0

–5

Array pattern (dB)

–10

–15

–20

–25

–30

–35 –100

Figure 6.9 = 10°.

–80

–60

–40

–20 0 20 AOD (Degrees)

40

60

80

100

Array beampattern, M = 8, desired signal (30°) 21 dB higher than interference (60°), AS

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–5

Array pattern (dB)

–10

–15

–20

–25

–30 –100

Figure 6.10

–80

–20 0 20 AOD (Degrees)

As=0 degrees

40

60

80

100

–5 –10 –15 –20 –100

–50

0 50 AOD (Degrees)

–5 –10 –15 –20 –100

100

–50

As=20 degrees

0

Array pattern (dB)

–10 –15 –20 –50

0 50 AOD (Degrees)

100

0 50 AOD (Degrees)

100

As=35 degrees

0

–5

–25 –100

As=10 degrees

0

Array pattern (dB)

Array pattern (dB)

–40

Array beampattern, M = 8, equal signals at 30° and 60°, AS = 15.

0

Array pattern (dB)

–60

–5 –10 –15 –20 –25 –30 –100

–50

0 50 AOD (Degrees)

100

Figure 6.11 Array beampattern, M = 6, desired signal (30°) 21 dB higher than interference (60°), AS = 0°, 10°, 20°, 35°.

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than the desired user’s on the uplink and more power is spread in the system on the downlink (i.e., more interference is created).

6.4 Downlink Beamforming Because of the asymmetric nature of the traffic demands of data applications, it is expected that the forward link or the downlink will be the limiting link as far as capacity is concerned in CDMA systems. In TDD-based CDMA systems, the same carrier frequency is used on both the uplink and downlink. Hence, the channel conditions on the two links can be assumed to be identical between consecutive time slots and the weights generated on the uplink can be used on the downlink. However, the situation is different in FDD systems because there is a large separation between the uplink and downlink frequencies. Several techniques have been published in the literature that attempt to overcome this problem. On the uplink, the channel is first estimated; that is, we can estimate the spatial correlation matrix, which can then be employed in beamforming. The problem in the downlink is that we have no knowledge of the downlink channel or the downlink correlation matrix. The simplest approach is then to simply use the weights computed based on uplink measurements directly on the downlink. Clearly, this can result in suboptimal performance due to the large separation between the frequency bands. Another promising technique is based on the fact that the DOAs of the desired user signal should be independent of the frequency. Since the array steering vector is a function of the DOA, interelement spacing, and wavelength, we can use the directions of the dominant paths estimated from the uplink to calculate the downlink weights. However, this ignores the fact that the amplitudes and phases of the paths on the downlink will be different from those on the uplink due to the different carrier frequency, even though the DOAs can be assumed to be the same. Of course, a feedback technique can be used so that the mobile users can estimate the downlink channel and feed the information back to the base station to be used in calculating the adaptive weights. As we discussed in an earlier chapter, this method has several drawbacks. First, the amount of feedback will increase as the number of elements increase. This will reduce the uplink capacity because we are increasing the overhead signaling. However, a moderate hit to the uplink capacity may be tolerable in some cases in exchange of a much needed higher capacity in the downlink. Another drawback is that this method would be inefficient in high-speed situations where the fading experienced by the mobiles might be too fast for the feedback loop to compensate. Finally, such a technique would require changes to be introduced to the current standards. Hence, a more optimal approach would be to try to estimate the downlink spatial correlation matrix from the uplink measurements. [63, 64] propose a

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technique called the frequency calibrated (FC) algorithm, in which an uplink covariance matrix is used to estimate the downlink covariance matrix. Reference [63] uses the FC algorithm to estimate the downlink correlation matrix RDL from the uplink measurements, then uses this information to compute the optimum weights using eigen decomposition. On the other hand, [64] uses the same FC technique to calculate the uplink weights, form the covariance matrix of those weights, and then use that to estimate the covariance matrix of the downlink weights.

6.5 Vector Rake Receivers In 3G CDMA systems, reverse link or uplink pilots were introduced as a reference to aid in channel estimation. In the WCDMA system case, the data carried on the DPDCH channel is modulated on the I-branch, whereas the DPCCH channel carrying control and signaling information, including pilot bits, is modulated on the Q-branch. The two channels are spread using different PSCs. At the base station, the received pilot bits of the DPCCH channel are used to estimate the channel parameters, namely the complex fading coefficients. This information is then used in the demodulation of the DPDCH channel in the Rake receiver. The operation of a 1D Rake receiver in a CDMA system can be summarized as follows: • The time delay positions of the paths with the most significant energies

are identified and correlators or Rake fingers are assigned to those peaks. • Within each Rake finger the fast-changing amplitude and phase are

tracked and compensated for. • Finally, the demodulated and phase-adjusted symbols from all active

fingers are MRC combined and the output is fed to the decoder.

Since pilot bits are predefined, they can be used to perform channel estimation. This is performed by first despreading the pilot channel and then multiplying the output by the conjugate of the pilot pattern. This effectively produces an instantaneous estimate of the complex fading channel coefficient, which is then filtered and averaged to remove the effects of noise. The same principle can be extended to combine spatial processing via beamforming and Rake temporal processing, resulting in the so-called 2D or vector Rake receivers.

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6.6 Channel Estimation Figure 6.12 shows a block diagram of the channel estimation process based on the DPCCH channel. From [65] we can write the desired signal at the output of the correlator as y 1 (τ ) =

K1 1 jϕ b 1 (n )∑ α1 ,k e l ,k a 1 ( θ 1 ,k )δ( τ − τ 1 ,k ) + S I ( τ ) + N ( τ ) Rb k =1

where αl,k and ϕl,k are the amplitude and phase of the kth multipath component of the lth user, respectively. SI(τ) is the multiple access interference (MAI) vector and N(τ) is the noise vector. The first step in channel estimation is to obtain the delay profile for each pilot bit. Each DPCCH 10 ms frame has 15 slots with Np pilot bits per slot. Since each user’s pilot is scrambled using a different code sequence, after despreading, the MAI can be considered as independent Gaussian noise. Let the output of the correlator at the mth antenna element when the nth pilot bit is received be denoted by y 1m, n . Scrambling code sin wot

Correlator

Coherent integration

Correlator

Coherent integration

Noncoherent integration

Threshold/ decision sin wot

Scrambling code

Correlator

Coherent integration

sin wot Scrambling code

Figure 6.12

Channel estimation in WCDMA beamforming.

DOA Esitimation

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Signals at each array element will have certain delay profiles that, when coherently integrated and averaged, will result in a mean delay profile that can be written as g 1m ( τ ) =

1 N

N

∑y n =1

m 1,n

(τ )

Since the correlator is matched to the desired user’s signal whose multipath will be phase-aligned, it follows that the magnitudes of the paths belonging to the desired user will be enhanced or increased by Np times after coherent integration, whereas the paths at time delays belonging to other users will be significantly reduced because of the randomness introduced by scrambling. Now if the delay profiles from all M elements were combined, the MAI effect can be further reduced, thereby improving the overall SINR. This results in a mean delay profile that can be written as [65] g 1 (τ ) =

1 M

M

∑g m =1

m 1

(τ )

The mean delay profile can be further processed to estimate the DOAs of the incoming signals. This delay profile is essentially a discrete time signal that can be Fourier transformed, yielding peaks in the spatial domain representing the DOAs with magnitudes representing the amplitudes and phases of the multipath components.

6.7 Beamforming Now that the channel delay profile and the multipath DOAs have been determined, this information can be employed in the beamforming weight calculations, which are then applied to the DPDCH data. The main goal here is that we need to invert the channel effects on the transmitted signals. Using the estimated mean delay profile, this can be accomplished by simply multiplying the correlator output of each antenna element by the complex conjugate of the corA responding profile or ( g 1m ( τ )) . The output of the array elements can then be averaged and fed to the Rake combiners, which perform the MRC scheme. Alternatively, [65] proposes another approach in which the estimated DOAs of the multipath components can be used to construct the array response or steering vectors and use those vectors as the weights of the array antenna. The beampattern resulting from this operation will then have main lobes toward the DOA directions. The output of the beamformers are then fed to the Rake

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receiver for temporal processing, which further removes the effects of channel fading by multiplying its input by the conjugate of the complex fading coefficients. On the downlink several beamforming techniques can be used. Based on the assumptions that the downlink multipath channel will retain the same DOAs as the uplink but with different amplitudes and phases, a DOA-based beamforming technique can be effective. Three choices exist when this DOA-based approach is employed, which can be summarized in the following: • Select the direction of the dominant path and use its DOA to form the

array response vector, which can then be used as the beamformer weight vector, that is w = a(θmax). This will create a single beam toward the direction of the strongest path. • Select the directions of the Km strongest paths and use their correspond-

ing DOAs to form multiple beams. The multiple beams can be assigned equal energy ratios, in which case the weight vector becomes Km

w m = ∑ a( θ k ), or they can be weighted by the corresponding paths k =1

Km

magnitudes, resulting in w m = ∑ αk a( θ k ). The weights are then nork =1

malized to unity to make the total transmitted energy equivalent to the one antenna case.

6.8 Conclusion A number of training-based and blind adaptive algorithms have been presented to compute the optimum weight vector for a smart antenna system with an antenna array at the base station. These include classical techniques such as the LMS and RLS originally developed for adaptive filtering, which rely on comparing the array output to a reference signal. Other approaches belonging to the so-called blind beamforming attempt to exploit specific signal structures or properties such as constant envelope or cyclostationarity. Other members of this class include methods based on well-known optimization techniques such as the conjugate gradient and Lagrange multipliers. Several methods especially suitable for CDMA systems take advantage of the unique characteristics of spread spectrum signals and the resulting correlation matrix structure. Moreover, it has been shown that the computational load of these techniques can be reduced using reasonable approximations. An emerging technique based on using neural networks to approximate the nonlinear Wiener solution was also discussed. The impact of angular spread on the eigenvalue spread of the spatial correlation

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matrix was also analyzed, along with its effect on the beampattern of the maximum SINR beamforming criterion. Results presented show how large angular spread can degrade the array performance.

References [1] Godara, L. C., and D. B. Ward, “A General Framework for Blind Beamforming”, Proc. of the IEEE Region 10 Conference, Vol. 2, No., December 1999, pp. 1240–1243. [2] Alam, F., D. Shim, and B. Woerner, “Comparison of Low Complexity Algorithms for MSNR Beamforming,” IEEE 55th Vehicular Technology Conference, Vol. 4, 2002, pp. 1776–1780. [3] Bengtsson, M., and B. Ottersten, Uplink and Downlink Beamforming for Fading Channels,” IEEE 2nd Workshop on Signal Processing Advances in Wireless Communications, 1999, pp. 350–353. [4] Chang, T., J. Kim, and C. Kim,” Investigation of the Trade-off Characteristics of Beamforming Performance in DS-CDMA System,” IEEE 52nd Vehicular Technology Conference, Vol. 1, No., 2000 pp. 110–115. [5] Shim, D., and F. Alam, “A New Adaptive Downlink Beamforming Method for WCDMA System,” IEEE 53rd Vehicular Technology Conference, Vol. 1, No., 2001 pp. 157–161. [6] Koutalos, A. C., J. Thompson, and P. Grant, “Downlink Adaptive Antenna Techniques for WCDMA,” IEEE 55th Vehicular Technology Conference, Vol. 3, No., 2002, pp. 1135–1139. [7] Kwon, S., I. Oh, and S. Choi, “Adaptive Beamforming from the Generalized Eigenvalue Problem with a Linear Complexity for a Wideband CDMA Channel,” IEEE 50th Vehicular Technology Conference, Vol. 3, No., 1999, pp. 1890–1894. [8] Choi, S., et al., “A Novel Adaptive Beamforming Algorithm for Antenna Array CDMA Systems with Strong Interferers,” IEEE Trans. on Vehicular Technology, Vol. 51, No. 5, September 2002, pp. 808–816. [9] Choi, S., and D. Yun, “Design of an Adaptive Antenna Array for Tracking the Source of Maximum Power and its Application to CDMA Mobile Communications,” IEEE Trans. on Antennas and Propagations, Vol. 45, No. 9, September 1997, pp. 1393–1404. [10] Shim, D., and S Choi, “A New Blind Adaptive Algorithm Based on Lagrange Formula for a Smart Antenna System in CDMA Mobile Communications.” [11] Choi, S , and D. Shim, “A Novel Beamforming Algorithm for a Smart Antenna System in a CDMA Mobile Communication Environment,” IEEE Trans. on Vehicular Technology, Vol. 49, No. 5, September 2000, pp. 1793–1806. [12] Godara, L. C., “Application of Antenna Arrays to Mobile Communications, Part II: Beamforming and Direction-of-Arrival Considerations,” Proc. of the IEEE, Vol. 85, No. 8, August 1997, pp. 1195–1245. [13] Naguib, A. F., “Adaptive Antennas for CDMA Wireless Networks”, Ph.D. dissertation, Stanford University, 1996.

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[14] Widrow, B., and J. M. McCool, “A Comparison of Adaptive Algorithms Based on the Methods of Steepest Descent and Random Search,” IEEE Trans. Antennas Propagat., Vol. AP-24, 1976, pp. 615–637. [15] Iltis, R. A., and L. B. Milstein, “An Approximate Statistical Analysis of the Widrow LMS Algorithm with Application to Narrow-band Interference Rejection,” IEEE Trans. Commun., Vol. COM-33, 1985, pp. 121–130. [16] Clarkson, P. M., and P. R. White, “Simplified Analysis of the LMS Adaptive Filter Using a Transfer Function Approximation,” IEEE Trans. Acoust., Speech, Signal Processing, Vol. ASSP-35, 1987, pp. 987–993. [17] Gardner, W. A., “Comments on Convergence Analysis of LMS Filters with Uncorrelated Data,” IEEE Trans. Acoust., Speech, Signal Processing, Vol. ASSP-34, 1986, pp. 378–379. [18] Foley, J. B., and F. M. Boland, “A Note on the Convergence Analysis of LMS Adaptive Filters with Gaussian Data,” IEEE Trans. Acoust., Speech, Signal Processing, Vol. 36, 1988, pp. 1087–1089. [19] Solo, V., “The Limiting Behavior of LMS,” IEEE Trans. Acoust., Speech, Signal Processing, Vol. 37, 1989, pp. 1909–1922. [20] Feuer, A., and E. Weinstein, “Convergence Analysis of LMS Filters with Uncorrelated Gaussian Data,” IEEE Trans. Acoust., Speech, Signal Processing, Vol. ASSP-33, 1985, pp. 222–229. [21] Jaggi, S., and A. B. Martinez, “Upper and Lower Bounds of the Misadjustment in the LMS Algorithm,” IEEE Trans. Acoust., Speech, Signal Processing, Vol. 38, 1990, pp. 164–166. [22] Boland, F. B., and J. B. Foley, “Stochastic Convergence of the LMS Algorithm in Adaptive Systems,” Signal Process., Vol. 13, 1987, pp. 339–352. [23] Ko, C. C., “A Fast Adaptive Null-steering Algorithm Based on Output Power Measurements,” IEEE Trans. Aerosp. Electron. Syst., Vol. 29, 1993, pp. 717–725. [24] Ko, C. C., G. Balabshaskar, and R. Bachl, “Unbiased Source Estimation with an Adaptive Null Steering Algorithm,” Signal Process., Vol. 31, 1993, pp. 283–300. [25] Benesty, J., and P. Duhamel, “A Fast Exact Least Mean Square Adaptive Algorithm,” IEEE Trans. Signal Processing, Vol. 40, 1992, pp. 2904–2920. [26] Godara, L. C., “Improved LMS Algorithm for Adaptive Beamforming,” IEEE Trans. Antennas Propagat., Vol. 38, 1990, pp. 1631–1635. [27] Ohgane, T., et al., “BER Performance of CMA Adaptive Array for High-Speed GMSK Mobile Communication—A Description of Measurements in Central Tokyo,” IEEE Trans. Veh. Technol., Vol. 42, 1993, pp. 484–490. [28] Eweda, E., and O. Macchi, “Convergence of the RLS and LMS Adaptive Filters,” IEEE Trans. Circuits Syst., Vol. CAS-34, 1987, pp. 799–803. [29] Fabre, P., and C. Gueguen, “Improvement of the Fast Recursive Least-squares Algorithms via Normalization: A Comparative Study,” IEEE Trans. Acoust., Speech, Signal Processing, Vol. ASSP-34, 1986, pp. 296–308. [30] Mantey, P. E., and L. J. Griffiths, “Iterative Least-squares Algorithm for Signal Extraction,” in 2nd Int. Hawaii Conf. System Science, Honolulu, HI., 1969, pp. 767–770.

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[31] Eleftheriou, E., and D. D. Falconer, “Tracking Properties and Steady State Performance of RLS Adaptive Filter Algorithms,” IEEE Trans. Acoust., Speech, Signal Processing, Vol. ASSP-34, 1986, pp. 1097–1110. [32] Mueller, M. S., “Least Squares Algorithms for Adaptive Equalizers,” Bell Syst. Tech. J., 1981, pp. 1905–1925. [33] Cioffi, J. M., and T. Kailath, “Fast Recursive-Least-Square, Transversal Filters for Adaptive Filtering,” IEEE Trans. Acoust., Speech, Signal Processing, Vol. ASSP-32, 1984, pp. 998–1005. [34] Chiba, I., W. Chujo, and M. Fujise, “Beamspace Constant Modulus Algorithm Adaptive Array Antennas,” Proc. Inst.Elect. Eng. 8th Int. Conf. Antennas and Propagation, Edinburgh, Scotland, 1993, pp. 975–978. [35] Godard, D. N., “Self-recovering Equalization and Carrier Tracking in Two-dimensional Data Communication Systems,” IEEE Trans. Commun., Vol. COM-28, 1980, pp. 1867–1875. [36] Treichler, J. R., and B. G. Agee, “A New Approach to Multipath Correction of Constant Modulus Signals,” IEEE Trans. Acoust., Speech, Signal Processing, Vol. ASSP-31, 1983, pp. 459–472. [37] Shynk, J. J., and C. K. Chan, “Performance Surfaces of the Constant Modulus Algorithm Based on a Conditional Gaussian Model,” IEEE Trans. Signal Processing, Vol. 41, 1993, pp. 1965–1969. [38] T. E., Reed, J. H., and W. H Biedka Tranter, “Mean Convergence Rate of a Decision Directed Adaptive Beamformer with Gaussian Interference,” Proc. of the 2000 IEEE Sensor Array and Multichannel Signal Processing Workshop, March 16–17, 2000, pp. 68–72. [39] Jwa, O. Hyunseo, and K. Mungeon, “Decision-directed Chip-level Beamforming in WCDMA Antenna Array System,” IEEE 55th Hyekyung Vehicular Technology Conference, Vol. 1 , May 6–9, 2002. pp. 322–326. [40] Duhamel, P., M. Montazeri, and K. Hilal, “Classical Adaptive Algorithms (LMS, RLS, CMA, Decision Directed) Seen as Recursive Structures,” IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 3, April 27–30, 1993, pp. 496–499. [41] Povey, G. J. R., P. M. Grant, and R. D. Pringle, “A Decision-directed Spread-spectrum RAKE Receiver for Fast-fading Mobile Channels,” IEEE Trans. on Vehicular Technology, Vol. 45, No. 3, August 1996, pp. 491–502. [42] Swindlehurst, A. L., S. Daas, and Y. Jiankan, “Analysis of a Decision Directed Beamformer,” IEEE Trans. on Signal Processing, Vol. 43 , No. 12 , December 1995, pp. 2920–2927. [43] Altuna, J., and B. Mulgrew, “A Comparison of Cyclostationary Blind Equalisation Techniques Multipath Countermeasures,” IEE Colloquium on , May 23, 1996, pp. 8/1–8/6. [44] Abed-Meraim, K., et al., “Blind Source-separation Using Second-order Cyclostationary Statistics,” IEEE Trans. on Signal Processing, (see also IEEE Trans. on Acoustics, Speech, and Signal Processing), Vol. 49, No. 4 , April 2001, pp. 694–701. [45] Enserink, S., and Cochran, D., “On Detection of Cyclostationary Signals,” International Conference on Acoustics, Speech, and Signal Processing, Vol. 3, May 9–12, 1995, pp. 2004– 2007.

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[46] Castedo, L., et al., “Linearly-constrained Adaptive Beamforming Using Cyclostationary Signal Properties,”IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. iv, April 19–22, 1994, pp. IV/249–IV/252. [47] Castedo, L., C. Y Tseng,.and L. J.Griffiths, “A New Cost Function for Adaptive Beamforming Using Cyclostationary Signal Properties,” IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 4, April 27–30, 1993, pp. 284–287. [48] Litva, J., and T. K. Yeung Lo, Digital Beamforming in Wireless Communications, Norwood, MA: Artech House, 1995. [49] El Zooghby A. H., C. G. Christodoulou, and M. Georgiopoulos, “Neural Network-Based Adaptive Beamforming for One and Two Dimensional Antenna Arrays,” IEEE Trans. on Antennas and Propagation, December 1998. [50] El Zooghby A. H., C. G. Christodoulou, and M. Georgiopoulos, “A Neural Network-based Linearly Constrained Minimum Variance Beamformer,” submitted to The Microwave and Optical Technology Letters. Microwave and Optical Technology Letters, 1999, p.p. 451–455. [51] El Zooghby A. H., C. G. Christodoulou, and M. Georgiopoulos, “Neural Network based Beamforming for Interference Cancellation,” Proc. of the SPIE’s AeroSense Conference, Orlando, FL, 1998. [52] El Zooghby A. H., C. G. Christodoulou, and M. Georgiopoulos, “A Novel Approach to Adaptive Nulling with Neural Networks,” Proc. SoutheastCon 98, Orlando, FL, 1998. [53] El Zooghby A. H., C. G. Christodoulou, and M. Georgiopoulos, “Adaptive Interference Cancellation with Neural Networks,” Proc. of the 8th Virginia Tech Symposium on Wireless Personal Communications, Blacksburg, VA, June 1998, pp. 281–292. [54] El Zooghby A. H., C. G. Christodoulou, and M. Georgiopoulos, “Neural Network based Smart Antennas for Mobile Satellite Communications,” Proc. of the International Symposium on Electromagnetic Theory, Thessaloniki, Greece, May 1998, pp. 336–339. [55] El Zooghby A. H., C. G. Christodoulou, and M. Georgiopoulos, “Adaptive Interference Cancellation in Circular Arrays with Radial Basis Function Neural Networks,” IEEE AP/URSI Symposium, Atlanta, 1998. [56] El Zooghby A. H., C. G. Christodoulou, and M. Georgiopoulos, “Radial Basis Function Neural Network Algorithm for Beamforming in Cellular Communication Systems,” IEEE-APS Conference on Antennas and Propagation for Wireless Communications, Waltham, MA, 1998, pp. 52–56. [57] El Zooghby A. H., C. G. Christodoulou, and M. Georgiopoulos, “Multiple Sources Neural Network Direction Finding with Arbitrary Separations,” IEEE-APS Conference on Antennas and Propagation for Wireless Communications, Waltham, MA, 1998, pp. 57–60. [58] El Zooghby A. H., C. G. Christodoulou, and M. Georgiopoulos, “Multiple Mobile User Tracking with Neural Network-based Adaptive Array Antennas,” accepted/to be published in the SPIE’s AeroSense 99 Conference, Orlando, FL. [59] Haykin, S., Neural Networks: A Comprehensive Foundation, Macmillan College Publishing. [60] Mulgrew, B., “Applying Radial Basis Functions,” IEEE Signal Processing Magazine, March 1996, Vol. 13, No. 2, pp. 50–65. [61] Tou, J. T., and R. C. Gonzalez, Pattern Recognition Principles, Reading, MA: Addison Wesley, 1975.

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[62] Moody, T. J., and C. J. Darken, “Fast Learning in Networks of Locally Tuned Processing Units,” Neural Computation, Vol. 1, 1989, pp. 281. [63] Liang, Y. C, and F. P. S. Chin, “Downlink Channel Covariance Matrix Estimation and Its Application in Wireless DS-CDMA Systems,” IEEE Journal on Selected Areas in Communications, Vol. 19, No. 2, February 2001, pp. 222–232. [64] Liang, Y. C, and F. P. S. Chin, “FDD DS-CDMA Downlink Beamforming by Modifying Uplink Beamforming Weights,” IEEE 52nd Vehicular Technology Conference, Vol. 1, Sept. 24–28, 2000, pp. 170–174. [65] Li, H. J, and T. Y Liu; “Comparison of Beamforming Techniques for W-CDMA Communication Systems,”IEEE Trans. on Vehicular Technology, Vol. 52, No. 4, July 2003, pp. 752–760.

7 Coverage and Capacity Improvements in 3G Networks 7.1 Introduction CDMA2000 and WCDMA have many similarities as well as some differences. They use different types of channelization and spreading codes, pilot transmission schemes, coding, interleaving and modulations, physical channel structure, frame structure, power control, and handoff algorithms, as well as higher layer algorithms. These differences, however, have little impact on the system’s relative coverage or capacity because they are optimized up to certain limits, mainly as a result of the required backward compatibility with their respective 2G systems (IS-95 and GSM). On the other hand, the fundamental difference—spreading bandwidth—affects the dimensioning process more significantly. Each CDMA2000 carrier uses 1.25 MHz of the total available bandwidth with a chip rate of 1.2288 Mcps, whereas WCDMA spreads the signals into a 5M-Hz band, using a chip rate of 3.6864 Mcps. A wide bandwidth gives additional diversity (i.e., improved capacity for WCDMA). A narrow bandwidth gives reduced own-cell interference (i.e., improved capacity for CDMA2000). Thus, the overall impact is small in most cases (i.e., CDMA2000 and WCDMA have similar basic performance). Since in general CDMA is an interference-limited system, it is always capacity limited. However, when the capacity is not the main issue, radio design will be based on the link budget and coverage. This is typically the case in the initial deployment of a new system or in the design of isolated rural cells, where the goal is to cover the area with a minimum number of base stations. Similarly, when reliable coverage for high bit rate indoor users from outdoor base stations is a requirement, cell sizes will be 191

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small, which provide more capacity per km2 than is needed. This chapter compares the CDMA2000 and WCDMA radio links. The impact of issues such as available bandwidth, detailed environment characteristics (which are important in network optimization), the suitability or compatibility of either WCDMA or CDMA2000 for overlaying on, or co-existing with (in a gradual migration), a particular deployed 2G system are not taken into account. In other words, we assume that the choice would be based only on the related performances of these two systems in terms of capacity and coverage.

7.2 Link Budgets and Coverage For narrowband systems such as FDMA or TDMA, the cell loading does not affect coverage design. However, for CDMA, due to universal frequency reuse, capacity and coverage analyses are tied together as more users are admitted into the network, the interference level is increased, and coverage area of the cells shrinks. Therefore, to have reliable coverage at the cell boundary, link budget and coverage analysis are usually based on a certain cell loading (that is, a certain interference margin is considered in the link budget). For the uplink (UL), a cell loading of 50% is commonly considered, which corresponds to a noise rise of 3 dB. This might be regarded as the design objective in the initial deployment of a system. Therefore, the need for additional base stations would occur only after penetration has reached this limit (50% load). An uplink budget is employed to predict the coverage of a CDMA RF network based on the limitations of the subscriber stations to communicate back to the base station. This fundamental limitation on coverage in most cases will be discussed later. Therefore, the limitation of service is based on the uplink budget(s). The uplink budget can vary with the subscriber type, subscriber configuration, subscriber mobility, and morphology classification. A separate link budget should be created for each permutation of these parameters. The parameters included in the link budget are provided below. 7.2.1

Mobile Station Parameters

Here are the mobile station parameters: • Tx power: Transmit power subscriber station; • Cable loss: Attenuation due to connector and cables to the antenna; • Mobile station effective antenna gain: Gain of antenna on MS terminal; • Effective isotropic radiated power (EIRP): The mobile station’s transmit

power less any cable loss and effective antenna gain.

Coverage and Capacity Improvements in 3G Networks 7.2.2

193

Base Station Parameters

Here are the base station parameters: • BS Rx antenna gain: Gain of BS antenna; • BS cable loss: Attenuation for BS cable from antennas to receiver front

end, including any couplers and multiplexers; • Noise figure: Noise figure of BS receiver. 7.2.3

System Parameters

Here are the system parameters: • Maximum traffic channel data rate; • Required BS Eb/No: Required ratio of traffic channel energy per bit to

interference energy. Sometimes this is also referred to as Eb/Io. The value is based on fading environment of subscribers. Highly mobile subscribers tend to require a higher ratio to overcome more severe fading. 7.2.4

Margins

The following are margins: • Building or vehicle penetration: RF attenuation of signal passing into

building or mobile vehicle. The penetration margin tends to be associated with the building classification or morphology. • Fade margin: Margin allocated to overcome effects of shadowing and lognormal fading. The amount of margin is determined by the desired confidence in coverage at the cell edge. Typically, the fade margin ranges from 4.5 dB to 11 dB. • Soft handoff (SHO) gain: Gain produced by mobile soft handoff diversity on two or more RBSs. The gains to the link budget are assumed due to the statistical independence of the RF fading between the subscriber terminal and any two base stations. No handoff gain is assumed for fixed users since their directional antennas or orientation inside the buildings tends to increase the correlation in fade paths and reduce the amount of soft handoff. 7.2.5

Other Parameters

The following are other parameters:

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• Thermal noise density: Ambient RF noise floor of environment set at

–174 dBm/Hz. • Signal bandwidth: Bandwidth of CDMA channel. • Load margin: Rise in thermal noise floor on uplink due to increased

interference levels produced by increased traffic. A traffic load of 50% equates to a 3.0 dB rise in the uplink noise floor. • Receiver sensitivity (unloaded) = Thermal noise density + BS

receiver noise figure + Required BS mean Eb/No + 10 log (maximum data rate). • Maximum allowable path loss (MAPL): Maximum path loss for the

given parameters the subscriber station can tolerate and still communicate reliably on the uplink. • Maximum path loss = Maximum subscriber Tx EIRP – Head Loss –

Building penetration loss + Soft handoff gain – Fading margin + BS antenna gain – BS cable loss – BS receiver sensitivity (unloaded) – Load margin. 7.2.6

Fade Margin

The confidence of coverage at cell edge and area is characterized statistically in terms of a normal distribution curve. To achieve a certain confidence in having service at the predicted edge of coverage, a particular fading margin must be applied to the link budget. The amount of margin added depends on the level of confidence required and the standard deviation of lognormal fading. For the case where handoffs occur near the cell edge of coverage, the transmitter must only provide enough power to overcome background noise and interference. However, with shadowing, the transmitter has to increase the transmitted power to overcome the random fluctuation in path loss due to lognormal fading. The margin set in transmitted power due to compensate for log- normal fades is known as the lognormal shadow margin. Lognormal shadow margin, µ, can be calculated by the following equation:   1 Confidence level (cell edge) = 1 −   2⋅π 

∞ −x2   2   dx e ∫    µ − 10 ⋅ n ⋅ log (r )    δ

Coverage and Capacity Improvements in 3G Networks

n = path loss exponent r = Normalized cell radius (on the cell boundary, r = 1 )  δ = Log normal shadow standard deviation  ∞ −x2    e 2 dx   1  ∫   = Outage probability (cell edge )  µ − 10 ⋅ n ⋅ log (r ) 2 ⋅ π     δ  It is the percentage of time when a receiver experiences  " shadowed" performance during shadowing

195

(7.1)

For example, for lognormal shadow standard deviation of 8 dB, a lognormal shadow margin of 10.3 dB is required for 90% confidence if hard handoffs occur at the edge between hexagonal cells. The higher the confidence, the higher the required margin and the smaller the cell radiuses are. (For example, assuming flat earth, no clutter and 35 dB per decade morphology loss slope, 1 dB decrease in margin will result in 6% decrease in cell radius and 14% decrease in cell area). A 90% confidence level indicates that 90% of the time the receivers on the cell edge can expect to be able to access the RF network. 7.2.7

Confidence (Cell Area)

The cell area confidence level is that percentage of time when those active subscribers randomly distributed over the cell sites coverage area can overcome the shadow fading to be received by the RBS receiver. Confidence level within a cell is higher than that of the confidence level on the cell boundary for a given signal strength threshold xo. The confidence level within the cell, Fu, can be calculated as follow:  1 − 2 ⋅ x ⋅y    2 1  Fu = ⋅ 1 − erf ( x ) + e  y  2  

  1 − x ⋅ y   ⋅ 1 − erf     y    

where y=

10 ⋅ n ⋅ log 10 e

x = −µ

δ⋅ 2

δ⋅ 2 x 2 2 erf ( x ) = ⋅ ∫ e − t dt 0 p

(7.2)

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µ = Log normal shadow margin for a given xo (see “log normal shadow margin” section for equation n = Path loss exponent δ = Log normal shadow standard deviation in dB Figure 7.1 shows the relation between confidence level for cell area and cell edge against lognormal shadow margin (hard handoff) of 8 dB standard deviation. 7.2.8

CDMA Traffic Loading

With CDMA technology, all subscribers are using common frequencies to receive and transmit, unlike earlier technologies that assigned distinct frequencies and/or time slots for each subscriber. Because the spectrum is shared, the primary factor limiting capacity in CDMA is the interference levels within the allocated CDMA spectrum. The uplink capacity is based on a formula called the pole capacity. The pole capacity represents a theoretical limit of simultaneous calls a single sector could support if all the subscribers could theoretically maintain their transmit power levels at their minimal values with perfect power control. But in reality, only a fraction of the pole capacity is practically achievable.

Figure 7.1

Cell edge versus cell area confidence.

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197

The higher the fraction of pole capacity the design attempts to achieve, the more the voice quality and/or grade of service must be compromised. The effect of rising interference requires the subscriber to increase its transmit power to maintain an acceptable frame error rate on the reverse link (RL). The maximum average traffic loading recommended is 65% of pole capacity. Cell loading is defined as the ratio of active users to the maximum allowable number of users expressed in terms of percentage of pole capacity. Uplink cell loading is calculated as [1, 2]  I  Nu ηUL = 1 + oc  ∑  I sc  j =1

1 W 1+ Rjνj Eb N o

(7.3)

where Nu: number of users; vj: activity factor for user j [0.67 for adaptive multirate (AMR) voice in WCDMA and 1 for data]; I oc : average other cell to same cell interference ratio (0.65 for three-sector I sc sites). The downlink (DL) traffic loading factor equation can be written as Nu

η DL = ∑ ν j j =1

N o )j  I  oc − α 1 + W Rj   I sc

(E b

(7.4)

where α is the average orthogonality factor (1 fully orthogonal, 0 no orthogonality).

7.3 Voice Services Table 7.1 summarizes the Eb/No values required to achieve acceptable quality based on the listed BLER targets for different WCDMA data rates [1]. This value is used as one of the inputs to the link budget and is one of the most important factors that determine the coverage and capacity for a given service.

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Table 7.1 WCDMA Link Level Performance Summary for Different Data Rates

UL Eb/No (dB)

DL Eb/No (dB)

BLER (%)

Additive white Gaussian noise (AWGN)

2.9

4.4

1%

12.2

Pedestrian A 3 km/hr

4.2

7

1%

12.2

Case 3

5.5

7

1%

64

AWGN

1

2.5

10%

64

Pedestrian A 3 km/hr

2.2

5.3

10%

64

Case 3

3.4

5.3

10%

384

AWGN

0.6

2.4

10%

384

Pedestrian A 3 km/hr

2

5.1

10%

384

Case 3

3.4

5.1

10%

Data Rate (Kbps)

Mobile Channel

12.2

7.3.1

Uplink Budgets

Tables 7.2 and 7.3 show the link budget for a pedestrian A (Ped. A) channel for urban, suburban, and rural environments for CDMA2000 and WCDMA, respectively. For CDMA2000, the assumptions and, hence, the different entries in the link budget, such as mobile transmit power, pilot overhead, BS and mobile antenna gains, and different losses at mobile station and BS are taken from [3]. For WCDMA, most of the data has been taken from [1, 2]. We assumed a vocoder output rate of 12.2 Kbps for WCDMA and 9.6 Kbps for CDMA2000. 7.3.2

Downlink Budgets

For downlink, we assumed a maximum BS transmit power of 20W and also assumed that the total available traffic power is given only to the voice users. Furthermore, we set a power limit for each user at 10% of the total available traffic power (which gives a maximum transmit power around 32 dBm). This value, although somehow arbitrarily chosen here, has significant effect on the downlink coverage area. In real scenarios, this value is set for each user depending on the number of users and their bit rates. For CDMA2000, the values shown in Table 7.4 are taken from [3]. In general, SHO gain in the forward link (FL) is higher than the reverse link. The reason is due to the use of MRC in the

Coverage and Capacity Improvements in 3G Networks

199

Table 7.2 CDMA2000 RL/UL Voice Link Budget Parameters

CDMA2000 Urban

Suburban

Rural

RL

RL

RL

Radio Configuration

RC3

RC3

RC3

Mobile Environment

Ped. A

Ped. A

Ped. A

Target FER/BLER

1.00%

1.00%

1.00%

Peak Data Rate (bps)

9,600

9,600

9,600

MS TX PO (Watts)

0.2

0.2

0.2

MS TX PO (dBm)

23.0

23.0

23.0

MS Combiner Loss (dB)

0.0

0.0

0.0

MS Cable Loss (dB)

0.0

0.0

0.0

MS Antenna Gain (dBi)

0.0

0.0

0.0

EIRP (dBm)

23.0

23.0

23.0

Total Reverse Link Overhead (dB)

–1.5

–1.5

–1.5

Traffic EIRP (dBm)

21.5

21.5

21.5

BS Antenna Gain (dBi)

18.0

18.0

18.0

BS Combiner Loss (dB)

0.0

0.0

0.0

BS RX Cable Loss (dB)

–1.0

–1.0

–1.0

BS Noise Figure (dB)

3

3

3

Thermal Noise (dBm/Hz)

–174

–174

–174

BS Receiver Noise Density (dBm/Hz)

–171

–171

–171

Peak Data Rate (dB)

39.8

39.8

39.8

MS Parameters

BS Parameters

Required Eb/No Set Point (dB)

5.5

5.5

5.5

Eb/No Std Deviation (dB)

0.0

0.0

0.0

Mean Eb/No (dB)

5.5

5.5

5.5

Effective BS Sensitivity (dBm)

–125.7

–125.7

–125.7

–2

–2

–2

Network Design Margins Body Loss (dB)

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Table 7.2 (continued) Parameters

CDMA2000 Urban

Suburban

Rural

RL

RL

RL

Building Penetration Loss (dB)

–20.0

–15.0

–10.0

Confidence (Cell Edge)

90%

90%

90%

Lognormal Shadow Std Dev (dB)

8.0

8.0

8.0

Path Loss Slope Y

3.5

3.5

3.5

Lognormal Shadow Margin (dB)

–10.3

–10.3

–10.3

Confidence (Cell Area)

97%

97%

97%

CDMA Soft Handoff Gain at 50% Correlation (dB)

3.0

3.0

3.0

CDMA Traffic Loading (%)

50%

50%

50%

CDMA Traffic Loading Effect (dB)

–3.0

–3.0

–3.0

Maximum Allowable Path Loss (dB)

131.9

136.9

141.9

Table 7.3 WCDMA RL/UL Voice Link Budget Parameters

WCDMA Urban

Suburban

Rural

UL

UL

UL

Mobile Environment

Ped. A

Ped. A

Ped. A

FEC Code

Convolution Convolution

Convolution

Target FER/BLER

1.00%

1.00%

1.00%

Peak Data Rate (bps)

12,200

12,200

12,200

MS TX PO (Watts)

0.125

0.125

0.125

widctlparMS TX PO (dBm)

21.0

21.0

21.0

MS Combiner Loss (dB)

0.0

0.0

0.0

MS Cable Loss (dB)

0.0

0.0

0.0

MS Antenna Gain (dBi)

0.0

0.0

0.0

EIRP (dBm)

21.0

21.0

21.0

Traffic EIRP (dBm)

21.0

21.0

21.0

MS Parameters

Coverage and Capacity Improvements in 3G Networks Table 7.3 (continued) Parameters

WCDMA Urban

Suburban

Rural

UL

UL

UL

BS Antenna Gain (dBi)

18.0

18.0

18.0

BS Combiner Loss (dB)

0.0

0.0

0.0

BS RX Cable Loss (dB)

–1.0

–1.0

–1.0

BS Noise Figure (dB)

3

3

3

Thermal Noise (dBm/Hz)

–174

–174

–174

BS Receiver Noise Density (dBm/Hz)

–171

–171

–171

Peak Data Rate (dB)

40.9

40.9

40.9

Required Eb/No Set Point (dB)

4.2

4.2

4.2

Eb/No Std Deviation (dB)

0.0

0.0

0.0

Mean Eb/No (dB)

4.2

4.2

4.2

Effective BS Sensitivity (dBm)

–125.9

–125.9

–125.9

Body Loss (dB)

–2.0

–2.0

–2.0

Building Penetration Loss (dB)

–20.0

–15.0

–10.0

Confidence (Cell Edge)

90%

90%

90%

BS Parameters

Network Design Margins

Lognormal Shadow Std Dev (dB)

8.0

8.0

8.0

Path Loss Slope Y

3.5

3.5

3.5

Lognormal Shadow Margin (dB)

–10.3

–10.3

–10.3

Confidence (Cell Area)

97%

97%

97%

CDMA Soft Handoff Gain at 50% Correlation (dB)

3.0%

3.0%

3.0%

CDMA Traffic Loading (%)

50%

50%

50%

CDMA Traffic Loading Effect (dB)

–3.0

–3.0

–3.0

Maximum Allowable Path Loss (dB)

131.6

136.6

141.6

201

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Table 7.4 CDMA2000 FL/DL Voice Link Budget

CDMA2000 Peak Data Rate (bps)

9,600

9,600

Urban

Suburban Rural

Origin

DL

DL

DL

BS TX Power (Watts)

Input:

20.0

20.0

20.0

BS TX Power (dBm)

Calc:

43.0

43.0

43.0

Forward Overhead (%)

Input:

25%

25%

25%

Power Allocated to All Voice Users (%) Input:

100%

100%

100%

Maximum Power Allocated to a User (%)

Input:

10%

10%

10%

Maximum TX Traffic Power/User (Watts)

Calc:

1.5

1.5

1.5

Maximum TX Traffic Power/User (dBm)

Calc:

32

32

32

BS Combiner Loss (dB)

Input:

0.0

0.0

0.0

BS Cable Loss (dB)

Input:

–1.0

–1.0

–1.0

BS Antenna Gain (dBi)

Input:

18.0

18.0

18.0

EIRP/User (dBm)

Calc:

48.8

48.8

48.8

MS Antenna Gain (dBi)

Input:

0.0

0.0

0.0

MS Combiner Loss (dB)

Input:

0.0

0.0

0.0

MS RX Cable Loss (dB)

Input:

0.0

0.0

0.0

FLMS Noise Figure (dB)

Input:

8

8

8

Thermal Noise (dBm/Hz)

Calc:

–174

–174

–174

MS Receiver Noise Density (dBm/Hz)

Calc:

–166

–166

–166

Peak Data Rate (dB)

Calc:

39.8

39.8

39.8

Required Eb/No Set Point (dB)

Input:

6.0

6.0

6.0

Eb/No Std Deviation (dB)

Input:

0.0

0.0

0.0

Mean Eb/No (dB)

Calc:

6.0

6.0

6.0

Effective MS Sensitivity (dBm)

Calc:

–120.2

–120.2

–120.2

Variable

Input

9,600

BS Parameters

MS Parameters

Coverage and Capacity Improvements in 3G Networks

203

Table 7.4 (continued)

CDMA2000 Peak Data Rate (bps)

9,600

9,600

Urban

Suburban Rural

Origin

DL

DL

DL

Body Loss (dB)

Input:

0.0

0.0

0.0

Building Penetration Loss (dB)

Input:

–20.0

–15.0

–10.0

Confidence (Cell Edge)

Input:

90%

90%

90%

Lognormal Shadow Std Dev (dB)

Input:

8.0

8.0

8.0

Lognormal Shadow Margin (dB)

Calc:

–10.3

–10.3

–10.3

Soft Handoff Gain at 50% Correlation (dB)

Input:

3.0

3.0

3.0

50%

50%

50%

–3.0

–3.0

–3.0

137.9

142.9

147.9

Variable

Input

9,600

Network Design Margins

Target Loading (%) Interference Margin (Noise Rise) (dB) Maximum Allowable Path Loss (dB)

Input

forward link, as opposed to the selection diversity employed in the uplink. The WCDMA DL budget is shown in Table 7.5. Whether we consider CDMA2000 or WCDMA, the coverage versus capacity trade-off remains the same with only slight differences in the actual MAPL and cell sizes. In the remainder of this chapter, the analysis will focus on WCDMA networks, although the same methodology is equally applicable to CDMA2000 networks. In Figure 7.2, we compare the uplink and downlink coverage versus capacity trade-off for 12.2 Kbps WCDMA voice in a macrocell, where we can see that for small loads or throughputs the uplink is the limiting link for coverage. At very high number of users, the downlink may become the coverage limiting link, assuming that sector capacity can be supported.

7.4 Data Applications Tables 7.6 and 7.7 show the uplink and downlink budgets for a pedestrian A channel for WCDMA. For the downlink (Table 7.6), we have assumed that 80% of the total available traffic power is given to data users. Furthermore, the maximum power of each user is limited to 30% of the total traffic power.

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Table 7.5 WCDMA FL/DL Voice Link Budget

WCDMA Urban

Suburban

Rural

Peak Data Rate (bps)

Input

12,200

12,200

12,200

Variable

Origin

DL

DL

DL

Input:

20.0

20.0

20.0

Calc:

43.0

43.0

43.0

Input:

0%

0%

0%

Power Allocated to All Voice Users (%)

Input:

100%

100%

100%

Maximum Power Allocated to a User (%)

Input:

10%

10%

10%

Maximum TX Traffic Power/User (Watts)

Calc:

2

2

2

Maximum TX Traffic Power/User (dBm)

Calc:

33

33

33

BS Combiner Loss (dB)

Input:

0.0

0.0

0.0

BS Cable Loss (dB)

Input:

–1.0

–1.0

–1.0

BS Antenna Gain (dBi)

Input:

18.0

18.0

18.0

EIRP/User (dBm)

Calc:

50

5

50

MS Antenna Gain (dBi)

Input:

0.0

0.0

0.0

MS Combiner Loss (dB)

Input:

0.0

0.0

0.0

MS RX Cable Loss (dB)

Input:

0.0

0.0

0.0

MS Noise Figure (dB)

Input:

8

8

8

Thermal Noise (dBm/Hz)

Calc:

–174

–174

–174

MS Receiver Noise Density (dBm/Hz)

Calc:

–166

–166

–166

Peak Data Rate (dB)

Calc:

40.9

40.9

40.9

Required Eb/No Set Point (dB)

Input:

7.0

7.0

7.0

Eb/No Std Deviation (dB)

Input:

0.0

0.0

0.0

Mean Eb/No (dB)

Calc:

7.0

7.0

7.0

Effective MS Sensitivity (dBm)

Calc:

–118.1

–118.1

–118.1

BS Parameters BS TX Power (Watts) BS TX Power (dBm) Forward Overhead (%)

1

MS Parameters

1. Overhead already accounted for in Eb/No values

Coverage and Capacity Improvements in 3G Networks

205

Table 7.5 (continued)

WCDMA Urban

Suburban

Rural

Peak Data Rate (bps)

Input

12,200

12,200

12,200

Variable

Origin

DL

DL

DL

Body Loss (dB)

Input:

0.0

0.0

0.0

Building Penetration Loss (dB)

Input:

–20.0

–15.0

–10.0

Confidence (Cell Edge)

Input:

90%

90%

90%

Lognormal Shadow Std Dev (dB)

Input:

8.0

8.0

8.0

Lognormal Shadow Margin (dB)

Calc:

–10.3

–10.3

–10.3

Soft Handoff Gain at 50% Correlation (dB)

Input:

3.0

3.0

3.0

50%

50%

50%

–3.0

–3.0

–3.0

135.9

140.9

145.9

Network Design Margins

Target Loading (%) Interference Margin (Noise Rise) (dB) Maximum Allowable Path Loss (dB)

Figure 7.2

Input

WCDMA 12.2-Kbps voice coverage versus capacity in uplink and downlink.

Figure 7.3 shows the results of coverage versus capacity analysis for data users in an urban area. Similar to voice application, the uplink remains as the limiting link for coverage for data users. Note that the data rate service with the lowest allowed propagation loss determines the cell range. The voice coverage for the uplink is smaller than downlink for both systems and for all environments. This

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Table 7.6 WCDMA UL Data Link Budget

Parameters

WCDMA Urban

Suburban

Rural

UL

UL

UL

Mobile Environment

Ped. A

Ped. A

Ped. A

ntblTarget FER

10.00%

10.00%

10.00%

Peak Data Rate (bps)

64,000

64,000

64,000

MS TX PO (Watts)

0.2

0.2

0.2

MS TX PO (dBm)

23

23

23

MS Combiner Loss (dB)

0.0

0.0

0.0

MS Cable Loss (dB)

0.0

0.0

0.0

MS Antenna Gain (dBi)

0.0

0.0

0.0

EIRP (dBm)

23

23

23

Traffic EIRP (dBm)

23

23

23

BS Antenna Gain (dBi)

18.0

18.0

18.0

BS Combiner Loss (dB)

0.0

0.0

0.0

BS RX Cable Loss (dB)

–1.0

–1.0

–1.0

BS Noise Figure (dB)

3

3

3

Thermal Noise (dBm/Hz)

–174

–174

–174

BS Receiver Noise Density (dBm/Hz)

–171

–171

–171

Peak Data Rate (dB)

48.1

48.1

48.1

Required Eb/No Set Point (dB)

2.2

2.2

2.2

Eb/No Std Deviation (dB)

0.0

0.0

0.0

Mean Eb/No (dB)

2.2

2.2

2.2

Effective BS Sensitivity (dBm)

–120.7

–120.7

–120.7

Body Loss (dB)

0.0

0.0

0.0

Building Penetration Loss (dB)

–20

–15

–10

Confidence (Cell Edge)

90%

90%

90%

Lognormal Shadow Std Dev (dB)

8.0

8.0

8.0

MS Parameters

BS Parameters

Design Margin

Coverage and Capacity Improvements in 3G Networks

207

Table 7.6 (continued)

Parameters

WCDMA Urban

Suburban

Rural

UL

UL

UL

Path Loss Slope Y

3.5

3.5

3.5

Lognormal Shadow Margin (dB)

–10.3

–10.3

–10.3

Confidence (Cell Area)

97%

97%

97%

CDMA Soft Handoff Gain at 50% Correlation (dB)

2.0

2.0

2.0

CDMA Traffic Loading (%)

50%

50%

50%

CDMA Traffic Loading Effect (dB)

–3

–3

–3

Maximum Allowable Path Loss (dB)

129.5

134.5

139.5

Table 7.7 WCDMA DL Budget

Urban

Suburban

Rural

Variable

DL

DL

DL

Peak Data Rate (bps)

384,000

384,000

384,000

FER

10.00%

10.00%

10.00%

BS TX Power (Watts)

20.0

20.0

20.0

BS TX Power (dBm)

43.0

43.0

43.0

Power Allocated to All Data Users (%)

80%

80%

80%

Maximum Data Power Allocated to a User (%)

30%

30%

30%

Maximum TX Traffic Power/User (Watts)

4.8

4.8

4.8

Maximum TX Traffic Power/User (dBm)

36.8

36.8

36.8

BS Combiner Loss (dB)

0.0

0.0

0.0

BS Cable Loss (dB)

–1.0

–1.0

–1.0

BS Antenna Gain (dBi)

18.0

18.0

18.0

EIRP/User (dBm)

53.8

53.8

53.8

BS Parameters

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Table 7.7 (continued)

Urban

Suburban

Rural

DL

DL

DL

MS Antenna Gain (dBi)

0.0

0.0

0.0

MS Combiner Loss (dB)

0.0

0.0

0.0

MS RX Cable Loss (dB)

0.0

0.0

0.0

MS Noise Figure (dB)

8

8

8

Thermal Noise (dBm/Hz)

–174

–174

–174

MS Receiver Noise Density (dBm/Hz)

–166

–166

–166

Peak Data Rate (dB)

55.8

55.8

55.8

Required Eb/No Set Point (dB])

5.3

5.3

5.3

Eb/No Std Deviation (dB)

0.0

0.0

0.0

Mean Eb/No (dB)

5.3

5.3

5.3

Effective MS Sensitivity (dBm)

–104.9

–104.9

–104.9

Body Loss (dB)

0.0

0.0

0.0

Building Penetration Loss (dB)

–20.0

–15.0

–10.0

Confidence (Cell Edge)

90%

90%

90%

Lognormal Shadow Std Dev (dB)

8.0

8.0

8.0

Lognormal Shadow Margin (dB)

–10.3

–10.3

–10.3

Soft Handoff Gain at 50% Correlation (dB)

2.0

2.0

2.0

Target Loading (%)

50%

50%

50%

Interference Margin (Noise Rise) (dB)

–3.0

–3.0

–3.0

Maximum Allowable Path Loss (dB)

127.4

132.4

137.4

Variable MS Parameters

Design Margins

is favorable to 3G asymmetric services, such that the coverage design can be done based on the uplink and support higher data rates for downlink. As the bit rate goes high, the coverage shrinks. From the results shown in Figures 7.2 and 7.3, we can draw the following observations:

Coverage and Capacity Improvements in 3G Networks

Figure 7.3

209

WCDMA 64-Kbps data coverage versus capacity in uplink and downlink.

• The downlink has a better MAPL than the uplink for symmetric ser-

vices (e.g., voice services and data services with 64Kbps peak rate on both links). • This implies that in those cases, the coverage will be uplink limited. This means that some base station transmit power that would have otherwise been used up in a larger cell size on the downlink is now available and can be employed to support higher data rates on the downlink at the cell edge. • In the case of asymmetric services (e.g., 384 Kbps on the DL and 64 Kbps on the UL), the downlink coverage can become the limiting factor. The load factor on the DL is much higher than the UL. Moreover, the base station can run out of power much faster than in the case of voice services, in which system capacity is more likely to become power limited than code limited. In CDMA systems there is always a trade-off between coverage and capacity. To provide large cell sizes, especially in early deployment phases, the network is designed with a low UL load factor that limits system capacity. Increasing capacity requires an increase in the target load factor, which results in a smaller cell size. The UL load factors for voice and 64 Kbps data are shown in Figures 7.4 and 7.5.

7.5 Limiting Links for Coverage and Capacity To determine how to optimize and maximize the impact of employing smart antennas to improve system performance, we first need to identify the areas that limit that performance or, more precisely, we need to identify whether the system is coverage limited or capacity limited and which link is the limiting one.

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Smart Antenna Engineering

Figure 7.4

WCDMA 12.2-Kbps voice interference rise versus uplink load.

Figure 7.5

WCDMA 64-Kbps data interference rise versus uplink load.

7.5.1

Coverage Limited Scenarios

In general, for low bit rate services such as voice and for small cell loading (small throughput situations), the uplink is the limiting link for coverage. The reason is mainly due to the limitation of the mobile terminal transmit power. This is despite other factors in favor of the uplink, such as receive antenna diversity and better receiver design, available at the base station. On the other hand, as the number of users increases and/or for higher bit rates (i.e., as the sector throughput is allowed to increase, for example, by increasing the target uplink load), the downlink becomes the limiting link. This is shown in Figure 7.6, where for 384 Kbps on the downlink and 64 Kbps on the uplink, the coverage becomes downlink limited. The reason is that in the downlink, the maximum transmit power is the same, regardless of the number of users or their bit rates, and is

Coverage and Capacity Improvements in 3G Networks

Figure 7.6

211

WCDMA 384/64-Kbps data coverage versus capacity in uplink and downlink.

always shared among all downlink users, whereas in the uplink, each additional user has its own power amplifier. Therefore, in the downlink, in addition to the maximum base station total power, individual users have power limits that depend on the number of users in the cell and their bit rates. This power limit might become the deciding factor in the coverage. 7.5.2

Capacity Limited Scenarios

The two key factors in determining which link is limiting the system capacity are the base station transmit power and uplink target load factor. As we have seen earlier, there is a trade-off between system coverage and capacity; that is, increasing the sector throughput (in other words, the capacity or number of users) shrinks the coverage. Normally, the initial system design is based on the coverage requirement, which is why the initial target uplink load factor is low so that the coverage area is maximized. This would lead to an uplink capacity limited scenario as the maximum uplink load is reached before the base station runs out of transmit power. For a network designed with high uplink load, such as in urban environments, the system may become downlink capacity limited when the base station runs out of transmit power before the uplink load is reached. This is especially true when the traffic is asymmetric with higher traffic (bit rate) in the downlink. Table 7.8 lists key factors by which the coverage and capacity limiting links are evaluated for the downlink and uplink. Techniques that can be used to alleviate the limiting link(s) are listed in Table 7.9.

7.6 Smart Antennas Impact on Uplink Coverage and Capacity Because coverage is mostly uplink limited, except for very high data rates or low PA capabilities (e.g., microcells), this section will focus on evaluating the coverage improvements that can be obtained using antenna arrays at the base station. The sensitivity of the BS receiver is determined by the noise figure, the

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Table 7.8 Limiting Link Evaluation

Link

Coverage

Uplink

Limiting Factors

Reasons

Limiting Factors

Reasons

Required mobile ERP.

MAPL inversely proportional to data rate.

UL loading.

Low target uplink load factor.

Highest data rate. Downlink

Capacity

Number of active users/sector.

BS power shared BS transmit power. among all downlink Traffic channels/ users. sector.

Symmetric traffic. High target uplink load factor. Asymmetric traffic (higher traffic on the DL).

Table 7.9 Coverage and Capacity Improvement Techniques

Limiting Link

Coverage Indications

Uplink

Downlink

Solutions

UL MAPL < DL Improve UL MAPL link budget

DL MAPL < UL Improve DL MAPL link budget

Capacity Indications

Solutions

UL load at maximum.

Improve UL load equation.

Total BS power below maximum.

Improve BS Eb/No.

UL load below maximum.

Improve DL load equation.

BS transmit power at maximum.

Improve DL link budget. Improve DL Eb/No.

maximum symbol rate received, the noise floor of the RF environment, and the Eb/No set point. If we can reduce this sensitivity requirement this would translate to a capacity increase and/or reduced mobile transmit power. Let C be the carrier power and N and IMAI be noise and multiple access interference power; then the receiver sensitivity is given by [4]

Coverage and Capacity Improvements in 3G Networks

S=

C C = N + I MAI I tot

213

(7.5)

and the cell load can be shown to be [5] α=

I MAI I tot

(7.6)

where the multiple access interference is given by I MAI = I SC + I OC

(7.7)

If the smart antenna can lower the receiver’s sensitivity requirement to S´, a capacity increase of β and a power reduction of δ can be achieved as follows S'=

δC C = N '+I ' M (1 − α)I tot + βδαI tot

(7.8)

It follows that the capacity gain and power reduction are given by

( )

δ S S' + α − 1 β= δα δ=

(1 − α) S

S' − βα

(7.9)

(7.10)

In Figure 7.7, we plotted δ as a function of the net gain that can be achieved using smart antennas for different load factors. The net gain is defined as the total sum of the directivity gain, diversity gain or loss, and other types of losses, such as combining loss associated with some fixed beam implementations. In a 75% loaded system, a 1 dB gain in the receiver’s sensitivity could lead to a 3-dB power reduction. When the transmit power is reduced the mobile battery life is extended. On the other hand, if the same mobile transmit power is maintained, this could translate to a range extension. Figure 7.8 shows the expected capacity increase we can achieve with no power reduction. A 3-dB gain with a 40% loaded system corresponds to about a 250% capacity increase. However, in the above situation if we allow the capacity to increase, the system load will also increase.

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Figure 7.7

Power reduction versus gain.

Figure 7.8

Capacity increase versus net gain with no power reduction.

To control and maintain the same system load, the above expression for the capacity improvement is modified as follows

β=

( )

δ S S' 1− α+ δα

(7.11)

We plotted (7.11) in Figure 7.9 in a 50% loaded system for different power reduction factors. It can be seen how capacity increase and power

Coverage and Capacity Improvements in 3G Networks

Figure 7.9

215

Capacity increase and power reduction trade-off in a 50% loaded system.

reduction can be traded off. For a 3-dB gain, power can be reduced by 50% and capacity will increase by 33%. A capacity improvement of 67.5 % could be obtained with a 4-dB gain. Several approaches can be used to evaluate the performance impacts of smart antennas [5–34]. They vary from extensive link and system level simulations to analytical techniques that look at outage probabilities and statistical capacity analysis. An alternative way to evaluate the impact of smart antennas on the uplink that is adopted in this chapter is to use the link budgets and load equations. Using smart antennas on the uplink provides two link level benefits, the first is a reduction of the required BS Eb/No and the second is an aperture gain, both of which improve the uplink coverage. From (7.3) we can see that the uplink load factor is a function of the data rate, Eb/No, and number of users. Therefore, reducing Eb/No lowers the load factor for a given number of users or sector throughput. This reduction in uplink load factor for a given system throughput improves both coverage and capacity. The coverage is improved because the lower load factor results in a higher MAPL. The capacity is increased because the number of users and, hence, the sector throughput can be increased until the load factor reaches the target load. Figure 7.10 illustrates the coverage improvements achievable using four fixed beam antennas, assuming an Eb/No reduction of 3 dB [23]. We notice that the gains are dependent on the number of users (throughput) or, more precisely, on the load factor. The gains are largest for high loads because, at those levels, the interference rise over thermal would normally be so high that when smart antennas are used significant reductions can be obtained. This is demonstrated in Figure 7.11, where we can see the greatest impact at high loads. Similarly, capacity gains can

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Figure 7.10

Uplink coverage improvements with multiple fixed beam antennas.

Figure 7.11

Uplink interference reduction with multiple fixed beam antennas.

be obtained, as shown in Figure 7.12. The actual increase in the uplink capacity is a function of the target load factor. The higher the target load the higher the capacity gains. In summary, with smart antennas at the base station, we can increase the uplink capacity by raising the uplink target load factor without sacrificing coverage because the reduction in Eb/No and array gain can limit the interference level and improve coverage.

7.6.1

Smart Antenna Impact on Downlink Capacity

As we have seen earlier, the capacity is typically downlink limited. In this section, we will present the link level performance gains of two different

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217

(a)

(b)

Figure 7.12 antennas.

(a, b) Uplink capacity improvements versus load factor with multiple fixed beam

approaches, namely transmit diversity and fixed beam antennas, and show how the downlink sector capacity can be increased. 7.6.1.1 Transmit Diversity

Transmit diversity techniques were discussed in detail in Chapter 5. We will now examine the diversity gain that can be obtained using open loop and closed loop transmit diversity techniques and show how this gain can be traded off for capacity improvements. Figures 7.13 and 7.14 compare the diversity gains of OTD, STS, and TXAA approaches versus speed and multipath for CDMA2000 9.6-Kbps voice users [8, 19].

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Smart Antenna Engineering

Figure 7.13

Open loop versus closed loop transmit diversity gains in CDMA2000.

Figure 7.14

OTD versus STS transmit diversity gains in CDMA2000.

Figure 7.15 shows the diversity gains obtained in WCDMA 12.2-Kbps voice with STTD and CLTD techniques [35]. The diversity gain can be defined in different ways; here, we consider the reduction in transmit Ec/Ior (ratio of traffic channel power to total transmit power) or Eb/No. We can summarize the results as follows: • Closed loop techniques such as TXAA provide the largest gains, fol-

lowed by STS and then OTD. This is because the feedback from the mobile stations improves channel estimation and the application of more accurate weights.

Coverage and Capacity Improvements in 3G Networks

Figure 7.15

219

Open loop versus closed loop transmit diversity gains in WCDMA.

• Diversity gain is greatest when time and multipath diversity perfor-

mance is poor (i.e., for low-mobility users with little multipath). This is because such users spend more time in periods with deep fades, thus affecting the performance. • High-speed users experience far less diversity gain and almost negligible

performance improvement. This is in contrast to beamforming techniques that offer performance improvements independent of the user’s speed, as we will discuss later in this chapter. Figure 7.16 shows the diversity gain as a function of the geometry G, defined as the ratio of the intracell interference power spectral density to the

Figure 7.16

Transmit diversity gains versus geometry in CDMA2000.

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Smart Antenna Engineering

intercell interference power spectral density or I sc / I oc = I$or / I oc . We can see that regardless of the diversity technique used, the diversity gain is higher for low G values (i.e., when the intercell or other cell interference is dominant). This is usually the case at the cell edge. For high G values (i.e., close to the base station), the intracell interference is more dominant and we do not get as much diversity gain. Diversity gain or, in other words, the reduction in Ec/Ior or Eb/No can improve both coverage and capacity on the downlink. For instance, in microcells where the coverage might be downlink limited due to low total base station transmit power, this diversity gain can be employed to improve coverage. In macrocells, the coverage is typically uplink limited and the capacity is usually downlink limited, in which case we can make use of the diversity gain to improve the downlink capacity. Figure 7.17 shows the reduction in downlink load factor and, consequently, the interference rise as the sector throughput (load) is increased in a WCDMA system carrying voice traffic at 12.2 Kbps, assuming an STTD diversity gain of 2 dB. Note that the maximum capacity gain with this performance improvement would be 100.2 or roughly 60%. However, the actual capacity gain depends on the target load factor to which the system is designed. The higher the load factor (smaller coverage), the higher the capacity gain, as illustrated in Figure 7.18. For example, at 40% target load the sector throughput can be increased from 483 Kbps to 724 Kbps, a 50% capacity improvement, whereas the capacity can be increased from about 905 Kbps to 1,450 Kbps, equivalent to 60% capacity improvement at 90% target load. Similarly, the downlink coverage can be improved with transmit diversity, as can be seen from Figure 7.19, where an STTD gain of 2 dB was assumed. We note the same behavior as well where the coverage gain is dependent on the load

Figure 7.17

Downlink load reduction with STTD.

Coverage and Capacity Improvements in 3G Networks

Figure 7.18

Downlink capacity improvements versus load factor with STDD macrocell.

Figure 7.19

Downlink coverage improvements with STTD macrocell.

221

factor and we do not get the same coverage improvement for all loads. One important factor that must be taken into account when we discuss the capacity improvements obtained from spatial techniques such as TD is that even if we assume that all the transmit power reduction is traded off for capacity gains, the system may simply become code limited instead of power limited and those capacity improvements would not be possible to obtain in practice unless we introduce secondary scrambling codes into the cell. This is because in voice services with SF of 128, there are approximately 60–65 users that can be supported when the common channel and handoff reduction factor are taken into account. To achieve the full benefits of TD, we must therefore increase the number of available codes. In WCDMA, a second PSC code with a corresponding group of

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orthogonal codes can be allocated, although this second group will cause some interference with the group of orthogonal codes associated with the first PSC. Similarly, CDMA2000 overcomes this problem by allocating additional codes using quasi-orthogonal functions (QOFs). Alternatively, instead of increasing the voice capacity the extra base station transmit power that is freed up as a result of the diversity gain can be used to support more high data rate users in the cell or increase the peak data rate at the cell edge. 7.6.1.2 Beamforming

Although coverage and capacity are coupled in the sense that conventionally when one is improved the other will be degraded, when smart antennas are used for coverage and capacity improvement it is possible to improve both simultaneously. Antenna arrays with more than two elements that implement either fixed beam or user-specific schemes can provide greater gains than transmit diversity. The gain has two main components, aperture gain and spatial filtering gain. The aperture gain is proportional to the number of elements M and is given by 10Log (M). Spatial filtering confines the interference in a limited AS, therefore the gain is greatest for low AS because the interference is confined to a small angular region and is decreased as the AS is increased, since more interference is received or spread into the system. Figure 7.20 presents the gains in terms of reduction in downlink Eb/No in a macrocell [2]. We can see that the gains increase as the number of beams (elements) is increased and are greatest for low AS. In the previous section we showed how the uplink coverage and capacity can be improved when beamforming is implemented at the base station. Figures 7.21 and 7.22 show the improvement we can achieve when smart antennas are

Figure 7.20

Beamforming gains versus angular spread in WCDMA [2].

Coverage and Capacity Improvements in 3G Networks

223

145

MAPL [dB]

140 Capacity gain

135 130 125

Coverage gain

150

120

UL (3-sectors) UL (4 Fixed beams) DL (3-sectors) DL (2 Fixed beams,AS=2)

115 110 105 100 0

200

400

600

800

1000 1200 1400

Throughput [kbps]

Figure 7.21 Downlink/uplink coverage and capacity improvements with multiple fixed beam antennas. Macrocell, small AS.

Figure 7.22 Downlink/uplink coverage and capacity improvements with multiple fixed beam antennas. Macrocell, large AS.

used at the base station on both the uplink and downlink for 12.2-Kbps voice service. With four fixed beams on the uplink providing approximately 3 dB of gain in Eb/No and two beams on the downlink with Eb/No gain of 2.2 dB at an AS of 2°, the sector throughput can be increased from 905 Kbps to 1,208 Kbps, whereas the coverage is improved by about 9 dB. The Eb/No gains for AS of 20°

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are slightly less (1.8 dB). Higher AS results in more interference received on the uplink or spread in the downlink since the angular region is wider, which reduces the performance gains slightly. The gains are a result of improvements in the uplink budget, uplink load factor (lower interference rise), aperture gain, and lower interference in the downlink. That is an improvement in both the link budget and load equation. Higher gains are possible if four beams are used on the downlink as well. For instance, the downlink coverage is significantly improved for 384/64 Kbps data, as shown in Figure 7.23, with the antenna array providing 4.5-dB gain in Eb/No. The actual capacity gains in terms of in sector throughput are dependent on the downlink target load. Figure 7.24 compares those gains for different target loads.

Figure 7.23

Downlink coverage improvements with multiple fixed beam antennas.

Figure 7.24

Downlink capacity improvements with multiple fixed beam antennas.

Coverage and Capacity Improvements in 3G Networks

225

7.6.1.3 Microcell Environments

In a microcell the base station transmit power is usually limited and is much lower than the macrocell case. Therefore, the coverage in a microcell is more likely to be downlink limited. Another major difference between the two types of cells is that the angular spread in a microcell is much larger than in a macrocell. As we have seen in previous chapters, as the AS increases the correlation between antenna elements is decreased; as a result, the diversity gain also increases. Therefore, transmit diversity might be more effective in these environments because the beamforming performance in terms of interference reduction is affected by the large AS. In [2] STTD and CLTD capacity increases of 50% and 75%, respectively, were reported in a microcell environment. Figure 7.25 compares the reduction in the interference rise (gains in capacity) using STTD in both macrocells and microcells. The downlink orthogonality factor and

(a)

(b)

Figure 7.25 Downlink capacity improvements versus interference rise with STTD: (a) macrocell, and (b) microcell.

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other-to-same cell interference ratio used in the calculations are 0.9 and 0.4, respectively, for the microcell, as opposed to 0.5 and 0.65 for the macrocell case. For a 40% target load factor (about 2.2 dB noise rise), a 65% improvement in the throughput can be achieved for the microcell case versus an approximate 50% increase in the macrocell case.

7.7 Conclusions The applications of different smart antenna techniques in 3G networks were discussed in this chapter, including transmit diversity and beamforming. Table 7.10 provides a brief comparison between TD and Beamforming. Methods to determine the coverage and capacity limiting links and techniques to improve the overall system performance were described. On the uplink, antenna arrays at the base station enable the system to tune itself for optimized signal reception. The result is that the received signal level is improved by a factor of M (number of antenna elements); at the same time, interference is significantly reduced. Similar gains also occur when smart antennas are used on the downlink. When the system is tuned for optimized signal transmission the power level of the transmitted signal is a factor of M over the power emitted by a single antenna at the base station. At the same time, less interference is spread on the downlink. The reduction in interference allows an increase either in the number of subscribers using the system or in the overall signal quality, which enables higher data throughput. The benefit of this reduction in interference networkwide is, in both cases, an increase in spectral efficiency. Smart antenna gains are not limited to just providing an aperture gain, but they also provide improvements in Eb/No that directly translate into coverage and capacity gains. All wireless systems suffer some degree of fading. Since the environment is dynamic, this fading is time varying. The consequence for wireless system designers is that the air interface must be robust to sudden outages and margins against fading must be introduced into link budgets and cell planning, which reduces coverage. Fading is substantially mitigated when multiple antennas are used. When one antenna fades in the array, chances are that others do not. The output of the array is, therefore, much smoother over time. Thus, there is a reduction in the needed margin against fading, which is often referred to as a “diversity gain” that is in addition to the aperture gain. The amount of this gain depends on the targeted outage probability, the detailed assumptions regarding the fading process, and the number of antennas. Simply put, smart antenna systems fundamentally improve the coverage and spectral efficiency trade-offs of wireless systems, although some trade-offs between cost, coverage, and capacity remain in the wireless system design.

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227

Table 7.10 Transmit Diversity Versus Beamforming Performance Comparison

Transmit Diversity

Beamforming

Mobile Speed

Performance degrades with increasing mobile speed.

Performance independent of mobile speed.

Angular Spread

Performance improves with increasing AS.

Performance degrades with increasing AS.

Interference

Not effective against interference.

Very effective in interference limited scenarios. Significant interference reduction capabilities. Performance improves with increasing number of beams/elements.

FLMacrocell

Less effective than beamforming due to relatively lower AS.

Effective. Largest performance improvements in low AS cases.

Microcell

More suitable than beamforming.

Performance affected by the increased AS.

UL Coverage Limited

N/A

Performance gains could be traded off for coverage improvement.

UL Capacity Limited

N/A

Performance gains could be traded off for capacity improvement.

DL Coverage Limited

Performance gains could be traded off for coverage improvement.

Performance gains could be traded off for coverage improvement.

DL Capacity Limited

Performance gains could be traded off for capacity improvement.

Performance gains could be traded off for capacity improvement.

Geometry G

Better gains at cell edge (low geometry).

Performance independent of geometry.

References [1]

Holma, H., and A. Toskala, WCDMA for UMTS: Radio Access for Third Generation Mobile Communications, 3rd ed., New York: John Wiley & Sons, 2004.

[2]

Laiho, J., T. Novosad, and A. Wacker, Radio Network Planning and Optimization for UMTS, New York: John Wiley & Sons, 2001.

[3]

Kim, K. L., Handbook of CDMA System Design, Engineering, and Optimization, Prentice Hall, N. J., 2000.

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[4] EL Zooghby, A. H., “Potentials of Smart Antennas in CDMA Systems and Uplink Improvements,” IEEE Antennas and Propagation Magazine, Vol. 43, No. 5, October 2001, pp. 172–177. [5] Lee, H. W., J. Yeom, and D. K. Sung, “Performance Analysis of Downlink Time Switched Transmit Diversity (TSTD) in W-CDMA System,” IEEE 51st Vehicular Technology Conference Proc., Vol. 1, May 15–18, 2000, pp. 561–565. [6] Tiirola, E., and J. Ylitalo, “Comparison of Beamforming and Diversity Approaches for the Coverage Extension of W-CDMA Macro Cells,” IEEE 54th Vehicular Technology Conference, Vol. 3, Oct. 7–11, 2001 pp. 1274–1278. [7] Dabak, A. G., et al., “A Comparison of the Open Loop Transmit Diversity Schemes for Third Generation Wireless Systems,” IEEE Wireless Communications and Networking Conference, Vol. 1, September 23–28, 2000, pp. 437–442. [8] Soni, R. A., and R. M. Buehrer, “On the Performance of Open-Loop Transmit Diversity Techniques for IS-2000 Systems: A Comparative Study,” IEEE Trans. on Wireless Communications, Vol. 3, No. 5, September 2004, pp. 1602–1615. [9] Rohani, K., “Open-loop Transmit Diversity for CDMA Forward Link,” IEEE Emerging Technologies Symposium: Broadband, Wireless Internet Access, April 10–11, 2000, p. 5. [10] Hak-Seong K., W. Lee and Y. Shin, “Performance Analysis of an Open Loop Transmit Diversity for Rician Multipath Wideband CDMA Channels,” IEEE Seventh International Symposium on Spread Spectrum Techniques and Applications, Vol. 2, September 2–5, 2002, pp. 400–404. [11] Qiang, Y., and D. Li, “Performance Analysis of Several Open-loop Transmit Diversity Schemes for IMT-2000 Systems,” International Conference on Communication Technology Proc., Vol. 2, April 9–11, 2003, pp. 1107–1110. [12] Canales, M., et al., “Performance Analysis of Downlink Transmit Diversity System Applied to the UTRA FDD Mode,” IEEE Seventh International Symposium on Spread Spectrum Techniques and Applications, Vol. 2, September 2–5, 2002, pp. 410–414. [13] Soni, R. A., and R. M. Buchrer, “Open-Loop Transmit Diversity in IS-20000 Systems,” Conference Record of the Thirty-Third Asilomar Conference on Signals, Systems, and Computers, Vol. 1, October 24–27, 1999, pp. 654–658. [14] Hamalainen, J., and R. Wichman, “Closed-Loop Transmit Diversity for FDD WCDMA Systems,” Conference Record of the Thirty-Fourth Asilomar Conference on Signals, Systems, and Computers, Vol. 1, Oct. 29–Nov. 1, 2000, pp. 111–115. [15] Vanganuru, K., and A. Annamalai, “Analysis of Transmit Diversity Schemes: Impact of Fade Distribution, Spatial Correlation and Channel Estimation Errors,” IEEE Wireless Communications and Networking, Vol. 1, March 16–20, 2003, pp. 247–251. [16] Vishwakarma, R., and K. S. Shanmugan, “Performance Analysis of Transmit Antenna Diversity in 3G WCDMA System,” IEEE International Conference on Personal Wireless Communications, December 17–20, 2000, pp.1–4. [17] Rohani, K., M. Harrison, and K. Kuchi, “A Comparison of Base Station Transmit Diversity Methods for Third Generation Cellular Standards,” IEEE 49th Vehicular Technology Conference, Vol. 1, May 16–20, 1999, pp. 351–355. [18] Rajan, D., and S. D. Gray, “Transmit Diversity Schemes for CDMA-2000,” IEEE Wireless Communications and Networking Conference, September 21–24, 1999, pp. 669–673.

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[19] Derryberry, R. T., et al., “Transmit Diversity in 3G CDMA Systems,” IEEE Communications Magazine, Vol. 40, No. 4, April 2002, pp. 68– 75. [20] Goransson, B., et al., “Advanced Antenna Systems for WCDMA: Link and System Level Results,” 11th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Vol. 1, September 18–21, 2000, pp. 62–66. [21] Sousa, V. A., Jr., et al., “Coverage and Capacity of WCDMA Systems with Beam Steering Antennas,” IEEE 58th Vehicular Technology Conference, Vol.2, Oct. 6–9, 2003, pp. 826–830. [22] Bae, K. K., J. Jiang, and W. H. Tranter, “Downlink WCDMA Performance Analysis with Diversity Techniques Combined with Beamforming,” IEEE Wireless Communications and Networking, Vol. 1, March 16–20, 2003, pp. 202–206. [23] Ylitalo, J., and E. Tiirola, “Performance Evaluation of Different Antenna Array Approaches for 3G CDMA Uplink,” IEEE 51st Vehicular Technology Conference Proc., Tokyo, Vol. 2, May 15–18, 2000, pp. 883–887. [24] Kogiantis, A. G., “Uplink Capacity Studies for Adaptive Antenna Arrays in Third Generation CDMA Wireless Systems,” IEEE Wireless Communications and Networking Conference, Sept. 21–24, 1999, pp. 679–683. [25] Lee, J., and R. Arnott, “System Performance of Multisector Smart Antenna Base Stations for WCDMA,” Second International Conference on 3G Mobile Communication Technologies, (Conf. Publ. No. 477), March 26–28, 2001, pp. 1–6. [26] Baumgartner, T., T. Neubauer, and E. Bonek, “Performance of Downlink Beam Switching for UMTS FDD in the Presence of Angular Spread,” IEEE International Conference on Communications, Vol. 2, April 28–May 2, 2002, pp. 851–855. [27] Yong Z., and F. Zhenghe, “Capacity Analysis of CDMA Networks with Smart Antenna,” Canadian Conference on Electrical and Computer Engineering, Vol. 2, May 13–16, 2001, pp. 1333–1336. [28] Soni, R. A., R. M. Buehrer, and R. D. Benning, “Intelligent Antenna System for CDMA2000,” IEEE Signal Processing Magazine, Vol. 19, No. 4, July 2002, pp. 54–67. [29] Goransson, B., B. Hagerman, and J. Barta, “Adaptive Antennas in WCDMA Systems-Link Level Simulation Results Based on Typical User Scenarios,” IEEE 52nd Vehicular Technology Conference, Vol. 1, September 24–28, 2000, pp. 157–164. [30] Barta, J., S. Petersson, and B. Hagerman, “Downlink Capacity and Coverage Trade-offs in WCDMA with Advanced Antenna Systems,” IEEE 55th Vehicular Technology Conference, Vol. 3, May 6–9, 2002, pp. 1145–1149. [31] Barta, J., et al., “Interference Distributions in Mixed Service WCDMA Systems—Opportunities for Advanced Antenna Systems,” IEEE 53rd Vehicular Technology Conference, Vol. 1, May 6–9, 2001, pp. 263–267. [32] Osseiran, A., et al., “Downlink Capacity Comparison Between Different Smart Antenna Concepts in a Mixed Service W-CDMA System,” IEEE 54th Vehicular Technology Conference, Vol. 3, October 7–11, 2001, pp. 1528–1532. [33] Ericson, M., et al., “Capacity Study for Fixed Multibeam Antenna Systems in a Mixed Service WCDMA System,” 12th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Vol. 1, September 30–October 3, 2001, pp. A-31–A-35.

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[34] Zhou, Y., et al., “Performance Comparison of Transmit Diversity and Beamforming for the Downlink of DS-CDMA System,” IEEE Trans. on Wireless Communications, Vol. 2, No. 2, March 2003, pp. 320–334. [35] Parkvall, S., et al., “Transmit Diversity in WCDMA: Link and System Level Results,” IEEE 51st Vehicular Technology Conference Proc., Tokyo, Vol. 2, May 15–18, 2000, pp. 864–868.

8 Smart Antennas System Aspects 8.1 Introduction Air interface standards have recently started to take smart antenna techniques into account, making it possible to optimize radio system design for spatial processing and integrate those advanced antennas into future adaptive modems. The main advantages expected with smart antennas include higher sensitive reception, interference cancellation in uplink and downlink functions, and mitigation of multipart fading effects. On the system level, this will lead to higher capacity, extended range, improved coverage of current dead spots, higher quality of service, lower power consumption at the mobile, and improved power control (PC). While smart antennas increase system complexity and cost, they also provide an additional degree of freedom for radio network control and planning. To improve radio network performance, the propagation and interference environment as well as expected traffic and user’s mobility in the cell should be taken into account when optimizing the smart antenna receiver structures and algorithms. While smart antenna receiver parameters are important for capacity, coverage, and interference planning, they also interact with different network control protocols. In addition, when evaluating different smart antenna receivers and algorithms one must consider their high degree of dependence on the air interface and its parameters, such as the multiple access method, the type of duplexing, pilot availability, modulation, diversity, physical channels splitting, and frame structure. Smart antenna algorithms should also be compatible with radio network protocols because link level control protocols must maintain the required link quality dynamically while carrying out channel and interference monitoring. Several smart antennas strategies are available in the radio network design and planning, including: 231

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• The use of smart antennas at the BS in the uplink, only to increase cov-

erage. Figure 8.1 illustrates this concept. • The use of smart antennas at the uplink and downlink simultaneously

to improve coverage and capacity. • The use of smart antennas at mobiles alone without installing them at

the BS, achieving similar improvement in coverage and capacity as with smart antennas at the BS, although the extent of such an approach would be limited by the cost and complexity of the mobile stations. • The use of smart antennas at both ends to allow several parallel chan-

nels to be established between the mobile and BS. In this case, higher bit rate transmission can be achieved by splitting data streams between parallel channels. • The use of space-time (ST) coding, which exploits transmit diversity

techniques with a MIMO channel. Several of these topics are discussed in more detail in the remainder of this chapter.

8.2 Third Generation Air Interfaces and Protocol Stacks Wireless mobile communications standards are layered architectures defined in terms of a protocol stack, which describes different layers that handle different

Adaptive beamforming

Fixed multiple beams

Sectorization

Coverage (cell range/area)

Adaptive beamforming

Fixed multiple beams

Sectorization

Low interference environment

Figure 8.1

Coverage improvement comparison.

High interference environment

Smart Antennas System Aspects

233

functions on the physical, logical, and other levels. The interactions and mappings between the layers are also defined. The two major 3G standards are the CDMA2000 evolution of IS-95 and the WCDMA evolution of GSM/GPRS. In this section, we will briefly discuss and compare the differences and similarities between those two technologies, both based on CDMA. The WCDMA protocol stack has a uniform structure[1–5]. Signaling and user data (voice, packet-switched data, circuit-switched data) all flow through the RLC and MAC layers by using logical and transport channels configured to support the appropriate quality of service. Mobility management, call and session management, short message service, and supplementary services functions are defined in the nonaccess stratum (NAS) layer specifications. The architecture, functionality, and interfaces of the NAS layers are specified in 3GPP specifications in a way that is tightly coupled with the access stratum layers. The CDMA2000 protocol stack is layered, but the layering is not as uniform as in the WCDMA protocol stack. For example, the link access control (LAC) layer corresponds to the RLC layer of WCDMA, but only signaling flows through the LAC layer. User data flows directly into the MAC layer. The Radio Link Protocol (RLP) sublayer is architecturally part of the MAC layer, but it is not specified in the MAC layer specification. Instead, it is specified as part of the IS-707 data services specification. In CDMA2000, upper layer signaling, or layer 3, includes functions that in WCDMA would be separated into NAS and RRC layers. Layer 3 is responsible for radio resource functions, such as handoffs and channel configurations, as well as mobility management and call control functions, such as registrations, call origination, and call release [6–10]. A comparison between the WCDMA and CDMA2000 stacks is shown in Figure 8.2.

8.3 Physical Layer 8.3.1

Data Multiplexing

One main difference between WCDMA and CDMA2000 is the way different data streams are multiplexed in the physical layer, as shown in Figure 8.3. Data streams can be voice, signaling, or data at various data rates, quality requirements, block sizes, traffic patterns, and such. To accommodate the need to carry multiple data streams, WCDMA uses time multiplexing, whereas CDMA2000 uses code multiplexing. CDMA2000 does include time multiplexing of multiple data streams at the multiplex sublayer, which is above the physical layer. Because this time multiplexing occurs above the physical layer, however, all the multiplexed data streams receive the same treatment with respect to error correction, interleaving, and so on. In WCDMA, multiplexing occurs in the physical layer after error correction and interleaving are individually applied to each transport channel. A lot of the differences in the details between the WCDMA

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Smart Antenna Engineering

NAS

Upper layer signaling

Layer 3

Data services

RRC

Data services

Voice services

Layer 3 Voice services

LAC sublayer

RLC

MAC sublayer

Layer 2

RLP RLP RLP

Layer 2

MAC sublayer

SRBP Multiplexing and QOS

Multiplexing and QOS

Layer 1

Layer 1

Physical layer

Physical layer (b)

(a)

Figure 8.2

3G standards protocol stacks: (a) WCDMA and (b) CDMA2000 protocol stacks.

WCDMA Voice

Data

CDMA2000 Signaling

Voice

Data

Signaling

Multiplexing

TrCh

TrCh

TrCh

TrCh

Physical layer

TrCh multiplexing

PhCh

PhCh

PhCh

Physicaionl channel segmentation Channeliztion Channeliztion Channeliztion Channelization

PN scrambling

Figure 8.3

Physical layer interaction with upper layers.

PN scrambling

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and CDMA2000 physical layers stem from this difference in basic approach. The WCDMA standard allows the time-multiplexed data stream to be split among multiple code channels; however, data streams passed from higher layers are always time multiplexed into a single stream first. In almost all known deployments, this multicode option is not used and the time-multiplexed data is sent over a single code channel. 8.3.2

Transmit Chain UL/RL PN Scrambling/Spreading

QPSK modulation is used for both WCDMA and CDMA2000 air interfaces. QPSK is a four-quadrant signaling pattern, where each signal is represented by a single point in the quadrant. All possible transitions between constellation points are allowed between successive QPSK symbols. HPSK is a variation of this, where only certain transitions are allowed. In HPSK, on alternate symbols, 180° phase transitions, otherwise known as zero-crossings, are not allowed; these symbols can only move to adjacent quadrants (90° phase transitions). This helps reduce the waveforms power peak to average ratio. In WCDMA, gold codes are used for scrambling, where each MS is assigned one of 224 codes by the network. CDMA2000 employs M-sequences for spreading. Each MS calculates a mask applied to the long code (of period 242 - 1). The masked long code is XORed with the CDMA2000 I-Q short codes. 8.3.3

DL/FL Physical Channel Formatting

A frame is the basic building block of a physical channel. In WCDMA, a frame is 10 ms long, whereas a CDMA2000 frame is 20 ms long. The frame structures of the forward and reverse links of the dedicated channels of both CDMA2000 and WCDMA are shown in Figure 8.4. In WCDMA, a downlink (forward link) frame consists of data bits: transport format combination indicatior (TFCI) bits, used to signal the data format to avoid the need to perform blind transport format detection; time-multiplexed power control (TPC) bits; and dedicated pilot bits. The TPC bits are essentially the inner loop power commands that inform the transmitter to increase or decrease its power. In WCDMA, channel estimation is based on the primary CPICH and can be improved if the dedicated pilot bits are also included, resulting in a better SIR estimation for downlink power control. The most important function of the dedicated pilot bits is their use in user-specific beamforming, which will be explained in detail later. In addition to TFCI, TPC, and dedicated pilot bits, a WCDMA uplink frame also includes FBI bits. The FBI bits are used only with closed loop transmit diversity to enable the MS to transmit the necessary set of weights back to the base station based on the quality of channel estimation. The major difference between the downlink and uplink frames is that data is time multiplexed with the control bits on the

236

Smart Antenna Engineering WCDMA 10 ms frame

0.66 ms slot

Data 2

TPC

TFCI

Data 2

Pilot

CDMA2000 20 ms frame

1.25 ms power control group (PCG) Data

Data FL power control subchannel (a) WCDMA 10 ms frame

0.66 ms slot I Q

Pilot

Data TFCI

FBI

TPC

CDMA2000 20 ms frame

I Q

Figure 8.4

1.25 ms power control group (PCG) Pilot Data (b)

PC

Physical layer frame structures. (a) FL/DL frame structure. (b) RL/UL frame structure.

downlink but sent on the in-phase branch on the uplink, whereas control bits (signaling) are sent on the quadrature-phase branch. In CDMA2000, a forward power control subchannel is punctured into the traffic channel and carries the inner loop power control commands, whereas on the reverse link the data bits are sent on the I branch while power control and reverse link pilot bits are sent on the Q branch. Note that the dedicated pilot and TFCI bits improve the system performance at the expense of increased overhead. This overhead is small for high data rates; however, at low data rates (e.g., voice), these add significant overhead. This necessitates a larger share of the OVSF code space per user. If most users in a cell have favorable downlink conditions, the cell could run out of OVSF codes even before the cell’s maximum power limit is reached. Additional

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OVSF codes can be assigned on one or more secondary scrambling codes, but these would cause undesirable cross-interference with users on the primary scrambling code.

8.4 Mobile Call States 8.4.1

WCDMA

WCDMA provides several connected mode substates that conserve air interface resources by freeing up physical layer resources. In these substates, the layer 3 connection context is preserved in times of low traffic activity to accommodate the bursty nature of packet data traffic. Transition into these substates is controlled by the network, and each substate reflects a progressively lower level of traffic activity. These include: • CELL_DCH: dedicated traffic channels are allocated to the MS. • CELL_FACH: dedicated traffic channels are released. The MS continu-

ously monitors the FACH. There is no transmission on the uplink, and a random access procedure is needed to get back into CELL_DCH. • CELL_PCH and URA_PCH: dedicated traffic channels are released,

and the MS operates in a discontinuous receive (DRX) mode to preserve battery life (same as idle except the layer 3 connection context is preserved). URA_PCH typically requires less frequent updates than CELL_PCH by the MS to notify the network that its location has changed. The different call states are shown in Figure 8.5, whereas the procedure by which the mobile station requests access to the system over the RACH channel is illustrated in Figure 8.6. 8.4.2

CDMA2000

CDMA2000 provides for layer 3 connection preservation, though it is not reflected in the call states defined in the air interface specification. Dedicated channels are allocated to the mobile when it is in the mobile control on the traffic channel state. Layer 3 connection preservation is handled independently by the upper layers. The mobile transitions to idle state when the upper layers are in the preserved state. In the traffic channel substate (control hold mode), dedicated channels are still allocated and the mobile continuously monitors them, but the only reverse link transmission is a gated pilot. For the most efficient use of system capacity, the forward link dedicated channel would be DCCH operating in discontinuous transmission (DTX) mode. This substate uses more system capacity than the CELL_FACH state of WCDMA because of the gated reverse

238

Smart Antenna Engineering WCDMA connected modes URA_PCH

CELL_PCH

CELL_DCH

CELL_FACH Establish connection

Release connection

Establish connection

Release connection

Idle mode (a) CDMA2000 MS control on traffic channel state Traffic channel substate (active mode)

Traffic channel substate (control hold mode)

Traffic channel init substate

Release substate

System access state

Mobile station initialization state

Mobile idle state (b)

Figure 8.5

Mobile station call states: (a) WCDMA, and (b) CDMA2000.

link pilot; however, dedicated operations can be resumed more quickly because a random access procedure is not required. The CDMA2000 access procedure is shown in Figure 8.7.

8.5 Mobility Procedures to Support High-Speed Data Transfer During periods of low or no data activity, dedicated high-speed data resources are released. To reestablish high-speed data transfer, the network must keep track of the location of the MS, as described next.

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239

Start

Yes

Max preamble cycles?

Tx unsuccessful

No End Wait 10 ms

Persistence pass?

No

Wait backoff interval

Yes L1 PRACH procedure

Wait 10 ms

No

NACK

ACK received?

Wait 10 ms

Yes Transmit message

Figure 8.6

WCDMA access procedure.

Start

Max attempts?

Yes

Tx unsuccessful

No End Wait 20 ms

No

Persistence pass? Yes Transmit preamble and message

Wait backoff interval

No

ACK received? Yes Done

Figure 8.7

CDMA2000 access procedure.

240

8.5.1

Smart Antenna Engineering

Cell_FACH State or Control Hold Mode

In WCDMA Cell_FACH state, UTRAN keeps track of the location of the MS by using the cell update procedure. Whenever it moves to a new cell, the UE sends a cell update message on the RACH. In CDMA2000 control hold mode, a dedicated channel is allocated to the UE (either FCH or the discontinuous DCCH). Traffic channel handoff procedures allow the network to know in which cell or cells the UE is located. 8.5.2

Idle, Cell_PCH, or URA_PCH States

In WCDMA idle state, the UE performs routing area updates when it moves to a new routing area. To reestablish dedicated channels, UTRAN must page the UE over all cells of a routing area. If the UE is in Cell_PCH state, the cell update procedure allows UTRAN to page the mobile in a single cell. In URA_PCH state, the URA update procedure allows UTRAN to page the mobile over all cells in a UTRAN registration area (URA). In CDMA2000 idle state, the network must page the UE according to its registration and paging policy (e.g., if zone-based registration is used, a page is sent over the last registered zone).

8.6 Procedures to Reestablish High-Speed Data Transfer 8.6.1

Cell_FACH State or Control Hold Mode

In WCDMA Cell_FACH state, UTRAN sends the radio bearer setup message on the FACH common channel to assign high-speed dedicated channels. In CDMA2000 control hold mode, the network sends a message on the dedicated, discontinuous DCCH to assign high-speed dedicated channels. 8.6.2

Idle Mode, Cell_PCH, or URA_PCH States

In WCDMA idle state, the signaling is similar to initial call establishment (RRC connection, authentication, and security procedures). In Cell_PCH and URA_PCH, the RRC connection, authentication, and security procedures are not required because the RRC connection is preserved in these states. In CDMA2000 idle state, signaling is similar to initial call establishment.

8.7 Packet Data Services Because of differences in their respective protocol stacks, the WCDMA and CDMA2000 standards employ different packet scheduling approaches, as outlined next.

Smart Antennas System Aspects 8.7.1

241

WCDMA Approach

Channel Allocation

The RNC assigns a DPCH to the UE, which carries both data and signaling. The peak DPCH data rate determines the OVSF code space required. The RNC reconfigures the DPCH to a lower peak rate when less data needs to be sent or when other users have higher priority [11]. The UE sends traffic volume measurement reports to the RNC to inform the RNC of the amount of uplink data queued in the UE. Reconfiguration of the signaling channel is required every time the data channel configuration needs to be changed. The DPCH data rate can be changed on a TTI basis. This reduces the overall transmit power used by DPCH. Each transport channel has its own QoS, which includes a BLER target. However, because transport channels are multiplexed together on the same physical channel, the required SIR is effectively the same for all of the transport channels. 8.7.2

CDMA2000 Approach

Channel Allocation

The base station scheduler assigns an SCH to a mobile on an as-needed basis. The SCH data rate is a function of loading, scheduler policy, data buffer depth, and channel condition. On the reverse link, the mobile requests a channel, and the base station determines what rate to allow the mobile to use. In release 0, when an SCH is assigned, the data rate is fixed for the duration of the assignment. Only the assigned rate or zero rate is allowed. In release A, frame-by-frame variable data rates are supported. Because the data and signaling can be sent on separate channels, the power used is variable. The SCH has independent power control from the FCH/DCCH.

8.8 Pilot Channels In CDMA, a common pilot channel is broadcast throughout a sector to provide cell identification, phase reference, and timing information to the mobile stations. When a sector is subdivided into multiple narrow fixed beams, a common pilot channel cannot be used for channel estimation because the reference signal (pilot) used for channel estimation must go through the exact same path (including antennas) as the data (traffic); consequently, each antenna beam requires a separate pilot. 8.8.1

CDMA2000

In addition to the common forward pilot channel, the following pilot channels are specified in the CDMA2000 standard:

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Forward Transmit Diversity Pilot Channel

If transmit diversity is supported on the forward CDMA channel, then the transmit diversity pilot channel is used and is spread using Walsh code W16128 and is transmitted at a power level of 0, –3, –6, or –9 dB relative to the power level of the forward pilot channel. Auxiliary Pilots

When antenna arrays and antenna transmit diversity schemes are employed on the forward link, a separate pilot for channel estimation and phase tracking is needed. In CDMA2000, such pilots, referred to as auxiliary pilots, are code multiplexed with other forward link channels and use orthogonal Walsh codes. Since a pilot contains no data, auxiliary pilots may use a longer Walsh sequence to lessen the reduction of orthogonal Walsh codes available for traffic. Generating Auxiliary Pilots

Code-multiplexed auxiliary pilots are generated by assigning a different Walsh code to each auxiliary pilot. This results in a reduction of the number of orthogonal codes available for traffic channels. To overcome this limitation, the size of the Walsh code set used for auxiliary pilots is expanded. Any Walsh code W mn denoting the mth Walsh code of length n can be used to generate Nw auxiliary Walsh codes, where Nw must be a power of 2. A longer Walsh sequence is built by concatenating Nw timesW mn where each concatenatedW mn may have a different polarity. The sequence of polarity is selected such that Nw additional orthogonal Walsh sequences of order Nw × n are generated. For the case Nw = 2, the two possible Walsh functions of order 2 × n areW mnW mn andW mnW mn , where the overbar denotes a polarity change. The CDMA2000 standard specifies 512 as the maximum length of the Walsh functions that is allowed for Walsh function spreading or quasi-orthogonal function spreading of an auxiliary pilot. Obviously, Walsh function W 0n cannot be used to generate auxiliary pilots because they would interfere with the common pilot channel; in addition, concatenation of certain Walsh functions such as W16128 is not allowed by the standard. Since Walsh sequenceW i n is orthogonal to all otherW jn only if j ≠ i, it follows that if WalshW i n is used to generate an auxiliary pilot it cannot be used by another traffic channel. Auxiliary Transmit Diversity Pilot Channel

When transmit diversity is supported on the forward CDMA channel associated with an auxiliary pilot channel, then an auxiliary transmit diversity pilot channel spread with a Walsh function or a quasi-orthogonal function is used.

Smart Antennas System Aspects 8.8.2

243

WCDMA

Common Pilot Channel

The CPICH is a fixed rate downlink physical channel that carries a continuous predefined bit/symbol sequence. The spreading factor of the CPICH is 256 and the rate of the CPICH is 30 Kbps. If transmit diversity is used in the cell then the CPICH is transmitted with both antennas, where a different pilot sequence is used for the second antenna. There are two types of CPICHs, the primary and secondary CPICH. They differ in their use and the limitations placed on their physical features [12]. Primary Common Pilot Channel

The primary common pilot channel (P-CPICH) is always spread with channelization code Cch.256.0 and scrambled with the primary scrambling code of the cell. Hence, the P-CPICH can be used in the mobile to determine the scrambling code used for scrambling the downlink channels of the cell. The P-CPICH is broadcast over the entire cell and there is always only one unique P-CPICH per cell. The P-CPICH is always the phase reference for the SCH, P-CCPCH, acquisition indication channel (AICH), and PICH, and it is the default phase reference for all other downlink physical channels [1]. Secondary Common Pilot Channel

The secondary common pilot channel (S-CPICH) can be spread by any channelization code of length 256 and can be scrambled by either the primary or a secondary scrambling code. There may be zero, one, or several S-CPICHs per cell. A S-CPICH may be transmitted over the entire cell or only into a part of the cell. The S-CPICH can be the phase reference for S-CCPCH and DPCH. If this is the case, the terminal is informed by higher layer signaling [5].

8.9 Channels Applicable for Downlink Beamforming The radio interface of UMTS has been designed to allow the use of smart antennas. In the downlink there are basically two possibilities for beamforming. The two options are to transmit an S-CPICH on the beams or not. If an S-CPICH is transmitted on a beam together with the user data, then the terminals can use the S-CPICH for channel estimation, which will give a good channel estimate. If no S-CPICH is transmitted together with the user data then the dedicated pilot bits have to be used for channel estimation. Hence, only physical channels with pilot bits can use this beamforming method. Table 8.1 shows which beamforming method, if any, is applicable for the different physical channels.

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Table 8.1 WCDMA Downlink Channels Applicable for Beamforming

Physical Channel

Multiple Fixed Beams with S-CPICH

Flexible Beams with Dedicated Pilot Bits

P-CPICH

No

No

P-CCPCH

No

No

SCH

No

No

S-CCPCH (carrying PCH and FACH)

No

No

S-CCPCH (carrying only FACH)

Yes

Yes

S-CCPCH (carrying only PCH)

No

No

DPCH

Yes

Yes

PDSCH

Yes

Yes

PICH

No

No

AICH

No

No

CSICH

No

No

As was discussed in Chapter 1, smart antenna strategies that do not rely on cooperation or feedback from the mobiles can be classified into fixed beam methods and user-specific beamforming methods that dedicate a single beam or transmission pattern to each user. Figure 8.8 illustrates the difference between fixed beams and user-specific beamforming.

8.10 Overview of Major Radio Network Algorithms 8.10.1 Power Control

The aim of the PC is to keep the UL and DL quality at the required level. The PC algorithm consists of the inner loop PC and the outer loop PC. The inner loop PC adjusts the power every slot/PCG to fulfill the SIR target. The outer loop PC uses measurements of the quality in both RL/UL and FL/DL transmission and compares the measured quality with a quality target. If the quality is too low, the outer loop increases the SIR target, and vice versa. The quality measurements could be the FER/BLER/CRC but even BER and SIR might be used in more advanced schemes. The PC measurements and targets operate on each individual MS in the system.

Smart Antennas System Aspects

(a)

245

(b)

Figure 8.8 Fixed versus adaptive beamforming. (a) Fixed multiple beams. (b) User tracking with adaptive antennas.

8.10.2 Initial Power Setting

The initial power procedure is used to set the initial power of both UL and DL transmission. The procedure is used both by common and dedicated channels (i.e., the same procedures are used for continuous and discontinuous services). Before the random access procedure, the MS needs to find the appropriate random access transmit power. This is done by the preamble power ramping scheme. According to [3], the initial preamble power in WCDMA is calculated by the mobile as follows Preamble _ Initial _ Power = Primary _ CPICH _ DL _TX _ Power − CPICH _ RSCP +UL _ interference + Constant

(8.1)

The primary CPICH transmit power, Primary_CPICH_DL_TX_Power, the total interference on the UL, UL_interference, and the constant value, Constant, are all sent to the MS from UTRAN. The only parameter that is measured by the MS is the received power of the primary CPICH by the MS, CPICH_RSCP. In CDMA2000, the corresponding initial power settings to be used on the first access probe is given by Preamble _ Initial _ Power = NOM _ PWR + INT _ PWR − Pr − P _ CNST (8.2)

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Smart Antenna Engineering

The nominal correction factor for the base station, NOM_PWR, and the correction factor for the base station from path loss decorrelation between the FL and RL frequencies, INT_PWR, are signaled to the MS in the access parameters message. 8.10.3 Admission Control

The admission control (AC) algorithm accepts or rejects requests to establish new radio links. This is done by checking if the requested amount of resources is available. The algorithm is located in the RNC/BSC, where relevant information from several sectors can be obtained. The radio link request is admitted if all UL and DL AC criteria are fulfilled. The specific AC algorithms are not standardized and therefore each vendor will have its own solution. In the literature a few AC strategies are proposed, which are based on the DL power, UL interference, and throughput [13–18]. A new user is admitted if the resulting total UL interference level is lower than a predefined threshold. This could be summarized by the following equation: Th I UL + ∆I UL < I UL

(8.3)

Th where I UL is the UL interference threshold, I UL is the existing total UL interference, and ∆I UL is the UL interference increase caused by the new user, which is E a function of the bit rate b and the voice activity factor. No

8.10.4 Congestion Control

The purpose of the congestion control (CC) is to quickly stabilize the system when an overload situation occurs. An overload situation is the result of fluctuations of IUL, PDL, which mainly are caused by fading and intercell interference or variations in the carried traffic of a link. The CC algorithm uses parameters from Node B and events from the RNC as inputs. The input parameters from Node B are the measurement of UL interference, received signal strength (RSSI), and DL transmitted power, TxCPwr. The CC may use IUL, PDL and code usage for its decision. Thus, a similar reasoning as for AC can be performed. However, CC and AC are interdependent and differ in certain respects. 8.10.5 Soft/Softer Handoff

In the current CDMA standards, soft handoff between cells is based on measurements on the primary common pilot channel. There can only be one primary common pilot per sector. When the MS is in the dedicated mode it sends

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measurement reports to the base station. These messages contain any of the following measurements: • FL/DL RSCP (for received signal code power); • FL/DL Ec/No; • Path loss (i.e., distance gain, antenna gain and fading).

The measurement type and the sectors to be measured are decided by the base station (UTRAN). The MS will enter the soft handoff state when, for example, the measured DL Ec/No from a new BS is within the configured handoff margin. 8.10.6 Hard Handoff

Hard handoff (HHO) is when the system executes a handoff by removing all links in the active set and establishing a new radio link. There are several different types of HHO: • Inter BSC/RNC HHO; • Interfrequency HHO; • Intersystem HHO.

All HHO types employ the same measurements as the SHO (i.e., Ec/No, RSCP, and path loss). The exception is the intersystem HHO in WCDMA, where only RSSI received signal strength is used.

8.11 System Impact of Advanced Spatial Techniques While most of the smart antenna literature has focused on algorithm/architecture development and performance analysis of using antenna arrays, the interaction between the previously described radio network algorithms and advanced antenna systems concepts [19] has received less attention. In this section, the impact of smart antenna architectures, namely transmit diversity and beamforming on power control, admission control, and handoff, as well as other radio network algorithms, are analyzed and discussed. 8.11.1 Transmit Diversity

In both the WCDMA and CDMA2000 standards, a number of transmit diversity schemes are defined, as described in an earlier chapter. The schemes can be

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Smart Antenna Engineering

divided into open and closed loop schemes. The open loop transmit diversity schemes, STTD and STS, use a space-time code in FL/DL without any MS feedback. The closed loop schemes, on the other hand, optimize the link by adjusting the phase and amplitude of the transmission according to a feedback signaling message. Antennas used for transmit diversity should be placed as far apart as possible to experience as low correlation as possible between the antennas. The impact of transmit diversity on different radio network algorithms is discussed next. 8.11.1.1 Initial Power Setting

The traffic channels in TD are optimized for each individual user but the initial power setting is based on measurements of the common pilot. Therefore, the initial power setting algorithm will not be affected. 8.11.1.2 Admission Control

Similar to the single antenna case, the base station can just average the downlink power of both antennas and then use this single measurement in the AC algorithm (i.e., the FL/DL power is split equally). Therefore, admission control is not affected by transmit diversity. 8.11.1.3 Code Allocation

With transmit diversity and favorable channel conditions, the sector capacity is increased. This could lead to situations where the capacity becomes limited by the number of available orthogonal codes. In WCDMA, this problem is solved by allocating a second PSC code with a corresponding group of orthogonal codes, although this second group will cause some interference with the group of orthogonal codes associated with the first PSC. Similarly, CDMA2000 overcomes this problem by allocating additional codes using QOFs. 8.11.2 Fixed Beam Approach

In this scheme, the fixed beams transmit only channels carrying user-specific data, while the common channels like P-CPICH, SCH, P-CCPCH, and so on for WCDMA or pilot, paging, and sync channels in CDMA2000 are transmitted into the entire sector using a single antenna element of the antenna array. Typically, the data sent on the different beams is scrambled with the same scrambling code [WCDMA] or assigned the same PN offset; [CDMA 2000] therefore, the channels sent on the different beams are orthogonal to each other. The receive power of the common pilot channel at the mobile determines the serving sector. Within the serving sector, the mobile will be served with the beam with the highest uplink/reverse link receive power.

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8.11.2.1 Additional Equipment at the Base Station

The beamforming for the multiple fixed beam method can be done either with a passive beamforming network, as in Figure 8.9, or digital in baseband, as illustrated in Figure 8.10. In the passive beamforming network implementation, the following components are needed: • A signal processing unit capable of processing the signals of the beams

and the common channels sent/received by the sector antenna. • One transceiver chain per beam plus an additional transceiver for the

common channels transmitted and received in/from the entire sector. • A beamforming network and an antenna array per sector.

In case of digital beamforming at baseband, the following components would be needed: • A signal processing unit that is capable for handling all beams plus the

common channels sent/received with the sector antenna. M elements

Nb beams

Transceiver

Uplink/downlink passive beamforming network

Transceiver

Transceiver

Calibration needed

Figure 8.9

Passive networks fixed multiple beam architecture.

Baseband processing for multiple fixed beams and sector beam

250

Smart Antenna Engineering N b beams

M elements

Transceiver

Transceiver

Baseband processing and digital beamforming for multiple fixed beams plus sector beam

Transceiver

Calibration needed

Figure 8.10

Digital baseband fixed multiple beam architecture.

• An antenna array per sector. • A transceiver chain per antenna element of the antenna array is needed. 8.11.2.2 Scrambling Code/PN Offset Assignment

If there are so many users in a sector that the number of required orthogonal codes exceeds the codes offered by an OVSF code tree, then a new code tree is used and the new channels are scrambled with another secondary scrambling code up to 15 secondary scrambling codes per cell, as supported in WCDMA. CDMA2000 uses QOF for the same purpose. These secondary scrambling codes/QOF are not orthogonal to each other and the PSC/short PN sequence, so it is beneficial if all data channels transmitted with one beam are scrambled/spread with the same scrambling code/PN offset. To reduce the interference from the common pilot and the control channels that are sent into the whole sector and are always scrambled/spread with the PSC/PN offset, as many beams as possible should use the PSC/PN offset. If a secondary scrambling code/QOF has to be used it should first be assigned to the outermost beam that is next to the border of a sector because the neighboring sector always uses different codes.

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8.11.2.3 Channel Estimation

By default, mobiles in a CDMA system use the P-CPICH/forward pilot channel for channel estimation. In the fixed beam method the P-CPICH/forward pilot channel are transmitted with a single antenna element, whereas the data (traffic channels) are sent on a beam. Since the data channel and the P-CPICH/forward pilot channel experience different propagation and multipath channels, the common pilot channel cannot be used for channel estimation. The UMTS specification allows the signaling of the mobile via higher layer protocols to use either the dedicated pilot bits that are time multiplexed with the user data or to use an S-CPICH that is transmitted with the same beam as the user data, for channel estimation. The S-CPICH has the same structure as the P-CPICH but is spread with a different channelization code and scrambled with the same scrambling code as the data channels on the beam. There are two drawbacks for using the dedicated pilot bits for channel estimation in the multiple fixed beam case. First, since the dedicated pilot bits are transmitted with low energy compared with a P-CPICH or S-CPICH, the channel estimation in the mobile will be worse, which would in turn increase the Eb/No to achieve the required service quality [20]. Second, since a single beam serves more than one user it is more beneficial to use S-CPICHs. One drawback of using the S-CPICH is that some of the base station transmit power will need to be allocated to the pilots associated with every beam (e.g., 6 dB below the P-CPICH), thereby increasing the overhead and reducing the power available for dedicated channels. For a small number of beams this should be offset by the increased capacity inherent in using beamforming. The spreading and scrambling code of the S-CPICH to use for channel estimation is signaled to the mobile via higher layer messages generated at the RNC. In UMTS it is not possible that a mobile uses two different S-CPICHs from one sector as phase reference, so each mobile can be served only by one beam per sector. In CDMA2000, each beam can be assigned a code multiplexed auxiliary pilot, which decouples the pilot from the actual traffic user data being sent. Hence, the pilot reference is not bound to a particular user data stream. This permits multiple mobile stations to be placed in the same fixed beam using a common auxiliary pilot. 8.11.2.4 Beam Transition

For scenarios where the Node B uses multiple fixed beams with one S-CPICH assigned per beam, the mobile’s active set is determined by measuring the mobile’s uplink receive power in the different beams. In order for the mobile to transition from one beam to another, it needs to get informed that it should use another S-CPICH. This requires higher layer signaling between Node B and RNC. For each MS, the Node B can measure the uplink-received power of the pilot symbols in all the beams where an S-CPICH is assigned. These measurements are averaged in the Node B and then reported to the RNC. Based on

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Smart Antenna Engineering

these measurements, the RNC determines whether a beam transition or handoff is needed or not. Hence, the beam handoff algorithm can be implemented in coherence with the conventional sector handover algorithms. 8.11.2.5 Soft-Handoff Procedures in Fixed Beam Mode

From an operational perspective, each code multiplexed auxiliary pilot used on a particular sector can be part of the sector’s neighbor list. The mobile station will then search for that auxiliary pilot in a similar way to the search for other sectors’ pilots (the mobile station will search for a different Walsh pilot instead of searching for other PN offsets). The mobile station can report the auxiliary pilot (that is, the fixed beam) using the same procedures as used for reporting another regular sector. In this way, mobile stations can go in and out of the coverage of the beam in a similar way to the soft-handoff mechanism between sectors. This allows placement of mobile stations in the coverage of the beam when interference conditions require it. This extra capability can be made optional by not instructing the mobile stations to search for auxiliary pilots (e.g., for individual beam mode). 8.11.2.6 Power Control

When an MS moves through a cell it will change the DL beam depending on the position. This may result in a change of the antenna and channel gain. However, it is likely that the MS cannot separate the beam change from ordinary fast fading and no specific problems will arise. 8.11.2.7 Preamble Initial Power Setting

The initial power algorithm will be affected by the introduction of a fixed beam concept. To explain how, first the UL and then the DL initial power algorithm is examined. Uplink Preamble Power Setting

To calculate the correct UL initial preamble power value for a fixed beam system, the MS ideally needs to measure the UL gain and the corresponding UL interference per beam for the cell. This will enable the MS to correctly estimate the necessary initial preamble power. The 3GPP standard states that the MS must measure the DL gain on the primary CPICH (see 8.1). A solution to this problem could be to have the MS measure the received power of the primary CPICH transmitted by the wide beam, RSCP. However, this leads to some difference in the interference level due to the difference between the wide beam gain and the narrow beam gain. If the total RSSI at the base station is used for UL interference measurement, the measured UL interference is higher than the interference in a separate beam (due to the spatial suppression/reduction of the interference in the UL). Thus, there will be some difference between the wide

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253

beam UL interference and the narrow beam UL interference. This difference can easily be compensated or accounted for by adjusting the constant in (8.1) both for the antenna gain difference and the UL interference difference. Downlink Initial Power Setting

The DL initial DCH power setting assumes that the MS is able to measure the SIR on the downlink primary CPICH and then transmit the measurement to the UTRAN. Even though the gain from the wide beam may differ from the narrow beam gain, the DL interference measured by the MS is correct. If the difference between the wide beam gain and the envelope of the narrow beam gain is known (equal for all angles or as a function of the angle), this can be exploited by the UTRAN. The power setting algorithm simply adjusts the initial power with the gain difference. 8.11.2.8 Reverse Link/Uplink Call Admission Control

When the base station receives a new call setup request from a mobile station, it checks its own resources and then the call is either admitted if there are available resources or otherwise blocked. System performance in terms of stability is typically measured using blocking and dropping probabilities, that is, the number of blocked calls and dropped calls over the total number of call attempts. Blocking occurs when SINR < SINRreq, whereas existing calls are dropped when the SINR drops below a certain threshold (i.e., in the reverse link/uplink, a conventional CDMA network estimates the load of a sector cell with the help of the wide band received power at the base station I$tot , which reflects the interference at the base station). Let the uplink load ηUL of a sector be given as a percentage of the pole capacity (i.e., 0≤ηUL≤1) and the noise rise be given by Nrise where N rise =

I$tot σ N2

(8.4)

and σ 2N is the background and receiver noise. The relation between the sector load and noise rise is given by N rise =

1 1 − ηUL

(8.5)

As new mobiles attempt to get admission to the system, the interference increases due to the new user, ∆I based on the actual sector load, and the required Eb/No for the requested service. Admission is granted as long as the expected resulting interference after admission of the user does not exceed a certain threshold.

254

Smart Antenna Engineering

I tot + ∆I ≤ I th

(8.6)

The value of the threshold is up to the system designer but the threshold should be chosen so that at the corresponding load a small increase in system load does not cause any tremendous interference increase. From Figure 8.11, a reasonable threshold for Nrise is around 6 dB. In [21] Hara suggests a new admission control scheme on the reverse link that takes into account adaptive beamforming at the base station. The algorithm’s flowchart is shown in Figure 8.12 and can be summarized as follows: • When a call is attempted, the mobile’s ID is retrieved and the corre-

sponding steering vector/matrix A is calculated. • The interference plus noise correlation matrix RN is then calculated and 2 the SINR for the new user is estimated as SINR = σ 2s AH R −1 N A, where σ s is the signal power.

• The estimated SINR is then compared with the required SINR and the

call is admitted if SINR > SINRreq, otherwise, the call is blocked. 8.11.2.9 Radio Resource Management

The radio resource management includes admission control, load control, packet scheduling, handoff control, and congestion control and is implemented in the RNC/BSC, as the RNC/BSC has knowledge of the load of all sectors in its radio network subsystem. The task of the admission control is to ensure that the network stays stable after a new user has been granted access to the network. The handoff control checks if the system stays stable after any update of a mobile’s active set, whereas the task of the packet scheduler is to adjust the data

Figure 8.11

Interference rise over thermal noise.

Smart Antennas System Aspects

255

Receive a new access probe / RACH message

Retrieve mobile station ID

Calculate array steering vector

Estimate SINR for new MS

Estimated SINR > SINR req ?

No

Block new call

Yes Admit new call

Figure 8.12

Uplink call admission control.

rates of the nonreal-time services so that the system is operating as close as possible at its maximum throughput. 8.11.2.10 Forward Link/Downlink Admission Control

For the downlink/forward link, it is well known that the total transmit power of a base station, Ior, is a good measure of the load of a CDMA sector [17]. A new mobile is granted access if the estimated average transmit power of a sector after admission of the new mobile stays below a certain threshold I or + ∆I or < I orth

(8.7)

where I orth is a function of the power amplifier capacity, power control dynamic range, and power allocated to common channels. The increase of the transmit power of the sector after admission of the new user, ∆Ior, is estimated using the measured path loss to the new mobile, the interference at the mobile, and the required Eb/No of the requested service. The same procedure is also followed for handoffs. Similarly, the task of the packet scheduler is to adjust the data rate of the downlink nonreal-time services so that the actual total downlink transmit power of the cells is as close as possible to I orth . The same holds true for the uplink, where the task is to send as much data as possible so that the maximum

256

Smart Antenna Engineering

sector load is reached as close as possible while not exceeding the maximum allowed sector load. Since the capacity gains from using adaptive antennas is highly dependent on the spatial interference distribution, which in turn depends on the user’s spatial distribution and data rates, the AC algorithm applied in conjunction with adaptive antennas must also take the directions of the mobiles into account, as suggested by Pedersen in [22–24]. From Chapter 5 we know that the antenna array power pattern is given by I or ( θ ) = I or A H RA = I orG ( θ )

(8.8)

where G (θ) is the normalized antenna array gain. As we described earlier, the common channels are all transmitted from a sector wide beam, whereas the dedicated traffic channels are transmitted from directional narrow beams. In the multiple fixed beam case, we will assume that every MS is served by only one beam on the downlink and an auxiliary pilot/S-CPICH is transmitted on each beam. Note that the common pilot power, auxiliary pilot/S-CPICH power, and overhead common channels powers are constant, whereas the dedicated traffic channels are power controlled. Assuming we have Nb beams, we can express the average transmit power per beam as I or n = P nSCPICH + P nDPCHtot I or n = P nAux .Pilot + P nTraffic tot

for WCDMA

(8.9)

for CDMA2000

(8.10)

It then follows that the total base station transmitted power in all Nb beams is given by [24] I or = MG ( θ )I beams + P common or

(8.11)

where

[

I beams = I or1 or

I or 2

L I orN b

]

T

(8.12)

Hence, a directional power-based AC algorithm can be formulated as follows, the load/power transmitted in every beam is monitored, and a new user is only admitted to the beam if the following condition is satisfied: I beams ( θ n ) < I th or

∀n ∈ [1, 2, L N b ]

(8.13)

That is, before a mobile is admitted, the power increase in all beams must be calculated to ensure that the above condition is met to grant admission. This

Smart Antennas System Aspects

257

is due to the fact that in CDMA when a new user is admitted to a sector/beam the downlink interference level in the system is increased and, consequently, the transmit power in all other beams will also increase to overcome the interference. In a conventional CDMA network, when an MS is denied admission to a sector because it violates (8.7), the sector capacity cannot increase. When the directional power-based AC is applied in conjunction with adaptive arrays, an MS at some AOA θ 1 can be denied admission in beam number i if it violates (8.13) but another MS at a different AOA θ 2 requesting admission into beam j, E i j could satisfy (8.13) and gains admission. This is because c , the required I or1 E dedicated channel power to support MS at θ 1 , might be much greater than c , I or2 the power necessary to support MS at θ 2 . The admission control algorithm can be summarized in the following steps. First, the current system resources [e.g., the base station transmit power level (load)] need to be estimated. The second step involves estimating the amount of transmit power that would be required to support the new user as well as the amount of power increase needed to support existing users. The reason for the power rise is that admitting the new user will raise the interference level in the sector. Let us assume that the new user requests admission to beam number k, then an estimate of the power required for each beam can be expressed as traffic I or n , new = P nPilot + P nTraffic tot ⋅ ∆ p , n + P new

I or n , new = P nPilot + P nTraffic tot ⋅ ∆ p , n

for n = k

for n ≠ k

(8.14) (8.15)

where ∆ p , n is a factor that expresses the increase in transmit power for existing users after admission of the new user given by

∆ p ,n

E 1  1+ b ⋅  N t 2PG = E 1 1 + b ⋅ G (θ )  N t 2PG

(8.16)

To estimate the transmit power required to support the new user, we start E with the required traffic b expressed as Nt

258

Smart Antenna Engineering

MLG ( θ )  Eb  W = ⋅ P Traffic ⋅   N th + I oc + I sc  N t  Traffic R

(8.17)

Rearranging the above equation we get the following expression for the transmit power for the new user E  R N + I oc + I sc Traffic P new = b ⋅ ⋅ th = MLG ( θ )  N t  Traffic W N + I oc + I sc  Eb  1 R ⋅ ⋅ ⋅ th   L  N t  Traffic W MG ( θ )

(8.18)

where M is the number of antenna elements and L is the path loss. In CDMA systems, the pilot is continuously measured by the MS and the measurement is sent to the base station in the measurement report message/pilot strength measurement message, either on a periodic basis or when specific events are triggered. The actual pilot measurement is expressed as E  Ec  RSCP Pilot P Pilot L L ⋅ = = = I ortot ⋅  c    RSSI N th + I oc + I sc  I or  Pilot N th + I oc + I sc  N o  Pilot (8.19)

8.12 Beam Steering/Adaptive Beamforming Beam steering is a user-specific beamforming method, where each user is served with an individual beam. In the multiple fixed beams method, users are served with the beam with the lowest path loss. Beam steering produces a unique beam for each user in order to transmit the signal for a user only into the direction where the signal experiences the lowest path loss while traveling to the user while simultaneously keeping the transmit power into other directions as low as possible. However, this only applies to dedicated traffic channels, although the common channels still have to be transmitted in the entire sector with a single antenna element of the antenna array. Just as it is done in a conventional UMTS system, the serving sectors are determined through measurement of the P-CPICH quality at the mobile. The scrambling code assignment for beam pointing is analog to the switched beam method. For radio resource management, an analog algorithm for the switched beam method can be used where (4.10) is checked for a set of equidistant directions. Figure 8.13 illustrates the equipment needed for user-specific beamforming, including:

Smart Antennas System Aspects

259

M elements Transceiver

Transceiver

Baseband processing and digital beamforming for multiple users plus sector beam

Transceiver

Calibration needed

Figure 8.13

Adaptive beamforming architecture.

• A signal processing unit capable of serving, calculating, and applying

appropriate antenna weights for all users, plus common channels sent/ received into/from the entire sector. • An antenna array. • A power amplifier per antenna element of the antenna array. 8.12.1 Channel Estimation at the Mobile

Like in systems that use the fixed beam scheme, the mobiles served by a userspecific beam cannot use the common pilot channel for channel estimation because this pilot sent with a single antenna element experiences a different radio channel than that experienced by the user data sent with a narrow beam. When user-specific beamforming like beam steering is used in a UMTS system, then each MS is served by an individual beam. Due to the high number of beams and the fact that only a single mobile is served by each beam, it is not effective to transmit an S-CPICH on every beam because this will significantly increase the downlink power allocated to overhead, thus reducing the system

260

Smart Antenna Engineering

capacity. The solution for this is to use the dedicated pilot bits for channel estimation. The use of the dedicated pilot bits causes a degraded channel estimate at the mobile as the energy in the dedicated pilot bits compared with the energy of the CPICH is lower. Therefore, the mobiles using the dedicated pilot bits for channel estimation will require a higher Eb/No to achieve their desired service quality than mobiles that could use a CPICH [20]. When adaptive beam steered channels are employed in CDMA2000 systems, an L3 message directs the mobile station to a beam steered channel where an auxiliary pilot channel will be used by the mobile station to coherently demodulate the traffic channel. The mobile station continues receiving the transmission on the beam steered channel until an L3 message directs the mobile station to a nonbeam steered channel or terminates transmission on the beam steered channel. 8.12.2 Advantages and Disadvantages

The advantage of beam steering is that the transmit power is essentially concentrated toward the desired users. Therefore, beam steering should provide a higher capacity gain than the fixed beam methods. In addition, as the serving beam tracks the mobile, there is no need to transition the mobile from beam to beam while it is traveling through the coverage area of the sector, like it is necessary in fixed beam schemes. Hence, far less signaling is needed than in fixed beam methods, where signaling is necessary every time a mobile moves from the coverage area of one beam to another. The main disadvantage of beam steering is that the base station has to determine the optimum transmit direction for all active users in the sector, which is computationally very intensive compared with the determination of the serving beam in the fixed beam method. 8.12.3 Uplink Beamforming

For a particular mobile station transmit power, demodulation performance of the reverse link can be improved by using narrow beams and increasing the antenna gain in the direction of one or more mobile stations. Adaptive beamforming of the RL mobile station transmission is one such method. As discussed in a previous chapter, in many beam-forming applications a reference signal is required for adjusting the RL beam pattern. The RL pilot channel provides the reference for adjusting the RL beam pattern for each mobile station. Unlike the FL, one RL pilot channel is used for traffic channel demodulation regardless of the technique used for adjusting the antenna pattern. 8.12.3.1 Power Control

Unlike the case of fixed beams, no changes of antenna and channel gain are anticipated. Therefore, beam steering/adaptive beamforming techniques will impact the current power control algorithms.

Smart Antennas System Aspects 8.12.3.2

261

Initial Power Setting

As far as the initial power setting for an access probe/RACH, the MS will not be able to differentiate between a steered beam solution and a fixed beam solution. Therefore, the expected impact in both cases is similar. 8.12.3.3 Admission Control

The same AC concepts previously discussed for fixed multiple beam systems are equally applicable with steered beams/adaptive beamforming techniques. For instance, a per-beam AC algorithm based on the DL/FL power can be employed, whereas an SINR-based algorithm can be used on the uplink. 8.12.3.4 Soft/Softer/Hard Handoff

The steering beam architecture interaction with the SHO algorithm is very similar to the fixed beam case. The only difference is that it is not feasible to assign one pilot per beam if a beam is to track a single user.

8.13 Conclusion In this chapter, system aspects of smart antennas and the interaction of different techniques with the various layers were provided. Figure 8.14 illustrates the main system components or processes affected by smart antennas. A summary of the impact of all three smart antennas techniques discussed in this Radio network control (Resource management, power control, etc...)

Network planning and dimensioning

Smart antennas

Receiver structure and algorithms

Air interface

Figure 8.14

Smart antenna integration in system design.

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Smart Antenna Engineering

chapter, namely transmit diversity, fixed beamforming, and adaptive (user-specific) beamforming, on various radio network algorithms is also provided in Table 8.2.

Table 8.2 Smart Antennas’ Impact on Radio Network Algorithms

Concept

Impact Power Control

Preamble Power Setting

Admission Control

Congestion Control

SHO/HHO

Code Allocation

Transmit Diversity

None

None

None

None

None

Minor

Fixed Multiple Beams

Minor

Minor

Significant

Significant

Medium

Significant

Adaptive Beamforming

None

Medium

Significant

Significant

Medium

Significant

References [1] 3GPP TS 25.211, Physical Channels and Mapping of Transport Channels onto Physical Channels (FDD). [2] 3GPP TS 25.213, Spreading and Modulation (FDD). [3] 3GPP TS 25.214, FDD: Physical Layer Procedures. [4] 3GPP TS 25.302, Services Provided by the Physical Layer. [5] 3GPP TS 25.331, Radio Resource Control (RRC) Protocol Specification. [6] TIA/EIA/IS-2000.2, Physical Layer Standard for CDMA2000 Spread Spectrum Systems. [7] TIA/EIA/IS-2000.3, Medium Access Control (MAC) Standard for CDMA2000 Spread Spectrum Systems—Addendum 2. [8] TIA/EIA/IS-2000.4, Signaling Link Access Control (LAC) Standard for CDMA2000 Spread Spectrum Systems—Addendum 2. [9] TIA/EIA/IS-2000.5, Upper Layer (Layer 3) Signaling Standard for CDMA2000 Spread Spectrum Systems—Addendum 2. [10] Vanghi, V., A. Damnjanovic, and B. Vojcic, CDMA2000 System for Mobile Communications, Prentice Hall PTR, March 2004. [11] Holma, H., and and A. Toskala, WCDMA for UMTS, Radio Access for Third Generation Mobile Communications, New York: John Wiley & Sons, 2000.

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263

[12] 3GPP TR 25.887 V6.0.0, Technical Report Beamforming Enhancements. [13] Kazmi, M., and P. Godlewski, “Admission Control Strategy and Scheduling Algorithms for Downlink Packet Transmission in WCDMA,” Proc. IEEE Vehicular Technology Conference, Boston, MA, September 2000. [14] Yates, R., and C. Y. Huang, “Call Admission in Power Controlled CDMA System,” Proc. IEEE Vehicular Technology Conference, 1996. [15] Magda El Zarki Zhao Liu, “SIR-based CAC for DS-CDMA Cellular Systems,” IEEE Journal in Selected Areas in Communications, May 1994. [16] Redana, S., and A. Capone, “Received Power-Based Call Admission Control Techniques for UMTS Uplink,” IEEE 56th Vehicular Technology Conference Proc., Vol. 4, 2002, pp. 2206–2210. [17] Perez-Romero, J., et al., “A Downlink Admission Control Algorithm for UTRA-FDD,” 4th International Workshop on Mobile and Wireless Communications Network, 2002, pp. 18–22. [18] Kuri, J., and P. Mermelstein, “Call Admission on the Uplink of a CDMA System Based on Total Received Power,” IEEE International Conference on Communications, Vol. 3, 1999, pp. 1431–1436. [19] Boukalov, A. O., and S. G. Häggman, “System Aspects of Smart-Antenna Technology in Cellular Wireless Communications—An Overview,” IEEE Trans. on Microwave Theory and Techniques, Vol. 48, No. 6, June 2000, pp. 919–929. [20] Qaraqe, K. A., and S. Roe, “Channel Estimation Algorithms for Third Generation WCDMA Communication Systems,” IEEE VTS, 53rd Vehicular Technology Conference, 2001. VTC 2001 Spring. Vol. 4, Mau 6–9, 2001. pp. 2,675–2,679 [21] Hara, Y., “Call Admission Control Algorithm for CDMA Systems with Adaptive Antennas,” Proc. IEEE Vehicular Technology Conference, Boston, MA, September 2000. [22] Ramiro-Moreno, J., K. Pedersen, and P. Mogensen. “Direction Power-Based Admission Control for WCDMA Systems Using Antenna Arrays,” Proc. of 53rd IEEE Vehicular Technology Conference, Vol. 1, May 2001, pp. 53–57. [23] Pedersen, K. I., P. E. Mogensen, and J. Ramiro-Moreno, “Application and Performance of Downlink Beamforming Techniques in UMTS,” IEEE Communications Magazine, Vol. 41, No. 10, October 2003, pp. 134–143. [24] Pedersen, K. I. and P. E. Mogensen, “Directional Power-Based Admission Control for WCDMA Systems Using Beamforming Antenna Array Systems,” IEEE Trans. Vehicular Technologies, Vol. 51, No. 6, November 2002, pp. 1294–1303.

9 Mobile Stations’ Smart Antennas 9.1 Introduction With the advent of mobile high-speed data applications, it is expected that the downlink of 3G CDMA systems will be the limiting link as far as capacity is concerned. Hence, it is important to investigate methods that can increase the downlink capacity to cope with increasing demand. Performance enhancements that are possible with diversity reception in wireless systems are well known and have been employed in cellular systems for decades on base station receivers [1, 2]. Commercial implementation of some form of diversity in wireless devices has also been reported [3–11]. Widespread implementation of diversity-equipped wireless devices has not been achieved yet for several reasons. The most important hurdle is that the implementation of diversity reception increases the complexity and cost of mobile stations. The second reason, the lack of widespread commercialization of diversity handsets for cellular systems, relates to the fact that the benefits that could be obtained from such devices are technology dependent. CDMA-based networks are well suited to benefit from any reduction in the interference levels in the system even if only a portion of the mobiles are equipped with diversity receivers. The capacity improvement in the downlink is simply proportional to the percentage of the advanced handsets (i.e., those equipped with receiver diversity). This is the case because a dual receiver unit requires a smaller amount of base station transmit power, thus allowing more simultaneous connections for an equivalent amount of spectrum and average power constraint. The downlink capacity of a CDMA system is inversely proportional to the base station transmit power required to maintain a given level of service to each user [12]. Under power control, the base station (or multiple base stations when in soft handoff) adjusts the power fraction 265

266

Smart Antenna Engineering

transmitted to each user to maintain a given level of service. Let the energy per E information bit over total noise plus interference density be denoted by b ; this Nt is the key parameter that characterizes the performance of any digital communication system. For a given transmission format and receiver design, the value of Eb determines the error performance of the digital link and is a function of the Nt channel characteristics. In CDMA systems, this value must be varied to maintain a given error rate over a variety of channel conditions. This is accomplished through outer loop power control, which sets a target value based on the desired quality of service. Let the processing gain, defined as the ratio between the W spread spectrum chip rate and the information bit rate, be denoted by PG = . Rb For a given signal bandwidth, W is fixed [e.g., 1.2288 Mcps in a 1.25-MHz carrier (for IS-95 and CDMA2000) or 3.84 Mcps in a 5-MHz carrier for WCDMA systems]. Note that the information bit rate Rb will vary depending on the type of service (e.g., CDMA2000 voice services can use variable rate speech coders, resulting in Rb of 9,600 bps, 4,800 bps, 2,400 bps, and 1,200 bps, yielding processing gains of 128, 256, 512, and 1024, respectively, or higher data rate applications up to 153.6 Kbps for 1XRTT and 3.1 Mbps for 1XEV-DV systems). Similarly current WCDMA systems can support 64, 128, 256, or 384 Kbps on the downlink. Let the total base station transmit power be denoted Ior and the base station transmit power on the traffic or dedicated channel for the ith user be denoted Ec. Then the fraction of the transmit power dediE  cated to the ith user is given by  c  . This fraction varies over time subject to  I or  i power control. Finally, let SINR represent the received signal to interference + noise ratio, which is a function of the location of the mobile user within the coverage area and the associated channel conditions. The relationship between the above quantities for the ith user then becomes Eb W = N t Rb

E  ⋅  c  SINR  I or  i

(9.1)

from which we can write the fraction of the transmit power dedicated to the ith user as Rb E b Ec  1 ⋅ ⋅   =  I or  i W N t SINR

(9.2)

Mobile Stations’ Smart Antennas

267

For voice services, it also becomes a function of the voice activity factor v: Rb E b Ec  1 ⋅ ⋅ ⋅ν   =  I or  i W N t SINR

(9.3)

Equation (9.3) assumes that each mobile user has only a single traffic channel from only one sector. When mobiles are in softer handoff (with active links coming from sectors belonging to the same base station) or in soft handoff (the active set contains sectors from different base stations), then the total power required by this user from the system perspective would depend on the handoff state. Assuming the user has Ns sectors in the active set and is being served by Ns E sectors simultaneously, then the expression for the required c should be modiI or fied to Rb E b Ec  1 ⋅ ⋅ ⋅ ν ⋅N s   =  I or  i W N t SINR The expected or average value of

(9.4)

Ec then becomes I or

 E   R E  1  ⋅ ν ⋅N s E  c   = b ⋅ b ⋅ E   SINR    I or  i  W N t

(9.5)

Since the number of active links for all data rates is limited by the base station transmit power, we can express the average number of users that can be supported in a sector or simply the sector capacity as N max_users =

1 − ( E c I or )Tot _Overhead  E   E  c     I or  i 

(9.6)

It is obvious that the smaller the fraction necessary to support each user, the higher the number of users that can be supported (i.e., the capacity of the Ec downlink). One way to reduce the average value of is downlink I or beamforming using antenna arrays at the base station. As we have discussed in a previous chapter, the FDD gap in CDMA systems leads to suboptimal

268

Smart Antenna Engineering

performance when downlink weights are obtained based on uplink measurements. Another approach that overcomes this problem is to use mobile stations equipped with multiple antennas versus that of a single receiver station. This technique eliminates the need for accurate downlink channel estimation at the base station because this task can be moved to the MS, which has all the information necessary to estimate the downlink correlation matrix accurately. It should be noted that regardless of the number of antennas used in the receiver  E   E design, b and PG remain the same. The reduction in E   c   results from Nt   I or  i  the enhancements achieved in the SINR through the use of antenna arrays or diversity reception. Let us group currently available commercial mobile stations into two groups, the first being handsets that support voice and moderate data services and the second including advanced mobile terminals that could physically accommodate multiple antennas. The most practical and obvious solution would then be to add a second antenna to form a dual-antenna handset that has a primary antenna used for both transmit and receive and a secondary antenna used for receive only. This secondary antenna can be designed to occupy much less volume than the primary antenna. The secondary antenna’s small volume allows it to be put inside the plastics of even small phones.

9.2 Multiple-Antenna MS Design Dual-antenna receiver designs offer several gains over single-antenna designs. The first part of these gains can be attributed to diversity. Just as using two antennas for spatial or polarization diversity improves the uplink performance, using two antennas at the MS should provide some diversity gain. Diversity gain refers to the improvement in received signal level if the better of two receive antennas is used (switched diversity) or the signals from both antennas were combined, as discussed in Chapter 5. As long as the fading is not completely synchronized between the two antennas, it is possible to improve the average signal level. It is well known that antenna diversity is obtained when the antennas have different reception characteristics so that the signals received by each antenna have low cross correlation. This can be accomplished using spatial diversity, which requires the antennas to be separated by multiple wavelengths. Therefore, this type of diversity is largely limited to base stations and renders it impractical to mobile stations. To understand the requirements for spatial diversity we need to look at the correlation between two antennas. A model for the cross correlation between two antennas separated by a distance d was derived in [13], assuming the signals DOAs have a uniform PDF. It was shown that the correlation between the real parts of the signals at the nth and mth antenna elements Rr and that between the real and imaginary parts Ri are given by

Mobile Stations’ Smart Antennas

269

∞ sin (kAS )  d   d  R r (m − n ) = J 0  2 π (m − n ) + 2 ∑ J 2k  2 π (m − n ) cos( 2kθ )     λ λ (kAS ) k =1

(9.7) AS   sin ( 2k + 1) d 2     R i (m − n ) = 2 ∑ J 2k + 1  2 π (m − n ) sin [( 2k + 1)θ ]   AS  λ  k =0 ( 2k + 1) 2  (9.8) ∞

where AS is the angle spread in degrees. The envelope correlation is thus given by R mn = R r2 + R i2

(9.9)

Figure 9.1 shows how the envelop correlation changes as the AS is increased for 0° DOA. We can see that for large AS the correlation is low;

1 0.9

AS=20

Envelope correlation

0.8 0.7

AS=40 AS=80

0.6 AS=180

0.5

AS=120

0.4 0.3 0.2 0.1 0 0

Figure 9.1

0.1

0.2

0.3 0.4 0.5 0.6 0.7 Antenna separation d in wavelengths

Envelope correlation versus antenna separation (d/λ), DOA = 0°.

0.8

0.9

1

270

Smart Antenna Engineering 180 Envelope correlation < 0.3 Envelope correlation < 0.5

160

AS (degrees)

140

120

100

80

60 0.25

0.3

0.35

0.4

0.45

0.5

0.55

0.6

0.65

0.7

0.75

d in wavelength

Figure 9.2 Relationship between AS and minimum separation to achieve a given envelope correlation, DOA = 0°.

however, zero correlation requires separations that are large compared with a mobile station size. For low AS we can observe that the correlation is quite high and diversity performance degradation should be expected. On the other hand, this high correlation improves the performance of optimum combining methods, which could help offset some of the degradation in diversity gain. The effect of correlation on the performance of diversity systems with two antennas was studied in [2] and it was shown that correlations below 0.3 do not add significant diversity gain, whereas performance degradations are small up to correlations of 0.7. Figure 9.2 shows the minimum required antenna separation to achieve correlations of 0.3 and 0.5 as a function of the AS for 0° DOA. This further confirms that improved diversity performance requires large separations. From Figure 9.2, a correlation of 0.5 could serve as a good trade-off between diversity and optimum combining, which requires high correlation between the signals at the antenna elements. Although spatial diversity techniques might not be suitable for mobile stations, other forms of diversity that do not require large separations can be employed. These include pattern or polarization diversity, as described in [14]. Polarization diversity makes use of the fact that different filed components experience different channel conditions, whereas pattern diversity exploits the fact that antennas with different patterns will

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271

receive different paths and thus are able to achieve some level of signal decorrelation. Possible designs might include placing the two whip antennas on either side of a phone, using the standard whip antenna as a primary antenna and a back-mounted antenna such as a planar-inverted F antenna (PIFA) as a secondary antenna. These antenna designs would achieve low signal cross correlation based on their separation, polarization, or different patterns. The results shown in [14] confirm that such designs can achieve sufficient signal decorrelation that some diversity gain can be obtained. The second gain component can be termed array gain. The aperture of M antennas should be about M times as large as the aperture of one antenna, thus an M -element array can provide a gain of M. In practice, this might not always be true in dual-antenna handsets. If the antennas are too close together, then their apertures will overlap, and the total aperture will be less than the sum of the individual apertures. Moreover, mutual coupling between the antennas will tend to cause losses that will reduce the overall array gain. In addition, if the secondary antenna is less efficient than the primary antenna, the total aperture would be expected to be less than twice the aperture of the primary antenna. Finally, the last gain component that can be achieved results from the antenna array interference reduction capabilities. Optimum weights could be applied to the signals received by each antenna according to one of the adaptive beamforming criteria discussed in Chapter 5, which could in the case of a strong signal and a single strong interferer nearly completely cancel the interference out, resulting in a high SINR. With multiple interferers and receiver noise cases, adaptive beamforming will make the best possible trade-off between signal enhancement and interference reduction. Unlike antennas at the base station, single- and dual-antenna handsets suffer from a unique problem that should be considered, the mean effective gain (MEG), and its possible imbalance between the primary and secondary antennas. The MEG [15] is a good way to characterize the effectiveness of an antenna at coupling to the signals it receives. Let us denote the base station transmit power by Ior and the total power incident on the antenna (from all directions) after it has gone through the channel by I$or , while the mean power received (absorbed) by the antenna is Prec, then the MEG is given by MEG =

P rec I$

(9.10)

or

The unique situation we have with handset antennas is that the presence of the head or hand will affect the ability of the antenna to couple the incident energy and therefore will affect its MEG. The MEG of a whip antenna on a handset held vertically with no head or hand near it is likely to be 0 dB. However, the following factors will reduce the MEG: the antenna will be held with

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45°–60° of tilt away from vertical, the head will block some directions and absorb some radiation, and the hand will provide additional blockage and absorption. The biggest effect of holding the handset is a reduction in the gain, especially in the direction of the head. The effect of this on the MEG is to reduce it by an amount that depends on the operating frequency. Obviously, this reduction in MEG could impact the receiver performance, so the question is by how much? The receiver performance is mainly dictated by its sensitivity, which is limited by the interference picked and the noise added to the receiver chain. It is well known that receivers in CDMA systems are mostly interference limited [i.e., the received interference powers are much stronger than the noise floor except at locations where the received signal power is too close to the noise floor (e.g., edge of coverage)]. It then follows that the receiver SINR in interference-limited situations is dominated by the SIR or signal-to-interference ratio. But since the antenna gain or MEG will affect the received signal and interference powers equally, any reduction in MEG should have a minimal impact on the receiver performance.

9.3 Combining Techniques Several combining techniques have been previously discussed in Chapter 5. The following is a summary of some methods applicable to mobile stations. 9.3.1

Selection (Switched) Diversity

This diversity combining technique assumes that the better of the two antennas is always and immediately chosen. In a realistic algorithm, there would be a reduction in the performance of this approach due to the real overhead of the switching system. 9.3.2

Maximal Ratio Combining

In the MRC scheme, the antennas are combined to maximize sensitivity under the assumption that the noise and interference the two antennas are receiving have low cross correlation. The weights applied to the antennas’ inputs are a function of the received SNR.

9.4 Adaptive Beamforming or Optimum Combining This is the most optimal combining scheme, where the antennas are combined with full knowledge of the interference correlation between the two antennas. Several adaptive beamforming techniques have been discussed in Chapter 5.

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Since the goal is to reduce the interference and improve the SINR, null steering can be employed to cancel or reduce some of the interference seen by the mobile station. However, null steering might not be of much practical use in this case due to the size constraints of the mobile stations. Optimum combining (OC) techniques such as those based on the Wiener-Hopf type calculations, resulting in an MMSE or max SINR, are thus more suitable. In the remainder of this subsection we will show why this is the case by means of simulations and by studying some relevant array characteristics. It is known that an adaptive antenna array of M elements can null M-1 interferers in both LOS and multipath environments. In [16, 17], it is stated that an antenna array can separate and combine closely spaced signals such as the case in multipath fading environments as long as the array beamwidth is smaller than the AS of the received paths. The performance of the array improves with M, larger AS, and denser multipaths. From [18], we know that at the mobile station, assuming a power azimuth spectrum with uniform distribution over 360°, the resulting AS is 104°. Figure 9.3 shows the array beamwidth for small element separations for two-, three-, and four-element broadside arrays. We can see from the plot that for M = 3 and 4, the beamwidth is smaller than the AS (104°), even for small separations d. For M = 2, we notice that larger values of d (about 0.3λ) are required to achieve the necessary beamwidth. To compare the performance of optimum combining techniques such as max SINR to that of null steering we will consider both LOS and multipath fading cases. Recall from Chapter 5 that the null steering weights are given by H W NS = [1 0 L 0 ] A −1

θ

T

λ

Figure 9.3

3-dB beamwidth as a function of interelement separation for M = 2, 3, and 4.

(9.11)

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Smart Antenna Engineering

whereas the optimum weight vector obtained from the max SINR criterion is given by W opt = K SINR R N−1 A

(9.12)

where A is the steering matrix whose columns are the array steering vectors (LOS) or spatial signatures (multipath). After null steering is applied, the array output can be written as y (t ) = s d (t ) + w H N (t )

(9.13)

It then follows that the SINR at the array output after null steering is SINR NS =

σ 2s σ 2N w H w

(9.14)

where σ 2s is the signal power and σ 2N is the noise variance. On the other hand, the SINR at the array output as a result of the max SINR criterion is SINR OC _LOS = σ 2s a H ( θ d )R IN−1 a( θ d )

(9.15)

SINR OC _ NLOS = σ s2 A H R IN−1 A

(9.16)

where NLOS stands for the multipath case, θd is the DOA of the desired signal, A denotes the spatial signature vector of the desired user, and RIN is the interference plus noise correlation matrix. To compare the performance of these two techniques, simulations were run for different environments, element spacing, number of multipaths, and DOA of desired and interfering signals. Figures 9.4 and 9.5 compare the SINR achieved using a two-element array in LOS environment for d = 0.5 λ and 0.2 λ, respectively, for one desired signal at 0° and one interferer at 3°. Figures 9.6 and 9.7 show the performance when one desired and one interfering signal have two paths each. Finally, Figures 9.8 and 9.9 compare the performance when the desired signal and interfering signal each has four paths. For all the results shown, the desired and interfering signals have the same power, 5,000 samples were used for each data point, and the mean SINR was plotted as a function of the input SNR. For the multipath cases, different paths were assumed to have random phases between [0,2π]. From these plots we can conclude that the max SINR technique significantly outperforms null steering

Mobile Stations’ Smart Antennas 30

275

M=2, LOS uncorrelated sources at 0, 3 degrees, d=0.5 (wavelength) Max SINR Null steering

20

SNR (dB)

10

0

–10

–20

–30

–40 –20

–10

0

10

20

30

40

SNR (dB)

Figure 9.4 Optimum SINR versus null steering, M = 2, desired signal at 0°, interference at 3°, d = 0.5 λ in LOS. M=2, uncorrelated sources (LOS) at 0 and 3 degrees, d=0.2 (wavelength) 20

10

SNR (dB)

0

–10

–20

Max SINR Null steering

–30

–40

–50 –20

–10

0

10

20

30

40

SNR (dB)

Figure 9.5 Optimum SINR versus null steering, M = 2, desired signal at 0°, interference at 3°, d = 0.2 λ in LOS.

276

Smart Antenna Engineering M=2, Multipath fading at 0 and 2 degrees, interference at 4 and 6 degrees, d=0.5 (wavelength) 30

20

SNR (dB)

10

0

–10 Max SINR Null steering

–20

–30

–40 –20

–10

0

10

20

30

40

SNR (dB)

Figure 9.6 Optimum SINR versus null steering, M = 2, desired signal with two paths at 0° and 2° interference with two paths at 4° and 6°, d = 0.5 λ. Equal power signals. M=2, Multipath fading, desired signal at 0 and 2 interference at 4 and 6, d=0.2 (wavelength) 20

10

SNR (dB)

0

–10

–20

–30

Max SINR Null steering

–40

–50 –20

–10

0

10

20

30

40

SNR (dB)

Figure 9.7 Optimum SINR versus null steering, M = 2, desired signal with two paths at 0° and 2° interference with two paths at 4° and 6°, d = 0.2 λ. Equal power signals.

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M=2, Multipath fading, desired signal paths at 0, 2, 4, and 6 degrees, and interference at 3, 7, 9, and 11 degrees, d=0.2 (wavelength) 30

20

SNR (dB)

10

0

–10

–20 Max SINR Null steering

–30

–40 –20

–10

0

10

20

30

40

SNR (dB)

Figure 9.8 Optimum SINR versus null steering, M = 2, desired signal with four paths at 0°, 2°, 4°, 5° and interference with four paths at 3°, 7°, 9°, 11°, d = 0.2 λ. Equal power signals.

30

M=2, Multipath fading, desired signal paths at 0, 2, 4, and 6 degrees, and interference at 3, 7, 9, and 11 degrees, d=0.5 (wavelength)

20

SNR (dB)

10

0

–10

–20

–30

–40 –20

Max SINR Null steering –10

0

10

20

30

40

SNR (dB)

Figure 9.9 Optimum SINR versus null steering, M = 2, desired signal with four paths at 0°, 2°, 4°, 5° and interference with four paths at 3°, 7°, 9°, 11°, d = 0.5 λ. Equal power signals.

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for low SNR regardless of the environment. This is because maximizing the SINR, which is equivalent to maintaining a high gain at the direction of the desired signal while taking into account additive noise (maximizing S and minimizing N), is more effective than just reducing the interference (reducing I) alone. For high SNR, since S is already high, both techniques achieve similar performance. In fact, the performance becomes identical for very high SNR. This is due to the fact that as SNR increases, because we are assuming the interference and desired signals have similar power, reducing or nulling the interference will greatly enhance the SINR (by reducing I). Under these conditions, it is very beneficial to null or reduce the interference because the signal power is already much higher than the noise and so it is less important to increase the gain in the direction of the desired signal. Based on the above, it is recommended that an optimum combining technique such as max SINR or the MMSE be selected over null or beam steering to achieve the desired perfor E   mance improvements in E   c  .   I or  i 

9.5 RAKE Receiver Size A RAKE receiver combines multiple copies (fingers) of the received signal to synthesize a single, higher SINR copy. The multifinger RAKE receiver allows efficient detection of CDMA signals because it makes use of the available multipath. The RAKE allows most of the power from the different paths to be combined in the receiver to enhance the SINR. In soft handoff, the signal to the phone is sent from multiple sectors in the network. If at least one finger is assigned to signals arriving from each serving sector, then the phone receiver can combine the signal energies from all sectors simultaneously. Existing CDMA phones based on the IS-95 or CDMA2000 standard with a chip rate of 1.2288 Mcps use four-finger RAKE receivers. Commercial CDMA deployments have shown that increasing the number of fingers beyond four would add complexity to the handset with only a small improvement in diversity performance, hence it is widely accepted that this is a good size for the RAKE receiver for single-antenna phones in narrowband CDMA systems. For dual-antenna phones, the RAKE receiver must assign two fingers for each path arriving at the phone from each antenna so that the gains from combining that path as seen on the two separate antennas are achieved. Since the four-finger RAKE is limited to combining only two paths if each path is combined on two antennas, the full exploitation of dual-antenna diversity requires eight fingers. For WCDMA systems, the chip rate is 3.84 Mcps, which allows multipath components separated by as little as one chip or 0.26 µs to be resolvable. The increased number of

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usable multipath from the channel because of the higher system bandwidth implies that more fingers can be added to the RAKE receiver to improve the diversity gain.

9.6 Mutual Coupling Effects Due to the size constraints of mobile stations, placing the diversity antennas very closely results in mutual coupling, which can affect the performance of the array. As previously described, in a dual-antenna diversity mobile station a single antenna is used to transmit and is connected to one receive chain while the second antenna is connected to the second receive chain; that is, the secondary antenna is connected to a receive filter among other parts of the chain. The presence of the second antenna and its associated terminating load will affect the amount of energy coupled from the primary antenna. This in turn will affect the isolation and return loss parameters of the array, leading to coupling and mismatch losses. Let us denote the return loss and isolation by S11 and S21, respectively. We can then define the coupling loss as L coup = −10 log 10 (1 − 10 S 21 10 )

(9.17)

and the mismatch loss as L m = −10 log 10 (1 − 10 S 11 10 )

(9.18)

Figure 9.10 plots Lcoup and Lm as a function of S11 and S21. We can see that any degradation in the return loss or isolation will result in some loss, which will reduce the antenna’s efficiency and hence the gain. The total loss in the antenna efficiency can be written as L tot = L coup + L m + L other

(9.19)

where Lother accounts for losses such as dielectric and conductor losses. Another problem that arises from mutual coupling is the transmit band coupling, which leads to reduction in the transmit efficiency. A portion of the transmit power can be coupled from the primary antenna to the secondary antenna. Depending on the termination of the secondary antenna this energy can either be absorbed or reflected by the antenna. If the secondary antenna absorbs this energy, the transmit efficiency of the primary antenna will be reduced and hence its gain will be reduced. If the energy is reflected, it will be radiated by the secondary antenna and will cause interference to the primary antenna transmission, which could affect its beam pattern.

280

Smart Antenna Engineering 3.5

Coupling/mismatch loss (dB)

3

2.5

2

1.5

1

0.5

0 –25

Figure 9.10

–20

–15

–10 S11/S21 (dB)

–5

0

Coupling loss as a function of return/insertion loss.

9.7 Dual-Antenna Performance Improvements Extensive field and lab testing of dual-antenna handsets have been carried out and reported in [19]. A commercially available CDMA phone with an external whip antenna has been modified by adding an internal antenna as the secondary antenna and several combining techniques, including switched diversity, MRC, and optimum combining, were employed. Figure 9.11 shows the CDF of the E E required c necessary to achieve an b of 3.9 dB (target corresponding to 1% I or Nt FER) with eight RAKE fingers. The required traffic power relative to the total transmit power available from sectors in the system calculations included the effect of handoff. Table 9.1 shows the forward link capacity improvement with respect to the whip antenna feeding a four-finger RAKE receiver. Table 9.1 shows that the whip antenna performance sees little improvement when the number of fingers is varied from four to eight. The capacity gains reported here are the average reductions in transmit power per user (i.e.,  E   E   c  . From Table 9.1, we can draw the following set of conclusions:   I or  i 

Mobile Stations’ Smart Antennas Required TX power for 8-finger RAKE

1 Whip Int Switched OC MRC

0.9 0.8

Probability that TX > x-axis

281

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 –30

–25

–20 –15 TX fraction system, dB

–10

–5

 E   Figure 9.11 E  c   required for eight-finger RAKE. (From: [19].  Qualcomm, Inc. Reprinted  I or  i  with permission.)

Table 9.1 Performance Improvements with Different Combining Techniques

Number of Whip Fingers Antenna

Switched Diversity

MRC

Optimum Combining

Capacity Increase Relative to Whip Antenna (dB) PCS Band

Cellular Band

Source: [19].

4

Reference

1

1.8

2.5

6

0.3

1.3

2.4

3.2

8

0.4

1.3

2.7

3.5

4

Reference

0.9

1.7

2.9

6

0.1

0.9

2.3

3.6

8

0.1

1

2.4

3.8

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• Optimum combining outperforms all other techniques and results in

the most significant gains, which can reduce the average required transmit power by about 3 dB, thereby doubling the capacity. • OC outperforms MRC by 0.7–1.4 dB, regardless of the number of

RAKE fingers used. • Increasing the number of fingers improves the capacity gains. We can

see significant gains in OC and MRC by going from four to six fingers but only a small improvement (0.2–0.3 dB) when we add another two fingers. When the phone is in soft handoff (i.e., the phone is being served by multiple sectors), the load it presents to the system increases. For instance, when in Ns-way handoff, the phone has Ns sectors in its active set, which in turn requires a larger number of RAKE receiver fingers. Since in the dual-receive-chain diversity architecture two fingers are devoted to each time-resolvable path being tracked by the receiver, the diversity performance will be compromised during high handoff states with smaller RAKE sizes. Table 9.2 summarizes the impact of the handoff state on capacity improvements for the PCS band. In any given handoff state, we can see some capacity improvements by increasing the number Table 9.2 Impact of Handoff on Capacity Gains, PCS Band

Handoff State

Number of Fingers

Whip Antenna

OC

MRC

Switched

Capacity Increase Relative to Whip Antenna (dB) Single Sector

Two-way SHO

Three-way SHO

Source: [19].

4

Reference

3.2

2.4

1.6

6

0.1

3.5

2.7

1.7

8

0.1

3.6

2.8

1.7

4

Reference

2.4

1.7

1.0

6

0.3

3.2

2.4

1.2

8

0.4

3.6

2.7

1.3

4

Reference

2.0

1.3

0.6

6

0.4

2.7

2.0

1.0

8

0.6

3.3

2.6

1.1

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of fingers because more fingers will allow the receiver to combine gains from the dual-antenna architecture and conventional soft handoff. On the other hand, as the handoff state increases (i.e., as we have more sectors in the active set), we see a drop in the capacity improvements, as is the case of three-way SHO. This is due to the fact that the receiver is unable to combine all the available paths from both the spatial domain (dual-antenna) and temporal domain (RAKE) because it is limited by the number of fingers (recall that a path from one sector requires at least two fingers in this architecture to achieve maximum capacity gains). The impact of the difference in the received power on both antennas on the performance is shown in Figure 9.12. We can observe two distinct performance regions, one in which there is a large difference in the powers received by each antenna, which corresponds to the right- and left-hand side. In this situation we see that the dual-antenna improvements are not much better than the performance with the single antenna. In the second region, where the received powers on each antenna are similar, the capacity gains are significant with dual-antenna receivers since this would be the optimum conditions to achieve diversity and beamforming gains. Another observation we can draw from these results is that OC always outperforms other techniques, followed by MRC.

Relative capacity gain 10 Whip Int OC MRC Switched

Capacity relative to Whip, dB

8 6 4 2 0 –2 –4 –6 –8

–15

–10

–5

0 5 PWhip /PINT , dB

10

15

Figure 9.12 Relative capacity gains versus receive power difference between whip and internal for PCS band, six-finger RAKE. (From: [19].  Qualcomm, Inc. Reprinted with permission.)

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9.8 Downlink Capacity Gains Ec results in a capacity increase that can I or be used in several ways. First, in parts of the system where capacity is limited by the downlink, dual-antenna phones will raise the downlink capacity to match the uplink capacity or until the higher uplink capacity becomes the limit. Second, new high-speed data services demand more resources on the downlink than the uplink. By introducing dual-antenna phones, downlink capacity is freed-up for use in data services. Finally, the downlink capacity increase can be matched by an uplink capacity increase using antenna arrays at the base station. Let us E assume that all the improvement in c is traded for increased capacity on the I or downlink; we will then proceed to show how much capacity gain can be achieved by using dual-antenna mobile stations with dual-receive chain. From (9.6) the downlink capacity of a CDMA system with conventional mobile stations is given by

As we have seen earlier, a reduction in

C conv =

1 − ( E c I or )Tot _Overhead  E   E  c     I or  i  conv

(9.20)

This expression is derived based on the premise that the downlink capacity is limited by the total base station transmit power, part of which will be devoted to the common or overhead channels with the remaining part allocated to the traffic or dedicated channels of the users in the sector. Similarly, the capacity achievable with dual-antenna or diversity mobile stations is given by C div =

1 − ( E c I or )Tot _Overhead  E   E  c     I or  i  div

(9.21)

It then follows that

C conv C div

 E   E  c     I or  i  div =  E   E  c     I or  i  conv

(9.22)

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285

where we have assumed that both types of mobile stations experience the same size of active sets [i.e., the handoff reduction factor (see Chapter 2) is equivalent in both cases]. Now, let us assume that the sector is supporting mixed mobile stations with Nconv and Ndiv representing the number of conventional and diversity enabled stations, respectively. We can then write  E    E    E   N conv E   c   + N div E   c   = E   c   C conv (9.23)   I or  i  div   I or  i  conv   I or  i  conv where we use the fact that the portion of the base station transmit power available for traffic in the case of mixed and conventional mobile stations is the same. Equation (9.23) can be rewritten as E  N conv + N div ∆  c  = C conv  I or  where  E   E  ∆  c  = E  c    I or    I or  i  div

 E   E  c     I or  i  conv

Defining the capacity of the sector with mixed mobile stations as Cmixed we get C mixed = C conv

N conv + N div N conv + N div

E  ⋅ ∆ c   I or 

=

1

(1 − α div ) + α div

E  ⋅ ∆ c   I or 

(9.24)

where αdiv is the percentage of diversity-enabled mobile stations in the sector. Equation (9.24) actually represents the achievable capacity gain and it is shown E  in Figure 9.13 for different values of αdiv and ∆  c  . We can see that as the  I or  percentage of diversity-enabled phones is increased, the capacity gain increases. E  Notice that the capacity is doubled when ∆  c  = -3 dB.  I or 

286

Smart Antenna Engineering 2 Delta Ec/Ior = –1 dB Delta Ec/Ior = –2 dB Delta Ec/Ior = –3 dB

1.9 1.8

Relative capacity gain

1.7 1.6 1.5 1.4 1.3 1.2 1.1 1

0

10

20

30 40 50 60 70 Percentage of dual-antenna mobile stations

80

90

100

Figure 9.13

Relative sector capacity versus percentage penetration of dual-antenna mobile staE  tions for different ∆  c  .  I or 

9.9 Conclusions The downlink capacity of a CDMA sector can be doubled if all the mobile stations in that sector use dual-antenna receivers. Capacity increase is achieved because the average signal sensitivity of the phone receiver is doubled through adaptively combining the two antenna-receiver chains. With twice the sensitivity, average base station transmit power per phone can be cut in half. Thus, twice as many downlink active calls can be served. The downlink capacity is increased incrementally in proportion to the penetration of dual-antenna phones into the system. The capacity increase can be used in three ways. First, where capacity is limited by the downlink, dual-antenna phones will raise total network capacity. Second, dual-antenna phones free up capacity for new downlink capacity-intensive data services. Finally, the downlink capacity increase can be matched by an uplink capacity increase using antenna arrays at the base station. The primary mechanisms by which the sensitivity is improved are aperture gain, interference reduction, and diversity gain.

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References [1] Lee, W. C. Y., Mobile Cellular Communications Systems, New York: McGraw-Hill, 1989. [2] Jakes, W. C., (ed.), Microwave Mobile Communications, New York: IEEE Press, 1994. [3] Taga, T., and K. Tsunekawa, “Performance Analysis of a Built-In Planar Inverted-F Antenna for 800-MHz Band Portable Radio Units,” IEEE Journal on Selected Areas in Communications, Vol. JSAC-5, June 1987, pp. 921–929. [4] Fuhl, J., P. Nowak, and E. Bonek, “Improved Internal Antenna for Hand-Held Terminals,” Electronics Letters, Vol. 30, October 1994, pp. 1816–1818. [5] Erätuuli, P., et al., “Performance of Internal Microstrip Handset Antennas,” 46th IEEE Vehicular Technology Conference, Atlanta, GA, April 28–May 1, 1996, pp. 344–347. [6] Haruki, H., and A. Kobayashi, “The Inverted-F Antenna for Portable Radio Units,” Conv. Rec. IECE Japan (in Japanese), March 1982, p. 613. [7] Winters, J. H., “Optimum Combining in Digital Mobile Radio with Co-Channel Interference,” IEEE Trans. Veh. Technol., Vol. VT-33, No. 3, 1984, pp. 144–155. [8] Erätuuli, P., and E. Bonek, “Diversity Arrangements for Internal Handset Antennas,” 8th International Symposium on Personal, Indoor and Mobile Radio Communications, Helsinki, Finland, September 1–4, 1997, pp. 589–593. [9] LeFevre, M., M. A. Jensen, and M. D. Rice, “Indoor Measurement of Handset Dual-antenna Diversity Performance,” IEEE VTC’97, Phoenix, AZ, May 1997. [10] Braun, C., G. Engblom, and C. Beckmam, “Antenna Diversity for Mobile Telephones,” IEEE AP-S’98, Atlanta, GA, July 1998, pp. 2220–2223. [11] Cox, D. C., “Antenna Diversity Performance in Mitigating the Effects of Portable Radiotelephone Orientation and Multipath Propagation,” IEEE Trans. Commun., Vol. COM-31, May 1983, pp. 620–628. [12] Viterbi, A. J., CDMA Principles of Spread Spectrum Communication, Reading, MA: Addison-Wesley, 1995. [13] Salz, J., and J. Winters, “Effect of Fading Correlation on Adaptive Arrays in Digital Mobile Radio,” IEEE Trans. on Vehicular Technology, Vol. 43, No. 4, November 1994, pp. 1049–1057. [14] Colburn, J. S., et al., “Evaluation of Personal Communications Dual Antenna Handset Diversity Performance,” IEEE Trans. on Vehicle Technology, Vol. 37, August 1998, pp. 737–746. [15] Taga, T., “Analysis of Mean Effective Gain of Mobile Antennas in Land Mobile Radio Environments,” IEEE Trans. on Vehicle Technology, Vol. 39, May 1990, pp. 117–131. [16] Winters, J., “Smart Antennas for Wireless Systems,” IEEE Personal Communications, February 1998, pp. 23–27. [17] Winters, J., “Optimum Combining in Digital Mobile Radio with Cochannel Interference,” IEEE JSAC, Vol. 2, No. 4, July 1984, pp. 528–539. [18] 3GPP-3GPP2 SCM-121, Spatial Channel Model Text Description, March 14, 2003. [19] Wengler, M. J., et al., “Capacity_Increase_CDMA2000.pdf,” found at: http://www. qualcomm.com/technology/1xev-do/publishedpapers/Capacity_Increase_CDMA2000.pdf, 2003.

10 MIMO Systems 10.1 Introduction In conventional wireless communication systems, one end of a link, typically the base station, is equipped with more than one antenna (e.g., two antennas for receive diversity or, more recently, an antenna array with more than two elements for beamforming or beam steering). Several chipset and handset vendors have recently started developing and designing phones with dual antennas for mobile receive diversity (MRD). In a MIMO system the communication link has both a transmitter as well as a receiver that are equipped with multiple antenna elements, as shown in Figure 10.1. The idea behind MIMO is to use AA at both the transmitter and receiver in combination with space-time modulation and coding techniques to achieve very high spectral efficiencies. The motivation for using such a setup is to achieve improvements that can be used to increase both the quality of service and the revenues significantly. Examples of applications that can benefit from MIMO systems include cellular/PCS system deployment based on fixed user terminals, microcells, picocells, and wireless LANs, as well as portable applications such as laptops and PDAs. Coding and modulation are essentially temporal techniques. A MIMO system is a space-time signal processing approach in which the time dimension is complemented with the spatial dimension through the use of multiple spatially distributed antennas. As such, MIMO systems can be viewed as an extension of smart antennas. Therefore, we can classify multiantenna schemes as a family that includes spatial techniques such as adaptive antennas, beamforming, and spatial diversity, as well as spatial multiplexing (SM) and space-time coding (STC) such as MIMO. Multiantenna schemes have great potential for significant information theoretic capacity increase. As we have seen in previous chapters, the main 289

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Matrix channel

Tx

MT

Figure 10.1

Rx

MR

MIMO wireless communications systems.

impairments to the performance of a wireless communication system are fading due to multipath and interference. The different multiantenna techniques deal with those impairments in different ways to either mitigate their impact or exploit their presence to improve the link and system level performance. A key feature of MIMO systems is the ability to exploit multipath by taking advantage of random fading [1–5], effectively extending the benefits of smart antennas a step further by achieving a multifold increase in transfer rates. This chapter provides an introductory overview of the most important aspects of MIMO systems, points out their relation to other spatial processing approaches, and summarizes the key issues and factors that affect their performance. An excellent discussion on MIMO systems can be found in [6] and the references therein.

10.2 Principles of MIMO Systems Table 10.1 shows a comparison of all the possible combinations of multiantenna techniques. In general, we define the number of transmit antennas as MT and the number of receive antennas as MR. We can think of the wireless channel as a vector channel with dimensionality MR × 1 or MT × 1. If we use only one antenna for transmission, the data rate will always be limited by the performance of that single antenna. One way to recover from the channel impairments would be by using a multiantenna at the receive end to reverse all or part of the channel effects. In this case the single transmit antenna becomes the bottleneck in a way. However, the performance is often superior to that obtained with single antennas at both ends. The same effect can be accomplished using multiple antennas at the transmitter end in situations where implementation issues prevent their use at the receiver end, such as in mobile handsets to try to create signal conditions at the receiver similar or close to those present had it been equipped with multiple antennas to enable it to take advantage of additional degrees of freedom, such as higher diversity orders. The resulting performance is roughly the same when the same number of elements is used. On the other hand, in MIMO systems data are transmitted over a matrix channel created by MT transmit and MR receive antennas rather than a vector channel, creating new types of gains beyond just diversity or array gain benefits. It was shown in [2] how one may,

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under certain conditions, transmit independent data streams simultaneously over the eigenmodes of a matrix channel. Let us denote the transmit signal by s (t), the received signal by y(t), the received noise by n(t), and the channel matrix by H(τ, t). We can then write y (t ) = H( τ,t )s(t ) + n (t )

(10.1)

10.2.1 SISO

In single input single output (SISO) systems, shown in Figure 10.2, MT = MR = 1, y(t) and s(t) are 1 × 1 vectors, and the channel matrix H(τ, t) = h is also a 1 × 1 vector. The normalized Shannon capacity in this case is given by C = log 2 (1 + h ⋅ SINR 2

)

(10.2)

The limitation of SISO systems is that the capacity increases very slowly with the log of SINR and in general it is low. Moreover, fading can cause large fluctuations in the signal power level, increasing the variance. Only temporal and frequency domain processing are possible but the spatial domain is neglected. 10.2.2 SIMO

In this case, both y(t), h are MT × 1 vectors, whereas s(t) is 1 × 1, as shown in Figure 10.3 Example applications of single input multiple output (SIMO) systems include receive diversity, beamforming, beam steering and null steering. The normalized Shannon capacity in this case is given by MR  2 C = log 2 1 + SINR ⋅ ∑ hi    i =1

(10.3)

Similar to the SISO case, capacity increases logarithmically as both MT and SINR are increased. The average channel capacity is, however, higher than the

Tx

Figure 10.2

SISO scheme.

Channel

Rx

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Vector channel

Tx

Rx

MR

Figure 10.3

SIMO scheme.

SISO case. The actual performance depends on the nature of the channel and the correlation across the antenna elements. For low correlation, SIMO systems provide diversity gain, which helps reduce fading effects when coherent combining is employed [7]. When the signals across the antennas are highly correlated (e.g., in the beamforming case), the system provides an array gain in addition to interference reduction or cancellation. 10.2.3 MISO

In this case, both s(t) and h are MT × 1 vectors and y (t) is a 1 × 1vector, as shown in Figure 10.4. Transmit diversity and beamforming are example multiple input single output (MISO) schemes. The normalized channel capacity in the case where the channel knowledge is unknown (open loop) is given by  SINR M T 2 C = log 2 1 + ⋅ ∑ hi  M T i =1  

(10.4)

We can see that the SINR is normalized by MT, ensuring fixed total transmit power. It is also clear that there is no array gain in this case and the capacity increases logarithmically with SINR. Assuming the channel coefficients are given by MT

∑h i =1

Tx

MISO scheme.

= MT

Vector channel MT

Figure 10.4

2 i

(10.5)

Rx

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293

then we get C = log 2 (1 + SINR )

(10.6)

That is, the capacity does not increase when the number of transmit antennas is increased. However, that does not mean we cannot increase capacity using open loop transmit diversity schemes such as STTD and STS. In fact, we have seen in Chapter 7 that STS and STTD provide diversity gain, which can be traded off for capacity improvements. This capacity increase is not a result of increasing the number of antenna elements but is rather due to SINR improvements for a given BLER or FER target compared with the SISO case. On the other hand, when the channel is known to the transmitter, such as the case in closed loop transmit diversity (TXAA and CLTD), then we get from [6] that MT  2 C = log 2 1 + SINR ⋅ ∑ hi    i =1

(10.7)

It then follows that for channel coefficients given by (10.5), the capacity is given by C = log 2 (1 + SINR ⋅ M T )

(10.8)

This is consistent with the results in Chapter 7, where CLTD outperformed STTD. 10.2.4 MIMO

In the case of MIMO, s(t) is a MT × 1 vector, y(t) is a MR × 1 vector, and H is a MT × MT matrix. An example MIMO setup is shown in Figure 10.5. Let the channel matrix be given by  h11 h 21 H=  M h  MT 1

h12 h 22 M hMT 2

L L

h1 M R h2M R M

O L hMT M R

     

(10.9)

In the absence of channel knowledge at the transmitter, the capacity is given by   SINR C = log 2 det  I M R + HH H  MT  

(10.10)

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Tx

MR

MT

Figure 10.5

Rx

Vector channel

MIMO scheme.

which can rewritten as [6, 8] L  SINR ⋅ λi  C = ∑ log 2 1 +  MT   i =1

(10.11)

Equation (10.11) clearly shows that the MIMO channel capacity can be expressed as the sum of the capacities of L SISO channels or multiple spatial data pipes. To maximize the MIMO channel capacity, given a fixed total channel power y such that H

2

L

= ∑ λi = γ,H, H must be orthogonal [8] (i.e., i =1

given by) h11 0 H=  M 0 

0 h 22 M 0

L 0 L 0 O M L hMT M R

     

(10.12)

Hence, the channel capacity is maximized when the channel matrix is diagonal (i.e., when the subchannels are uncorrelated, such as parallel independent subchannels). Any correlation between the different subchannels results in increased fading and a reduction in channel capacity. To achieve this very high capacity, the channel matrix must be made diagonal through signal processing at the receiver. The capacity can then be rewritten in the simple form  SINR  C = M T log 2 1 +  MT  

(10.13)

Consider the multiantenna system diagrams in Figure 10.6. A digital input signal is fed to a serial to parallel splitter after error control coding and mapping to complex modulation symbols. The splitter produces several separate symbol streams and each are then mapped onto one of the multiple transmit

MIMO Systems

S/P

Modulation/ weighting

Signal processing

MT

Figure 10.6

295

P/S

MR

Spatial multiplexing with MIMO.

antennas, which may include spatial weighting of the antenna elements or antenna space-time precoding. At the receiver, the signals are captured by multiple antennas and the signals are recovered after demodulation and demapping. This can be considered as an extension to conventional smart antenna applications. The intelligence of the multiantenna system lies in the weight selection algorithm and can offer a more reliable communications link in the presence of adverse propagation conditions such as multipath fading and interference. Figure 10.7 compares the performance of all four schemes versus the SINR and number of receive and transmit array elements. For the adaptive antenna array case (beamforming), only the effect of the increased array gain was considered in the comparison. The actual performance is better when the interference reduction capabilities are factored in. A comparison between the different spatial techniques already discussed is shown in Table 10.1

10.3 Transmission Strategies It is important to realize that the performance improvements achievable with MIMO systems by going to multiple transmit antenna do not derive from

Figure 10.7

Performance comparison between different spatial techniques.

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Table 10.1 Multielement Spatial Schemes

Scheme

MT

MR

Example

Benefits

SISO

1

1

No transmit or receive diversity

No diversity

SIMO

1

>1

Receive diversity, beamforming, beam steering

Diversity proportional to MR.

MISO

MIMO

>1

>1

1

>1

Array gain interference reduction with beamforming.

Transmit diversity, beamforming, beam steering

Diversity proportional to MT.

Use of multiple antennas at both the transmitter and receiver

Diversity proportional to the product of MT and MR.

Array gain interference reduction with beamforming.

Array gain (coherent combining assuming prior channel estimation).

increased transmit power, which would be a very inefficient approach to overcome interference or improve spectral efficiency. Rather, in MIMO systems a peak power constraint of Pmax is imposed on each transmit antenna so that the total power available at the transmitter is Ptot = MT Pmax and is equivalent to the single transmit antenna case. It is possible to allocate this total power over all N nonzero eigenmodes of the channel in a variety of ways, as long as the per-antenna power limit and the total power limit are not exceeded. Common methods include water filling, uniform power allocation, beamforming, and beam steering. 10.3.1 Water Filling

Assuming a total transmit power constraint, Ptot = MT Pmax, the optimum, capacity achieving power allocation strategy for the N parallel channels is found by waterfilling [9]. The waterfilling method performs a distribution of the available power over the eigenmodes in such a way that the mode with the lowest noise variance receives the greatest fraction of total power. The waterfilling power allocation technique is optimal under constraint of total power. However, performance degrades when the per-antenna power limit is taken into account. 10.3.2 Uniform Power Allocation

One transmission method is to allocate the total power evenly over all modes. This uniform power allocation method assigns power Ptot /N to each mode; this

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297

power allocation results in equal power on each antenna. When the number of modes is less than the number of transmit antennas, a scaling coefficient can be used to meet the constraint. It is worth noting that the effective result is the same as if the maximum power Pmax were allocated to each mode. 10.3.3 Beamforming

The beamforming power allocation strategy places all of the available power on a single eigenmode. To approach capacity, the total transmit power is assigned to the eigenmode corresponding to the highest eigenvalue. Thus, the SINR is maximized given the constraint of using a single mode. 10.3.4 Beam Steering

In the beamforming transmission strategy already described, both the amplitude and phases of the principal eigenmode are used at the transmitter. The beam steering transmission strategy also places the total available power on the single data stream (eigenmode); however, instead of using both the amplitude and phase information of the principal eigenmode, only the phase information is used. The amplitude information is discarded by normalizing the principal eigenvector such that all coefficients of the vector have unity amplitude. To ensure that the power across each transmit antenna is Pmax a rescaling coefficient equal to Pmax/Ptot is applied. As discussed in Chapter 5, the beam steering approach better uses the total available power by increasing the transmitted power in the direction of the desired user. In the beamforming strategy, the scaling ensures that the highest antenna power is equal to the per-antenna power limit, whereas the beam steering scheme forces the power on all transmit antennas to equal Pmax, thus, resulting in a higher overall transmit power and a higher effective SINR.

10.4 MIMO Approaches There are several approaches to implement MIMO systems that are based on the presence or absence of channel information at the transmitter. A summary of the different MIMO schemes is shown in Table 10.2. The best performance can be achieved with fixed terminals where the receive array size is not severely constrained by the physical dimensions. Low fading rates or, more precisely, lack of mobility allows for accurate channel estimation, hence the full CSI-based approach is possible. Moreover, the use of directional antennas in the receive array helps improve performance. For portable terminals, which are usually stationary during usage (e.g., laptop), the array size is somewhat more constrained but the use of directional and/or omni

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Table 10.2 MIMO Schemes

MIMO Scheme

Pros

Cons

Applications

Transmitter equipped with channel state information (full CSI)

Best performance

Increased overhead since a CSI feedback channel is required

Fixed terminals

Transmitter does not have No CSI feedback CSI (non-CSI) channel required

Worst performance

Mobile terminals

Transmitter has limited CSI (partial CSI)

Performance between CSI and non-CSI schemes

Portable terminals

A CSI limited feedback channel is till required → reduced overhead relative to CSI

receive antennas is still possible. In this case, a mixed approach is more suitable where the mode can be adaptively selected between full, partial, or no CSI. Finally, for mobile terminals, the array size is severely constrained (e.g., phone) and the terminals are typically restricted to omni receive antennas. This results in a low to moderate SINR environment that favors a partial or no CSI-based approach. This is because in high mobility the fade rates are also high, leading to less accurate channel estimates. Although the feedback frequency can be increased so that the transmitter has meaningful channel estimates faster than changes in the channel, this would significantly increase the overhead.

10.5 MIMO Advantages and Key Performance Issues The biggest advantage of MIMO systems is their ability to provide tremendous capacity gains under certain conditions compared with other spatial techniques [7, 10–20]. In SIMO/MISO systems capacity improves by about 1 bps/Hz when the SINR is doubled, whereas in a MIMO system doubling SNR improves capacity by ~ N bps/Hz, N = min(MT, MR). Another benefit of MIMO systems that derives from the increased diversity order is improved link reliability. As diversity increases, the probability that a given data rate cannot be reliability sustained is reduced: P outage = κ ⋅ (SINR )

−N

(10.14)

where κ is a constant and N is the diversity order. In a SIMO or MISO system N = MR & MT, respectively, whereas in a MIMO system N = MR * MT.

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299

10.6 RF Propagation Characterization MIMO performance advantages occur when the RF propagation channel is richly scattered (i.e., multipath, fading) such as in nonLOS applications, which typically have good scattering. In LOS cases, the use of cross-polarized antennas can preserve capacity by capturing more multipath. To achieve the orthogonality required in (10.12), which maximizes the capacity, the signals across the array elements must be uncorrelated. Therefore, the correlation between array elements has a big impact on performance. As we have discussed in previous chapters, this correlation is a function of the array element spacing and angular spread.

10.7 SINR Environment MIMO gains over diversity systems increase with the SINR, as was demonstrated in Figure 10.7. Figure 10.8 shows how the performance compares for low to moderate SINR. It follows that in situations where the system is interference limited (low SINR), MIMO gains will be reduced. In those situations, enhanced interference management techniques, such as power control and directional antennas, can be employed to improve a MIMO system performance. Effect of SINR on MIMO Capacity

MIMO capacity gain over that of a SISO system also increases as the SINR is increased. This is shown in Figure 10.9, where we can see that the gain is marginal for SINRs below 0 dB, but significant gains can be achieved at high

Figure 10.8 SINR.

Performance comparison between different spatial techniques at low to moderate

300

Figure 10.9

Smart Antenna Engineering

Benefits of MIMO systems over SISO systems.

SINRs. It can be shown that as the SINR → ∞, CMIMO/CSISO → N, where N = min(MT, MR), assuming the same total power for both schemes. This can also be seen in Figure 10.9, where the gain converges toward two and four for the 2 × 2 and 4 × 4 cases, respectively. Figure 10.10 also illustrates how MIMO and receive diversity performances compare for the same total number of antenna elements. We can see that MIMO starts outperforming receive diversity only at high SINRs, above 10 dB, and this gain improves as the SINR is increased. We can summarize the performance of MIMO systems with respect to the SINR in Table 10.3

10.8 Spatial Multiplexing The combined use of transmit and receive arrays as seen earlier offers capacities that increase linearly with the number of array elements. SM in MIMO systems

Figure 10.10

Comparison between MIMO and receive diversity for four total antennas.

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Table 10.3 MIMO Performance Comparison Low SINR region

Some diversity order. Reduced outage probability → lower link margin required →better coverage. No significant increase in average data rate.

Moderate to high SINR region

Significant increase in average data rates over SIMO (by exploiting parallel channels to increase link throughput). High SNR → throughput increases with link dimensionality (see Figure 10.9). Large diversity reduces variability of link data rate.

can be employed in cases where high peak data rates and very low service outage probability are required. The operating requirements necessary to achieve those gains with SM can be summarized as follows: • Sufficiently rich signal scattering; • Data rate much higher than maximum Doppler spread (e.g., fixed or

stationary users); • Moderate to high SNRs. A comparison between the performance of MIMO systems and beamforming, receive, and transmit diversity is reported in [21, 22]. Transmit diversity gains and capacity improvements increase as the number of array elements or diversity branches is increased but the gains diminish beyond four elements [23]. Beamforming performance improves with increasing the number of elements as the beamwidth decreases and the array gain increases. However, since decreasing the beamwidth below the angle spread does not provide additional gains, there is an upper bound on the number of elements. This is shown in Figure 10.11, where we can see that in macrocells where the AS is generally below 15°, the upper bound on the number of elements is 20 for AS of 5° and about eight for AS of 12°. A depiction of the performance of the different approaches discussed in this chapter is shown in Figure 10.12. We can see that for users with high SINR (i.e., those close to the base station), SM outperforms all other techniques. In moderate SINR, both beamforming and SM have similar performances, whereas beamforming outperforms all others for users with low SNR (e.g., at cell edge).

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Figure 10.11

Upper bound on number of elements versus AS.

Spatial Multiplexing MIMO

Increasing peak data rates

Spatial Multiplexing MIMO Beamforming

Beamforming

Beamforming Diversity Diversity

Spatial Multiplexing MIMO Diversity

High SNR user close to BS

Figure 10.12

Moderate SNR

Low SNR user close to cell edge

Comparison between MIMO, beamforming, and diversity.

10.9 Conclusion MIMO techniques are being widely proposed as a key technique to enhance the radio channel capacity of cellular systems. This technology can enhance the SINR and improve spectral efficiency by enabling multistream transmission. To fully use these potential features, MIMO technology should be combined with flexible link adaptation, also known as adaptive modulation and coding mechanisms, which can map high SINR values into high user data rates. That is why it

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is natural to combine MIMO together with the HSDPA, a new feature that is part of the Release 5 specifications of the 3GPP WCDMA/UTRA-FDD standard. HSDPA takes the maximum peak data rate in a 5-MHz WCDMA carrier to 14.4 Mbps from the currently commercial 384 Kbps. Combining MIMO with HSDPA improves the maximum data rate up to 21.6 Mbps in the same 5-MHz bandwidth [24]. Even though it might appear that few mobile applications could require this very high peak data rate, the real motivation for using MIMO is to actually increase the sector throughput. In currently deployed WCDMA networks, the sector throughput is typically around 0.9–1 Mbps, which improves by about twofold with HSDPA [25]. Fixed beamforming with HSDPA has been studied in [26]. A capacity gain of at least 2.5 was reported using four beams. That would further improve the sector throughput with HSDPA from around 2 Mbps to ~ 5 Mbps. It is expected that by using MIMO schemes, based on the comparisons we have shown earlier in this chapter, the sector throughput can be increased much further.

References [1] Foschini, G. J., and M. J. Gans, “On Limits of Wireless Communications in a Fading Environment When Using Multiple Antennas,” Wireless Pers. Commun., Vol. 6, March 1998, pp. 311–335. [2] Foschini, G. J., “Layered Space-Time Architecture for Wireless Communication in a Fading Environment When Using Multielement Antennas,” Bell Labs Tech. J., Fall 1996, pp. 41–59. [3] Telatar, E., “Capacity of Multiantenna Gaussian Channels,” AT&T Bell Laboratories, Tech. Memo, June 1995. [4] Raleigh, G., and J. M. Cioffi, “Spatial-Temporal Coding for Wireless Communications,” IEEE Trans. Commun., Vol. 46, 1998, pp. 357–366. [5] Bölcskei, H., D. Gesbert, and A. J. Paulraj, “On the Capacity of OFDM-Based Spatial Multiplexing Systems,” IEEE Trans. Commun., Vol. 50, February 2002, pp. 225–234. [6] Gesbert, D., et al., “From Theory to Practice: An Overview of MIMO Space-Time Coded Wireless Systems,” IEEE Journal on Selected Areas in Communications, Vol. 21, No. 3, April 2003, pp. 281–302. [7] Shiu, D., et al., “Fading Correlation and Its Effect on the Capacity of Multielement Antenna Systems,” IEEE Trans. Commun., Vol. 48, March 2000, pp. 502–513. [8] Paulraj, A., et al., Introduction to Space-Time Wireless Communications, New York: Cambridge University Press, May 2003. [9] Smith, P. J., and M. Shafi, “Waterfilling Methods for MIMO Systems,”Proc. 3rd Australian Communication Theory Workshop, Canberra, Australia, 2002. [10] Paulraj, A., and C. B. Papadias, “Space-Time Processing for Wireless Communications,” IEEE Signal Processing Mag., Vol. 14, November 1997, pp. 49–83.

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[11] Winters, J. H., “On the Capacity of Radio Communication Systems with Diversity in a Rayleigh Fading Environment,” IEEE J. Select. Areas Commun., Vol. SAC-5, June 1987, pp. 871–878. [12] Foschini, G. J., et al., “Simplified Processing for Wireless Communication at High Spectral Efficiency,” IEEE J. Select. Areas Commun.—Wireless Commun. Series, Vol. 17, 1999, pp. 1841–1852. [13] Lozano, A., and C. Papadias, “Layered Space Time Receivers for Frequency Selective Wireless Channels,” IEEE Trans. Commun., Vol. 50, January 2002, pp. 65–73. [14] Foschini, G. J., et al., “Analysis and Performance of Some Basic Space-Time Architectures,” IEEE VTS 54th Vehicular Technology Conference, 2001. VTC Fall 2001 Vol. 2 pp. 1220–1224. [15] Blum, R., J. Winters, and N. Sollenberger, “On the Capacity of Cellular Systems with MIMO,” Proc. IEEE Vehicular Technology Conf., Atlantic City, NJ, October 2001. [16] Catreux, S., P. F. Driessen, and L. J. Greenstein, “Attainable Throughput of an Interference-Limited Multiple-input Multiple-Output Cellular System,” IEEE Trans. Commun., Vol. 48, August 2001, pp. 1307–1311. [17] Dai, H., A. Molisch, and H. V. Poor, “Downlink Capacity of Interference Limited MIMO Systems with Joint Detection,” IEEE Trans. Wireless Commun., submitted for publication. Vol. 47, Issue No. 2, pp. 173–176, 1999. [18] Driessen, P. F., and G. J. Foschini, “On the Capacity Formula for Multiple-input Multiple-output Wireless Channels: A Geometric Interpretation,” IEEE Trans. Commun., Vol. 47, February 1999, pp. 173–176. [19] Andersen, J. B., “Array Gain and Capacity for Known Random Channels with Multiple Element Arrays at Both Ends,” IEEE J. Select. Areas Commun., Vol. 18, November 2000, pp. 2172–2178. [20] Lizhong Zheng; Tse, Diversity and Multiplexing: A Fundamental Trade-Off in Multiple-Antenna Channels,” IEEE Trans. on D.N.C. Information Theory, Vol. 49, No. 5, May 2003, pp. 1073–1096. [21] Lozano, A., F. R. Farrokhi, and R. A. Valenzuela, “Lifting the Limits on High-Speed Wireless Data Access Using Antenna Arrays,” IEEE Commun. Mag., Vol. 39, September 2001, pp. 156–162. [22] “Emerging Wireless SIG–New Multiple-Input-Multiple-Output (MIMO) Smart Antenna Technology to Boost Wireless Bandwidth, Capacity and Range,” General Atomics, http://www.sdtelecom.org/comdir/Documents.cfm?NID=3572&ckid=2. [23] 3GPP, Tx Diversity Solutions for Multiple Antennas Release 5, Tech. Rep. 3G TR 25.869 v 1.0.0, 2001. [24] 3GPP, Multiple-Input Multiple Output in UTRA, Tech. Rep. TR 25.876 V1.5.1, 2004. [25] Holma, H., et al., WCDMA for UMTS: Radio Access for Third Generation Mobile Communications, 3rd ed., New York: John Wiley & Sons, 2004. [26] Pedersen, K. I., and P. E. Mogensen, “Performance of WCDMA HSDPA in a Beamforming Environment Under Code Constraints,” IEEE 58th Vehicular Technology Conference, Vol. 2, October 6–9, 2003, pp. 995–999.

List of Acronyms 2G

second generation

3G

third generation

3GPP

Third Generation Partnership Project

3GPP2

Third Generation Partnership Project 2

AA

adaptive antenna

AC

admission control

AF

array factor

AMPS

advanced mobile phone service

AMR

adaptive multirate

AOA

angle of arrival

AOD

angle of departure

ARQ

automatic repeat request

AS

angle spread

AWGN

additive white Gaussian noise

BCCH

broadcast control channel

BER

bit error rate

BLER

block error rate

BPSK

binary phase shift keying 305

306

Smart Antenna Engineering

BS

base station

BSC

base station controller

CAPICH

common auxiliary pilot channel

CDF

cumulative distribution function

CDM

code division multiplexing

CDMA

code division multiple access

C/I

carrier-to-interference ratio

CLTD

closed loop transmit diversity

COST

European COoperation in the field of Scientific and Technical research

CPICH

common pilot channel

CRC

cyclic redundancy check

DAPICH

dedicated auxiliary pilot channel

DCCH

dedicated control channel

DCH

dedicated channel

DL

downlink

DOA

direction of arrival

DPCCH

dedicated physical control channel

DPCH

dedicated physical channel

DPDCH

dedicated physical data channel

DTCH

dedicated traffic channel

EIA

electronic industry alliance

EIRP

effective isotropic radiated power

EV-DO

EVolution Data Optimized

FACH

forward access channel

F-CAPICH

forward common auxiliary pilot channel

FCC

Federal Communications Commission

FCH

fundamental channel

FDD

frequency division duplex

List of Acronyms

FDMA

frequency division multiple access

FEC

forward error correction

FER

frame error rate

FL

forward link

GSM

global system for mobile communication

HHO

hard handoff

HSDPA

High Speed Downlink Packet Access

IMT

International Mobile Telecommunications

IR

incremental redundancy

IS

interim standard

ITU

International Telecommunication Union

Kbps

kilobits per second

L1

OSI layer 1: physical layer

LMS

least mean square

LOS

line of sight

LS

least squares

MAC

medium access control

MAPL

maximum allowable path loss

MEG

mean effective gain

MIMO

multiple input multiple output

MISO

multiple input single output

MMSE

minimum mean square error

MRC

maximal ratio combining

MS

mobile station

MVDR

minimum variance distortionless response

NF

noise figure

OC

optimum combining

OLTD

open loop transmit diversity

OTD

orthogonal transmit diversity

307

308

Smart Antenna Engineering

OVSF

orthogonal variable spreading factor

PAS

power azimuth spectrum

P-CCPCH

primary common control channel

PCG

power control group

PCH

paging channel

PG

processing gain

PN

pseudorandom noise

PSC

primary synchronization code

QOF

quasi-orthogonal function

QPCH

quick paging channel

QPSK

quadrature phase shift keying

R99

Release 99

RBFNN

radial basis function neural network

RBS

radio base station

RF

radio frequency

RL

reverse link

RLS

recursive least squares

RMS

root mean square

RNC

radio network controller

SCCH

supplemental code channel

SCH

supplemental channel

S-CPICH

secondary common pilot channel

SDMA

space division multiple access

SF

spreading factor

SHO

soft handoff

SIMO

single input multiple output

SINR

signal-to-interference plus noise ratio

SISO

single input single output

SM

spatial multiplexing

List of Acronyms

SNR

signal-to-noise ratio

SS

spatial signature

STS

space time spreading

STTD

space time transmit diversity

TD

transmit diversity

TDD

time division duplex

TDM

time division multiplexing

TDMA

time division multiple access

TIA

Telecommunication Industry Association

TXAA

transmit antenna array

UE

user equipment

UL

uplink

UMTS

universal mobile telecommunication system

VA

voice activity factor

WCDMA

wideband code division multiple access

309

About the Author Ahmed El Zooghby received his Ph.D. in electrical engineering from the University of Central Florida in 1999 and his B.S. and M.S. in electrical engineering from Alexandria University in Egypt in 1991 and 1994, respectively. He was a faculty member at the Arab Academy for Science and Technology and Maritime Transport from 1992–1995. Dr. El Zooghby is currently a UMTS product manager with QUALCOMM CDMA Technologies, working on the development of advanced chipset solutions for current and future 3G wireless communications systems. Dr. El Zooghby has also held previous positions at Ericsson Wireless Communications, Inc., where he worked on CDMA infrastructure. At Ericsson, he was responsible for managing and supervising technical issues related to defining, configuring, planning, and evaluating requirement outlines of wireless CDMA infrastructure systems for key Ericsson customers. Dr. El Zooghby’s main research interests include smart antennas, neural network–based adaptive array processing, and direction finding. Dr. El Zooghby has published more than 25 transaction and conference papers on smart antennas. He has also lectured and offered several seminars about smart antennas.

311

Index beam steering, 124 blind, 161 fixed multiple beams vs., 130–32 maximum SINR, 125–26 MMSE, 126–27 MVDR, 127–28 null steering, 124–25 optimum SINR, 128–30 See also Beamforming Adaptive cell sectorization, 114–15 defined, 114 load balancing, 115 See also Sectorization Adaptive modulation and coding (AMC), 3, 43 Admission control (AC) algorithm, 246 forward link/downlink, 255–58 reverse link/uplink, 253–54 system impact, 248 uplink beamforming, 261 Advanced mobile phone service (AMPS), 2, 14 Angle diversity, 118–20 concept illustration, 120 defined, 119 performance, 120 Angle of arrival (AOA) of clusters and paths, 73, 76 computing, 76 distribution, 70 spread, 73

1xEV-DO, 40–43 downlink, 41, 42 link adaptation, 43 optimization, 41 sector power usage comparison, 42 1xRTT defined, 37 sector power usage comparison, 42 2G networks, 5 3-dB beamwidth. See Half-power beamwidth 3G cellular users forecast, 6 evolution paths towards, 5 technology comparisons, 6 3G networks, 36–49, 191–227 coverage/capacity limitations, 209–11 data applications, 203–9 impact on uplink coverage/capacity, 211–26 link budgets and coverage, 192–97 voice services, 197–203 A Access channel (ACH), 52 Adaptive arrays, 10–11, 117–50 downlink beamforming, 142–49 downlink processing, 132–41 uplink processing, 117–32 See also Antenna arrays Adaptive beamforming, 122–32 architecture illustration, 259

313

314

Smart Antenna Engineering

Angle spread, 147 base station, 69–73 beamforming gains vs., 222 impact, 77–80 impact on optimum beamforming, 175–81 mobile station, 74 Antenna arrays, 89–105 array factor, 89 broadside, 91–92 coordinate system, 90 directivity, 99 element spacing, impact of, 93–96 end-fire, 91–92 first null beamwidth, 96–97 fundamentals, 89–105 gain, 100, 271 half-power beamwidth, 97–99 interference reduction capabilities, 271 number of elements, impact, 92–93 planar, 101–5 radiation pattern, 89, 90 trade-off analysis, 100 Asymmetric beamformers, 107 Auxiliary pilots, 242 Azimuth power spectrum, 69–74 base station, 69–73 mobile station, 74 B Base stations additional equipment at, 249–50 angle spread, 69–73 azimuth power spectrum, 69–73 parameters (link budget), 193 Beamformers, 105–7 4 x 4 Butler matrix, 106 asymmetric, 107 defined, 106 orthogonal, 106 spatial filtering, 109–11 symmetric, 107 Beamforming, 105–7, 184–85, 222–24, 296 adaptive, 122–32 approach comparison, 150 blind adaptive, 161 channel estimation, 183–84 DOA-based, 146–47, 170–75 downlink, 142–49, 181–82

gains vs. angular spread (WCDMA), 222 MIMO comparison, 301, 302 optimum, 175–81 performance, 145 spatial signature-based, 145–46 transmission strategy, 297 transmit diversity performance comparison, 227 See also Antenna arrays Beam pointing, 258 Beam steering, 124, 258–61 advantages/disadvantages, 260 channel estimation at mobile, 259–60 defined, 258 transmission strategy, 297 Beam transition, 251–52 Beamwidths half-power, 97–99 null-to-null (NNBW), 96–97 tailored, 89 Blind adaptive beamforming, 161 Broadside arrays, 91–92 defined, 91 polar pattern, 94–96 radiation patterns along z-axis, 92 See also Antenna arrays Butler matrix, 107–9 4 x 4 beamformer, 106 defined, 107 phases at array elements, 108 phase shifters, 107 power dividers, 107 C Capacity CDMA, 21, 24, 84 cell, 54 coverage trade-off, 55–57 downlink, 132, 133 downlink, of CDMA sector, 286 downlink gains, 284–86 downlink improvements, 221, 224 embedded, 53–55 gain, 220, 282, 283 improvements vs. load factor, 217 improvement techniques, 212 increase vs. gain, 214 isolated, 24–25 limited scenarios, 211 limiting links for, 209–11

Index MIMO, 294, 299–300 normalized Shannon, 291 power reduction trade-off, 215 relative sector, 286 smart antennas impact, 211–26 STTD, 220 voice coverage vs., 205 WCDMA cell, 54 See also Coverage CDMA2000, 3, 5, 7, 37–44, 191 1x-EV-DO, 40–43 1XRTT, 37, 42 access procedure, 239 cell capacity, 54 defined, 37 fast forward link power control mechanism, 37–38 FEC, 37 mobile call states, 237–38 M-sequences, 235 multimedia services, 38 multiplexing, 233 open loop vs. closed loop transmit diversity gains, 218 OTD vs. STS transmit diversity gains, 218 packet data services, 241 pilot channels, 241–42 protocol stack, 234 radio links, 192 transmit diversity gains vs. geometry, 219 voice link budget, 199–200, 202–3 See also Code division multiple access (CDMA); Third-generation systems CDMAOne operators, 5 Cell breathing, 56 Cells area confidence, 195–96 CDMA layout, 49 edge, 196 embedded capacity, 53–55 isolated capacity, 24–25 macrocells, 63, 70, 221 microcells, 63, 225–26 minicells, 63 picocells, 63 Channel estimation, 183–84, 251 Channel quality indicator (CQI), 3

315

Closed loop power control, 35–36 Closed loop transmit diversity (CLTD), 134, 137–40 capacity increases, 225 CL1 mode, 141 CL2 mode, 141 defined, 137 OLTD schemes vs., 138 TXAA, 137–38 TXAA architecture, 139 TXAA operation, 140–41 See also Transmit diversity Cochannel interference, 18–19 Cochannel separation, 18 Code allocation, 248 Code division multiple access (CDMA), 2, 20–36 access state, 52–53 access technology, 20 acquisition state, 49–52 advantage, 21 basic procedures, 49–53 call setup, 52–53 call states, 50 capacity, 21, 24, 84 carrier frequency, 22 cell breathing, 56 cell layout, 49 codes, 25–29 coverage vs. capacity trade-off, 55–57 defined, 20 direct-sequence (DS-CDMA), 43 downlink capacity, 133 embedded cell capacity, 53–55 forward link channels, 30–32 fundamentals, 21–36 idle state, 52 IS-95, 24, 29–36 isolated cell capacity, 24–25 multipath fading, 55 power control, 32–34 RAKE receivers, 32 receiver block diagram, 30 receivers, 22 reverse link channels, 32 reverse link closed loop power control, 35–36 reverse link open loop power control, 34–35

316

Smart Antenna Engineering

Code division multiple access (CDMA) (continued) sectorization gain (SG), 85–86 soft capacity, 84 soft handoff, 36 systems, 20–21 TDD-based, 181 traffic loading, 196–97 traffic or dedicated state, 53 transmitter block diagram, 30 See also Multiple access techniques Coherence bandwidth, 65–66 Combining maximal ratio, 272 optimum, 272–78 performance improvement, 281 selection diversity, 272 techniques, 272 Common pilot channel (CPICH), 44 Confidence, 195–96 Congestion control (CC), 246 Conjugate gradient (CG) algorithm, 164–67 approximation, 165 computational load, 167 forgetting factor, 165 subsequent updates, 165 See also Reference signal methods Constant modulus algorithm (CMA), 162 Conventional sectorization, 83–89 limitations, 88–89 scheme illustration, 88 three-sector pattern, 84 COST-231 model, 67 Coupling loss, 280 mutual effects, 279–80 Coverage CDMA trade-off, 55–57 downlink improvements, 221, 224 improvements with multiple fixed beam antennas, 216 improvement techniques, 212 limited scenarios, 210–11 limiting links for, 209–11 link budgets and, 192–97 smart antennas impact, 211–26 voice, vs. capacity, 205 See also Capacity

Cumulative distribution function (CDF), 118 Cyclic redundancy check (CRC), 47 Cyclostationary algorithms, 163–64 D Data applications, 203–9 Data multiplexing, 233–35 Decision-directed algorithm, 162–63 Dedicated channel (DCH), 46 Dedicated physical channel (DPCH), 45 Dedicated physical control channel (DPCCH), 45 Dedicated physical data channel (DPDCH), 45, 184 Dedicated pilot bits, 235, 243 Delay spread, 65 Delta rule, 173 Direction on arrival (DOA), 10 characterization, 68 downlink beamforming and, 181 multipath, 184 Directivity arrays, 99 planar arrays, 104–5 direct-sequence CDMA (DS-CDMA), 43 Diversity angle, 118–20 gain, 118, 119, 220, 268 mobile receive (MRD), 289 polarization, 270–71 selection, 272 space, 120 techniques, 117–18 transmit, 93, 134–41, 217–22 DOA-based beamforming, 146–47 neural network, 170–75 PAS, 147 uplink channel correlation matrix, 146 See also Beamforming Doppler shift, 66, 147 Doppler spread, 66 Downlink admission control, 255–58 beamforming, 142–49, 181–82 budgets, 198–203 capacity, 132, 133 capacity gains, 284–86 capacity improvements, 221, 224 capacity of CDMA sector, 286

Index coverage improvements, 221, 224 initial power setting, 253 load reduction with STTD, 220 smart antenna capacity impact, 216–26 Downlink processing, 132–41 transmit diversity concepts, 134 transmit diversity in 3G CDMA standards, 134–41 See also Adaptive arrays Downlink shared channel (DSCH), 46 Dual-antenna diversity mobile station, 279 performance improvements, 280–83 relative sector capacity vs. percentage penetration, 286 E Element pattern, 101 Element spacing half-power beamwidth as function, 98 impact of, 93–96 spatial correlation vs., 144 End-fire arrays, 91–92 Envelope correlation, 269, 270 Equal-gain combining, 121 EV-DO systems, 7 F Fade margin (link budget), 194–95 Fading, 34–35 envelope, 77, 78, 79, 80 fast, 34, 35 flat, 55, 66 frequency, 77 frequency selective, 66 as function of time, 34 multipath, 55 Rayleigh, 35, 118 Rician, 120 slow, 34, 35 spatial, 77 temporal, 77 Fast fading, 34 Fast forward link power control, 37–38 Fixed beam antennas, 9–10, 83–115 adaptive cell sectorization, 114–15 arrays, 89–105 beamforming, 105–7 Butler matrix, 107–9 conventional sectorization, 83–89

317

multiple, 113 system impact, 248–58 See also Smart antennas Fixed beamforming, 110 Flat fading, 55, 66 Forward error correction (FEC), 37 Forward link channels, 30–32 forward traffic, 32 paging, 31 pilot, 31 sync, 31 types of, 30 Forward traffic channels, 32 Four-finger RAKE receivers, 278 Frame error rate (FER), 54 Frequency calibrated (FC) algorithm, 182 Frequency division duplex (FDD), 43, 146 defined, 43 gaps, 148 Frequency division multiple access (FDMA), 1 concept illustration, 15 spectrum division, 15 systems, 14–15 Frequency planning, 16 Frequency reuse, 16–18 defined, 17 plans, 18 Frequency selective fading, 66 Fundamental channel (FCH), 53 G Gain(s) array, 100, 271 beamforming, angular spread vs., 222 capacity, 220, 282, 283 capacity increase vs., 214 diversity, 118, 119, 220, 268 downlink capacity, 284–86 mean effective (MEG), 271–72 MIMO, 299 power reduction vs., 214 Gaussian minimum-shift keying (GMSK), 164 Gold codes, 235 Grating lobes, 93 H Half-power beamwidth, 97–99 defined, 97

318

Smart Antenna Engineering

Half-power beamwidth (continued) formula, 98 as function of element spacing, 98 scanning angle effect, 99 Handoffs hard (HHO), 247 impact on capacity gains, 282 soft, 36, 246–47 softer, 86, 87, 246–47 zones, 89 Hard handoff (HHO), 247, 261 Hata’s model, 66–67 High-speed data transfer, 239–40 mobility procedures, 238–40 reestablish procedures, 240 High-speed dedicated physical control channel (HS-DPCCH), 47 High Speed Downlink Packet Access (HSDPA), 7, 45–49 adaptive modulation and coding, 47 defined, 45–46 for end users, 46 fast scheduling, 48–49 hybrid-ARQ with soft combining, 47–48 peak data rate, 46 scheduler algorithms, 48 High-speed downlink shared channel (HS-DSCH), 46, 47 High-speed shared control channel (HS-SCCH), 47 High-speed uplink packet access (HSUPA), 3 Hybrid-automatic repeat request (HARQ), 3 defined, 47–48 functionality implementation, 48 Hybrid couplers, 108 I Indoor office radio environment, 64 Initial power setting, 245–46 downlink, 253 fixed beam approach, 252–53 preamble, 252–53 system impact, 248 uplink, 252–53 uplink beamforming, 261 Interference average, 22 cochannel, 18–19 reduction capabilities, 271 uplink reduction, 216

worst-case, 22 Interference-limited systems, 132 International Telecommunications Union (ITU), 4–5 IS-95 CDMA, 24, 29–36 defined, 29 forward link channels, 30–32 IS-2000 vs., 44 RAKE receivers, 32 reverse link channels, 32 WCDMA vs., 44 See also Code division multiple access (CDMA) IS-2000, 38–40 forward link physical channels, 38, 39 IS-95 vs., 44 reverse link physical channels, 38–40 reverse link pilot, 40 reverse link pilot frame structure, 41 WCDMA vs., 44 L Lagrange multiplier method, 167–69 Least mean square (LMS) algorithm, 159–61 Least squares (LS) algorithm, 161–62 Limiting link capacity scenarios, 211 coverage scenarios, 210–11 evaluation, 212 Line-of-sight (LOS), 61, 274 Link adaptation, 43 Link budgets, 192–97 base station parameters, 193 datalink, 206–7 fade margin, 194–95 margins, 193 maximum allowable path loss (MAPL), 194 mobile station parameters, 192 receiver sensitivity, 194 signal bandwidth, 194 system parameters, 193 thermal noise density, 194 voice services, 197–203 Load reduction, 220 Lyapunov functions, 170–71 M Macrocells, 63 base station model, 70

Index STTD, 221 Margins, 193 fade, 194–95 list of, 193 Maximal ratio combining, 272 Maximum likelihood (ML) principle, 10 Maximum ratio combining (MRC), 10, 131, 132 Mean effective gain (MEG), 271–72 defined, 271 reduction in, 272 whip antenna, 271 Microcells, 63, 225–26 MIMO, 11 advantages, 298 approaches, 297–98 beamforming and diversity comparison, 302 capacity, 299–300 channel capacity, 294 channel matrix, 293 conclusion, 302–3 data transmission, 290 gains, 299 introduction, 289–90 mixed approach, 298 performance, 297 performance comparison, 301 performance issues, 298 principles, 290–95 receive diversity comparison, 300 RF propagation characterization, 299 scheme illustration, 294 sector throughput, 303 SINR environment, 299–300 SISO systems vs., 300 spatial multiplexing, 295, 300–302 systems, 289–303 transmission strategies, 295–97 wireless communication systems, 290 Minicells, 63 Minimum mean square error (MMSE), 10, 125–26 criterion, 126 disadvantage, 127 gradient, 126 Minimum variance distortionless response (MVDR), 127–28 beamformer, 10, 128

319

defined, 128 Mobile call states, 237–38 CDMA2000, 237–38 illustrated, 238 WCDMA, 237 Mobile market, growth, 3–4 Mobile radio environments, 63–64 indoor office, 64 outdoor to indoor environment, 64 propagation channel, 62 vehicular environment, 64 Mobile receive diversity (MRD), 289 Mobile station parameters (link budget), 192 Mobile stations angle spread, 74 azimuth power spectrum, 74 diversity-enable, 285 smart antennas, 265–86 SNR, 134, 139 Mobility procedures, 239–40 Multimedia messaging service (MMS), 3 Multipath channels, 64–65 Multipath DOAs, 184 Multipath fading, 55 channel characterization, 65–66 impact, 55 Multiple access techniques, 14–21 CDMA, 20–36 cochannel interference, 18–19 FDMA, 14–15 frequency reuse, 16–18 TDMA, 15–16 Multiple-antenna MS design, 268–72 Multiple fixed beam systems, 113 adaptive beamforming vs., 130–32 additional equipment at base station, 249–50 beam transition, 251–52 channel estimation, 251 digital baseband architecture, 250 downlink capacity improvements, 224 downlink coverage improvements, 224 downlink/uplink coverage/capacity improvements, 223 forward link/downlink admission control, 255–58 passive networks architecture, 249 power control, 252

320

Smart Antenna Engineering

Multiple fixed beam systems (continued) preamble initial power setting, 252–53 radio resource management, 254–55 reverse link/uplink call admission control, 253–54 scrambling code/PN offset assignment, 250 soft-handoff procedure, 252 uplink capacity vs. load factor, 217 uplink coverage improvements, 216 uplink interference reduction, 216 Multiple input multiple output. See MIMO Multiple input single output (MISO), 292–93 defined, 292 scheme illustration, 292 Mutual coupling effects, 279–80 N Near-far problem, 23 Neural network DOA-based beamforming, 170–75 Noise-dominated systems, 131–32 Nonaccess stratum (NAS) layer specification, 233 Nonline-of-sight (NLOS), 62, 274 Nordic Mobile Telephone (NMT), 14 Normalized Shannon capacity, 291 Null steering beamforming, 124–25 defined, 124 disadvantages, 125 optimum SINR vs., 275–77 See also Beamforming Null-to-null beamwidth (NNBW), 96–97 O Okumura-Hata propagation models, 66–67 COST-231 model, 67 Hata’s model, 66–67 Open loop power control, 34–35 Open loop transmit diversity (OLTD), 134–35 Optimum combining (OC) conclusions, 280–82 defined, 273 performance, 282 Orthogonal beamformers, 106 Orthogonal spreading factor codes (OVSF), 43

Orthogonal transmit diversity (OTD), 135–36 defined, 135 illustrated, 135 See also Transmit diversity Outdoor to indoor radio environment, 64 P Packet data services, 240–41 CDMA2000 approach, 241 WCDMA approach, 241 Paging channel, 31 Paging indication channel (PICH), 45 Path loss models, 66–67 Personal communications systems (PCS), 1 Physical common packet channel (PCPCH), 45 Physical layer, 233–37 data multiplexing, 233–35 formatting, 235–37 frame structures, 236 interaction with upper layers, 234–35 transmit chain, 235 Physical random access channel (PRACH), 45 Picocells, 63 Pilot channels, 31, 241–43 auxiliary transmit diversity, 232 CDMA2000, 241–42 common (CPICH), 243 forward transmit diversity, 242 primary common (P-CPICH), 243 secondary common (S-CPICH), 243 WCDMA, 243 Pilot measurement, 258 Pilot pollution, 114 Planar arrays, 101–5 array factor, 102 defined, 101 directivity, 104–5 geometry, 103 See also Antenna arrays Planar-inverted F antenna (PIFA), 271 Polarization diversity, 270–71 Power azimuth spectrum (PAS), 147, 149 Power control, 32–34 fast forward link, 37–38 fixed beam approach, 252 inner loop, 35 reverse link closed loop, 35–36

Index reverse link open loop, 34–35 uplink beamforming, 260 Power control bit (PCB), 35 Power control (PC) algorithm, 244 Power reduction, 214–15 capacity increase trade-off, 215 gain vs., 214 Primary common control physical channel (P-CCPCH), 44 Protocol stacks, 232–37 CDMA2000, 234 WCDMA, 234 Q Quadrature phase shift keying (QPSK), 3 Quasi-orthogonal functions (QOFs), 222 R Radial basis function neural network (RBFNN), 171, 172–73 block diagram, 171 contents, 172–73 delta rule, 173 input-output mapping, 173 performance phase, 173, 174–75 training data, 174 Radial-basis functions (RBF), 172 Radiation patterns, 142–43 four-element array, 143 two-element array, 142 Radio configurations (RC), 38 Radio environments, 63–64 Radio network algorithms, 244–47 admission control (AC), 246 congestion control (CC), 246 hard handoff (HHO), 247 initial power setting, 245–46 power control (PC), 244 smart antennas’ impact on, 262 soft/softer handoff, 246–47 Radio network controller (RNC), 46 Radio resource management, 254–55 Rake combiners, 184 RAKE receivers, 32 block diagram, 33 eight-finger, 281 fingers, adding, 279 four-finger, 278 searcher finger, 32 size, 278–79

321

vector, 182 Rayleigh fading, 35, 118 Recursive least squares (RLS) algorithm, 161 Reference signal methods, 159–70 blind adaptive beamforming, 161 CG algorithm, 164–67 CMA algorithm, 162 comparison, 169–70 cyclostationary algorithms, 163–64 decision-directed algorithm, 162–63 Lagrange multiplier method, 167–69 LMS algorithm, 159–61 LS algorithm, 161–62 RLS algorithm, 161 SCORE algorithm, 164 Reverse link admission control (AC) algorithm, 253–54 channels, 32 closed loop power control, 35–36 open loop power control, 34–35 physical channels, 38–40, 45 pilot, 40 Rician fading, 120 S Scalloping, 112, 113 SCORE algorithm, 164 Scrambling, 235, 250 Secondary common control physical channel (S-CCPCH), 44 Sectorization adaptive cell, 114–15 conventional, 83–89 defined, 83 efficiency, 87 techniques, 8–9 Sectorization gain (SG), 84, 85–86 CDMA, 85–86 defined, 84 illustrated, 86 Selection diversity, 272 Short message service (SMS), 3 Signal-to-interference and noise ratio (SINR) maximum, 125–26 mean, 274 MIMO, 299–300 optimum, 128–30, 275–77 Signal-to-interference ratio (SIR), 19

322

Smart Antenna Engineering

Signal-to-noise ratio (SNR) high, 278 maximum, 147–49 mobile stations, 134, 139 Single input multiple output (SIMO) systems, 291–92 defined, 291 illustrated, 292 low correlation, 292 normalized Shannon capacity, 291 Single input single output (SISO) systems, 291, 300 Slow fading, 34, 35 Smart antennas advanced spatial techniques, impact of, 247–58 benefits, 7–8 downlink capacity impact, 216–26 fixed beam, 9–10, 83–115 impact on radio network algorithms, 262 impact on uplink coverage and capacity, 211–26 integration in system design, 261 performance impacts, 68, 215 sectorization techniques, 8–9 switched beam, 9–10, 111–13 system aspects, 9, 231–62 types of, 8–9 on uplink, 215 why?, 7 Softer handoff, 246–47 defined, 86 overlap areas, 87 uplink beamforming, 261 See also Handoffs Soft handoff, 36, 246–47 basis, 246 candidate set, 36 defined, 36 in fixed beam mode, 252 neighbor set, 36 state, 247 uplink beamforming, 261 See also Handoffs Space diversity performance, 120 Space division multiple access (SDMA), 8 Space-time coding (STC), 289 Space-time spreading (STS) defined, 136

illustrated, 137 transmission matrix, 136 Space-time transmit diversity (STTD), 136–37 capacity increases, 225 defined, 136 downlink capacity improvements, 221 downlink coverage improvements, 221 downlink load reduction with, 220 gain, 220 illustrated, 138 macrocell, 221 Walsh code, 137 See also Transmit diversity Spatial channel modeling, 67–74 AOA, 71–73 application in system simulations, 74–76 base station azimuth power spectrum, 69–71 distribution of clusters/scatterers, 69 number of clusters, 69 parameters, 68 Spatial filtering, with beamformers, 110–11 Spatial multiplexing (SM), 289, 295, 300–302 Spatial signature-based beamforming, 145–46 Spreading, 235 Spread spectrum composite signals, 28 processing gain, 22, 84 signal effect, 28, 29 signals, 26, 27 Stop and Wait (SAW) protocol, 47 Superframes, 31 Switched beam antennas, 9–10, 111–13 advantages, 111–12 architecture, 112 defined, 111 limitation, 112–13 Symmetric beamformers, 106–7 Synchronization channel (SCH), 50 System parameters (link budget), 193 T Third Generation Partnership Project 2 (3GPP2), 70 Third-generation systems, 36–49 CDMA2000, 37–44 HSDPA, 45–49

Index WCDMA, 43–45 Time division duplex (TDD), 43, 146 Time division multiple access (TDMA), 2 concept illustration, 16 defined, 15 disadvantages, 16 systems, 15–16 Time-division-multiplexed (TDM) waveform, 41, 42 Time-multiplexed power control (TPC), 235 Total Access Communications System (TACS), 14 Traffic loading, 196–97 Transmission strategies, 295–97 beamforming, 296 beam steering, 297 uniform power allocation, 296–97 water filling, 296 Transmit adaptive antennas (TXAA), 137–38, 218 architecture, 139 diversity gain, 140 operation, 140–41 use of, 138 Transmit diversity, 93, 134–41, 217–22 in 3G CDMA standards, 134–41 beamforming performance comparison, 227 closed loop, 134, 137–40 concepts, 134 gains vs. geometry (CDMA2000), 219 open loop, 134–35 open loop vs. closed loop gains (CDMA2000), 218 open loop vs. closed loop gains (WCDMA), 219 orthogonal, 135–36 OTD vs. STS gains, 218 space-time, 136–37 system impact, 247–48 Transport format combination indicator (TFCI), 235 U Uniform power allocation, 296–97 Uplink call admission control, 253–54 capacity improvements vs. load factor, 217 coverage improvements, 216

323

interference reduction, 216 preamble power setting, 252–53 smart antennas on, 215 Uplink beamforming, 260–61 admission control, 261 initial power setting, 261 power control, 260 soft/softer/hard handoff, 261 See also Beamforming Uplink budgets, 198 Uplink processing, 117–32 adaptive beamforming, 122–32 angle diversity, 118–20 diversity techniques, 117–18 maximum ratio combining, 121 See also Adaptive arrays User-specific beamforming methods, 244 V Vector RAKE receivers, 182 Vehicular radio environment, 64 Voice services, 197–203 CDMA2000, 199–200, 202–3 coverage vs. capacity, 205 downlink budgets, 198–203 uplink budgets, 198 voice coverage vs. capacity, 205 WCDMA, 200–201, 204–5 Voice traffic, projected growth, 5 W Walsh codes, 25 complementary, 136 generation, 25 PN codes comparison, 31 spreading signal effect, 29 STTD, 137 Water filling, 296 Weight selection algorithm, 295 Weiner-Hopf type calculations, 273 Wideband CDMA (WCDMA), 2, 43–45, 191 access procedure, 239 acquisition steps, 51–52 beamforming, channel estimation, 183 beamforming gains vs. angular spread, 222 cell capacity, 54 cell search signals, 50 data coverage vs. capacity, 209

324

Smart Antenna Engineering

Wideband CDMA (WCDMA) (continued) data interface rise vs. uplink load, 210 datalink budget, 206–7 defined, 43 DL budget, 207–8 downlink channels applicable for beamforming, 244 European bands, 148 FDD forward link physical channels, 43–45 FDD reverse link physical channels, 45 gold codes, 235 IS-95 vs., 44 IS-2000 vs., 44

mobile call states, 237, 238 multiplexing, 233–35 open loop vs. closed loop transmit diversity gains, 219 packet data services, 241 pilot channels, 243 protocol stack, 233, 234 radio links, 192 voice interface rise vs. uplink load, 210 voice link budget, 200–201, 204–5 See also Code division multiple access (CDMA) Wireless communications systems, 1–3, 14–21, 290

Recent Titles in the Artech House Mobile Communications Series John Walker, Series Editor

3G CDMA2000 Wireless System Engineering, Samuel C. Yang 3G Multimedia Network Services, Accounting, and User Profiles, Freddy Ghys, Marcel Mampaey, Michel Smouts, and Arto Vaaraniemi 802.11 WLANs and IP Networking: Security, QoS, and Mobility, Anand R. Prasad, Neeli R. Prasad Advances in 3G Enhanced Technologies for Wireless Communications, Jiangzhou Wang and Tung-Sang Ng, editors Advances in Mobile Information Systems, John Walker, editor Advances in Mobile Radio Access Networks, Y. Jay Guo Applied Satellite Navigation Using GPS, GALILEO, and Augmentation Systems, Ramjee Prasad and Marina Ruggieri CDMA for Wireless Personal Communications, Ramjee Prasad CDMA Mobile Radio Design, John B. Groe and Lawrence E. Larson CDMA RF System Engineering, Samuel C. Yang CDMA Systems Capacity Engineering, Kiseon Kim and Insoo Koo CDMA Systems Engineering Handbook, Jhong S. Lee and Leonard E. Miller Cell Planning for Wireless Communications, Manuel F. Cátedra and Jesús Pérez-Arriaga Cellular Communications: Worldwide Market Development, Garry A. Garrard Cellular Mobile Systems Engineering, Saleh Faruque The Complete Wireless Communications Professional: A Guide for Engineers and Managers, William Webb EDGE for Mobile Internet, Emmanuel Seurre, Patrick Savelli, and Pierre-Jean Pietri

Emerging Public Safety Wireless Communication Systems, Robert I. Desourdis, Jr., et al. The Future of Wireless Communications, William Webb GPRS for Mobile Internet, Emmanuel Seurre, Patrick Savelli, and Pierre-Jean Pietri GPRS: Gateway to Third Generation Mobile Networks, Gunnar Heine and Holger Sagkob GSM and Personal Communications Handbook, Siegmund M. Redl, Matthias K. Weber, and Malcolm W. Oliphant GSM Networks: Protocols, Terminology, and Implementation, Gunnar Heine GSM System Engineering, Asha Mehrotra Handbook of Land-Mobile Radio System Coverage, Garry C. Hess Handbook of Mobile Radio Networks, Sami Tabbane High-Speed Wireless ATM and LANs, Benny Bing Interference Analysis and Reduction for Wireless Systems, Peter Stavroulakis Introduction to 3G Mobile Communications, Second Edition, Juha Korhonen Introduction to Digital Professional Mobile Radio, Hans-Peter A. Ketterling Introduction to GPS: The Global Positioning System, Ahmed El-Rabbany An Introduction to GSM, Siegmund M. Redl, Matthias K. Weber, and Malcolm W. Oliphant Introduction to Mobile Communications Engineering, José M. Hernando and F. Pérez-Fontán Introduction to Radio Propagation for Fixed and Mobile Communications, John Doble Introduction to Wireless Local Loop, Second Edition: Broadband and Narrowband Systems, William Webb IS-136 TDMA Technology, Economics, and Services, Lawrence Harte, Adrian Smith, and Charles A. Jacobs

Location Management and Routing in Mobile Wireless Networks, Amitava Mukherjee, Somprakash Bandyopadhyay, and Debashis Saha Mobile Data Communications Systems, Peter Wong and David Britland Mobile IP Technology for M-Business, Mark Norris Mobile Satellite Communications, Shingo Ohmori, Hiromitsu Wakana, and Seiichiro Kawase Mobile Telecommunications Standards: GSM, UMTS, TETRA, and ERMES, Rudi Bekkers Mobile Telecommunications: Standards, Regulation, and Applications, Rudi Bekkers and Jan Smits Multiantenna Digital Radio Transmission, Massimiliano “Max” Martone Multiantenna Wireless Communications Systems, Sergio Barbarossa Multipath Phenomena in Cellular Networks, Nathan Blaunstein and Jørgen Bach Andersen Multiuser Detection in CDMA Mobile Terminals, Piero Castoldi Personal Wireless Communication with DECT and PWT, John Phillips and Gerard Mac Namee Practical Wireless Data Modem Design, Jonathon Y. C. Cheah Prime Codes with Applications to CDMA Optical and Wireless Networks, Guu-Chang Yang and Wing C. Kwong QoS in Integrated 3G Networks, Robert Lloyd-Evans Radio Engineering for Wireless Communication and Sensor Applications, Antti V. Räisänen and Arto Lehto Radio Propagation in Cellular Networks, Nathan Blaunstein Radio Resource Management for Wireless Networks, Jens Zander and Seong-Lyun Kim RDS: The Radio Data System, Dietmar Kopitz and Bev Marks Resource Allocation in Hierarchical Cellular Systems, Lauro Ortigoza-Guerrero and A. Hamid Aghvami

RF and Baseband Techniques for Software-Defined Radio Peter B. Kenington RF and Microwave Circuit Design for Wireless Communications, Lawrence E. Larson, editor Sample Rate Conversion in Software Configurable Radios, Tim Hentschel Signal Processing Applications in CDMA Communications, Hui Liu Smart Antenna Engineering, Ahmed El Zooghby Software Defined Radio for 3G, Paul Burns Spread Spectrum CDMA Systems for Wireless Communications, Savo G. Glisic and Branka Vucetic Third Generation Wireless Systems, Volume 1: Post-Shannon Signal Architectures, George M. Calhoun Traffic Analysis and Design of Wireless IP Networks, Toni Janevski Transmission Systems Design Handbook for Wireless Networks, Harvey Lehpamer UMTS and Mobile Computing, Alexander Joseph Huber and Josef Franz Huber Understanding Cellular Radio, William Webb Understanding Digital PCS: The TDMA Standard, Cameron Kelly Coursey Understanding GPS: Principles and Applications, Elliott D. Kaplan, editor Understanding WAP: Wireless Applications, Devices, and Services, Marcel van der Heijden and Marcus Taylor, editors Universal Wireless Personal Communications, Ramjee Prasad WCDMA: Towards IP Mobility and Mobile Internet, Tero Ojanperä and Ramjee Prasad, editors Wireless Communications in Developing Countries: Cellular and Satellite Systems, Rachael E. Schwartz Wireless Intelligent Networking, Gerry Christensen, Paul G. Florack, and Robert Duncan

Wireless LAN Standards and Applications, Asunción Santamaría and Francisco J. López-Hernández, editors Wireless Technician’s Handbook, Second Edition, Andrew Miceli

For further information on these and other Artech House titles, including previously considered out-of-print books now available through our In-Print-Forever® (IPF®) program, contact: Artech House

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