Current Technology Developments of WiMax Systems

Current Technology Developments of WiMax Systems

Maode Ma Editor

Current Technology Developments of WiMax Systems

13

Editor Dr. Maode Ma Nanyang Technological University School of Electrical & Electronic Engineering 50 Nanyang Avenue Singapore 639798 Singapore

ISBN: 978-1-4020-9299-2

e-ISBN: 978-1-4020-9300-5

DOI 10.1007/978-1-4020-9300-5 Library of Congress Control Number: 2008936718 Springer Science+Business Media B.V. 2009 No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work.

c 

Printed on acid-free paper 9 8 7 6 5 4 3 2 1 springer.com

Preface

Recent developments on wireless communication technology have resulted in tremendous innovations to make broadband wireless networks able to compete with 3G cellular network. IEEE 802.16X standards have not only specified WiMax wireless access networks but also designed a framework of wireless metropolitan area networks with mobility functionality. It is obvious that with further development of various WiMax technologies, wide range of high-quality, flexible wireless mobile applications and services could be provided, which will revolutionarily improve our modern life to achieve the goal of accessing the global information at any place and at any time by any mobile devices in the near future. WiMax technology is the most promising global telecommunication technology recently. WiMax technology and various WiMax networks are specified by the IEEE 802.16X standards, which define the Medium Access Control (MAC) layer and the Physical (PHY) layer of fixed and mobile broadband wireless access systems. This edited book has been produced by many contributors who have much knowledge of the standards and rich teaching and/or research experience of the WiMax technology. This edited book is intended to be a comprehensive reference book to address the recent developments of WiMax technologies for both academia and industry. It can serve as an introduction book for beginners to get the fundamental knowledge of various aspects of WiMax systems. It is also expected to be a good reference for researchers and engineers to understand the recent developments of the technology in order to promote further development of WiMax technologies and systems. The book consists of 13 chapters. Each of the chapters is either a technical overview or literature survey on a particular topic or a proposed solution to a research issue of Wimax technology. The 13 chapters can be roughly classified into 3 parts. The first part is major on the fundamental issues in WiMax point-to-multipoint (PMP) topology, consisting of Chapter 1 to Chapter 7. Chapter 1 addresses the deployment of multi-antenna base stations in WiMax systems and corresponding design of signal processing algorithms for interference mitigation involved in the deployment. Two main solutions have been proposed for interference management at the physical layer of WiMax systems. Chapter 2 presents a general background introduction on various dynamic bandwidth allocation schemes and introduces a Two-Phase Proportionating (TPP) algorithm as a solution to achieve a feasible dyv

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Preface

namic bandwidth allocation in order to increase the utilization of precious bandwidth and provide service differentiation. Chapter 3 is to address the frame allocation issue for the Quality of Service (QoS) provisioning, particularly based on the Orthogonal Frequency Division Multiple Access (OFDMA) PHY layer technology. A modular framework to handle frame allocation problems has been proposed to decouple the constraints of data region allocation into the MAC frames. Chapter 4 provides a detailed overview of the state-of-the-art scheduling techniques employed in WiMax systems and various issues related to the implementation of some of the scheduling algorithms. Chapter 5 presents an introduction and literature review on the QoS provisioning architecture and various technologies of traffic management at different levels including call admission control (CAC) and traffic scheduling to provide QoS guarantee in WiMax systems. In Chapter 6, a load-balancing approach to handle radio resource management in the mobile WiMax networks has been presented. This approach is a set of algorithms including call admission control, adaptive transmission, horizontal handover, and dynamic bandwidth allocation algorithms, which jointly maximize the network capacity and guarantee QoS requirements from different types of applications. Chapter 7 is a comparison study on the random access technologies in various wireless systems including the third generation (3G) of cellular systems like Wideband Code Division Multiple Access (WCDMA) and CDMA2000, mobile WiMax, and 3G Long Term Evolution (LTE). The second part is major on the mobility issues in WiMax cellular networks, consisting of Chapter 8 and Chapter 9. Chapter 8 is a proposed improvement on a handover mechanism, which is the Fast Base Station Switching (FBSS), used in mobile WiMax systems. The proposed FBSS with Reuse Partitioning Cell Structure scheme is to enhance the performance of the traditional FBSS. Chapter 9 is a literature review on various handover schemes recently proposed to improve the standard one specified in the IEEE 802.16e. It has presented the recent research efforts to reduce the latency introduced in the handover process with aim to provide QoS to different types of traffic during the handovers. The third part is major on other topologies of WiMax networks and the integration of WiMax networks with other wireless/wired networks, consisting of Chapter 10 to Chapter 13. Chapter 10 is a comprehensive study on WiMax systems with a proposal of a multiservice CAC mechanism, which can significantly improve the performance of an existing CAC scheme, and a proposal on the architecture of an interworking wireless network composed of WiMax and Wireless Local Area Network (WLAN) systems. Chapter 11 examines the power consumption performance in WiMax relay networks specified by IEEE 802.16j. Chapter 12 studies the issue to provide large-scale reliable multicast and broadcast services by social psychology principles and game theory. Finally, Chapter 13 addresses physical layer technologies such as Multiple Input Multiple Output (MIMO) antennas and Adaptive Modulation and Coding (AMC), the operations of WiMax mesh networks, and the integration of wireless and wired/optical MANs. It is obvious that without the great contributions and profound, excellent knowledge of WiMax technologies from the authors of each chapter, this book could not be published to serve as a reference book to the world. I wish to thank each contributor

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of the book for his/her time, huge efforts, and great enthusiasm to the publication of the book. I would also thank the publisher of the book and the representatives, Mr. Mark de Jongh, Mrs. Cindy Zitter, and Ms. Deivanai Loganathan, Integra for their patience and great helps in the publication process. Singapore

Dr. Maode Ma

Contents

1 Deployment and Design of Multi-Antenna WiMax Systems in a Non-Stationary Interference Environment . . . . . . . . . . . . . . . . . . . . . . . . M. Nicoli, S. Savazzi, O. Simeone, R. Bosisio, G. Primolevo, L. Sampietro and C. Santacesaria

1

2 Dynamic Bandwidth Allocation for 802.16E-2005 MAC . . . . . . . . . . . . . 17 Yi-Neng Lin, Shih-Hsin Chien, Ying-Dar Lin, Yuan-Cheng Lai and Mingshou Liu 3 A Downlink MAC Frame Allocation Framework in IEEE 802.16e OFDMA: Design and Performance Evaluation . . . . . . . . . . . . . . . . . . . . 31 Andrea Bacioccola, Claudio Cicconetti, Alessandro Erta, Luciano Lenzini, Enzo Mingozzi and Jani Moilanen 4 Scheduling Techniques for WiMax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Aymen Belghith and Loutfi Nuaymi 5 QoS Provision Mechanisms in WiMax . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 Maode Ma and Jinchang Lu 6 Mobile WiMax Performance Optimization . . . . . . . . . . . . . . . . . . . . . . . . 115 Stanislav Filin, Sergey Moiseev and Mikhail Kondakov 7 A Comparative Study on Random Access Technologies of 3G and B3G Mobile Communications Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Jungchae Shin and Ho-Shin Cho 8 An Improved Fast Base Station Switching for IEEE 802.16e with Reuse Partitioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 I-Kang Fu, Hsiang-Jung Chiu and Wern-Ho Sheen ix

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9 Fast Handover Schemes in IEEE 802.16E Broadband Wireless Access System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 Qi Lu, Maode Ma and Hui Ming Liew 10 Addressing Multiservice Classes and Hybrid Architecture in WiMax Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Kamal Gakhar, Mounir Achir, Alain Leroy and Annie Gravey 11 Energy-Efficient Multimedia Delivery in WMAN Using User Cooperation Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 Ki-Dong Lee, Byung K. Yi and Victor C.M. Leung 12 Game Theory Modeling of Social Psychology Principle for Reliable Multicast Services in WiMax Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 Markos P. Anastasopoulos, Athanasios V. Vasilakos and Panayotis G. Cottis 13 IEEE 802.16: Enhanced Modes of Operation and Integration with Wired MANs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 Isabella Cerutti, Luca Valcarenghi, Piero Castoldi, Dania Marabissi, Filippo Meucci, Laura Pierucci, Enrico Del Re, Luca Simone Ronga, Ramzi Tka and Farouk Kamoun

Biography of the Editor

Dr. Maode Ma received his BE degree in Computer Engineering from Tsinghua University in 1982, ME degree in Computer Engineering from Tianjin University in 1991 and PhD degree in Computer Science from Hong Kong University of Science and Technology in 1999. He is an Associate Professor in the School of Electrical and Electronic Engineering at Nanyang Technological University in Singapore. He has extensive research interests including wireless networking, optical networking, and so forth. He has been a member of the technical program committee for more than 80 international conferences. He has been a technical track chair, tutorial chair, publication chair, and session chair for more than 40 international conferences. Dr. Ma has published more than 130 international academic research papers on wireless networks and optical networks. He currently serves as an Associate Editor for IEEE Communications Letters, an Editor for IEEE Communications Surveys and Tutorials, an Associate Editor for International Journal of Wireless Communications and Mobile Computing, an Associate Editor for International Journal of Security and Communication Networks, and an Associate Editor for International Journal of Vehicular Technology.

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Contributors

M. Achir TELECOM Bretagne, France M. P. Anastasopoulos Wireless & Satellite Communications Group, School of Electrical & Computer Engineering, National Technical University of Athens, Greece A. Bacioccola Dipartimento di Ingegneria dell’Informazione, University of Pisa, Italy A. Belghith TELECOM Bretagne R. Bosisio Dip. di Elettronica e Informazione, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, Italy P. Castoldi Scuola Superiore Sant’Anna, Pisa, Italy, [email protected] I. Cerutti Scuola Superiore Sant’Anna, Pisa, Italy, [email protected] S.-H. Chien National Chiao Tung University, University Road, Hsinchu, Taiwan H.-J. Chiu Department of Communication Engineering, National Chiao Tung University, Hsinchu, Taiwan H.-S. Cho School of Electrical Engineering and Computer Science, Kyungpook National University, Daegu, Korea C. Cicconetti Dipartimento di Ingegneria dell’Informazione, University of Pisa, Italy P. G. Cottis Wireless & Satellite Communications Group, School of Electrical & Computer Engineering, National Technical University of Athens, Greece E. Del Re Universit`a degli Studi di Firenze, Italy, [email protected] xiii

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Contributors

A. Erta IMT Lucca Institute for Advanced Studies, Lucca, Italy S. Filin National Institute of Information and Communications Technology, 3-4, Hikarino-oka, Yokosuka, 239-0847, Japan I.-K. Fu Department of Communication Engineering, National Chiao Tung University, Hsinchu, Taiwan K. Gakhar 68 Rue Gallieni; 92100 Boulogne Billancourt, France, [email protected] A. Gravey Department of Computer Science, TELECOM Bretagne, France ´ F. Kamoun Ecole Nationale des Sciences de l’Informatique, Manouba, Tunisia, [email protected] M. Kondakov Kodofon, 97, Moskovsky Prospekt, Voronezh, Russia Y.-C. Lai National Taipei University of Science and Technology, Taipei, Taiwan K.-D. Lee LD Electronics Mobile Research, San Diego, CA 92131, USA, [email protected] L. Lenzini Dipartimento di Ingegneria dell’Informazione, University of Pisa, Italy A. Leroy TELECOM Bretagne, France V. C. M. Leung LD Electronics Mobile Research, San Diego, CA 92131, USA H. M. Liew School of Electrical and Electronic Engineering, Nanyang Technological Unversity, Singapore Y.-N. Lin National Chiao Tung University, University Road, Hsinchu, Taiwan Y.-D. Lin National Chiao Tung University, University Road, Hsinchu, Taiwan M. Liu Intel Innovation Center, Taiwan J. Lu School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore Q. Lu School of Electrical and Electronic Engineering, Nanyang Technological Unversity, Singapore M. Ma School of Electrical and Electronic Engineering, Nanyang Technological Unversity, Singapore

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D. Marabissi Universit`a degli Studi di Firenze, Italy, [email protected] F. Meucci Universit`a degli Studi di Firenze, Italy, [email protected] E. Mingozzi Dipartimento di Ingegneria dell’Informazione, University of Pisa, Italy, [email protected] J. Moilanen Nokia Siemens Networks, Helsinki, Finland S. Moiseev Kodofon, 97, Moskovsky Prospekt, Voronezh, Russia M. Nicoli Dip. di Elettronica e Informazione, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, Italy, [email protected] L. Nuaymi TELECOM Bretagne L. Pierucci Universit`a degli Studi di Firenze, Italy, [email protected] G. Primolevo WISYTech, Via Cadore, 21, 20035 Lissone, Milano Italy L. S. Ronga CNIT, Firenze, Italy, [email protected] L. Sampietro Nokia Siemens Networks S.p.A. Com CRD MW, S.S. 11 Padana Superiore Km. 158, 20060 Cassina de’ Pecchi (Milano), Italy C. Santacesaria Nokia Siemens Networks S.p.A. Com CRD MW, S.S. 11 Padana Superiore Km. 158, 20060 Cassina de’ Pecchi (Milano), Italy S. Savazzi Dip. di Elettronica e Informazione, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, Italy W.-H. Sheen Department of Communication Engineering, National Chiao Tung University, Hsinchu, Taiwan J. Shin School of Electrical Engineering and Computer Science, Kyungpook National University, Daegu, Korea O. Simeone CCSPR, New Jersey Institute of Technology (NJIT), University Heights 07102, Newark, USA U. Spagnolini Dip. di Elettronica e Informazione, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, Italy

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Contributors

´ R. Tka Ecole Nationale des Sciences de l’Informatique, Manouba, Tunisia, [email protected] L. Valcarenghi Scuola Superiore Sant’Anna, Pisa, Italy, [email protected] A. V. Vasilakos Wireless & Satellite Communications Group, School of Electrical & Computer Engineering, National Technical University of Athens, Greece B. K. Yi LG Electronics Mobile Research, San Diego, CA 92131, USA

Chapter 1

Deployment and Design of Multi-Antenna WiMax Systems in a Non-Stationary Interference Environment M. Nicoli, S. Savazzi, O. Simeone, R. Bosisio, G. Primolevo, L. Sampietro and C. Santacesaria

Abstract WiMax has already been adopted worldwide by operators attracted by promises of large throughput and coverage for broadband wireless access. However, towards the goal of an efficient deployment of the technology, a thorough analysis of its performance in presence of frequency reuse under realistic traffic conditions is mandatory. In particular, in both fixed and mobile WiMax applications, an important performance limiting factor is inter-cell interference, which has strong time-varying and non-stationary features. Two main solutions have been proposed for interference management at the physical layer of WiMax systems, namely, multi-antenna technology and random subcarrier permutation (as in the latest version of the standard, IEEE 802.16-2005). In this chapter, system deployment of multi-antenna base stations, and related design of signal processing algorithms for interference mitigation, are discussed. Extensive numerical results for realistic interference models show the advantages of the optimized multi-antenna deployment and design in combination with subcarrier permutation.

1.1 Introduction WiMax (Worldwide Interoperability for Microwave Access) is a standard-based technology that provides last mile broadband wireless access. Operators worldwide have already embraced this solution as either a complement or an alternative to existing wired and wireless technologies, such as cable, Digital Subscriber Line (DSL) or second/third generation (2G/3G) cellular systems [1]. Applications range from the provision of wireless services for rural or developing areas, to intensive and real-time applications on notebooks and other mobile devices. A first version of the standard, IEEE 802.16-2004 [2], was designed to provide broadband access M. Nicoli (B) Dip. di Elettronica e Informazione, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, Italy e-mail: [email protected]

M. Ma (ed.), Current Technology Developments of WiMax Systems, C Springer Science+Business Media B.V. 2009 DOI 10.1007/978-1-4020-9300-5 1, 

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to fixed subscriber stations, while the recently approved IEEE 802.16-2005 [3] supports both fixed and mobile access. Among the various options proposed in the IEEE 802.16 physical (PHY) layer specifications, the Orthogonal Frequency Division Multiplexing mode with 256 subcarriers in [2] and the Scalable OFDMA mode in [3] (here referred to as, respectively, 802.16-OFDM-256 and 802.16-SOFDMA) have sparkled the most interest as access solutions for the deployment of a cellular Wireless Metropolitan Area Network (WMAN). While analysis of single WiMax links for fixed applications is by now well studied (see, e.g., the survey in [1]), the impact of a deployment of WiMax (either fixed or mobile) over a given geographical area is currently under investigation. WiMax access points distributed over the coverage area form a cellular structure with given frequency reuse factor (see Fig. 1.1(a) for square cells with reuse factor

(a)

(c) Δθ =

33 7

SINR [dB]

4

45

deg 3.5

40

SS1

SS2

BS1

Linear array

Δ e [λ]

2.5

SS3 BS3

BS0

Δi

35

BS2 SS0

Δe

3

30

2 1.5

25

1

20

1000 m

Δe

0.5

15

(d)

(b)

SINR [dB]

4

50

620 m 3.5

45

Δθ =4 SS2

Δ e [λ]

deg

SS0

40

6.1

SS1 BS0

3

Δe

2.5 35 2 30 1.5

Δi

25

Δe

1

SS3 Linear array

20

0.5

No-SD Max-FD 0.5

1

1.5

2

2.5

3

3.5

4

15

Δ i [λ]

Fig. 1.1 Deployment of a non-uniform linear antenna array at the base station of a WiMax multicell system. Uplink cellular layouts are considered with either square (a) or hexagonal (b) cells. The average SINR at the output of the MVDR filter is shown versus the antenna spacings ⌬e and ⌬i , for the square (c) and hexagonal (d) layouts

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4, and Fig. 1.1(b) for hexagonal cells with reuse factor 3). In such a scenario, the main technological challenge is the mitigation of inter-cell interference. This issue is even more relevant in WiMax than in existing 2G/3G cellular systems due to the larger data rates that WiMax promises to offer, which call for more sophisticated interference-reduction techniques. The task of designing a WiMax system robust to inter-cell interference is made even more challenging by the non-stationary nature of interference in both fixed and mobile applications. In a fixed system, based on IEEE 802.16-OFDM-256, asynchronous transmission in interfering cells causes the inter-cell interference to vary within a given communication session. In fact, in a typical scenario, out-of-cell subscriber stations (SS) are expected to start and end their transmission on a time scale that cannot be controlled by the interfered cells. On the other hand, mobile WiMax based on IEEE 802.16-SOFDMA adopts random subcarrier permutation within a time-frequency grid (as in the Partially Used Subchannelization, PUSC, mode [3, 4]) in order to provide interference diversity. After the permutation, subcarriers allocated to a given user are subject to the interference of different out-of-cell subscribers, thus leading to a non-stationary interference scenario. This chapter presents an overview of existing solutions to the problem of (nonstationary) interference-mitigation techniques at the PHY layer of a WiMax-compliant system for both IEEE 802.16-OFDM-256 and SOFDMA. As recognized at academic and industrial levels, a satisfactory interference management hinges on multi-antenna technology (see, e.g., [5, 6]). Therefore, in this chapter we focus on deployment and design of WiMax systems in presence of multi-antenna at the base station (BS). As a case study, we investigate the uplink (UL), i.e. the communication from SSs to BS, as illustrated in Fig. 1.1(a, b). We first study optimal deployment of an antenna array at WiMax base stations as a trade-off between diversity and interference-rejection capability of the antenna array (Section 1.3). The advantages of an optimized array in terms of coverage and average throughput with respect to conventional antenna array deployments are shown through extensive numerical simulations in Section 1.4. Then, signal processing techniques at the BS that allow to cope efficiently with non-stationary interference are addressed. These techniques are based on the exploitation of the pilot subcarriers prescribed by the IEEE 802.16-OFDM-256 standard for adaptive estimation of channel/interference parameters (Section 5.1). Finally, subcarrier permutation as defined by the PUSC mode of IEEE 802.16-SOFDMA is considered, in combination with multi-antenna technology, in order to cope with non-stationary interference. Performance of such a solution is discussed to validate its suitability (Section 5.2).

1.2 WiMax System In this section we present a brief overview of the PHY layer specified by the standards IEEE 802.16-OFDM-256 and IEEE 802.16-SOFDMA. We focus on the UL side of the radio link, where complex receiver algorithms at the BS side can give

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the most relevant gains in cell coverage and link quality. Moreover, we introduce the basic model of a multi-cell WiMax system, that will be used for performance assessment.

1.2.1 Overview of IEEE 802.16 PHY Layer Both the OFDM-256 [2] and the SOFDMA [3] modes of IEEE 802.16 are targeted to bandwidths in the low 2-11 GHz range (for performance evaluation purposes, in the remainder of the chapter we will consider 3.5 GHz as the carrier frequency) and employ OFDM modulation for their basic symbol structure. Apart from these fundamental similarities, the two PHY layers exhibit profound differences in both the chosen multiple access scheme and the system parameters, and require a separate introduction. A summary of the basic system parameters of IEEE 802.16 is given in Table 1.1. Table 1.1 Basic parameter for IEEE 802.16-OFDM-256 and IEEE 802.16-SOFDMA Overall subcarriers Guard subcarriers

IEEE 802.16 OFDM-256

IEEE 802.16 SOFDMA

256 (irrespective of BW)

128

512

1024

2048

43

91

183

367

55

Active subcarriers

192 data+ 8 pilots

N. of slots in the BW

–

Mandatory coding scheme

Concatenated RS-CC

48 data + 24 pilots (6 tiles × 3 symbols) 3

15

30

60

Tail Biting CC (also CTC)

1.2.1.1 IEEE 802.16-OFDM-256 The OFDM mode has been tailored for a deployment with fixed users, as a lastmile access solution. Users are accommodated on the UL frame by a Time Division Multiple Access (TDMA) scheme: different users transmit in physically separate bursts, each including a long preamble (the first OFDM symbol) used for channel estimation and synchronization, followed by a sequence of OFDM symbols carrying coded data. Eight pilot subcarriers are also embedded in each OFDM data symbol (see Table 1.1). Since there is at most one active interferer per adjacent cell on a given OFDM symbol, and the interferers can be reasonably expected to transmit continuously for a number of OFDM symbols, interference rejection in the OFDM mode is a feasible task. However, bursts are not necessarily synchronized among different cells and thus the transmission from the desired user might experience different interferers switching on and off. Specific techniques are therefore needed so as to gather data about all interferer’s spatial signature within each transmitted burst, track it for the burst duration and reject its transmission (if needed). To this end, the receiver can exploit both the burst preamble and the pilots embedded in the subsequent OFDM data symbols (see Section 1.5.1).

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1.2.1.2 IEEE 802.16-SOFDMA In the SOFDMA mode, a hybrid Time/Frequency Division Multiple Access (TDMA/ FDMA) scheme is utilized. The logical structure of the UL frame can be visualized as a time/frequency grid, where each chunk, dubbed slot in the standard, represents a set of subcarriers observed over a certain number of OFDM symbols. All user allocations are then defined as contiguous sets of slots in the frame. The standard provides various options for grouping the subcarriers into slots. The basic option, and the most likely to be utilized in the first generation devices, is the UL-PUSC [4]. In this case, each slot spans 24 subcarriers, observed over 3 OFDM symbols (see Table I). However, the subcarriers assigned to a slot are not contiguous in frequency, as each slot selects 6 groups of 4 contiguous subcarriers by means of a pseudo-random permutation. When observed over 3 OFDM symbols each group is called tile (spanning 12 subcarriers, of which 8 for data and 4 pilots). The permutation depends on both a cell-specific identifier and the time index of the slot in the frame. This results in a pseudo-random spreading of the signal over the frequency domain that improves the system diversity in the following way: 1 The transmission of any user is spread over the available bandwidth, so that subcarriers exhibiting bad channel conditions impact only on a portion of the transmitted data (frequency diversity); 2 The transmission of a strong interferer affects only part of the signals transmitted by a given user. In fact, the interferer’s tiles are likely assigned to different slots and thus to different terminals in the cell of the considered user. Furthermore, since data is coded, the corrupted portion of the received signal might be recovered. Random permutation can thus yield a significant performance improvement with respect to fixed channel assignments where a strong interference affects the entire codeword (interference diversity). The effects of random subcarrier permutation will be investigated in Section V-B.

1.2.2 System Model 1.2.2.1 Multi-cell Layout We consider the UL of a IEEE 802.16 compliant system [2, 3]. Figure 1.1 exemplifies the scenario of interest for, respectively: (a) square layout with cell side r = 1 km and frequency reuse factor F = 4; (b) hexagonal layout with cell side r = 620 m and frequency reuse factor F = 3. In these examples, the transmission by the subscriber station SS0 to its own base station BS0 at a given time instant and frequency is impaired by the interference from at most N I = 3 out-of-cell subscriber NI (see the shaded cells in the Fig. 1.1(a,b) representing the first ring stations {SSi }i=1 of interferers). Base station BS0 is equipped with a linear symmetric array of M antennas, while SS’s have a single antenna.

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1.2.2.2 Modeling the Radio Environment Let us consider the antenna-array receiver at the base station BS0 . The (base-band) signal received on the t-th subcarrier on a given OFDM symbol can be written as yt = ht xt + nt ,

(1.1)

where xt denotes the symbol transmitted by the desired station SS0 , while ht is a vector gathering the M (complex) channel gains between the transmitter SS0 and the M antennas of BS0 . These gains account for path-loss, shadowing and fast-fading effects due to the propagation from SS0 to BS0 [7]. Notice that long-term fading effects due to shadowing can be, to a certain extent, mitigated through power control. Generally, the propagation channel ht is the superposition of the contributions of several paths, each characterized by a complex amplitude, a time of arrival and a direction of arrival (DOA). In our performance analysis, the multipath components are modelled according to the Stanford University Interim (SUI) channel models [8], and DOAs are considered as Gaussian distributed around the main direction SS0 BS0 , with a moderate angular spread. The baseline case of signal coming from a single direction that might be different from the line-of-sight (LOS) one (i.e., with null angular spread) will also be considered and referred to as a no-spatial-diversity (No-SD) channel. It is perfectly understood that this particular case has limited applicability either in the fixed and in the mobile wireless environment, although it might model, in some cases, propagation scenarios where the BS0 and the SS0 are marginally surrounded by scattering. As further performance references, we consider two simplified fading models that deviate from the SUI one and can be seen as extreme cases of frequency selectivity:

r r

No frequency diversity (No-FD): the channel gains are constant over the subcarriers (as for a null delay spread or, equivalently, a frequency-flat channel); Maximum frequency diversity (Max-FD): the channel gains are uncorrelated over the subcarriers (as for the ideal case of a maximum delay spread).

The focus of this chapter is on the effect of the noise vector nt , that is given by the sum of the background noise and inter-cell interference. The latter is generated by the set It of users, {SSi }i∈It , that are active in the nearby cells on the same subcarrier and the same OFDM symbol as the desired transmission. Propagation from interferers to BS0 is modelled similarly to the user SS0 . The main difference is that shadowing effects on the interfering channels cannot be compensated by power control and lead to fluctuations of the interference level up to 20-30dB. 1.2.2.3 Characterization of the Inter-cell Interference According to the discussion above, inter-cell interference is characterized at the base station BS0 by the multipath channels corresponding to the propagation from the users {SSi }i∈It to BS0 . This information is summarized by the spatial covariance of the interference-plus-noise signal nt , defined as Qt = E[nt nH t ], that collects the

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noise correlation between any pair of antennas [6]. This quantity is fundamental when treating the interference as Gaussian; it depends not only on the propagation environment but also on the inter-element spacing used at the antenna array (see Section 1.3). Accurate estimation and tracking algorithms for the covariance matrix Qt are necessary tools in order to design interference mitigation algorithms at the BS. As explained above, while this task is feasible in IEEE 802.16-OFDM-256 systems (see Section 1.5.1), this is highly impractical in the UL-PUSC mode of the SOFDMA standard (see Section 1.5.2).

1.3 Antenna Array Design In this section, we tackle the problem of optimal antenna deployment at the base station BS0 . In particular, as firstly proposed in [9], we investigate the optimal antenna spacings for a non-uniform linear antenna array. Herein, we focus on IEEE 802.16-OFDM-256 systems with interference scenarios sketched in Fig. 1.1(a) and 1.1(b). According to the standard specifications, only one user is active within each cell in the bandwidth of interest. Thereby, in both cases up to three interferers impair the transmission from SS0 to BS0 . To reduce the effects of this interference, BS0 applies a spatial filter (beamforming) to the received signal (1.1). The optimal beamforming technique is the minimum variance distortionless (MVDR) filter [6], which minimizes the output power subject to the constraint of unitary gain in the steering direction SS0 -BS0 . This leads to effective interference-rejection capabilities, as nulls are steered in directions of strong interferers. The interference-rejection capability may be quantified in terms of signal-tointerference-plus-noise ratio (SINR) at the output of the spatial filter. This depends on the channel response (ht ), the spatial pattern of the interference (Qt ) and the antenna spacing. The first two quantities are determined by the cellular layout geometry, the SS positions and the propagation environment, while the antenna spacings are free parameters that can be designed for a specific layout/environment so as to maximize the SINR performance. 3 placed at the center of their Let us at first consider the three interferers {SSi }i=1 respective cells and focus on the simplified propagation model with no shadowing, path-loss simulated according to the Hata-Okamura model [7] (with path-loss exponent 4), maximum frequency-diversity (Max-FD) and null angular spread (No-SD). As shown in Fig. 1.1, a non-uniform antenna array with M = 4 elements is considered at the BS: the array structure consists of two clusters of two antenna each that are positioned at a distance ⌬i among each other, antennas in each cluster are ⌬e spaced. In Fig. 1.1(c) and 1.1(d) the average SINR at the output of the MVDR filter, for M = 4 antennas, is plotted in gray scale versus the external (⌬e ) and internal (⌬i ) spacings of the antenna array, for the two plannings in Fig. 1.1(a) and 1.1(b), respectively. For each pair (⌬e , ⌬i ), the SINR value is obtained by averaging over fading, noise and the position of the desired user SS0 (uniformly distributed within the cell). The results obtained for both the layouts show that the minimum-length array that

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maximizes the average SINR is a uniform linear array (ULA) with ⌬i = ⌬e = ⌬opt , being ⌬opt = 1.8λ the optimal spacing for the layout (a) and ⌬opt = 1.4λ for the layout (b) (λ denotes the carrier wavelength). This confirms the analytical results of [10], where the optimal spacing is found to be ⌬opt = nλ/ sin(⌬θ ) where n is a non-zero integer and ⌬θ the angular separation between interferers (n = 1 for minimum-length array). We recall that the inter-element spacing normally used for beamforming purposes is the one that maximizes the DOA resolution under the non-alias constraint, i.e., ⌬m = λ/[2 sin(θmax )] where θmax is the largest DOA admissible for the considered cellular layout. In particular, this equals the usual spacing ⌬m = λ/2 when the antenna array covers the whole sector of 180deg (θmax = π/2), while for the plannings in Fig. 1.1 it is: θmax = π/4 and ⌬m = 0.71λ for the square layout; (b) θmax = π/3 and ⌬m = 0.58λ for the hexagonal layout. With respect to this standard antenna deployment with spacing ⌬m , the optimal antenna array is wider (⌬opt > ⌬m ) and provides a larger SINR at the output. From Fig. 1.1(c,d), the SINR gain with respect to the first solution is around 5dB. Such a gain can be justified by noticing that the optimal spacing introduces a certain degree of angular equivocation in the directivity function of the array, so that the three interferers with DOAs [θ1 , θ2 , θ3 ] = [−⌬θ, 0, +⌬θ ] are grouped together along the unique direction θ = 0. The spatial wave numbers associated to the DOAs of the interferers SS1 and ⌬ SS3 are indeed ω1 = ω3 = ±2π λopt sin(⌬θ ) = ± 2πn and coincide with that of the broadside interferer SS2 (ω2 = 0). This effect renders interference mitigation more effective, as one null of the directivity function on the broadside is enough to virtually reject three interferers, thus leaving other degrees of freedom to increase the spatial diversity. When the position of the interferers is not known a-priori at the time of the antenna deployment, or it varies due to terminal mobility as prescribed in IEEE 802.16-SOFDMA [3], these concepts have to be adapted to randomness of SS’s positions. In such scenarios, the SINR has to be averaged over the expected positions of both user and interferers within their cells before being optimized. Interestingly, simulation results show that the optimal spacing remains essentially the same as in the static scenario considered above (for n = 1), due to the symmetry of the problem at hand. The performance gain with respect to standard antenna deployment reduces to 2dB (the reader may refer to [11] for a numerical validation).

1.4 Coverage Analysis In this section we compare the average cell throughput provided by the optimized ULA with inter-element spacing ⌬opt and MVDR processing, as derived in the previous section, with that obtained by a conventional ULA with spacing ⌬m . The performance gain of the optimized array is evaluated for an IEEE 802.16-OFDM256 system with bandwidth 4MHz. As prescribed in [2], seven possible transmis7 , can be used, with throughput ranging from 1.2 Mbit/s (T1 ) to sion modes, {Ti }i=1

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9

11.9 Mbits/s (T7 ) for the selected bandwidth. According to the adaptive modulation and coding approach, the transmission mode is selected based on specific channel measurements so as to guarantee a fixed bit error rate. The average throughput for each position of the user SS0 in the cell is obtained for fixed interferers placed at the center of their respective cells (as indicated in Fig. 1.1(a,b)), as follows. For each position of SS0 , the average BER (averaged with respect to the channel, the noise and the interference) at the output of the decoder 7 . The best transmission mode is then is evaluated for all transmission modes {Ti }i=1 selected as the one that satisfies the constraint BER≤ 10−6 (e.g., to model applications that have stringent reliability requirements) and provides the largest bit-rate. This allows to obtain a coverage map, detailed for all transmission modes, as those exemplified for the square layout in Fig. 1.2 and for the hexagonal layout in Fig. 1.3, for the channel model Max-FD with null angular spread (No-SD). In these examples the number of receiving antennas M ranges from 1 (left figures) to 4 (right figures), while the inter-element spacing is the conventional one (⌬m ) used for beamforming (top) or the optimized one (⌬opt ) for throughput maximization (bottom). Once the coverage maps have been obtained, the average throughput R¯ b [bit/s] for the overall cell can be evaluated through a weighted average of the throughputs associated to the different transmission modes, using as weighting factors the (normalized) areas where the modes are supported. The results are summarized in Fig. 1.4 for the square layout and in Fig. 1.5 for the hexagonal layout. The number of antennas ranges between M = 1 and 4, both the case of omnidirectional (on the top) and directional antennas (on the bottom) at the BS are considered; performances are evaluated for the two antenna spacings ⌬opt and ⌬m . Each column refers to a different channel model: no frequency diversity and null angular spread (No-FD No-SD, column (1); SUI-3 with null angular spread (SUI-3 No-SD, column (2); M=2

M=3

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Δ = Δ m = 0.7λ

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5.94 Mbps

4.61 Mbps

6.80 Mbps

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T6 T5 T4

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Δ = Δ opt = 1.8

T7

Δθ

SS1

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SS 3

T2

6.70 Mbps

8.05 Mbps

8.90 Mbps T1

7 Fig. 1.2 Coverage for all transmission modes {Ti }i=1 represented in gray-scale for the square planning shown on the right. The arrows indicate the directions of arrival of the interferers. The BS antenna array has a number of elements ranging from M = 1 (left) to M = 4 (right), and inter-element spacing optimized for beamforming ⌬m (top) or for coverage ⌬opt (bottom)

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M=3

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Δ = Δ m = 0.6λ

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4.47 Mbps

5.56 Mbps

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Δ = Δ opt = 1.4 λ

T6 SS SS

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7 Fig. 1.3 Coverage for all transmission modes {Ti }i=1 represented in gray-scale for the hexagonal planning shown on the right. The arrows indicate the directions of arrival of the interferers. The BS antenna array has a number of elements ranging from M = 1 (left) to M = 4 (right), and inter-element spacing optimized for beamforming ⌬m (top) or for coverage ⌬opt (bottom)

SUI-4 with null angular spread (SUI-4 No-SD, column (3); maximum frequency diversity with null angular spread (Max-FD No-SD, column (4); no frequency diversity with angular spread 5deg (No-FD SD, column (5). For instance, the results obtained in Fig. 1.2 (and in Fig. 1.3) can be easily found in the top section of the tables in Fig. 1.4 (and in Fig. 1.5) in the columns referring to No-FD No-SD channel. As expected, from the exploitation of diversity either in space or in frequency, the overall system throughput may be considerably enhanced. In addition, for the square cell planning, an optimized array is shown to provide substantial throughput improvements with respect to the conventional beamforming-oriented array. On the

l ne an as Ch nn te An

No-FD No-SD

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3.76

6.51

Fig. 1.4 Average throughput [Mbit/s] for the UL of the square cellular planning shown on the right, with an antenna array of M elements at the BS and a single antenna at each SS. We consider both omnidirectional antennas (on top) and directional antennas (on the bottom) cases

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l ne an as nn te An

No-FD No-SD

SUI 3 No-SD

11

SUI 4 No-SD

Max-FD No-SD

No-FD SD

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7.78

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11.40

5.94

7.07

Fig. 1.5 Average throughput [Mbit/s] for the UL of the hexagonal cellular planning shown on the right, with an antenna array of M elements at the BS and a single antenna at each SS. We consider both omnidirectional antennas (on top) and directional antennas (on the bottom) cases

other hand, for the hexagonal cell planning, due to the reduced frequency reuse factor that increases the co-channel average interference level, a substantially lower throughput improvement is observed. In this case, directional antennas can be used for optimizing performances.

1.5 Impact of Non-Stationary Inter-Cell Interference In this section we analyze the impact of non-stationary inter-cell interference on both fixed [2] and mobile [3] WiMax. As discussed in Section 1.2, in IEEE 802.16-OFDM-256 systems [2], estimation and tracking of the interferers’ spatial properties along with the use of an optimized array for beamforming is a viable solution to mitigate time-varying interference. On the other hand, in IEEE 802.16SOFDMA systems [3], the fast variability of the interference and the limited number of pilot symbols in each tile makes the previous approach unfeasible; as a consequence, a robust exploitation of the interference diversity is mandatory at the receiver.

1.5.1 Time-Varying Interference Mitigation in IEEE 802.16-OFDM-256 The design of an effective beamforming, as discussed in Section III-IV, requires an estimate of the channel gains ht (defined by the multipath channel of user SS0 ) and an estimate of the interference covariance matrix Qt (which defines the power and spatial features of interferers). Such channel/interference parameters have to be evaluated for each subcarrier and OFDM symbol (i.e., for each index value t).

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The estimation of the channel/interference parameters may be obtained from pilots through the least-squares (LS) method, then followed by interpolation to extend the estimate over the whole time-frequency bandwidth [12]. Here, we consider a IEEE 802.16-OFDM-256 fixed access scenario, where the channel coherence time is large enough to make the channel gains invariant over the whole frame interval. The whole bandwidth is assigned to one user at a time, thus the set of interferers It and the corresponding covariance matrix Qt do not vary over the frequency domain. However, due to the asynchronous access of users in neighboring cells, even in this fixed scenario the set It of active interferers can vary over the time (within the burst interval), generating abrupt changes in the signal interfering on user SS0 and thus in its covariance matrix Qt . In [13] a method was proposed to estimate the channel and to track the power/ spatial features of the interference by exploiting both the preambles and the pilots included in each OFDM symbol of the frame. At first, the signals measured in several preambles of the frame are jointly processed to obtain an estimate of the channel ht (which is constant for the whole frame) and a first estimate of the interference covariance matrix Qt in each preamble. The channel ht is evaluated through a weighted average of the LS estimates obtained separately from the different preambles: the average accounts for the stationarity of the channel, while the weighting accounts for the possible variations of the interference scenario. The interference covariance matrix Qt is then updated within each burst, by using the embedded pilots. Abrupt variations of the interference are detected by comparing the covariance estimate obtained from the current pilots with the one extracted from the previous OFDM symbol, in order to decide whether the spatial structure of the interference has changed or not: if the correlation ρ between the two subsequent covariance estimates is larger than a given threshold ρ, ¯ the interference covariance estimate is refined by averaging, otherwise is re-initialized according to the new estimate value. An example is shown in Fig. 1.6 for the square cellular layout in Fig. 1.1(a). The optimized ULA with M = 4 elements and inter-element spacing ⌬opt is adopted by BS0 . The receiver consists of MVDR filtering, soft demodulation and convolutional/Reed-Solomon (CC/RS) decoding. The user SS0 transmits with power 27dBm and transmission mode T2 . Interferers {SSi }i∈It are uniformly distributed in their cells; their power (subject to log-normal shadowing with standard deviation 8dB) and transmission mode are adaptively selected based on the channel state so as to guarantee a BER≤10−3 . Multipath channels are modelled according to the SUI-3 model, DOAs of both user and interferers are drawn from a Gaussian distribution with standard deviation 5 deg. We consider the transmission of 3 bursts of 10 symbols each, with the user SS0 placed in broadside at a distance d = 0.8 km from BS0 . The interference scenario changes at the third and seventh symbol of each burst, with positions of the three interferers selected uniformly within their cell. Figure 1.6 shows the BER (top) and the interference correlation (bottom) over the OFDM symbols. The estimation of the interference matrix Qt is obtained using three different approaches: estimation only from the preamble of the current burst (thick line); re-estimation within each OFDM symbol without memory (dashed line); tracking in

Deployment and Design of Multi-Antenna

Fig. 1.6 Performance of IEEE 802.16-OFDM-256 in presence of non-stationary inter-cell interferece: BER (top) and interferencecovariance-matrix correlation ρ (bottom) as a function of the time index over the frame. The positions of preambles are indicated by vertical thick line, while vertical thin lines denote changes (in power and DOA) of interference scenario

13

10–1 Interference estimation: from preamble only from current pilots (no memory) with detection/tracking

BER

1

10–2

10–3

5

10

15 20 Symbol index

25

30

5

10

15 20 Symbol index

25

30

Correlation ρ

1

ρ

0.5

each OFDM symbol with interference-change detection (thin line). The BER results confirm that the proposed tracking method with interference-change detection is an effective approach for time-varying interference mitigation.

1.5.2 Non-Stationary Interference in IEEE 802.16-SOFDMA As shown in Section 1.2, the IEEE 802.16-SOFDMA standard adopts a random subcarrier permutation that artificially induces fluctuations on the interference scenario along the transmission of the reference user. This non-stationarity enhances the robustness of the system against strong interferers, as the interfered slots are spread over different SSs in the cell (interference diversity). On the contrary, such a behavior of the interference becomes difficult to predict. The problem is particularly relevant in multi-antenna systems, where the non-stationarity of the interference and the limited number of pilots per tile makes the estimation/tracking of the interference spatial covariance (and thus beamforming for interference-mitigation) unfeasible. A more suited multi-antenna solution is obtained by largely spaced antennas so as to get signal uncorrelation and thus spatial diversity. Since the interference is spatially uncorrelated (i.e., uncorrelated over the antennas), the optimal MVDR receiver in this case reduces to a maximum ratio combiner (MRC) [6].

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A first analysis of the impact of inter-cell interference on the throughput of IEEE 802.16-SOFDMA has been derived in [14]. BER performance evaluation after decoding in UL-PUSC systems [3] shows that the performance heavily depends on the amount of interference-state information available at the decoder [15]. More specifically, optimum decoding can be performed when the detector knows the instantaneous interference power (i.e., the interference power in each tile), while a conventional receiver approximates the interference as stationary along the coded data packet with remarkable performance degradation. This means that the interference diversity can be fully exploited only when some knowledge of the interference statistics is available at the BS. A practical solution [15] consists in a joint interference-power estimation and decoding. Differently from spatial covariance, the estimation of a single scalar parameter (the interference power) is feasible even from a limited set of pilots. For performance assessment, we assume that the user SS0 requests two slots for the transmission of a data-packet of 192 bits, by using the rate-1/2 convolutional (CC) code for QPSK modulation [3]. BS0 is equipped with M = 2 antennas and the simplified channel model No-FD is employed (the reader might refer to [16] for the frequency selective case). We assume a slow fading scenario so that the channel can be assumed as stationary over three consecutive OFDM symbols. We consider N I = 3 interfering cells as in Fig. 1.1(b), with interferers’ power subject to lognormal shadowing fluctuations. The system load υ (fraction of slots allocated out of the whole number of available slots) is assumed to be the same for all the cells. Figure 1.7(a) shows the BER versus the signal-to-interference-ratio (SIR) for system load υ = {0.9; 0.5}. The figure compares the simulation results (markers) and the analytical analysis (dashed/dotted lines) obtained by truncating to the first term the union bound approximation for the bit error probability [15]. The optimum receiver offers a considerable performance gain with respect to conventional scheme for moderate load (about 2 dB when υ = 0.5). The motivation is that for limited

(b)

(a) 10–2

100

SIR = 15db

10–1

BER

υ = 0.5

υ = 0.9

10–3 10

–4

10–5 –5

BER

10–3

10–2

10–4

Conventional receiver Practical receiver Optimum receiver Analytic Conventional Analytic Optimum

0

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SIR = 20db

Conventional receiver Practical receiver Optimum receiver

15

20

25

10–5

1

0.9

0.8

0.7

0.6

0.5

0.4

υ

Fig. 1.7 Analytic (dashed/dotted lines) and simulated (markers) BER at the output of the CC decoder in IEEE 802.16-SOFDMA: transmission mode with code rate r = 1/2 and QPSK modulation, N I = 3 inter-cell interferers, M = 2 receiving antennas, SNR=30dB. (a) BER versus SIR for load υ ∈ {0.5, 0.9}; (b) BER vs. load υ for SIR∈ {15, 20}dB

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load (υ → 0) the number of interfering users sharing the same subcarrier abruptly changes from tile to tile, thus making the interference to heavily fluctuate along the coded packet. On the other hand, for large load (υ → 1) the number of collisions with the interferers is almost constant (N I ) and the influence of the interference non-stationarity is mitigated. As a consequence, the conventional receiver is mainly effective in systems with large load. The results are corroborated by Fig. 1.7(b) that shows the BER versus the system load υ for SIR= {15; 20}dB.

1.6 Conclusions This chapter has focused on technological solutions at the PHY layer for management of inter-cell interference in WiMax-compliant systems. The study has assumed components and features as defined in the standards IEEE 802.16-OFDM-256 [2] and SOFDMA [3]. The main conclusion is that an appropriate system design (deployment and signal processing) allows to harness relevant performance gains in terms of transmission quality-of-service. From an evolutionary perspective, further enhancements in the interference rejection capabilities of the PHY layer could be achieved by: (1) introducing multi-cell cooperation: decoding at different cell-sites is performed jointly by capitalizing on the existing high-capacity backbone: (2) cross-layer optimization of PHY layer and higher layers functionalities, such as scheduling. Acknowledgments The authors would like to acknowledge the former students D. Archetti, A. Bonfanti, M. Sala and A. Villarosa for their contribution to the development of the simulator for IEEE 802.16 systems. This work was supported by Nokia Siemens Networks S.p.A. Com CRD MW, Cassina de’ Pecchi, Italy and by MIUR-FIRB Integrated System for Emergency (InSyEme) project under the grant RBIP063BPH.

References 1. A. Ghosh, D. R. Wolter, J. G. Andrews, R. Chen, “Broadband wireless access with WiMax/802.16: current performance benchmarks and future potential,” IEEE Commun. Mag., Vol. 43, No. 2, pp. 129–136, Feb. 2005. 2. IEEE Std 802.16TM -2004, “802.16TM IEEE standard for local and metropolitan area networks Part 16: Air interface for fixed broadband wireless access systems,” Oct. 2004. 3. IEEE Std 802.16eTM -2005 and IEEE Std 802.16TM -2004/Cor 1-2005 5, “IEEE standard for local and metropolitan area networks Part 16: Air interface for fixed and mobile broadband wireless access systems. Amendment 2: Physical and medium access control layers for combined fixed and mobile operation in licensed bands and Corrigendum 1,” Sep. 2005. 4. H. Yaghoobi, “Scalable OFDMA physical layer in IEEE 802.16 WirelessMAN”, Intel Tech. J., Vol. 8, No. 3, pp. 201–212, Aug. 2004. 5. A. Salvekar, S. Sandhu, Q. Li, M. Vuong, X. Qian, “Multiple-antenna technology in WiMax Systems,” Intel Technology Journal, Vol. 8, No. 3, pp. 229–240, Aug. 2004. 6. H. L. Van Trees, Optimum Array Processing, Wiley, 2002.

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7. A. Goldsmith, Wireless Communications, Cambridge University Press, 2005. 8. IEEE 802.16.3c-01/53, IEEE 802.16 Broadband Wireless Access Working Group, “Simulating the SUI channel models,” April 2004. 9. R. Jana and S. Dey, “3G wireless capacity optimization for widely spaced antenna arrays,” IEEE Pers. Commun., Vol. 7, No. 6, pp. 32–35, Dec. 2000. 10. S. Savazzi, O. Simeone, and U. Spagnolini, “Optimal design of linear arrays in a TDMA cellular system with Gaussian interference,” EURASIP Journ. on Wireless Comm. and Networking, Smart Antennas for Next Generation Wireless Systems, 2006. 11. M. Nicoli, L. Sampietro, C. Santacesaria, S. Savazzi, O. Simeone, U. Spagnolini, “Throughput optimization for non-uniform linear antenna arrays in multicell WiMax systems,” Int’l. ITGIEEE Workshop on Smart Antennas, March 2006. 12. Y. Li, L. J. Cimini and N. R. Sollenberger, “Robust channel estimation for OFDM systems with rapid dispersive fading,” IEEE Trans. Commun., Vol. 46, No. 7, pp. 902–915, July 1998. 13. M. Nicoli, M. Sala, L. Sampietro, C. Santacesaria, O. Simeone, “Adaptive array processing for time-varying interference mitigation in IEEE 802.16,” Proc. IEEE Int’l. Symp. on Pers. Indoor and Mobile Radio Commun. (PIMRC’06), Helsinki, Sep. 2006. 14. S-E. Elayoubi, B. Fouresti`e, and X. Auffret, “On the capacity of OFDMA 802.16 systems,” Proc. IEEE Int’l. Conf. on Commun. (ICC’06), June 2006. 15. R. Bosisio and U. Spagnolini, “Collision model for the bit error rate analysis of multicell multiantenna OFDMA systems,” Proc. IEEE Int’l. Conf. on Commun. (ICC’07), June 2007. 16. D. Molteni, M. Nicoli, R. Bosisio, L. Sampietro, “Performance analysis of multiantenna WiMax systems over frequency selective fading channels,” Proc. IEEE Int’l. Symp. on Pers. Indoor and Mobile Radio Commun. (PIMRC’07), Athens, Sep. 2007.

Chapter 2

Dynamic Bandwidth Allocation for 802.16E-2005 MAC Yi-Neng Lin, Shih-Hsin Chien, Ying-Dar Lin, Yuan-Cheng Lai and Mingshou Liu

Abstract The IEEE 802.16e-2005 is designed to support high bandwidth for the wireless metropolitan area network. However, the link quality is likely to degrade drastically due to the unstable wireless links, bringing ordeals to the real-time applications. Therefore, a feasible bandwidth allocation algorithm is required to utilize the precious bandwidth and to provide service differentiation. This article presents the general background of allocation schemes and introduces a Two-Phase Proportionating (TPP) algorithm to tackle the above challenges. The first phase dynamically determines the subframe sizes while the second phase further differentiates service classes and prevents from bandwidth waste. Performance comparison with other algorithms confirms that TPP achieves the highest bandwidth utilization and the most appropriate differentiation. Keywords WiMax · Dynamic · Bandwidth Allocation · Proportion · Service Differentiation

2.1 Introduction General broadband technologies have been used to provide multimedia applications with stable connectivity. However, for a growing volume of hand-held devices running these applications, those technologies are unable to meet the requirements such as ubiquitous access, low deployment cost, and mobility support. Broadband wireless access (BWA), standardized as 802.16e-20051 [1] and known as WiMax, has emerged to be a potential candidate to meet these criteria. The standard defines signaling mechanisms [2] between base stations (BSs) and subscriber stations (SSs) considering both fixed and mobile wireless broadband. It supports not only seamless handover at vehicle speeds but also an extra service class compared to the previous version, 802.16-2004 [3]. Y.-N. Lin (B) National Chiao Tung University, University Road, Hsinchu, Taiwan 1

In the following contexts we use 802.16 to represent 802.16e-2005.

M. Ma (ed.), Current Technology Developments of WiMax Systems, C Springer Science+Business Media B.V. 2009 DOI 10.1007/978-1-4020-9300-5 2, 

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However, the nature of wireless communication makes it difficult to provide stable signal quality, and could lead to much degraded bandwidth. For example, signal gradually fades as the transmission distance stretches, and channels are usually interfered with each other. Furthermore, though 802.16 defines service classes for differentiation, no mechanism is specified to fulfill the QoS guarantees. Therefore, a feasible algorithm is required to utilize and fairly allocate the bandwidth considering the following issues. First, the Grant Per SS (GPSS) scheme specified in the standard needs to be adhered to. In this scheme, the BS grants requested bandwidth to each SS rather than to each connection, so that the SS can flexibly respond to different QoS requirements of the connections. Second, in order to make the best use of the link, the separation between uplink and downlink subframes and the number of physical-layer slots needed given a certain amount of requested bytes, have to be carefully determined. Similar situations to design allocation algorithm in 802.16 can be seen in systems such as Wi-Fi (Wireless Fidelity) [4] and DOCSIS (Data over Cable System Interface Specifications) [5] because of the similar point-to-multipoint architectures. However, Wi-Fi adopts arbitrary contention for transmission opportunities in any time and is thus not appropriate in the WiMax environment having lengthy roundtrip delay. Also little can be referenced from works regarding the DOCSIS since it follows the Grant Per Connection (GPC) scheme [6] which is not flexible for SSs to be adaptive to connections of real-time applications and is not supported by the standard. Several works [7–10] investigating allocation algorithms over 802.16 are proposed, but again only the GPC scheme is supported. The solution researched by [11] is based on GPSS, but the separation of the uplink and downlink channels is fixed so that bandwidth is usually not properly utilized. In this article, a novel bandwidth allocation algorithm, Two-Phase Proportionating (TPP), is introduced to maximize the bandwidth utilization as well as to meet the QoS requirements under the Time Division Duplexing (TDD) mode. TDD, compared to the Frequency Division Duplexing (FDD), is frequently favored because of the flexibility to divide a time frame into adequate uplink and downlink subframes so that bandwidth waste could be minimized. Employing the concept of proportionate allocation, the algorithm dynamically adjusts the uplink and downlink subframes considering different slot definitions, and fairly allocates each subframe to queues of different classes. Simulation results further validate the efficiency of bandwidth utilization and service differentiation. The rest of this article is organized as follows. We brief the IEEE 802.16 MAC and review the related works to justify our problems. Then we introduce the TPP algorithm and exemplify the operations, followed by the simulation setup and results. Some conclusive remarks are given finally, outlining some future directions.

2.2 Background Unlike Wi-Fi which is used for small range communications, WiMax is mainly applied to metropolitan area networks and therefore must master all data transmission decisions to/from SSs to avoid synchronization problems. In this section, we

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brief the WiMax frame structure under TDD mode, describe the five service classes whose connections fill up the frame, and detail the packet flow in the BS MAC. The bandwidth allocation module as well as its input and output is identified according to the flow. Some related researches investigating the allocation problem are discussed.

2.2.1 Overview of the MAC Protocol 2.2.1.1 TDD Subframe The frame structure under TDD includes (1) UL-MAP and DL-MAP for control messages, and (2) downlink and uplink data bursts whose scheduled time is determined by the bandwidth allocation algorithm and is indicated in the MAP messages. All UL-MAP/DL-MAP and data bursts are composed of a number of OFDMA (Orthogonal Frequency Division Multiplexing Access) slots, in which a slot is one subchannel by three OFDMA symbols in uplink and one subchannel by two OFDMA symbols in downlink. This mode is named PUSC (Partial Usage of Subchannels), the mandatory mode in 802.16, and is considered throughout the work. 2.2.1.2 Uplink Scheduling Classes The 802.16 currently supports five uplink scheduling classes, namely the Unsolicited Grant Service (UGS), Real-time Polling Service (rtPS), Non-real-time polling Service (nrtPS), Best Effort (BE), and the lately proposed Extended Realtime Polling Service (ertPS). Each service class defines different data handling mechanisms to carry out service differentiation. The UGS has the highest priority and reserves a fixed amount of slots at each interval for bandwidth guarantee. rtPS, nrtPS, and BE rely on the periodic polling to gain transmission opportunities from BS, while the ertPS reserves a fixed number of slots as UGS does and notifies the BS in the contention period of possible reservation changes. nrtPS and BE also contend, according to their pre-configured priority, for transmission opportunities if they fail to get enough bandwidth from polling. An nrtPS service flow is always superior to that of BE. 2.2.1.3 Detailed Packet Flow in the MAC Layer The complete packet flow in the uplink and downlink of a BS MAC is illustrated as follows. For the downlink processing flow, both IP and ATM packets in the network layer are transformed from/to the MAC Convergence Sublayer (CS) by en/de-capsulating the MAC headers. According to the addresses and ports, packets are classified to the corresponding connection ID of a service flow which further determines the QoS parameters. Fragmentation and packing are then performed to form a basic MAC Protocol Data Unit (PDU), whose size frequently adapts to the channel quality, followed by the allocation of resulting PDUs into queues. Once the allocation starts, the bandwidth management unit arranges the data burst

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transmissions to fill up the frame. The MAP builder then writes the arrangement, namely the allocation results, into the MAP messages to notify the PHY interface when to send/receive the scheduled data in the time frame. Encryption, header checksum and frame CRC calculations are carried out to the PDUs before they are finally sent to the PHY. The uplink processing flow is similar to that of the downlink except that the BS also receives standalone or piggybacked bandwidth requests. Among the above operations, it is obvious that the bandwidth management, and thus the bandwidth allocation algorithm, are critical and need to be carefully designed in order to improve the performance of the system.

2.2.2 Related Work A number of studies regarding the bandwidth allocation over 802.16 can be found. Hawa and Petr [7] propose a QoS architecture applicable for both DOCSIS and 802.16 using semi-preemptive priority for scheduling UGS traffic while priorityenhanced Weighted Fair Queuing (WFQ) for others. Chu et al. [8] employ the Multi-class Priority Fair Queuing (MPFQ) for the SS scheduler and the Weighted Round Robin (WRR) for that of the BS. Though innovative in the architectural design, both of them do not present experiment results validating the architecture. Wongthavarawat and Ganz [9] introduce the Uplink Packet Scheduling (UPS) for service differentiation. It applies the Strict Priority to the selection among service classes, in which the UL and DL have same capacity, and each service class adopts a certain scheduling algorithm for queues within it. However, this scheme deals with only uplink channel so that overall bandwidth utilization suffers. The Deficient Fair Priority Queue (DFPQ) [10], which uses the maximum sustained rate as the deficit counter to specify the transmission quantum, dynamically adjusts the uplink and downlink proportion. Nonetheless, this method is suitable only for GPC rather than GPSS. Maheshwari et al. [11] support GPSS using proportion, though the proportion is not alterable in run-time. Furthermore, the above schemes do not consider the slot definition when translating data bytes requested by SSs into OFDMA slots to practically determine the allocation of a time frame.

2.2.3 Goals To solve the allocation problem which could lead to long latency and serious jittering, a well-designed bandwidth allocation algorithm shall possess three merits. First and obviously, the algorithm must implement GPSS to comply with the standard as well as to provide flexible packet scheduling in SSs. Second, service classes should adhere to the corresponding QoS requirements such as Maximum Sustained Traffic Rate (MSTR) and Minimum Reserved Traffic Rate (MRTR) for differentiated guarantees. The former prevents a certain class from consuming too much bandwidth while the latter sustains a service class with least feeds. Third, in order to achieve

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high throughput, the proportion of the uplink and downlink subframes should be able to be dynamically adjusted. The separator was previously fixed and failed to adapt to situations in which uplink and downlink bandwidth needs vary.

2.3 Two-Phase Proportionating This section details the concept and procedure of the proposed Two-Phase Proportionating (TPP) algorithm. Each phase manipulates different levels of allocation to achieve high bandwidth utilization and QoS guarantees. An example is presented finally.

2.3.1 Overview of the Algorithm The goal of bandwidth allocation in 802.16 is actually to fill up the whole TDD time frame, in which the proportions of the uplink and downlink subframes can be dynamically adjusted. Every subframe is further allocated to service classes/queues of different QoS requirements. Observing these two targets, the Two-Phase Proportionating (TPP) is proposed in this work to well utilize the bandwidth. The first phase decides the subframe sizes according to the requested sizes of both downlink and uplink, while the second phase distributes the bandwidth to each queue based on the corresponding QoS parameter represented as weight, and an adjustment factor reflecting the practical demand. Finally the TPP adheres to the GPSS by granting SSs the allocated bandwidth of each queue. The operations of the algorithm are depicted in Fig. 2.1 and elaborated in the following subsections.

Convergence Sublayer

Translator

Downlink Queues with/o latency

Downlink data

Uplink

Assign slots for queues Determine UL/DL subframe First phase proportionating

UGS

Second phase proportionating

Second phase proportionating

Assign slots to SSs

Assign slots to SSs

Write in DL-MAP

Write in UL-MAP

ertPS

Translator Bandwidth requests

rtPS nrtPS

Two-Phase Proportionating

BE

PHY Layer

Fig. 2.1 Architecture of the Two-Phase Proportionating (TPP)

Downlink frame

Uplink frame

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2.3.2 Detailed Operations of TPP 2.3.2.1 Bandwidth Translation and Slot Dispatching A service flow in an SS issues a bandwidth request whenever necessary. After the BS receives the data traffic from the backbone network or the uplink bandwidth requests from SSs, the TPP translates them from data bytes into the OFDMA slots, which are the basic transmission unit in PHY. This can be done by dividing the data bytes by the OFDMA slot size, in which the OFDMA slot size is derived by multiplying the number of bits that can be encoded over a subchannel by the number of symbols in a slot. Notably the number of symbols in a slot is three for UL while two in DL, and the data bytes should include the requested bandwidth from a SS, MAC headers, and PHY overhead such as the Forward Error Correction (FEC), preamble, and guardtime. These slots are then dispatched to the corresponding service queues comprising the five uplink classes as well as the two downlink classes with/o the latency guarantee. Each queue employs three variables, the bandwidth request slots (BRQ), Rmax , and Rmin , to accumulate the number of requested slots, MSTR and MRTR, respectively. All of them are translated from data rate to number of slots per frame duration. 2.3.2.2 First Phase: Dividing a Frame into Downlink and Uplink Subframes To fit the traffic data into the time frame, TPP determines the proportion of the uplink and downlink subframes according to their accumulated BRQs in each frame. However, this is not trivial because of different slot definitions of the uplink and downlink, and could result in unused symbols. For example, if the uplink is proportionally allocated 19 symbols, only 18 of them will be used to form 18/3 = 6 slot columns, where a slot column contains three consecutive symbols. This problem is solved as follows. Depicted in Fig. 2.2, the most appropriate placement of the separator dividing uplink and downlink subframes is assumed to be x steps from the right, in which one step is considered 6 symbols, the least common multiple of the uplink and downlink slots. This is to ensure that all symbols are used up after the division. Two cases need to be discussed here, namely when S, the number of symbols in a frame, is odd and when S is even. If S is odd, the scheme starts with an initial condition in which a slot column exists in the uplink subframe so that the number of remaining symbols, S-3, is dividable by 2 in the downlink, leaving no unused symbols. Then the separator moves x steps toward left, which is supposed to be the correct position, resulting in 1 + 2x slot columns for the uplink and (S − 3)/2 − 3x slot columns in the downlink. The ratio should be the same as the ratio of the uplink and downlink requested slots, namely 1 + 2x UR = , S−3 DR − 3x 2

(2.1)

Dynamic Bandwidth Allocation

23

Fig. 2.2 The placement of the separator in the first phase

S symbols 1 slot comprises 2 symbols

1 slot comprises 3 symbols

UR

S −3 − 3x slot col. 2 UR 1 + 2x = DR S − 3 − 3 x 2

Separator (finial placement)

Control messages

Subchannel index

DR

S −3 slot col. 2 Downlink Subframe

TTG

1+2x slot col.

moving x steps, namely 6x symbols

Separator (initial placement)

2

Slot col.

Uplink Subframe

where UR and DR represents the BRQ of the uplink and downlink, respectively. Similar concept can be applied to the case when S is even, except that in the initial condition no slot column exists in the uplink whereas S/2 slot columns are derived in the downlink, 2x UR . = S DR − 3x 2

(2.2)

The x can be obtained after solving the equation and notably is rounded off if it has a fraction. 2.3.2.3 Second Phase: Allocating Subframes to Queues After properly dividing the frame into uplink and downlink subframes, in the second phase we start to allocate them to service queues. In this phase, the Rmin of all queues are firstly satisfied for minimum slots guarantee, followed by the proportionating of the remaining slots to queues except the UGS and ertPS whose requested slots are already served. Since higher service classes typically have higher Rmax values, we take the Rmax as the weight for proportion. However, only referring to Rmax may cause bandwidth waste or starvation of some queues. An example for the former case is a high class queue having a BRQ very close to Rmin . The additional number of slots assigned will be excessive because of the large Rmax , leading to unnecessary bandwidth waste. Similarly, a low class queue yet having a BRQ close to Rmax may not get enough feed. We use an adjustment factor, B R Q − Rmin Rmax − Rmin

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referred to as the A-Factor, for the Rmax of each queue to fix this problem so that a high class queue requiring less bandwidth (BRQ) will be reflected and offset while a low class queue demanding much will be compensated. The remaining slots are therefore allocated according to the following proportion rt P S nr t P S BE B R Q r t P S − Rmin B R Q nr t P S − Rmin B R Q B E − Rmin rt P S nr t P S BE Rmax : Rmax : Rmax . rt P S nr t P S BE B E r t P S nr t P S Rmax − Rmin Rmzx − Rmin Rmax − Rmin (2.3)

2.3.2.4 Per-SS Bandwidth Grant within Each Queue The slots allocated to each queue are finally distributed to SSs in the fashion of GPSS. Similar to the second phase, the minimum number of requested slots of each SS is satisfied first. Nevertheless, the remaining slots of each queue are evenly assigned to SSs since there is no priority among them. 2.3.2.5 Example This section gives an example of the TPP, in which UR and DR are 60 and 40, respectively. Suppose S is 26, then the separator should be moved toward left with number of steps x = 3 according to Eq. (2.1), indicating 6x/3 = 6 slot columns for uplink while (26−6x)/2 = 4 slot columns for downlink. If we use direct proportion, however, the number of symbols for uplink is 26 × [60/(60 + 40)] ∼ = 16, in which only 15 symbols are effective. The uplink is adopted as an example for the second phase. Assuming 6 subchannels in a symbol, 6 × 6 = 36 slots are thus allocated to the uplink after the first phase. Rmin , BRQ, and Rmax of the five service classes are as in Table 2.1. The scheduler allocates the guaranteed minimum number of slots to each queue, and later proportionate the remaining slots to queues of the lower three classes according to Eq. (2.2) since the UGS and ertPS are already satisfied. As we can see in the table, using Rmax as the weight without the A-Factor causes three slots to be unnecessarily assigned to the rtPS.

Table 2.1 Parameters and allocation results of the second phase Item

UGS

ertPS

rtPS

nrtPS

BE

Rmax

8

8

16

8

12

BRQ

8

8

6

8

12

Rmin

8

8

6

4

2

BRQ-Rmin

n/a

n/a

0

4

10

Rmax with A-Factor

n/a

n/a

0

2

6

Rmax without A-Factor

n/a

n/a

3

3

2

2

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25

2.4 Simulation Through OPNET simulation we evaluate the TPP algorithm, focusing on the bandwidth utilization and the differentiated guarantee among service classes.

2.4.1 Simulation Setup We have made several modifications on the original DOCSIS module of OPNET to adapt to the IEEE 802.16 requirements. The topology consists of one BS serving 20 SSs, and two remote stations including an FTP server and a voice endpoint. Five service classes are supported and each class involves four SSs. The UL and DL channel capacity is 10.24 Mbps and the frame duration is 5ms. All classes in Figs. 2.4, 2.5 and 2.6 run voice applications with G.711 codec and 64Kbps bit rate. The Rmax of rtPS, nrtPS and BE are 8, 6, and 4, respectively, while Rmin are 4, 2, and 1, respectively.

2.4.2 Numerical Results 2.4.2.1 Subframe Allocation: Static vs. Dynamic The first-phase of TPP is advantageous in utilizing the bandwidth when the load of the uplink and downlink are different, as Fig. 2.3 proves. The FTP traffic load of the downlink is three times of the uplink. In Fig. 2.3a the downlink utilization is bound to 50% because of the static subframe allocation. However, by stealing the unused uplink slot columns for the downlink, TPP improves the overall link utilization from 75 to 96%. (b) 100 90 80 70 60 50 40 30 20 10 0

Total

0

10

Downlink

20

30

Uplink

40 50 Time (sec)

Bandwidth utilization (%)

Bandiwidth utilization (%)

(a)

60

70

80

100 90 80 70 60 50 40 30 20 10 0

Total

0

10

20

30

Downlink

40 50 Time (sec)

60

Uplink

70

80

Fig. 2.3 Bandwidth utilization: (a) static subframe allocation with UL:DL = 1:1; (b) dynamic subframe allocation with UL:DL = 1:3

2.4.2.2 Effectiveness of the A-Factor As introduced previously, the A-Factor helps avoid bandwidth waste by reflecting the requested amount of classes. To understand the effectiveness, we compare it with

26 1.4

Rmax with A-Factor Rmin Rmax BRQ BRQ-Rmin

1.2 Grant Ratio.

Fig. 2.4 Effectiveness of the A-Factor. Four schemes with a simple weight such as Rmin , Rmax , BRQ, and BRQ-Rmin are involved for comparison

Y.-N. Lin et al.

1 0.8 0.6 0.4 0.2 0

rtPS

nrtPS

BE

four schemes which simply use a weight such as Rmin , Rmax , BRQ, and BRQ–Rmin , for each class. A term named Grant Ratio is defined as the ratio of number of allocated slots to the number of requested ones. A grant ratio larger than 1 means that the service class is allocated more than requested, resulting in bandwidth waste. As presented in Fig. 2.4, the Grant Ratios of rtPS using Rmin , Rmax and BRQ are about 1.2, implying excessive allocations, while appropriate amounts are provided when using the A-Factor and BRQ–Rmin . The nrtPS using Rmax with A-Factor obtains more slots than those in other schemes. In BE, though the one using BRQ–Rmin has the highest Grant Ratio, this scheme is not feasible because it tends to favor classes with a small Rmin which oftentimes is BE, and therefore violates the spirit of service differentiation. Service Differentiation — Figure 2.5a displays the number of granted and minimally reserved slots, respectively, as well as the average delay for each class under different numbers of SSs. As we can see in the figure, the UGS and ertPS sustain the number of reserved slots even when the number of SSs advances 60. For other classes, the system guarantees the differentiated Rmin , namely 4:2:1, until the number of SSs exceeds 50. For the average delay depicted in Fig. 2.5b, only minor difference is observed among classes initially until the number of SSs reaches 40, rather than 50. This is because not enough additional slots can be allocated but only the minimum requirement is satisfied. Again, the delay of the UGS and ertPS are always kept under 10 ms. Performance — The performance of TPP is compared with the Deficit Fair Priority Queue (DFPQ) and Strict Priority (SP) in terms of bandwidth utilization, as depicted in Fig. 2.6a. From the figure we can learn that the bandwidth utilizations of the three algorithms increase linearly but start to decrease when hitting a certain level: 85.5% for TPP, 80.6% for DFPQ and 68.4% for SP. The reason why they are not fully utilized is explored by looking into the average frame occupation of service classes, as presented in Fig. 2.6b. Each class has an unused portion, which occurs during the translation from requested bytes to slots. Since the calculation, namely dividing the requested bytes by slot size, always rounds up, the resulted assignment is often larger than expected. As an example shown in Fig. 2.6c, assuming that a slot contains 64 bytes, which is one of the supported sizes, and the amount requested by service flow (SF) #1 is 213 bytes, the number of requested slots is thus four, causing a 256 − 213 = 43

Dynamic Bandwidth Allocation

Fig. 2.5 Service differentiation: (a) the variation of minimally reserved slots and granted slots of each request under each class; (b) average delay between service classes. To make the differences recognizable, in (a) the numbers of allocated slots per request for (rtPS-total, nrtPS-total, BE), which is (47.2, 52.7, 53.7) for 10 SSs and (22.8, 23,2, 21,7) for 20 SSs are omitted

27 UGS nrtPS-min nrtPS-total

(a) Number of allocated slots per request

2

ertPS BE-min BE-total

rtPS-min rtPS-total

10 8 6 4 2 0

10

20

30 40 Number of SSs

50

60

(b) Average delay (ms)

10000 UGS ertPS rtPS nrtPS BE

1000 100 10 1

10

20

30 40 Number of SSs

50

60

bytes waste. However, TPP alleviates this effect by reserving minimum required slots first, rather than paying up all requested slots at once for an SF. Take Fig. 2.6c for instance and assume that the number of available slots is nine and the MRTR of each SF is three, TPP breadth-firstly allocates every SF three slots which are slightly insufficient whereas the allocated slots are not wasted; nonetheless, the DFPQ depth-firstly tries to satisfy all SFs’ requested slots but results in the waste for the first two SFs and the starvation of the third which has the lowest priority. The SP has a largest waste also because of its static allocation. Besides, the UGS contributes to the relatively more amount of unused portion than other classes, revealing the drawback of unnecessary slot reservation. Finally, aside the high efficiency in bandwidth consumption, TPP is advantageous in service differentiation. As depicted in Fig. 2.6b, the ratio of allocated bandwidth for rtPS, nrtPS and BE is very close to 4:2:1, compared to other two algorithms.

2.5 Conclusions and Future Work This work considers the problem of bandwidth allocation for 802.16 in order to well utilize the precious wireless link and to support service differentiation. Among others, an allocation scheme called TPP is presented. The uplink and downlink

Y.-N. Lin et al. Bandwidth utilization (%)

28 100 90 80 70 60 50 40 30 20 10 0

TPP DFPQ SP

5

10

15

(a)

20 25 30 35 Number of SSs

40

45

50

Frame occupation (%)

35 UGS ertPS rtPS nrtPS BE unused

30 25 20 15 10 5 0

(b)

TPP

DFPQ

Unused

SP

213 bytes

4 slots 256 bytes

SF1

SF1

Requested 64 bytes per slot

Allocated Allocated but unused

Requested

(c)

SF1

SF2

SF3

TPP

SF1

SF2

DFPQ

SF3

SF1

SF2

SF3

Fig. 2.6 Performance comparison with SP and DFPQ: (a) bandwidth utilization; (b) frame occupation under three schemes with 48 SSs; (c) example of allocations by TPP and DFPQ, in which number of slots to be allocated to three service flows is 9

bandwidth allocations are considered at the same time so that the allocation can be dynamically adjusted. Simulation results confirm that bandwidth utilization increases 20% by applying the first phase proportionating; differentiation among classes is appropriately achieved in the second phase.

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Though service differentiation is carried out in BS, the SSs should also be capable of providing similar support in order to meet the QoS requirement of various applications. Therefore, the future work will be focusing on designing a sophisticated allocation algorithm for the SS to manipulate the per-SS grant. The ultimate target will be implementing both algorithms in real BSs and SSs for performance validation.

References 1. IEEE 802.16 Working Group, “Air Interface for Fixed and Mobile Broadband Wireless Access Systems – Amendment for Physical and Medium Access Control Layers for Combined Fixed and Mobile Operation in Licensed Bands,” Feb. 2006. 2. G. Nair et al., “IEEE 802.16 Medium Access Control and Service Provisioning,” Intel Technology Journal, Vol 8, Issue 3, Aug. 2004. 3. IEEE 802.16 Working Group, “Air Interface for Fixed Broadband Wireless Access Systems,” Oct. 2004. 4. IEEE 802.11 Working Group, “Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications,” Sep. 1999. 5. Cable Television Laboratories Inc., “Data-Over-Cable Service Interface Specifications - Radio Frequency Interface Specification v1.1,” July 1999. 6. W. M. Yin, C. J. Wu and Y. D. Lin, “Two-phase Minislot Scheduling Algorithm for HFC QoS Services Provisioning,” GLOBECOM, Nov. 2001. 7. M. Hawa and D. W. Petr, “Quality of Service Scheduling in Cable and Broadband Wireless Access Systems,” IWQoS, May 2002. 8. G. S. Chu, D. Wang and S. Mei, “A QoS Architecture for the MAC Protocol of IEEE 802.16 BWA System,” Communications, Circuits and Systems and West Sino Expositions, IEEE, July 2002. 9. K. Wongthavarawat and A. Ganz, “IEEE 802.16 Based Last Mile Broadband Wireless Military Networks with Quality of Service Support,” MILCOM, Oct. 2003. 10. J. Chen, W. Jiao and H. Wang, “A Service Flow Management Strategy for IEEE802.16 Broadband Wireless Access Systems in TDD Mode,” ICC, May 2005. 11. S. Maheshwari, S. lyer and K. Paul, “An Efficient QoS Scheduling Architecture for IEEE 802.16 Wireless MANs,” Asian International Mobile Computing Conference, Jan. 2006.

Chapter 3

A Downlink MAC Frame Allocation Framework in IEEE 802.16e OFDMA: Design and Performance Evaluation Andrea Bacioccola, Claudio Cicconetti, Alessandro Erta, Luciano Lenzini, Enzo Mingozzi and Jani Moilanen

Abstract The IEEE 802.16e standard specifies a connection-oriented centralized Medium Access Control (MAC) protocol, based on Time Division Multiple Access (TDMA), which adds mobility support defined by the IEEE 802.16 standard for fixed broadband wireless access. To this end, Orthogonal Frequency Division Multiple Access (OFDMA) is specified as the air interface. In OFDMA, the MAC frame extends over two dimensions: time, in units of OFDMA symbols, and frequency, in units of logical sub-channels. The Base Station (BS) is responsible for allocating data into the frames so as to meet the Quality of Service (QoS) guarantees of the Mobile Stations’ (MSs) admitted connections. This is done on a frame-by-frame basis by defining the content of map messages, which advertise the position and shape of data regions reserved for transmission to/from MSs. We refer to the process of defining the content of map messages as frame allocation. Through a detailed analysis of the standard, we show that the latter is an overly complex task. We then propose a modular framework to solve the frame allocation problem, which decouples the constraints of data region allocation into the MAC frame, i.e. the definition of the position and shape of the data regions according to a set of scheduled grants, from the QoS requirements of connections. Allocation is carried out by means of the Sample Data Region Allocation algorithm (SDRA), which also supports Hybrid Automatic Repeat Request (H-ARQ), an optional feature of IEEE 802.16e. Finally, we evaluate the effectiveness of SDRA by means of Monte Carlo analysis in several scenarios, involving mixed Voice over IP (VoIP) and Best Effort (BE) MSs with varied modulations, with different sub-carrier permutations and frequency re-use plans. Keywords IEEE 802.16 · Medium access control protocols · Resource allocation

E. Mingozzi (B) Dipartimento di Ingegneria dell’Informazione, University of Pisa, Italy e-mail: [email protected]

M. Ma (ed.), Current Technology Developments of WiMax Systems, C Springer Science+Business Media B.V. 2009 DOI 10.1007/978-1-4020-9300-5 3, 

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3.1 Introduction During the last few years, we have witnessed a daunting increase of the use of electronic communication devices in everyday life. This is due to the spread of sophisticated handheld equipments, such as mobile phones and palmtops, which are available at an increasingly lower cost. This has boosted a technology advance in the area of mobile Broadband Wireless Access (BWA), since these devices, by necessity, cannot rely on the use of wired connections. A by-product of these two factors is that people are becoming more and more accustomed to portable communication devices, which, in turn, produces unforeseen needs and requirements. For instance, the well-known GSM technology, primarily targeted at traditional voice applications, has recently moved towards the GPRS/EDGE architectures for packet-based data transmission. Packet access is also implemented for multimedia services in UMTS, by means of High-Speed Downlink/Uplink Packet Access (HSDPA/HSUPA), in addition to the legacy code-division multiple access (CDMA) based circuit-switched mode. In the context of mobile BWA, a novel standard has been published recently by the IEEE, namely IEEE 802.16e [1], which extends the 2004 version of IEEE 802.16 for fixed BWA [2,3], so that high transmission efficiency for mobile users is coupled with Quality of Service (QoS) support to enable multimedia services. A non-profit association, the Worldwide Interoperability for Microwave Access (WiMax) forum, was formed to define the specifications for compatibility and interoperability of the IEEE 802.16 wireless equipments. According to the WiMax forum, Orthogonal Frequency Division Multiple Access (OFDMA) is the target air interface for mobile BWA with IEEE 802.16e. In fact, OFDMA has been shown to provide mobile users with an improved resilience against multi-path fading in non-line-of-sight environments, with respect to alternative technologies, like FDM/TDM and CDMA, which are used in competing mobile wireless standards, respectively GPRS, EDGE, and UMTS [4]. Furthermore, a multiple access technique based on OFDMA has been proposed by the 3GPP consortium as the downlink air interface in the context of the Long Term Evolution (LTE) project, which is an ongoing effort to lead the current UMTS standard towards 4G wireless technologies [5]. An IEEE 802.16e cell consists of a number of Mobile Stations (MSs) served by a Base Station (BS), which controls the access to the wireless medium in a centralized manner. Before transmitting to/receiving from the BS, any MS must request the admission of a new connection. If the connection is accepted, the BS is then responsible for meeting the requested QoS guarantees. The access to the medium is scheduled on a frame basis. MAC frames extend over two dimensions: time, in units of OFDMA symbols, and frequency, in units of logical sub-channels [4]. Data packets are thus conveyed into bi-dimensional (i.e. time and frequency) data regions, which are advertised by the BS via specific in-band control messages, which share the same resources as data. In the following, the process of defining the content of maps is referred to as frame allocation, which is left unspecified by the standard. Since the frame allocation problem is a complex task, which can significantly impact on the overall performance of the system, we propose a modular framework

3

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based on a pipeline approach. Specifically, our framework decouples the process of scheduling data to the admitted connections from the activity of allocating the data regions into the MAC frames. The SDRA algorithm is then provided as a simple solution to the latter, i.e. allocation, which does not depend on the specific scheduling algorithms adopted by the BS to meet the QoS requirements of the admitted connections. SDRA has been originally proposed in [6] and is extended here to support Hybrid Automatic Repeat Request (H-ARQ), which is an optional feature of the IEEE 802.16e to enhance coverage and capacity in mobile applications. An extensive analysis of the proposed algorithm is carried out through Monte Carlo analysis to evaluate its performance under varied system configurations.

3.2 IEEE 802.16e The IEEE 802.16e MAC protocol is connection-oriented and explicitly supports QoS by defining five different QoS scheduling services: namely, Unsolicited Grant Service (UGS), real-time Polling Service (rtPS), extended real-time Polling Service (ertPS), non real-time Polling Service (nrtPS), and Best Effort (BE). Each scheduling service is designed to support a specific class of applications and is therefore characterized by a different set of QoS requirements (see [7] for further details). Connections are uni-directional, i.e. either uplink or downlink, while MSs can establish multiple connections with the BS. MAC Service Data Units (SDUs), which are used to convey data from the upper layers, e.g. Internet Protocol version 4 (IPv4) datagrams or Ethernet frames, are encapsulated into MAC Protocol Data Units (PDUs), which are then transmitted to the peer MAC layer through the physical layer. Furthermore, time is partitioned into frames of fixed duration, which are, in turn, divided into downlink and uplink sub-frames1 . The former is used by the BS to transmit data to the MSs, whereas the MSs transmit to the BS in the latter. Fig. 3.1 reports the frame structure in case of Time Division Duplex (TDD). OFDMA is a multiplexing technique for Non-Line-of-Sight (NLOS) operations which subdivides the bandwidth into multiple frequency sub-carriers then grouped into subsets, called sub-channels [8, 9]. The IEEE 802.16e standard specifies a few sub-carrier permutations, i.e. the mapping of logical sub-channels onto physical subcarriers. Different mappings are tailored to different transmission environments and user characteristics, as described in [10]. Two sub-carrier permutations are mandatory for downlink transmission: Partial Usage of Sub-channels (PUSC) and Full Usage of Sub-channels (FUSC). For uplink transmission, PUSC is the only mandatory sub-carrier permutation. A zone is a portion of the frame in which one of the above sub-carrier permutations is applied. As shown in Fig. 3.1, multiple zones within the same downlink or uplink sub-frame, employing different sub-channels mapping schemes, may exist. 1 Frame partition into sub-frames can occur both in the time domain, i.e. TDD, and in the frequency domain, i.e. FDD. The latter is supported by the standard, but TDD is the option currently deployed in the first mobile WiMax releases.

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Fig. 3.1 IEEE 802.16e OFDMA sample TDD frame structure. The number of OFDMA symbols and sub-channels reported in the figure are not realistic

With a slight abuse of notation, we can consider a frame as a matrix where rows and columns are sub-channels and OFDMA symbols, respectively (see Fig. 3.1). The number of OFDMA symbols and sub-channels per frame depends on the frame duration, the channel bandwidth, the Fast Fourier Transform (FFT) size, the Cyclic Prefix length, the direction, and the sub-carrier permutation. In any case, the minimum resource allocation unit is the OFDMA slot (hereafter slot) and consists of one or more sub-channels by one or more OFDMA symbols. With regard to the mandatory sub-carrier permutations, a downlink PUSC slot is two OFDMA symbols by one sub-channel large, a downlink FUSC slot is one OFDMA symbol by two sub-channels large, and an uplink PUSC slot is three OFDMA symbols by one sub-channel large. Furthermore, the standard specifies several Modulation and Coding Schemes (MCSs) which can be used by the BS and MSs to adapt the transmission rate to the channel conditions. Therefore, while the dimensions of a slot are determined by the physical layer parameters and sub-carrier permutation only, the amount of data that can be conveyed into a single slot depends on the robustness of the MCS. The more robust the MCS is, the less the number of data bytes per slot are. The set of available MCSs, both for uplink and downlink transmission, is advertised by the BS on a regular basis through dedicated control messages, called Downlink Channel Descriptor (DCD) and Uplink Channel Descriptor (UCD). As illustrated in Fig. 3.1, the downlink sub-frame begins with a physical preamble needed by the MSs for synchronization and channel quality estimation. The preamble, which consists of a known sequence of modulated pilots, lasts one OFDMA symbol in the time domain and covers all the available sub-channels in the frequency domain. After the preamble, the BS transmits the Frame Control Header (FCH), which defines the structure of the first downlink PUSC zone and the length in slots of the downlink map (DL-MAP) message. The DL-MAP message

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is thus allocated within the downlink sub-frame in column-wise order from the end of the FCH. If the DL-MAP exceeds the number of rows in a column, the allocation continues from the beginning of the next column. The remaining part of the downlink sub-frame is allocated as a number of data regions, which consist of twodimensional portions of the downlink sub-frame formed by a group of contiguous logical sub-channels in a group of contiguous OFDMA symbols. A data region may be visualized as a rectangle of OFDMA slots as shown in Fig. 3.1, and cannot span over two zones. However, MAC SDUs can be fragmented into many MAC PDUs, which in turn can be conveyed in different data regions2 . In other words, data that are intended to be conveyed in a data region can actually span over multiple data regions, provided that the appropriate slot boundaries are taken into account. If the size of the MAC PDUs of a data region is not a multiple of the number of bytes contained in the data region slots, the remaining portion is padded with stuff bytes. The size of each data region and its coordinates, with respect to the upper-left corner of the downlink sub-frame, depend on the frame allocation process and are specified by the BS in the downlink map (DL-MAP) message, which is advertised at the beginning of the downlink sub-frame in a PUSC zone. The standard allows that data directed to MSs employing the same MCS can be grouped into a single data region to reduce the overhead. In fact, each data region is specified through an Information Element (IE) into the DL-MAP, which can optionally include the list of connections to which the data region is addressed, namely the data region recipients. With regard to the uplink direction, MSs access the medium in accordance with the uplink data regions advertised by the BS. Unlike downlink, uplink data regions are explicitly defined by the standard as a number of contiguous slots, starting from the upper-left corner of the uplink sub-frame, allocated in row-wise order as shown in Fig. 3.1. The end of each uplink data region is the beginning of the next one. Like downlink, each uplink data region incurs the overhead of one IE in the uplink map (UL-MAP) message. The latter is transmitted by the BS in the PUSC zone of the downlink sub-frame. In the following we call maps the DL-MAP and UL-MAP messages. Since maps have to be decoded by all the MSs in the cell, the BS must employ the most robust MCS among those currently in use by the MSs. Therefore, even though the number of bits required to encode a single IE is rather limited, the total amount of overhead, in slots, required for the transmission of maps can be significant. To increase the range and robustness of data transmission, the IEEE 802.16e standard specifies several advanced techniques. For instance, H-ARQ can be enabled on a per-connection basis for fast recovering from channel errors at an intermediate level between the MAC and physical layers [10]. A H-ARQ data region consists of a set of H-ARQ sub-bursts, each containing one or more MAC PDUs followed by a Cyclic Redundancy Check (CRC). The latter is used by the recipient to determine 2

This is only true provided that variable-length fragmentation of MAC SDUs into multiple MAC PDUs is allowed. While the latter is an optional feature of the IEEE 802.16 MAC, it can be effectively employed to reduce the MAC overhead due to padding the unused portion of data regions, and is thus specified as a mandatory feature by the WiMax compliance datasheets.

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whether the MAC PDUs have been received correctly, in which case it sends a positive indication to the sender. Otherwise, the H-ARQ sub-burst is retransmitted, employing the same MCS, until either it is received correctly or the maximum number of retransmissions is exceeded. Unlike classical ARQ schemes employed at the MAC layer of wireless MAC protocols (e.g. IEEE 802.11), the H-ARQ recipient keeps a copy of all the failed transmissions, and it combines them together to infer the correct sequence of bytes transmitted. Furthermore, acknowledgments of the correct/incorrect reception of the H-ARQ sub-bursts are sent via a dedicated logical channel in the MAC frame, which ensures fast convergence of the H-ARQ process, and hence low transmission latencies. From the frame allocation point of view, H-ARQ adds to major changes with respect to non-H-ARQ data transmissions. First, H-ARQ sub-bursts must be transmitted as a whole, that is, they cannot span over two data regions. Second, in the downlink direction only, H-ARQ sub-bursts that are addressed to MSs with different MCSs can be packed into a single downlink H-ARQ data region. In fact, each H-ARQ downlink data region is advertised in the DL-MAP by means of a specific IE, which specifies the starting point (top-left corner), width (number of OFDMA symbols), height (number of logical sub-channels), and number of H-ARQ subbursts. Each H-ARQ sub-burst is then advertised within the downlink H-ARQ data region IE by means of the length (in number of slots), the MCS, and the recipient.

3.3 OFDMA Frame Allocation An IEEE 802.16e BS is in charge of allocating capacity in both downlink and uplink, by advertising maps on a frame-by-frame basis. We call this process frame allocation. Since the IEEE 802.16e standard specifies neither a mandatory nor a reference algorithm to perform such task, it is assumed to be manufacturer-specific. However, the standard specifications impose a number of constraints and/or requirements on the candidate algorithm to be implemented, which makes its definition an extremely complex task. More specifically, three main issues may be identified as follows. First, the BS has to ensure that admitted connections are provided with the negotiated QoS guarantees [7]. MAC frame allocation could significantly impact the QoS of a given connection. Therefore, the issue of determining which MSs are granted capacity in the next frame, and how many bytes they are allowed to transmit or receive, must be based on the QoS requirements of every admitted connection. Second, the capacity available for data transmission in a frame cannot be granted arbitrarily, but rather it must obey to a number of constraints derived straightforwardly from the standard specification of the OFDMA MAC frame structure. In fact, grants must be organized into a set of data regions in order to transmit (receive) data to (from) the scheduled MSs. Furthermore, downlink data regions must have rectangular shape and must be allocated in the sub-frame without overlapping with each other and without spanning over multiple zones. Finally, use of H-ARQ introduces an additional constraint to data regions, in that they cannot be split into

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smaller data regions. It is easy to see that to satisfy all these constraints is a very challenging task. Note that, although in principle one could think of granting capacity on a slot-by-slot basis, this immediately proves to be unfeasible in practice in an IEEE 802.16 OFDMA system, due to the unbearable cost in terms of the resulting map overhead. Third, signaling in IEEE 802.16 is in-band, i.e. the overall capacity of the downlink sub-frame is shared between control messages, including maps, and data messages, i.e. MAC PDUs transporting user data. As mentioned above, each downlink (uplink) data region is advertised by a different IE in the DL-MAP (UL-MAP). Therefore, when performing frame allocation, one desirable objective would be that of minimizing the MAC control overhead (map overhead hereafter) so as to maximize the capacity available to users. The complexity of the frame allocation task comes from the fact that the above mentioned issues are often in contrast one to each other. On the one hand, to satisfy the QoS constraints that a given user may require, e.g. real-time traffic with strict deadlines, the BS may be forced to grant bandwidth to any MS in certain frames. Yet, should each MS receive a small amount of capacity in every frame, for the sake of service timeliness, this would very likely cause an unacceptable loss of capacity due to the map overhead. On the other hand, one could follow the simplistic approach of scheduling only one MS per sub-frame, which minimizes the map overhead. However, this would lead to an increased latency between two consecutive capacity grants to the same MS. While such an approach would be fairly acceptable for elastic best-effort applications, such as File Transfer Protocol (FTP), it is very unlikely to fit the maximum delay requirements of real-time interactive applications envisaged for IEEE 802.16 networks, such as Voice over IP (VoIP). Finally, given a number of PDUs to be transmitted in a given sub-frame, not all their possible arrangements into a set of data regions are equivalent from the point of view of the corresponding map overhead, thus calling for data region arrangement strategies which aim at minimizing the map overhead. Matters are further complicated by the fact that a data region arrangement which would minimize the map overhead can possibly entail no feasible frame allocations complying with the frame structure constraints.

3.3.1 A Modular Framework In order to tackle the complexity of the frame allocation problem, a general framework can be assumed, which is based on a pipeline approach: each of the above issues is basically solved independently of each other, i.e. forcing when necessary the breaking of mutual dependencies, and the corresponding solutions are then combined in sequence in order to provide the final allocation.3

3 A similar approach has been followed in [18], where frame allocation constraints came from the Subscriber Stations being half-duplex transmission capable only, while the system works in Frequency Division Duplex mode.

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In particular, we envisage the frame allocation task as being split into three separate and sequential sub-tasks, namely grant scheduling, data region arrangement, and data region allocation. The grant scheduling sub-task takes as input the connections’ QoS requirements, their current traffic load and the available frame capacity, and determines how many MAC PDUs must be transmitted to each MS to satisfy the requirements of its admitted connections. Provided that a given MCS is associated to each MS, this turns into an amount of capacity, in slots, to be granted to each MS, irrespective of its assignment to a specific data region and frequency and time domain allocation in the MAC frame. The data region arrangement sub-task takes as input the output of the grant scheduling sub-task, and arranges the selected grants into different groups according to specific arrangement strategies, which aim at, e.g., minimizing the map overhead or providing better service in terms of packet delay to the MS. Such groups are tentatively targeted to being mapped one-to-one to as many data regions to be allocated in the frame. These data regions are then fed to the last sub-task, i.e. data region allocation, which is responsible for defining the final contents of maps, possibly re-arranging data regions to fit them into the OFDMA frequency and time domains, and re-sizing scheduled grants to make the allocation feasible. So far we have not distinguished between uplink and downlink in our discussion. However, in the uplink sub-frame, the IEEE 802.16e standard explicitly defines the procedures for the data region arrangement and the data region allocation sub-tasks, whereas grant scheduling is left up to manufacturers. Conversely, in the downlink sub-frame, all the three sub-tasks are left unspecified. Therefore, in the following, we will mainly refer to the downlink sub-frame allocation procedure unless otherwise specified. A schematic representation of the modular framework with a numerical example is reported in Fig. 3.2. Grant scheduling selects a list of MSs and a number of slots for each MS that will be transmitted in the forthcoming downlink sub-frame. The data region arrangement module groups the list of grants into a set of data regions, according to a data region arrangement strategy (described in Section 3.3.2). In the example, we assume that data addressed to the MSs employing the same MCSs are grouped together so as to form a single data region (e.g. grants addressed to MS 1 and MS 8 are grouped together into a data region with area equal to 30 slots). Finally, the data region allocation module is in charge of defining the final layout

Fig. 3.2 Modular pipelined approach

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of the downlink sub-frame. Note that, in the example of Fig. 3.2, the data region allocation module is not able to allocate all the data regions as they are received from the data region arrangement module. Thus, the area of data region 4 is allocated into two distinct data regions. It is evident that such a modular approach may result in sub-optimal solutions, even though the optimal solution to each sub-problem is found, because constraints are considered separately. However, we argue that it has substantial advantages, which are outlined in the following. On the one hand, with this approach, QoS provisioning only depends on the algorithm which is implemented by the grant scheduling sub-task. Therefore, the task of scheduling bandwidth for QoS support can be confined to a well-identified functional sub-module, and is naturally abstracted from the details originating from grant allocation within the MAC frame. On the other hand, such isolation allows for implementing scheduling algorithms as a result of simple adaptation of well-known algorithms already proposed for wired networks, where this discipline has been extensively studied [11–13]. Assuming that the available literature on scheduling algorithms is able to provide adequate solutions to the issue of supporting QoS, we dedicate the next two subsections to discuss in detail those issues which are more specific to IEEE 802.16e, i.e. the data region arrangement and allocation sub-tasks, respectively.

3.3.2 Data Region Arrangement As mentioned above, different data region arrangement strategies, both in downlink and uplink sub-frames, may impact differently on the MAC control overhead. Since the control signaling is in-band, reducing the size of maps is beneficial as it allows more data to be conveyed into the same downlink sub-frame, thus possibly increasing the overall throughput while preserving QoS guarantees. We investigate separately how the different data region arrangement strategies affect the MAC control overhead in the downlink and the uplink sub-frame, respectively. We focus on the downlink direction first. IEEE 802.16e provides standard mechanisms for the purpose of reducing the DL-MAP overhead. First, data addressed to MSs with the same MCS can be packed into a single data region. In fact, as mentioned in Section 3.2, for each IE, the BS is allowed to omit the list of MSs to which the PDUs are addressed, provided that the latter are all transmitted with the same MCS. While this may result in a considerable reduction of the DL-MAP size, it has the drawback that each MS is forced to decode all data regions transmitted with its own MCS4 , due to the lack of information with regard to the actual receivers of the data contained into the data region. Therefore, on average, an MS incurs greater energy consumption than needed. This might be a problem especially with mobile terminals, due to their limited energy capabilities. 4 Note that this does not violate the security functions of the IEEE 802.16 to ensure privacy of data transmitted over-the-air, since the payload of MAC PDUs can be encrypted so that only the intended recipient is able to retrieve the original data sent by the BS.

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On the other hand, energy wastage can be mitigated by explicitly specifying the lists of data region recipients in the IEs, which reduces the chance that an MS unnecessarily decodes the data region, at the cost of increasing the overall map overhead. In fact, in this case, an MS only decodes those data regions actually containing data addressed to it. However, an MS can still incur some energy wastage, due to the fact that a data region may also convey data addressed to other MSs with the same MCS. Yet, having exactly one MS for each data region completely avoids waste of energy, though at the cost of the highest overhead. To evaluate the relevance of such overhead, we define the DL-MAP size theoretical lower bound (TLB), with respect to a set of data regions, as the size of the minimum DL-MAP needed to allocate them. Specifically, TLB is computed assuming that each data region never needs to be split. However, this assumption is in general over-optimistic, since there might not be any feasible allocation of all data regions without splitting one or more of them. In Fig. 3.3 we show the DL-MAP overhead, in terms of the ratio between TLB and the downlink sub-frame duration against the total number of MSs served in the downlink and uplink sub-frames. We assume that each MS is provided with one connection. Numerical results have been obtained with the following system parameters: the frame duration is 10 ms with an FFT size of 1024 and a channel bandwidth of 10 MHz; the downlink and uplink sub-frames consist of 28 and 18 OFDMA symbols, respectively; the number of sub-channels is 30; and MSs employ four different MCSs. The curves reported in Fig. 3.3 are related to three different strategies for the definition of the DL-MAP: (i) MSs are grouped per MCS without

Fig. 3.3 DL and UL map overhead

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specifying the list of the downlink data region recipients; (ii) same as (i), but the list of the downlink data region recipients5 is specified; (iii) each MS is assigned to only one downlink data region. As shown in Fig. 3.3, with strategy (i) the overhead remains constant with the number of MSs. In fact, the size of the DL-MAP does not depend on the number of MSs served in the downlink sub-frame, but it only depends on the number of different downlink MCSs currently used, i.e. four in this scenario. On the other hand, strategy (iii) exhibits an increasing overhead with the number of MSs per frame which severely affects the downlink sub-frame capacity by consuming 34% of the sub-frame with 23 MSs. Intermediate results have been obtained with strategy (ii), where the list of the downlink data region recipients is also advertised within the IE relative to a specific MCS. With regard to the uplink direction, the IEEE 802.16e standard explicitly requires the BS to add an IE to the UL-MAP for each MS served in the uplink sub-frame, regardless of the MCS of the MS. In other words, the UL-MAP size only depends on the number of MSs served in the uplink sub-frame. Therefore, unlike downlink, the BS cannot count on any mechanism of the standard to reduce the control messages overhead. Note that the UL-MAP overhead affects the capacity available for transmission in the downlink sub-frame, although it depends on the number of MSs served in the uplink sub-frame. For the sake of completeness, we also reported the UL-MAP overhead in Fig. 3.3, which confirms that the UL-MAP overhead increases linearly with the number of MSs due to the presence of one IE for each MS. We stress the fact that the frame allocation procedure of the uplink sub-frame is explicitly defined by the standard. Therefore, the UL-MAP curve only depends on the number of MSs actually scheduled to transmit data in the uplink sub-frame since no data region arrangements are permitted, except that specified by the standard. We can conclude that, in an IEEE 802.16e system, the overhead due to maps can significantly reduce the capacity available for transmitting data. This problem can be mitigated by employing a data region arrangement strategy which limits the number of IEs to be advertised.

3.3.3 Downlink Data Region Allocation We now describe the downlink data region allocation sub-task, which consists of selecting both the shape, i.e. width and height, and the position, i.e. the OFDMA symbol and sub-channel offsets, of each data region. These two problems are discussed separately. With regard to data region shaping, in general, there are several ways to shape the same number of scheduled slots into a data region. For example, assume that the BS has to transmit eight slots of data to an MS. If only one data region is used,

5 Note that since we assume that each MS has only one connection, the number of the data region recipients is equal to the number of MSs per frame.

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there are four shapes available: 2×4, 4×2, 1×8 or 8×1. Still, it is possible for the BS to employ multiple data regions, e.g. two data regions of four slots each. Finally, the BS can inflate the data regions so that more than eight slots are used, e.g. a single 3 × 3 data region, in which case part of the inflated data regions remains empty. However, not all alternatives are equivalent, since the MAC overhead varies depending on the fragmentation of MAC PDUs over various data regions, and the padding of slots that are not completely used for user data. As far as positioning of data regions is concerned, this problem can be viewed as a bi-dimensional bin packing problem, where a number of items (i.e. data regions) with a specified width and height have to be packed into a fixed-size bin (i.e. the downlink sub-frame). This problem is well known in the literature of operations research and has been proved to be NP-hard [14]. Therefore it is not feasible to implement exact methods at the BS, since positioning of data regions is a hard real-time task, with a deadline comparable to the frame duration. Additionally, no efficient heuristics to solve general bin packing problems are known. We thus argue that simple, yet effective, heuristics should be envisaged, by taking into account the inherent properties of IEEE 802.16. For instance, a joint approach between shaping and placing data regions might be employed, so that the shape of items to be allocated is modified to broaden the space of solutions. Furthermore, the use of H-ARQ introduces an additional constraint when shaping data regions because H-ARQ data regions cannot be split at arbitrary boundaries. For example, assume that the BS decides to split an H-ARQ data region consisting of two H-ARQ sub-bursts each consisting of four slots into two data regions. The only feasible configuration is {4, 4}, while {5, 3}, {6, 2}, {7, 1} are not allowed since they would require H-ARQ sub-bursts to be fragmented. 3.3.3.1 A Sample Data Region Allocation Algorithm In this section, we propose an algorithm, that we name Sample Data Region Allocation (SDRA) algorithm, as a solution to the data region allocation problem. According to our modular framework, SDRA works independently of the specific grant scheduling algorithm and the data region arrangement strategy adopted. Without loss of generality, we describe our algorithm in the case where data addressed to MSs which employ a common MCS are grouped into a single data region (strategy (i) in Section 3.3.2). The SDRA algorithm is then extended to support H-ARQ as described at the end of this section. The performance of SDRA is assessed by means of Monte Carlo analysis in Section 3.4. For the ease of readability, we describe the SDRA algorithm in the case of data transmitted in the FUSC zone only. Since the IEEE 802.16e standard specifies that the DL-MAP is always transmitted in the PUSC zone, the slots that lie “below” it, if any, are left unallocated. The extension to the more general case of combined allocation of data into the PUSC and FUSC zones is straightforward. Likewise, we omit the procedure to allocate the UL-MAP, which must be transmitted as a single data region in the PUSC zone, because its size does not depend on the process of downlink data region allocation.

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In the following a slot is said to be allocated if the DL-MAP defines a data region which covers it. More formally, slot (i, j) is allocated if there is at least one IE such that x ≤ i ≤ x + w ∧ y ≤ j ≤ y + h, where x, y are the time, sub-channel upper-left coordinates of the data region, respectively, and w, h are the width, height data region dimensions, respectively. Note that an allocated slot might not contain any data actually. Moreover, we define the number of slots in a row of the downlink sub-frame as W; likewise, the number of slots in a column of the downlink subframe is H. Lastly, we assume without loss of generality that columns and rows are numbered starting from 1. SDRA is based on the following key concepts: (i) the order in which the data regions are passed to SDRA is preserved when they are allocated into the downlink sub-frame; (ii) allocation proceeds backwards in column-wise order, i.e. any slot of column i is not allocated until all the slots of columns j, i + 1 < j ≤ W are allocated. These design choices are motivated as follows. Because of (i) the scheduler is free to decide the priority of the downlink MAC PDUs that have to be transmitted, which can be exploited to assign to certain MAC PDUs a higher priority than others. Examples of MAC PDUs which may need a higher priority than others include: the UL-MAP (in the PUSC zone) and other MAC management messages; data that have not been allocated in previous frames; data belonging to admitted connections with strict QoS requirements, e.g. UGS connections. Additionally, a (partially) opportunistic approach could also be devised, where MAC PDUs of MSs enjoying higher transmission efficiency (i.e. less robust MCS) are more likely to be placed than the others. The impact of this choice on the network utilization is investigated in the performance analysis at the end of this section. Furthermore, choice (ii) is motivated by the implementation concern that the DL-MAP grows in column-wise order starting from the beginning of the downlink sub-frame, according to the standard specifications. Therefore, by letting data filling up the sub-frame from the opposite direction, any grant needs only be allocated once and for all by the allocation procedure, as described below. We first describe the procedure to allocate a set of non-H-ARQ data regions, which are called pending data regions, while those actually placed into the downlink sub-frame are called allocated data regions. The procedure for H-ARQ data regions is described afterwards. Note that these procedures are described separately for the clarity of illustration, but they can be seamlessly integrated into a single allocation procedure to jointly allocate both non-H-ARQ and H-ARQ data regions. As already discussed in Section 3.2, we assume that any non-H-ARQ data region can be split into smaller data regions arbitrarily, though at slot boundaries. Thus, since data regions are allocated in column-wise order, each data region can be split at most into three smaller data regions. The scheduler, thus, must overprovision the amount of slots requested for each data region so as to include the MAC overhead due to (up to) two fragmented MAC SDUs. However, the quantitative impact on the network utilization is negligible. An example of the resulting allocation of four data regions, labeled A, B, C, and D, is depicted in Fig. 3.4. As can be seen, the four data regions input produces eight data regions: A is split into two data regions, C into three, D into two, while B is allocated as a whole. Under

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Fig. 3.4 Example of non-H-ARQ allocation via SDRA, with FUSC

the assumption of employing FUSC as the sub-carrier permutation, no more data can be allocated into the downlink sub-frame, because the free room that lies below the DL-MAP (column 3 in Fig. 3.4) cannot be exploited. The detailed allocation procedure is described by means of the pseudo-code in Appendix A of this paper. With H-ARQ, the allocation procedure described above is modified according to the fact that H-ARQ data regions can only be split at H-ARQ sub-bursts boundaries. To achieve this, each data region is always allocated as a rectangle with height equal to the number of rows available. The number of columns is then set to the minimum value such that the sum of the sizes of all the H-ARQ sub-bursts contained into the data region is smaller than or equal to the data region size. This way, up to H – 1 slots can remain unused in each data region. A sample allocation of three H-ARQ data regions, A with three sub-bursts, and B and C with two sub-bursts, is depicted in Fig. 3.5.

Fig. 3.5 Example of H-ARQ allocation via SDRA

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There is a major difference between the non-H-ARQ and the H-ARQ versions of SDRA. With H-ARQ, each input data region produces at most one allocated data region, while non-H-ARQ data regions can be split into up to three data regions. However, the latter can always be allocated provided that there is free room in the downlink sub-frame left by the DL-MAP. On the other hand, a H-ARQ data region can contain a sub-burst that does not fit into the remaining space, and thus needs to remain unallocated, while some sub-bursts of other H-ARQ data regions may be successfully allocated. Therefore, the allocation procedure with H-ARQ must proceed even when one or more sub-bursts are discarded, which is never the case with non-H-ARQ. The detailed allocation procedure is described by means of the pseudo-code in Appendix A of this paper.

3.4 Performance Evaluation The performance evaluation is carried out by means of Monte Carlo analysis. Specifically a set of input numerical instances is defined based on the system and network configuration parameters reported below. An input numerical instance is defined as a list of data regions, each with a specified size and MCS; with H-ARQ, the data region also contains the list of H-ARQ sub-bursts. The procedure used to generate a numerical instance is described in Appendix B. The parameters are classified into static and dynamic parameters: static parameters are set in a deterministic manner and are used to derive the dynamic parameters; dynamic parameters are sampled from a random distribution during the numerical instance generation, based on the set of static parameters. Any combination of static parameters is defined as a snapshot. The allocation procedure is then fed with several variations of the snapshot, by initializing the random number generator functions with different seeds. Performance metrics, defined in Section 3.4.1, are estimated for a given snapshot by averaging their respective values over all the numerical instances of that snapshot. No weighting is applied; therefore all numerical instances are assumed as equally probable states of the system, and confidence intervals can be derived using the standard method of independent replications [15]. Confidence intervals are however not drawn whenever negligible with respect to the estimated average. The system and network configuration parameters are reported in Table 3.1. As outlined in Section 3.2, a slot consists of a two-dimensional time/sub-channel rectangle, whose exact dimensions depend on the sub-carrier permutation, i.e. PUSC or FUSC. This difference accounts for the varying size of the downlink sub-frames reported in Table 3.1. Additionally, the greater downlink sub-frame sizes, i.e. 17×30 and 34×16, correspond to the case of 10 MHz physical bandwidth, with a frame duration equal to 5 ms, and ratio between the downlink and uplink sub-frames set to 35/12. The same profile is used for the cases 17×10 and 34×5, respectively for PUSC and FUSC, where a physical re-use of three is assumed. In any case, the downlink sub-frame size includes the slots used to transmit the DL-MAP, which is computed according to the standard specifications as a function

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Parameter name

Symbol

Type

Possible values

Sub-carrier permutation H-ARQ support Downlink sub-frame size (slots)

h s

static static static

PUSC, FUSC enabled, disabled 17 × 30, 17 × 10 (PUSC) 34 × 16, 34 × 5 (FUSC) seven, three random (RND), More Robust First (MRF), Less Robust First (LRF) [0, 1] 0.2, 0.4, 0.6, 0.8 72 192 Seven MCSs QPSK-1/2 (6), QPSK-3/4 (9), 16QAM-1/2 (12), 16QAM-3/4 (18), 64QAM-1/2 (18), 64QAM-2/3 (24), 64QAM-3/4 (27) Three MCSs QPSK-1/2 (6), 16QAM-1/2 (12), 64QAM-1/2 (18) VoIP, BE 1, 2, 4

Number of MCSs Data region order

static static

Target offered load Target percentage of VoIP users Size of VoIP PDUs, bytes Size of BE PDUs, bytes MCS (number of bytes/slot)

tol v Sv Sb mcs

static static static static dynamic

User type Average number of BE PDUs per user

u 1/ pbe

dynamic dynamic

of the number of data regions (both non-H-ARQ and H-ARQ) and H-ARQ subbursts (H-ARQ only). For the purpose of analysis, we define the offered load (in slots, ol S ) as the fraction of the overall downlink sub-frame size s required to be allocated as user data: n ols =

j=1

s

aj

,

where a j is the size of the j-th data region provided as input to the allocation procedure, and n is the number of data regions. On the other hand, the target offered load for a given numerical instance is the reference value used as a stopping criterion during the numerical instance generation (see below). It is worth noting that the offered load associated to a given instance may differ from the target offered load, due to the integer nature of the numerical instance generation process. Finally, the offered load (in bytes, ol B ) is computed as the sum of the bytes conveyed by the data regions provided as input to the allocation procedure. As far as data region arrangement is concerned, we assume that all MAC PDUs directed to users with the same MCS are combined into the same data region. The number of MCSs employed depends on the static parameter specified in Table 3.1.

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Specifically, if seven MCSs are used, this means that each user is served with the MCS that best fits its current channel conditions, as reported by the MSs [16]. On the other hand, with three MCSs we simulate a system where the BS can transmit data to some MSs, i.e. the ones that would be served with the MCSs that are unavailable in the three MCSs case, using a more robust MCS. This approach leads to a slightly lower transmission efficiency for some MSs, but reduces the number of IEs that are advertised on average in the DL-MAP, and hence the map overhead. Lastly, the order in which data regions are fed to the allocation algorithm is specified as a static parameter. Three possible approaches are defined:

r r r

Random (RND): Data regions, each related to a different MCS, are shuffled in a random fashion. More Robust First (MRF): Data regions are sorted in decreasing robustness order. In other words, data that have the least transmission efficiency, in terms of the number of bytes conveyed per slot, have the highest chance of being allocated. Less Robust First (LRF): This is opposite approach than MRF, i.e. data regions are sorted in increasing robustness order.

The impact on the performance of both the ordering approach selected and the number of employed MCSs is evaluated in the performance analysis in Section 3.4.2.

3.4.1 Metrics The following performance metrics are defined. The carried load (in slots, cl S ) is defined as the fraction of the downlink sub-frame size s that is allocated as user data by the allocation algorithm. Note that the slots formally allocated, but not used for data transmission, are not included in this computation. On the other hand, the carried load (in bytes, cl B ) is defined as the number of bytes that can be conveyed by the data regions resulting from the output of the allocation algorithm, depending on their MCSs. Then, the success probability is defined as the probability that the carried load is equal to the offered load. It measures the capability of the algorithm to find a solution that satisfies all the specified constraints. The map overhead is defined as the fraction of the downlink sub-frame size s that is allocated to transmit the DL-MAP message. The unused slots per frame is defined as the fraction of the downlink sub-frame size s that is not allocated for any transmission. The padding overhead is defined as the fraction of the downlink sub-frame size s that is allocated in any data region, as a result of map definition, but is not actually used for data transmission. This accounts for slots that are allocated so as to define data regions as rectangles, but are not needed to accommodate transmission foreseen in that data region. This metric is only meaningful when H-ARQ is enabled, as discussed in Section 3.3.3.1.

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3.4.2 Results In order to evaluate the impact of the system and network configuration parameters on the performance of the downlink MAC frame allocation alone, the results in this section have been obtained without taking into account the MAC overhead due to the management messages other than the DL-MAP, including the UL-MAP. Furthermore, in all the evaluated scenarios, we verified that the performance improvement with PUSC is negligible with respect to FUSC. In other words, the gain due to the number of slots that lie below the DL-MAP, which cannot be allocated in FUSC mode, is quantitatively small in terms of the metrics defined in Section 3.4.1. Therefore, we only report the results with FUSC for clarity of illustration. We start with the analysis of the downlink MAC frame allocation procedure without H-ARQ support, by evaluating the carried load (in slots, cl S ), the map overhead, and the success probability when the offered load (in slots, ol S ) increases from 0.2 to 1. For the ease of presentation, the carried load plotted also includes the map overhead. In Fig. 3.6 we first compare the results with different downlink sub-frame sizes, i.e. 34 × 16 and 34 × 5. As can be seen, a large portion of the downlink sub-frame (i.e. about 20% with a 34×16 sub-frame) is consumed by the transmission of the DL-MAP. Furthermore, this overhead does not depend significantly on the offered load. In fact, the size of the DL-MAP only depends on the number of IEs contained, i.e. data regions allocated, which varies in a narrow interval, i.e. between 1 and 7,

Fig. 3.6 FUSC mode without H-ARQ. Carried load (in slots), map overhead, and success probability vs. the offered load (in slots), with different downlink sub-frame size, i.e. 34 × 16 and 34 × 5, seven MCSs and data regions randomly passed to the allocation algorithm

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which is the maximum number of MCSs. Note that the map overhead does not decrease linearly with the sub-frame size, because of the fixed amount of information that needs to be conveyed in the DL-MAP, regardless of the number of data regions allocated. As far as the carried load is concerned, both the 34 × 16 and the 34 × 5 curves increase when the offered load increases until there is enough room in the downlink sub-frame to allocate the input data completely. On the other hand, when the success probability becomes smaller than one, the carried load saturates to a constant value. In other words, after some value of the offered load that depends on the size of the sub-frame, the latter is always completely allocated (i.e. the sum of the carried load and the map overhead is one), but some input bursts are still discarded (i.e. the offered load is greater than the carried load). Since this effect is due to the DL-MAP transmission overhead, the actual value of the offered load that saturates the sub-frame depends on the sub-frame size: the offered load with a 34×16 sub-frame saturates earlier than that with a 34×5 sub-frame. Note that there is a transient interval of the success probability where most, but not all, the numerical instances fail to be allocated. In the results above, all seven MCSs have been considered. This case is compared to that with three MCSs only in Fig. 3.7, with a 34 × 16 sub-frame. As can be seen, having a smaller number of MCSs reduces the map overhead, which in turn improves the success probability. In fact, with three MCSs the latter drops below one at a value of the offered load about 6% greater than that with seven MCSs.

Fig. 3.7 FUSC mode without H-ARQ. Carried load (in slots), map overhead, and success probability vs. the offered load (in slots), with different number of MCSs employed, i.e. seven and three, 34 × 16 downlink sub-frame size, and data regions randomly passed to the allocation algorithm

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Fig. 3.8 FUSC mode without H-ARQ. Carried load (in bytes) vs. offered load (in bytes), with different number of MCSs employed, i.e. seven and three, 34 × 16 downlink sub-frame size, and data regions randomly passed to the allocation algorithm

However, the lower overhead due to maps does not improve significantly the performance, in terms of the carried load (in bytes) conveyed in the allocated regions, which is reported in Fig. 3.8 against the offered load (in bytes). This can be explained as follows. With three MCSs only, some PDUs that can be transmitted with a less robust MCS (e.g. 16-QAM-2/3) are allocated instead by means of a less efficient MCS (e.g. 16-QAM-1/2). While this reduces the number of data regions allocated on average, and hence the map overhead, the average transmission efficiency is also lessened. Specifically, the plot in Fig. 3.8 exhibits three phases. First, when the offered load can be entirely allocated (i.e. when it is smaller than ∼3500 bytes), the carried load with both seven and three MCSs coincide with the former. Then, there is a phase (i.e. with the offered load between ∼3500 bytes and ∼4500 bytes) where the carried load does not increase in a linear manner anymore. In this interval having seven MCSs yields a higher carried load, in terms of bytes, due to the higher transmission efficiency. Finally, when the offered load is greater than ∼4500 bytes, the effect due to the map overhead reduction with three MCSs overruns that of the transmission efficiency, which leads to improved performance with three MCSs only. Thus, while the number of slots allocated per sub-frame, reported in Fig. 3.7, is always greater with a smaller number of MCSs, the performance in terms of bytes carried shows a trade-off depending on the input rate, in bytes, to the allocation algorithm. We now investigate the performance in the cases where the data regions, before being fed to the allocation algorithm, are sorted according to the MRF and the LRF strategies described above. In this scenario, we consider seven MCSs.

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Fig. 3.9 FUSC mode without H-ARQ. Carried load (in slots), map overhead, and success probability vs. the offered load (in slots), with different ordering of data regions depending on their MCSs, i.e. MRF and LRF, 34 × 16 downlink sub-frame size, and seven MCSs employed by the BS

As can be seen in Fig. 3.9, the specific ordering strategy does not affect significantly the performance in terms of carried load (in slots). In fact, with both the MRF and the LRF strategies all the curves (almost) overlap. However, beyond the value of about 0.8 of the offered load, the map overhead curves exhibit a divergent trend. Specifically, the MRF strategy entails an increasing smaller map overhead than the LRF one. This can be explained as follows. When the offered load is such that the success probability is smaller than one, i.e. the allocation algorithm fails to allocate all the data regions, the number of data regions allocated on average, and thus the map overhead, is greater with LRF than with MRF. In fact, the greater the transmission efficiency of an MCS, the smaller the size of the data region to convey the same amount of data. Therefore, allocating the data regions in decreasing robustness of MCS produces, on average, a smaller number of data regions which are actually allocated. This accounts for the overhead reduction in the MRF case when the value of the offered load is greater than 0.8. Conversely, the higher the transmission efficiency, the higher the carried load, in terms of bytes, which can be achieved. This result is shown in Fig. 3.10 where the carried load (in bytes) versus the offered load is reported. With an offered load greater than ∼3500 bytes the MRF and LRF curves no longer overlap. The higher transmission efficiency obtained by the LRF strategy yields a carried load up to ∼5300 bytes while the MRF strategy reaches ∼4200 bytes at most. So far we have analyzed scenarios where the H-ARQ is not considered. We now extend our analysis to the case of FUSC sub-carrier permutation with H-ARQ.

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Fig. 3.10 FUSC mode without H-ARQ. Carried load (in bytes) vs. offered load (in bytes), with different ordering of data regions depending on their MCSs, i.e. MRF and LRF, 34×16 downlink sub-frame size, and seven MCSs employed by the BS

Again, in Fig. 3.11, we report the carried load (in slots), the map overhead and the success probability versus the offered load (in slots) with 34×16 downlink subframes and seven MCSs. The padding overhead is also reported as a measure of the capacity wasted due to the H-ARQ allocation algorithm. The same considerations of the results described earlier without H-ARQ still hold. However, remarkable is the impact of the padding overhead. The latter is comparable with the map overhead, reaching the value of about 0.3. As an effect of the padding overhead, note that the point in which the success probability starts dropping to zero is when the offered load reaches the value of about 0.5 which is actually much less than the value of about 0.75 obtained when H-ARQ is disabled (see Fig. 3.6). The padding overhead also impacts on the carried load (in bytes), which is plotted in Fig. 3.12 against the offered load (in bytes). The results with both seven MCSs and three MCSs are reported. While the curves exhibit a similar trend to the case with H-ARQ disabled (see Fig. 3.8), the quantitative results are much different. Specifically, the maximum value of carried load obtained with H-ARQ is ∼4000 bytes whereas it is ∼5000 bytes when the H-ARQ is disabled. Furthermore, as the offered load increases beyond the value of ∼3000 bytes, the combined effect of the transmission efficiency and the map overhead described in Fig. 3.8 is smoothed by the presence of the padding overhead. Therefore, it is no longer possible to precisely identify the phases in which the curve with seven MCSs lies above the one with three MCSs and vice-versa. We can thus conclude that H-ARQ yields poorer performance, in terms of the carried load, due to the padding overhead.

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Fig. 3.11 FUSC mode with H-ARQ. Carried load (in slots), map overhead, padding overhead, and success probability vs. the offered load (in slots), with 34 × 16 downlink sub-frame size, seven MCSs and data regions randomly passed to the allocation algorithm

Fig. 3.12 FUSC mode with H-ARQ. Carried load (in bytes) vs. offered load (in bytes), with different number of MCSs employed, i.e. seven and three, 34 × 16 downlink sub-frame size, and data regions randomly passed to the allocation algorithm

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Fig. 3.13 FUSC mode with H-ARQ. Carried load (in bytes) vs. offered load (in bytes), with different ordering of data regions depending on their MCSs, i.e. MRF and LRF, 34 × 16 downlink sub-frame size, and seven MCSs employed by the BS

Finally, in Fig. 3.13 we compare the carried load (in bytes) with the MRF and LRF strategies versus the offered load (in bytes). The results obtained are similar to those without H-ARQ, which have been reported in Fig. 3.10. In particular, note that the LRF curve lies above the MRF curve as soon as the offered load becomes greater than ∼3000 bytes.

3.5 Conclusions In this work we have studied the frame allocation problem in IEEE 802.16e with the OFDMA air interface. Through a detailed analysis of the standard we have identified the constraints and requirements that need to be met by the BS, which are related to both complying with the QoS guarantees of the admitted connections and satisfying the MAC and physical layer specifications. Since addressing all the issues together is an overly complex task, the frame allocation problem has been split into three separated sub-tasks, namely grant scheduling, data region arrangement, and data region allocation. A solution for data region arrangement and allocation, called SDRA, is proposed. SDRA is designed to work with both non-H-ARQ and H-ARQ data regions. An extensive performance evaluation of SDRA has been carried out by means of Monte Carlo analysis under varied system and network configuration parameters,

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with both VoIP and BE users. The following conclusions can be drawn from the results obtained. First, the smaller the MAC frame, the smaller the impact of the map overhead on the overall frame capacity, which anyway consumes a significant amount of the available bandwidth (i.e. up to 20%), and therefore the higher the probability that SDRA completely allocates the set of input data regions. Second, employing a reduced set of MCSs, i.e. three instead of the full set of seven MCSs, greatly reduces the map overhead, as well. However, there is a trade-off between the number of slots that are allocated per frame and the number of bytes that can be actually conveyed by them. In fact, lessening the set of available MCSs reduces the average transmission efficiency. Finally, the performance of SDRA has been shown to greatly depend on the order in which data regions are fed to the algorithm. For instance, ordering the data regions in increasing robustness order achieves the highest amount of data actually carried per frame, while ordering the data regions in decreasing robustness order produces the least amount of map overhead. While the same conclusions hold both for non-H-ARQ and H-ARQ data regions, the latter exacerbates the trade-off between transmission efficiency and map overhead.

Appendix A: SDRA Algorithm Pseudo-Codes In this section we report and describe the pseudo-codes of the SDRA algorithm both in the non-H-ARQ and the H-ARQ cases which have been informally introduced in Section 3.3.3.1. Figure 3.14 reports the pseudo-code of SDRA without H-ARQ support. Data structures are initialized by creating an empty map (1) and a list of allocated data regions (2), and by setting the working variables w and h to the dimensions of the downlink sub-frame, respectively width and height (3–4). These variables will keep, at each step of the procedure, the coordinates of the next slot to be allocated. The allocation procedure is performed by placing one of the data regions in list pending at each iteration of the main loop (7). The iteration terminates when either there are no more data regions to be allocated (7) or adding a new IE to the DL-MAP would make the latter overlap with already allocated data regions (12, 23, 32), whichever comes first. The size of the DL-MAP is updated via the add harq region() function. Newly allocated data regions are added to the list allocated data structures, which consists of a list of data region coordinates, expressed in terms of upper-left corner (i.e. starting column and row, respectively, in slots) and dimensions (i.e. width and height, respectively, in slots). After the data region currently under allocation is extracted from the pending list (9), the allocation consists of (up to) three steps:

r

The first step (11–19) allocates a data region upwards starting from h into the remaining portion of the current column, i.e. w. The number of slots allocated is thus equal to h, provided that there are enough slots in the data region, otherwise the whole data region is allocated (e.g. B in Fig. 3.4). This step is skipped if the next slot to be allocated is at the bottom of downlink sub-frame, i.e. h is equal to H.

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Fig. 3.14 Pseudo-code of SDRA, without H-ARQ support

r r

The second step (22–29) allocates a data region that spans over multiple columns (cols) (e.g. the second data region resulting from the allocation of C in Fig. 3.4). This step is skipped if the amount of remaining slots to be allocated for the data region is smaller than a full-height column. The third step (32–36) allocates the remaining portion of the data region, which is by construction smaller than a full-height column (e.g. the third portion of C

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in Fig. 3.4). This step is skipped only if the number of slots of the data region before the second step is a multiple of H. The detailed allocation procedure of H-ARQ data regions is described by means of the pseudo-code in Fig. 3.15. Data structures are initialized by creating an empty map (1) and a list of allocated data regions (2), and by setting the working variable width to zero (3). This variable will keep, at each step, the number of columns allocated so far. Unlike with non-H-ARQ data regions, there is no need to store the number of rows allocated so far, since the size of H-ARQ data regions is enforced to be a multiple of the downlink sub-frame height. The allocation procedure is performed by placing one of the data regions in list pending at each iteration of the main loop (5). The iteration terminates when there are no more data regions to be allocated. The size of the DL-MAP is updated via the add harq region() function (8). As for the non-H-ARQ case, newly allocated data regions are added to the list allocated data structure (12), which consists of a list of data region coordinates, expressed in terms of upper-left corner (i.e. starting column and row, respectively, in slots) and dimensions (i.e. width and height, respectively, in slots). After the data region currently under allocation is extracted from the pending list (6), the number of columns needed to fully allocate it are computed and stored into new w (7). Then, there are two cases:

r

The data region fits into the remaining space, after the size of the DL-MAP has been updated (8). In this case the variable width is updated (11), a new data region is added to list allocated (12), and the current data region is removed from the pending list (13). Allocation then restarts with the next data region in list pending.

Fig. 3.15 Pseudo-code of SDRA, with H-ARQ support

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Otherwise, a sub-burst is removed from the current data region (18) and the allocation procedure restarts. If the sub-burst removed is the only one of the data region, then the latter is removed, as well (19). Note that the size of the DL-MAP must be also restored to its previous value (17) since no data region was allocated in this step.

To conclude this section we now compute the computational complexity of SDRA. With both non-H-ARQ and H-ARQ, the procedure is run as a single iteration, each of which only includes simple constant time operations (e.g. addition to/removal from the head of a list, elementary mathematical operations with integer values). Therefore, we can state that the worst-case computational complexity is O(n), where n is the number of data regions or the number of sub-bursts, respectively in the non-H-ARQ and H-ARQ cases.

Appendix B: Numerical Instance Generation A numerical instance is generated as follows. Starting from an empty list, the input list of data regions is produced iteratively according to the following steps: 1. Initialize the offered load in slots (ol S ) and bytes (ol B ) to 0. 2. Consider a new SDU to be transmitted to a new user. 3. Determine the associated MCS mcs according to a probability distribution reflecting a typical MSs’ deployment scenario [17]. 4. Determine the SDU type (i.e. application) according to the corresponding probability distribution: the SDU is VoIP with probability v, BE with probability 1 – v. 5. Determine the number and size (in bytes) of PDUs used to transmit the SDU: 6. If VoIP was selected at point 3), then only one PDU is needed, of size v; 7. Otherwise, if BE was selected, the number n be of PDUs is drawn from a geometric distribution with average 1/ pbe ; 8. Determine the number and size (in slots) to transmit the above PDUs according to mcs. Each PDU is added to the data region reserved for MCS mcs. Moreover, in H-ARQ enabled snapshots, the PDUs are appended to the list of H-ARQ sub-bursts of data region mcs. 9. Update the offered load, both in bytes and slots. If ol S < tol restart from step 1. 10. As the final step, the set of data regions is sorted according to the ordering approach described above. There are three cases: a) RND: Data regions are permuted according to a random uniform distribution. b) MRF: Data regions are sorted deterministically in decreasing robustness order. c) LRF: Data regions are sorted deterministically in increasing robustness order.

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References 1. IEEE 802.16e-2005 (February 2006), IEEE Standard for Local and metropolitan area networks – Part 16: Air Interface for Fixed Broadband Wireless Access Systems – Amendment 2: Physical and Medium Access Control Layers for Combined Fixed and Mobile Operation in Licensed Bands and Corrigendum 1. 2. IEEE 802.16-2004 (October 2004), IEEE Standard for Local and metropolitan area networks – Part 16: Air Interface for Fixed Broadband Wireless Access Systems. 3. A. Ghosh, D. R. Wolter, J. G. Andrews, and R. Chen, Broadband Wireless Access with WiMax/802.16: Current Performance Benchmarks and Future Potential, IEEE Comm. Mag. 43(2), 129–136 (2005). 4. WiMax forum (June 2006), Mobile WiMax: A technical overview and performance evaluation. 5. H. Holma and A. Toskala, HSDPA/HSUPA for UMTS: High Speed Radio Access for Mobile Communications (John Wiley & Sons, Hoboken, NJ, 2006). 6. A. Bacioccola, C. Cicconetti, A. Erta, L. Lenzini, E. Mingozzi, A Downlink Data Region Allocation Algorithm for IEEE 802.16e OFDMA, Proc. Information, Communications & Signal Processing (ICICS), Singapore (China), Dec. 10–13, 2007. 7. C. Cicconetti, L. Lenzini, E. Mingozzi, and C. Eklund, Quality of Service Support in IEEE 802.16 Networks, IEEE Network (20)2, 50–55 (2006). 8. L. J. Cimini, Analysis and Simulation of a Digital Mobile Channel Using Orthogonal Frequency Division Multiplexing, IEEE Trans. Comm. (33)7, 665–675 (1985). 9. R. Van Nee and R. Prasad, OFDM for Wireless Multimedia Communications (Artech House, Norbrook, MA, 2000). 10. H. Yaghoobi, Scalable OFDMA Physical Layer in IEEE 802.16 WirelessMAN, Intel Tech. J. (8)3, 201–212 (2004). 11. C. Cicconetti, A. Erta, L. Lenzini, and E. Mingozzi, Performance Evaluation of the IEEE 802.16 MAC for QoS Support, IEEE Trans. Mobile Comput. (6)1, 26–38 (2007). 12. M. Shreedhar and G. Varghese, Efficient Fair Queueing Using Deficit Round Robin, IEEE/ACM Trans. Networking, (4)3, 375–385 (1996). 13. D. Stiliadis and A. Varma, Latency-rate Servers: A General Model for Analysis of Traffic Scheduling Algorithms, IEEE/ACM Trans. Networking, (6)5, 675–689 (1998). 14. S. Martello and P. Toth, Knapsack Problems: Algorithms and Computer Implementations (John Wiley & Sons, Hoboken, NJ, 1990). 15. A. M. Law and W. D. Kelton, Simulation Modeling and Analysis (McGraw-Hill, Columbus, OH, 2000). 16. C. Eklund, R. B. Marks, S. Ponnuswamy, K. L. Stanwood, and N. J. M. Van Waes, WirelessMAN: Inside the IEEE 802.16 Standard for Wireless Metropolitan Area Networks (IEEE Press, 2006). 17. J. Moilanen, OFDMA Allocation Numerical Guidelines, Technical Report, 2006 (unpublished). 18. A. Bacioccola, C. Cicconetti, A. Erta, L. Lenzini, and E. Mingozzi, Half Duplex Station Scheduling in IEEE 802.16 Wireless Networks, IEEE Trans. Mobile Comput. (6)12, 1384–1397 (2007).

Chapter 4

Scheduling Techniques for WiMax Aymen Belghith and Loutfi Nuaymi

Abstract This chapter proposes a state-of-the-art of scheduling techniques for WiMax. We first summarize the practical considerations of a WiMax scheduling algorithm in order to make a link between a scheduling algorithm and its implementation in WiMax. Then, we analyze the proposed use of some known algorithms for WiMax and then some scheduling algorithms specifically proposed for WiMax. Finally, we draw a comparison between the different possible scheduling methods and highlight the main points of each of them. Keywords Scheduling · WiMax

4.1 Introduction The Worldwide Interoperability for Microwave Access (WiMax) [1] system is based on the IEEE 802.16-2004 standard [2] and its amendment IEEE 802.16e [3]. The IEEE 802.16 standard defines the physical (PHY) and medium access control (MAC) layer of fixed and mobile broadband wireless access system. The use of the (2–11 GHz) frequency band for WiMax allows this technology to perform a non line of sight (NLOS) propagation. WiMax is a technology that promises high throughput and spectrum efficiency and provides powerful Quality of Service (QoS) support. The QoS support in wireless networks is a difficult task due to the characteristics of the wireless link and, in the case of a multimedia system such as WiMax, the high variability of the traffic. The IEEE 802.16-2004 MAC specifies four scheduling services, also known as QoS classes, in order to fulfil QoS requirements: Unsolicited Grant Service (UGS), real-time Polling Service (rtPS), non-real-time Polling Service (nrtPS), and Best Effort (BE). The IEEE 802.16e MAC added a fifth service class: extended real-time Polling Service (ertPS). The radio resources have to be scheduled according to the QoS requirements. The WiMax/IEEE 802.16 standard does not define a mandatory scheduling algorithm. Only the framework is given in the standard. Therefore, the choice of the A. Belghith (B) TELECOM Bretagne

M. Ma (ed.), Current Technology Developments of WiMax Systems, C Springer Science+Business Media B.V. 2009 DOI 10.1007/978-1-4020-9300-5 4, 

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Fig. 4.1 Packets Scheduling in BS and SS. The uplink scheduler may have different scheduling classes depending on the service type

algorithm is left to the vendor or the operator. The choice of a scheduling algorithm for WiMax/IEEE 802.16 is an open question. There are many known scheduling techniques. There are also scheduling techniques that are specifically proposed for WiMax. The scheduling must be applied to the downlink or the uplink direction in the Base Station (BS). Only uplink scheduling is applied in subscribers stations (SS) (see Fig. 4.1). Since the scheduling is a very active field, we cannot describe all the algorithms proposed for WiMax and then we have selected some of them. The rest of the chapter is organized as follows. Section 4.4.2 presents the considerations to take into account in the design of a scheduling technique. Section 4.4.3 describes some known scheduling methods and the performance evaluation of the deployment of some schedulers in WiMax. Section 4.4.4 presents schedulers specifically proposed for WiMax as well as their performance evaluations. Section 4.5 presents a synthesis of the deployment of different schedulers in WiMax. The conclusion is in Section 4.4.6.

4.2 WiMax practical Scheduling Considerations 4.2.1 The BS Announces its Scheduling Decisions In WiMax, the MAC architecture is centralized at the BS. The BS scheduler is responsible for the whole control access for the different wireless subscribers. In order to indicate the assignment of the downlink and uplink transmission intervals

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Fig. 4.2 Time Division Duplexing (TDD) frame structure in IEEE 802.16

(or bursts) in each frame, the BS transmits the downlink map (DL-MAP) and uplink map (UL-MAP) MAC management messages, respectively. These messages are transmitted at the beginning of the downlink subframe (see Fig. 4.2). When it receives an UL-MAP management message, the SS determines if it can access to the uplink channel during the current frame. Since the SS may have different connections, an uplink scheduler is required in each SS.

4.2.2 The WiMax Scheduling Classes The scheduling algorithm is not specified by the standard. However, the IEEE 802.16-2004 document [2] defines four scheduling service classes in order to fulfil QoS requirements:

r r

r

r

Unsolicited Grant Service (UGS): designed to support real-time applications. The packets have a fixed size and are generated periodically. An UGS connection never requests bandwidth. real-time Polling Service (rtPS): designed to support real-time applications. The packets have a variable size and are generated periodically. An rtPS connection requests bandwidth by responding to unicast polls which are transmitted periodically by the BS. The most important QoS parameters are the minimum reserved traffic rate and the maximum latency. non-real-time Polling Service (nrtPS): designed to support applications that do not have delay requirements. An nrtPS connection requests bandwidth by responding to broadcast polls which are transmitted periodically by the BS. Each nrtPS connection has a minimum reserved traffic rate parameter. This parameter determines the minimum amount of bandwidth to reserve. Best Effort (BE): designed to support applications that have not delay requirements. A BE connection requests bandwidth by responding to broadcast polls which are transmitted periodically by the BS.

The 802.16e [3] added a fifth scheduling service class, called extended real-time Polling Service (ertPS). An ertPS connection benefits from the advantages of both UGS and rtPS connections. This kind of connection is designed to support real-time

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applications. The packets have a variable size and are generated periodically like in an rtPS connection. Unicast grants are provided in an unsolicited manner like in an UGS connection. However, the ertPS allocations are dynamic and the BS must not change the size of the allocations until receiving a new bandwidth change request from the subscribers.

4.2.3 Link Adaptation and Scheduling In the IEEE 802.16 standard, the frame has a fixed number of OFDM symbols. The number of symbols depends on some parameters such as the frequency bandwidth and Cyclic Prefix (CP). However, the number of useful data bits is variable and depends on the used Modulation Coding Scheme (MCS). The MCS to be used is defined by the link adaptation procedure. The choice of the appropriate MCS depends on the value of the receiver Signal-to-Noise Ratio (SNR). The IEEE 802.16 standard proposes some thresholds of the SNR (see Table 4.1) only as indicative values. For example, when the SNR is equal to 7.0 dB, the station uses QPSK 1/2. The different MCS that are defined by the standard are the following: BPSK 1/2, QPSK 1/2, QPSK 3/4, 16QAM 1/2, 16QAM 3/4, 64QAM 1/2, and 64QAM 3/4. In general, the subscriber switches to a more energy efficient MCS if the SNR is good. The SS can also switch to a more robust MCS if the SNR is poor. Once the MCS is defined, the number of bits per symbol, and then the useful number of bits per frame, can be computed. Therefore, the BS must take into account the link adaptation in its scheduling considerations. Table 4.1 Receiver SNR thresholds (values proposed by the IEEE 802.16e standard) Modulation

Coding

Receiver SNR (dB)

BPSK QPSK QPSK 16-QAM 16-QAM 64-QAM 64-QAM

1/2 1/2 3/4 1/2 3/4 2/3 3/4

3.0 6.0 8.5 11.5 15.0 19.0 21.0

4.3 Well-Known Scheduling Methods Some well-know scheduling methods are presented in this section. The list of the presented schedulers is not exhaustive. It contains the most used schedulers such as the Round Robin (RR), Maximum Signal-to-Interference (mSIR), Prorate, Weighted Round Robin (WRR), and Deficit Round Robin (DRR) schedulers.

4.3.1 Round Robin (RR) Scheduler The Round Robin scheduler, also called cyclic scheduler, equitably distributes the channel resources to the multiplexed packet data calls. This technique is suitable if

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the subscribers have the same traffic and radio characteristics. Indeed, the radio characteristics permit the determination of the Modulation and Coding Scheme (MCS) to use. Therefore, when all the subscribers use the same MCS and have the same traffic, they need the same resources and then the RR scheduler is suitable in these conditions. Nevertheless, these conditions are generally not applicable in a WiMax context.

4.3.2 Maximum Signal-to-Interference (mSIR) Scheduler The maximum Signal-to-Interference (mSIR) scheduler allocates the radio resources to subscriber stations (SS) having the highest Signal-to-Interference Rate (SIR). Then, this scheduler offers high spectrum efficiency. Nevertheless, subscribers having a SIR that is always small may never be served.

4.3.3 Prorate Scheduler The resources allocation in the Prorate scheduler depends on the number of symbols that are required by the different connections. The allocated portion of symbols (of the total number of available symbols) for the connection i is equal to the number of required symbols by the connection i divided by the total number of symbols required by all the connections. The higher the demand of a subscriber is, the more symbols are allocated to this subscriber. However, there are no considerations for the SIR or the MCS used.

4.3.4 Weighted Round Robin (WRR) Scheduler The weighted round robin scheduler is an extension of Round Robin scheduler based on the static weight. An example of WRR algorithm execution is represented in Fig. 4.3. In this example, there are:

r r

Three queues: A, B, and C. The weight of the queues A, B, and C are equal to 2, 1, and 3, respectively.

Fig. 4.3 Packets queues for Weighted Round Robin (WRR) scheduling algorithm

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In WiMax, the connections have different QoS parameters and the subscribers use different MCS. In another hand, the subscribers have not generally the same traffic. Therefore, the connections do not need the same resources. The use of the WRR scheduler can be suitable for WiMax because different values of weights can be assigned to the different queues to take into account the different requested resources.

4.3.5 Deficit Round Robin (DRR) Scheduler The Deficit Round Robin scheduler associates a fixed quantum (Q i) and a deficit counter (DC i) with each flow i. At the start of each round, DC i is incremented by Q i for each flow i. The head of the queue Queue i is eligible to be dequeued if DC i is greater than the length of the packet waiting to be sent (L i). In this case, DC i is decremented by L i. At each round, one packet at most can be dequeued for each flow. An example of DRR algorithm execution is represented in Fig. 4.4. In this example, there are three queues: A, B, and C:

r r r

A contains three packets: a1 (200), a2 (750), and a3 (280). B contains two packets: b1 (500), and b2 (300). C contains four packets: c1 (100), c2 (900), c3 (300), c4 (250), and c5 (900).

Fig. 4.4 Packets queues for Deficit Round Robin (DRR) scheduling algorithm

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The DDR scheduler requires a minimum rate to be reserved for each packet flow before being scheduled. This characteristic can be useful in WiMax because the subscribers usually require an allocation of a minimum of resources.

4.3.6 DRR and WRR Schedulers Evaluated for WiMax 4.3.6.1 Choice of Schedulers for the BS and the SS Three schedulers must be chosen for the BS and SS; an uplink and downlink schedulers for the BS and an uplink scheduler for the SS. Authors in [4] make the following choice. To use the DRR scheduler, a station must have the knowledge of the packet size of each queue. Since the BS and SS have all the information about their downlink and uplink queues, respectively, they can use the DRR scheduler as a downlink and uplink schedulers, respectively. It remains to choose the uplink scheduler in the BS because the BS does not have the knowledge of the packet length of the different subscribers. However, the BS can estimate the amount of backlog of each connection through the bandwidth requests. Then, the WRR scheduler can be selected as the uplink scheduler in the BS (see Fig. 4.5).

Fig. 4.5 Use of DRR and WRR schedulers

4.3.6.2 Performance Evaluation The performance of IEEE 802.16 is analyzed by simulation in [4]. The main parameters of the simulation are the following: the frequency band is 2–11 GHz, the air interference is the WirelessMAN-OFDM (using orthogonal frequency-division multiplexing), the channel bandwidth is 7 MHz, the duplexing mode is Frequency Division Duplexing (FDD), and the frame duration is 10 ms.

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The metrics to evaluate are: Maximum achievable throughput: represents the maximum amount of data that a station can send successfully. Packet-transfer delay: represents the time between the arrival of the packet at the MAC layer of the source of the traffic and the time of the arrival of this packet at the upper layer of the destination of the traffic. Delay variation: represents the difference between the maximum packet-transfer delay and the packet transmission delay.

Two scenarios are considered: residential scenario and Small and Medium-sized Enterprises (SME) scenario. In the first scenario, the BS provides Internet access to SS by means of BE (Best Effort) connections. The results of the average delay and maximum achievable throughput depending on the offered load and number of subscribers are represented in Table 4.2. In the second scenario, the BS provides three types of services:

r r r

Voice over Internet Protocol (VoIP) service: each station has four VoIP sources multiplexed into an rtPS connection. Videoconference service: each station has two videoconference sources multiplexed into an nrtPS connection. Data service: each station has a data source provided by a BE connection. Table 4.2 Performance evaluation of the residential and SME scenario

Metric/Results Residential scenario: Average delay

Results for downlink traffic

r Constant at low cell load r Sharply increase at high cell load

r Downlink delay < uplink delay

Residential scenario: Maximum achievable throughput

Small and Medium-sized Enterprises scenario: Average delay

Small and Medium-sized Enterprises scenario: Delay variation

r Decrease when number of SS increases (because BS transmits higher MAC overhead)

r Constant for voIP service r Smoothly increase at low

Results for uplink traffic

r Constant at low cell load. r Sharply increase at high cell load.

r Uplink delay > downlink delay

r Decrease when number of SS increases (because SSs transmit higher number of physical preambles)

r Constant for voIP service r Smoothly increase at low

cell load for videoconfercell load for videoconference and data service ence and data service r Sharply increase at high r Sharply increase at high cell load for videoconfercell load for videoconference and data service ence and data service

r Delay variation of VoIP

service = delay variation of videoconference service

Downlink scheduler is DRR and uplink scheduler is WRR.

r Delay variation of VoIP

service < delay variation of videoconference service

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The BS, periodically, grants a unicast poll to each VoIP and videoconference services in order to allow SS to send bandwidth request. The results of the average delay and delay variation depending on the offered load, number of subscribers, and service types are represented in Table 4.2.

4.4 Schedulers Specially Proposed for WiMax 4.4.1 Temporary Removal Scheduler (TRS) The temporary removal scheduler (TRS) [5] is described as follows. The TRS identifies the packet calls under power radio conditions. These packet calls are temporarily removed from the scheduling list for a certain adjustable time period TR . The scheduling list contains all the SSs that can be served at the next frame. If TR expires, the temporarily removed packet is checked again. If the radio conditions are still poor, this packet is temporarily removed for another time period TR . The whole process is repeated up to L times. When the packet is removed for a period of L × TR , it is included in the scheduling list independently of the current radio conditions. Then, a penalty time TP prevents the packet call from being immediately selected once more. If an improvement is observed in the radio channel, the packet could be topped up in the scheduling list again. The temporary removal scheduling can be combined with a common scheduler. It can be combined with the RR, and mSIR schedulers. 4.4.1.1 Temporary Removal Scheduler + Round Robin The temporary TRS can be combined with the RR scheduler. The combined scheduler is called TRS+RR. For example, if there are k packet calls and only one of them is temporary removed, each packet call has a portion, equal to 1/(k − 1), of the whole channel resources. 4.4.1.2 Temporary Removal Scheduler + Maximum Signal to Interference Ratio The TRS can be combined with the mSIR scheduler. The combined scheduler is called TRS+mSIR. This scheduler assigns the whole channel resources to the packet call that has the maximum value of the Signal to Noise Ration (SNR). The station to be served has to belong to the scheduling list. 4.4.1.3 Performance Comparison The performance analysis TRS applied to WiMax is performed by simulation. The main parameters of the simulation are the following: the frequency band is 3.5 GHz, the air interference is the WirelessMAN-OFDM, the channel bandwidth is 3.5 MHz, and the frame duration is 2 ms. The schedulers to evaluate are the following: the RR,

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mSIR, TRS+RR, and TRS+mSIR schedulers. The results for File Transfer Protocol (FTP) 300 kByte download are represented below:

r r r r

The mSIR and TRS+mSIR schedulers provide the highest throughput whereas RR provides the worst results. The mSIR, TRS+mSIR, and TRS+RR schedulers provide the lowest download time whereas the RR scheduler provides the worst results. The TRS+mSIR and TRS+RR schedulers provide the lowest channel utilization whereas the RR and mSIR schedulers provide the worst results. The TRS+mSIR and TRS+RR schedulers provide the lowest packet call blocking whereas the RR and mSIR schedulers provide the worst results.

4.4.2 Opportunistic Deficit Round Robin (O-DRR) Scheduler 4.4.2.1 O-DRR Scheduler Description In [6], the Opportunistic Deficit Round Robin (O-DRR) scheduler is used as an uplink scheduler. The O-DRR scheduler works as follows. The BS polls all the subscribers periodically, every k frames. After each period, called a scheduling epoch, the BS determines the set of subscribers that are eligible to transmit as well as their bandwidth requirements. This set is called eligible set. A subscriber is eligible to transmit when:

r r

the subscriber has a non empty queue, and, the signal-to-interfernece-plus-noise ratio (SINR) of its wireless link is above a minimum threshold, called SINRth . A scheduled subscriber is a subscriber that:

r r

the subscriber is eligible at the start of the current scheduling epoch, and, the subscriber is eligible during a given frame of the current scheduling epoch.

The scheduled set is changed dynamically. This changing depends on the wireless link state of each eligible subscriber. At the beginning of a new scheduling epoch, the BS resets the eligible and scheduled set and performs the above process again. 4.4.2.2 Determination of the Polling Interval k The BS polls all the subscribers every k frames. A low value of k causes a polling overhead; thus, the efficiency will be low. Furthermore, a high value of k causes an unfair traffic and a non satisfaction of QoS requirements. Therefore, the BS objective is to minimize the worst-case relative fairness in bandwidth and the normalized delay.

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4.4.2.3 Bandwidth Assignment Using the O-DRR Scheduler It is considered that the BS knows the SINR of each channel. During a scheduling epoch, if the SINR of the wireless link of the subscriber i is above SINRth , then:

r r r

The quantum Qi of the subscriber i is distributed among the scheduled subscribers. The lead/lag counter of the subscriber i is incremented by Qi . The lead/lag counter of the scheduled subscriber j is decremented by the amount that the subscriber j receives above its quantum Qj .

4.4.2.4 Performance Evaluation of the O-DRR Scheduler The results of the number of slots assigned depending on the number of subscribers and k value show that the number of slots assignment increases when k increases. In fact, when k increases, the SINR becomes more variable for the different subscribers and the lead/lag counter has more influence on the bandwidth assignment. The results of the fairness in bandwidth using Jain’s Fairness Index [7] depending on the number of subscribers and k value show that Jain’s Fairness Index remains above 90%. This gives more choice to the provider to choose an appropriate value of k at which the fairness and the bandwidth requirements are both satisfied.

4.4.3 Uplink Packet Scheduler with Call Admission Control (CAC) Mechanism In [8], an uplink packet scheduler with Call Admission Control (CAC) is proposed. The CAC mechanism is based on the token bucket principle. The token bucket is a mechanism used to control network traffic rate. The uplink packet scheduler algorithm works as follows (see Fig. 4.6). First, All the UGS connections are granted. Then, the CAC is applied to the rtPS packets. The different deadlines of these packets are computed. Once the deadlines are determined, the Earliest Deadline First (EDF) scheduler is applied for the attribution of the priorities for the rtPS connections. The EDF scheduler attributes priorities to different packets according to their deadlines. The closer is the deadline of a packet; the higher is its priority. After the allocation of resources for the UGS and the the rtPS connections, resources for the nrtPS connections are allocated if there are remaining bandwidth and the bandwidth requirements of these connections are below a threshold, called TnrtPS . Then, resources for the BE connections are allocated if there are remaining bandwidth and the bandwidth requirements of the BE connections are below a threshold, called TBE . If there are still remaining bandwidth, the nrtPS connections and then BE connections are granted until the use of the whole available bandwidth. The simulation is used to validate the CAC. The main parameters are the following: the frame duration is 1 ms, the size of the bandwidth request is 48 bits, the size

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Fig. 4.6 Main steps of the uplink packet scheduler with CAC

of the UGS, rtPS, nrtPS, and BE packets are 64 bits, 256 bits, 256 bits, and 128 bits, respectively, and the number of flows are 1000. The simulation results show that the proposed uplink packet scheduler can receive a high number of rtPS connections and guarantee their delay requirements.

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4.4.4 Cross-layer Scheduling Algorithm with QoS Support 4.4.4.1 Cross-Layer Scheduler Description In [9], a novel scheduling algorithm is proposed for WiMax networks. This scheduler is based on affecting a priority to each connection. For the UGS connections, the scheduler must guarantee a fixed quantum of radio resources. The UGS connections are characterized by a constant number of time slots allocated. Therefore, the transmission mode is selected and remains the same during the whole service time. For the rtPS and nrtPS connections, the scheduler must guarantee the latency and the minimum reserved rate respectively. For the BE connections, there is no QoS guarantee but a Packet Error Rate (PER) should be maintained. After serving all the UGS connections, the scheduler allocates all the residual time slots to the rtPS, nrtPS, then BE connections that have the maximum value of a defined Priority Function (PFR). The PRFs, for the rtPS, nrtPS, and BE connections consequently depends on the delay satisfaction indicator, the rate of the average transmission rate over the minimum reserved rate, and the normalized channel quality. The PRFs details are presented in [9]. 4.4.4.2 Performance Evaluation When there is sufficient available bandwidth, the simulation results show that the delay outage probabilities of the rtPS connections are always below 5%. Therefore the latency constraints are guaranteed. The results also show that the average reserved rate of each nrtPS connection is greater than its minimum reserved rate. However, the average transmission rates of the BE connections have large variations and sometimes are null. This behaviour is expected since there is no guarantees for the BE connections. When the residual slots decrease, the performance of the BE connections (then the nrtPS connections) degrades. This is due to the insufficient available bandwidth and the fact that the nrtPS connections have higher priority than the BE connections.

4.4.5 Hybrid Scheduling Algorithm 4.4.5.1 Hybrid Scheduler Description The hybrid scheduling algorithm for QoS in WiMax in [10] works as follows. The Earliest Due Date (EDD) scheduler is used for the real time services while the Weighted Fair Queue (WFQ) scheduler is used for the non-real time services. This is then a hybrid scheduling algorithm. The EDD scheduler is based on dynamic priority. In an EDD queue, the packets are classified in order of their deadline values. The expected deadline time of a packet is calculated by adding the packet arrival time and maximum service time of this packet.

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The WFQ scheduler provides a required throughput rate for each service. The delay in WFQ for a service is computed as follows: DWFQ =

 i=1..n

wi

 (R ∗ wi )

Where:

r r r

n: represents the number of services. wi : represents the weight given to the queue i. R: represents the link transmission rate.

In [11], the same hybrid scheduler is proposed; the EDD scheduler is used for the UGS QoS class and the WFQ scheduler is used for nrtPS and BE QoS classes. The only difference is that the nrtPS QoS class has more priority than that of the BE QoS class. 4.4.5.2 Performance Evaluation In [10], the hybrid scheduler is compared with the EDD scheduler employed for the real and non-real time services. The simulation results show that the hybrid scheduler provides, for real time services, less delay than the EDD scheduler. This is due to the competition of the non-real time packets for channel access when the EDD scheduler is used. However, the non-real packets wait for more time when the hybrid scheduler is used. Since the real time services have delay requirements, it is recommended to use the hybrid scheduler in WiMax. In [11], the number of contention slots is investigated using the same hybrid scheduler. The contention slots are used by the SSs to send their bandwidth requests through contention. The simulation results show that the throughput increases when the number of contention slots increases without exceeding the half BE connections. This is due to the decrease of the probability of bandwidth request collisions. If the number of contention slots is grater than the half of the BE connections, the throughput decreases when the number of contention slots increases. This is because less radio resources are reserved for the data transmission.

4.4.6 Frame Registry Tree Scheduler (FRTS) 4.4.6.1 Frame Registry Tree Scheduler (FRTS) Description The Frame Registry Tree Scheduler (FRTS) scheduler [12] contains three operations: packet/request arrival, frame creation, and subscriber’s modulation type change or connection QoS service change. The basic idea of the packet/request operation is to distribute packet transmissions in time frames, based on their deadline. For UGS and rtPS services, the packet deadline is equal to the arrival time plus

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the latency of this packet. The subtree of the last time frame where this packet can be transmitted is updated, if it exists. Otherwise, it is created. For nrtPS and BE services, the packet deadline does not need to be calculated. Then, the subtree of the last existing time frame is updated. The frame creation procedure decides on the frame contents. There are three cases:

r r r

If the subtree of the first time frame contains a number of packets equal to one time frame, all these packets fill up the frame content. If the subtree of the first time frame contains a number of packets less than one time frame, the empty slots are occupied by packets from the next time frame subtrees and/or will be left for contention. If the subtree of the first time frame contains a number of packets more than one time frame, packets for BE service are moved to the next time frame subtree. If there are still excess packets to transmit, first nrtPS packets, then rtPS packets and finally UGS packets are deleted until the number of packets fit exactly into one time frame.

A change in a subscriber’s modulation type or connection QoS service causes a moving of the corresponding subtree to the right modulation substructure or service substructure. 4.4.6.2 Performance Evaluation The FRTS scheduler is compared with a simple scheduler that serves higher priority before lower priority traffic (UGS has the highest priority, then rtPS, nrtPS, and finally BE). Simulation results show that the FRTS scheduler provides better throughput. This is due to the less lost packets. Indeed, FRTS takes into account the deadline of the real time packets (UGS and rtPS). Simulations results also show that the FRTS scheduler can serve nrtPS and BE connections even if the load is high. This is because this scheduler profits from the latency tolerance of the real time packets.

4.4.7 Adaptive rtPS Scheduler 4.4.7.1 Adaptive rtPS Scheduler Description The adaptive rtPS scheduler [13] is used only for the rtPS QoS class. It is based on the prediction of the rtPS packets arrival. As defined in the IEEE 802.16 standard, the BS allocates bandwidth for rtPS traffic after receiving a bandwidth request. When the request is granted by the BS, the subscriber may receive from upper layers new rtPS packets. These packets will wait for the next grant to be sent and, therefore, suffer from extra delay. The basic idea of the adaptive rtPS scheduler is to propose an rtPS bandwidth request process in which the subscriber requests time slots for the data present in the rtPS queue and also for the data which will arrive. The authors of [13] define a stochastic prediction algorithm in order to estimate the data arrival.

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4.4.7.2 Performance Evaluation The adaptive rtPS scheduler is compared with the weighted scheduler. The simulation results show that the adaptive rtPS scheduler provides better average delay in low and medium load. This is because this scheduler also considers the data generated between the time of the bandwidth request sending and the time of the bandwidth allocation by the BS. Therefore, the adaptive rtPs scheduler requires less buffer size for the rtPS data queue. In high load, the adaptive rtPS and weighted scheduler have the same performance. This is due to the saturation of the network.

4.4.8 Scheduler Ensuring QoS Requirements 4.4.8.1 Description of the Scheduling Proposal In [14], a simple scheduler that ensures the QoS requirements for the different QoS service classes is proposed. This scheduler consists of the allocation of the minimum bandwidth requirements to all the connections and then the allocation of free slots to the rtPS, nrtPS, and BE connections without exceeding their maximum bandwidth requirements (see Fig. 4.7). The number of slots to allocate to the UGS and ertPS is constant since the minimum and maximum bandwidth requirements of these connections are the same. The minimum and maximum number of slots to allocate to the rtPS and nrtPS connexions depends on the minimum and maximum bandwidth requirements, respectively. Since BE connection has no QoS requirements, its minimum number of slots to allocate is null and its maximum number depends on its bandwidth request. After satisfying the minimum bandwidth requirements of all the connections, the unused slots are allocated to the rtPS and nrtPS connections and then distributed between the BE connections.

4.4.8.2 Performance Evaluation When the system contains different type of QoS service classes, the simulations results show that the proposed scheduler allocates the whole requested bandwidth of the UGS and erTPS connections, provides a bandwidth greater than the minimum bandwidth requirements of the rtPS and nrtPS connections and the remaining bandwidth is allocated to the BE connections. The simulations results show that the proposed scheduler fairly distributes the bandwidth between the different BE connections. The simulation results also show that the throughput decreases when the number of the served connections increases. This is due to the increase of the overhead size. Despite of the change of the MCS used and therefore the number of slots to reserve, the proposed scheduler provides the bandwidth requirements.

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Fig. 4.7 Main steps of a scheduler ensuring QoS requirements

4.4.9 Adaptive Bandwidth Allocation Scheme (ABAS) 4.4.9.1 Adaptive Bandwidth Allocation Scheme (ABAS) Description An Adaptive Bandwidth Allocation Scheme (ABAS) for 802.16 TDD systems is defined in [15]. It aims to dynamically determine the suitable downlink-to-uplink bandwidth ratio. In fact, the IEEE 802.16 standard specifies the frame structure but it is up to operators to choose the downlink-to-uplink bandwidth ratio. ABAS

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performs as follows. The BS determines the different information of the downlink and uplink connections such as their bandwidth requests and the number of downlink and uplink connections. Then, the BS determines the number of slots allocated to the downlink and uplink subframe and adjusts the split between the two parts of the frame. Finally, the BS informs the different SSs about its decision using DLMAP and UL-MAP MAC management messages. The mechanism is repeated at the beginning of each frame. 4.4.9.2 Performance Evaluation ABAS is compared with a static downlink to uplink bandwidth ratio mechanism. Only Transmission Control Protocol (TCP) transfers and BE QoS service class are considered. The results of the throughput of the downlink connections depending on the number of TCP transfers show that ABAS provides better throughput. This is because that ABAS determines the most appropriate value of the downlink to uplink bandwidth ratio. On the other hand, the static downlink to uplink bandwidth ratio mechanism can degrade the throughput if the initial static ratio is not suitable with the traffic characteristics. Moreover, the number of subscribers in the system as well as their connections characteristics can change every time. The results of the throughput of the downlink and uplink connections depending on the ratio of downloading to uploading TCP transfers show that ABAS also provides better throughput. This is due to the tacking into account the traffic characteristics in the determination of the suitable value of the downlink to uplink bandwidth ratio.

4.4.10 Adaptive Polling Service (aPS) 4.4.10.1 Adaptive Polling Service (aPS) Description A novel adaptive Polling Service (aPS) for WiMax system is defined in [16]. The main idea of the proposed mechanism is to adjust the polling period based on the reception of bandwidth request. The BS initializes the polling period with Tmin . Tmin is determined using the average packet arrival rate. If the BS does not receive bandwidth request after N polls, it exponentially increases the polling period until reaching Tmax . Tmax is determined using the tolerable delay of the connection since the aPS mechanism is defined for real-time applications. When the BS receives a bandwidth request, it resets the polling period to Tmin . 4.4.10.2 Performance Evaluation The aPS mechanism is compared with the rtPS mechanism defined in the IEEE 802.16 standard. The simulation results of an ON/OFF TCP traffic show that the aPS mechanism reduces the signalling overhead by 50% to 66%. This reduction comes from the increase of the polling period during the OFF periods. On the other

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hand, the aPS mechanism provides higher delay than the rtPS mechanism. However, the delay is still acceptable for almost of the applications. The simulations results of a TCP-based application show that the aPS mechanism reduces the signalling overhead by 66%. Like for the ON/OFF traffic, the increase of the delay is still acceptable. The simulation results of an online game application working over User Datagram Protocol (UDP) shows that the aPS mechanism reduces the signalling overhead by 50–75%. However, the delay increases and becomes not suitable with all the applications. The increase of delay depends on the parameters of the aPS mechanism. In the different scenarios, the authors investigates only Tmax and show that when Tmax increases, the reduction of the signalling overhead and delay increase. Then, we can choose the suitable value of Tmax depending on the tolerate delay.

4.4.11 Modified Maximum Signal-to-Interference Ratio (mmSIR) Scheduler 4.4.11.1 Modified Maximum Signal-to-Interference Ratio (mmSIR) Scheduler Description In [17], a problem that may exist with the rtPS QoS class is highlighted. If the BS allocates unicast request opportunities and resource grants for rtPS connections in the same frame, the BS cannot immediately take into account the new length of the uplink data connection of the subscriber. The reason is that the BS allocates symbols for rtPS connections before receiving the latest unicast bandwidth request (see Fig. 4.8). Moreover, the mSIR scheduler serves those subscribers having the highest SIR at each frame. So, subscribers having a slightly smaller SIR may be not served and then the mean delay to deliver data increases. The proposed scheduler is based on the

Fig. 4.8 Allocation of symbols for rtPS connection

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Fig. 4.9 Main steps of the proposed mmSIR scheduler

modification the mSIR scheduler in order to decrease the mean time of sojourn and called modified maximum Signal-to-Interference Ratio (mmSIR) scheduler. The main idea of the mmSIR scheduler is that the BS only serves the subscribers that do not have unicast request opportunities in the same frame. The main steps of this proposed scheduler are shown in Fig. 4.9. 4.4.11.2 Performance Evaluation The mmSIR scheduler is compared with the mSIR scheduler. Simulation results show that the mmSIR provides a decrease in the mean sojourn time. This is mainly due to the non freezing of the SSs having a small SIR. Indeed, the BS serves these SSs when it has already allocated unicast request opportunities to SSs having a higher SIR. The simulation results also show that the mmSIR scheduler provides better throughput. Indeed, the mmSIR scheduler, like the mSIR scheduler, favors those

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SSs having the highest SIR. If it does not serve an SS having unicast request opportunities, it gives priority to other SSs having higher SIR. Furthermore, the mSIR scheduler cannot immediately benefit from the unicast request opportunities of SSs since it has already reserved resources for rtPS connections before receiving the bandwidth requests. Moreover, the mmSIR scheduler serves fewer SSs than the mSIR scheduler. As a preamble is added to each uplink burst (see Section 8.3.5.1 of Ref. [2]), the BS schedules more useful symbols when it serves fewer SSs per frame.

4.5 Synthesis of Different Schedulers Deployed in WiMax After presenting different scheduling methods as well as their performance evaluations, a synthesis of deployment of different schedulers is presented in Table 4.3. If the BS can use a scheduler in downlink traffic, the SS can use the same kind of scheduler in the uplink direction between its connections. In fact, in the two situations, the queues contents are well known. Therefore, the synthesis table contains only the downlink and uplink schedulers in the BS (and not the uplink scheduler in the SS). Further to this synthesis, we can identify some schedulers that can be used for the downlink or/and the uplink traffic. The RR, mSIR, and Prorate scheduler are not suitable to use in a WiMax context for most of the cases. Indeed, the RR scheduler is only suitable for subscribers that have the same characteristics such as the SNR and the traffic load. The mSIR and Prorate schedulers may block the traffic that is generated by subscribers having a poor SIR value or less data, respectively. The DRR scheduler can be used only in the downlink traffic because the BS does not know the queue length of the subscribers. This prevents the BS to perform the DRR scheduler for the uplink traffic. The CAC is performed to control the connections that are transmitted by the subscribers. So, the uplink scheduler with CAC mechanism is only proposed for the uplink traffic. The WRR, TRS, O-DRR, cross-layer, hybrid, and FRTS schedulers can be used in the downlink and uplink traffic. An important research topic is the performance evaluation intended to determine adequate values to the different parameters of these schedulers. A use of the WRR or the hybrid (EDD+WFQ) schedulers requires the determination of the suitable weights. The removal time, penalty time, and maximum number of repetitions are the main parameters of the TRS scheduler. Different values of these parameters completely change the behaviour and performance of the TRS scheduler. For the O-DRR scheduler, the polling interval and minimum threshold of the SINR parameters have an immediate impact on the delay and throughput, respectively. The adaptive rtPS and mmSIR are proposed for the rtPS QoS class in the uplink direction. The parameters of the stochastic algorithm defined for the adaptive rtPS scheduler.

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Scheduler

Possibility of use for WiMax

Comments

Algorithm parameters

Round Robin

No

–

Maximum SIR

Usually not

Prorate

Usually not

Not suitable. Subscribers do not have the same traffic, radio characteristics, and QoS requirements. Subscribers having a poor SIR may be scheduled after an excessive delay. Subscribers, having less data, may be scheduled after an excessive delay. Can be used for the downlink and uplink traffic. Can be used only for the downlink traffic; BS does not know the packet sizes at the SS queues. Can be used for the downlink and uplink traffic.

Weighted Round Yes Robin Deficit Round Robin Yes

Temporary Removal Scheduler

Yes

Opportunistic Deficit Yes Round Robin

Can be used for the downlink and uplink traffic.

Uplink scheduler with CAC mechanism

Yes

Was proposed for the uplink traffic.

Cross-layer

Yes

Hybrid (EDD+WFQ) Frame Registry Tree Scheduler Adaptive rtPS

Yes

Yes

Modified mSIR

Yes

Can be used for the downlink and uplink traffic. Can be used for the downlink and uplink traffic. Can be used for the downlink and uplink traffic. Was proposed for rtPS QoS class in the uplink direction. Was proposed for rtPS QoS class in the uplink direction.

Yes

–

–

Static weights. Fixed quantum.

Removal time (TR ), number of repetitions (L), and penalty time (TP ). Polling interval, and minimum threshold of the SINR (SINRth ). Parameters of token bucket, thresholds of the AC used for the nrtPS and BE connections (TnrtPS and TBE ). – Weights for WFQ scheduler. – Stochastic prediction algorithm. –

4.6 Conclusion In this chapter, we propose a state-of-the-art of WiMax Scheduling. We first present the scheduling framework of WiMax and then describe different proposed trends for WiMax scheduling algorithms. The choice of the scheduling algorithm is highly dependent on the transmission service type and the traffic shape in addition to other

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QoS requirements. We propose a synthesis table for some of the proposed scheduling algorithms. Evidently, all the proposed WiMax scheduling algorithms could not be studied in this chapter. The efficiency of a scheduling algorithm can be estimated through simulations. We propose an NS-2 (Network Simulator) Module for WiMax scheduling in [18]. The details of our WiMax module are presented in [19].

References 1. WiMax forum, http://www.wimaxforum.org/home/, last visited in 17-07-2008. 2. IEEE Std 802.16-2004, Part 16: Air interface for Fixed Broadband Wireless Access Systems, 1 October 2004. 3. IEEE Std 802.16e, Part 16: Air interface for Fixed and Mobile Broadband Wireless Access Systems, 28 February 2006. 4. C. Cicconetti, L. Lenzini, E. Mingozzi, C. Eklund, “Quality of Service in IEEE 802.16 Networks”, IEEE Network – Special Issue on Multimedia Over Broadband Wireless Network, Vol. 20, No. 2, pp. 50–55, March/April 2006. 5. C.F. Ball, F. Treml, X. Gaube, A. Klein, “Performance Analysis of Temporary Removal Scheduling applied to mobile WiMax Scenarios in Tight Frequency Reuse”, The 16th Annual IEEE International Symposium on Personal Indoor and Mobile Radio Communications, PIMRC’2005, Berlin, 11–14 September 2005. 6. H. K. Rath, A. Bhorkar, V. Sharma, “An Opportunistic DRR (O-DRR) Uplink Scheduling Scheme for IEEE 802.16-based Broadband Wireless Networks”, IETE, International Conference on Next Generation Networks (ICNGN), Mumbai, 9 February 2006. 7. R. Jain, D. Chiu, W. Hawe, “A quantitative Measure Of Fairness And Discrimination For Resource Allocation In Shared Computer Systems”, DEC Research Report TR-301, September 1984. 8. T. Tsai, C. Jiang, C. Wang, “CAC and Packet scheduling Using Token Bucket for IEEE 802.16 Networks”, Journal of Communications, Vol. 1, No. 2, May 2006. 9. Q. Liu, X. Wang, G. B. Giannakis, A. Ramamoorthly, “A Cross-Layer Scheduling Algorithm With QoS Support in Wireless Networks”, IEEE Transactions on Vehicular Technology, Vol. 55, No. 3, May 2006. 10. K. Vinay, N. Sreenivasulu, D. Jayaram, D. Das, “Performance Evaluation of End-to-end Delay by Hybrid Scheduling Algorithm for QoS in IEEE 802.16 Network”, Wireless and Optical Communications Networks, 2006 IFIP International Conference on, 11–13 April 2006. 11. D. Tarchi, R. Fantacci, and M. Bardazzi, “Quality of Service Management in IEEE 802.16 Wireless Metropolitan Area Network”, International Conference Communications, ICC’06, Istanbul, Turkey, 11–15 June 2006. 12. S. A. Xergias, N. Passas, and L. Marekos, “Flexible Resource Allocation in IEEE 802.16 Wireless Metropolitan Area Networks”, the 14th IEEE Workshop on Local and Metropolitan Area Networks, LANMAN 2005, Chania, Greece, 18–21 September 2005. 13. R. Mukul, P. Singh, D. Jayaram, D. Das, N. Sreenivasulu, K. Vinay, and A. Ramamoorthly, “An Adaptive Bandwidth Request Mechanism for QoS Enhancement in WiMax Real Time Communication”, Wireless and Optical Communications Networks, 2006 IFIP International Conference on, Bangalore, India, 11–13 April 2006. 14. A. Sayenko, O. Alanen, J. Karhila, and T. H¨am¨al¨ainen, “Ensuring the QoS Requirements in 802.16 Scheduling”, the 9th ACM international symposium on Modeling analysis and simulation of wireless and mobile systems, MSWiM 06, Malaga, Spain, 2–6 October 2006. 15. C. Chiang, W. Liao, and T. Liu, “Adaptive Downlink/Uplink Bandwidth Allocation in IEEE 802.16 (WiMax) Wireless Networks: A Cross-Layer Approach”, Global Telecommunications Conference, 2007, GLOBECOM 07, Washington, USA, 26–30 November 2007.

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16. C. Nie, M. Venkatachalam, X. Yang, “Adaptive Polling Service for Next-Generation IEEE 802.16 WiMax Networks”, Global Telecommunications Conference, 2007, GLOBECOM 07, Washington, USA, 26–30 November 2007. 17. A. Belghith, L. Nuaymi, “Comparison of WiMax scheduling algorithms and proposals for the rtPS QoS class”, 14th European Wireless 2008, EW2008, Prague, Czech Republic, 22–25 June 2008. 18. Design and implementation of a QoS included WiMax module for NS-2 simulator: https://perso.enst-bretagne.fr/aymenbelghith/tools/, last visited in 17-07-2008. 19. A. Belghith and L. Nuaymi, “Design and implementation of a QoS included WiMax module for NS-2 simulator”, First International Conference on Simulation Tools and Techniques for Communications, Networks and Systems, SimuTools 2008, Marseille, France, 3–7 March 2008.

Chapter 5

QoS Provision Mechanisms in WiMax Maode Ma and Jinchang Lu

Abstract This chapter presents QoS support mechanisms in WiMax networks. Existing proposals with the state-of-the-art technology have been classified into three main categories: QoS support architecture, Bandwidth management mechanism and Traffic management mechanism. Representative schemes from each of the categories have been evaluated with respect to major distinguishing characteristics of the WiMax MAC layer and PHY layer as specified in the IEEE 802.16d standard. Future research issues and trends are also highlighted. Keywords QoS Provisioning · WiMax · Traffic Scheduling · Admission Control

5.1 Background Broadband Wireless Access (BWA) systems, e.g. IEEE 802.16d standard [1], provide fixed-wireless access between the subscriber station and the Internet service provider (ISP) through the base station. BWA systems have been deployed not only to be complement and expansion of existing last mile wired networks such as cable modem and xDSL but also to be competitor to wired broadband access networks. Due to the upcoming air interface technologies, which promise to deliver high transmission data rates, BWA systems become an attractive alternative. The MAC Layer of IEEE 802.16d was designed for PMP broadband wireless access applications. It is designed to meet the requirements of very-high-data-rate applications with a variety of quality of service (QoS) requirements. The MAC layer is composed of three sub-layers. From bottom to top: the Security Sub-layer (PS), the MAC Common Part Sub-layer (CPS), and the Service Specific Convergence Sub-layer (CS). The former deals with security and network access authentication procedures. CPS carries out the key MAC functions. The CS sub-layer provides the interface to the upper layer; decides the MAC service class for the specific connection and initializes the resource allocation requests of the CPS. M. Ma (B) Nanyang Technological University, Singapore

M. Ma (ed.), Current Technology Developments of WiMax Systems, C Springer Science+Business Media B.V. 2009 DOI 10.1007/978-1-4020-9300-5 5, 

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The bandwidth request and grant mechanism has been designed to be scalable, efficient, and self-correcting. The 802.16d does not lose efficiency when presented with multiple connections per terminal, multiple QoS levels per terminal, and a large number of statistically multiplexed users. It takes advantage of a wide variety of request mechanisms, balancing the stability of contentionless access with the efficiency of contention-oriented access. The IEEE 802.16d PHY specifies 4 different PHY specifications, namely, WirelessMAN-SC PHY specification, WirelessMAN-SCa PHY specification, WirelessMAN-OFDM PHY specification, WirelessMAN-OFDMA PHY specification. IEEE 802.16d supports both frequency division duplex (FDD) and time division duplex (TDD) PHYs. The standard defines three different modulation schemes. On the uplink, support for QPSK is mandatory, while 16-QAM and 64-QAM are optional. The downlink supports QPSK and 16-QAM, while 64-QAM is optional. In addition to these modulation schemes, the 802.16d PHY also defines various forward error correction (FEC) schemes on the uplink as well as the downlink. These include Reed-Solomon (RS) codes, RS concatenated with inner Block Convolution Codes (BCC), and turbo codes. Support for such a wide variety of modulation and coding schemes permits vendors to tradeoff efficiency for robustness depending on the channel conditions. The advanced technology of the 802.16d PHY requires equally advanced radio link control (RLC), particularly the capability of the PHY to transition from one burst profile to another. The RLC must control this capability as well as the traditional RLC functions of power control and ranging. Figure 5.1 [2, 3] shows the existing QoS architecture of IEEE 802.16d. Uplink Bandwidth Allocation scheduling resides in the BS to control all the uplink packet transmissions. The communication path between SS and BS has two directions: uplink channel (from SS to BS) and downlink channel (from BS to SS). On the downlink (from BS to SS), the transmission is relatively simple because the BS is the only one that transmits packets during the downlink sub-frame. The data packets are broadcasted to all SSs and an SS only picks up the packets destined to it. The uplink channel is shared by SSs. Time in the uplink channel is usually slotted (mini-slots) called by time-division multiple access (TDMA), whereas on the downlink channel BS uses a continuous time-division multiplexing (TDM) scheme. The BS dynamically determines the duration of these sub-frames. Each sub-frame consists of a number of time slots. SSs and BS have to be synchronized and transmit data into predetermined time slots. On the uplink (from SS to BS), the BS determines the number of time slots that each SS will be allowed to transmit in an uplink sub-frame. This information is broadcasted by the BS through the uplink map message (UL-MAP) at the beginning of each frame. UL-MAP contains information element (IE), which includes the transmission opportunities, i.e. the time slots in which the SS can transmit during the uplink sub-frame. After receiving the UL-MAP, each SS will transmit data in the predefined time slots as indicated in IE. The BS uplink-scheduling module determines the IEs using bandwidth request PDU (BW-request) sent from SSs to BS.

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Fig. 5.1 Existing QoS architecture of IEEE 802.16d

The 802.16d MAC provides QoS differentiation for different types of applications that might operate over 802.16d networks. The 802.16d standard defines the following types of services: Unsolicited Grant Services (UGS): UGS is designed to support Constant Bit Rate (CBR) services, such as T1/E1 emulation, and Voice Over IP (VoIP) without silence suppression. Real-Time Polling Services (rtPS): rtPS is designed to support real-time services that generate variable size data packets on a periodic basis, such as MPEG video or VoIP with silence suppression. Non-Real-Time Polling Services (nrtPS): nrtPS is designed to support nonreal-time services that require variable size data grant burst types on a regular basis. Best Effort (BE) Services: BE services are typically provided by the Internet today for Web surfing. Since IEEE 802.16d MAC protocol is connection oriented, the application first establishes the connection with the BS as well as the associated service flow (UGS, rtPS, nrtPS or BE). BS will assign the connection with a unique connection ID (CID). The connection can represent either an individual application or a group of applications (all in one SS) sending data with the same CID. All packets from the application layer in the SS are classified by the connection classifier based on CID and are forwarded to the appropriate queue. At the SS, the Scheduler will retrieve the packets from the queues and transmit them to the network in the appropriate time slots as defined by the UL-MAP sent by the BS. The UL-MAP is determined by the Uplink Bandwidth Allocation Scheduling module based on the BW-request messages that report the current queue size of each connection in SS.

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IEEE 802.16d defines the required QoS signaling mechanisms such as BWRequest and UL-MAP, but it does not define the Uplink Scheduler, i.e. the mechanism that determines the IEs in the UL-MAP. IEEE 802.16d defines the connection signaling (connection request, response) between SS and BS but it does not define the admission control process. IEEE 802.16d medium access control, which is based on the concepts of connections and service flows, specifies QoS signaling mechanisms (per connection or per station) such as bandwidth requests and bandwidth allocation. However, IEEE 802.16d standard left the details of the QoS based packet-scheduling algorithms and reservation management that determine the uplink and downlink bandwidth allocation, undefined. IEEE 802.16d PHY gives AMC and the conceptually power control. It also left details of AMC and adaptive power control algorithm, undefined.

5.2 Overview of the Quality of Service in WiMax Network The QoS term can be interpreted in different ways. In general, QoS can be described from two perspectives: user perspective and network perspective. In user perspective, QoS refers to the application quality as perspective by the user. In network perspective, QoS refers the service quality that the network offers to applications or users in term of network QoS parameters that include: latency or delay of packets traveling across the network, reliability of packet transmission and throughput. From the network perspective, the networks’ goal is to provide the QoS services that adequately to meet the users’s needs while maximizing the network resources’ utilization. To achive this goal, the networks analyze the application requirements, manage the network resources and deploy various network QoS mechanisms. QoS parameters quantitatively represent the applications’s QoS requirements. They are: 1. 2. 3. 4. 5.

Throughput Delay Delay jitter Error rate Packet loss rate

Networks may use a combination of QoS services, i.e., per-flow and quantitative, per-class and quantitative. Some networks may include multiple types of QoS services in order to support a wide range of applications. In recently years, QoS support architecture and QoS support algorithms for WiMax system have been proposed [4–36]. They can be classified according to the following taxonomy as shown in Fig. 5.2.

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QoS Support Mechanisms in WiMAX QoS Support Architecture

QoS Support Schemes

Traffic Handling

Bandwidth management

Cross Layer Approach

MAC - Higher layer

Bandwidth/ Resource Reservation

Admission Control

Cross layer APA

Holonomic

Link Adaptation

Congestion Avoidance

Scheduling

Buffer tuning

Traffic Policing

Cross layer

Hierarchical

Channel access

Classification

Explicit congestion notification

Tail-drop

Fig. 5.2 Hierarchical taxonomy of QoS mechanisms that support different level Quality of Service in IEEE 802.16d

5.3 QoS Support Architecture in WiMax Networks

Downstream

Paper [4] proposed an inclusive architecture to support QoS mechanisms in IEEE 802.16 standard as shown in Fig. 5.3. Authors developed some compatible methods for specific modules such as Scheduler, Traffic Shaper, and Request and Grant Manager to optimize Delay, Throughput and Bandwidth Utilization metrics. In paper [5], authors proposed MAC layer cross to network upper layer QoS framework in the downlink mode and uplink mode to provide QoS support in WiMax networks as shown in Figs. 5.4 and 5.5. The proposed cross-layer QoS framework integrated L3 and L2 QoS in the IEEE 802.16 network. Main functional blocks in the framework include: QoS mapping from L3 to L2, Admission control, Fragment Control, and Remapping. Fragment Control handles the data frames from the same IP datagram as a group in L2 operations to reduce useless transmission. Remapping is designed for more flexible use of L2 buffers by changing the mapping rules from IP QoS to L2 service type under congested situation of the rtPS queue.

Base Station

Upstream Upstream Analyzer

Downstream Analyzer Connection Establishment Request

Data

Service Specification

Total Grant

UpLink Service Flow Data Base

Data

Subscriber Station

Call Admission Control

Classifier

Bandwidth Request

Shaper and Policer

UpLink Service Flow Data Base

DownLink Service Flow Data Base

Grant Allocator UGS

rPS

nrtPS

Classifier

Shaper and Policer

UGS

BE

rPS

nrtPS

BE

Polling Manager Downlink Scheduler Uplink Scheduler

Request Generator Connection Requests

Request Selector

UP Stream Generator

Upstream

Fig. 5.3 The proposed architecture to provide QoS in IEEE 802.16 standards

Downstream Generator

Downstream

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Fig. 5.4 Cross-layer QoS framework in the downlink mode in WiMax

Fig. 5.5 Cross-layer QoS framework in the uplink mode in WiMax

Paper [6] proposed cross-layer design frameworks for 802.16e OFDMA systems that are compatible with WiBro based on various kinds of cross-layer protocols for performance improvement: a cross-layer adaptation framework and a design example of primitives for cross-layer operation between its MAC and PHY layers as shown in Fig. 5.6. In the proposed model, the MAC layer contains a user grouper, scheduler, and resource controller. Each functional entity exploits physical layer information to increase system throughput. The physical layer consists of a diversity channel PPDU controller, AMC channel PPDU controller, control information controller, and HARQ functional blocks. AMC subchannel users and diversity subchannel users are classified by the user grouper. Since the properties of AMC subchannels and diversity subchannels are quite different, the grouping of users into two channel types is essential if system throughput is to be increased.

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Fig. 5.6 Cross layer adaptation scheme for efficient resource allocation in WiMax

The scheduler determines the scheduling of users and the quantity of packets that should be scheduled in the current frame. For cross-layer optimization, the scheduler should be designed to exploit not only PHY information but also application layer information.

5.4 Bandwidth Management QoS Mechanisms in WiMax Networks Bandwidth management mechanisms are mechanisms that manage the network resources by coordinating and configuring network devices’ traffic handling mechanism. The main mechanisms are: 1. Resource reservation 2. Connection admission control 3. Cross layer approach bandwidth management mechanisms

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5.4.1 Resource Reservation Mechanisms Resource reservation mechanisms inform the network entities on the QoS requirements of the applications using the network resources. The network devices will use this information to manage the network resources in order to meet such requirements. The resource reservation mechanisms include the following functions: 1. provision of resource reservation signaling that notifies all devices along the communication path on the mulitimedia application’s QoS requirements. 2. Delivery of QoS requirements to the connection admission control mechanism that decides if there are available resoureces to meet the new request QoS requirements. 3. Notification of the application regarding the admission result. The representive proposal tailored for WiMax is Dynamic Resource Reservation (DRR) scheme [7] as shown in Fig. 5.7. The basic principle of DRR [7] is: the reserved bandwidth will vary between a minimum and a maximum value as per the bandwidth utilized by the clients. The proposal is able to optimize reservation and utilization of bandwidth for Committed Bandwidth (CB) type traffic. However, it is very difficult to select parameters Cm and T. And the fluctuations of the reserved bandwidth from CM to Cm will increases signaling costs. Fig. 5.7 The amount of reserved bandwidth varies according to the number of active flows

5.4.2 The Connection Admission Control Mechanism Admission control is a network Quality of Service (QoS) procedure. Admission control determines how bandwidth and latency are allocated therefore need to be implemented between network edges and core to control the traffic entering the network. The role of CAC is to control the number of connection flows into the network. A new connection request is progressed only when sufficient resources are available at each successive network element to establish the connection through the whole network based on its service category, traffic contract, and QoS, while

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the agreed QoS of all existing connections are still maintained. Admission control is useful in situations where a certain number of connections may all share a link, while an even greater number of connections causes significant degradation in all connections to the point of making them all useless such as in congestive collapse. The proposed CAC schemes [8–13, 30] can be classified mainly into two strategies. The first one based on service degradation. It would consist in gracefully degrading existing connections to make room for the new one [8–13]. The second strategy is without degradation strategy. It would maintain the QoS provided for ongoing connections and simply reject the new service flow if no sufficient resources are available [30]. This first category of CAC schemes include all the CAC algorithms based on service degradation [8], bandwidth borrowing [9–12], or bandwidth stealing [13] strategies. The main idea of these policies is to decrease—when necessary and possible—the resources provided to ongoing connections in order to be able to accept a new service flow. The strategy could be combined by a threshold-based capacity sharing approach in order to avoid starvation [13] or a guard channel strategy that reserves a dedicated amount of bandwidth for more bandwidth-sensitive flows [12]. The second category [30] has no graceful service degradation of existing connections to accept a new flow. Thus, a new connection is accepted only if (1) it will receive QoS guarantees in terms of both bandwidth and delay—for real-time flows—and (2) the QoS of existing connections is maintained. Paper [11] proposed R-CAC which is characterized by defining thresholds of allocated bandwidth at BS for each class of service based on the priority of the service. The proposal introduced two parameters, denoted as Cu and Cr as shown in Fig. 5.8. Cu is the bandwidth exclusively reserved for UGS service, which is allocated with the highest priority in the four service classes supported in IEEE 802.16d networks. Cr is the bandwidth exclusively reserved for UGS and rtPS to mitigate the bandwidth competition coming from the other two types of service. The residual bandwidth (i.e., C – Cu – Cr) is the only part that a BS can assign to the nrtPS connection requests to meet their minimum bandwidth requirements, while it can also be assigned to UGS and rtPS services.

C: total bandwidth of the system Cu: Bandwidth that only can be used by UGS connections Cr: Bandwidth that only can be used by UGS and rtPS connections C – Cu – Cr: bandwidth that can used by UGS, rtPS and nrtPS connections

Fig. 5.8 Proposed R-CAC bandwidth allocation scheme

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D-CAC [12] is a Priority Support-based CAC. The proposed scheme can give the highest priority for UGS flows and maximizes the bandwidth utilization by bandwidth borrowing and degradation. Token bucket based CAC [13] is to control each connection. The connection is controlled by two token bucket parameters: token rate ri (bps) and bucket size bi (bits). When a traffic flow wants to establish a connection with BS, it sends these two parameters to BS and waits for response from BS. An extra parameter, delay requirement di , will be sent by rtPS flow. The proposed call admission control algorithm is detailed as followings: Step 1. Calculate the remainder uplink capacity Cremain : Cremain = Cuplink -CUGS CrtPS -CnrtPS -CBE . Step 2. Compare Cremain to the bandwidth requirement of the new connection. If there is enough capacity, accept it. If not, go to Step 3. Step 3. First look at the connections that belong to lower classes than this new connection. If there is a class that uses more capacity than its threshold, calculate CL, which means how much capacity can be stolen from it. If the sum of CL and Cremain is greater than or equal to the bandwidth requirement of this new connection, accept it. If not, look at if the capacity occupied by the class of the new connection is less than its threshold. If not, deny it. In order to avoid starvation of some classes, the proposal suggested that a threshold is set for each class. Combining with a proper scheduler, Token bucket based CAC can reserve bandwidth needed by real-time flows and thus delay requirements of rtPS flows can be promised. Other CAC schemes like Binary search approach fairly allocating bandwidth CAC [14] used Gaussian model for aggregated traffic in large network and Chernoff bound method to obtain upper bound blocking probability. Based on the analysis result, binary search approach was applied to solve the problem that given a total bandwidth, fairly allocating bandwidth to each class of multimedia traffic in 802.16d MAN. The total bandwidth (C) is completely portioned for four classes of traffic, and the partition value Ci(i = 1, 2, 3, 4) is calculated by above binary search algorithm using Chernoff bound. If a new connection of UGS and BE arrives at SS, it send request to BS for bandwidth. It is granted bandwidth if the new aggregated bandwidth including this connection is less than Ci(i = 1, 2, 3, 4). Else, it is blocked. If a new connection of rtPS and nrtPS arrives at SS, the connection is always admitted, but the burst within the connection will be blocked when the total used bandwidth larger than Ci(i = 2, 3). This mechanism can guarantee the pre-required upper bound blocking probability for UGS and BE connections, and the burst blocking probability for rtPS and nrtPS.

5.4.3 Cross Layer Approach Bandwidth Management Mechanism There is a few prior works that deals with MAC cross to the higher layers scheduling, i.e. the MAC to application and transport layers. Paper [15] aims at providing

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end to end QoS guarantee using IntServ and DiffServ in connection oriented WiMax PMP and mesh networks. The work maps the RSVP in the IP layer and DSA/DSC/DSD in MAC layer. The author proposed that message exchange for DSA and DSC can be deployed to carry QoS parameters of IntServ services for end-to-end resource (bandwidth/buffer) reservation. For DiffServ services, on the other hand, a number of per-hop behaviors (PHBs) for different classes of aggregated traffic can be mapped into different connections directly. When a new application flow arrives in IP layer, it is firstly parsed according to the definition in PATH message (for InteServ) or Differentiated Services Code Point (DSCP for DiffServ); then classified and mapped into one of four types of services (UGS, rtPS, nrtPS or BE). The proposed dynamic service model in SS sends a request message to the BS, where the admission control determines whether this request is approved or not. If it is not approved, the service module informs the upper layer to deny this service; else the admission control notifies the scheduling module to make a provision based on the parameter values in the request message. At the same time the accepted service is transferred into a traffic grooming module. According to the grooming result, the SS will send Bandwidth Request message to BS. The centralized scheduling module in BS will retrieve the requests and generate UL-MAP message carrying the bandwidth allocation results. Finally, the SS will package SDUs from IP layer into PDUs and upload them in its allocated uplink slots to BS. Figure 5.9 shows the PATH, RESERV messages for IntServ. Here the sender sends a PATH message including traffic specification (TSpec) information. Parameters such as max/min bounds for bandwidth, delay and jitter is mapped into parameters of the DSA message such as Maximum Sustained Traffic Rate, Minimum Reserved Traffic Rate, Tolerated Jitter and Maximum Latency. According to the response received for the DSA message, the provisioned bandwidth can also be mapped into Reserved Specification (RSpec) in the RESV message. These parameters can be used at the MAC scheduler to allocate resources according to the QoS requirements of flows along their path.

Fig. 5.9 Traffic classification and mapping for IntServ services

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5.5 Traffic Handling Mechanisms QoS Support in WiMax Networks Traffic handling mechanisms are mechnism that classsify, handle, police and monitor the traffic across the network. The main mechanisms are: 1. 2. 3. 4. 5. 6. 7.

Classification Channel access Traffic policing Buffer management Congestion avoidance Packet scheduling Cross layer APC

5.5.1 Traffic Classification The classification mechanism identifies and separates different traffic into flows or group of flows. Therefore each flow or group of flows can be handled differently. Application traffic is identified by the classification mechanism and is forwarded to the appropriate queue awaiting service from other mechanism such as traffic shaping and packet scheduling. The granularity level of the classification mechanism can be per-user, per-flow or per-class depending on the type of QoS services provided. To identify and classify the traffic, the traffic classification mechanism requires some form of tagging or marking of packets. Paper [16] proposed two- stage packet classification algorithm. Authors suggested that prefix-based fields and range-based fields should be processed separately in two stages. In the first stage, packets are classified by a matching scheme with their prefix-based fields, while other range-based fields are processed by a rangebased scheme in the second stage. The Prefix-Matching-Tree (PMT) used in the first stage to handle prefix-based fields. The PMT is constructed by a prefix-based matching scheme that can speedup the searching process by simultaneously processing multiple prefix-based fields. Each tree node of the first stage is connected to the second stage according to protocol types (TCP or UDP). The Range-MatchingTree(RMT) is employed to deal with range-based fields in the second stage.

5.5.2 Channel Access Mechansim In wireless networks, all hosts communicate through a shared wireless medium. When multiple hosts try to transmit packets on the shared communication channel, collisions can occur. Therefore, wireless networks need a channel access mechanism which controls the access to the shared channel.collision-based chanael such as Random access and collision-free channel access such as TDMA or Polling channel access mechanism can provide different QoS services.

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In IEEE802.16d, there are two access protocols based on multi-channel slotted Aloha and periodic polling. The former is a contention based access protocol, while the latter provides periodic polling services without contention, such as unsolicited grant service (UGS), e.g., Voice over IP and real time or non-real time services. In addition, IEEE802.16d allows unicast-polling for a single SS or multicast-polling for groups of SSs as periodic polling service. In paper [17], the performance of IEEE802.16d Random Access Protocol is evaluated by using Transient Queueing Analysis. The paper derived the random access success probability from the system equilibrium. Retransmission probability is also derived by including a binary exponential backoff algorithm. In paper [18], authors considered a capacity allocation scheme of periodic polling services for a multimedia traffic in an IEEE802.16d system. Considering a base station assigns a subscriber station contiguous M uplink subframes for uplink traffic transmission and v vacation frames for saving power and opportunities for other subscriber stations, they proposed a capacity allocation scheme for a multimedia traffic in WiMax network. The bandwidth allocated to an SS will be returned to a BS, when the SS’s queue is empty during M contiguous uplink subframes. The returned bandwidth will be allocated to other types of services requested from the multichannel slotted Aloha.

5.5.3 Traffic Policing The traffic policing is the mechanism that monitors the admitted sessions’ traffic so that the sessions do not violate their QoS contract. The traffic policing mechanism makes sure that all traffic that passes through it will confirm to agreed traffic parameters. In case vialation is found, a traffic poling mechanism is enforced by shaping the traffic and dropping traffic to enforce compliance with that contract. Traffic sources which are aware of a traffic contract sometimes apply Traffic Shaping in order to ensure their output stays within the contract and is thus not dropped. Due to fact that traffic policing shapes the traffic based on some known quantative traffic parameters, multimetedia (real-time) application are naturally compatible to traffic policing. Most multimedia application traffic (Voice, video) is generated by a standard codec which generally provides certain knowledge of the quantitative traffic parameters. Traffic policing can be applied to individual multimedia flows. Non-real time traffic does not provide quantitative traffic parameters and usaually demands bandwidth as much as possible. Therefore, traffic policing enforces non-real time traffic based on network policy. Such policing is usually enforced on aggregated non-real time traffic flows. Traffic policing, in cooperation with other QoS mechanisms usually, can provide QoS support. In [19], a new traffic shaping based on concept of “Fair Marker”(FM) was proposed to enforce fairness among distinct flows. FM controls token distribution from the token bucket to the flows originating from the same subscriber network. The FM explores the duality between packet queueing and token bucket utilization. Fairness in token distribution is a function of the fair allocation algorithm used by FM. In

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order to reach this purpose, it records information regarding the consumption of tokens by the monitored flows.

5.5.4 Buffer Management Buffer management refers to any particular discipline used to regulate the occupancy of a particular queue where packets may be held (or dropped). Buffer is set to improve link utilization and system performance, but it also increases packet’s queue delay. With the increase of user demands for service quality, providing stable and low delay has been the primary requirement of real-time services. The most important and easy to control part of total delay is queue delay. So how to set the capacity of the buffer, how to efficiently control buffer length while network circumstance is dynamic, and how to achieve the tradeoff between throughput and queue delay are the important problems to be solved in buffer management and QoS control of whole networks. At present, support [20] for wired and wireless network is included for drop-tail (FIFO) queueing, Random Early Detection (RED) buffer management, class based queueing (CBQ) (including a priority and round-robin scheduler), and variants of Fair Queueing including Fair Queueing (FQ), Stochastic Fair Queueing (SFQ), and Deficit Round-Robin (DRR). RED is the most intensive researched class of AQM (Active Queue Management), it selectively discards packets of some flows based on probability determine. In WiMax networks, the base station (BS) is a likely bottleneck for downlink (DL) TCP connections due to difference in available bandwidth between the fixed network and the wireless link. This may result in buffer overflows or excessive delays at the BS, as these buffers are connection-specific. In order to avoid buffer overflows, different Active Queue Management (AQM) methods may be applied at the BS. Paper [21] analyzed of RED; Packet Discard Prevention Counter (PDPC) and time-to-live based RED AQM mechanisms and proved that they are indeed very useful: AQM reduces considerably DL delays at the WiMax BS without sacrificing TCP throughput.

5.5.5 Congestion Control Congestion control concerns controlling traffic entry into a network, so as to avoid congestive collapse by attempting to avoid oversubscription of any of the processing or link capabilities of the intermediate nodes and networks and taking resource reducing steps, such as reducing the rate of sending packets. In congestion control, the packet loss information can serve as an index of network congestion for effective rate adjustment, but in a wireless network environment, common channel errors due to multipath fading, shadowing, and attenuation may cause bit errors and packet loss, which are quite different from the packet loss caused by network

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congestion. Therefore wireless packet loss can mistakenly lead to dramatic performance degradation. If the link capacity in one system is temporarily degraded due to a high traffic load in the WLAN system (congestion) or due to interference, two effects, leading to inefficiency on the IEEE802.16d link, could occur:

r r

Loss of data due to buffer overflow and therewith unnecessary retransmissions. Waste of bandwidth due to unused reserved transmission opportunities.

To avoid the above-mentioned effects, a congestion control mechanism needs to be worked out to dynamically adapt the QoS demands of a connection during runtime for a specifically defined period of time. Paper [22] proposed Dynamic Service Change (DSC) congestion control mechanism to support the Explicit Congestion Notification mechanism in future deployment of TCP. The paper contributed to IEEE 802.16-REVd D1 standard by adding MAC DSC TEMP.request and MAC DSC TEMP.indication message in MAC entity to provide the temporary traffic reduction mechanism to overcome congestion effect. Since network congestion is directly related to the congestion packet loss, packet loss can be caused by either congestion loss or wireless channel errors, resulting from mulitipath fading, shadowing, or attenuation. New approach of congestion control over wireless network is to perform packet loss classification so that congestion control algorithms can more effectively adapt the sending rate based on congestion loss instead of from wireless loss. Paper [23] considered two packet loss classes, congestion loss and wireless loss and proposed packet loss classification (PLC) method based on the trend of ROTT (relative one-way trip time) to assist packet loss classification in the ambiguous area of ROTT distribution. By taking advantage of the QoS features offered by one of the four proposed WiMax service flow arrangement, paper [24] aimed at more flexible layer constructing and subscription while reliable in diverse channel conditions and fitting users’ demand. Through effective integration of packet loss classification (PLC) [23], endto-end available bandwidth probing, congestion control via layered structure and packet level FEC, for layered multicast applications over WiMax for disseminating scalable extension of H.264/AVC compressed video is proposed. The optimality comes from the best tradeoff of number of video layers subscription with number of additional FEC packets insertion simultaneously to satisfy the estimated available bandwidth and wireless channel error condition.

5.5.6 Packet Scheduling Algorithm Packet scheduling refers to the decision process used to choose which packets should be serviced or dropped. Packet scheduling is the process of resolving contention for bandwidth. A scheduling algorithm has to determine the allocation of bandwidth among the users and their transmission order. One of the most important

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tasks of a scheduling scheme is to satisfy the Quality of Service (QoS) requirements of its users while efficiently utilizing the available bandwidth. Many legacy scheduling algorithms, capable of providing certain guaranteed QoS, have been developed for wireline networks. However, these existing service disciplines, such as fair queueing scheduling, virtual clock, and EDD, are not directly applicable in wireless networks because they do not consider the varying wireless link capacity and the location-dependent channel state. The characteristics of wireless communication pose special problems that do not exist in wireline networks. These include: 1) 2) 3) 4) 5)

high error rate and bursty errors; location-dependent and time-varying wireless link capacity; scarce bandwidth; user mobility; and power constraint of the mobile hosts.

All of the above characteristics make developing efficient and effective scheduling algorithms for wireless networks very challenging. WiMax networks provide services for heterogeneous classes of traffic with different quality of service (QoS) requirements. Currently, there is an urgent need to develop new technologies for providing QoS differentiation and guarantees in WiMax networks. Among all the technical issues that need to be resolved, packet scheduling in WiMax networks is one of the most important. In this sub-section, we assess proposed scheduling algorithms for QoS support in WiMax networks thoroughly with respect to the characteristics of the IEEE 802.16d MAC layer and PHY layer. We classify those scheduling algorithms in WiMax into three categories: holonomic approach [25–28]; hierarchical approach [29–34] and cross-layer approach [35–38] with respect to the nature of scheduling algorithm mechanism as holonomic approach uses single layer scheduling scheme, in contrast, hierarchical approach uses several layers or stages scheduling schemes and cross layer approach uses information from several layers in OSI model. Each of three categories can be classified as per–flow, per-class, per-packet and hybrid scheduling algorithms. Representative schemes in each of these categories will be discussed next. 5.5.6.1 Holonomic Packet Scheduling Algorithm Paper [25] applied holonomic approach and proposed one layer hybrid scheduling scheme which combines per-flow and per-class scheduler termed as “Frame Registry Tree Scheduler” (FRTS). The proposal aims at providing differentiated treatment to data connections, based on their QoS characteristics. This approach focuses on properly preparing future transmitted frames by using a tree based approach. The tree consists of six levels; root, time frame, modulation, subscriber, QoS service and connection level. First level is taken to be the root. The second level represents time frames immediately after the current time frame. The third level represents the available modulation types. The fourth level organizes all the connections according

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to the SS each SS has one uplink node and one downlink node at this level. The fifth level organizes the connections according to their QoS. The last level consists of leaves for each active connection queue. The data structure presented achieves time frame creation and reduces the processing needs at the beginning of each frame. The algorithm schedules each packet at the last time frame before its deadline. This allows more packets to be transmitted and hence an increased throughput. This method also avoids fragmentation of transmissions to/from the same SS or same modulation. Another good feature is its ability to handle changes in the connection characteristics like modulation type or service type of the channel. In [26], the authors proposed a Token Bank Fair Queueing (TBFQ) scheduling algorithm at packet level for BWA systems as shown in Fig. 5.10. TBFQ uses the priority index Ei/ri to keep track of the normalized service received by backlogged flows. Ei is the number of tokens exchanged between the ‘bank’ and the flow i. Ei is negative if the flow continues to borrow tokens from the bank to serve the traffic that exceeds its average rate. Ei is positive if the traffic is below its average rate. The flows are first served based on their token generation rate to guarantee the throughput and latency, and then the remaining bandwidth is distributed based on their priority index. The parameters (debt limit, credit burst, and creditable threshold) determine the dynamic behavior of the algorithm. TBFQ can be adapted to operate under varying channel error conditions. It has demonstrated its effectiveness in achieving fairness, maximizing channel utilization, and fast convergence to guaranteed throughput. Its ability to serve and isolate real-time traffic and data traffic that is under severe error conditions makes it a suitable candidate as the wireless packet scheduling algorithm for BWA systems.

ri P1

λ1

Ei

D1 r2

Token Denk Size B

P2

λ2

E2

D2 rn Wireless Terminals

Pn λ3

En

D3

Fig. 5.10 TBFQ Algorithm

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In order to maximize throughput of non-real-time traffics with satisfying QoS requirements of real-time traffics, paper [27] proposed urgency and efficiency based packet scheduling (UEPS) algorithm which was designed not only to support multiple users simultaneously but also to offer real-time and non-real-time services to a user at the same time. The UEPS algorithm uses the time-utility function as a scheduling urgency factor and the relative status of the current channel to the average one as an efficiency indicator of radio resource usage. The proposed packet scheduler assigns priorities to the packets to be transmitted, based on the channel status reported by the user equipments as well as the QoS statistics maintained by the BS. Since the scheduler works in a global timeline, a time utility function (TUF) is used for the scheduling. Two scheduling factors, the urgency of scheduling and the efficiency of radio resource usage, are used to schedule RT and NRT traffic packets at the same time. The TUF is used to represent the urgency of scheduling while the channel state is used to indicate the efficiency of radio resource usage. There are three steps in the UEPS scheduling algorithm. In the first step, the incoming packets are stored in the buffer corresponding to the SS and the traffic type. The QoS profiles of each arrived packet, such as the arrival time, deadline, packet type, head of line (HOL) delay, and packet size are also maintained. In the next step, at each scheduling instance, the urgency factor of each HOL packet of each buffer is calculated from the TUF. The TUF of a real-time traffic is a straight line up till a threshold with a dead drop. Because it is a hard and discontinuous function in delay, the unit change of utility can not be obtained directly at its delay time. Thus a continuous TUF having straight line up till a threshold with a gradual z-shaped drop is used instead. The gradual decay occurs in the marginal scheduling time interval (MSTI) which is a small time window of delay jitters around the deadline. This relaxed z-shaped function is given by U RT (t) = e−a(t−c) /(1+e−a(t−c) ) where a and c are the parameters which determine the slope and location of the point of inflection. The unit change of utility of a real time traffic at the inflection point (t = c) is given by |U ’ RT (t = c)| = a/4, and this value forms the urgency factor. For non real time traffics, the TUFs are monotonic decreasing functions in time (delay). TUF of a non-real time traffic is f N RT (t) = 1 − ex p(at)/ex p(D) where D is the maximum time used for normalization purpose. The resulting urgency function is |U ’N RT (t)| = aex p(at)/ex p(D). Once the urgency has been calculated, the highest urgency factor for an SS out of the four buffers for that SS is taken as the representative for that user. The efficiency factor of each SS is then calculated. It is a moving average of the channel state of the SS, given by R(t) = (1 − 1/W )R’(t − 1) + (1/W )R(t), where R(t) is the channel state at time t, R’(t) is the scheduling priority value of each SS is calculated by p(t) = U ’(t)∗ (R(t)/R’(t)). As a last step, based on the priorities calculated for each SS, the top n number of packets having the highest priorities are transmitted, where n is the number of different streams the OFDMA can send simultaneously. Paper [28] proposed a new per-flow based scheduling algorithm, referred as Service Criticality (SC) based scheduling scheme. SC is based on a dual of buffer occupancies at nodes and allowable latencies for a particular service. In the proposed

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scheme, a flow would receive the service through bandwidth allocation depending on degree of service criticality (SCindex ) computed at SS and conveyed to BS during bandwidth request burst of UL. The core of the proposed SC scheme is the way SS computes the SCindex for active flows existing at node. The computation of SCindex depends on both, the buffer occupancy and latency experienced by flow. Proposed scheme employs linear dependence on buffer occupancy and tunable sigmoid like relation with experienced latency to compute SCindex . SCindex encompasses two service parameters, maximum allowable latency and permissible packet loss and it is derived from two elements, referred to as Service Desperation (SD) and Buffer Occupancy (BO). SD denotes urgency of bandwidth allocation or inversely denotes the amount of time a flow (service) can tolerate not being provisioned. BO denotes current level up to which flow buffer is full with respect to allocated buffer. Both of these elements are normalized over all the different flows in the system. The normalization process ensures fairness amongst multiple flows. Normalization also implies that SD and BO denote a ratio between 0 and 1. The maximum of SD and BO is chosen as SCindex such that when SCindex is close to 1 the flow must be provisioned, else it will be timed out. 5.5.6.2 Hierarchical Scheduling Scheme In paper [29], the issue of differentiated service provisioning will be addressed with the non-real-time polling service in WiMax systems. The proposed solution has been designed to have an ability to accommodate integrated traffic in the networks with effective scheduling schemes. A hierarchical scheduling algorithm to provide service differentiation to enhance the nrtPS service in WiMax systems was proposed. In order to meet the time constraints of real-time messages as much as possible and avoid scheduling starvation of the non-real-time messages, the data transmission scheduling has been divided into two levels, inter-class scheduling and intra-class scheduling as shown in Fig. 5.11. With the objective to provide service differentiation between the real-time and non-real-time classes of traffic, the proportional delay differentiation (PDD) model was proposed as inter-class scheduling algorithm. Intra-class scheduling scheme was designed as a priority assignment scheme based on tardy rate, the message with less transmission time will have higher priority to be served. The message which

Fig. 5.11 Structure of two levels hieratical scheduling scheme

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Fig. 5.12 Hierarchical structure of bandwidth allocation

has been delayed a longer time in the queue will also have higher priority to be transmitted. With this scheme, the total time for messages to stay in the network could be reduced and the starvation of the messages with longer transmission time could be avoided. Paper [30] proposed a hybrid scheduling algorithm that combines EDF, WFQ and FIFO scheduling algorithms. The overall allocation of bandwidth is done in a strict priority manner. EDF scheduling algorithm is used for SSs of the rtPS class, WFQ is used for SSs of the nrtPS class and FIFO for SSs of the BE class. In paper [31], authors enhanced the proposal scheduling architecture in [30], the scheduling architecture is similarly divided into two layers as shown in Fig. 5.12. The first layer is for bandwidth requests. The authors suggested a Deficit Fair Priority Queue (DFPQ) for scheduling at this layer. The second layer scheduling is for the data traffic. UGS are not scheduled because they already have a reserved bandwidth. For the other three traffic classes, a hybrid of scheduling algorithm is proposed. The authors suggested Earliest Deadline First (EDF) for rtPS traffic, where the packets with earliest deadline are scheduled first. For nrtPS, WFQ is proposed. The bandwidth left is allocated to each BE traffic in a round robin (RR) manner. Compared with fixed bandwidth allocation, the proposed solution [31] improves the performance of throughput under unbalanced uplink and downlink traffic. And better performance in fairness can be achieved by the proposed DFPQ algorithm than strict PQ scheduling. Paper [32] extended the scheduling architecture in paper [31]. It proposed a preemptive DFPQ scheduling algorithm that enhances the DFPQ algorithm proposed in [31], and improves the performance of the rtPS service flow class. The Preemptive DFPQ defines for each non-preemptive queue a Quantum Critical (Q crit ) to give the queue another chance to service critical packets. Q crit value is a percentage of the original value of the queue’s quantum. Queues are allowed to use Q crit to serve critical packets only. The processing of critical packets continues until the Q crit of the non-preemptive queue becomes less than or equal to zero. Q crit is initialized

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only once per frame and not at every round like the quantum Q. This algorithm gives more chances to rtPS packets to get serviced before the expiration of their deadlines. The authors in [33] proposed an architecture consisting of three schedulers. The first scheduler is concerned with UGS and rtPS flow, as well as rtPS and nrtPS polling flow. An EDF scheduling is applied in this scheduler. The second scheduler is concerned with flows requiring a minimum bandwidth mainly nrtPS. WFQ scheduling is used here where the weight is the size of the requested bandwidth. The third scheduler is used for BE traffic and here too a WFQ scheduling is employed where the weight is the traffic priority. Among the schedulers, the first level has the highest priority, and only after all the packets have been served, is the second scheduler considered. The third scheduler comes when the first two have become free. The delay and delay jitter character for UGS, rtPS, nrtPS and BE can be improved simultaneously using the proposed architecture. In Paper [34] authors considered a scheduling algorithm should possess the following features: efficient link utilization, bounded delay, enough fairness, high throughput, low implementation complexity, graceful performance degradation, strong isolation, more delay/bandwidth decoupling, and flexible scalability and proposed solution follows the framework specified in the IEEE 802.16d standard with the following unique features as shown in Fig. 5.13. (1) The Pre-scale Dynamic Resource Reservation (PDRR) scheme is proposed to allocate bandwidth to UL subframe and DL subframe dynamically. (2) The Priority-based Queue Length Weighted (PQLW) scheduling algorithm was proposed for inter-class scheduling and the Max-Min Fair Sharing (MMFS) scheduling was introduced for inter-SS scheduling within each class of service at the BS as Tier 1 scheduling. (3) The Self-Clocked Fair Queuing (SCFQ) and Weighted Round Robin (WRR) scheduling schemes have been applied to inter-connection scheduling within each class of service at each SS as Tier 2 scheduling. (4) Earliest Deadline First (EDF) and Shortest Packet Length First (SPLF) scheduling have been applied to packet scheduling within each of the connections carrying burst traffic as Tier 3 scheduling. 5.5.6.3 Cross-Layer QoS Scheduling for WiMax Networks Wireless communication systems have unique characteristics – namely, timingvarying channel conditions and multi-user diversity. New MAC design and new scheduling solutions need to be developed that are specifically tailored for this Wireless communication environment [35]. Opportunistic MAC (OMAC) is the modern view of communicating over spatiotemporally varying wireless link. The cross-layer nature embeds OMAC with the potential to revolutionize the design of wireless data networks from physical to data link layers. The wireless resources (bandwidth and power) are more scarce and expensive than their wired counterparts, because the overall system performance degrades dramatically due to multi-path fading, Doppler, and time-dispersive effects caused by the wireless air interface. Unlike wired networks, even if large bandwidth/power is allocated to a certain wireless connection, the loss and delay requirements may not

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Fig. 5.13 Proposed 3-tier scheduling framework

be satisfied when the channel experiences deep fades. In this scenario, the scheduler plays extremely important role. An OMAC seeks to pick among competing users the one who is currently experiencing the relatively best channel conditions in each scheduling instant. The judicious schemes should be developed to support prioritization and resource reservation in wireless networks, in order to enable guaranteed QoS with efficient resource utilization. Cross-layer MAC designs tailored for WiMax have been proposed [36–38]. Some researchers have considered cross layer scheduling using the MAC scheduler and the PHY resource allocator. In paper [36], authors proposed a cross layer design of packet scheduling and resource allocation in OFDMA wireless networks which concentrates on downlink scheduling in the BS. In OFDMA systems, each carrier is subdivided into a number of sub-carriers which can be controlled or allocated separately making the PHY very robust. In the proposed system, the sub carriers are grouped into allocation units (AU) for allocation so that overheads are minimized. The BS estimates the sub channel condition of each user and allocated resources to the users on a frame-by-frame basis. The authors considered a system where some SS have real time traffic and other systems have non-real time traffic, i.e. real time and non-real time traffic do not co-exist in the same SS. The BS can estimate the channel gain of each user on a sub channel and an AU can be independently allocated to a particular user. The aim is to maximize the overall utilization while satisfying the rate requirements of individual users. To do this, author in [36] formulates a linear programming method and subsequently obtain a sub-optimal solution which reduces the computational time. The scheme also proposes a packet scheduler

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at the BS MAC layer which provides equal priority to both real time and non-real time traffic except if the ratio of the waiting time of a real time packet in the MAC queue and the delay constraint of the packet exceeds a certain threshold. Associated with cross-layer packet scheduler, the paper [36] proposed a 3-stage resource allocation algorithm where the first stage schedules the urgent packets. The second stage schedules the non-real time packets and the real-time packets which are not urgent, higher priority is given to users whose channel quality is better regardless of the traffic type. The third stage allocates the unallocated AU’s if any. After the packet scheduler decides the rate requirements of each user, the actual resources are allocated in the PHY layer. This is carried out by using a sub-optimal heuristic algorithm which first allocates the sub-channels (or subcarriers) constraints in order to maximize the utilization (or minimize the transmission power). After that, the algorithm relocates (or swaps) sub-channels so as to satisfy the rate requirements of each user. Though this kind of cross layer scheme can be followed in the downlink at the BS, it cannot be adopted for uplink scheduling since individual nodes cannot decide the channel condition and place request for those particular channels. A priority-based scheduler [37] was proposed at the medium access control (MAC) layer for multiple connections with diverse QoS requirements, where each connection employs adaptive modulation and coding (AMC) scheme at the physical (PHY) layer. The priority-based scheduler is termed as priority function (PRF) for each connection admitted in the system and update it dynamically depending on the wireless channel quality, QoS satisfaction, and service priority across layers. Thus, the connection with the highest priority is scheduled each time. The connection with the highest priority is scheduled each time. Efficient bandwidth utilization for a prescribed PER performance at the PHY can be accomplished with AMC schemes, which match transmission parameters to the time-varying wireless channel conditions adaptively and have been adopted by WiMax networks. Authors defined Transmission Mode(TM) through the information of adaptive modulation and coding scheme according to wireless channel condition abstracted from IEEE802.16d standard as shown in Table 5.1. To simplify the AMC design, authors approximated the PER expression in AWGN channels as  P E Rn (γ ) ≈

1, i f γ ≤ γ pn an exp(−gn γ ), i f

γ ≥ γ pn

(5.1)

where n is the mode index and γ is the received SNR. Parameters an , gn are modedependent. With packet length N b = 128 bytes/packet, the fitting parameters for transmission modes in TM are provided in Table 5.1. AMC design guarantees that the PER is less than or equal to Minimum PER by determining minimum SNR required boundary and updating the transmission mode as in Table 5.1. Authors further developed a cross-layer opportunistic scheduling algorithm termed as priority function (PRF) to schedule rtPS, nrtPS, and BE connections.

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Mode n

1

2

3

4

5

6

Modulation RS Code CC Code Rate Coding Rate Rc Rn (bits/symbol) an (dB) gn γ pn (dB)

QPSK (32,24,4) 2/3 1/2 1.0 232.9242 22.7925 3.7164

QPSK (40,36,2) 5/6 3/4 1.5 140.7922 8.2425 5.9474

16QAM (64,48,8) 2/3 1/2 2.0 264.0330 6.5750 9.6598

16QAM (80,72,4) 5/6 3/4 3.0 208.5741 2.7885 12.3610

64QAM (108,96,6) 3/4 2/3 4.0 216.8218 1.0675 16.6996

64QAM (102,108,6) 5/6 3/4 4.5 220.7515 0.8125 17.9629

Each connection i, where i denotes the connection identification (CID) of rtPS, nrtPS, and BE services, adopts AMC at the PHY. Given a prescribed PER ξ i, the SNR thresholds γ pn (1), for connection i are determined by setting P0 = ξ i. Thus, the possible transmission rate (capacity), i.e., the number of packets that could be carried by Nr time slots for connection i at time t (frame index), can be expressed as Ci(t) = Nr Ri(t)

(5.2)

where Ri(t) is the number of packets that can be carried by one time slot and is determined by the channel quality of connection i via AMC as in Table 5.1. Either Ri(t) or Ci(t) indicates the channel quality or capacity, which is accounted for by the scheduler. At the MAC, the scheduler simply allocates all Nr time slots per frame to the connection i = arg max ϕi(t)

(5.3)

where ϕi(t) is the PRF for connection i at time t, which is specified differently for rtPS and nrtPS and BE service classes. If multiple connections have the same value max {ϕi(t)}, the scheduler will randomly select one of them with even opportunity. For each rtPS connection, the scheduler timestamps each arriving packet according to its arrival time and defines its timeout if the waiting time of such a packet in queue is over the maximum latency (deadline) Ti. The PRF for a rtPS connection i at time t is defined as: ⎧ R (t) 1 i ⎪ ⎨βr t R N Fi (t) , If Fi (t) ≥ 1, Ri (t) = 0 ϕi(t) = βr t , If Fi (t) < 1, Ri (t) = 0 ⎪ ⎩ 0 Ri (t) = 0

(5.4)

ε where βr t ε[0, 1] is the rtPS-class coefficient and Fi(t) is the delay satisfaction indicator, which is defined as: Fi (t) = Ti − ⌬Ti − Wi (t) + 1

(5.5)

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With ⌬Ti ε[0, Ti ] denoting the guard time region ahead of the deadline Ti and W i(t)ε[0, Ti ] denoting the longest packet waiting time, i.e., the head of the line (HOL) delay. If Fi(t) ≥ 1, i.e., W i(t)ε[0, T i − ⌬T i], the delay requirement is satisfied, and the effect on priority is quantified as 1/Fi(i)ε[0, 1]: Large values of Fi(t) indicate high degree of satisfaction, which leads to low priority. If Fi(t) < 1, i.e., W i(t)ε[T i − ⌬T i, T i], the packets of connection i should be sent immediately to avoid packet drop due to delay outage, so that the highest value of PRF βr t is set. Parameter R N = max{Ri(t)}, and the factor Ri(t)/R N ε[0, 1] quantifies the normalized channel quality because high received SNR induces high capacity, which results in high priority. When Ri(t) = 0, the channel is in deep fade and the capacity is zero, so that connection i should not be served regardless of delay performance. The value of ϕi(t) for rtPS connection i lies in [0, βr t ]. For each nrtPS connection, guaranteeing the minimum reserved rate ηi means that the average transmission rate should be greater than ηi. In practice, if data of connection i are always available in queue, the average transmission rate at time t is usually estimated over a window size tc based on (5.3) and (5.4) as:  ηˆ i (t + 1) =

ηˆ i (t)/(1 − 1/tc ) ηˆ i (t)/(1 − 1/tc ) + Ci (t)/tc

If i = i ∗ If i = i ∗

(5.6)

In order to guarantee ηi(t) ˆ ≥ ηi during the entire service period. The PRF for an nrtPS connection i at time t is defined as: ⎧ Ri (t) 1 ⎪ ⎪ , If Fi (t) ≥ 1, ⎪βnr t ⎪ ⎪ R N Fi (t) ⎨ Ri (t) = 0 ϕi(t) = (5.7) ⎪ ⎪ , If F (t) < 1, R (t) = 0 β ⎪ nr t i i ⎪ ⎪ ⎩0 Ri (t) = 0 where βnr t ε [0, 1] is the nrtPS-class coefficient, and Fi(t) is the ratio of the average transmission rate over the minimum reserved rate. Fi(t) = ηi(t)/ηi. ˆ

(5.8)

Quantity Fi(t) here is the rate satisfaction indicator. If Fi(t) ≥ 1, the rate requirement is satisfied, and its effect on priority is quantified as 1/Fi(t)ε [0, 1]. If Fi(t) < 1, the packets of connection i should be sent as soon as possible to meet the rate requirement; in this case, the upper-bound value βnrt is set for ϕi(t). Once again, the value of ϕi(t) lies in [0, βnrt]. For BE connections, there is no QoS guarantee. The PRF for a BE connection i at time t is φi (t) = β B E

Ri (t) RN

(5.9)

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where βBEε [0, 1] is the BE-class coefficient. Notice that ϕi(t) varies in [0, βBE], which only depends on the normalized channel quality regardless of delay or rate performance. The role of βrtPS, βnrtPS, and βBE is to provide different priorities for different QoS classes. For example, if the priority order for different QoS classes is rtPS > nrtPS > BE, the coefficients can be set under the constraint βrtPS > βnrtPS > βBE; e.g., βrtPS = 1.0 > βnrtPS = 0.8 > βBE = 0.6. Thus, the QoS of connections in a high-priority QoS class can be satisfied prior to those of a lowpriority QoS class because the value of ϕi(t) for QoS unsatisfied connections will equal the upper-bound βrtPS, βnrtPS, and βBEforrtPS, nrtPS, and BE connections, respectively. The purpose of normalizing ϕi(t) in [0, βrtPS], [0, βnrtPS], and [0, βBE], respectively, is to provide comparable priorities among connections with different kinds of services, which enable exploiting multiuser diversity among all connections with rtPS, nrtPS, and BE services. In paper [38], authors enhanced the proposal in [6] by proposing an optimal resource allocation scheme and an opportunistic scheduling rule similar as in paper [37] that can satisfy the QoS requirements of the application layer and optimize MAC-PHY cross-layer performance. The main concept of the proposed Optimal Resource Allocation is to solve optimization problem. Assume that a downlink OFDM system with N subchannels and M time slots. There are K users, J connections and L packets in the system. The objective of the resource allocation is to maximize the overall system throughput while guaranteeing the provision of QoS, which is formulated into the following constrained optimization problem:

arg

max

Ci (m,n),g( j)

M  N L  

Ci (m, n)Ri (m, n)

(5.10)

i=1 m=1 n=1

subject to 

Ci (m, n) − 1 ≤ 0,Ci (m, n) ∈ {0, 1}∀m, n

(5.11)

i

Wi ≤ T j ∀i, i → j

Cmin ( j) , P j (t) , ∀ j g( j) ≥ d( j)

(5.12) (5.13)

where Ci (m, n) identifies whether packet i is allocated to slot (m, n) and Ci (m, n) is equivalent data rate packet i can obtain on this slot. Since CSI is usually assumed to keep constant per frame, it simplifies as Ri (n). (5.11) is to ensure one slot can only be allocated to one packet while (5.12) and (5.13) correspond to the QoS requirements in terms of delay and throughput. Here Tj and Cmin ( j) are maximum latency of rtPS connection and minimum reserved data rate of nrtPS connection respectively with d(j) being connection j’s packet length and Pj(t) being number of

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packets present in connection j. Moreover, Wi denotes waiting time of packet i and g(j) is number of connection j’s scheduled packets.

5.5.7 MAC-PHY Cross Layer Approaches with APA and CAC Appropriate power control can not only reduce power consumption, but also improve system capacity by adjusting coverage ranges and improve spatial reuse efficiency. Higher transmission rates require higher SINR to maintain the same bit error rate. Due to this close relationship between rate and channel condition, incorporating transmission rate selection into MAC design is another promising way to increase the system performance. Paper [39] considered Adaptive Power Allocation (APA) emphasizes how to share the limited power resource of base station among different WiMax subscribers and further influences the access bandwidth of each subscriber; CAC highlights how to assign a subscriber’s access bandwidth to different types of applications. Authors suggested that APA and CAC have to work cooperatively to provide cross-layer resource management to build a WiMax access network. A power allocation scheme that produces optimal revenue and this is known as the optimal revenue criterion was studied. In order to investigate the APA revenue of a certain scheme, the revenue rate of each type of service as the revenue generated by a bandwidth unit was defined. Let rerUGS , rerrtPS , rernrtPS , and rerBE be the revenue rates of the following, respectively:

r r r r

Unsolicited Grant Service Real-Time Polling Service Non-Real-Time Polling Service Best-Effort Service

Obviously, different services have different prices due to their specific QoS requirements. As a result, r er U G S , r er r t P S , r er nr t P S , and r er B E always take distinct values. To maximize the APA revenue, the potential revenue of each subscriber, which is defined as the revenue that could be achieved if all arriving traffic is served, must be investigated. The potential revenue of a given subscriber k is determined by the amount of UGS, rtPS, nrtPS, and BE traffic load in its local network and the price of service (i.e., r er U G S , r er r t P S , r er nr t P S , and r er B E ). Let T Lk D denote the arriving downlink traffic load in subscriber k’s local network, and suppose this traffic load can generate potential revenue R Dk . Then, the revenue-to-bandwidth ratio of the kth subscriber is defined as RBRkD = RDk /TLk D . Since different WiMax subscribers can have different amount of UGS, rtPS, nrtPS, and BE traffics in their local networks, they hold distinct revenue-to-bandwidth ratios. The optimal revenue-criterion-based APA optimization has inherent preference to allocate more power resource to the sub-carriers that belong to the subscriber of high revenue-to-bandwidth ratio.

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5.6 Summary This chapter presents various QoS support mechanisms in WiMax networks. Existing proposals with state-of-art technology have been classified into three main categories: QoS support architecture; Bandwidth management mechanism and Traffic handling mechanism. Representative schemes from each of the categories have been evaluated with respect to major distinguishing characteristics of the WiMax MAC layer and PHY layer as specified in the IEEE 802.16d standard. Since scheduling algorithms provide mechanisms for bandwidth allocation and multiplexing at the packet level. Admission control and congestion control policies are all dependent on the specific scheduling disciplines used. For the uplink traffic, the scheduling algorithm has to work in tandem with Call Admission Control (CAC) to satisfy the QoS requirements. The CAC algorithm ensures that a connection is accepted into the network only if its QoS requirements can be satisfied as well as the performance of existing connections in the network is not deteriorated. The computational complexity of a proposed algorithm strongly influences its scalability. We suggest that the QoS support framework in WiMax network could be hierarchical structure. Since opportunistic MAC (OMAC) is the modern view of communicating over spatiotemporally varying wireless link whereby the multi-user diversity is exploited rather than combated to maximize band width efficiency or system throughput. The cross-layer nature embeds OMAC with the potential to revolutionize the design of wireless data networks from physical to data link layers. We suggest that cross-layer opportunistic scheduling can combine with adaptive power control scheme to provide QoS support in WiMax. Different mechanisms can address different issues in QoS support in WiMax network. How to combine Bandwidth management mechanisms; Traffic handling mechanisms in a cross-layer struture to provide new QoS support scheme in WiMax are open issues.

References 1. “IEEE Standard for Local and Metropolitan Area Networks Part 16: Air Interface for Fixed Broadband Wireless Access Systems,” IEEE Std 802.16–2004 (Revision of IEEE Std 802.16-2001), pp:1–857, 2004. 2. A. Ghosh, David R. Wolter J. G. Andrews and Runhua Chen, “Broadband Wireless Access with WiMax/802.16: Current Performance Benchmarks and Future Potential,” Communications Magazine, IEEE Volume 43, Issue 2, Feb. 2005, pp:129–136. 3. Dong-Hoon Cho, Jung-Hoon Song, Min-Su Kim, and Ki-Jun Han, “Performance Analysis of the IEEE 802.16 Wireless Metropolitan Area Network,” First International Conference on Distributed Frameworks for Multimedia Applications, 2005, DFMA ’05, pp:130–136, Feb. 2005. 4. Alavi, H.S.; Mojdeh, M., Yazdani, N. “A Quality of Service Architecture for IEEE 802.16Standards,” Asia-Pacific Conference on Communications, pp:249–253, Oct. 2005. 5. Yi-Ting Mai; Chun-Chuan Yang; Yu-Hsuan Lin, “Cross-Layer QoS Framework in the IEEE 802.16 Network,” The 9th International Conference on Advanced Communication Technology, Volume 3, pp:2090–2095, Feb. 2007.

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6. T. Kwon, H. Lee, S. Choi, J. Kim, and D. Cho, “Design and Implementation of a Simulator Based on a Cross-Layer Protocol between MAC and PHY layers in a WiBro Compatible IEEE802.16e OFDMA System,” IEEE Communication Magazine, Dec. 2005, Vol. 43, no.12, pp:136–146. 7. K. Gakhar, M. Achir, A. Gravey, “Dynamic Resource Reservation in IEEE 802.16 Broadband Wireless Networks,” IWQoS 2006, Jun. 2006, pp:140–148. 8. Y. Ge and G.-S. Kuo, “An Efficient Admission Control Scheme for Adaptive Multimedia Services in IEEE 802.16e Networks,” In IEEE 64th Vehicular Technology Conference, VTC-2006, pp: 1–5, Sept. 2006. 9. D. Niyato and E. Hossain, “Radio Resource Management Games in Wireless Networks: An Approach to Bandwidth Allocation and Admission Control for Polling Service in IEEE 802.16,” IEEE Wireless Communications, 14(1), Feb. 2007. 10. L. Wang, F. Liu, Y. Ji, and N. Ruangchaijatupon, “Admission Control for Non-preprovisioned Service Flow in Wireless Metropolitan Area Networks,” In Fourth European Conference on Universal Multiservice Networks, ECUMN ’07, pp:243–249, Feb. 2007. 11. H. Fen; H. Pin-Han; S. Xuemin; “WLC17-1: Performance Analysis of a Reservation Based Connection Admission Scheme in 802.16 Networks,” IEEE GLOBECOM’06, Nov. 2006, pp:1–5. 12. H. Wang; W. Li; D.P. Agrawal, “Dynamic admission control and QoS for 802.16 wireless MAN,” Wireless Telecommunications Symposium, April, 2005, pp:60–66. 13. C.-H. Jiang; T.-C. Tsai, “Token bucket based CAC and packet scheduling for IEEE 802.16 broadband wireless access networks,” Consumer Communications and Networking Conference, CCNC 2006, Volume 1, pp:183–187, 2006. 14. H. Wang; B. He; D.P. Agrawal, “Admission control and bandwidth allocation above packet level for IEEE 802.16 wireless MAN,” Parallel and Distributed Systems, 2006. ICPADS 2006, Jul. 2006. 15. J. Chen, W. Jiao, and Q. Guo, “An integrated QoS control architecture for IEEE 802.16 broadband wireless access systems,” in Proc. Global Telecommunication Conf., (Globecom) 2005, Vol. 6 pp:3330–3335, 2005. 16. W.T. Chen; S.B. Shih; J.L. Chiang, “A Two-Stage Packet Classification Algorithm,” Advanced Information Networking and Applications, 2003. AINA 2003, Mar. 2003, pp:762–767. 17. S. Jun-Bae; L. Hyong-Woo; C. Choong-Ho, “Performance of IEEE802.16 Random Access Protocol - Transient Queueing Analysis,” IEEE Global Telecommunications Conference, 2006, GLOBECOM’06, pp:1–6, Nov. 2006. 18. J.-B. Seo; S.-J. Kim; H.-W. Lee; C.-H. Cho, “An Efficient Capacity Allocation Scheme of Periodic Polling Services for a Multimedia Traffic in an IEEE802.16 System,” Mobile Adhoc and Sensor Systems (MASS) 2006, pp:11–20, Oct. 2006. 19. Lu´ıs Felipe M. de Moraes and Paulo Ditarso Maciel Jr,” An Alternative QoS Architecture for the IEEE 802.16 Standard,” www ravel.ufrj.br/arquivosPublicacoes/conext06.pdf. 20. Y. Chen; L. Li “A Random Early Expiration Detection Based Buffer Management Algorithm for Real-time Traffic over Wireless Networks,” Computer and Information Technology, 2005, CIT 2005, pp:507–511, Sept. 2005. 21. J. Lakkakorpi; A. Sayenko; J. Karhula; O. Alanen; J. Moilanen. “Active Queue Management for Reducing Downlink Delays in WiMax,” IEEE Vehicular Technology Conference, 2007, VTC-2007, 66th Volume, pp:326–330 , Fall 2007. 22. M. Engels, P. Coenen (IMEC). Christian Hoymann, “Congestion control mechanism for interworking between WLAN and WMAN,” IEEE C802.16d-03/83. Congestion Control. wirelessman.org/tgd/contrib/C80216d-03 83.pdf. 23. H.-F. Hsiao; A. Chindapol; J.A. Ritcey; Yaw-Chung Chen; Jenq-Neng Hwang, “A new multimedia packet loss classification algorithm for congestion control over wired/wireless channels,” Acoustics, Speech, and Signal Processing, 2005. ICASSP ’05, Volume 2, pp:ii/1105 - ii/1108, Mar. 2005.

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24. C.-W. Huang; J.-N. Hwang; D.C.W. Chang, “Congestion and error control for layered scalable video multicast over WiMax,” IEEE Mobile WiMax Symposium, 2007, pp:114–119, Mar. 2007. 25. S.A. Xergias; N. Passas; L. Merakos “Flexible resource allocation in IEEE 802.16 wireless metropolitan area networks,” Local and Metropolitan Area Networks, 2005. LANMAN 2005, pp:6pp, Sept. 2005. 26. W.K. Wong; H. Tang; Shanzeng Guo; Leung, “Scheduling algorithm in a point-to-multipoint broadband wireless access network,” Vehicular Technology Conference, 2003. VTC 2003, Volume 3, pp:1593–1597. Fall 2003. 27. S. Ryu; B. Ryu; H. Seo; M. Shi, “Urgency and efficiency based wireless downlink packet scheduling algorithm in OFDMA system,” IEEE Vehicular Technology Conference, 2005, 61st Volume 3, pp:1456–1462, May. 2005. 28. A. Shejwal; A. Parhar, “Service Criticality Based Scheduling for IEEE 802.16 WirelessMAN,” The 2nd International Conference on Wireless Broadband and Ultra Wideband Communications (AusWireless 2007), pp:12–12, Aug. 2007. 29. M. Ma, B.C. Ng, “Supporting Differentiated Services in Wireless Access Networks,” Communication systems, ICCS 2006, pp:1–5. 2006. 30. K. Wongthavarawat, A. Ganz, “Packet scheduling for QoS support in IEEE 802.16 broadband wireless access systems”, International Journal of Communication Systems, vol. 16, issue 1, pp:81–96, Feb.2003. 31. J. Chen, W. Jiao, H. Wang, “A service flow management strategy for IEEE 802.16 broadband wireless access systems in TDD mode,” IEEE International Conference, ICC 2005. Volume: 5, pp:3422–3426, 2005. 32. H. Safa.; H. Artail.; M. Karam.; R. Soudan.; S. Khayat, “New Scheduling Architecture for IEEE 802.16 Wireless Metropolitan Area Network,” IEEE/ACS International Conference on Computer Systems and Applications, 2007. AICCSA07, pp:203–210, May. 2007. 33. N. Liu; X. Li; C. Pei; B. Yang, “Delay Character of a Novel Architecture for IEEE 802.16 Systems,” Proceedings of the Sixth International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT’05), pp:293–296, Dec. 2005. 34. M. Ma, J. Lu, Sanjay Kumar Bose, and Boon Chong Ng. “A Three-Tier Framework and scheduling to Support QoS Service in WiMax,” Information, Communications & Signal Processing, 2007, 6th International Conference, pp:1–5, Dec. 2007. 35. X. Liu, “Optimal Opportunistic Scheduling in Wireless Networks”. IEEE 58th Vehicular Technology Conference, 2003, Vol. 3, pp:1417–1421, Oct. 2003. 36. J. Sang, D. Jeong, and W. Jeon, “Cross-layer Design of Packet Scheduling and Resource Allocation in OFDMA Wireless Multimedia Networks,” in Proc. 63r d Vehicular Technology Conf. (VTC) 2006, pp:309–313, 2006. 37. Q. Liu, X. Wang and G. B. Giannakis, “A Cross-Layer Scheduling Algorithm With QoS Support in Wireless Networks,” IEEE Transactions on Vehicular Technology, May. 2006, Vol. 55, no.3, pp:839–847. 38. L. Wan, W. Ma, Z. Guo, “A Cross-layer Packet Scheduling and Subchannel Allocation Scheme in 802.16e OFDMA System,” Wireless Communications and Networking Conference, 2007, WCNC 2007, pp:1865–1870, Mar. 2007. 39. B. Rong Y. Qian Hsiao-Hwa Chen , “Adaptive power allocation and call admission control in multiservice WiMax access networks,” IEEE Wireless Communications, Feb. 2007, Volume: 14, Issue: 1, pp:: 14–19.

Chapter 6

Mobile WiMax Performance Optimization Stanislav Filin, Sergey Moiseev and Mikhail Kondakov

Abstract The Mobile WiMax system is a promising solution for delivering broadband wireless access services to mobile users. Radio resource management (RRM) algorithms play a key role in the Mobile WiMax network. The Mobile WiMax network has a number of distinct features that complicate the use of conventional RRM algorithms. We propose load-balancing approach to RRM in the Mobile WiMax network. To illustrate the advantages of the load-balancing approach, we present RRM algorithms, including call admission control, adaptive transmission, horizontal handover, and dynamic bandwidth allocation algorithms. These algorithms jointly maximize the network capacity and guarantee users quality-of-service requirements. Keywords Mobile WiMax · OFDMA · Performance optimization · Loadbalancing · System load · Quality-of-service · Radio resource management · Call admission control · Adaptive transmission · Handover · Dynamic bandwidth allocation

6.1 Introduction IEEE standards 802.16 [1] and 802.16e [2] specify the requirements for the medium access control (MAC) and physical (PHY) layers of the WiMax and Mobile WiMax systems. These systems are attracting huge interest as a promising solution for delivering fixed and mobile broadband wireless access services. The standards have incorporated such key technologies as quality-of-service (QoS) mechanisms, adaptive coding and modulation, power control, selective and hybrid automatic repeat request, orthogonal frequency division multiplexing (OFDM) and orthogonal frequency division multiple access (OFDMA), support of adaptive antenna systems and multiple-input multiple-output transmission. This provides great potential for satisfying users and operators needs.

S. Filin (B) National Institute of Information and Communications Technology, 3-4, Hikarino-oka, Yokosuka, 239-0847, Japan

M. Ma (ed.), Current Technology Developments of WiMax Systems, C Springer Science+Business Media B.V. 2009 DOI 10.1007/978-1-4020-9300-5 6, 

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From a user’s perspective, a wireless system should deliver his/her data with the required QoS level, while an operator aims at maximizing network capacity and revenue [3]. These two goals are achieved by radio resource management (RRM) algorithms. These algorithms play a key role in the Mobile WiMax system but their complexity is high due to a lot of degrees of freedom and optimization opportunities. RRM in the OFDMA-based Mobile WiMax network includes call admission control, adaptive transmission, handover, and dynamic bandwidth allocation algorithms. Call admission control algorithm handles system overloading and satisfies users QoS by limiting the number of users in the system [4]. Adaptive transmission algorithms enable QoS-guaranteed opportunistic data transmission over a wireless medium [5]. They include scheduling, adaptive coding and modulation, power control, and time-frequency resource allocation. Seamless horizontal handover guarantees continuous service by assigning new serving base stations to a user during his/her mobility and system load variations. Dynamic bandwidth allocation algorithm distributes signal bandwidth within a group of sectors to balance their load. The Mobile WiMax network has a number of distinct features that complicate the use of conventional RRM algorithms. Users may have multiple service flows with different traffic and QoS requirements, be in different receiving conditions, and use different coding and modulation schemes and transmission power values. Consequently, it is difficult to determine the time-frequency and power resources required for the given number of users. This is the main challenge of the call admission control algorithm. In adaptive transmission algorithms, the key problems are computational intensity due to a large number of degrees of freedom, complicated frame structure, and complex MAC and PHY layers processing procedures. Traditional horizontal handover algorithms are based on the received signal level or signal-to-interference-plus-noise ratio (SINR). However, such handover algorithms neither guarantee QoS requirements nor maximize the capacity in the Mobile WiMax OFDMA network. In the Mobile WiMax network several sectors may form a group of sectors sharing the same signal bandwidth by using different groups of subcarriers. This provides additional degree of freedom for optimizing network capacity and QoS. Therefore, RRM algorithms taking into account the distinct features of the Mobile WiMax OFDMA network should be provided. We describe load-balancing approach to RRM in the Mobile WiMax OFDMA network, which is based on our system load model [6]. This model takes into account different traffic, QoS requirements, and receiving conditions of the users and efficiently combines time-frequency and power resources, as well as downlink and uplink resources in the expression for the system load. To illustrate the advantages of the load-balancing approach, we present RRM algorithms, including call admission control, adaptive transmission, horizontal handover, and dynamic bandwidth allocation algorithms. We demonstrate the advantages of our algorithms over the conventional ones by means of system level simulation. Each algorithm individually and all algorithms as a whole satisfy QoS requirements and maximize the network capacity. We define the network capacity as the maximum achievable network throughput when QoS requirements are satisfied for all the users served.

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First, we describe our system load model for the Mobile WiMax OFDMA network. Then, based on this system load model we present call admission control, adaptive transmission, horizontal handover, and dynamic bandwidth allocation algorithms for the Mobile WiMax OFDMA network.

6.2 System Load Model A system load model is traditionally used in call admission control algorithms. System load characterizes the degree of system resources consumption. When all system resources have been consumed, new users are not admitted to the system. We have proposed a system load model for the Mobile WiMax OFDMA network in [6]. We consider the Mobile WiMax network comprising some sectors and some users. The sectors transmit data to the users in the downlink and the users transmit data to the sectors in the uplink. Each user may have several downlink service flows and several uplink service flows, where a service flow is a flow of data packets from an application. Different service flows may have different traffic arrival rates. The network uses the OFDM technology, the OFDMA multiple access, and the time division duplex. Each sector uses frames for the downlink and uplink data transmission, where a frame comprises a downlink subframe and an uplink subframe (see Fig. 6.1). The frame boundary between the downlink and uplink subframes may be adaptively adjusted. In the time domain the frame comprises OFDM symbols, while in the frequency domain it comprises subcarriers. In the frame, the time resource is equal to the number of OFDM symbols and the size of the frequency resource is defined by the number of subcarriers in one OFDM symbol. In the OFDMA, each subcarrier can be assigned to any user. When a subcarrier is assigned to a user, a coding and modulation scheme and a transmission power value are selected for this user on this subcarrier. In addition, sectors in the downlink and users in the uplink have the maximum transmission power constraints. The adaptation parameters available in the Mobile WiMax network are frame boundary

Fig. 6.1 OFDMA frame structure in Mobile WiMax network

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position, subcarriers assignment, coding and modulation schemes, and transmission power values. Data packets of a service flow should be transmitted with the required QoS. The set of QoS requirements includes the minimum average data rate, the maximum data block reception error probability, and the maximum average data block transmission delay. All these QoS requirements can be satisfied by selecting an appropriate transmission power value for the given coding and modulation scheme and the given receiving conditions [7–9]. The system load model of the Mobile WiMax network should satisfy the following requirements:

r r

r

The Mobile WiMax network has shared and individual resources. For example, transmission power of a sector is a shared system resource, because it is concurrently used by several users. Transmission power of a user is its individual resource. The system load model should include shared system resources only. When adaptive coding and modulation and power control are employed, different adaptive transmission algorithms having different target functions may be used, which leads to different amount of the consumed system resource. Consequently, the amount of currently consumed system resource cannot characterize the system load. The system load should be equal to the minimum amount of the system resources needed to satisfy QoS requirements for all the users served. The amount of the available system resource may be different in different sectors. To compare system load values of different sectors, the system load should be normalized to the available system resource. In addition, normalization simplifies RRM algorithms.

Our system load model includes uplink load, downlink load, sector load, and network load. To calculate each system load, we use the following approach. First, we write an expression for the amount of the normalized shared system resources, consumed by all users, as a function of adaptation parameters. Then, we find the system load by minimizing this expression over adaptation parameters under the constraint on the individual system resources, while satisfying the QoS requirements for all users. In the uplink, the shared system resource is the time-frequency resource of the uplink subframe. Transmission power of each user is an individual system resource. Adaptation parameters are the set of the assigned uplink subcarriers, coding and modulation schemes, and transmission power values. In the downlink, the shared system resources are the time-frequency resource of the downlink subframe and the downlink transmission power. Adaptation parameters are the set of the assigned downlink subcarriers, coding and modulation schemes and transmission power values. In a sector, the shared system resources are the uplink resources and the downlink resources. A new adaptation parameter, that is, the frame boundary position between the downlink and uplink subframes is added to the uplink and downlink adaptation parameters.

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The proposed system load model takes into account all distinct features of the Mobile WiMax network, including time-frequency and power resources, QoS requirements, adaptive coding and modulation and power control, time division duplex and adaptation of the frame boundary between the downlink and uplink subframes, different users with different traffics and receiving conditions.

6.3 Call Admission Control Any wireless network has constrained resources and can serve a limited number of users with a given QoS level. Hence, a call admission control algorithm that decides whether new users should be admitted to the network is required. The admission criteria may be different. The known call admission control schemes are based on SINR, interference, bandwidth, load, or system capacity [10]. In the Mobile WiMax network the most suitable scheme is the one maximizing network capacity while satisfying QoS requirements for all admitted users. Such scheme maximizes operator’s revenue and guarantees user’s satisfaction. This call admission control algorithm admits a new user in the sector, if the sector load remains less than one when a new user and all previous users are served by the sector. Since the system load is equal to the minimum required system resources in our model, this call admission control algorithm maximizes the sector capacity. More details on the call admission control algorithm in the Mobile WiMax OFDMA network can be found in [11].

6.4 Adaptive Transmission Adaptive transmission algorithms include scheduling, adaptive coding and modulation, power control, and adaptive resource allocation. A scheduler guarantees QoS and fairness and can also handle user priorities by making a decision, how much data and of which service flows will be transmitted in the current frame. To satisfy the required QoS level, different pairs of coding and modulation scheme number and transmission power value can be used. The selection of a pair for data transmission is based on the target function. For example, when the total transmission power is minimized, a coding and modulation scheme with the minimum transmission rate and a corresponding transmission power are selected. However, when the total allocated time-frequency resource is minimized, a coding and modulation scheme with the maximum transmission rate and a corresponding transmission power are selected. In the OFDMA, adaptive resource allocation algorithm plays an important role. Receiving conditions are different for different users on the same subcarrier. Moreover, they are different for the same user on different subcarriers. Users can be assigned to the subcarriers with the best receiving conditions, thereby multi-user diversity gain is obtained. To enable opportunistic data transmission over a wireless medium a cross-layer approach should be used [5]. In this case, scheduling, adaptive

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coding and modulation, power control, and adaptive resource allocation algorithms should have the same target function and should be jointly optimized. Optimization of the OFDMA systems is a subject of a considerable literature. Time-frequency resource minimization, transmission power minimization, throughput maximization, and utility function optimization are traditionally performed. However, most of the known algorithms do not consider the distinct features of the Mobile WiMax network. First, MAC and PHY layers processing is not taken into account. Data blocks of a service flow arrive from the upper layers at the MAC layer, where they are converted into data packets using fragmentation and packing operations (see Fig. 6.2). Also, cyclic redundancy check and automatic repeat request mechanisms can be used. The set of data packets of the service flow arrives at the PHY layer, where it is converted into coding blocks. Each coding block is coded and decoded independently. QoS requirements are specified for the data blocks, while the coding blocks are transmitted and received. Hence, MAC and PHY layers processing should be taken into account to enable QoS-guaranteed data transmission [7–9]. Moreover, most of the known algorithms do not perform joint downlink and uplink optimization. A common problem for all the adaptive transmission algorithms in the OFDMA networks is their computational complexity due to a large number of degrees of freedom. Using our system load model results in an efficient and fast adaptive transmission algorithm. We proposed the adaptive transmission algorithm maximizing the sector capacity while satisfying the QoS requirements for all service flows scheduled for transmission in the current frame in [12]. In our algorithm, we consider such adaptation parameters as a position of the frame boundary between the downlink and uplink subframes, coding and modulation schemes, transmission power values, and positions of the service flows within the frame. O