3G, HSPA and FDD versus TDD networking smart antennas and adaptive modulation

3G, HSPA and FDD versus TDD Networking

3G, HSPA and FDD
versus TDD Networking
Smart Antennas and Adaptive Modulation
Second Edition
L. Hanzo, University of Southampton, UK
J. S. Blogh, Anritsu, UK
S. Ni, Panasonic Mobile Communication, UK
IEEE Communications Society, Sponso
r
John Wiley & Sons, Ltd
Copyright
c
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Library of Congress Cataloging-in-Publication Data
Hanzo, Lajos, 1952-
3G, HSPA and FDD versus TDD Networking / by L. Hanzo, J. S. Blogh and S. Ni – 2nd ed.
p. cm.
Includes bibliographical references and index.
ISBN 978-0-470-75420-7 (cloth)
1. Wireless communication systems. 2. Cellular telephone systems.
I. Blogh, J. S. (Jonathan S.) II. Title.
TK5103.2.H35 2008
621.382’1–dc22
2007046621
British Library Cataloguing in Publication Data
A catalogue record for this book is available from the British Library
ISBN 978-0-470-75420-7 (HB)

Typeset by the authors using L
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Printed and bound in Great Britain by Antony Rowe Ltd, Chippenham, England.
This book is printed on acid-free paper responsibly manufactured from sustainable forestry in which at least two trees are
planted for each one used for paper production.
Contents
About the Authors xv
Other Wiley and IEEE Press Books on Related Topics xvii
Preface xix
Acknowledgments xxxi
1 Third-generation CDMA Systems 1
1.1 Introduction . 1
1.2 BasicCDMASystem 2
1.2.1 Spread Spectrum Fundamentals . 2
1.2.1.1 Frequency Hopping . . 3
1.2.1.2 DirectSequence 3
1.2.2 The Effect of Multipath Channels 6
1.2.3 Rake Receiver . . 9
1.2.4 Multiple Access . 13
1.2.4.1 DLInterference 14
1.2.4.2 UplinkInterference 15
1.2.4.3 GaussianApproximation 18
1.2.5 SpreadingCodes 19
1.2.5.1 m-sequences 20
1.2.5.2 GoldSequences 21
1.2.5.3 Extended m-sequences 21
1.2.6 ChannelEstimation 22

1.2.6.1 DLPilot-assistedChannelEstimation 22
1.2.6.2 ULPilot-symbolAssistedChannelEstimation 23
1.2.6.3 Pilot-symbol Assisted Decision-directed Channel
Estimation 24
1.2.7 Summary 26
vi CONTENTS
1.3 Third-generationSystems 26
1.3.1 Introduction . . . . . 26
1.3.2 UMTS Terrestrial Radio Access (UTRA) . . . . 29
1.3.2.1 CharacteristicsofUTRA 29
1.3.2.2 TransportChannels 33
1.3.2.3 PhysicalChannels 34
1.3.2.3.1 DedicatedPhysicalChannels 35
1.3.2.3.2 CommonPhysicalChannels 37
1.3.2.3.2.1 Common Physical Channels of the FDD
Mode. 37
1.3.2.3.2.2 Common Physical Channels of the TDD
Mode. 40
1.3.2.4 Service Multiplexing and Channel Coding in UTRA . . . . 43
1.3.2.4.1 CRCAttachment 43
1.3.2.4.2 TransportBlockConcatenation. 43
1.3.2.4.3 Channel-coding 43
1.3.2.4.4 RadioFramePadding 46
1.3.2.4.5 FirstInterleaving 46
1.3.2.4.6 RadioFrameSegmentation 46
1.3.2.4.7 RateMatching. 46
1.3.2.4.8 Discontinuous Transmission Indication. . . . . . 47
1.3.2.4.9 Transport Channel Multiplexing. . . 47
1.3.2.4.10 PhysicalChannelSegmentation. 47
1.3.2.4.11 SecondInterleaving. 47

1.3.2.4.12 PhysicalChannelMapping 47
1.3.2.4.13 Mapping Several Multirate Services to the UL
PhysicalChannelsinFDDMode 48
1.3.2.4.14 Mapping of a 4.1 Kbps Data Service to the DL
DPDCHinFDDMode 49
1.3.2.4.15 Mapping Several Multirate Services to the UL
PhysicalChannelsinTDDMode 50
1.3.2.5 Variable-rate and Multicode Transmission in UTRA . . . . 52
1.3.2.6 Spreading and Modulation . . . . . . 52
1.3.2.6.1 Orthogonal Variable Spreading Factor Codes. . . 55
1.3.2.6.2 UplinkScramblingCodes. 57
1.3.2.6.3 DownlinkScramblingCodes 57
1.3.2.6.4 Uplink Spreading and Modulation. . 58
1.3.2.6.5 Downlink Spreading and Modulation. . . . . . . 58
1.3.2.7 Random Access . 60
1.3.2.7.1 Mobile-initiated Physical Random Access
Procedures 60
1.3.2.7.2 Common Packet Channel Access Procedures. . . 61
1.3.2.8 PowerControl 61
1.3.2.8.1 Closed-loop Power Control in UTRA. . . . . . . 62
1.3.2.8.2 Open-loop Power Control in TDD Mode. . . . . 62
CONTENTS vii
1.3.2.9 CellIdentification 63
1.3.2.9.1 CellIdentificationintheFDDMode 63
1.3.2.9.2 CellIdentificationintheTDDMode 65
1.3.2.10 Handover 66
1.3.2.10.1 Intra-frequency Handover or Soft Handover 66
1.3.2.10.2 Inter-frequency Handover or Hard Handover. . . 67
1.3.2.11 Intercell Time Synchronization in the UTRA TDD Mode . 68
1.3.3 The cdma2000 Terrestrial Radio Access . 68

1.3.3.1 Characteristics of cdma2000 . 70
1.3.3.2 Physical Channels in cdma2000 71
1.3.3.3 Service Multiplexing and Channel Coding . 74
1.3.3.4 Spreading and Modulation . . 74
1.3.3.4.1 Downlink Spreading and Modulation. . . 75
1.3.3.4.2 Uplink Spreading and Modulation. 77
1.3.3.5 Random Access 79
1.3.3.6 Handover 81
1.3.4 Performance-enhancementFeatures 82
1.3.4.1 DownlinkTransmitDiversityTechniques 82
1.3.4.1.1 Space Time Block Coding-based Transmit
Diversity 82
1.3.4.1.2 Time-switchedTransmitDiversity 82
1.3.4.1.3 Closed-loopTransmitDiversity. 82
1.3.4.2 AdaptiveAntennas 84
1.3.4.3 Multi-user Detection/Interference Cancellation . . . 84
1.3.5 Summaryof3GSystems 84
1.4 SummaryandConclusions 85
2 High Speed Downlink and Uplink Packet Access 87
2.1 Introduction . 87
2.2 High Speed Downlink Packet Access . . . 88
2.2.1 PhysicalLayer 92
2.2.1.1 High Speed Physical Downlink Shared Channel
(HS-PDSCH) 94
2.2.1.2 HighSpeedSharedControlChannel(HS-SCCH) 96
2.2.1.3 High Speed Dedicated Physical Control Channel
(HS-DPCCH) 98
2.2.2 Medium Access Control (MAC) Layer . 98
2.3 High Speed Uplink Packet Access 99
2.3.1 PhysicalLayer 102

2.3.1.1 E-DCH Dedicated Physical Data Channel (E-DPDCH) . . 104
2.3.1.2 E-DCH Dedicated Physical Control Channel (E-DPCCH) . 106
2.3.1.3 EDCHHARQIndicatorChannel(E-HICH) 106
2.3.1.4 E-DCHAbsoluteGrantChannel(E-AGCH) 107
2.3.1.5 E-DCHRelativeGrantChannel(E-RGCH) 107
2.3.2 MACLayer 108
2.4 ImplementationIssues 112
viii CONTENTS
2.4.1 HS-SCCHDetectionAlgorithm 112
2.4.1.1 Viterbi’sPathMetricDifferenceAlgorithm 112
2.4.1.2 Yamamoto–ItohAlgorithm 113
2.4.1.3 MinimumPathMetricDifferenceAlgorithm 113
2.4.1.4 AveragePathMetricDifferenceAlgorithm 114
2.4.1.5 Frequency of Path Metric Difference Algorithm . . . 114
2.4.1.6 LastPathMetricDifferenceAlgorithm 114
2.4.1.7 DetectionAlgorithmPerformances 114
2.4.2 16QAM 115
2.4.2.1 AmplitudeandPhaseEstimation 115
2.4.2.2 Equalizer 116
2.4.3 HARQResultProcessingTime 116
2.4.4 CrestFactor 117
3 HSDPA-style Burst-by-Burst Adaptive Wireless Transceivers 119
3.1 Motivation 119
3.2 Narrowband Burst-by-Burst Adaptive Modulation . . . . 120
3.3 Wideband Burst-by-Burst Adaptive Modulation . . . . . 123
3.3.1 Channel Quality Metrics . . 123
3.4 WidebandBbB-AQAMVideoTransceivers 126
3.5 BbB-AQAMPerformance 129
3.6 WidebandBbB-AQAMVideoPerformance 131
3.6.1 AQAMSwitchingThresholds 133

3.6.2 Turbo-coded AQAM Videophone Performance . 135
3.7 Burst-by-Burst Adaptive Joint-Detection CDMA Video Transceiver . . 136
3.7.1 Multi-user Detection for CDMA . . 136
3.7.2 JD-ACDMA Modem Mode Adaptation and Signalling . 138
3.7.3 TheJD-ACDMAVideoTransceiver 139
3.7.4 JD-ACDMAVideoTransceiverPerformance 141
3.8 Subband-adaptive OFDM Video Transceivers . . . . . . 145
3.9 SummaryandConclusions 150
4 Intelligent Antenna Arrays and Beamforming 151
4.1 Introduction . . 151
4.2 Beamforming 152
4.2.1 AntennaArrayParameters 152
4.2.2 Potential Benefits of Antenna Arrays in Mobile Communications . . 153
4.2.2.1 Multiple Beams . 153
4.2.2.2 AdaptiveBeams 155
4.2.2.3 NullSteering 155
4.2.2.4 DiversitySchemes 155
4.2.2.5 Reduction in Delay Spread and Multipath Fading . . 158
4.2.2.6 ReductioninCo-channelInterference 160
4.2.2.7 CapacityImprovementandSpectralEfficiency 161
4.2.2.8 IncreaseinTransmissionEfficiency 161
4.2.2.9 ReductioninHandovers 161
CONTENTS ix
4.2.3 SignalModel 162
4.2.4 ABeamformingExample 165
4.2.5 AnalogBeamforming 166
4.2.6 DigitalBeamforming 167
4.2.7 Element-spaceBeamforming 167
4.2.8 Beam-spaceBeamforming 168
4.3 AdaptiveBeamforming 169

4.3.1 FixedBeams 170
4.3.2 TemporalReferenceTechniques 171
4.3.2.1 LeastMeanSquares 174
4.3.2.2 NormalizedLeastMeanSquaresAlgorithm 176
4.3.2.3 SampleMatrixInversion 176
4.3.2.4 RecursiveLeastSquares 183
4.3.3 SpatialReferenceTechniques 184
4.3.3.1 AntennaCalibration 185
4.3.4 BlindAdaptation 187
4.3.4.1 Constant Modulus Algorithm . 188
4.3.5 AdaptiveArraysintheDownlink 189
4.3.6 AdaptiveBeamformingPerformanceResults 191
4.3.6.1 Two Element Adaptive Antenna Using Sample Matrix
Inversion 191
4.3.6.2 Two Element Adaptive Antenna Using Unconstrained
LeastMeanSquares 195
4.3.6.3 Two Element Adaptive Antenna Using Normalized Least
MeanSquares 197
4.3.6.4 Performance of a Three Element Adaptive Antenna Array . 199
4.3.6.5 ComplexityAnalysis 212
4.4 SummaryandConclusions 213
5 Adaptive Arrays in an FDMA/TDMA Cellular Network 215
5.1 Introduction . 215
5.2 Modelling Adaptive Antenna Arrays . . . 216
5.2.1 AlgebraicManipulationwithOptimalBeamforming 216
5.2.2 Using Probability Density Functions . . . 218
5.2.3 SampleMatrixInversionBeamforming 219
5.3 ChannelAllocationTechniques 220
5.3.1 OverviewofChannelAllocation 221
5.3.1.1 FixedChannelAllocation 222

5.3.1.1.1 ChannelBorrowing 224
5.3.1.1.2 FlexibleChannelAllocation. 226
5.3.1.2 DynamicChannelAllocation 226
5.3.1.2.1 CentrallyControlledDCAAlgorithms 228
5.3.1.2.2 DistributedDCAAlgorithms 228
5.3.1.2.3 LocallyDistributedDCAAlgorithms. 229
5.3.1.3 HybridChannelAllocation 230
5.3.1.4 TheEffectofHandovers 231
x CONTENTS
5.3.1.5 TheEffectofTransmissionPowerControl 232
5.3.2 SimulationoftheChannelAllocationAlgorithms 232
5.3.2.1 The Mobile Radio Network Simulator, “Netsim” . . . 232
5.3.2.1.1 PhysicalLayerModel. 235
5.3.2.1.2 ShadowFadingModel. 235
5.3.3 OverviewofChannelAllocationAlgorithms 236
5.3.3.1 FixedChannelAllocationAlgorithm 237
5.3.3.2 Distributed Dynamic Channel Allocation Algorithms 237
5.3.3.3 Locally Distributed Dynamic Channel Allocation
Algorithms 238
5.3.3.4 PerformanceMetrics 239
5.3.3.5 Nonuniform Traffic Model . . . . . . 240
5.3.4 DCA Performance without Adaptive Arrays . . . 241
5.4 EmployingAdaptiveAntennaArrays 242
5.5 Multipath Propagation Environments 245
5.6 NetworkPerformanceResults 251
5.6.1 SystemSimulationParameters 252
5.6.2 Non-wraparound Network Performance Results . 261
5.6.2.1 PerformanceResultsoveraLOSChannel 262
5.6.2.2 Performance Results over a Multipath Channel 268
5.6.2.3 Performance over a Multipath Channel using Power

Control 272
5.6.2.4 Transmission over a Multipath Channel using Power
Control and Adaptive Modulation . . . 278
5.6.2.5 Power Control and Adaptive Modulation Algorithm . 281
5.6.2.6 Performance of PC-assisted, AQAM-aided Dynamic
ChannelAllocation 284
5.6.2.7 Summary of Non-wraparound Network Performance 291
5.6.3 Wrap-around Network Performance Results . . . 292
5.6.3.1 PerformanceResultsoveraLOSChannel 293
5.6.3.2 Performance Results over a Multipath Channel 297
5.6.3.3 Performance over a Multipath Channel using Power
Control 300
5.6.3.4 Performance of an AQAM based Network using Power
Control 307
5.7 SummaryandConclusions 315
6 HSDPA-style FDD Networking, Adaptive Arrays and Adaptive Modulation 317
6.1 Introduction . . 317
6.2 Direct Sequence Code Division Multiple Access . . . . . 318
6.3 UMTS Terrestrial Radio Access . . 320
6.3.1 Spreading and Modulation . 321
6.3.2 CommonPilotChannel 325
6.3.3 PowerControl 326
6.3.3.1 UplinkPowerControl 327
6.3.3.2 DownlinkPowerControl 328
CONTENTS xi
6.3.4 SoftHandover 328
6.3.5 Signal-to-interferenceplusNoiseRatioCalculations 329
6.3.5.1 Downlink 329
6.3.5.2 Uplink 330
6.3.6 Multi-user Detection . . . 331

6.4 SimulationResults 332
6.4.1 SimulationParameters 332
6.4.2 TheEffectofPilotPoweronSoftHandoverResults 336
6.4.2.1 Fixed Received Pilot Power Thresholds without Shadowing 336
6.4.2.2 Fixed Received Pilot Power Thresholds with 0.5 Hz
Shadowing 339
6.4.2.3 Fixed Received Pilot Power Thresholds with 1.0 Hz
Shadowing 342
6.4.2.4 Summary 342
6.4.2.5 Relative Received Pilot Power Thresholds without
Shadowing 344
6.4.2.6 Relative Received Pilot Power Thresholds with 0.5 Hz
Shadowing 346
6.4.2.7 Relative Received Pilot Power Thresholds with 1.0 Hz
Shadowing 348
6.4.2.8 Summary 351
6.4.3 E
c
/I
o
PowerBasedSoftHandoverResults 351
6.4.3.1 Fixed E
c
/I
o
Thresholds without Shadowing 351
6.4.3.2 Fixed E
c
/I
o

Thresholdswith0.5HzShadowing 354
6.4.3.3 Fixed E
c
/I
o
Thresholdswith1.0HzShadowing 355
6.4.3.4 Summary 357
6.4.3.5 Relative E
c
/I
o
Thresholds without Shadowing . . . 358
6.4.3.6 Relative E
c
/I
o
Thresholdswith0.5HzShadowing 359
6.4.3.7 Relative E
c
/I
o
Thresholdswith1.0HzShadowing 361
6.4.3.8 Summary 363
6.4.4 OverviewofResults 363
6.4.5 Performance of Adaptive Antenna Arrays in a High Data Rate
PedestrianEnvironment 365
6.4.6 Performance of Adaptive Antenna Arrays and Adaptive
Modulation in a High Data Rate Pedestrian Environment . . . 373
6.5 SummaryandConclusions 380
7 HSDPA-style FDD/CDMA Performance Using Loosely Synchronized

Spreading Codes 383
7.1 Effects of Loosely Synchronized Spreading Codes on the Performance
ofCDMASystems 383
7.1.1 Introduction . . . . 383
7.1.2 LooselySynchronizedCodes 384
7.1.3 SystemParameters 386
7.1.4 SimulationResults 388
7.1.5 Summary 391
xii CONTENTS
7.2 EffectsofCellSizeontheUTRAPerformance 392
7.2.1 Introduction . . . . . 392
7.2.2 SystemModelandSystemParameters 393
7.2.3 SimulationResultsandComparisons 395
7.2.3.1 Network Performance using Adaptive Antenna Arrays 395
7.2.3.2 Network Performance using Adaptive Antenna Arrays and
Adaptive Modulation . . 398
7.2.4 SummaryandConclusion 400
7.3 Effects of SINR Threshold on the Performance of CDMA Systems . . . 401
7.3.1 Introduction . . . . . 401
7.3.2 SimulationResults 402
7.3.3 SummaryandConclusion 406
7.4 Network-layer Performance of Multi-carrier CDMA . . . 407
7.4.1 Introduction . . . . . 407
7.4.2 SimulationResults 413
7.4.3 SummaryandConclusions 419
8 HSDPA-style TDD/CDMA Network Performance 421
8.1 Introduction . . 421
8.2 UMTS FDD versus TDD Terrestrial Radio Access . . . . 422
8.2.1 FDDversusTDDSpectrumAllocationofUTRA 422
8.2.2 PhysicalChannels 423

8.3 UTRATDD/CDMASystem 424
8.3.1 TheTDDPhysicalLayer 425
8.3.2 CommonPhysicalChannelsoftheTDDMode 425
8.3.3 PowerControl 426
8.3.4 TimeAdvance 428
8.4 InterferenceScenarioinTDDCDMA 428
8.4.1 Mobile-to-MobileInterference 429
8.4.2 BaseStation-to-BaseStationInterference 429
8.5 SimulationResults 430
8.5.1 SimulationParameters 431
8.5.2 Performance of Adaptive Antenna Array Aided TDD CDMA
Systems 433
8.5.3 Performance of Adaptive Antenna Array and Adaptive Modulation
AidedTDDHSDPA-styleSystems 438
8.6 Loosely Synchronized Spreading Code Aided Network Performance
ofUTRA-likeTDD/CDMASystems 442
8.6.1 Introduction . . . . . 442
8.6.2 LSCodesinUTRATDD/CDMA 444
8.6.3 SystemParameters 445
8.6.4 SimulationResults 446
8.6.5 SummaryandConclusions 449
CONTENTS xiii
9 The Effects of Power Control and Hard Handovers on the UTRA
TDD/CDMA System 451
9.1 AHistoricalPerspectiveonHandovers 451
9.2 HardHOinUTRA-likeTDD/CDMASystems 452
9.2.1 RelativePilotPower-basedHardHO 453
9.2.2 SimulationResults 454
9.2.2.1 Near-symmetricUL/DLTrafficLoads 455
9.2.2.2 AsymmetricTrafficloads 458

9.3 PowerControlinUTRA-likeTDD/CDMASystems 464
9.3.1 UTRATDDDownlinkClosed-loopPowerControl 464
9.3.2 UTRATDDUplinkClosed-loopPowerControl 466
9.3.3 Closed-loopPowerControlSimulationResults 466
9.3.3.1 UL/DLSymmetricTrafficLoads 467
9.3.3.2 ULDominatedAsymmetricTrafficLoads 470
9.3.3.3 DLDominatedAsymmetricTrafficLoads 473
9.3.4 UTRATDDULOpen-loopPowerControl 475
9.3.5 Frame-delay-basedPowerAdjustmentModel 476
9.3.5.1 UL/DLSymmetricTrafficLoads 480
9.3.5.2 AsymmetricTrafficLoads 483
9.4 SummaryandConclusion 486
10 Genetically Enhanced UTRA/TDD Network Performance 489
10.1 Introduction . 489
10.2TheGeneticallyEnhancedUTRA-likeTDD/CDMASystem 490
10.3SimulationResults 494
10.4SummaryandConclusion 499
11 Conclusions and Further Research 501
11.1SummaryofFDDNetworking 501
11.2SummaryofFDDversusTDDNetworking 506
11.3FurtherResearch 511
11.3.1 AdvancedObjectiveFunctions 513
11.3.2 OtherTypesofGAs 513
Glossary 515
Bibliography 521
Subject Index 547
Author Index 553

About the Authors
Lajos Hanzo () FREng, FIEEE,

FIET, DSc received his degree in electronics in 1976 and his doctorate
in 1983. During his 31-year career in telecommunications he has held
various research and academic posts in Hungary, Germany and the UK.
Since 1986 he has been with the School of Electronics and Computer
Science, University of Southampton, UK, where he holds the chair
in telecommunications. He has co-authored 15 books on mobile radio
communications totalling in excess of 10 000, published in excess of
700 research papers, acted as TPC Chair of IEEE conferences, presented
keynote lectures and been awarded a number of distinctions. Currently he is directing an
academic research team, working on a range of research projects in the field of wireless
multimedia communications sponsored by industry, the Engineering and Physical Sciences
Research Council (EPSRC) UK, the European IST Programme and the Mobile Virtual Centre
of Excellence (VCE), UK. He is an enthusiastic supporter of industrial and academic liaison
and he offers a range of industrial courses. He is also an IEEE Distinguished Lecturer of both
the Communications Society (ComSoc) and the Vehicular Technology Society (VTS) as well
as a Governor of both ComSoc and the VTS. For further information on research in progress
and associated publications please refer to
Jonathan Blogh was awarded an MEng. degree with Distinction in
Information Engineering from the University of Southampton, UK in
1997. In the same year he was also awarded the IEE Lord Lloyd
of Kilgerran Memorial Prize for his interest in and commitment to
mobile radio and RF engineering. Between 1997 and 2000 he conducted
postgraduate research and in 2001 he earned a PhD in mobile com-
munications at the University of Southampton, UK. His current areas of research include
the networking aspects of FDD and TDD mode third generation mobile cellular networks.
Following a spell with Radioscape, London, UK, working as a software engineer, currently
he is a senior researcher with Anritsu, UK.
xvi ABOUT THE AUTHORS
Song Ni received his BEng degree in Information detection and instru-
mentation from Shanghai Jiaotong University in 1999. Subsequently, he

was employed by Winbond Electronics (Shanghai) Ltd. as a Software
Engineer. His primary responsibility was telecom products R & D.
In 2001 he started a PhD on Intelligent Wireless Networking at the
University of Southampton, which was sponsored by IST SCOUT
project. During four years research, he developed a simulation platform
for the UTRA TDD network layer in the UMTS WCDMA system and
studied various technologies to enhance achievable performance of UTRA systems. Dr Song
Ni is currently a system engineer with Panasonic Mobile Communication, UK.
Other Wiley and IEEE Press
Books on Related Topics
1
• R. Steele, L. Hanzo (Ed): Mobile Radio Communications: Second and Third Genera-
tion Cellular and WATM Systems, John Wiley and IEEE Press, 2nd edition, 1999, ISBN
07 273-1406-8, 1064 pages.
• L. Hanzo, T.H. Liew, B.L. Yeap: Turbo Coding, Turbo Equalisation and Space-Time
Coding, John Wiley and IEEE Press, 2002, 751 pages.
• L. Hanzo, C.H. Wong, M.S. Yee: Adaptive Wireless Transceivers: Turbo-Coded,
Turbo-Equalised and Space-Time Coded TDMA, CDMA and OFDM Systems, John
Wiley and IEEE Press, 2002, 737 pages.
• L. Hanzo, L-L. Yang, E-L. Kuan, K. Yen: Single- and Multi-Carrier CDMA: Multi-
User Detection, Space-Time Spreading, Synchronization, Networking and Standards,
John Wiley and IEEE Press, June 2003, 1060 pages.
• L. Hanzo, M. M¨unster, T. Keller, B-J. Choi, OFDM and MC-CDMA for Broadband
Multi-User Communications, WLANs and Broadcasting, John-Wiley and IEEE Press,
2003, 978 pages.
• L. Hanzo, S-X. Ng, T. Keller and W.T. Webb, Quadrature Amplitude Modulation: From
Basics to Adaptive Trellis-Coded, Turbo-Equalised and Space-Time Coded OFDM,
CDMA and MC-CDMA Systems, John Wiley and IEEE Press, 2004, 1105 pages.
• L. Hanzo, T. Keller: An OFDM and MC-CDMA Primer, John Wiley and IEEE Press,
2006, 430 pages.

• L. Hanzo, F.C.A. Somerville, J.P. Woodard: Voice and Audio Compression for Wireless
Communications, John Wiley and IEEE Press, 2nd edition, 2007, 858 pages.
1
For detailed contents and sample chapters please refer to
xviii OTHER WILEY AND IEEE PRESS BOOKS ON RELATED TOPICS
• L. Hanzo, P.J. Cherriman, J. Streit: Video Compression and Communications:
H.261, H.263, H.264, MPEG4 and HSDPA-Style Adaptive Turbo-Transceivers John
Wiley and IEEE Press, 2nd edition, 2007, 680 pages.
Preface
Background and Overview
Wireless communications is experiencing an explosive growth rate. This high demand for
wireless communications services requires increased system capacities. The simplest solution
would be to allocate more bandwidth to these services, but the electromagnetic spectrum is a
limited resource, which is becoming increasingly congested [1]. Furthermore, the frequency
bands to be used for the Third-Generation (3G) wireless services have been auctioned in
various European countries, such as Germany and the UK, at an extremely high price.
Therefore, the efficient use of the available frequencies is paramount [1, 2].
The digital transmission techniques of the Second-Generation (2G) mobile radio net-
works have already improved upon the capacity and voice quality attained by the analog
mobile radio systems of the first generation. However, more efficient techniques allowing
multiple users to share the available frequencies are necessary. Classic techniques of
supporting a multiplicity of users are frequency, time, polarization, code or spatial division
multiple access [3]. In Frequency Division Multiple (FDMA) Access [4, 5] the available
frequency spectrum is divided into frequency bands, each of which is used by a different
user. Time Division Multiple Access (TDMA) [4,5] allocates each user a given period of time,
referred to as a timeslot, over which their transmission may take place. The transmitter must
be able to store the data to be transmitted and then transmit it at a proportionately increased
rate during its timeslot constituting a fraction of the TDMA frame duration. Alternatively,
Code Division Multiple Access (CDMA) [4, 5] allocates each user a unique code. This code
is then used to spread the data over a wide bandwidth shared with all users. For detecting the

transmitted data the same unique code, often referred to as the user signature, must be used.
The increasing demand for spectrally efficient mobile communications systems motivates
our quest for more powerful techniques. With the aid of spatial processing at a cell site,
optimum receive and transmit beams can be used for improving the system’s performance in
terms of the achievable capacity and the Quality of Service (QoS) measures. This approach
is usually referred to as Spatial Division Multiple Access (SDMA) [3, 6], which enables
multiple users in the same cell to be accommodated on the same frequency and timeslot
by exploiting the spatial selectivity properties offered by adaptive antennas [7]. In contrast,
if the desired signal and interferers occupy the same frequency band and timeslot, then
xx PREFACE
“temporal filtering” cannot be used to separate the signal from the interference. However,
the desired and interfering signals usually originate from different spatial locations and
this spatial separation may be exploited in order to separate the desired signal from the
interference using a “spatially selective filter” at the receiver [8–10]. As a result, given a
sufficiently large distance between two users communicating in the same frequency band,
there will be negligible interference between them. The higher the number of cells in a region,
owing to using small cells, the more frequently the same frequency is re-used and, hence, the
higher the teletraffic density per unit area that can be carried.
However, the distance between co-channel cells must be sufficiently high so that the
intra-cell interference becomes lower than its maximum acceptable limit [3]. Therefore, the
number of cells in a geographic area is limited by the base stations’ transmission power level.
A method of increasing the system’s capacity is to use 120
◦
sectorial beams at different
carrier frequencies [11]. Each of the sectorial beams may serve the same number of users
as supported in ordinary omni-directional cells, while the Signal-to-Interference Ratio (SIR)
can be increased owing to the antenna’s directionality. The ultimate solution, however, is to
use independently steered high-gain beams for tracking the individual users [3] roaming in
the network.
High Speed Downlink Packet Access (HSDPA)-style Adaptive Quadrature Amplitude

Modulation (AQAM) [12,13] is another technique that is capable of increasing the achievable
spectral efficiency. The philosophy behind adaptive modulation is to select a specific
modulation mode, from a set of modes, according to the instantaneous radio channel
quality [12,13]. Thus, if the channel quality exhibits a high instantaneous Signal-to-Interface
plus Noise Ratio (SINR), then a high-order modulation mode may be employed, enabling the
exploitation of the temporarily high channel capacity. In contrast, if the channel has a low
instantaneous SINR, using a high-order modulation mode would result in an unacceptably
high Frame Error Ratio (FER) and, hence, a more robust, but lower throughput modulation
mode would be invoked. Therefore, adaptive modulation not only combats the effects of a
poor quality channel, but also attempts to maximize the throughput, whilst maintaining a
given target FER. Thus, there is a trade-off between the mean FER and the data throughput,
which is governed by the modem mode switching thresholds. These switching thresholds
define the SINRs, at which the instantaneous channel quality requires the current modulation
mode to be changed, i.e. where an alternative AQAM mode must be invoked.
A more explicit representation of the wideband HSDPA-style AQAM mode switching
regime is shown in Figure 1, which displays the variation of the modulation mode with respect
to the near-instantaneous SINR at average channel SNRs of 10 and 20 dB. In this figure, it can
be seen explicitly that the lower-order modulation modes were chosen when the pseudo-SNR
was low. In contrast, when the pseudo-SNR was high, the higher-order modulation modes
were selected in order to increase the transmission throughput. This figure can also be used
to exemplify the application of wideband AQAM in an indoor and outdoor environment. In
this respect, Figure 1(a) can be used to characterize a hostile low-SINR outdoor environment,
where the average channel quality was low. This resulted in the utilization of predominantly
more robust modulation modes, such as Binary Phase Shift Keying (BPSK) and 4 Quadrature
Amplitude Modulation (4QAM). Conversely, a less hostile high-SINR indoor environment
is exemplified by Figure 1(b), where the channel quality was consistently higher. As a
result, the wideband AQAM regime can adapt by suitably invoking higher-order modula-
tion modes, as evidenced by Figure 1(b). Again, this simple example demonstrated that
PREFACE xxi
0 100 200 300 400 500

Frame index
-20
-15
-10
-5
0
5
10
15
20
Pseudo SNR (dB)
0 100 200 300 400 500
Frame index
1
2
4
6
BPS
BPS
Pseudo SNR (dB)
(a)
0 100 200 300 400 500
Frame index
-10
-5
0
5
10
15
20

25
30
Pseudo SNR (dB)
0 100 200 300 400 500
Frame index
1
2
4
6
BPS
BPS
Pseudo SNR (dB)
(b)
Figure 1: Modulation mode variation with respect to the pseudo-SNR evaluated at the output of the
channel equalizer of a wideband AQAM modem for transmission over the TU Rayleigh
fading channel. The Bits per symbol (BPS) throughputs of 1, 2, 4 and 6 represent BPSK,
4QAM, 16QAM and 64QAM, respectively. Channel SNR of (a) 10 dB and (b) 20 dB.
HSDPA-style wideband AQAM can be utilized in order to provide a seamless, near-
instantaneous reconfiguration for example between indoor and outdoor environments. The
most convincing argument in favor of HSDPA-style AQAM is that a fixed-mode system
would increase the required uplink (UL) or downlink (DL) transmit power for the sake of
maintaining a given user’s target Bit Error Ratio (BER), hence the system is expected to
inflict a higher Multi-User Interface (MUI) upon all other system users. Therefore, all of the
other users would in turn also increase their power requirement, which may result in a system
instability. In contrast, an AQAM system would simply adjust the AQAM mode used, in order
to use the system’s resources as judiciously as possible.
In this book we study the network capacity gains that may be achieved with the
advent of adaptive antenna arrays and HSDPA-style adaptive modulation techniques in both
FDMA/TDMA and CDMA-based mobile cellular networks employing Frequency Division
Duplexing (FDD) as well as Time Division Duplexing (TDD). The advantages of employing

adaptive antennas are multifold, as outlined in the following.
Reduction of Co-channel Interference
Antenna arrays employed by the base station allow the implementation of spatial filtering, as
shown in Figure 2, which may be exploited in both transmitting as well as receiving modes
in order to reduce co-channel interferences [1, 2, 14, 15] experienced in the UL and DL of
wireless systems. When transmitting with an increased antenna gain in a certain direction
of the DL, the base station’s antenna is used to focus the radiated energy in order to form a
high-gain directive beam in the area where the mobile receiver is likely to be. This, in turn,
implies that there is a reduced amount of radiated energy and, hence, reduced interference
inflicted upon the mobile receivers roaming in other directions where the directive beam
has a lower gain. The co-channel interference generated by the base station in its transmit
xxii PREFACE
Base Station
Mobile Stations
Figure 2: A cell layout showing how an antenna array can support many users on the same carrier
frequency and timeslot with the advent of spatial filtering or SDMA.
mode may be further reduced by forming beams exhibiting nulls in the directions of other
receivers [6, 16]. This scheme deliberately reduces the transmitted energy in the direction of
co-channel receivers and, hence, requires prior knowledge of their positions.
The employment of antenna arrays at the base station for reducing the co-channel
interference in its receive mode has been also reported widely [1,2,6,16–18]. This technique
does not require explicit knowledge of the co-channel interference signal itself, however, it
has to possess information concerning the desired signal, such as the direction of its source,
a reference signal, such as a channel sounding sequence, or a signal that is highly correlated
with the desired signal.
Capacity Improvement and Spectral Efficiency
The spectral efficiency of a wireless network refers to the amount of traffic a given system
having a certain spectral allocation could handle. An increase in the number of users of
the mobile communications system without a loss of performance increases the spectral
efficiency. Channel capacity refers to the maximum data rate a channel of a given bandwidth

can sustain. An improved channel capacity leads to an ability to support more users of a
specified data rate, implying a better spectral efficiency. The increased QoS that results from
the reduced co-channel interference and reduced multipath fading [18, 19] upon using smart
antennas may be exchanged for an increased number of users [2,20].
Increase of Transmission Efficiency
An antenna array is directive in its nature, having a high gain in the direction where the
beam is pointing. This property may be exploited in order to extend the range of the base
station, resulting in a larger cell size or may be used to reduce the transmitted power of the
mobiles. The employment of a directive antenna allows the base station to receive weaker
signals than an omni-directional antenna. This implies that the mobile can transmit at a lower
power and its battery recharge period becomes longer, or it would be able to use a smaller
battery, resulting in a smaller size and weight, which is important for hand-held mobiles.
PREFACE xxiii
A corresponding reduction in the power transmitted from the base station allows the use of
electronic components having lower power ratings and, therefore, lower cost.
Reduction of the Number of Handovers
When the amount of traffic in a cell exceeds the cell’s capacity, cell splitting is often used in
order to create new cells [2], each with its own base station and frequency assignment. The
reduction in cell size leads to an increase in the number of handovers performed. By using
antenna arrays for increasing the user capacity of a cell [1] the number of handovers required
may actually be reduced. More explicitly, since each antenna beam tracks a mobile [2], no
handover is necessary, unless different beams using the same frequency cross each other.
Avoiding Transmission Errors
When the instantaneous channel quality is low, conventional fixed-mode transceivers typi-
cally inflict a burst of transmission errors. In contrast, adaptive transceivers avoid this problem
by reducing the number of transmitted bits per symbol, or even by disabling transmissions
temporarily. The associated throughput loss can be compensated for by transmitting a higher
number of bits per symbol during the periods of relatively high channel qualities. This
advantageous property manifests itself also in terms of an improved service quality, which
is quantified in this book in terms of the achievable video quality.

However, realistic propagation scenarios are significantly more complex than that
depicted in Figure 2. Specifically, both the desired signal and the interference sources
experience multipath propagation, resulting in a high number of received uplink signals
impinging upon the base station’s receiver antenna array. A result of the increased number of
received uplink signals is that the limited degrees of freedom of the base station’s adaptive
antenna array are exhausted, resulting in reduced nulling of the interference sources. A
solution to this limitation is to increase the number of antenna elements in the base station’s
adaptive array, although this has the side effect of raising the cost and complexity of the array.
In a macro-cellular system it may be possible to neglect multipath rays arriving at the base
station from interfering sources, since the majority of the scatterers are located close to the
mobile station [21]. In contrast, in a micro-cellular system the scatterers are located in both
the region of the reduced-elevation base station and that of the mobile, and hence multipath
propagation must be considered. Figure 3 shows a realistic propagation environment for both
the UL and the DL, with the multipath components of the desired signal and interference
signals clearly illustrated, where the UL and DL multipath components were assumed to
be identical for the sake of simplicity. Naturally, this is not always the case and, hence, we
investigate the potential performance gains, when the UL and DL beamforms are determined
independently.
To elaborate a little further, the design of wireless networks is based on a complex
interplay of the various performance metrics as well as on a range of other often contradictory
trade-offs, which are summarized in the stylized illustration seen in Figure 7.4. For example,
Figure 7.4 suggests that it is always possible to reduce the call dropping probability by
increasing the call blocking probability, since this implies admitting less users to the system.
In contrast, we may admit more users to the system for the sake of reducing the call blocking
probability, which however results in an increased call dropping probability. Furthermore,