Advanced Wireless Communications

Advanced Wireless Communications

4G Cognitive and Cooperative Broadband Technology

Second Edition

Savo G. Glisic

University of Oulu, Finland

B J C E N T E N N I A L

1 8 O 7

SWILEY

2 O O 7

B I C E N T fcNNIAI.

John Wiley & Sons, Ltd

Contents

Preface to the Second Edition xxi

1 Fundamentals 1 1.1 4G and the book layout 1

1.2 General Structure of 4G Signals 4 1.2.1 Advanced time division multiple access (ATDMA) 5

1.2.2 Code division multiple access (CDMA) 5 1.2.3 Orthogonal frequency division multiplexing (OFDM) 6

1.2.4 Multicarrier CDMA (MC CDMA) 8 1.2.5 Ultra wide band (UWB) Signals 11

References 16

Adaptive Coding 21 2.1 Adaptive and reconfigurable block coding 21

2.2 Adaptive and reconfigurable convolutional codes 26 2.2.1 Punctured convolutional codes/code reconngurability 31

2.2.2 Maximum likelihood decoding/Viterbi algorithm 32 2.2.3 Systematic recursive convolutional codes 33

2.3 Concatenated codes with interleavers 36 2.3.1 The iterative decoding algorithm 37 2.4 Adaptive coding, practice and prospects 43

2.5 Distributed source coding 44 2.5.1 Continuous valued source 46 2.5.2 Scalar quantization and trellis-based coset construction 48

2.5.3 Trellis-based quantization and memoryless 50

2.5.4 Performance examples 50 Appendix 2.1 Maximum a posteriori detection 53

References 56

viii CONTENTS

Adaptive and Reconfigurable Modulation 63

3.1 Coded modulation 63 3.1.1 Euclidean distance 64 3.1.2 Examples of TCM schemes 65

3.1.3 Set partitioning 68 3.1.4 Representation of TCM 70 3.1.5 TCM with multidimensional constellation 70

3.2 Adaptive coded modulation for fading Channels 72

3.2.1 Maintaining a fixed distance 73

3.2.2 Information rate 74

References 75 Space-Time Coding 79 4.1 Diversity gain 79

4.1.1 Two-branch transmit diversity scheme with

one receiver 80 4.1.2 Two transmitters and M receivers 82

4.2 Space-time coding 84 4.2.1 The System model 84 4.2.2 The case of independent fade coefficients 85

4.2.3 Rayleigh fading 86 4.2.4 Design criteria for Rayleigh space-time codes 86

4.2.5 Code construction 87 4.2.6 Reconfiguration efficiency of space-time coding 91

4.2.7 Delay diversity 94 4.3 space-time block codes from orthogonal designs 96

4.3.1 The Channel model and the diversity criterion 96

4.3.2 Real orthogonal designs 97 4.3.3 Space-time encoder 97 4.3.4 The diversity order 97 4.3.5 The decoding algorithm 98 4.3.6 inear processing orthogonal designs 98

4.3.7 Generalized real orthogonal designs 99

4.3.8 Encoding 99 4.3.9 The Alamouti scheme 100

4.3.10 Complex orthogonal designs 100 4.3.11 Generalized complex orthogonal designs 100

4.3.12 Special codes 101 4.3.13 Performance results 102 4.4 Channel estimation imperfections 102

4.4.1 Channel estimator 106 4.5 Quasi-orthogonal space-time block codes 107

4.5.1 Decoding 108 4.5.2 Decision metric 108 4.6 Space-time convolutional codes 109

4.7 Algebraic space-time codes 111

4.7.1 Füll spatial diversity 116

4.7.2 QPSK modulation 116

CONTENTS

4.8 Differential space-time modulation 116 4.8.1 The encoding algorithm 122 4.8.2 Differential decoding 123 4.9 Multiple transmit antenna differential detection from generalized

orthogonal designs 125 4.9.1 Differential encoding 126

4.9.2 Received signal 126 4.9.3 Orthogonality 127 4.9.4 Encoding 127 4.9.5 Differential decoding 128

4.9.6 Received signal 129 4.9.7 Demodulation 130 4.9.8 Multiple receive antennas 130

4.9.9 The number of transmit antennas lower than the number

of symbols 130 4.9.10 Final result 131 4.9.11 Real constellation set 131

4.10 Layered space-time coding 133 4.10.1 Receiver complexity 134 4.10.2 Group interference suppression 134

4.10.3 Suppression method 134 4.10.4 The null space 134 4.10.5 Receiver 135 4.10.6 Decision metric 135 4.10.7 Multilayered space-time coded modulation 135

4.10.8 Diversity gain 136 4.10.9 Adaptive reconfigurable transmit power allocation 136

4.11 Concatenated space-time block coding 140

4.11.1 System model 140 4.11.2 Product sum distance 141 4.11.3 Error rate bound 141 4.11.4 The case of low SNR 142

4.11.5 Code design 142 4.12 Estimation of MIMO Channel 145

4.12.1 System model 146 4.12.2 Training 148 4.12.3 Performance measure 148

4.12.4 Definitions 148 4.12.5 Channel estimation error 148

4.12.6 Error statistic 149 4.12.7 Results 149 4.13 Space-time codes for frequency selective Channels 151

4.13.1 Diversity gain properties 153 4.13.2 Coding gain properties 154 4.13.3 Space-time trellis code design 155 4.14 Optimization of a MIMO system 157

4.14.1 The Channel model 157

4.14.2 Gain optimization by singular value decomposition (SVD) 158

CONTENTS

4.14.3 The general (M, AO case 159 4.14.4 Gain optimization by iteration for a reciprocal Channel 161

4.14.5 Spectral efficiency of parallel Channels 162

4.14.6 Capacity of the (M, N) array 163 4.15 MIMO Systems with constellation rotation 163

4.15.1 System model 163 4.15.2 Performance in a Rayleigh fading Channel 165

4.16 Diagonal algebraic space-time block codes 167

4.16.1 System model 167 4.16.2 The DAST coding algorithm 169

4.16.3 The DAST decoding algorithm 170 Appendix 4.1 QR Factorization 173 Appendix 4.2 Lattice code decoder for space-time codes 175

Appendix 4.3 MIMO Channel capacity 176

References 180 Multiuser Communication 191

5.1 Pseudorandom sequences 191 5.1.1 Binary shift register sequences 191

5.1.2 Properties of binary maximal length sequences 193

5.1.3 Crosscorrelation spectra 193 5.1.4 Maximal connected sets of m-sequences 194

5.1.5 Gold sequences 194 5.1.6 Gold-like and dual-BCH sequences 195

5.1.7 Kasami sequences 196 5.1.8 JPL sequences 197 5.1.9 Kronecker sequences 197 5.1.10 Walsh functions 198 5.1.11 Optimum PN sequences 199

5.1.12 Golaycode 199 5.2 Multiuser CDMA receivers 201

5.2.1 Synchronous CDMA Channels 202 5.2.2 The decorrelating detector 202 5.2.3 The Optimum linear multiuser detector 202

5.2.4 Multistage detection in asynchronous CDMA [43] 203

5.2.5 Non-coherent detector 205 5.2.6 Non-coherent detection in asynchronous multiuser

Channels [45] 205 5.2.7 Multiuser detection in frequency non-selective Rayleigh

fading Channels 207 5.2.8 Multiuser detection in frequency selective Rayleigh

fading Channels 210 5.3 Minimum mean Square error (MMSE) linear

multiuser detection 216 5.3.1 System model in multipath fading Channels 217

5.3.2 MMSE detector structures 220

5.3.3 Spatial processing 221

CONTENTS xi

5.4 Single user LMMSE receivers for frequency selective

fading Channels 225 5.4.1 Adaptive precombining LMMSE receivers 225

5.4.2 Blind least Squares receivers 230 5.4.3 Least Squares (LS) receiver 230 5.4.4 Method based on the matrix inversion lemma 231

5.5 Signal subspace-based Channel estimation for

CDMA Systems 232 5.5.1 Estimating the signal subspace 234

5.5.2 Channel estimation 235 5.6 Iterative receivers for layered space-time coding 236

5.6.1 LST architectures 237 5.6.2 LST receivers 241 5.6.3 QR decomposition/SIC detecor 242

5.6.4 MMSE/SIC detector 244 5.6.5 Iterative LST receivers 246 Appendix 5.1 Linear and matrix algebra 253

Definitions 253 Special matrices 254 Matrix manipulation and formulas 255

Theorems 257 Eigendecompostion of matrices 257

Calculation of eigenvalues and eigenvectors 258

References 259 Channel Estimation and Equalization 269

6.1 Equalization in the digital data transmission System 269

6.1.1 Zero-forcing equalizers 269

6.2 LMS equalizer 275 6.2.1 Signal model 275 6.2.2 Adaptive weight adjustment 276

6.2.3 Automatic Systems 276 6.2.4 Iterative algorithm 277 6.2.5 The LMS algorithm 277 6.2.6 Decision feedback equalizer (DFE) 277

6.2.7 Blind equalizers 278 6.3 Detection for a statistically known, time varying Channel 279

6.3.1 Signal model 279 6.3.2 Channel model 279 6.3.3 Statistical description of the received sequence 281

6.3.4 The ML sequence (block) estimator for a statistically

known Channel 281 6.4 LMS-adaptive MLSE equalization on multipath

fading Channels 284 6.4.1 System and Channel modeis 284

6.4.2 Adaptive Channel estimator and LMS estimator model 285

6.4.3 The Channel prediction algorithm 285

CONTENTS

6.5 Adaptive Channel identification and data demodulation 288

6.5.1 System model 288 6.5.2 Joint Channel and data estimation 288

6.5.3 Data estimation and tracking for a fading Channel 292

6.5.4 The static Channel environment 293 6.5.5 The time varying Channel environment 296

6.6 Turbo equalization 301 6.6.1 Signal format 301 6.6.2 Equivalent discrete time Channel model 302

6.6.3 Equivalent System State representations 302

6.6.4 Turbo equalization 302 6.6.5 Viterbi algorithm 303 6.6.6 Iterative implementation of turbo equalization 304

6.6.7 Performance 304 6.7 Kaiman Filter based Joint Channel estimation and data

detection over fading Channels 305 6.7.1 Channel model 308 6.7.2 The received signal 308 6.7.3 Channel estimation alternatives 308

6.7.4 Implementing the estimator 309 6.7.5 The Kaiman filier 310

6.7.6 Implementation issues 310 6.8 Equalization using higher order signal statistics 311

6.8.1 Problem Statement 311 6.8.2 Signal model 313 6.8.3 Derivation of algorithms for DFE 313

6.8.4 The equalizer coefficients 314 6.8.5 Stochastic gradient DFE adaptive algorithms 315

6.8.6 Convergence analysis 316 6.8.7 Kurtosis-based algorithm 318 6.8.8 Performance results 321

References 321 Orthogonal Frequency Division Multiplexing—OFDM

and Multicarrier CDMA 329 7.1 Timing and frequency offset in OFDM 329

7.1.1 Robust frequency and timing synchronization

for OFDM 331 7.2 Fading Channel estimation for OFDM Systems 334

7.2.1 Statistics of mobile radio Channels 334

7.2.2 Diversity receiver 335 7.2.3 MMSE Channel estimation 335

7.2.4 FIR Channel estimator 337

7.2.5 System Performance 338

7.2.6 Reference generation 339

7.3 64 DAPSK and 64 QAM modulated OFDM Signals 339

CONTENTS

7.4 Space-time coding with OFDM Signals 344 7.4.1 Signal and Channel parameters 345 7.4.2 The wireless asynchronous transfer mode System 349

7.4.3 Space-time coded adaptive modulation for OFDM 349 7.4.4 Turbo and space-time coded adaptive OFDM 349

7.5 Layered space-time coding for MIMO OFDM 351 7.5.1 System model (two times two transmit antennas) 352

7.5.2 Interference cancellation 353 7.5.3 Four transmit antennas 353 7.6 Space-time coded TDMA/OFDM reconfiguration efficiency 356

7.6.1 Frequency selective Channel model 356

7.6.2 Front end prefilter 357 7.6.3 Time-invariant Channel 357 7.6.4 Optimization problem 358 7.6.5 Average Channel 358 7.6.6 Prefiltered M-BCJR equalizer 358

7.6.7 Decision 359 7.6.8 Prefiltered MLSE/DDFSE equalizer complexity 359

7.6.9 Delayed decision feedback sequence estimation (DDFSE) 360

7.6.10 Equalization schemes for STBC 360 7.6.11 Single-carrier frequency domain equalized space-time

block coding SC FDE STBC 361

7.7 Multicarrier CDMA System 369 7.7.1 Datademodulation 370 7.7.2 Performance examples 371 7.8 Multicarrier DS-CDMA broadcast Systems 371

7.9 Frame by frame adaptive rate coded multicarrier DS-CDMA System 375

7.9.1 Transmitter 377 7.9.2 Receiver 378 7.9.3 Rate-compatible punctured convolutional (RCPC) codes 379

7.9.4 Rate adaptation 380 7.10 Intermodulation interference suppression

in Multicarrier CDMA Systems 382

7.10.1 Transmitter 382 7.10.2 Non-linear power amplifier model 383

7.10.3 MMSE receiver 383 7.11 Successive interference cancellation

in Multicarrier DS-CDMA Systems 386 7.11.1 System and Channel model 386 7.12 MMSE detection of multicarrier CDMA 387

7.12.1 Tracking the fading processes 390 7.13 Approximation of Optimum multiuser receiver for space-time coded

multicarrier CDMA Systems 393 7.13.1 Frequency selective fading Channels 397

7.13.2 Receiver Signal model of STBC MC CDMA Systems 398

7.13.3 Blind approach 399

CONTENTS

7.13.4 Bayesian optimal blind receiver 400 7.13.5 Blind Bayesian Monte Carlo multiuser receiver approximation 400

7.13.6 Gibbs sampler 400 7.13.7 Prior distributions 401 7.13.8 Conditional posterior distributions 401

7.13.9 Gibbs multiuser detection 402 7.13.10 Sampling space of data 403 7.13.11 The orthogonality property 403 7.13.12 Blind turbo multiuser receiver 403 7.13.13 Decoder-assisted convergence assessment 404

7.13.14 Performance example 404 7.14 Parallel interference cancellation in OFDM Systems in time-varying

multipath fading Channels 405 7.15 Zero forcing OFDM equalizer in time-varying multipath

fading Channels 411 7.16 Channel estimation for OFDM Systems 415

7.17 Turbo processing for an OFDM-based MIMO System 418

7.18 PAPR reduction of OFDM Signals 420

Appendix 424 References 425 Ultra Wide Band Radio 433 8.1 UWB multiple access in a gaussian Channel 433

8.1.1 The multiple access Channel 433

8.1.2 Receiver 434 8.2 The UWB Channel 436

8.2.1 Energy capture 436 8.2.2 The received signal model 436

8.2.3 The UWB signal propagation experiment 1 436

8.2.4 UWB propagation experiment 2 437 8.2.5 Clustering modeis for the indoor multipath

propagation Channel 438 8.2.6 Path loss modeling 440 8.3 UWB system with M-ary modulation 442

8.3.1 Performance in a Gaussian Channel 442 8.3.2 Performance in a dense multipath Channel 446

8.3.3 Receiver and BER Performance 447

8.3.4 Time variations 447 8.3.5 Performance example 448 8.4 M-ary PPM UWB multiple access 448

8.4.1 M-ary PPM signal sets 451 8.4.2 Performance results 453 8.5 Coded UWB schemes 453

8.5.1 Performance 457 8.5.2 The uncoded System as a coded system with repetition 457

8.6 Multiuser detection in UWB radio 458

CONTENTS xv

8.7 UWB with space-time processing 460

8.7.1 Signal model 460 8.7.2 The monopulse tracking System 464

8.8 Beamforming for UWB radio 467 8.8.1 Circular array 467

References 492 9 Linear Precoding for MIMO Channels 497

9.1 Space-time precoders and equalizers for MIMO Channels 497

9.1.1 ISI modelling in MIMO Channels 497 9.1.2 MIMO system precoding and equalization 499

9.1.3 Precoder and equalizer design for

STBC Systems 502 9.2 Linear precoding based on convex optimization theory 504

9.2.1 Generalized MIMO Systems 505 9.2.2 Convex optimization 506 9.2.3 Precoding for power optimization 507

9.2.4 Precoder for SINR optimization 510

9.2.5 Performance example 512 9.3 Convex optimization-theory-based beamforming 513

9.3.1 Multicarrier MIMO signal model 514

9.3.2 Channel diagonalization 516 9.3.3 Convex optimization-based beamforming 520

9.3.4 Constraints in multicarrier Systems 526

9.3.5 Performance examples 527

References 533 10 Cognitive Radio 537

10.1 Energy-efficient cognitive radio 537 10.1.1 Frame length adaptation 537 10.1.2 Frame length adaptation in flat fading Channels 539

10.1.3 The adaptation algorithm 542 10.1.4 Energy-efficient adaptive error control 542

10.1.5 Processing gain adaptation 545 10.1.6 Trellis-based processing/adaptive maximum likelihood

sequence equalizer 547 10.1.7 Hidden Markov Channel model 548

10.1.8 Link layer Performance with inadequate equalization 549 10.1.9 Link layer Performance with adequate equalization 551 10.2 A cognitive radio architecture for linear multiuser detection 556

10.2.1 A unified architecture for linear multiuser detection

and dynamic reconfigurability 556

10.2.2 Experimental results 563 10.2.3 The effects of quantization 564 10.2.4 The effect on the 'near-far' resistance 565

10.3 Reconfigurable ASIC architecture 567

xvi CONTENTS

10.3.1 10.3.2 10.3.3 10.3.4 10.3.5 10.3.6 10.3.7 10.3.8 10.3.9 10.3.10 10.3.11 10.3.12 10.3.13 10.3.14

Motivation and present art 569 Alternative implementations 570 Example architecture versus an FPGA 570

DSP against the example architecture 571 Computation of a complex 16-point DFT - the Goertzel

FFTmode 571 Fixed coefficient Alters 573

Real FIR/correlator 574 Real IIR/correlator 574 Cascading fixed coefficient Alters 574

Adaptive filtering 574

Direct digital frequency synthesis 576

CORDIC algorithm [83] 577 Discrete Fourier transform 578 Goertzel algorithm 578

References 580 11 Cooperative Diversity in Cognitive Wireless Networks 587

11.1 System modeling 587 11.1.1 System capacity 588

11.1.2 Probability of outage 591 11.1.3 Cellular coverage 592 11.2 Cooperative diversity protocols 593

11.2.1 System and Channel modeis 593 11.2.2 Coperative diversity protocols 594

11.2.3 Outage probabilities 595 11.2.4 Performance bounds for cooperative diversity 598

11.3 Distributed space-time coding 600 11.3.1 System description 600 11.3.2 BER analysis in DSTC 603 11.4 Generalization of distributed space-time-coding based

on cooperative diversity 605 11.4.1 System and Channel model 605

11.4.2 Cooperative diversity based on repetition 608 11.4.3 Cooperative diversity using space-time coding 612 Appendix 11.1 Asymptotic CDF approximations 614 Appendix 11.2 Amplify-and-forward mutual information 619 Appendix 11.3 Input distributions for transmit diversity bound 620

References 621 12 Cognitive UWB Communications 625

12.1 Introduction 625 12.2 Signal and interference modeis 627

12.3 Receiver structure and Performance 628 12.3.1 Interference rejection circuit model 629

12.4 Performance examples 635

References 641

CONTENTS xvii

13 Positioning in Wireless Networks 645 13.1 Mobile Station location in cellular networks 645

13.1.1 Introduction 645 13.1.2 MS location estimation using AD and RD measurements 646

13.1.3 The circular, hyperbolic, and mixed multilateration 646

13.1.4 WLS Solution ofthe location problem 648

13.1.5 Accuracy measure 649 13.1.6 Circular multilateration 650 13.1.7 Hyperbolic multilateration 651 13.1.8 Mixed multilateration 652 13.1.9 Performance results for three stations 652

13.1.10 Performance results for N stations 654 13.2 Relative positioning in wireless sensor networks 655

13.2.1 Performance bounds 656 13.2.2 Relative location estimation 659 13.3 Average Performance of circular and hyperbolic geolocation 664

13.3.1 Signal modeis and Performance limits 664 13.3.2 Performance of location techniques 666 13.3.3 Average Performance of location techniques 667

References 671 14 Channel Modeling and Measurements for 4G 675

14.1 Macrocellular environments (1.8 GHz) 675 14.1.1 PDF of shadow fading 677 14.2 Urban spatial radio Channels in macro/microcell (2.154 GHz) 681

14.2.1 Description of environment 682

14.2.2 Results 682 14.3 MIMO Channels in microcell and picocell environments

(1.71/2.05 GHz) 688 14.3.1 Simulation of Channel coefficients 690

14.3.2 Measurement Setups 690 14.3.3 Validation of the stochastic MIMO Channel

model assumptions 690 14.3.4 Input parameters to the Validation of the MIMO model 692

14.3.5 The eigenanalysis method 693 14.4 Outdoor mobile Channel (5.3 GHz) 696

14.4.1 Path loss modeis 700 14.4.2 Window length for averaging fast fading components

at 5 GHz 702 14.4.3 Spatial and frequency correlations 702

14.4.4 Path number distribution 705 14.4.5 Rotation measurements in an urban environment 706

14.5 Microcell Channel (8.45 GHz) 708 14.5.1 Azimuth profile 709 14.5.2 Delay profile for the forward arrival waves 710

14.5.3 Short-term azimuth spread (AS) for forward

arrival waves 712

xviii CONTENTS

14.6 Wireless MIMO LAN environments (5.2 GHz) 714

14.6.1 Data evaluation 715 14.6.2 Capacity computation 716 14.6.3 Measurement environments 717 14.7 Indoor WLAN Channel (17 GHz) 718 14.8 Indoor WLAN Channel (60 GHz) 727

14.8.1 Definition of the Statistical parameters 728

14.9 UWB Channel model 732 14.9.1 The large-scale statistics 736

14.9.2 The small-scale statistics 739 14.9.3 Correlation of MPCs among different delay bins 741

14.9.4 The Statistical model 741 14.9.5 Simulation Steps 742

References 745 15 Adaptive 4G Networks 753

15.1 Adaptive MAC layer 753 15.1.1 Signal variations and the power control problem 755

15.1.2 Spectral efficiency and effective load factor of the multirate

DS-CDMA PRN 755 15.1.3 CLSP/DS-CDMA packet access and traffic model 756

15.1.4 Bit rate adaptation 756 15.1.5 The correlated fading model and optimal packet size 758

15.1.6 Performance 760 15.2 Minimum energy peer-to-peer mobile wireless networks 770

15.2.1 Network layer requirements 770 15.2.2 The power consumption model 771 15.2.3 Minimum power networks 772 15.2.4 Distributed network routing protocol 773

15.2.5 Distributed mobile networks 775 15.3 Least resistance routing in wireless networks 778

15.3.1 Least resistance routing (LRR) 778 15.3.2 Multimedia least resistance routing (MLRR) 779

15.3.3 Network Performance examples: LRR versus MLRR 780 15.3.4 Sensitivity to the number of allowable word erasures 783 15.4 Power optimal routing in wireless networks for

guaranteed TCP layer QoS 786 15.4.1 Constant end-to-end error rate 786

15.4.2 Optimization problem 788 15.4.3 Error rate modeis 789 15.4.4 Properties of power optimal paths 790

References 791 16 Cognitive Networks and Game Theory 797

16.1 Cognitive power control 797 16.1.1 Noncooperative power control game 797

16.1.2 Nash equilibrium 799

CONTENTS xix

16.1.3 Pareto optimality 800 16.1.4 Supermodular games and social optimality 801

16.2 Power control game with QOS guarantee 805 16.3 Power control game and multiuser detection 809 16.4 Power control game in MIMO Systems 811 16.5 Game theory based MAC for AD HOC networks 813

16.6 Tit-for-Tat (TFT) game theory based packet forwarding strategies

in AD HOC networks 815 16.6.1 Strategy modeis 815 16.6.2 Network nodes dependency graph and System metamodel 817

16.6.3 The payoff of iterative game 819 16.7 TFT game theory based modeling of node Cooperation

with energy constraint 823 16.7.1 Acceptance rate 823 16.7.2 Pareto optimum 823 16.7.3 Prisoner's dilemma and TFT game 825

16.8 Packet forwarding model based on dynamic Bayesian games 828 16.9 Game theoretic modeis for routing in wireless sensor networks 830

16.9.1 Cognitive wireless sensor network model 830

16.9.2 Optimal rout computation 832 16.10 Profit driven routing in cognitive networks 832

16.10.1 Algorithmic mechanism design 832 16.10.2 Profit driven pricing mechanism 833 16.10.3 Truthful behavior in cognitive networks 835 16.10.4 Collusion of nodes in cognitive networks 836 16.11 Game theoretical model of flexible spectra sharing in cognitive

networks with social awareness 838 16.12 A game theoretical modelling of slotted ALOHA protocol 839

16.13 Game-theory-based modeling of admission in competitive

wireless networks 842 16.13.1 System model 842 16.13.2 Equilibrium Solutions 845 16.14 Modelling access point pricing as a dynamic game 846

16.14.1 The System model 846 16.14.2 Modelling service reselling 848

16.14.3 File transfer model 848 16.14.4 Bayesian model for unknown traffic 849

References 851