SIGNAL PROCESSING FOR

MOBILE COMMUNICATIONS

HANDBOOK

Edited by

Mohamed Ibnkahla

CRC PRESS 2005.

CONTENTS

Preface 4

Contributors 6

PART I: INTRODUCTION

1
• CHAPTER 1. SIGNAL PROCESSING FOR FUTURE MOBILE COMMUNICATIONS SYSTEMS: CHALLENGES AND PERSPECTIVES
2
• 1.1 Introduction 12
• 1.2 Channel Characterizations 12
3
• 1.2.1 Large-Scale Propagation Models 12
4
• 1.2.1.1 Deterministic Approach 12
5
• 1.2.1.1.1 Free-Space Propagation Model 12
• 1.2.1.1.2 Log-Distance Path Loss Model 13
4
• 1.2.1.2 Stochastic Approach 13
5
• 1.2.1.2.1 Lognormal Shadowing Model 13
4
3
• 1.2.2 Small-Scale Propagation Models 14
4
• 1.2.2.1 Parameters of Mobile Multipath Channel 14
5
• 1.2.2.1.1 Fading 14
• 1.2.2.1.2 Doppler Shift 14
• 1.2.2.1.3 Excess Delay 14
• 1.2.2.1.4 Power Delay Profile, I c ( T) 14
• 1.2.2.1.5 Delay Spread (T m) 15
• 1.2.2.1.6 Coherence Bandwidth (B 15
• 1.2.2.1.7 Doppler Spread (Bd) 15
• 1.2.2.1.8 Coherence Time (Tcoh) 15
4
• 1.2.2.2 Types of Small-Scale Fading 15
5
• 1.2.2.2.1 Flat Fading 16
• 1.2.2.2.2 Frequency-Selective Fading 16
• 1.2.2.2.3 Fast Fading 16
• 1.2.2.2.4 Slow Fading 16
4
• 1.2.2.3 Statistical Representation of the Small-Scale Propagation Channel 17
5
• 1.2.2.3.1 Rayleigh Fading Channel 17
• 1.2.2.3.2 Ricean Fading Channel 17
• 1.2.2.3.3 Nakagami Fading Channel 17
4
• 1.2.2.4 Statistical Models for Multipath Fading Channels 18
5
• 1.2.2.4.1 Two-Ray Fading Channel Model 18
• 1.2.2.4.2 Motif Model 19
• 1.2.2.4.3 Finite-State Markov Chain Model 19
• 1.2.2.4.4 Loo's Satellite Channel Model 20
• 1.2.2.4.5 Multiple-Input Multiple-Output Channel Models 21
6
• 1.2.2.4.5.2 Physical Scattering Model 22
• 1.2.2.4.5.1 Matrix Channel Model 21
5
4
3
2
• 1.3 Modulation Techniques 23
3
• 1.3.1 Modulation Schemes: The Classification 23
• 1.3.2 Different Modulation Schemes 23
4
• 1.3.2.1 Phase Shift Keying 23
• 1.3.2.10 Challenges in the Next-Generation System Concerning Different Modulation 29
• 1.3.2.2 Pulse Amplitude Modulation 25
• 1.3.2.3 Quadrature Amplitude Modulation 25
• 1.3.2.4 Frequency Shift Keying 26
• 1.3.2.5 Continuous-Phase FSK 27
• 1.3.2.6 Continuous-Phase Modulation 28
• 1.3.2.7 Minimum Shift Keying 28
• 1.3.2.8 Gaussian MSK 28
• 1.3.2.9 Orthogonal Frequency Division Multiplexing 28
3
2
• 1.4 Coding Techniques 30
3
• 1.4.1 Shannon's Capacity Theorem 30
• 1.4.2 Different Coding Schemes 31
4
• 1.4.2.1 Block Codes 32
5
• 1.4.2.1.1 Vector Space and Subspace 32
• 1.4.2.1.2 Linear Block Code 32
• 1.4.2.1.3 Coding Gain 32
• 1.4.2.1.4 Hamming Codes 32
• 1.4.2.1.5 Hamming Distance 32
• 1.4.2.1.6 Implementation Complexity 32
• 1.4.2.1.7 BCH (Bose-Chaudhuri-Hocquenghem)Codes 33
• 1.4.2.1.8 Reed-Solomon Codes 33
• 1.4.2.1.9 Interleaving 33
4
• 1.4.2.2 Convolutional Codes 34
5
• 1.4.2.2.1 Pictorial Representation of Convolutional Encoder 34
6
• 1.4.2.2.1.1 State Diagram 35
• 1.4.2.2.1.2 Tree Diagram 35
• 1.4.2.2.1.3 Trellis Diagram 36
5
4
• 1.4.2.3 Space-Time Coding 36
• 1.4.2.4 Turbo Coding 36
• 1.4.2.5 Coded Modulation Techniques 37
5
• 1.4.2.5.1 Trellis Coded Modulation 37
• 1.4.2.5.2 Block Coded Modulation 37
• 1.4.2.5.3 Multilevel Coded Modulation 37
• 1.4.2.5.4 Turbo Coded Modulation 37
4
3
• 1.4.3 Coding in Next-Generation Mobile Communications: Some Research 37
2
• 1.5 Multiple Access Techniques 39
• 1.5 Multiple Access Techniques 11
3
• 1.5.1 Fundamental Multiple-Access Schemes 39
4
• 1.5.1.1 Frequency Division Multiple Access 39
5
• 1.5.1.1.1 Merits 39
• 1.5.1.1.2 Demerits 39
4
• 1.5.1.2 Time Division Multiple Access 40
5
• 1.5.1.2.1 Merits 40
• 1.5.1.2.2 Demerits 40
4
• 1.5.1.3 Code Division Multiple Access 40
5
• 1.5.1.3.1 Wideband CDMA 40
• 1.5.1.3.2 Merits 40
• 1.5.1.3.3 Demerits 41
4
3
• 1.5.2 Combination of OFDM and CDMA Systems 41
4
• 1.5.2.1 MT-CDMA Scheme 42
• 1.5.2.2 MC-CDMA Scheme 42
• 1.5.2.3 MC-DS CDMA Scheme 42
3
• 1.5.3 OFDM/TDMA 43
4
• 1.5.3.1 Merits 43
• 1.5.3.2 Demerits 43
3
• 1.5.4 Capacity of MAC Methods 43
4
• 1.5.4.1 FDMA Capacity 44
• 1.5.4.2 TDMA Capacity 44
• 1.5.4.3 CDMA Capacity 44
3
• 1.5.5 Challenges in the MAC Schemes 44
2
• 1.6 Diversity Technique 45
3
• 1.6.1 Classifications of the Diversity Techniques 45
• 1.6.2 Classifications of Diversity Combiners 46
4
• 1.6.2.1 Predetection Diversity Combiners 46
5
• 1.6.2.1.1 Selection Diversity Combining 46
• 1.6.2.1.2 Maximal Ratio Combining 46
• 1.6.2.1.3 Equal Gain Combining 46
4
• 1.6.2.2 Postdetection Diversity Combiners 46
3
• 1.6.3 Diversity for Next-Generation Systems: Some Research Evidence 47
• 1.6.4 Challenges in the Diversity Area 47
2
• 1.7 Conclusions 48
1

0

PART II: CHANNEL MODELING AND ESTIMATION

1
• CHAPTER 2. MULTIPATH PROPAGATION MODELS FOR BROADBAND WIRELESS SYSTEMS
2
• 2.1 Introduction 54
• 2.2 Narrowband, Wideband, and Directional Channel Modeling 55
3
• 2.2.1 Intuitive Description 55
• 2.2.2 Mathematical Description: Deterministic Case 57
• 2.2.3 Mathematical Description: Stochastic Case 58
• 2.2.4 Condensed Parameters 59
• 2.2.5 Directional Description 60
2
• 2.3 Modeling Methods for Multipath Channels 62
3
• 2.3.1 Measured Channel Impulse Responses 62
• 2.3.2 Deterministic Channel Computation 63
4
• 2.3.2.1 Full Electromagnetic Description 64
• 2.3.2.2 High-Frequency Approximations 64
3
• 2.3.3 Tapped Delay Lines 66
• 2.3.4 Stochastic MIMO Models 67
• 2.3.5 Geometry-Based Stochastic Channel Models 68
2
• 2.4 Propagation Aspects and Parameterization 69
3
• 2.4.1 Amplitude Statistics 70
• 2.4.2 Arrival Times 71
• 2.4.3 Average Time Dispersion 72
• 2.4.4 Average Angular Dispersion at the BS 73
• 2.4.5 Average Angular Dispersion at the MS 74
• 2.4.6 MIMO Parameters 75
• 2.4.7 Polarization 75
• 2.4.8 Millimeter Wave Propagation 75
• 2.4.9 Ultrawideband Systems 76
2
• 2.5 Standard Models 76
3
• 2.5.1 The COST 207 Model 76
• 2.5.2 The ITU-R Models 77
• 2.5.3 IEEE 802.11/HIPERLAN Models 79
• 2.5.4 The 802.15 Ultrawideband Channel Model 79
• 2.5.5 The 3GPP-3GPP2 Model 81
• 2.5.6 The COST 259 Model 82
2
• 2.6 Conclusions 83
1
• CHAPTER 3. MODELING AND ESTIMATION OF MOBILE CHANNELS
2
• 3.1 Introduction 97
• 3.2 Channel Models 99
3
• 3.2.1 Time-Variant Channels 99
4
• 3.2.1.1 Tapped Delay Line Model 99
• 3.2.1.2 Basis Expansion Models 101
3
• 3.2.2 Time-Invariant Channels 102
2
• 3.3 Channel Estimation 104
3
• 3.3.1 Training-Based Channel Estimation 105
4
• 3.3.1.1 Time-Variant Channels 105
3
• 3.3.2 Blind Channel Estimation 106
4
• 3.3.2.1 Combined Channel and Symbol Estimation 106
5
• 3.3.2.1.1 Stochastic Maximum Likelihood Estimation 106
• 3.3.2.1.2 Deterministic Maximum Likelihood Estimation 107
4
• 3.3.2.2 The Methods of Moments 108
5
• 3.3.2.2.1 SISO Channel Estimation 108
6
• 3.3.2.2.1.1 Indirect Channel Estimation 108
5
• 3.3.2.2.2 SIMO Channel Estimation 109
6
• 3.3.2.2.2.3 Multistep Linear Prediction 113
• 3.3.2.2.2.2 Noise Subspace Approach 112
• 3.3.2.2.2.1 The Cross-Relation Approach 111
5
4
3
• 3.3.3 Semiblind Approaches 115
• 3.3.4 Hidden Pilot-Based Approaches 115
4
• 3.3.4.1 Equalization 117
3
2
• 3.4 Simulation Examples 118
• 3.4 Simulation Examples 97
3
• 3.4.1 Example 1 118
• 3.4.2 Example 2 119
2
• 3.5 Conclusions 121
1
• CHAPTER 4. MOBILE SATELLITE CHANNELS: STATISTICAL MODELS AND PERFORMANCE ANALYSIS
2
• 4.1 Introduction 125
• 4.2 Statistical Propagation Models 126
3
• 4.2.1 Narrowband Statistical Models 128
4
• 4.2.1.1 Single-State Statistical Models 128
5
• 4.2.1.1.1 Single-State First-Order Characterization 129
6
• 4.2.1.1.1.9 Patzold et al. Distribution 132
• 4.2.1.1.1.6 Suzuki Distribution 130
• 4.2.1.1.1.10 GRLN Distribution 133
• 4.2.1.1.1.11 Xie and Fang Distribution 133
• 4.2.1.1.1.4 Nakagami Distribution 130
• 4.2.1.1.1.5 Norton Distribution 130
• 4.2.1.1.1.2 Rayleigh Distribution 129
• 4.2.1.1.1.7 Loa Distribution 131
• 4.2.1.1.1.3 Rice Distribution 129
• 4.2.1.1.1.1 Lognormal Distribution 129
• 4.2.1.1.1.12 Other Models 134
• 4.2.1.1.1.8 RLN Distribution 131
5
• 4.2.1.1.2 Single-State Second-Order Characterization 134
6
• 4.2.1.1.2.1 Doppler Spectrum 135
• 4.2.1.1.2.2 Level Crossing Rate and Average Fade Duration 136
• 4.2.1.1.2.3 Fade and Non-fade Duration Statistics 137
• 4.2.1.1.2.4 Shadowing Correlation Function 138
5
4
• 4.2.1.2 Multistate Statistical Models 138
5
• 4.2.1.2.1 Lutz Model 138
• 4.2.1.2.2 Barts and Stutzman Model 140
• 4.2.1.2.3 Vucetic and Du Model 140
• 4.2.1.2.4 Rice and Humphreys Model 141
• 4.2.1.2.5 Karasawa et al. Model 141
• 4.2.1.2.6 Fontan et al. Model 141
• 4.2.1.2.7 Wakana model 141
4
3
• 4.2.2 Wideband Statistical Models 142
4
• 4.2.2.1 DLR Wideband Model 142
• 4.2.2.2 Saunders et al. 143
• 4.2.2.3 IMR Channel Model 143
3
2
• 4.3 Detection Performance Analysis 125
• 4.3 Detection Performance Analysis 144
3
• 4.3.1 Uncoded Transmission over LMS Channels 144
4
• 4.3.1.1 M-ary Coherent Detection 145
• 4.3.1.2 M-ary Orthogonal Noncoherent Detection 145
3
• 4.3.2 Coded Transmission over LMS Channels 146
4
• 4.3.2.1 Pseudo-coherent BPSK Detection 147
• 4.3.2.2 Non-coherent M-ary Detection 149
• 4.3.2.3 Convolutional Code Performance Analysis 150
• 4.3.2.4 Turbo Code Performance Analysis 154
3
2
• 4.4 Conclusions 155
1
• CHAPTER 5. MOBILE VELOCITY ESTIMATION FOR WIRELESS COMMUNICATIONS
2
• 5.1 Introduction 161
3
• 5.1.1 Importance of Velocity Estimation 161
• 5.1.2 Existing Velocity Estimators 163
• 5.1.3 Structure of the Chapter 164
2
• 5.2 Received Signal Model and Statistics 164
3
• 5.2.1 Received Signal Model 164
• 5.2.2 Multipath Component Model 165
• 5.2.3 The Scattering Distribution 166
• 5.2.4 Statistics of the Multipath Fading 166
2
• 5.3 Principles of Mobile Velocity Estimation 168
3
• 5.3.1 Examples of Derivations of the Velocity 168
• 5.3.2 Examples of Velocity Estimators 170
2
• 5.4 Performance Analysis of Velocity Estimators 171
3
• 5.4.1 Effect of Shadowing 172
• 5.4.2 Effect of AWGN and Nonisotropic Scattering 172
4
• 5.4.2.1 Derivation of the Normalized Bias 172
• 5.4.2.2 Performance in the Presence of AWGN and Isotropic Scattering 173
• 5.4.2.3 Performance in the Presence of Nonisotropic Scattering 174
3
2
• 5.5 Performance Analysis Using Simulations 176
3
• 5.5.1 Simulations of the Received Signal 176
• 5.5.2 Simulation Results 177
2
• 5.6 Rice Factor Estimation 180
3
• 5.6.1 Existing Methods 180
• 5.6.2 Envelope-Based Estimators 181
• 5.6.3 Simulation Results 184
2
• 5.7 Application on Handover Performance 184
3
• 5.7.1 Handover Decision Algorithms 184
• 5.7.2 Effect of an Error in Velocity Estimation on the System's 185
2
• 5.8 Conclusions and Perspectives 187
1

0

PART III: MODULATION TECHNIQUES FOR WIRELESS COMMUNICATIONS

1
• CHAPTER 6. ADAPTIVE CODED MODULATION FOR TRANSMISSION OVER FADING CHANNELS
2
• 6.1 Introduction 195
• 6.2 Adaptive System Model 197
3
• 6.2.1 Model for a Wireless Link 197
• 6.2.2 Adaptation in Response to Path Loss/Shadowing 198
• 6.2.3 Analytic Model for Fine-Scale Adaptation 199
2
• 6.3 Adaptivity in Single-Input Single-Output Systems 201
3
• 6.3.1 Information Theoretic Bounds 201
• 6.3.2 Design for Uncoded Systems 202
4
• 6.3.2.1 Design Rules 203
• 6.3.2.2 Numerical Results 204
3
• 6.3.3 Coded Modulation Structures 205
4
• 6.3.3.1 Coding Structures with (Nearly) Perfect Prediction 205
• 6.3.3.2 Coding Structures with Moderate Prediction Error Statistics 205
• 6.3.3.3 Coding Structures with Large Prediction Error Statistics 206
3
• 6.3.4 Designing with a Given Coded Modulation Structure 206
4
• 6.3.4.1 Design Rules 206
• 6.3.4.2 Performance Results 206
3
2
• 6.4 Adaptivity in Multiantenna Systems 207
3
• 6.4.1 Information Theoretic Considerations 207
4
• 6.4.1.1 MIMO Single-User Systems 207
• 6.4.1.2 Multiuser MIMO Systems 208
3
• 6.4.2 Adaptive Coded Modulation for MIMO Systems 208
2
• 6.5 Conclusions 209
1
• CHAPTER 7. SIGNALING CONSTELLATIONS FOR TRANSMISSION OVER NONLINEAR CHANNELS
2
• 7.1 Introduction 214
• 7.2 System Model 215
• 7.3 Craig's Method 216
• 7.4 probability of Symbol Error for 16-ary QAM Format 217
3
• 7.4.1 The (8,8) Constellation Format 217
4
• 7.4.1.1 The Structure 217
• 7.4.1.2 Probability of Error Analysis 218
3
• 7.4.2 The (4,12) Constellation Format 219
4
• 7.4.2.1 The Structure 219
• 7.4.2.2 Probability of Error Analysis 219
3
• 7.4.3 The (S,II) Constellation Format 220
• 7.4.4 The (6,10) Constellation Format 221
• 7.4.5 16-Rectangular Constellations with a Circular Format 222
4
• 7.4.5.1 The Structure 222
• 7.4.5.2 Probability of Error Analysis 223
3
• 7.4.6 The Effect of Nonlinearity and the Application 224
• 7.4.7 Total Degradation 226
• 7.4.8 Results and Discussions 227
2
• 7.5 probability of Symbol Error for the Circular 232
3
• 7.5.1 The (4,11,17) Constellation Format 232
• 7.5.2 The (5,11,16) Constellation Format 234
• 7.5.3 32-Rectangular Constellations with a Circular Format 235
4
• 7.5.3.1 The Structure 235
• 7.5.3.2 Probability of Error Analysis 236
3
• 7.5.4 The Effect of Nonlinearity and the Application 240
• 7.5.5 Results and Discussions 240
2
• 7.6 Conclusions 242
1
• CHAPTER 8. CARRIER FREQUENCY SYNCHRONIZATION FOR OFDM SYSTEMS
2
• 8.1 Introduction 244
• 8.10 Conclusions 262
• 8.2 Basics of OFDM 245
3
• 8.2.1 OFDM Modulation 246
• 8.2.2 Demodulation 247
2
• 8.3 Effect of CFO on System Performance 249
• 8.4 Carrier Frequency Offset Estimation 250
• 8.5 Repetitive Slots-Based CFO Estimation 251
3
• 8.5.1 Nonlinear Least Squares Method 251
• 8.5.2 Computationally Simpler Estimators 252
4
• 8.5.2.1 Approximate NLS Estimator 252
• 8.5.2.2 BLUE Estimator 253
3
2
• 8.6 Null-Subcarrier-Based CFO Estimation 253
3
• 8.6.1 Deterministic Maximum Likelihood Estimation 254
• 8.6.2 Special Case: Repetition of Identical Slots 255
4
• 8.6.2.1 Virtual Subcarriers Absent 255
• 8.6.2.2 Virtual Subcarriers Present 255
3
2
• 8.7 Identifiability 256
• 8.8 Performance Analysis 258
3
• 8.8.1 The Conditional CRB 258
• 8.8.2 The Unconditional CRB: Rayleigh Channel 259
• 8.8.3 Optimal Choice of Null-Subcarriers 259
2
• 8.9 Simulation Results 260
1
• CHAPTER 9. FILTER-BANK MODULATION TECHNIQUES FOR TRANSMISSION OVER FREQUENCY-SELECTIVE CHANNELS
2
• 9.1 Introduction 267
• 9.2 Critically Sampled Filter Banks 269
3
• 9.2.1 Orthogonality Conditions 269
• 9.2.2 Efficient Implementation 270
• 9.2.3 Example of Critically-Sampled Filter Bank 273
2
• 9.3 Discrete Multitone Modulation 274
• 9.4 O-QAM OFDM Modulation 276
• 9.5 Discrete Wavelet Multitone Modulation 281
• 9.6 Filtered Multitone Modulation 282
3
• 9.6.1 Filter-bank Design 286
• 9.6.2 Per-Subchannel Adaptive Equalization and Precoding 289
2
1

0

PART IV: MULTIPLE ACCESS TECHNIQUES

1
• CHAPTER 10. SPREAD-SPECTRUM TECHNIQUES FOR MOBILE COMMUNICATIONS
2
• 10.1 A Brief History of Wireless Communications 296
3
• 10.1.1 The Wireless Revolution 296
• 10.1.2 2G and 3G Cellular Systems 296
• 10.1.3 DSP Components for Wireless Communications 297
2
• 10.2 Fundamentals of Digital Spread-Spectrum Signaling 298
3
• 10.2.1 Narrowband 298
• 10.2.10 Short Code 301
• 10.2.11 Long Code 301
• 10.2.12 Processing Gain 301
• 10.2.13 Pseudorandom Sequence Generators 302
• 10.2.14 Basic Architecture of a DS/SS Modem 302
• 10.2.2 Spread-Spectrum 299
• 10.2.3 Frequency-Hopping Spread Spectrum 299
• 10.2.4 Direct-Sequence Spread Spectrum 299
• 10.2.5 DS/SS Signal Model 300
• 10.2.6 Real Spreading 300
• 10.2.7 Complex Spreading 300
• 10.2.8 DS/SS Bandwidth Occupancy 300
• 10.2.9 Spreading Factor 301
2
• 10.3 Code-Division Multiple Access 304
3
• 10.3.1 Frequency-, Time-, and Code-Division Multiplexing 304
• 10.3.2 Multirate Code-Division Multiplexing 306
• 10.3.3 Multiple Access Interference 306
• 10.3.4 Capacity of a CDMA System 307
• 10.3.5 Cellular Networks and the Universal Frequency Reuse 308
2
• 10.4 A Review of 2G and 3G Standards for CDMA 308
• 10.4 A Review of2G and 3G Standards for CDMA 295
3
• 10.4.1 IS-95 309
• 10.4.2 UMTS/UTRA 309
• 10.4.3 cdma2000 311
2
• 10.5 Synchronization for Spread-Spectrum and CDMA Signals 312
3
• 10.5.1 Synchronization Functions 312
• 10.5.2 Code Synchronization 312
• 10.5.3 Carrier Frequency and Phase Synchronization 314
2
• 10.6 Architecture of DSP-Based DS/SS and CDMA Receivers 315
3
• 10.6.1 IF vs. Baseband Sampling 315
• 10.6.2 Correlation Receiver 316
• 10.6.3 Rake Receiver 316
2
• 10.7 Multiuser Detection 318
3
• 10.7.1 Multiuser Detection in the UL 318
• 10.7.2 The Decorrelating and MMSE Detectors 320
2
• 10.8 Perspectives and Conclusions 321
3
• 10.8.1 The Challenge to Mobile Spread-Spectrum Communications 321
• 10.8.2 4G Wireless Communications Systems 322
• 10.8.3 Concluding Remarks 323
2
1
• CHAPTER 11. MULTIUSER DETECTION FOR FADING CHANNELS
2
• 11.1 Introduction 327
• 11.2 Signal and Channel Model 327
3
• 11.2.1 The Fading Channel Model 327
• 11.2.2 Transmitted Signal Model 329
• 11.2.3 Continuous-Time Received Signal Model 330
2
• 11.3 Multiuser Detection with Known CSI 331
3
• 11.3.1 Conventional Single-User Detection 332
• 11.3.2 Optimum Multiuser Detection 333
• 11.3.3 Linear Multiuser Detection 335
4
• 11.3.3.1 The Decorrelating Detector 335
• 11.3.3.2 Remarks 337
• 11.3.3.3 The MMSE Detector 338
• 11.3.3.4 Remarks on the MMSE Detector 339
3
• 11.3.4 Approximate MMSE Detection: Linear Serial 340
• 11.3.5 Constrained ML Detection: Nonlinear Serial 341
• 11.3.6 Performance Results 342
4
• 11.3.6.1 Analysis 342
3
• 11.3.7 Some Numerical Results 347
• 11.3.8 Sliding-Window One-Shot Multiuser Receivers 349
2
• 11.4 Multiuser Detection with Unknown CSI 350
3
• 11.4.1 Signal Representation in Unknown CSI 350
• 11.4.2 Available Strategies to Cope with Missing CSI 353
• 11.4.3 Channel Estimation Based on the Least Squares Criterion 353
• 11.4.4 Trained Adaptive Code-Aided Symbol Detection 355
• 11.4.5 Code-Aided Joint Blind Multiuser Detection and Equalization 358
• 11.4.6 Subspace-Based Blind MMSE Detection 359
• 11.4.7 Minimum Variance Blind Detection 360
• 11.4.8 Two-Stage Blind Detection 360
• 11.4.9 Performance Results 361
2
• 11.5 Conclusions 363
1

0

PART V: MIMO SYSTEMS

1
• CHAPTER 12. PRINCIPLES OF MIMO-OFDM WIRELESS SYSTEMS
2
• 12.1 Introduction 365
• 12.2 The Broadband MIMO Fading Channel 366
3
• 12.2.1 Basic Assumptions 366
• 12.2.2 Array Geometry 367
• 12.2.3 Fading Statistics 367
• 12.2.4 Ricean Component 368
• 12.2.5 Comments on the Channel Model 368
2
• 12.3 Capacity of Broadband MIMO-OFDM Systems 368
3
• 12.3.1 MIMO-OFDM 368
• 12.3.2 Capacity of MIMO-OFDM Spatial Multiplexing Systems 370
• 12.3.3 Impact of Propagation Parameters on Capacity 370
4
• 12.3.3.1 Impact of Cluster Angle Spread and Antenna Spacing 371
• 12.3.3.2 Impact of Total Angle Spread 371
• 12.3.3.3 Ergodic Capacity in the SISO and MIMO Cases 371
3
• 12.3.4 Numerical Results 373
2
• 12.4 Space-Frequency Coded MIMO-OFDM 373
3
• 12.4.1 Space--Frequency Coding 373
• 12.4.2 Error Rate Performance 375
• 12.4.3 Maximum Diversity Order and Coding Gain 375
4
• 12.4.3.1 Rayleigh Fading 375
5
• 12.4.3.1.1 Receive Correlation Only 376
• 12.4.3.1.2 Transmit Correlation Only 376
• 12.4.3.1.3 Joint Transmit-Receive Correlation 377
4
• 12.4.3.2 Ricean Fading 377
3
2
• 12.5 Impact of Propagation Parameters 378
3
• 12.5.1 Impact of Propagation Parameters 378
4
• 12.5.1.1 Impact of Cluster Angle Spread 378
• 12.5.1.2 Impact of Total Angle Spread 379
3
• 12.5.2 Simulation Results 380
4
• 12.5.2.1 Simulation Example 1 380
• 12.5.2.2 Simulation Example 2 380
• 12.5.2.3 Simulation Example 3 382
• 12.5.2.4 Simulation Example 4 382
5
• 12.5.2.4.1 Spatial Multiplexing 384
• 12.5.2.4.2 Delay Diversity Combined with Convolutional Code 384
4
3
2
1
• CHAPTER 13. SPACE-TIME CODING AND SIGNAL PROCESSING FOR BROADBAND WIRELESS COMMUNICATIONS
2
• 13.1 Introduction 387
• 13.2 Broadband Wireless Channel Model 387
• 13.2 Broadband Wireless Channel Model 389
• 13.3 Information- Theoretic Considerations 391
• 13.3 Information- Theoretic Considerations 387
3
• 13.3.1 Shannon Capacity of Fading ISI Channels 391
• 13.3.2 Diversity Order 391
• 13.3.3 Design Criteria for Space-Time Codes over Flat-Fading Channels 392
2
• 13.4 Signal Transmission Issues 393
3
• 13.4.1 Transmitter Techniques 393
4
• 13.4.1.1 Spatial Multiplexing (BLAST) 393
• 13.4.1.2 Transmit Diversity Techniques 394
• 13.4.1.3 Space-Time Coding 395
5
• 13.4.1.3.1 Space-Time Trellis Codes 396
• 13.4.1.3.2 Alamouti-Type Space-Time Block Codes 397
4
• 13.4.1.4 Diversity vs. Throughput Trade-Off 398
• 13.4.1.5 Orthogonal Frequency Division Multiplexing 399
3
• 13.4.2 Receiver Techniques 399
4
• 13.4.2.1 Coherent Techniques 400
5
• 13.4.2.1.1 Channel Estimation for Quasi-Static Channels 400
• 13.4.2.1.2 Channel Estimation and Tracking for Rapidly Time- Varying Channels 401
• 13.4.2.1.3 Joint Equalization/Decoding of Space-Time Trellis Codes 401
• 13.4.2.1.4 Joint Equalization/Decoding of Space-Time Block Codes 402
• 13.4.2.1.5 OFDM with Fast Channel Variations 404
• 13.4.2.1.6 Joint Equalization and Interference Cancellation 404
• 13.4.2.1.7 Adaptive Techniques 406
4
• 13.4.2.2 Noncoherent Techniques 407
3
2
• 13.5 Summary and Future Challenges 409
1
• CHAPTER 14. LINEAR PRECODING FOR MIMO SYSTEMS
2
• 14.1 Introduction 414
3
• 14.1.1 System Model 415
2
• 14.2 Optimum precoding 416
3
• 14.2.1 Jointly Optimum Design and Performance Bounds 417
4
• 14.2.1.1 MMSE Criterion under Transmit Power and Maximum Eigenvalue Constraint 418
• 14.2.1.2 Maximum Amin(SNR(F, G)) under Power and Maximum 419
• 14.2.1.3 Equivalent Decomposition into Independent Sub channels 420
• 14.2.1.4 Performance Measures 421
3
2
• 14.3 Performance Analysis and Random Matrices 423
3
• 14.3.1 Differential Forms and Random Matrix Techniques 423
• 14.3.2 The Statistics of the Capacity 427
2
• 14.4 Channel Estimation for MIMO Systems 432
• 14.4 Channel Estimation for MIMO Systems 414
3
• 14.4.1 Channel Estimation Algorithm 433
• 14.4.2 Cramer-Rao Lower Bound 435
• 14.4.3 Numerical Results 436
2
• 14.5 Conclusions 437
1
• CHAPTER 15. PERFORMANCE ANALYSIS OF MULTIPLE ANTENNA SYSTEMS
2
• 15.1 Introduction 441
• 15.2 MIMO Systems without Co-Channel Interference 442
3
• 15.2.1 System Model and Problem Statement 442
• 15.2.2 MIMO Channel Capacity without Channel State Information 442
4
• 15.2.2.1 Rician Fading 442
• 15.2.2.2 l.i.d. Rician Fading and LLd. Rayleigh Fading Channels 443
• 15.2.2.3 Correlated Rayleigh Fading Channels 444
• 15.2.2.4 Capacity CCDF 449
3
• 15.2.3 Capacity/Outage Probability of MIMO MRC (Beam-Forming) Systems 451
4
• 15.2.3.1 System Models and Problem Statement 451
• 15.2.3.2 MIMO MRC Systems Outage Probability 452
• 15.2.3.3 Capacity CCDF of MIMO MRC Systems 453
3
• 15.2.4 Water-Filling Capacity and Beam Forming Performance of Correlated 453
4
• 15.2.4.1 Water-Filling Capacity 453
• 15.2.4.2 Beam-Forming Performance 455
3
2
• 15.3 MIMO Systems in the Presence 458
3
• 15.3.1 Problem Statement 458
• 15.3.2 Capacity CCDF of MIMO Optimum Combining Scheme 459
4
• 15.3.2.2 Capacity of Optimum Combining 463
3
• 15.3.3 Statistics of the MIMO Capacity with Co-Channel Interference 463
4
• 15.3.3.1 Problem Statement 463
• 15.3.3.2 Capacity MGF 464
3
• 15.3.4 RicianjRayleigh Fading Scenarios 467
2
1

0

PART VI: EQUALIZATION AND RECEIVER DESIGN

1
• CHAPTER 16. EQUALIZATION TECHNIQUES FOR FADING CHANNELS
2
• 16.1 Introduction 472
• 16.2 Wireless Channel Model 473
3
• 16.2.1 TIV Channels 475
• 16.2.2 TV Channels 475
2
• 16.3 System Model 477
3
• 16.3.1 TIV Channels 478
• 16.3.2 TV Channels 478
2
• 16.4 Block Equalization 478
3
• 16.4.1 Block Linear Equalization 479
• 16.4.2 Block Decision Feedback Equalization 480
2
• 16.5 Serial Linear Equalization 481
3
• 16.5.1 TIV Channels 481
• 16.5.2 TV Channels 483
• 16.5.3 Equalizer Design 484
2
• 16.6 Serial Decision Feedback Equalization 485
3
• 16.6.1 TIV Channels 486
• 16.6.2 TV Channels 486
• 16.6.3 Equalizer Design 487
2
• 16.7 Frequency-Domain Equalization for TIV Channels 488
3
• 16.7.1 FD Linear Equalization 489
• 16.7.2 FD Decision Feedback Equalization 489
2
• 16.8 Existence of Zero-Forcing Solution 492
3
• 16.8.1 Linear Equalizers 492
• 16.8.2 Decision Feedback Equalizers 492
2
• 16.9 Complexity 493
3
• 16.9.1 Design Complexity 493
• 16.9.2 Implementation Complexity 494
2
1
• CHAPTER 17. LOW-COMPLEXITY DIVERSITY COMBINING SCHEMES FOR MOBILE COMMUNICATIONS
2
• 17.1 Introduction 503
• 17.2 System and Channel Models 504
3
• 17.2.1 System Model 504
• 17.2.2 Channel Model 505
2
• 17.3 Dual-Branch Switch and Stay Combining 505
3
• 17.3.1 Dual-Branch sse Schemes 506
• 17.3.2 Markov Chain-Based Analysis 507
• 17.3.3 Statistics of Overall Combiner Output 509
• 17.3.4 Application to Performance Analysis and Comparisons 511
2
• 17.4 Multibranch Switched Diversity 515
3
• 17.4.1 L-Branch sse 515
• 17.4.2 L-Branch SEC 516
2
• 17.5 Generalized Switch and Examine Combining (GSEC) 521
3
• 17.5.1 Mode of Operation of GSEC 521
• 17.5.2 MGF of Combiner Output 522
• 17.5.3 Application to Performance Analysis 524
• 17.5.4 Complexity Savings over GSC 527
2
• 17.6 Further Remarks 529
1
• CHAPTER 18. OVERVIEW OF EQUALIZATION TECHNIQUES FOR MIMO FADING CHANNELS
2
• 18.1 Introduction 531
• 18.2 Frequency-Selective MIMO Channel Model 531
• 18.2 Frequency-Selective MIMO Channel Model 532
3
• 18.2.1 General Framework 532
• 18.2.2 Simulation Framework 533
2
• 18.3 Block Linear and Decision-Feedback Equalizers 534
3
• 18.3.1 Block Linear Equalizers 534
• 18.3.2 Block Decision-Feedback Equalizers 535
4
• 18.3.2.1 Simulation Results 537
3
2
• 18.4 List-Type Equalizers 538
3
• 18.4.1 The Single Antenna Case 538
4
• 18.4.1.1 Viterbi Algorithm 538
• 18.4.1.2 Generalized Viterbi Algorithm 538
• 18.4.1.3 List-Type MAP Algorithm 539
3
• 18.4.2 Generalization to the MIMO Case 540
• 18.4.3 Simulation Results 541
2
• 18.5 The Multidimensional Whitened Matched Filter 531
• 18.5 The Multidimensional Whitened Matched Filter 542
3
• 18.5.1 Whitened Matched Filter 542
• 18.5.2 Prefiltered List-Type MAP Equalizer 543
• 18.5.3 WMF Implementation Using Linear Prediction 543
• 18.5.4 Energy Concentration 544
• 18.5.5 Simulation Results 546
2
• 18.6 The Block Equalizers vs. the Prefiltered 531
• 18.6 The Block Equalizers vs. the Prefiltered List-Type MAP 547
3
• 18.6.1 Complexity Comparison 547
• 18.6.2 Performance Comparison 548
2
• 18.7 Conclusion 549
1
• CHAPTER 19. NEURAL NETWORLZS FOR TRANSMISSION OVER NONLINEAR CHANNELS
2
• 19.1 Introduction 552
• 19.2 Identification of Memoryless Nonlinear Amplifiers 555
3
• 19.2.1 Natural Gradient Learning 556
• 19.2.2 Influence of the HP A Modeling Error 558
• 19.2.3 Application to Adaptive Predistortion 561
2
• 19.3 Modeling and Identification of Nonlinear 566
3
• 19.3.1 Neural Network Channel Identification 566
• 19.3.2 Learning Algorithm 567
• 19.3.3 Application to MLSE Receiver Design 568
• 19.3.4 Simulation Examples 568
2
• 19.4 Channel Equalization 572
3
• 19.4.1 Neural Network Structure 572
• 19.4.2 BP Algorithm 574
• 19.4.3 NG Algorithm 574
• 19.4.4 Simulation Examples 575
2
• 19.5 Conclusion 575
1

0

PART VII: VOICE OVER IP

1
• CHAPTER 20. VOICE OVER IP AND WIRELESS: PRINCIPLES AND CHALLENGES
2
• 20.1 Introduction 581
• 20.2 Speech Coding for IP and Wireless: Principles 581
3
• 20.2.1 Some Preliminary Notions 581
• 20.2.2 Speech Coding Basics 582
4
• 20.2.2.1 Differential Coding 582
• 20.2.2.2 Adaptive Quantization 583
• 20.2.2.3 Noise Masking 583
• 20.2.2.4 Quality Measures 584
3
• 20.2.3 Speech Coding for IP and Wireless 584
4
• 20.2.3.1 Waveform Coders 584
• 20.2.3.2 Parametric Coders 584
• 20.2.3.3 Hybrid Coders 584
• 20.2.3.4 Relevant Coder Attributes 585
5
• 20.2.3.4.1 Bit Rate 585
• 20.2.3.4.2 Complexity 586
• 20.2.3.4.3 Delay 586
• 20.2.3.4.4 Robustness 586
4
3
2
• 20.3 Voice over IP 586
3
• 20.3.1 An Overview 586
• 20.3.2 Technological Barriers 587
4
• 20.3.2.1 End-to-End Delay and Jitter 587
• 20.3.2.2 Packet Loss 587
• 20.3.2.3 Throughput 587
• 20.3.2.4 Internet Availability and Reliability 588
• 20.3.2.5 Security and Confidentiality 588
3
• 20.3.3 Quality of Service 588
• 20.3.4 Standard Speech Coders for VoIP 588
• 20.3.5 Packet loss Recovery Techniques 589
• 20.3.6 Other Packet-Based Alternatives for Voice Transport: Trends 590
2
• 20.4 Voice over Wireless 590
3
• 20.4.1 Wireless Voice Communications Systems 590
• 20.4.2 Fundamental Issues in Speech Coding for Wireless 591
4
• 20.4.2.1 Channel Quality and Adaptive Operation 591
• 20.4.2.2 Background Noise 591
• 20.4.2.3 Tandeming 591
• 20.4.2.4 Voice Activity Detection 591
• 20.4.2.5 Unequal Error Protection (UEP) 591
• 20.4.2.6 Frame Erasures 592
3
• 20.4.3 Standard Speech Coders for Wireless 592
4
• 20.4.3.1 European GSM and UMTS Standards 592
• 20.4.3.2 North American Cellular Systems 593
3
• 20.4.4 Some Trends 593
4
• 20.4.4.1 Joint Channel-Source Coding 593
• 20.4.4.2 Robustness Issues for Low-Bit-Rate Coders 594
• 20.4.4.3 Selectable Mode Vocoder (SMV [18]) 594
3
2
• 20.5 Voice over IP over Wireless 594
• 20.6 Voice-Enabled Services over IP and Wireless 595
3
• 20.6.1 Introduction 595
• 20.6.2 Technologies for Voice-Enabled Interfaces 595
• 20.6.3 Architectures for Automatic Speech Recognition 596
4
• 20.6.3.1 Local or Terminal-Based Speech Recognition 596
• 20.6.3.2 Remote or Network-Based Speech Recognition 597
• 20.6.3.3 Distributed Speech Recognition 597
3
• 20.6.4 ASR over IP 598
4
• 20.6.4.1 Coding Distortion 598
• 20.6.4.2 Packet Loss 598
3
• 20.6.5 ASR over Wireless 598
4
• 20.6.5.1 Noisy Scenarios 599
• 20.6.5.2 The Influence of Coding Distortion on Speech Recognition 599
• 20.6.5.3 Transmission Errors and Lost Frames 599
3
2
• 20.7 Conclusions and Challenges 580
• 20.7 Conclusions and Challenges 600
1

0

PART VIII: WIRELESS GEOLOCATION TECHNIQUES

1
• CHAPTER 21. GEOLOCATION TECHNIQUES FOR MOBILE RADIO SYSTEMS
2
• 21.1 Introduction 605
3
• 21.1.1 FCC Regulations for E-911 606
• 21.1.2 Location-Based Services (LBS) 606
2
• 21.2 Geolocation Methods 607
• 21.3 Geolocation Algorithms 609
3
• 21.3.1 Geometric Solutions 609
• 21.3.2 Least Squares Estimation 611
• 21.3.3 Other Location Algorithms 613
• 21.3.4 Hybrid Location Methods 613
• 21.3.5 Performance 614
2
• 21.4 Location Parameter Estimation 616
3
• 21.4.1 ADA Estimation 616
• 21.4.2 Range Estimation 617
• 21.4.3 Range Difference Estimation 617
• 21.4.4 Joint Parameter Estimation 618
2
• 21.5 Impairments to Accuracy 618
• 21.5 Impairments to Accuracy 605
3
• 21.5.1 Multipath Propagation 618
• 21.5.2 Hearability 619
• 21.5.3 Non-Line-of-Sight Propagation 619
2
• 21.6 Provisions in the Standards 621
3
• 21.6.1 3G Location Solutions 621
• 21.6.2 Locating Legacy Terminals 622
2
• 21.7 Summary 622
1
• CHAPTER 22. ADAPTIVE ARRAYS FOR GPS RECEIVERS
2
• 22.1 Introduction 628
• 22.2 GPS Signal Model 632
• 22.3 Interference Suppression Techniques in GPS 633
3
• 22.3.1 Broadband Interference Suppression Using Space-Time Array 633
4
• 22.3.1.1 MSINR 634
• 22.3.1.2 MMSE 634
• 22.3.1.3 MOP 635
• 22.3.1.4 Signal Distortion Introduced by Processor 635
3
• 22.3.2 Narrowband FM Interference Suppression 636
• 22.3.3 A Self-Coherence Antijamming GPS Receiver 640
2
• 22.4 Multipath Mitigation in GPS 646
3
• 22.4.1 Bias Due to Signal Multipath 646
4
• 22.4.1.1 Early-Late Discrimination Functions 647
• 22.4.1.2 Time-Delay Estimation 648
3
• 22.4.2 Single-Antenna Multipath Mitigation Techniques 649
4
• 22.4.2.1 Narrow Correlator 649
• 22.4.2.2 Multipath Elimination Delay Lock Loop 649
3
• 22.4.3 Time-Delay and Carrier-Phase Estimation Using Antenna Array 649
2
• 22.5 Conclusions 651
1

0

PART IX: POWER CONTROL AND WIRELESS NETWORKING

1
• CHAPTER 23. TRANSMITTER POWER CONTROL IN WIRELESS NETWORLZING: BASIC PRINCIPLES AND CORE ALGORITHMS
2
• 23.1 Intoduction 654
• 23.2 Power Control for Streamed Continuous Traffic 656
3
• 23.2.1 The Target SIR Formulation of the Power Control Problem 656
4
• 23.2.1.1 Example of the Simple Two-Link Network 657
• 23.2.1.2 The Optimal Power Vector P* 659
3
• 23.2.2 Autonomous Distributed Power Control 660
• 23.2.3 DPC with Active Link Protection 661
• 23.2.4 Admission Control under DPC/ ALP 663
4
• 23.2.4.1 Voluntary Dropout 666
• 23.2.4.2 Forced Dropout 666
• 23.2.4.3 Initial Power Level of New Links 667
3
• 23.2.5 Noninvasive Channel Probing and Selection 667
2
• 23.3 Power Control for Packetized Data Traffic 668
3
• 23.3.1 Power-Controlled Multiple Access: The Basic Model 668
• 23.3.2 Optimally Emptying the Transmitter Buffer 669
• 23.3.3 The Case of Per-Slot-Independent Interference 670
• 23.3.4 Structural Properties: Backlog Pressure and Phased Back-Off 671
• 23.3.5 Design of PCMA Algorithms: Responsive Interference 672
2
• 23.4 Final Remarks 673
1
• CHAPTER 24. SIGNAL PROCESSING FOR MULTIACCESS COMMUNICATION NETWORKS
2
• 24.1 Introduction 675
• 24.2 MPR at the Physical Layer 676
3
• 24.2.1 The Model 676
• 24.2.2 Assumptions and Properties 678
• 24.2.3 The Training-Based Zero-Forcing Receiver 678
• 24.2.4 The Semiblind Least Squares Smoothing Receiver 679
• 24.2.5 Further Remarks 681
2
• 24.3 The Interface between the Physical Layer and the MAC 682
3
• 24.3.1 Channel Reception Matrix 682
• 24.3.2 Resolvability 683
• 24.3.3 From Resolvability Function to Reception Matrix 684
• 24.3.4 Further Remarks 684
2
• 24.4 Impact of MPR on the Performance of Existing 685
3
• 24.4.1 Resolvability Comparison 685
• 24.4.2 Network Performance Comparison 685
• 24.4.3 Further Remarks 686
2
• 24.5 MAC Layer Design for Networks with MPR 687
3
• 24.5.1 The Network Model 687
• 24.5.2 The Multiqueue Service Room Protocol 687
• 24.5.3 The Dynamic Queue Protocol 688
• 24.5.4 Further Remarks 689
2
• 24.6 Approach High Performance from Both Physical 690
• 24.7 Conclusion 691
1

0

PART X: EMERGING TECHNIQUES AND APPLICATIONS

1
• CHAPTER 25. TIME-FREQUENCY SIGNAL PROCESSING FOR WIRELESS COMMUNICATIONS
2
• 25.1 Introduction 694
• 25.2 Time-Frequency Signal Processing Tools 695
3
• 25.2.1 Limitations of Traditional Signal Representations 695
• 25.2.2 Joint Time-Frequency Representations 695
4
• 25.2.2.1 Finding Hidden Information Using Time-Frequency Representations 695
• 25.2.2.2 What Is a Time-Frequency Representation? 696
5
• 25.2.2.2.1 Physical Interpretation of TFDs 698
• 25.2.2.2.2 Instantaneous Frequency and Group Delay 698
4
3
• 25.2.3 Quadratic Time-Frequency Distributions 699
4
• 25.2.3.1 Time-Varying Spectrum and the Wigner- Ville Distribution 699
• 25.2.3.2 Time-Varying Spectrum Estimates and Quadratic TFDs 700
• 25.2.3.3 Time, Lag, Frequency, and Doppler Domains and the Ambiguity Function 701
• 25.2.3.4 Quadratic TFDs, Multicomponent Signals, Cross-Terms Reduction, 703
5
• 25.2.3.4.1 Reduced Interference Distributions 704
• 25.2.3.4.2 Comparison of Quadratic TFDs 704
4
• 25.2.3.5 Time-Frequency Signal Synthesis 705
• 25.2.3.6 Other Properties 705
3
2
• 25.3 Spread-Spectrum Communications Systems Using TFSP 705
3
• 25.3.1 Channel Modeling and Identification 705
4
• 25.3.1.1 Wireless Communication LTV Channel Model 706
• 25.3.1.2 Estimation of LTV channels 708
• 25.3.1.3 Estimation of Scattering Function 709
3
• 25.3.2 Interference Mitigation 710
4
• 25.3.2.1 TV-NBI Suppression in DS-CDMA 710
• 25.3.2.2 Signal Modulation Design for ISI Mitigation 711
• 25.3.2.3 Multiple-Access Interference in MC-CDMA 713
3
2
• 25.4 Time-Frequency Array Signal Processing 714
3
• 25.4.1 The Spatial Time-Frequency Distributions 715
4
• 25.4.1.1 Structure under linear model 716
• 25.4.1.2 Structure under Unitary Model 716
3
• 25.4.2 STFD Structure in Wireless Communications 717
• 25.4.3 Advantages of STFDs over Covariance Matrix 717
• 25.4.4 Selection of Autoterms and Cross-Terms 718
• 25.4.5 Time-Frequency Direction-of-Arrival Estimation 719
4
• 25.4.5.1 Data Model 720
• 25.4.5.2 TF-MUSIC 720
3
• 25.4.6 Time-Frequency Source Separation 720
4
• 25.4.6.1 Separation of Instantaneous Mixture 721
• 25.4.6.2 Separating More Sources Than Sensors 722
• 25.4.6.3 Separation of Convolutive Mixtures 722
• 25.4.6.4 STFD-Based Separation 723
3
2
• 25.5 Other TFSP Applications in Wireless Communications 724
3
• 25.5.1 Precoding for LTV Channels 724
• 25.5.2 Signaling Using Chirp Modulation 724
• 25.5.3 Detection of FM Signals in Rayleigh Fading 725
• 25.5.4 Mobile Velocity/Doppler Estimation 726
2
• 25.6 Conclusion 727
1
• CHAPTER 26. MONTE CARLO SIGNAL PROCESSING FOR DIGITAL COMMUNICATIONS: PRINCIPLES AND APPLICATIONS
2
• 26.1 Introduction 735
• 26.2 MCMC Methods 736
3
• 26.2.1 General MCMC Algorithms 736
4
• 26.2.1.1 Metropolis-Hastings Algorithm 736
• 26.2.1.2 Gibbs Sampler 737
• 26.2.1.3 Other Techniques 737
3
• 26.2.2 Applications of MCMC in Digital Communications 738
4
• 26.2.2.1 MCMC Detectors in AWGN Channels 738
• 26.2.2.2 MCMC Equalizers in ISI Channels 740
• 26.2.2.3 MCMC Multiuser Detector in CDMA Channels 742
3
• 26.2.3 Other Applications 744
2
• 26.3 SMC Methods 744
3
• 26.3.1 General SMC Algorithms 744
4
• 26.3.1.1 Sequential Importance Sampling 744
• 26.3.1.2 SMC for Dynamic Systems 746
• 26.3.1.3 Mixture Kalman Filter 747
3
• 26.3.2 Resampling Procedures 748
• 26.3.3 Applications of SMC in Digital Communications 750
4
• 26.3.3.1 SMC Receiver in Flat-Fading Channels 750
• 26.3.3.2 SMC Receiver in MIMO ISI Channels 752
3
2
• 26.4 Concluding Remarks 756
1
• CHAPTER 27. PRINCIPLES OF CHAOS COMMUNICATIONS
2
• 27.1 What Is Chaos? 759
3
• 27.1.1 Nonlinear Dynamic Systems 759
• 27.1.2 Statistical Analysis of Chaotic Signals 761
4
• 27.1.2.1 Probability Density Functions 761
• 27.1.2.2 Autocovariance and Autocorrelation Function 762
3
• 27.1.3 Realization of Chaos Generators 765
4
• 27.1.3.1 Discrete-Time Systems 765
• 27.1.3.2 Bit Stream Generators 765
• 27.1.3.3 Continuous-Time Systems 766
• 27.1.3.4 Distributed Systems 766
• 27.1.3.5 Digital Realizations 766
3
• 27.1.4 Properties of Chaotic Signals 766
2
• 27.2 Communication: Requirements and Resources 759
• 27.3 Chaos in Communications 759
• 27.3 Chaos in Communications 768
3
• 27.3.1 The Broadband Aspect 768
• 27.3.2 The Complexity Aspect 768
• 27.3.3 The Orthogonality Aspect 768
2
• 27.4 Communication Using Broadband Chaotic Carriers 768
3
• 27.4.1 Chaos-Based Transmitters 769
4
• 27.4.1.1 Static Encoding/Modulation Methods 769
5
• 27.4.1.1.1 Chaotic Masking 769
• 27.4.1.1.2 Chaos Shift Keying 770
• 27.4.1.1.3 Chaotic On-Off Keying 770
• 27.4.1.1.4 Transmitted Reference Methods and Differential Chaos Shift Keying 771
• 27.4.1.1.5 Analysis of Schemes with Static Encoding/Modulation 771
4
• 27.4.1.2 Dynamic Encoding/Modulation Methods 772
5
• 27.4.1.2.1 Chaotic Modulation/Chaotic Switching 772
• 27.4.1.2.2 Encoding Messages into the Symbolic Dynamics of Chaos Generators 772
4
3
• 27.4.2 Receiver Design in Chaos Communications 773
4
• 27.4.2.1 Message Detection Using a Reference Signal 773
5
• 27.4.2.1.1 Reference Generation by Drive Response Synchronization 773
• 27.4.2.1.2 Reference Generation by Controlling Chaotic Systems 774
• 27.4.2.1.3 Demodulation Using Reference Signals: Application Examples 774
4
• 27.4.2.2 Message Detection Based on Signal Statistics 776
• 27.4.2.3 Inverse System Principles 776
• 27.4.2.4 Other Detection Principles 777
3
2
• 27.5 Chaos for Spreading Code Generation 777
• 27.6 Chaotic vs. Classical Communications
3
• 27.6.1 The Analysis of Chaos Communication Schemes in AWGN 778
4
• 27.6.1.1 The Analysis Problem 778
• 27.6.1.2 Analysis Methods 779
5
• 27.6.1.2.1 Discrete-Time Baseband Modeling 779
• 27.6.1.2.2 Statistical Analysis of Nonlinear Discrete-Time Baseband Models 780
4
• 27.6.1.3 Analysis Example 781
5
• 27.6.1.3.1 Baseband Model 781
• 27.6.1.3.2 Baseband Signal Models 781
• 27.6.1.3.3 Results of the Statistical Analysis: Gaussian Approximation 782
• 27.6.1.3.4 Results of the Statistical Analysis: Exact Solutions 782
4
3
• 27.6.2 Chaos Communication Methods in Comparison to Classical Solutions 783
2
1
• CHAPTER 28. ADAPTATION TECHNIQUES AND ENABLING PARAMETER ESTIMATION ALGORITHMS FOR WIRELESS COMMUNICATIONS SYSTEMS
2
• 28.1 Introduction 787
• 28.2 Overview of Adaptation Schemes 789
3
• 28.2.1 Link and Transmitter Adaptation 790
• 28.2.2 Adaptive System Resource Allocation 791
• 28.2.3 Receiver Adaptation 791
2
• 28.3 Parameter Measurements 792
• 28.3 Parameter Measurements 787
3
• 28.3.1 Channel Selectivity Estimation 792
4
• 28.3.1.1 Time Selectivity Measure: Doppler Spread 792
• 28.3.1.2 Frequency Selectivity Measure: Delay Spread 794
• 28.3.1.3 Spatial Selectivity Measure: Angle Spread 795
3
• 28.3.2 Channel Quality Measurements 796
4
• 28.3.2.1 Measures before Demodulation 797
• 28.3.2.2 Measures during and after Demodulation 797
• 28.3.2.3 Measures after Channel Decoding 799
• 28.3.2.4 Measures after Speech or Video Decoding 799
3
2
• 28.4 Applications of Adaptive Algorithms: Case Studies 800
3
• 28.4.1 Examples for Adaptive Receiver Algorithms 800
4
• 28.4.1.1 Channel Estimation with A Priori Information 800
• 28.4.1.2 Adaptive Channel Length Truncation for Equalization 801
• 28.4.1.3 Adaptive Interference Cancellation Receivers 801
• 28.4.1.4 Adaptive Soft Information Generation and Decoding 802
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• 28.4.2 Examples for Link Adaptation and Adaptive Resource Allocation 803
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• 28.4.2.1 Adaptive Power Control 803
• 28.4.2.2 Adaptive Modulation and Channel Coding 803
• 28.4.2.3 Adaptive Cell and Frequency Assignment 805
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• 28.5 Future Research for Adaptation 806
• 28.6 Conclusion 808
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