 <h4>SIGNAL PROCESSING FOR 
MOBILE COMMUNICATIONS 
HANDBOOK</h4>                                          
 <h5>Edited by 
Mohamed Ibnkahla</h5>                                   
 <h6>CRC PRESS 2005.</h6>                               
<h5>CONTENTS</h5>                                            
Preface 4
Contributors 6
PART I: INTRODUCTION
CHAPTER 1.  SIGNAL PROCESSING FOR FUTURE MOBILE COMMUNICATIONS SYSTEMS: CHALLENGES AND PERSPECTIVES  
1.1 Introduction 12
1.2 Channel Characterizations 12
1.2.1 Large-Scale Propagation Models 12
1.2.1.1 Deterministic Approach 12
1.2.1.1.1 Free-Space Propagation Model 12
1.2.1.1.2 Log-Distance Path Loss Model 13
1.2.1.2 Stochastic Approach 13
1.2.1.2.1 Lognormal Shadowing Model 13
1.2.2 Small-Scale Propagation Models 14
1.2.2.1 Parameters of Mobile Multipath Channel 14
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
1.2.2.2 Types of Small-Scale Fading 15
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
1.2.2.3 Statistical Representation of the Small-Scale Propagation Channel 17
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
1.2.2.4 Statistical Models for Multipath Fading Channels 18
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
1.2.2.4.5.2 Physical Scattering Model 22
1.2.2.4.5.1 Matrix Channel Model 21
1.3 Modulation Techniques 23
1.3.1 Modulation Schemes: The Classification 23
1.3.2 Different Modulation Schemes 23
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
1.4 Coding Techniques 30
1.4.1 Shannon's Capacity Theorem 30
1.4.2 Different Coding Schemes 31
1.4.2.1 Block Codes 32
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
1.4.2.2 Convolutional Codes 34
1.4.2.2.1 Pictorial Representation of Convolutional Encoder 34
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
1.4.2.3 Space-Time Coding 36
1.4.2.4 Turbo Coding 36
1.4.2.5 Coded Modulation Techniques 37
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
1.4.3 Coding in Next-Generation Mobile Communications: Some Research 37
1.5 Multiple Access Techniques 39
1.5 Multiple Access Techniques 11
1.5.1 Fundamental Multiple-Access Schemes 39
1.5.1.1 Frequency Division Multiple Access 39
1.5.1.1.1 Merits 39
1.5.1.1.2 Demerits 39
1.5.1.2 Time Division Multiple Access 40
1.5.1.2.1 Merits 40
1.5.1.2.2 Demerits 40
1.5.1.3 Code Division Multiple Access 40
1.5.1.3.1 Wideband CDMA 40
1.5.1.3.2 Merits 40
1.5.1.3.3 Demerits 41
1.5.2 Combination of OFDM and CDMA Systems 41
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
1.5.3 OFDM/TDMA 43
1.5.3.1 Merits 43
1.5.3.2 Demerits 43
1.5.4 Capacity of MAC Methods 43
1.5.4.1 FDMA Capacity 44
1.5.4.2 TDMA Capacity 44
1.5.4.3 CDMA Capacity 44
1.5.5 Challenges in the MAC Schemes 44
1.6 Diversity Technique 45
1.6.1 Classifications of the Diversity Techniques 45
1.6.2 Classifications of Diversity Combiners 46
1.6.2.1 Predetection Diversity Combiners 46
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
1.6.2.2 Postdetection Diversity Combiners 46
1.6.3 Diversity for Next-Generation Systems: Some Research Evidence 47
1.6.4 Challenges in the Diversity Area 47
1.7 Conclusions 48
PART II: CHANNEL MODELING AND ESTIMATION 
CHAPTER 2. MULTIPATH PROPAGATION MODELS  FOR BROADBAND  WIRELESS SYSTEMS  
2.1 Introduction 54
2.2 Narrowband, Wideband, and Directional Channel Modeling 55
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
2.3 Modeling Methods for Multipath Channels 62
2.3.1 Measured Channel Impulse Responses 62
2.3.2 Deterministic Channel Computation 63
2.3.2.1 Full Electromagnetic Description 64
2.3.2.2 High-Frequency Approximations 64
2.3.3 Tapped Delay Lines 66
2.3.4 Stochastic MIMO Models 67
2.3.5 Geometry-Based Stochastic Channel Models 68
2.4 Propagation Aspects and Parameterization 69
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
2.5 Standard Models 76
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
2.6 Conclusions 83
CHAPTER 3. MODELING AND ESTIMATION OF MOBILE  CHANNELS  
3.1 Introduction 97
3.2 Channel Models 99
3.2.1 Time-Variant Channels 99
3.2.1.1 Tapped Delay Line Model 99
3.2.1.2 Basis Expansion Models 101
3.2.2 Time-Invariant Channels 102
3.3 Channel Estimation 104
3.3.1 Training-Based Channel Estimation 105
3.3.1.1 Time-Variant Channels 105
3.3.2 Blind Channel Estimation 106
3.3.2.1 Combined Channel and Symbol Estimation 106
3.3.2.1.1 Stochastic Maximum Likelihood Estimation 106
3.3.2.1.2 Deterministic Maximum Likelihood Estimation 107
3.3.2.2 The Methods of Moments 108
3.3.2.2.1 SISO Channel Estimation 108
3.3.2.2.1.1 Indirect Channel Estimation 108
3.3.2.2.2 SIMO Channel Estimation 109
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
3.3.3 Semiblind Approaches 115
3.3.4 Hidden Pilot-Based Approaches 115
3.3.4.1 Equalization 117
3.4 Simulation Examples 118
3.4 Simulation Examples 97
3.4.1 Example 1 118
3.4.2 Example 2 119
3.5 Conclusions 121
CHAPTER 4. MOBILE SATELLITE CHANNELS: STATISTICAL  MODELS AND PERFORMANCE ANALYSIS  
4.1 Introduction 125
4.2 Statistical Propagation Models 126
4.2.1 Narrowband Statistical Models 128
4.2.1.1 Single-State Statistical Models 128
4.2.1.1.1 Single-State First-Order Characterization 129
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
4.2.1.1.2 Single-State Second-Order Characterization 134
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
4.2.1.2 Multistate Statistical Models 138
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
4.2.2 Wideband Statistical Models 142
4.2.2.1 DLR Wideband Model 142
4.2.2.2 Saunders et al. 143
4.2.2.3 IMR Channel Model 143
4.3 Detection Performance Analysis 125
4.3 Detection Performance Analysis 144
4.3.1 Uncoded Transmission over LMS Channels 144
4.3.1.1 M-ary Coherent Detection 145
4.3.1.2 M-ary Orthogonal Noncoherent Detection 145
4.3.2 Coded Transmission over LMS Channels 146
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
4.4 Conclusions 155
CHAPTER 5. MOBILE VELOCITY ESTIMATION  FOR WIRELESS COMMUNICATIONS 
5.1 Introduction 161
5.1.1 Importance of Velocity Estimation 161
5.1.2 Existing Velocity Estimators 163
5.1.3 Structure of the Chapter 164
5.2 Received Signal Model and Statistics 164
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
5.3 Principles of Mobile Velocity Estimation 168
5.3.1 Examples of Derivations of the Velocity 168
5.3.2 Examples of Velocity Estimators 170
5.4 Performance Analysis of Velocity Estimators 171
5.4.1 Effect of Shadowing 172
5.4.2 Effect of AWGN and Nonisotropic Scattering 172
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
5.5 Performance Analysis Using Simulations 176
5.5.1 Simulations of the Received Signal 176
5.5.2 Simulation Results 177
5.6 Rice Factor Estimation 180
5.6.1 Existing Methods 180
5.6.2 Envelope-Based Estimators 181
5.6.3 Simulation Results 184
5.7 Application on Handover Performance 184
5.7.1 Handover Decision Algorithms 184
5.7.2 Effect of an Error in Velocity Estimation on the System's 185
5.8 Conclusions and Perspectives 187
PART III: MODULATION TECHNIQUES FOR WIRELESS COMMUNICATIONS 
CHAPTER 6. ADAPTIVE CODED MODULATION FOR  TRANSMISSION OVER FADING CHANNELS  
6.1 Introduction 195
6.2 Adaptive System Model 197
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
6.3 Adaptivity in Single-Input Single-Output Systems 201
6.3.1 Information Theoretic Bounds 201
6.3.2 Design for Uncoded Systems 202
6.3.2.1 Design Rules 203
6.3.2.2 Numerical Results 204
6.3.3 Coded Modulation Structures 205
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
6.3.4 Designing with a Given Coded Modulation Structure 206
6.3.4.1 Design Rules 206
6.3.4.2 Performance Results 206
6.4 Adaptivity in Multiantenna Systems 207
6.4.1 Information Theoretic Considerations 207
6.4.1.1 MIMO Single-User Systems 207
6.4.1.2 Multiuser MIMO Systems 208
6.4.2 Adaptive Coded Modulation for MIMO Systems 208
6.5 Conclusions 209
CHAPTER 7. SIGNALING CONSTELLATIONS FOR  TRANSMISSION OVER NONLINEAR CHANNELS  
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
7.4.1 The (8,8) Constellation Format 217
7.4.1.1 The Structure 217
7.4.1.2 Probability of Error Analysis 218
7.4.2 The (4,12) Constellation Format 219
7.4.2.1 The Structure 219
7.4.2.2 Probability of Error Analysis 219
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
7.4.5.1 The Structure 222
7.4.5.2 Probability of Error Analysis 223
7.4.6 The Effect of Nonlinearity and the Application 224
7.4.7 Total Degradation 226
7.4.8 Results and Discussions 227
7.5 probability of Symbol Error for the Circular 232
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
7.5.3.1 The Structure 235
7.5.3.2 Probability of Error Analysis 236
7.5.4 The Effect of Nonlinearity and the Application 240
7.5.5 Results and Discussions 240
7.6 Conclusions 242
CHAPTER 8. CARRIER FREQUENCY SYNCHRONIZATION  FOR OFDM SYSTEMS  
8.1 Introduction 244
8.10 Conclusions 262
8.2 Basics of OFDM 245
8.2.1 OFDM Modulation 246
8.2.2 Demodulation 247
8.3 Effect of CFO on System Performance 249
8.4 Carrier Frequency Offset Estimation 250
8.5 Repetitive Slots-Based CFO Estimation 251
8.5.1 Nonlinear Least Squares Method 251
8.5.2 Computationally Simpler Estimators 252
8.5.2.1 Approximate NLS Estimator 252
8.5.2.2 BLUE Estimator 253
8.6 Null-Subcarrier-Based CFO Estimation 253
8.6.1 Deterministic Maximum Likelihood Estimation 254
8.6.2 Special Case: Repetition of Identical Slots 255
8.6.2.1 Virtual Subcarriers Absent 255
8.6.2.2 Virtual Subcarriers Present 255
8.7 Identifiability 256
8.8 Performance Analysis 258
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
8.9 Simulation Results 260
CHAPTER 9. FILTER-BANK MODULATION  TECHNIQUES FOR TRANSMISSION OVER  FREQUENCY-SELECTIVE CHANNELS  
9.1 Introduction 267
9.2 Critically Sampled Filter Banks 269
9.2.1 Orthogonality Conditions 269
9.2.2 Efficient Implementation 270
9.2.3 Example of Critically-Sampled Filter Bank 273
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
9.6.1 Filter-bank Design 286
9.6.2 Per-Subchannel Adaptive Equalization and Precoding 289
PART IV: MULTIPLE ACCESS TECHNIQUES
CHAPTER 10. SPREAD-SPECTRUM TECHNIQUES FOR MOBILE  COMMUNICATIONS  
10.1 A Brief History of Wireless Communications 296
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
10.2 Fundamentals of Digital Spread-Spectrum Signaling 298
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
10.3 Code-Division Multiple Access 304
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
10.4 A Review of 2G and 3G Standards for CDMA 308
10.4 A Review of2G and 3G Standards for CDMA 295
10.4.1 IS-95 309
10.4.2 UMTS/UTRA 309
10.4.3 cdma2000 311
10.5 Synchronization for Spread-Spectrum and CDMA Signals 312
10.5.1 Synchronization Functions 312
10.5.2 Code Synchronization 312
10.5.3 Carrier Frequency and Phase Synchronization 314
10.6 Architecture of DSP-Based DS/SS and CDMA Receivers 315
10.6.1 IF vs. Baseband Sampling 315
10.6.2 Correlation Receiver 316
10.6.3 Rake Receiver 316
10.7 Multiuser Detection 318
10.7.1 Multiuser Detection in the UL 318
10.7.2 The Decorrelating and MMSE Detectors 320
10.8 Perspectives and Conclusions 321
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
CHAPTER 11. MULTIUSER DETECTION FOR FADING CHANNELS  
11.1 Introduction 327
11.2 Signal and Channel Model 327
11.2.1 The Fading Channel Model 327
11.2.2 Transmitted Signal Model 329
11.2.3 Continuous-Time Received Signal Model 330
11.3 Multiuser Detection with Known CSI 331
11.3.1 Conventional Single-User Detection 332
11.3.2 Optimum Multiuser Detection 333
11.3.3 Linear Multiuser Detection 335
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
11.3.4 Approximate MMSE Detection: Linear Serial 340
11.3.5 Constrained ML Detection: Nonlinear Serial 341
11.3.6 Performance Results 342
11.3.6.1 Analysis 342
11.3.7 Some Numerical Results 347
11.3.8 Sliding-Window One-Shot Multiuser Receivers 349
11.4 Multiuser Detection with Unknown CSI 350
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
11.5 Conclusions 363
PART V: MIMO SYSTEMS
CHAPTER 12. PRINCIPLES OF MIMO-OFDM  WIRELESS SYSTEMS  
12.1 Introduction 365
12.2 The Broadband MIMO Fading Channel 366
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
12.3 Capacity of Broadband MIMO-OFDM Systems 368
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
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
12.3.4 Numerical Results 373
12.4 Space-Frequency Coded MIMO-OFDM 373
12.4.1 Space--Frequency Coding 373
12.4.2 Error Rate Performance 375
12.4.3 Maximum Diversity Order and Coding Gain 375
12.4.3.1 Rayleigh Fading 375
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
12.4.3.2 Ricean Fading 377
12.5 Impact of Propagation Parameters 378
12.5.1 Impact of Propagation Parameters 378
12.5.1.1 Impact of Cluster Angle Spread 378
12.5.1.2 Impact of Total Angle Spread 379
12.5.2 Simulation Results 380
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
12.5.2.4.1 Spatial Multiplexing 384
12.5.2.4.2 Delay Diversity Combined with Convolutional Code 384
CHAPTER 13. SPACE-TIME CODING AND SIGNAL  PROCESSING FOR BROADBAND WIRELESS  COMMUNICATIONS  
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
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
13.4 Signal Transmission Issues 393
13.4.1 Transmitter Techniques 393
13.4.1.1 Spatial Multiplexing (BLAST) 393
13.4.1.2 Transmit Diversity Techniques 394
13.4.1.3 Space-Time Coding 395
13.4.1.3.1 Space-Time Trellis Codes 396
13.4.1.3.2 Alamouti-Type Space-Time Block Codes 397
13.4.1.4 Diversity vs. Throughput Trade-Off 398
13.4.1.5 Orthogonal Frequency Division Multiplexing 399
13.4.2 Receiver Techniques 399
13.4.2.1 Coherent Techniques 400
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
13.4.2.2 Noncoherent Techniques 407
13.5 Summary and Future Challenges 409
CHAPTER 14. LINEAR PRECODING FOR MIMO SYSTEMS  
14.1 Introduction 414
14.1.1 System Model 415
14.2 Optimum precoding 416
14.2.1 Jointly Optimum Design and Performance Bounds 417
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
14.3 Performance Analysis and Random Matrices 423
14.3.1 Differential Forms and Random Matrix Techniques 423
14.3.2 The Statistics of the Capacity 427
14.4 Channel Estimation for MIMO Systems 432
14.4 Channel Estimation for MIMO Systems 414
14.4.1 Channel Estimation Algorithm 433
14.4.2 Cramer-Rao Lower Bound 435
14.4.3 Numerical Results 436
14.5 Conclusions 437
CHAPTER 15. PERFORMANCE ANALYSIS OF MULTIPLE  ANTENNA SYSTEMS  
15.1 Introduction 441
15.2 MIMO Systems without Co-Channel Interference 442
15.2.1 System Model and Problem Statement 442
15.2.2 MIMO Channel Capacity without Channel State Information 442
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
15.2.3 Capacity/Outage Probability of MIMO MRC (Beam-Forming) Systems 451
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
15.2.4 Water-Filling Capacity and Beam Forming Performance of Correlated 453
15.2.4.1 Water-Filling Capacity 453
15.2.4.2 Beam-Forming Performance 455
15.3 MIMO Systems in the Presence 458
15.3.1 Problem Statement 458
15.3.2 Capacity CCDF of MIMO Optimum Combining Scheme 459
15.3.2.2 Capacity of Optimum Combining 463
15.3.3 Statistics of the MIMO Capacity with Co-Channel Interference 463
15.3.3.1 Problem Statement 463
15.3.3.2 Capacity MGF 464
15.3.4 RicianjRayleigh Fading Scenarios 467
PART VI: EQUALIZATION AND RECEIVER DESIGN
CHAPTER 16. EQUALIZATION TECHNIQUES FOR  FADING CHANNELS  
16.1 Introduction 472
16.2 Wireless Channel Model 473
16.2.1 TIV Channels 475
16.2.2 TV Channels 475
16.3 System Model 477
16.3.1 TIV Channels 478
16.3.2 TV Channels 478
16.4 Block Equalization 478
16.4.1 Block Linear Equalization 479
16.4.2 Block Decision Feedback Equalization 480
16.5 Serial Linear Equalization 481
16.5.1 TIV Channels 481
16.5.2 TV Channels 483
16.5.3 Equalizer Design 484
16.6 Serial Decision Feedback Equalization 485
16.6.1 TIV Channels 486
16.6.2 TV Channels 486
16.6.3 Equalizer Design 487
16.7 Frequency-Domain Equalization for TIV Channels 488
16.7.1 FD Linear Equalization 489
16.7.2 FD Decision Feedback Equalization 489
16.8 Existence of Zero-Forcing Solution 492
16.8.1 Linear Equalizers 492
16.8.2 Decision Feedback Equalizers 492
16.9 Complexity 493
16.9.1 Design Complexity 493
16.9.2 Implementation Complexity 494
CHAPTER 17. LOW-COMPLEXITY DIVERSITY COMBINING  SCHEMES FOR MOBILE COMMUNICATIONS  
17.1 Introduction 503
17.2 System and Channel Models 504
17.2.1 System Model 504
17.2.2 Channel Model 505
17.3 Dual-Branch Switch and Stay Combining 505
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
17.4 Multibranch Switched Diversity 515
17.4.1 L-Branch sse 515
17.4.2 L-Branch SEC 516
17.5 Generalized Switch and Examine Combining (GSEC) 521
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
17.6 Further Remarks 529
CHAPTER 18. OVERVIEW OF EQUALIZATION TECHNIQUES  FOR MIMO FADING CHANNELS  
18.1 Introduction 531
18.2 Frequency-Selective MIMO Channel Model 531
18.2 Frequency-Selective MIMO Channel Model 532
18.2.1 General Framework 532
18.2.2 Simulation Framework 533
18.3 Block Linear and Decision-Feedback Equalizers 534
18.3.1 Block Linear Equalizers 534
18.3.2 Block Decision-Feedback Equalizers 535
18.3.2.1 Simulation Results 537
18.4 List-Type Equalizers 538
18.4.1 The Single Antenna Case 538
18.4.1.1 Viterbi Algorithm 538
18.4.1.2 Generalized Viterbi Algorithm 538
18.4.1.3 List-Type MAP Algorithm 539
18.4.2 Generalization to the MIMO Case 540
18.4.3 Simulation Results 541
18.5 The Multidimensional Whitened Matched Filter 531
18.5 The Multidimensional Whitened Matched Filter 542
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
18.6 The Block Equalizers vs. the Prefiltered 531
18.6 The Block Equalizers vs. the Prefiltered List-Type MAP 547
18.6.1 Complexity Comparison 547
18.6.2 Performance Comparison 548
18.7 Conclusion 549
CHAPTER 19. NEURAL NETWORLZS FOR TRANSMISSION  OVER NONLINEAR CHANNELS 
19.1 Introduction 552
19.2 Identification of Memoryless Nonlinear Amplifiers 555
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
19.3 Modeling and Identification of Nonlinear 566
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
19.4 Channel Equalization 572
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
19.5 Conclusion 575
PART VII: VOICE OVER IP
CHAPTER 20. VOICE OVER IP AND WIRELESS:  PRINCIPLES AND CHALLENGES  
20.1 Introduction 581
20.2 Speech Coding for IP and Wireless: Principles 581
20.2.1 Some Preliminary Notions 581
20.2.2 Speech Coding Basics 582
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
20.2.3 Speech Coding for IP and Wireless 584
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
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
20.3 Voice over IP 586
20.3.1 An Overview 586
20.3.2 Technological Barriers 587
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
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
20.4 Voice over Wireless 590
20.4.1 Wireless Voice Communications Systems 590
20.4.2 Fundamental Issues in Speech Coding for Wireless 591
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
20.4.3 Standard Speech Coders for Wireless 592
20.4.3.1 European GSM and UMTS Standards 592
20.4.3.2 North American Cellular Systems 593
20.4.4 Some Trends 593
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
20.5 Voice over IP over Wireless 594
20.6 Voice-Enabled Services over IP and Wireless 595
20.6.1 Introduction 595
20.6.2 Technologies for Voice-Enabled Interfaces 595
20.6.3 Architectures for Automatic Speech Recognition 596
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
20.6.4 ASR over IP 598
20.6.4.1 Coding Distortion 598
20.6.4.2 Packet Loss 598
20.6.5 ASR over Wireless 598
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
20.7 Conclusions and Challenges 580
20.7 Conclusions and Challenges 600
PART VIII: WIRELESS GEOLOCATION TECHNIQUES
CHAPTER 21. GEOLOCATION TECHNIQUES FOR MOBILE  RADIO SYSTEMS  
21.1 Introduction 605
21.1.1 FCC Regulations for E-911 606
21.1.2 Location-Based Services (LBS) 606
21.2 Geolocation Methods 607
21.3 Geolocation Algorithms 609
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
21.4 Location Parameter Estimation 616
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
21.5 Impairments to Accuracy 618
21.5 Impairments to Accuracy 605
21.5.1 Multipath Propagation 618
21.5.2 Hearability 619
21.5.3 Non-Line-of-Sight Propagation 619
21.6 Provisions in the Standards 621
21.6.1 3G Location Solutions 621
21.6.2 Locating Legacy Terminals 622
21.7 Summary 622
CHAPTER 22. ADAPTIVE ARRAYS FOR GPS RECEIVERS 
22.1 Introduction 628
22.2 GPS Signal Model 632
22.3 Interference Suppression Techniques in GPS 633
22.3.1 Broadband Interference Suppression Using Space-Time Array 633
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
22.3.2 Narrowband FM Interference Suppression 636
22.3.3 A Self-Coherence Antijamming GPS Receiver 640
22.4 Multipath Mitigation in GPS 646
22.4.1 Bias Due to Signal Multipath 646
22.4.1.1 Early-Late Discrimination Functions 647
22.4.1.2 Time-Delay Estimation 648
22.4.2 Single-Antenna Multipath Mitigation Techniques 649
22.4.2.1 Narrow Correlator 649
22.4.2.2 Multipath Elimination Delay Lock Loop 649
22.4.3 Time-Delay and Carrier-Phase Estimation Using Antenna Array 649
22.5 Conclusions 651
PART IX: POWER CONTROL AND WIRELESS NETWORKING
CHAPTER 23. TRANSMITTER POWER CONTROL IN WIRELESS  NETWORLZING: BASIC PRINCIPLES AND CORE  ALGORITHMS  
23.1 Intoduction 654
23.2 Power Control for Streamed Continuous Traffic 656
23.2.1 The Target SIR Formulation of the Power Control Problem 656
23.2.1.1 Example of the Simple Two-Link Network 657
23.2.1.2 The Optimal Power Vector P* 659
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
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
23.2.5 Noninvasive Channel Probing and Selection 667
23.3 Power Control for Packetized Data Traffic 668
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
23.4 Final Remarks 673
CHAPTER 24. SIGNAL PROCESSING FOR MULTIACCESS  COMMUNICATION NETWORKS  
24.1 Introduction 675
24.2 MPR at the Physical Layer 676
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
24.3 The Interface between the Physical Layer and the MAC 682
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
24.4 Impact of MPR on the Performance of Existing 685
24.4.1 Resolvability Comparison 685
24.4.2 Network Performance Comparison 685
24.4.3 Further Remarks 686
24.5 MAC Layer Design for Networks with MPR 687
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
24.6 Approach High Performance from Both Physical 690
24.7 Conclusion 691
PART X: EMERGING TECHNIQUES AND APPLICATIONS
CHAPTER 25. TIME-FREQUENCY SIGNAL PROCESSING  FOR WIRELESS COMMUNICATIONS  
25.1 Introduction 694
25.2 Time-Frequency Signal Processing Tools 695
25.2.1 Limitations of Traditional Signal Representations 695
25.2.2 Joint Time-Frequency Representations 695
25.2.2.1 Finding Hidden Information Using Time-Frequency Representations 695
25.2.2.2 What Is a Time-Frequency Representation? 696
25.2.2.2.1 Physical Interpretation of TFDs 698
25.2.2.2.2 Instantaneous Frequency and Group Delay 698
25.2.3 Quadratic Time-Frequency Distributions 699
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
25.2.3.4.1 Reduced Interference Distributions 704
25.2.3.4.2 Comparison of Quadratic TFDs 704
25.2.3.5 Time-Frequency Signal Synthesis 705
25.2.3.6 Other Properties 705
25.3 Spread-Spectrum Communications Systems Using TFSP 705
25.3.1 Channel Modeling and Identification 705
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
25.3.2 Interference Mitigation 710
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
25.4 Time-Frequency Array Signal Processing 714
25.4.1 The Spatial Time-Frequency Distributions 715
25.4.1.1 Structure under linear model 716
25.4.1.2 Structure under Unitary Model 716
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
25.4.5.1 Data Model 720
25.4.5.2 TF-MUSIC 720
25.4.6 Time-Frequency Source Separation 720
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
25.5 Other TFSP Applications in Wireless Communications 724
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
25.6 Conclusion 727
CHAPTER 26. MONTE CARLO SIGNAL PROCESSING  FOR DIGITAL COMMUNICATIONS:  PRINCIPLES AND APPLICATIONS  
26.1 Introduction 735
26.2 MCMC Methods 736
26.2.1 General MCMC Algorithms 736
26.2.1.1 Metropolis-Hastings Algorithm 736
26.2.1.2 Gibbs Sampler 737
26.2.1.3 Other Techniques 737
26.2.2 Applications of MCMC in Digital Communications 738
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
26.2.3 Other Applications 744
26.3 SMC Methods 744
26.3.1 General SMC Algorithms 744
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
26.3.2 Resampling Procedures 748
26.3.3 Applications of SMC in Digital Communications 750
26.3.3.1 SMC Receiver in Flat-Fading Channels 750
26.3.3.2 SMC Receiver in MIMO ISI Channels 752
26.4 Concluding Remarks 756
CHAPTER 27. PRINCIPLES OF CHAOS COMMUNICATIONS 
27.1 What Is Chaos? 759
27.1.1 Nonlinear Dynamic Systems 759
27.1.2 Statistical Analysis of Chaotic Signals 761
27.1.2.1 Probability Density Functions 761
27.1.2.2 Autocovariance and Autocorrelation Function 762
27.1.3 Realization of Chaos Generators 765
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
27.1.4 Properties of Chaotic Signals 766
27.2 Communication: Requirements and Resources 759
27.3 Chaos in Communications 759
27.3 Chaos in Communications 768
27.3.1 The Broadband Aspect 768
27.3.2 The Complexity Aspect 768
27.3.3 The Orthogonality Aspect 768
27.4 Communication Using Broadband Chaotic Carriers 768
27.4.1 Chaos-Based Transmitters 769
27.4.1.1 Static Encoding/Modulation Methods 769
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
27.4.1.2 Dynamic Encoding/Modulation Methods 772
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
27.4.2 Receiver Design in Chaos Communications 773
27.4.2.1 Message Detection Using a Reference Signal 773
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
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
27.5 Chaos for Spreading Code Generation 777
27.6 Chaotic vs. Classical Communications 
27.6.1 The Analysis of Chaos Communication Schemes in AWGN 778
27.6.1.1 The Analysis Problem 778
27.6.1.2 Analysis Methods 779
27.6.1.2.1 Discrete-Time Baseband Modeling 779
27.6.1.2.2 Statistical Analysis of Nonlinear Discrete-Time Baseband Models 780
27.6.1.3 Analysis Example 781
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
27.6.2 Chaos Communication Methods in Comparison to Classical Solutions 783
CHAPTER 28. ADAPTATION TECHNIQUES AND ENABLING  PARAMETER ESTIMATION ALGORITHMS  FOR WIRELESS COMMUNICATIONS  SYSTEMS  
28.1 Introduction 787
28.2 Overview of Adaptation Schemes 789
28.2.1 Link and Transmitter Adaptation 790
28.2.2 Adaptive System Resource Allocation 791
28.2.3 Receiver Adaptation 791
28.3 Parameter Measurements 792
28.3 Parameter Measurements 787
28.3.1 Channel Selectivity Estimation 792
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
28.3.2 Channel Quality Measurements 796
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
28.4 Applications of Adaptive Algorithms: Case Studies 800
28.4.1 Examples for Adaptive Receiver Algorithms 800
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
28.4.2 Examples for Link Adaptation and Adaptive Resource Allocation 803
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
28.5 Future Research for Adaptation 806
28.6 Conclusion 808
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