无线与移动通信中的信号处理新技术 英文版 第2册 单用户与多用户系统PDF|Epub|txt|kindle电子书版本网盘下载

无线与移动通信中的信号处理新技术 英文版 第2册 单用户与多用户系统PDF格式电子书版下载

注意：本站所有压缩包均有解压码： 点击下载压缩包解压工具

1 TIME-VARYING FADING CHANNELS1

1.1 Channel Model4

1.1.1 Deterministic Models4

1.1.2 Stochastic Models10

1.1.3 Channel Singular Functions12

1.1.4 Time-Frequency Analysis of LTV Channels Eigenfunctions16

1.2 Coding Strategies for Transmissions over LTV Channels20

1.2.1 Perfect CSI Available at both Transmit and Receive Sides21

1.2.2 Comparisons and Asymptotic Bounds24

1.2.3 Adaptive OFDM30

1.2.4 Coding with Partial CSI32

1.3 Channel Estimation and Prediction35

1.3.1 Cramér-Rao Bound for LTV Multipath Channels37

1.3.2 Channel Prediction40

1.3.3 Channel Parameter Estimation42

1.4 Conclusion43

1.5 Appendices45

1.5.1 Eigenfunction Model45

1.5.2 Time-frequency Representations48

1.5.3 Cramér-Rao Bounds49

Bibliography50

2 SPACE-TIME DIVERSITY59

2.1 Introduction59

2.1.1 Diversity59

2.2 The Set-Up60

2.3 General Framework for Detection62

2.3.1 A Bound on the SNR63

2.4 Space-Only Processing64

2.5.2 Eigenvalue Constraint65

2.5 Space-Time Processing65

2.5.1 Trace Constraint65

2.5.3 Comparison of the Schemes66

2.6 Bit Error Rate67

2.6.1 BER for a Non-fading Channel67

2.6.2 BER for a Rayleigh Fading Channel68

2.6.3 BER for other Modulation Schemes71

2.7 Data Rate72

2.7.1 Real Symbols72

2.7.2 Complex Symbols79

2.8 Discussion84

2.8.1 Space-only vs.Space-Time Processing84

2.8.2 Capacity vs.Diversity85

2.8.3 The Rank One Channel85

Bibliography86

2.8.4 Soft Failure86

2.9 Conclusions86

3 ALGEBRAIC CONSTANT MODULUS ALGORITHMS89

3.1 Introduction89

3.2 Preliminaries94

3.3 Derivation of the ACMA99

3.4 Analysis of the Noise-free Case103

3.5 ACMA in Noise106

3.6 Asymptotic Behavior108

3.7 Weighted ACMA113

3.8 Binary Source Separation115

3.9 Simulations117

3.10 Joint Diagonalization117

3.11 Concluding Remarks125

Bibliography126

4.1 Introduction131

4 PARAFAC TECHNIQUES FOR SIGNAL SEPARATION131

4.1.1 Historical Remarks133

4.2 Theory133

4.2.1 Notation and Preliminaries133

4.2.2 k-Rank136

4.2.3 Identifiability137

4.3 Algorithms for Fitting the PARAFAC Model140

4.3.1 Eigenanalysis-Based:GRAM/ESPRIT141

4.3.2 Alternating Least Squares142

4.3.3 Separable LS,Gauss-Newton and Levenberg-Marquardt143

4.3.4 Compression/COMFAC143

4.4 Determining Three-Way Array Rank147

4.5.1 Extracting Trilinear Structure out of Bilinear-Vandermonde Data151

4.5 Applications-PartⅠ：Data Modcling151

4.5.2 CDMA152

4.5.3 Multiple-Invariance Array Processing156

4.5.4 Deterministic Blind Beamforming159

4.5.5 Fluorescence Spectroscopy161

4.5.6 Sensory Profiling162

4.6 Applications-PartⅡ：Examples164

4.6.1 Numerical Example:COMFAC Performance and the CRB164

4.6.2 OFDMA with Base Station Antenna Array Example164

4.6.3 Fluorescence Spectroscopy Example166

4.6.4 Sensory Profiling Example167

4.7 PARAFAC Extensions:PARAFAC2170

4.8 Conclusions171

Bibliography172

5.1 Introduction181

5 MULTIPATH MITIGATION IN CDMA SYSTEMS181

5.2 Signal Model183

5.2.1 Vector Models186

5.2.2 Analogies with Array Processing Models187

5.3 Receiver Design188

5.3.1 Matched Filter and RAKE Reccivers188

5.3.2 MMSE Receivers189

5.4 Minimum Variance Receivers189

5.4.1 The Multipath Case190

5.4.2 Performance Analysis192

5.4.3 Illustrative Examples197

5.4.4 Time Recursive Implementations198

5.4.5 Convergence203

5.4.6 Numerical Examples205

5.5 Multipath Mitigation in Long Code Systems207

5.5.1 Parameter Estimation in Long Code Systems208

5.5.2 Blind Channel Estimation210

5.5.3 Idenfifiability Issues213

5.5.4 Single-User Receivers213

5.5.5 Numerical Examples214

5.6 Conclusions216

Bibliography217

6 BLOCK SPREADING FOR MULTIPATH-RESILIENTGENERALIZED MULTI-CARRIER CDMA223

6.1 Block Spreading Model225

6.1.1 Filterbank Block Precoding226

6.1.2 Asynchronous Multirate Receiver Design229

6.1.3 Quasi-Synchronous Model231

6.1.4 All-Digital Unification of Multi-carrier CDMA232

6.2 GMC-CDMA for MUI/ISI-free Multirate Transmissions237

6.2.1 Single Rate GMC-CDMA:AMOUR237

6.2.2 GMC-CDMA:Multirate Case247

6.2.3 Receiver Design:Blind Equalization249

6.2.4 Underloaded Systems251

6.3 Performance and Comparisons251

6.4 Conclusions and Discussion257

Appendix 6.A Dual Vandermonde-Lagrange Transceivers257

Appendix 6.B Modulo-Interpretation of GMC-CDMA259

6.B.1 The Modulo Interpretation259

6.B.2 Re-designing the Codes260

Bibliography261

7 MULTISTAGE INTERFERENCE CANCELLATION ALGORITHMS FOR DS/CDMA SIGNALS267

7.1 Introduction267

7.2 Multiuser Signal Model268

7.3.1 Conventional Detector270

7.3 Overview of CDMA Receivers270

7.3.2 Optimum Detector272

7.3.3 Linear Detectors273

7.3.4 Decision-Feedback Detectors274

7.4 Successive Interference Canceler(SIC)274

7.4.1 SIC Computer Simulations:Synchronous Signals277

7.4.2 SIC Computer Simulations:Asynchronous Signals280

7.5 Exact BER Analysis281

7.5.1 Synchronous Signal Model for Two Users282

7.5.2 Exact BER of the SIC Receiver283

7.5.3 Exact BER of the SIC with Amplitude Mismatch287

7.5.4 Numerical Example:Exact Analysis288

7.6 Approximate BER Analysis288

7.6.1 Approximate BER of the SIC Receiver289

7.6.2 Approximate BER of the SIC with Amplitude Mismatch291

7.6.3 Numerical Example:Approximate Analysis292

7.7 Adaptive SIC(ASIC)293

7.7.1 ASIC Implementation293

7.7.2 ASIC Computer Simulations295

7.8 Parallel Interference Canceler(PIC)297

7.9 BER Analysis for the PIC299

7.9.1 BER for Stage 1:Exact Analysis299

7.9.2 BER for Stage 2:Exact Analysis300

7.9.3 BER from Stage j-l to Stage j:Approximate Analysis303

7.9.4 PIC Computer Simulations305

7.10 State-Space Analysis308

7.10.1 Convergence of the Error Probabilities308

7.10.2 IC Receiver Design311

7.11 Conclusion311

Bibliography313

8 SIGNAL PROCESSING BASED COLLISION RESOLUTION315

8.1 Packet Collision in Slotted Aloha Ad Hoc Networks317

8.1.1 Random Access Ad Hoc Networks317

8.1.2 Packet Collision318

8.2 Packet Collision Model320

8.2.1 Channel Model320

8.2.2 Signal Structure322

8.2.3 Assumptions and Properties323

8.3 The Training-based Zero Forcing Receiver324

8.4 The Semi-blind Least Squares Smoothing Receiver326

8.4.1 The Elimination of ISI327

8.4.2 The Reduction of MAI331

8.5 Blind Receivers333

8.6 Resolvability Analysis335

8.6.1 Collision Resolvability335

8.6.2 Resolvability of the Training-based ZF Receiver336

8.6.3 Resolvability of the Semi-blind Lss Receivers338

8.6.4 Resolvability Comparisons339

8.7 Network Performance Analysis341

8.7.1 Network Model341

8.7.2 Node and Network Reception Matrices342

8.7.3 The Markov-Chain Characterization of the Network344

8.7.4 Throughput,Delay and Stability Analysis345

8.8 Numerical Examples347

8.8.1 Resolvability Comparison347

8.8.2 Network Performance Comparison347

8.9 Concluding Remarks351

Appendix351

Bibliography354

9.1 Introduction357

9 NON-DATA-ATDED DIGITAL SYNCHRONIZATION357

9.1.1 Classical Approachcs to NDA Synchronization358

9.1.2 Chapter Summary360

9.2 Signal Modcl360

9.3 Classical Unconditional Maximum Likelihood(UML)Approach365

9.3.1 NDA Symbol Timing Estimation367

9.4 Conditional Maximum Likelihood(CML)Approach371

9.4.1 Joint Parameter Estimation375

9.4.2 CML-based NDA Synchronization376

9.4.3 CML Timing and Frequency Synchronizers for Linear Modulations377

9.4.4 CML Timing and Frequency Synchronizers for Binary CPM Signals382

9.5 Minimum Conditioned Variance Compressed Likelihood Function(MCV-CML)Approach384

9.6 Bounds and Performance Evaluation389

9.6.1 The Modified Cramer-Rao Bound(MCRB)390

9.6.2 The Unconditional CRB(UCRB)393

9.6.3 The Conditional CRB(CCRB)395

9.7 Conclusions398

Bibliography400

10 EXPLOITING ANTENNA ARRAYS FOR SYNCHRONIZATION403

10.1 Introduction403

10.2 Data Model406

10.3 Maximum Likelihood Estimator409

10.3.1 Consistency411

10.3.2 Cramér-Rao Bound412

10.3.3 Computation of the Estimates413

10.4 An Asymptotically Equivalent Estimator414

10.4.1 Proof of the Asymptotic Equivalence414

10.4.2 Calculation of the Weighting Matrix415

10.5 Heuristic Derivations416

10.5.1 Series Expansion of the Logarithm416

10.5.3 First-Order Approximation417

10.5.2 Eigenvalue Weighting417

10.6 Calculating the Estimates with IQML and ESPRIT418

10.6.1 IQML Algorithm419

10.6.2 ESPRIT Algorithm420

10.7 Simulation Results421

10.7.1 Simulation Parameters421

10.7.2 Effect of the Number of Samples422

10.7.3 Effect of the Number of Sensors424

10.7.4 Effect of the SIR425

10.7.5 Closely Spaccd Signals425

10.7.6 Performance Using a Search426

10.8 Conclusions428

Appendix 10.A429

Bibliography430