Simplified Robust Adaptive Detection and Beamforming for Wireless Communications

Ayman Elnashar, PhD, has 20+ years of experience in the telecoms industry, including 2G/3G/LTE/WiFi/IoT/5G/Wireless Networks. He was part of three major start-up telecom operators in the MENA region (Orange/Egypt, Mobily/KSA, and du/UAE). Currently, he is Head of Core and Cloud planning with the Emirates Integrated Telecommunications Co. "du", UAE. He is the founder of the Terminal Innovation Lab and UAE 5G Innovation Gate (U5GIG). Prior to this, he was Sr. Director – Wireless Networks, Terminals and IoT, where he managed and directed the evolution, evaluation, and introduction of du wireless networks, terminals and IoT, including LTE/LTE-A, HSPA+, WiFi, NB-IoT, and is currently working towards deploying 5G network in the UAE.

About the Author xiii

About the Companion Website xiv

1 Introduction 1

1.1 Motivation 1

1.2 Book Overview 4

2 Wireless System Models 13

2.1 Introduction 13

2.1.1 Modulation and Coding Scheme and Link Adaptation 24

2.1.2 Link Adaptation 26

2.2 DS-CDMA Basic Formulation 27

2.2.1 Pulse-shaping Filter 30

2.2.2 Discrete Time Model 30

2.2.3 Channel Model 32

2.2.4 Matrix Formulation for DS/CDMA System Model 38

2.2.5 Synchronous DS/CDMA System 41

2.3 Performance Evaluation 43

2.3.1 Signal to Interference plus Noise Ratio 43

2.3.2 Bit Error Rate 44

2.4 MIMO/OFDM System Model 46

2.4.1 FFT and IFFT 49

2.4.2 Cyclic Prefix 52

2.4.3 Single-user MIMO/OFDM 53

2.4.3.1 3GPP LTE MIMO 55

2.4.4 Adaptive Resource Management 64

2.4.5 Multi-User MIMO/OFDM 69

2.4.6 Adaptive filtering in MIMO/OFDM System 71

2.4.7 Performance Evaluation of MIMO/MBER System 71

2.5 Adaptive Antenna Array 73

2.5.1 Uniform Linear Array 73

2.5.2 DS/CDMA with Antenna Array 78

2.6 Simulation Software 80

References 82

3 Adaptive Detection Algorithms 89

3.1 Introduction 89

3.2 The Conventional Detector 90

3.3 Multiuser Detection 91

3.3.1 Decorrelating Detector 93

3.3.2 Minimum Mean-squared Error Detector 93

3.3.3 Adaptive Detection 95

3.3.4 Blind Detection 95

3.3.4.1 Constrained Optimization 96

3.3.5 Constant Modulus Approach 105

3.3.6 Subspace Approach 107

3.4 Simulation Results 109

3.4.1 Linear Detectors 109

3.4.2 MOE Detectors 111

3.4.2.1 MOE Detector with Single Constraint 111

3.4.2.2 MOE Detector with Multiple Constraints 112

3.4.3 Channel Estimation Techniques 113

3.4.4 LCCMA Detector 115

References 118

4 Robust RLS Adaptive Algorithms 127

4.1 Introduction 127

4.2 IQRD-RLS Algorithm 131

4.3 IQRD-Based Receivers with Fixed Constraints 132

4.3.1 Direct-form MOE Detector 132

4.3.2 MOE Detector based on IQRD-RLS and PLIC 133

4.4 IQRD-based Receiver with Optimized Constraints 135

4.5 Channel Estimation Techniques 139

4.5.1 Noise Cancellation Schemes 139

4.5.1.1 Adaptive Implementation of Improved Cost Function 139

4.5.1.2 Adaptive Implementation of Modified Cost Function 140

4.5.2 Adaptive Implementation of POR Method 141

4.5.3 Adaptive Implementation of Capon Method 142

4.6 New Robust Detection Technique 144

4.7 Systolic Array Implementation 148

4.8 Simulation Results 153

4.8.1 Experiment 1 153

4.8.2 Experiment 2 155

4.8.3 Experiment 3 158

4.8.4 Experiment 4 160

4.8.5 Experiment 5 162

4.9 Complexity Analysis 163

Appendix 4.A Summary of Inverse QR Algorithm with Inverse Updating 167

Appendix 4.B QR Decomposition Algorithms 169

Appendix 4.C Subspace Tracking Algorithms 171

References 173

5 Quadratically Constrained Simplified Robust Adaptive Detection 181

5.1 Introduction 181

5.2 Robust Receiver Design 187

5.2.1 Quadratic Inequality Constraint 187

5.2.1.1 SP Approach 188

5.2.1.2 Tian Approach 189

5.2.1.3 A Simplified VL Approach 191

5.2.2 Optimum Step-size Estimation 194

5.2.3 Low-complexity Recursive Implementation based on PLIC 195

5.2.4 Convergence Analysis 198

5.3 Geometric Approach 199

5.4 Simulation Results 202

5.5 Complexity Analysis 213

Appendix 5.A Robust Recursive Conjugate Gradient (RCG) Algorithm 215

References 217

6 Robust Constant Modulus Algorithms 225

6.1 Introduction 225

6.2 Robust LCCMA Formulation 232

6.3 Low-complexity Recursive Implementation of LCCMA 234

6.4 BSCMA Algorithm 237

6.5 BSCMA with Quadratic Inequality Constraint 239

6.6 Block Processing and Adaptive Implementation 241

6.7 Simulation Results for Robust LCCMA 243

6.8 Simulation Results for Robust BSCMA 246

6.9 Complexity Analysis 250

References 253

7 Robust Adaptive Beamforming 263

7.1 Introduction 263

7.2 Beamforming Formulation 279

7.2.1 Capon Beamforming 279

7.2.2 LCMV Beamforming 281

7.3 Robust Beamforming Design 283

7.3.1 Adaptive Implementation 288

7.4 Cooperative Joint Constraint Robust Beamforming 292

7.4.1 Adaptive Implementation 295

7.5 Robust Adaptive MVDR Beamformer with Single WC Constraint 296

7.5.1 Lagrange Approach 299

7.5.2 Eigendecomposition Method 299

7.5.3 Taylor Series Approximation Method 300

7.5.4 Adaptive MVDR Beamformer with Single WC Constraint 300

7.5.4.1 Lagrange Multiplier Estimation 301

7.5.4.2 Recursive Implementation 303

7.6 Robust LCMV Beamforming with MBWC Constraints 304

7.7 Geometric Interpretation 306

7.7.1 Ellipsoidal Constraint Beamforming 306

7.7.2 Worst-case Constraint Beamforming 308

7.8 Simulation Results 310

7.8.1 Simulations Results for Ellipsoidal Constraint Beamforming 310

7.8.2 Simulation for WC Constraint Beamforming 322

7.8.2.1 DOA Mismatch Scenario 322

7.8.2.2 Small Angular Spread Scenario 328

7.8.2.3 Large Angular Spread Scenario 331

7.9 Summary 332

References 333

8 Minimum BER Adaptive Detection and Beamforming 345

8.1 Introduction 345

8.2 MBER Beamformer 347

8.2.1 AMBER 351

8.2.2 LMBER 352

8.2.3 Gradient Newton Algorithms 353

8.2.3.1 Newton-AMBER 354

8.2.3.2 Newton-LMBER 354

8.2.4 Normalized Gradient Algorithms 354

8.2.4.1 Normalized-AMBER 355

8.2.4.2 Normalized-LMBER 355

8.2.5 Normalized Newton Gradient Algorithms 355

8.2.5.1 Normalized-Newton-AMBER 355

8.2.5.2 Normalized-Newton-LMBER 356

8.2.6 Block-Shanno MBER 356

8.3 MBER Simulation Results 360

8.3.1 BER Performance versus SNR 361

8.3.2 Convergence Rate Comparison 366

8.3.3 BER Performance versus Number of Subscribers 370

8.3.4 Computational Complexity 371

8.4 MBER Spatial MUD in MIMO/OFDM Systems 372

8.4.1 AMBER 375

8.4.2 LMBER 376

8.4.3 Gradient Newton Algorithms 376

8.4.3.1 Newton-AMBER 377

8.4.3.2 Newton-LMBER 377

8.4.4 Normalized Gradient Algorithms 377

8.4.4.1 Normalized-AMBER 378

8.4.4.2 Normalized-LMBER 378

8.4.5 Normalized Newton Gradient Algorithms 378

8.4.5.1 Normalized-Newton-AMBER 378

8.4.5.2 Normalized-Newton-LMBER 379

8.4.6 Block-Shanno MBER 379

8.5 MBER Simulation Results 381

8.5.1 Convergence Rate Comparison 382

8.5.2 BER Performance versus SNR 384

8.6 Summary 386

References 387

Index 395

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