Analysis and Transceiver Design for the MIMO Broadcast Channel (eBook)

Contents 5
Abstract 8
1 Introduction 10
1.1 Overview 11
1.2 Used Symbols and Operators 18
1.3 Abbreviations 20
2 System Models 21
2.1 MIMO Broadcast Channel System Model 21
2.2 Dual MIMO Multiple Access Channel System Model 23
3 Dualities for the MIMO BC and the MIMO MAC with Linear Transceivers 25
3.1 Motivation 25
3.2 Mean Square Error Duality for Linear Transceivers 26
3.2.1 Literature Overview 26
3.2.2 Multi-Level Structure and MSE Expressions 27
3.2.3 BC to MAC Conversion 31
3.2.4 MAC to BC Conversion 39
3.2.5 Duality of BC and MAC 44
3.2.6 Advantages of the Presented Duality Compared to Hitherto Existing Ones 44
3.3 Rate Duality for Linear Transceivers Under Gaussian Signaling 45
3.3.1 BC to MAC Conversion 50
3.3.2 MAC to BC Conversion 56
3.3.3 Advantages of the Novel Rate Duality for Linear Transceivers 59
4 Rate Duality with Nonlinear Interference Cancelation 62
4.1 The Capacity Region of the MIMO MAC 62
4.1.1 The Two-User MIMO MAC 62
4.1.2 The Multi-User MIMO MAC 69
4.2 The Capacity Region of the MIMO BC 73
4.2.1 Single-Antenna Transmitter 73
4.2.2 Multi-Antenna Transmitter 77
4.3 State-of-the-Art Rate Duality 82
4.4 Novel Rate Duality 84
4.4.1 Dirty Paper Coding for Vector Signals 84
4.4.2 BC to MAC Conversion 87
4.4.3 MAC to BC Conversion 91
4.4.4 Advantages of the Novel Rate Duality 94
5 Matrix-Based Gradient-Projection Algorithm 97
5.1 Standard Gradient-Projection Algorithm 98
5.1.1 Precoder-Based Gradient-Projection Algorithm 99
5.1.2 Covariance-Based Gradient-Projection Algorithm 104
5.1.3 Step-Size Strategies 105
5.2 Relaxed Gradient-Projection Algorithm 111
5.3 Orthogonal Projection of a Block-Diagonal Matrix 114
5.3.1 Orthogonal Projection of Precoding Matrices 114
5.3.2 Orthogonal Projection of Covariance Matrices 118
6 MIMO BC Transceiver Design with Interference Cancelation 132
6.1 Sum Capacity Computation 132
6.1.1 Literature Overview 132
6.1.2 Covariance-Based Sum Capacity Computation 134
6.1.3 Precoder-Based Sum Capacity Computation 138
6.1.4 Simulation Results 141
6.2 Weighted Sum Rate Maximization 145
6.2.1 Motivation 145
6.2.2 Literature Overview 146
6.2.3 Covariance-Based Weighted Sum Rate Maximization 147
6.2.4 Precoder-Based Weighted Sum Rate Maximization 149
6.2.5 Simulation Results 150
7 Linear Transceiver Design for the MIMO BC 156
7.1 Motivation for Linear Transceivers 156
7.1.1 Typical Sequences Approach to Dirty Paper Coding 157
7.1.2 Modulo-Lattice Additive Noise Channel Approach 160
7.2 Sum Rate Maximization 162
7.2.1 Basic Properties of the Sum Rate Expression 163
7.2.2 Combinatorial Sum Rate Maximization 165
7.2.3 Successive Sum Rate Maximization 169
7.2.4 Simulation Results 170
7.3 Weighted Sum Rate Maximization 176
7.3.1 Weighted Sum Rate Expression and Wirtinger Derivatives 176
7.3.2 Simulation Results 177
7.4 Sum Mean Square Error Minimization 181
7.4.1 Dual MIMO MAC Approach 182
8 Asymptotic High Power Analysis of the MIMO BC 187
8.1 Overview and Asymptotic Notation 188
8.1.1 Definitions 188
8.1.2 Basic Principle and Literature Overview 190
8.2 Fixed Channel Weighted Sum Rate Analysis 193
8.2.1 Cooperative Point-to-Point MIMO System 194
8.2.2 MIMO Broadcast Channel with Dirty Paper Coding 200
8.2.3 MIMO Broadcast Channel with Linear Transceivers 218
8.3 Ergodic Weighted Sum Rate Analysis 233
8.3.1 Wishart and Inverse Wishart Distribution 234
8.3.2 Point-to-Point MIMO System 236
8.3.3 MIMO Broadcast Channel with Dirty Paper Coding 237
8.3.4 MIMO Broadcast Channel with Linear Transceivers 238
8.4 Simulation Results 242
9 Description of the Quality of Service Feasibility Region 245
9.1 Literature Overview on Feasibility and Power Minimization 246
9.2 Feasibility in the Vector Broadcast Channel 251
9.2.1 Regular Channels Scenario 255
9.2.2 Singular Channels Scenario 261
9.3 Feasibility in the MIMO Broadcast Channel 273
9.3.1 Feasibility Under Single-Stream Transmission 274
9.3.2 Optimality of Single-Stream Transmission 277
Appendix 280
Index 320