Multiple-Input Multiple-Output Channel Models: Theory and Practice

by Nelson Costa, Simon Haykin

Overview

A complete discussion of MIMO communications, from theory to real-world applications

The emerging wireless technology Wideband Multiple-Input, Multiple-Output (MIMO) holds the promise of greater bandwidth efficiency and wireless link reliability. This technology is just now being implemented into hardware and working its way into wireless standards such as the ubiquitous 802.11g, as well as third- and fourth-generation cellular standards.

Multiple-Input Multiple-Output Channel Models uniquely brings together the theoretical and practical aspects of MIMO communications, revealing how these systems use their multipath diversity to increase channel capacity. It gives the reader a clear understanding of the underlying propagation mechanisms in the wideband MIMO channel, which is fundamental to the development of communication algorithms, signaling strategies, and transceiver design for MIMO systems.

MIMO channel models are important tools in understanding the potential gains of a MIMO system. This book discusses two types of wideband MIMO models in detail: correlative channel models—specifically the Kronecker, Weichselberger, and structured models—and cluster models, including Saleh-Valenzuela, European Cooperation in the field of Scientific and Technical Research (COST) 273, and Random Cluster models. From simple to complex, the reader will understand the models' mechanisms and the reasons behind the parameters. Next, channel sounding is explained in detail, presenting the theory behind a few channel sounding techniques used to sound narrowband and wideband channels. The technique of digital matched filtering is then examined and, using real-life data, is shown to provide very accurate estimates of channel gains. The book concludes with a performance analysis of the structured and Kronecker models.

Multiple-Input Multiple-Output Channel Models is the first book to apply tensor calculus to the problem of wideband MIMO channel modeling. Each chapter features a list of important references, including core literary references, Matlab implementations of key models, and the location of databases that can be used to help in the development of new models or communication algorithms. Engineers who are working in the development of telecommunications systems will find this resource invaluable, as will researchers and students at the graduate or post-graduate level.

Product Details

ISBN-13:	9780470399835
Publisher:	Wiley
Publication date:	04/12/2010
Series:	Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control , #65
Pages:	248
Product dimensions:	6.30(w) x 9.30(h) x 0.90(d)

About the Author

Nelson Costa, PhD, is President and CEO of Deus Ex Machina Consulting Inc.

Simon Haykin is Distinguished University Professor at McMaster University, Ontario, Canada.

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Table of Contents

Preface xiii

Acknowledgments xv

1 Introduction 1

1.1 Historical Perspective 1

1.1.1 Electromagnetism 1

1.1.2 The Hertz Transmitter 3

1.1.3 Tesla and Wireless Power 4

1.1.4 Lodge and Tunable Circuits 4

1.1.5 Marconi and Trans-Atlantic Communication 5

1.2 MIMO Communications 6

1.3 MIMO Channel Models 9

1.3.1 The Channel Model Spectrum 9

1.3.2 Wideband MTMO Channel Models 11

1.4 Software Defined Radio 12

1.5 Overview 14

1.5.1 Chapter 2: Multiple Antenna Channels and Correlation 14

1.5.2 Chapter 3: Correlative Models 15

1.5.3 Chapter 4: Cluster Models 15

1.5.4 Chapter 5: Channel Sounding 16

1.5.5 Chapter 6: Experimental Verifications 16

1.5.6 Appendixes: Background and Definitions 17

2 Multiple Antenna Channels and Correlation 19

2.1 The Radio Channel: Definitions 19

2.1.1 The Physical Channel 19

2.1.2 The Analytical Channel 20

2.2 Channel Classifications 21

2.2.1 Linear Time-Invariant Channels 21

2.2.2 Time-Invariant Narrowband Channels 23

2.2.3 Time-Varying Wideband Channels and Bello's Model 23

2.2.4 The Tapped-Delay Line Model and the Physical Channel 27

2.2.5 Narrowband Diversity Channels 29

2.2.6 The Narrowband MIMO Channel 31

2.2.7 The Wideband MIMO Channel 32

2.2.8 The Wideband MIMO Channel Recast Using Tensors 33

2.3 Summary of Channel Classifications 34

2.4 Second-Order Statistics of Multiple Antenna Channels 34

2.4.1 Second-Order Statistics of the Vector Channel 36

2.4.2 Second-Order Statistics of the Narrowband MIMO Channel 36

2.5 Second-Order Statistics of the Wideband MIMO Channel 39

2.5.1 Eigenvalue Decomposition of the Wideband Correlation Matrix 41

2.6 Spatial Structure of Multiple Antenna Channels 42

2.6.1 SIMO Channels and Beamformers 42

2.6.2 MIMO Beamformers 47

2.7 Summary and Discussion 51

2.7.1 Channel Classifications 51

2.7.2 Multiantenna Channels 51

2.7.3 Spatial Structure and the APS 52

2.8 Notes and References 52

2.8.1 Channel Classifications 52

2.8.2 Second-Order Statistics of Multiantenna Channels 53

2.8.3 The Spatial Structure of Multiantenna Channels 53

3 Correlative Models 55

3.1 Vector Channel Synthesis from the Vector Correlation Matrix 56

3.2 Matrix Channel Synthesis from the Narrowband Correlation Matrix 57

3.2.1 Number of Model Parameters 57

3.3 One-Sided Correlation for Narrowband MIMO Channels 58

3.4 The Kronecker Model 59

3.4.1 The Narrowband Kronecker Model 59

3.4.2 The Wideband Kronecker Model 61

3.4.3 Notes on the Narrowband and Wideband Kronecker Models 61

3.5 The Weichselberger Model 63

3.5.1 The Vector Mode Model 63

3.5.2 H-matrix from Structured Vector Modes 64

3.6 The Structured Model 66

3.6.1 H-tensor Synthesis from the Wideband Correlation Tensor 67

3.6.2 One-Sided Correlation for Wideband MIMO Channels 68

3.6.3 Approximating the Wideband Correlation Matrix 70

3.6.4 Number of Parameters Comparison 72

3.7 Summary and Discussion 73

3.7.1 The Kronecker Model 73

3.7.2 The Weichselberger Model 73

3.7.3 The Structured Model 74

3.8 Notes and References 74

3.8.1 Correlative Models 74

3.8.2 Tensor Decomposition 75

4 Cluster Models 77

4.1 What is a Cluster? 78

4.2 The Saleh-Valenzuela Model 79

4.2.1 Model Summary 81

4.2.2 Model Implementation 83

4.2.3 Some Typical Parameters 83

4.3 Clusters in Time and Space 83

4.3.1 Azimuth, Elevation, and Delay Spreads 84

4.4 The Extended Saleh-Valenzuela Model 86

4.5 The COST 273 Model 87

4.5.1 Generic Channel Model 87

4.5.2 Environments 89

4.5.3 Receiver, Transmitter Placement 89

4.5.4 COST 273 Procedure 90

4.5.5 Features Not Yet Implemented and Omissions 98

4.5.6 Disadvantages and Advantages: COST 273 100

4.6 The Random Cluster Model 101

4.6.1 General Description 101

4.6.2 Determining the Environment PDF 105

4.6.3 Advantages and Disadvantages: The RCM 107

4.7 Summary and Discussion 108

4.8 Notes and References 109

5 Channel Sounding 111

5.1 Introduction 111

5.2 The WMSDR 112

5.2.1 Transmission 112

5.2.2 Reception 115

5.2.3 Timing and Carrier Offsets 118

5.3 Narrowband Channel Sounding 118

5.3.1 Periodic Pulse Sounding 118

5.3.2 Narrowband Single-Input, Single-Output Channel Sounding 120

5.3.3 Narrowband MIMO Channel Sounding 121

5.4 Wideband Sounding; Correlative Sounding 122

5.4.1 ML Sequences 124

5.4.2 Cross-correlation Using the FFT 126

5.4.3 Digital Matched Filters 127

5.5 Wideband Sounding: Sampled Spectrum Channel Sounding 134

5.6 Switched-Array Architectures 136

5.7 Timing and Carrier Recovery 137

5.7.1 Digital Timing Recovery Methods 140

5.7.2 Phase Recovery Using a Decision-Directed Feedback Loop 152

5.8 Summary and Discussion 157

5.9 Notes and References 158

6 Experimental Verifications 159

6.1 Validation Metrics 160

6.1.1 Channel Capacity 160

6.1.2 The Diversity and Correlation Metrics 161

6.1.3 The Demmel Condition Number 162

6.1.4 The Environmental Characterization Metric 163

6.1.5 Correlation Matrix Difference Metric 165

6.2 WMSDR Experimental Setup 166

6.2.1 Terminology 166

6.2.2 Measurement Description 166

6.3 BYU Wideband Channel Sounder Experimental Setup 167

6.3.1 BYU Transmitter Set 167

6.3.2 BYU Receiver Set 168

6.3.3 Measurement Description 169

6.4 Experimental Results 170

6.4.1 Capacity Measure: Methodology 171

6.4.2 Results: MTMO APS and Spatial Structure 176

6.4.3 Results: Wideband Correlation Matrices 181

6.5 Discussion 184

6.5.1 Accuracy of the Results 184

6.5.2 Sources of Error 184

6.6 Summary and Discussion 185

6.7 Notes and References 186

Appendix A An Introduction to Tensor Algebra 187

Appendix B Proof of Theorems from Chapter 3 193

Appendix C COST 273 Model Summary 201

Glossary 207

Bibliography 211

Index 221