MIMO Wireless Communications: From Real-World Propagation to Space-Time Code Design
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Table of contents
- Copyright Pageiv
- Contentsv
- List of Figuresxi
- List of Tablesxvii
- Prefacexix
- List of Abbreviationsxxi
- List of Symbolsxxv
- About the Authorsxxvii
- Chapter 1 Introduction to multi-antenna communications1
- 1.1 Brief history of array processing1
- 1.2 Space–time wireless channels for multi-antenna systems2
- 1.3 Exploiting multiple antennas in wireless systems6
- 1.3.1 Diversity techniques6
- 1.3.2 Multiplexing capability9
- 1.4 Single-input multiple-output systems10
- 1.4.1 Receive diversity via selection combining10
- 1.4.2 Receive diversity via gain combining11
- 1.4.3 Receive diversity via hybrid selection/gain combining14
- 1.5 Multiple-input single-output systems15
- 1.5.1 Switched multibeam antennas15
- 1.5.2 Transmit diversity via matched beamforming15
- 1.5.3 Null-steering and optimal beamforming16
- 1.5.4 Transmit diversity via space–time coding17
- 1.5.5 Indirect transmit diversity19
- 1.6 Multiple-input multiple-output systems19
- 1.6.1 MIMO with perfect transmit channel knowledge19
- 1.6.2 MIMO without transmit channel knowledge22
- 1.6.3 MIMO with partial transmit channel knowledge26
- 1.7 Multiple antenna techniques in commercial wireless systems27
- Chapter 2 Physical MIMO channel modeling29
- 2.1 Multidimensional channel modeling30
- 2.1.1 The double-directional channel impulse response30
- 2.1.2 Multidimensional correlation functions and stationarity35
- 2.1.3 Channel fading, K-factor and Doppler spectrum37
- 2.1.4 Power delay and direction spectra40
- 2.1.5 From double-directional propagation to MIMO channels42
- 2.1.6 Statistical properties of the channel matrix44
- 2.1.7 Discrete channel modeling: sampling theorem revisited47
- 2.1.8 Physical versus analytical models48
- 2.2 Electromagnetic models49
- 2.2.1 Ray-based deterministic methods49
- 2.2.2 Multi-polarized channels51
- 2.3 Geometry-based models53
- 2.3.1 One-ring model54
- 2.3.2 Two-ring model56
- 2.3.3 Combined elliptical-ring model56
- 2.3.4 Elliptical and circular models58
- 2.3.5 Extension of geometry-based models to dual-polarized channels59
- 2.4 Empirical models60
- 2.4.1 Extended Saleh-Valenzuela model60
- 2.4.2 Stanford University Interim channel models62
- 2.4.3 COST models63
- 2.5 Standardized models64
- 2.5.1 IEEE 802.11 TGn models64
- 2.5.2 IEEE 802.16d/e models65
- 2.5.3 3GPP/3GPP2 spatial channel models66
- 2.6 Antennas in MIMO systems66
- 2.6.1 About antenna arrays66
- 2.6.2 Mutual coupling67
- Chapter 3 Analytical MIMO channel representations for system design73
- 3.1 General representations of correlated MIMO channels73
- 3.1.1 Rayleigh fading channels74
- 3.1.2 Ricean fading channels76
- 3.1.3 Dual-polarized channels76
- 3.1.4 Double-Rayleigh fading model for keyhole channels81
- 3.2 Simplified representations of Gaussian MIMO channels81
- 3.2.1 The Kronecker model82
- 3.2.2 Virtual channel representation83
- 3.2.3 The eigenbeam model85
- 3.3 Propagation-motivated MIMO metrics87
- 3.3.1 Comparing models and correlation matrices87
- 3.3.2 Characterizing the multipath richness88
- 3.3.3 Measuring the non-stationarity of MIMO channels93
- 3.4 Relationship between physical models and analytical representations96
- 3.4.1 The Kronecker model paradox96
- 3.4.2 Numerical examples99
- 3.4.3 Comparison between analytical models: a system viewpoint105
- Chapter 4 Mutual information and capacity of real-world random MIMO channels109
- 4.1 Capacity of fading channels with perfect transmit channel knowledge110
- 4.2 Ergodic capacity of i.i.d. Rayleigh fast fading channels with partial transmit channel knowledge114
- 4.3 Mutual information and capacity of correlated Rayleigh channels with partial transmit channel kn123
- 4.3.1 Mutual information with equal power allocation123
- 4.3.2 Ergodic capacity of correlated Rayleigh channels with partial transmit channel knowledge129
- 4.4 Mutual information and capacity of Ricean channels with partial transmit channel knowledge133
- 4.4.1 Mutual information with equal-power allocation133
- 4.4.2 Ergodic capacity with partial transmit channel knowledge135
- 4.5 Mutual information in some particular channels136
- 4.5.1 Dual-polarized channels136
- 4.5.2 Impact of antenna coupling on mutual information138
- 4.6 Outage capacity and diversity-multiplexing trade-off in i.i.d. Rayleigh slow fading channels141
- 4.6.1 Infinite SNR142
- 4.6.2 Finite SNR148
- 4.7 Outage capacity and diversity-multiplexing trade-off in semi-correlated Rayleigh and Ricean slow151
- Chapter 5 Space–time coding over i.i.d. Rayleigh flat fading channels155
- 5.1 Overview of a space–time encoder155
- 5.2 System model156
- 5.3 Error probability motivated design methodology157
- 5.3.1 Fast fading MIMO channels: the distance-product criterion159
- 5.3.2 Slow fading MIMO channels: the rank-determinant and rank-trace criteria160
- 5.4 Information theory motivated design methodology163
- 5.4.1 Fast fading MIMO channels: achieving the ergodic capacity163
- 5.4.2 Slow fading MIMO channels: achieving the diversity-multiplexing trade-off165
- 5.5 Space–time block coding170
- 5.5.1 A general framework for linear STBCs171
- 5.5.2 Spatial multiplexing/V-BLAST178
- 5.5.3 D-BLAST190
- 5.5.4 Orthogonal space–time block codes192
- 5.5.5 Quasi-orthogonal space–time block codes198
- 5.5.6 Linear dispersion codes202
- 5.5.7 Algebraic space–time codes203
- 5.5.8 Global performance comparison209
- 5.6 Space–time trellis coding211
- 5.6.1 Space–time trellis codes211
- 5.6.2 Super-orthogonal space–time trellis codes221
- Chapter 6 Error probability in real-world MIMO channels223
- 6.1 A conditional pairwise error probability approach223
- 6.1.1 Degenerate channels223
- 6.1.2 The spatial multiplexing example227
- 6.2 Introduction to an average pairwise error probability approach230
- 6.3 Average pairwise error probability in Rayleigh fading channels234
- 6.3.1 High SNR regime234
- 6.3.2 Medium SNR regime245
- 6.3.3 Low SNR regime254
- 6.3.4 Summary and examples255
- 6.4 Average pairwise error probability in Ricean fading channels258
- 6.4.1 High SNR regime259
- 6.4.2 Medium SNR regime262
- 6.4.3 Low SNR regime264
- 6.4.4 Summary and examples265
- 6.5 Average pairwise error probability in dual-polarized channels267
- 6.5.1 Performance of orthogonal space–time block coding268
- 6.5.2 Performance of spatial multiplexing270
- 6.6 Perspectives on the space–time code design in realistic channels273
- Chapter 7 Space–time coding over real-world MIMO channels with no transmit channel knowledge275
- 7.1 Information theory motivated design methodology275
- 7.2 Information theory motivated code design in slow fading channels277
- 7.2.1 Universal code design criteria277
- 7.2.2 MISO channels281
- 7.2.3 Parallel channels281
- 7.3 Error probability motivated design methodology284
- 7.3.1 Designing robust codes284
- 7.3.2 Average pairwise error probability in degenerate channels285
- 7.3.3 Catastrophic codes and general design criteria289
- 7.4 Error probability motivated code design in slow fading channels296
- 7.4.1 Full-rank codes296
- 7.4.2 Linear space–time block codes296
- 7.4.3 Virtual channel representation based design criterion300
- 7.4.4 Relationship with information theory motivated design301
- 7.4.5 Practical code designs in slow fading channels303
- 7.5 Error probability motivated code design in fast fading channels313
- 7.5.1 'Product-wise' catastrophic codes313
- 7.5.2 Practical code designs in fast fading channels314
- Chapter 8 Space–time coding with partial transmit channel knowledge319
- 8.1 Introduction to channel statistics based precoding techniques321
- 8.1.1 A general framework321
- 8.1.2 Information theory motivated design methodologies322
- 8.1.3 Error probability motivated design methodologies323
- 8.2 Channel statistics based precoding for orthogonal space–time block coding324
- 8.2.1 Optimal precoding in Kronecker Rayleigh fading channels325
- 8.2.2 Optimal precoding in non-Kronecker Rayleigh channels330
- 8.2.3 Optimal precoding in Ricean fading channels331
- 8.3 Channel statistics based precoding for codes with non-identity error matrices333
- 8.4 Channel statistics based precoding for spatial multiplexing337
- 8.4.1 Beamforming338
- 8.4.2 Constellation shaping339
- 8.4.3 A non-linear approach to constellation shaping347
- 8.4.4 Precoder design for suboptimal receivers351
- 8.5 Introduction to quantized precoding and antenna selection techniques352
- 8.6 Quantized precoding and antenna selection for dominant eigenmode transmissions353
- 8.6.1 Selection criterion and codebook design in i.i.d. Rayleigh fading channels354
- 8.6.2 Antenna selection and achievable diversity gain355
- 8.6.3 How many feedback bits are required?357
- 8.6.4 Selection criterion and codebook design in spatially correlated Rayleigh fading channels357
- 8.7 Quantized precoding and antenna selection for orthogonal space–time block coding358
- 8.7.1 Selection criterion and codebook design359
- 8.7.2 Antenna subset selection and achievable diversity gain360
- 8.8 Quantized precoding and antenna selection for spatial multiplexing362
- 8.8.1 Selection criterion and codebook design363
- 8.8.2 Impact of decoding strategy on error probability364
- 8.8.3 Extension to multi-mode precoding364
- 8.9 Information theory motivated quantized precoding367
- Chapter 9 Space–time coding for frequency selective channels369
- 9.1 Single-carrier vs. multi-carrier transmissions370
- 9.1.1 Single-carrier transmissions370
- 9.1.2 Multi-carrier transmissions: MIMO-OFDM371
- 9.1.3 A unified representation for single and multi-carrier transmissions376
- 9.2 Information theoretic aspects for frequency selective MIMO channels378
- 9.2.1 Capacity considerations378
- 9.2.2 Mutual information with equal power allocation379
- 9.2.3 Diversity-multiplexing trade-off380
- 9.3 Average pairwise error probability381
- 9.4 Code design criteria for single-carrier transmissions in Rayleigh fading channels382
- 9.4.1 Generalized delay-diversity382
- 9.4.2 Lindskog-Paulraj scheme384
- 9.4.3 Alternative constructions385
- 9.5 Code design criteria for space-frequency coded MIMO-OFDM transmissions in Rayleigh fading channe386
- 9.5.1 Diversity gain analysis386
- 9.5.2 Coding gain analysis389
- 9.5.3 Space-frequency linear block coding392
- 9.5.4 Cyclic delay-diversity395
- 9.6 On the robustness of codes in spatially correlated frequency selective channels399
- 9.6.1 Degenerate taps399
- 9.6.2 Application to space-frequency MIMO-OFDM401
- Appendix A: Useful mathematical and matrix properties403
- Appendix B: Complex Gaussian random variables and matrices405
- B.1 Some useful probability distributions405
- B.2 Eigenvalues of Wishart matrices406
- B.2.1 Determinant and product of eigenvalues of Wishart matrices406
- B.2.2 Distribution of ordered eigenvalues407
- B.2.3 Distribution of non-ordered eigenvalues407
- Appendix C: Stanford University Interim channel models409
- Appendix D: Antenna coupling model411
- D.1 Minimum scatterers with regard to impedance parameters411
- D.1.1 Circuit representation411
- D.1.2 Radiation patterns413
- D.2 Minimum scatterers with regard to admittance parameters415
- Appendix E: Derivation of the average pairwise error probability417
- E.1 Joint space–time correlated Ricean fading channels419
- E.2 Space correlated Ricean slow fading channels420
- E.3 Joint space–time correlated Ricean block fading channels421
- E.4 I.i.d. Rayleigh slow and fast fading channels422
- Bibliography423
- Index445
- A445
- B445
- C445
- D445
- E446
- F446
- G446
- H446
- I446
- J446
- K446
- L446
- M446
- N447
- O447
- P447
- Q447
- R447
- S447
- T448
- U448
- V448
- W448
- Z448
Uniquely, this book proposes robust space-time code designs for real-world wireless channels. Through a unified framework, it emphasizes how propagation mechanisms such as space-time frequency correlations and coherent components impact the MIMO system performance under realistic power constraints. Combining a solid mathematical analysis with a physical and intuitive approach to space-time coding, the book progressively derives innovative designs, taking into consideration that MIMO channels are often far from ideal.
The various chapters of this book provide an essential, complete and refreshing insight into the performance behaviour of space-time codes in realistic scenarios and constitute an ideal source of the latest developments in MIMO propagation and space-time coding for researchers, R&D engineers and graduate students.
Features include
• Physical models and analytical representations of MIMO propagation channels, highlighting the strengths and weaknesses of various models
• Overview of space-time coding techniques, covering both classical and more recent schemes under information theory and error probability perspectives
• In-depth presentation of how real-world propagation affects the capacity and the error performance of MIMO transmission schemes
• Innovative and practical designs of robust space-time coding, precoding and antenna selection techniques for realistic propagation (including single-carrier and MIMO-OFDM transmissions)
"This book offers important insights into how space-time coding can be tailored for real-world MIMO channels. The discussion of MIMO propagation models is also intuitive and well-developed."
Arogyaswami J. Paulraj, Professor, Stanford University, CA
"Finally a book devoted to MIMO from a new perspective that bridges the boundaries between propagation, channel modeling, signal processing and space-time coding. It is of high reference value, combining intuitive and conceptual explanations with detailed, stringent derivations of basic facts of MIMO."
Ernst Bonek, Emeritus Professor, Technische Universität Wien, Austria
* Presents space-time coding techniques for real-world MIMO channels
* Contains new design methodologies and criteria that guarantee the robustness of space-time coding in real life wireless communications applications
* Evaluates the performance of space-time coding in real world conditions
The various chapters of this book provide an essential, complete and refreshing insight into the performance behaviour of space-time codes in realistic scenarios and constitute an ideal source of the latest developments in MIMO propagation and space-time coding for researchers, R&D engineers and graduate students.
Features include
• Physical models and analytical representations of MIMO propagation channels, highlighting the strengths and weaknesses of various models
• Overview of space-time coding techniques, covering both classical and more recent schemes under information theory and error probability perspectives
• In-depth presentation of how real-world propagation affects the capacity and the error performance of MIMO transmission schemes
• Innovative and practical designs of robust space-time coding, precoding and antenna selection techniques for realistic propagation (including single-carrier and MIMO-OFDM transmissions)
"This book offers important insights into how space-time coding can be tailored for real-world MIMO channels. The discussion of MIMO propagation models is also intuitive and well-developed."
Arogyaswami J. Paulraj, Professor, Stanford University, CA
"Finally a book devoted to MIMO from a new perspective that bridges the boundaries between propagation, channel modeling, signal processing and space-time coding. It is of high reference value, combining intuitive and conceptual explanations with detailed, stringent derivations of basic facts of MIMO."
Ernst Bonek, Emeritus Professor, Technische Universität Wien, Austria
* Presents space-time coding techniques for real-world MIMO channels
* Contains new design methodologies and criteria that guarantee the robustness of space-time coding in real life wireless communications applications
* Evaluates the performance of space-time coding in real world conditions