Space-Time.Processing.for.MIMO.Communications.pdf

圣斗士

学习MIMO的筒子要看的好书呀！
Contents
List of Contributors xi
Preface xiii
Acknowledgements xvii
1 MIMO Wireless Channel Modeling and Experimental Characterization 1
Michael A. Jensen and Jon W. Wallace
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 MIMOsystemmodel ......................... 2
1.1.2 Channelnormalization......................... 4
1.2 MIMOChannelMeasurement ......................... 5
1.2.1 Measurementsystem.......................... 6
1.2.2 Channelmatrixcharacteristics..................... 8
1.2.3 Multipath estimation . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.3 MIMOChannelModels ............................ 13
1.3.1 Random matrix models . . . . . . . . . . . . . . . . . . . . . . . . 13
1.3.2 Geometricdiscretescatteringmodels ................. 19
1.3.3 Statisticalclustermodels........................ 20
1.3.4 Deterministicraytracing........................ 24
1.4 TheImpactofAntennasonMIMOPerformance............... 24
1.4.1 Spatialdiversity ............................ 25
1.4.2 Pattern (angle and polarization) diversity . . . . . . . . . . . . . . . 26
1.4.3 Mutual coupling and receiver network modeling . . . . . . . . . . . 28
References....................................... 35
2 Multidimensional Harmonic Retrieval with Applications in MIMO Wireless
Channel Sounding 41
Xiangqian Liu, Nikos D. Sidiropoulos, and Tao Jiang
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.2 HarmonicRetrievalDataModel........................ 43
2.2.1 2-Dharmonicretrievalmodel ..................... 43
2.2.2 N-Dharmonicretrievalmodel .................... 44
2.2.3 Khatri–Rao product of Vandermonde matrices . . . . . . . . . . . . 45
2.3 Identifiability of Multidimensional Harmonic Retrieval . . . . . . . . . . . . 46
2.3.1 Deterministic ID of N-Dharmonicretrieval ............. 47
2.3.2 StochasticIDof2-Dharmonicretrieval ............... 48
2.3.3 Stochastic ID of N-Dharmonicretrieval............... 51
2.4 Multidimensional Harmonic Retrieval Algorithms . . . . . . . . . . . . . . 53
2.4.1 2-DMDF................................ 54
2.4.2 N-DMDF ............................... 54
2.4.3 N-DunitaryESPRIT.......................... 55
2.4.4 N-DMUSIC.............................. 57
2.4.5 N-DRARE............................... 58
2.4.6 Summary................................ 58
2.5 NumericalExamples .............................. 59
2.5.1 2-Dharmonicretrieval(simulateddata)................ 59
2.5.2 3-Dharmonicretrieval(simulateddata)................ 61
2.6 Multidimensional Harmonic Retrieval for MIMO Channel
Estimation.................................... 61
2.6.1 Parametricchannelmodeling ..................... 62
2.6.2 MIMO channel sounding . . . . . . . . . . . . . . . . . . . . . . . 65
2.6.3 Examples of 3-D MDF applied to measurement data . . . . . . . . 66
2.7 ConcludingRemarks .............................. 70
References....................................... 73
3 Certain Computations Involving Complex Gaussian Matrices with Applications
to the Performance Analysis of MIMO Systems 77
Ming Kang, Lin Yang, and Mohamed-Slim Alouini
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
3.2 Performance Measures of Multiple Antenna Systems . . . . . . . . . . . . . 78
3.2.1 Noise-limitedMIMOfadingchannels................. 78
3.2.2 MIMO channels in the presence of cochannel interference . . . . . 80
3.2.3 MIMObeamforming.......................... 83
3.3 SomeMathematicalPreliminaries ....................... 85
3.4 General Calculations with MIMO Applications . . . . . . . . . . . . . . . . 87
3.4.1 Mainresult ............................... 90
3.4.2 Application to noise-limited MIMO systems . . . . . . . . . . . . . 92
3.4.3 Applications to MIMO channels in the presence of
interference............................... 97
3.5 Summary .................................... 101
References....................................... 102
4 Recent Advances in Orthogonal Space-Time Block Coding 105
Mohammad Gharavi-Alkhansari, Alex B. Gershman, and Shahram Shahbazpanahi
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
4.2 Notations and Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
4.3 MathematicalPreliminaries........................... 106
4.4 MIMO System Model and OSTBC Background . . . . . . . . . . . . . . . 108
4.5 Constellation Space Invariance and Equivalent Array-Processing-Type
MIMOModel.................................. 111
4.6 CoherentMLDecoding ............................ 115
4.7 Exact Symbol Error Probability Analysis of Coherent ML Decoder . . . . . 119
4.7.1 Probability of error for a separable input constellation . . . . . . . . 119
4.7.2 Probability of error for a nonseparable input constellation . . . . . . 128
4.8 Optimality Properties of OSTBCs . . . . . . . . . . . . . . . . . . . . . . . 133
4.8.1 Sufficient conditions for optimal space-time codes with dimension-
constrainedconstellations ....................... 135
4.8.2 Optimality of OSTBCs for dimension-constrained
constellations.............................. 140
4.8.3 Optimality of OSTBCs for small-size constellations . . . . . . . . . 141
4.8.4 Optimality of OSTBCs among LD codes with the same number of
complexvariables ........................... 144
4.9 BlindDecodingofOSTBCs .......................... 145
4.9.1 Signalmodelanditsproperties .................... 146
4.9.2 Blindchannelestimation........................ 147
4.9.3 Relationship to the blind ML estimator . . . . . . . . . . . . . . . . 153
4.9.4 Numericalexamples .......................... 154
4.10 Multiaccess MIMO Receivers for OSTBCs . . . . . . . . . . . . . . . . . . 157
4.10.1 Multiaccess MIMO model . . . . . . . . . . . . . . . . . . . . . . . 158
4.10.2 Minimum variance receivers . . . . . . . . . . . . . . . . . . . . . . 159
4.10.3 Numericalexamples .......................... 161
4.11Conclusions................................... 163
References....................................... 163
5 Trace-Orthogonal Full Diversity Cyclotomic Space-Time Codes 169
Jian-Kang Zhang, Jing Liu, and Kon Max Wong
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
5.2 Channel Model with Linear Dispersion Codes . . . . . . . . . . . . . . . . 172
5.3 Good Structures for LD Codes: Trace Orthogonality . . . . . . . . . . . . . 174
5.3.1 An information-theoretic viewpoint . . . . . . . . . . . . . . . . . . 174
5.3.2 Adetectionerrorviewpoint ...................... 177
5.4 Trace-orthogonal LD Codes . . . . . . . . . . . . . . . . . . . . . . . . . . 182
5.4.1 Trace orthogonality . . . . . . . . . . . . . . . . . . . . . . . . . . . 182
5.4.2 Optimality of trace-orthogonal LD codes from a linear MMSE
receiver viewpoint . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
5.5 Construction of Trace Orthogonal LD Codes . . . . . . . . . . . . . . . . . 187
5.6 DesignofFullDiversityLDCodes ...................... 192
5.6.1 Some basic definitions and results in algebraic number theory . . . 192
5.6.2 DesignoffulldiversityLDcodes................... 194
5.7 Design of Full Diversity Linear Space-time Block Codes for N<M .... 197
5.8 DesignExamplesandSimulations....................... 200
5.9 Conclusion ................................... 204
References....................................... 205
6 Linear and Dirty-Paper Techniques for the Multiuser MIMO Downlink 209
Christian B. Peel, Quentin H. Spencer, A. Lee Swindlehurst, Martin Haardt,
and Bertrand M. Hochwald
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209
6.1.1 Problemoverview ........................... 209
6.1.2 Literaturesurvey............................ 210
6.1.3 Chapterorganization.......................... 212
6.2 Background and Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . 212
6.2.1 Capacity ................................ 213
6.2.2 Dirty-papercoding........................... 215
6.2.3 Discussion ............................... 216
6.3 Single Antenna Receivers . . . . . . . . . . . . . . . . . . . . . . . . . . . 217
6.3.1 Channelinversion ........................... 217
6.3.2 Regularizedchannelinversion..................... 218
6.3.3 Sphereencoding ............................ 219
6.3.4 Computationally ef&#64257;cient precoding . . . . . . . . . . . . . . . . . . 223
6.3.5 Powercontrol.............................. 226
6.4 Multiple Antenna Receivers . . . . . . . . . . . . . . . . . . . . . . . . . . 227
6.4.1 Channel block diagonalization . . . . . . . . . . . . . . . . . . . . . 227
6.4.2 Combined block diagonalization and MMSE THP precoding . . . . 228
6.4.3 Coordinated Tx/Rx beamforming . . . . . . . . . . . . . . . . . . . 231
6.5 OpenProblems ................................. 234
6.5.1 Codingandcapacity.......................... 234
6.5.2 PartialorimperfectCSI ........................ 234
6.5.3 Scheduling ............................... 235
6.5.4 Resourceallocation........................... 235
6.6 Summary .................................... 236
References....................................... 236
7 Antenna Subset Selection in MIMO Communication Systems 245
Alexei Gorokhov, Dhananjay A. Gore, and Arogyaswami J. Paulraj
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245
7.1.1 Signalandchannelmodel ....................... 246
7.2 SIMO/MISOSelection............................. 247
7.2.1 Maximumratiocombining....................... 247
7.2.2 Antennaselection ........................... 248
7.2.3 MRC versus antenna selection: performance comparison . . . . . . 248
7.3 MIMOSelection ................................ 251
7.3.1 Antenna selection for practical space-time processing . . . . . . . . 251
7.3.2 Antenna selection to maximize Shannon capacity . . . . . . . . . . 251
7.4 Diversity and Multiplexing with MIMO Antenna Selection . . . . . . . . . 254
7.4.1 Diversity versus multiplexing . . . . . . . . . . . . . . . . . . . . . 254
7.4.2 Transmit/receive antenna selection . . . . . . . . . . . . . . . . . . 255
7.4.3 Diversity and multiplexing: numerical example . . . . . . . . . . . 259
7.5 Receive Antenna Selection Algorithms . . . . . . . . . . . . . . . . . . . . 259
7.5.1 Incremental and decremental selection . . . . . . . . . . . . . . . . 260
7.5.2 Numericalstudy ............................ 262
7.6 Antenna Selection in MIMO Wireless LAN Systems . . . . . . . . . . . . . 262
7.7 Summary .................................... 265
References....................................... 266
8 Convex Optimization Theory Applied to Joint Transmitter-Receiver
Design in MIMO Channels 269
Daniel P&acute; erez Palomar, Antonio Pascual-Iserte, John M. Ciof&#64257;, and Miguel
Angel Lagunas
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269
8.2 Convex Optimization Theory . . . . . . . . . . . . . . . . . . . . . . . . . . 271
8.2.1 De&#64257;nitions and classes of convex problems . . . . . . . . . . . . . 271
8.2.2 Reformulating a problem in convex form . . . . . . . . . . . . . . . 273
8.2.3 Lagrange duality theory and KKT optimality conditions . . . . . . . 274
8.2.4 Ef&#64257;cient numerical algorithms to solve convex problems . . . . . . 275
8.2.5 Applications in signal processing and communications . . . . . . . 277
8.3 SystemModelandPreliminaries........................ 281
8.3.1 Signalmodel .............................. 281
8.3.2 Measures of quality . . . . . . . . . . . . . . . . . . . . . . . . . . 282
8.3.3 Optimum linear receiver . . . . . . . . . . . . . . . . . . . . . . . . 283
8.4 Beamforming Design for MIMO Channels: A Convex Optimization
Approach .................................... 284
8.4.1 Problemformulation.......................... 285
8.4.2 Optimal design with independent QoS constraints . . . . . . . . . . 287
8.4.3 Optimal design with a global QoS constraint . . . . . . . . . . . . . 289
8.4.4 Extensiontomulticarriersystems................... 295
8.4.5 Numericalresults............................ 296
8.5 An Application to Robust Transmitter Design in MIMO Channels . . . . . 298
8.5.1 Introduction and state-of-the-art . . . . . . . . . . . . . . . . . . . . 298
8.5.2 A generic formulation of robust approaches . . . . . . . . . . . . . 300
8.5.3 Problemformulation.......................... 301
8.5.4 Reformulating the problem in a simpli&#64257;ed convex form . . . . . . . 304
8.5.5 Convex uncertainty regions . . . . . . . . . . . . . . . . . . . . . . 307
8.5.6 Numericalresults............................ 308
8.6 Summary .................................... 311
References....................................... 313
9 MIMO Communications with Partial Channel State Information 319
Shengli Zhou and Georgios B. Giannakis
9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319
9.2 PartialCSIModels ............................... 319
9.2.1 Statisticalmodels............................ 320
9.2.2 Finite-ratefeedbackmodel....................... 322
9.3 Capacity-OptimalDesigns ........................... 323
9.3.1 Capacity optimization with statistical CSI . . . . . . . . . . . . . . 324
9.3.2 Capacity optimization with &#64257;nite-rate feedback . . . . . . . . . . . . 329
9.4 ErrorPerformanceOrientedDesigns...................... 331
9.4.1 Combining orthogonal STBC with linear precoding . . . . . . . . . 331
9.4.2 Finite-rate one-dimensional beamforming . . . . . . . . . . . . . . . 341
9.4.3 Furtherresults ............................. 345
9.5 Adaptive Modulation with Partial CSI . . . . . . . . . . . . . . . . . . . . . 347
9.5.1 Adaptive modulation based on 2D coder-beamformer . . . . . . . . 347
9.5.2 Adaptive modulation/beamforming with &#64257;nite-rate feedback . . . . . 350
9.5.3 Othercombinations........................... 352
9.6 Conclusions................................... 352
Appendix ....................................... 352
References....................................... 353
Index 357

apteye

Thanks for sharing

flyfreely23

好书，谢谢诶

cnyqw

good book

jsqdp

is this really a good book

vikingg

谢谢分享

qianhe

谢谢分享。

huatiantian

good  good  good

efwefwrrr

Thanks alot

linyh

Thanks for sharing