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Other titles in the Adaptive and Learning Systems for Signal Processing, Communications and Control series:
Multiple-Input, Multiple-Output Channel Models: Theory and Practice (Adaptive and Learning Systems for Signal Processing, Communications and Control)by Nelson Costa
Synopses & Reviews
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.
Because Wideband Multiple Input and Multiple Output (MIMO) technology is just now being implemented in hardware, there is currently a great desire for knowledge of both the theory and practicality of its channels. Bringing together theoretical and practical aspects of MIMO communications, Wideband MIMO Channel Models is the first book to apply tensor calculus to the problem of wideband MIMO channel modeling. The text contains important novel material for how to model a MIMO channel, making it suitable for graduate students, researchers, and engineers.
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.
Table of Contents
Chapter 1: Introduction.
1.1 Historical Perspective.
1.1.2 The Hertz Transmitter.
1.1.3 Tesla and Wireless Power.
1.1.4 Lodge and Tunable Circuits.
1.1.5 Marconi and Trans-Atlantic Communication.
1.2 MIMO Communications.
1.3 MIMO Channel Models.
1.3.1 The Channel Model Spectrum.
1.3.2 Wideband MIMO Channel Models.
1.4 Software Defined Radio.
1.5.1 Chapter 2: Multiple Antenna Channels and Correlation.
1.5.2 Chapter 3: Correlative Models.
1.5.3 Chapter 4: Cluster Models.
1.5.4 Chapter 5: Channel Sounding.
1.5.5 Chapter 6: Experimental Validation.
1.5.6 Appendices: Background and Definitions.
Chapter 2: Multiple Antenna Channels and Correlation.
2.1 The Radio Channel: Definitions.
2.1.1 The Physical Channel.
2.1.2 The Analytical Channel.
2.2 Channel Classifications.
2.2.1 Linear Time-Invariant Channels.
2.2.2 Time-Invariant Narrowband Channels.
2.2.3 Time-Varying Wideband Channels and Bello’s Model.
2.2.4 The Tapped-Delay Line Model and the Physical Channel.
2.2.5 Narrowband Diversity Channels.
2.2.6 The Narrowband MIMO Channel.
2.2.7 The Wideband MIMO Channel.
2.2.8 The Wideband MIMO Channel Recast Using Tensors.
2.3 Summary of Channel Classifications.
2.4 Second-Order Statistics of Multiple Antenna Channels.
2.4.1 Second-Order Statistics of the Vector Channel.
2.4.2 Second-Order Statistics of the Narrowband MIMO Channel.
2.5 Second-order Statistics of the Wideband MIMO Channel.
2.5.1 Eigenvalue Decomposition of the Wideband Correlation Matrix.
2.6 Spatial Structure of Multiple Antenna Channels.
2.6.1 SIMO Channels and Beamformers.
2.6.2 MIMO Beamformers.
2.7 Summary and Discussion.
2.7.1 Channel Classifications.
2.7.2 Multi-Antenna Channels.
2.7.3 Spatial Structure and the APS.
2.8 Notes and References.
2.8.1 Channel Classifications.
2.8.2 Second-Order Statistics of Multi-Antenna Channels.
2.8.3 The Spatial Structure of Multi-Antenna Channels.
Chapter 3: Correlative Models.
3.1 Vector Channel Synthesis from the Vector Correlation Matrix.
3.2 Matrix Channel Synthesis from the Narrowband Correlation Matrix.
3.2.1 Number of Model Parameters.
3.3 One-Sided Correlation for Narrowband MIMO Channels.
3.4 The Kronecker Model.
3.4.1 The Narrowband Kronecker Model.
3.4.2 The Wideband Kronecker Model.
3.4.3 Notes on the Narrowband and Wideband Kronecker Models.
3.5 The Weichselberger Model.
3.5.1 The Vector Mode Model.
3.5.2 H-matrix From Structured Vector Modes.
3.6 The Structured Model.
3.6.1 H-Tensor Synthesis from the Wideband Correlation Tensor.
3.6.2 One-Sided Correlation for Wideband MIMO Channels..
3.6.3 Approximating the Wideband Correlation Matrix.
3.6.4 Number of Parameters Comparison.
3.7 Summary and Discussion.
3.7.1 The Kronecker Model.
3.7.2 The Weichselberger Model.
3.7.3 The Structured Model.
3.8 Notes and References.
3.8.1 Correlative Models.
3.8.2 Tensor Decomposition.
Chapter 4: Cluster Models.
4.1 What is a Cluster?
4.2 The Saleh-Valenzuela Model.
4.2.1 Model Summary.
4.2.2 Model Implementation.
4.2.3 Some Typical Parameters.
4.3 Clusters in Time and Space.
4.3.1 Azimuth, Elevation, and Delay Spreads.
4.4 The Extended Saleh-Valenzuela Model.
4.5 The COST 273 Model.
4.5.1 Generic Channel Model.
4.5.3 Receiver, Transmitter Placement.
4.5.4 COST 273 Procedure.
4.5.5 Features Not Yet Implemented and Omissions.
4.5.6 Advantages/Disadvantages: COST 273.
4.6 The Random Cluster Model (RCM).
4.6.1 General Description.
4.6.2 Determining the Environment PDF.
4.6.3 Advantages/Disadvantages: The RCM.
4.7 Summary and Discussion.
4.8 Notes and References.
Chapter 5: Channel Sounding.
5.2 The WMSDR.
5.2.3 Timing and Carrier Offsets.
5.3 Narrowband Channel Sounding.
5.3.1 Periodic Pulse Sounding.
5.3.2 Narrowband Single-Input, Single-Output Channel Sounding.
5.3.3 Narrowband MIMO Channel Sounding.
5.4 Wideband Sounding: Correlative Sounding.
5.4.2 Cross-Correlation Using the FFT.
5.4.3 Digital Matched Filters.
5.5 Wideband Sounding: Sampled Spectrum Channel Sounding.
5.6 Switched-array Architectures.
5.7 Timing and Carrier Recovery.
5.7.1 Digital Timing Recovery Methods.
5.7.2 Phase Recovery Using a Decision Directed Feedback Loop.
5.8 Summary and Discussion.
5.9 Notes and References.
Chapter 6: Experimental Verifications.
6.1 Validation Metrics.
6.1.1 Channel Capacity.
6.1.2 The Diversity and Correlation Metrics.
6.1.3 The Demmel Condition Number.
6.1.4 The Environmental Characterization Metric.
6.1.5 Correlation Matrix Difference Metric.
6.2 WMSDR Experimental Setup.
6.2.2 Measurement Description.
6.3 BYU Wideband Channel Sounder Experimental Setup.
6.3.1 BYU Transmitter Set.
6.3.2 BYU Receiver Set.
6.3.3 Measurement Description.
6.4 Experimental Results.
6.4.1 Capacity Measure: Methodology.
6.4.2 Results: MIMO APS and Spatial Structure.
6.4.3 Results: Wideband Correlation Matrices.
6.5.1 Accuracy of the Results.
6.5.2 Sources of Error.
6.6 Summary and Discussion.
6.7 Notes and References.
Appendix A: An Introduction to Tensor Algebra.
Appendix B: Proof of Theorems from Chapter 3.
Appendix C: COST 273 Model Summary.
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