Serguei Primak, Valeri Kontorovitch

Wireless Multi-Antenna Channels (eBook, ePUB)

Modeling and Simulation

• Format: ePub

Produktbeschreibung

This book offers a practical guide on how to use and apply channel models for system evaluation In this book, the authors focus on modeling and simulation of multiple antennas channels, including multiple input multiple output (MIMO) communication channels, and the impact of such models on channel estimation and system performance. Both narrowband and wideband models are addressed. Furthermore, the book covers topics related to modeling of MIMO channel, their numerical simulation, estimation and prediction, as well as applications to receive diversity, capacity and space-time coding techniques. Key Features: * Contains significant background material, as well as novel research coverage, which make the book suitable for both graduate students and researchers * Addresses issues such as key-hole, correlated and non i.i.d. channels in the frame of the Generalized Gaussian approach * Provides a unique treatment of generalized Gaussian channels and orthogonal channel representation * Reviews different interpretations of scattering environment, including geometrical models * Focuses on the analytical techniques which give a good insight into the design of systems on higher levels * Describes a number of numerical simulators demonstrating the practical use of this material. * Includes an accompanying website containing additional materials and practical examples for self-study This book will be of interest to researchers, engineers, lecturers, and graduate students.

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Produktdetails

• Verlag: Polity
• Seitenzahl: 288
• Erscheinungstermin: 14. Oktober 2011
• Englisch
• ISBN-13: 9781119960867
• Artikelnr.: 37352617

Autorenporträt

Professor Serguei L. Primak, The University of Western Ontario, Canada Professor Primak is an Associate Professor at the University of the Western Ontario, Canada. His main areas of interest include modelling and performance evaluation of MIMO systems, Markov processes, non-Gaussian random processes and communications aspects of robotic assisted telesurgery. He has co-authored a book "Stochastic Methods and their Applications to Communications: Stochastic Differential Equations Approach", Wiley, 2004. Professor Valeri Kontorovich, CINVESTAV-IPN, Mexico Professor Kontorovich is a Professor at the CINVESTAV-IPN, Mexico. His main areas of interest include modelling and performance evaluation of MIMO systems, Markov processes, non-Gaussian random processes, fractal, electromagnetic compatibility and other related topics. Professor Kontorovich has co-authored a book "Stochastic Methods and their Applications to Communications: Stochastic Differential Equations Approach", Wiley, 2004, and has co-authored 4 other books (In Russian) and a large number of publications in the field of communications.

Inhaltsangabe

- About the Series Editors
1 Introduction
1.1 General remarks
1.2 Signals, interference, and types of parallel channels
2 Four-parametric Model of a SISO Channel
2.1 Multipath propagation
2.2 Random walk approach to modeling of scattering field
2.2.1 Random walk in two dimensions as a model for scattering field
2.2.2 Phase distribution and scattering strength
2.2.3 Distribution of intensity
2.2.4 Distribution of the random phase
2.3 Gaussian case
2.3.1 Four-parametric distribution family
2.3.2 Distribution of the magnitude
2.3.3 Distribution of the phase
2.3.4 Moment generating function, moments and cumulants of four-parametric distribution
2.3.5 Some aspects of multiple scattering propagation
3 Models of MIMO Channels
3.1 General classification of MIMO channel models
3.2 Physical models
3.2.1 Deterministic models
3.2.2 Geometry-based stochastic models
3.3 Analytical models
3.3.1 Channel matrix model
3.4 Geometrical phenomenological models
3.4.1 Scattering from rough surfaces
3.5 On the role of trigonometric polynomials in analysis and simulation of MIMO channels
3.5.1 Measures of dependency
3.5.2 Non-negative trigonometric polynomials and their use in estimation of AoD and AoA distribution
3.5.3 Approximation of marginal PDF using non-negative polynomials
3.6 Canonical expansions of bivariate distributions and the structure MIMO channel covariance matrix
3.6.1 Canonical variables and expansion
3.6.2 General structure of the full covariance matrix
3.6.3 Relationship to other models
3.7 Bivariate von Mises distribution with correlated transmit and receive sides
3.7.1 Single cluster scenario
3.7.2 Multiple clusters scenario
3.8 Bivariate uniform distributions
3.8.1 Harmonic coupling
3.8.2 Markov-type bivariate density
3.9 Analytical expression for the diversity measure of an antenna array
3.9.1 Relation of the shape of the spatial covariance function to trigonometric moments
3.9.2 Approximation of the diversity measure for a large number of antennas
3.9.3 Examples
3.9.4 Leading term analysis of degrees of freedom
3.10 Effect of AoA/AoD dependency on the SDoF
3.11 Space-time covariance function
3.11.1 Basic equation
3.11.2 Approximations
3.12 Examples: Synthetic data and uniform linear array
3.13 Approximation of a matrix by a Toeplitz matrix
3.14 Asymptotic expansions of diversity measure
3.15 Distributed scattering model
4 Modeling of Wideband Multiple Channels
4.1 Standard models of channels
4.1.1 COST 259/273
4.1.2 3GPP SCM
4.1.3 WINNER channel models
4.2 MDPSS based wideband channel simulator
4.2.1 Geometry of the problem
4.2.2 Statistical description
4.2.3 Multi-cluster environment
4.2.4 Simulation of dynamically changing environment
4.3 Measurement based simulator
4.4 Examples
4.4.1 Two cluster model
4.4.2 Environment specified by joint AoA/AoD/ToA distribution
4.4.3 Measurement based simulator
4.5 Appendix A. Simulation parameters
5 Capacity of Communication Channels
5.1 Introduction
5.2 Ergodic capacity of MIMO channel
5.2.1 Capacity of a constant (static) MIMO channel
5.2.2 Alternative normalization 102
5.2.3 Capacity of a static MIMO channel under different operation modes
5.2.4 Ergodic capacity of a random Channel
5.2.5 Ergodic capacity of MIMO channels
5.2.6 Asymptotic analysis of capacity and outage capacity
5.3 Effects of MIMO models and their parameters on the predicted capacity of MIMO channels
5.3.1 Channel estimation and effective SNR
5.3.2 Achievable rates in Rayleigh channels with partial CSI
5.3.3 Examples
5.4 Time evolution of capacity
5.4.1 Time evolution of capacity in SISO channels
5.4.2 SISO channel capacity evolution
5.5 Sparse MIMO channel model
5.6 Statistical properties of capacity
5.6.1 Some mathematical expressions
5.7 Time-varying statistics
5.7.1 Unordered eigenvalues
5.7.2 Single cluster capacity LCR and AFD
5.7.3 Approximation of multi-cluster capacity LCR and AFD
5.7.4 Statistical simulation results
6 Estimation and Prediction of Communication Channels
6.1 General remarks on estimation of time-varying channels
6.2 Velocity estimation
6.2.1 Velocity estimation based on the covariance function approximation
6.2.2 Estimation based on reflection coefficients
6.3 K-factor estimation
6.3.1 Moment matching estimation
6.3.2 I/Q based methods
6.4 Estimation of four-parametric distributions
6.5 Estimation of narrowband MIMO channels
6.5.1 Superimposed pilot estimation scheme
6.5.2 LS estimation
6.5.3 Scaled least-square (SLS) estimation
6.5.4 Minimum MSE
6.5.5 Relaxed MMSE estimators
6.6 Using frames for channel state estimation
6.6.1 Properties of the spectrum of a mobile channel
6.6.2 Frames based on DPSS
6.6.3 Discrete prolate spheroidal sequences
6.6.4 Numerical simulation
7 Effects of Prediction and Estimation Errors on Performance of Communication Systems
7.1 Kolmogorov-Szeg¨o-Krein formula
7.2 Prediction error for different antennas and scattering characteristics
7.2.1 SISO channel
7.2.2 SIMO channel
7.2.3 MISO channel
7.2.4 MIMO channel
7.3 Conclusions
7.4 Eigenstructure of two cluster correlation matrix
7.5 Preliminary comments on finite horizon prediction
7.6 SISO channel prediction
7.6.1 Wiener filter
7.6.2 Single pilot prediction in a two cluster environment
7.6.3 Single cluster prediction with multiple past samples
7.6.4 Two cluster prediction with multiple past samples
7.6.5 Role of oversampling
7.7 What is the narrowband signal for a rectangular array?
7.8 Prediction using the UIU model
7.8.1 Separable covariance matrix
7.8.2 1 × 2 unseparable example
7.8.3 Large number of antennas: no noise
7.8.4 Large number of antennas: estimation in noise
7.8.5 Effects of the number of antennas, scattering geometry, and observation time on the quality of prediction
7.9 Numerical simulations
7.9.1 SISO channel single cluster
7.9.2 Two cluster prediction
7.10 Wiener estimator
7.11 Approximation of the Wiener filter
7.11.1 Zero order approximation
7.11.2 Perturbation solution
7.12 Element-wise prediction of separable process
7.13 Effect of prediction and estimation errors on capacity calculations
7.14 Channel estimation and effective SNR
7.14.1 System Model
7.14.2 Estimation error
7.14.3 Effective SNR
7.15 Achievable rates in Rayleigh channels with partial CSI
7.15.1 No CSI at the transmitter
7.15.2 Partial CSI at the transmitter
7.15.3 Optimization of the frame length
7.16 Examples
7.16.1 P(0,0) Estimation
7.16.2 Effect of non-uniform scattering
7.17 Conclusions
7.18 Appendix A. Szeg¨o summation formula
7.19 Appendix B. Matrix inversion Lemma
8 Coding, Modulation, and Signaling over Multiple Channels
8.1 Signal constellations and their characteristics
8.2 Performance of OSTBC in generalized Gaussian channels and hardening effect
8.2.1 Introduction
8.2.2 Channel representation
8.2.3 Probability of error
8.2.4 Hardening effect
8.3 Differential time-space modulation (DTSM) and an effective solution for the non-coherent MIMO channel
8.3.1 Introduction to DTSM
8.3.2 Performance of autocorrelation receiver of DSTM in generalized Gaussian channels
8.3.3 Comments on MIMO channel model
8.3.4 Differential space-time modulation
8.3.5 Performance of DTSM
8.3.6 Numerical results and discussions
8.3.7 Some comments
- Bibliography
- Index