Robust Signal Processing for Wireless Communications / Edition 1

by Frank Dietrich

Overview

Optimization of adaptive signal processing algorithms for wireless communications is based on a model of the underlying propagation channel. In practice, this model is never known perfectly. For example, its parameters have to be estimated and are only known with significant errors.

In this book, a systematic treatment of this practical design problem is provided for signal processing in the physical layer with multiple antennas. The design of robust signal processing algorithms is based on a description of the errors and the uncertainties in the system's model. It applies principles of modern estimation, optimization, and information theory. Tutorial introductions to relevant literature and mathematical foundations give the necessary background and context to the reader.

The book provides detailed derivations and enlightening insights into the related technical problems covering the following topics in detail: An overview of the principles of training-based multiple-input multiple-output (MIMO) channel estimation. Robust minimax estimation of the wireless communication channel. Robust minimax prediction of the wireless communication channel based on the maximum Doppler frequency. Identification of channel and noise correlations (power delay profile, spatial and temporal correlations, spatial correlations of interference). Interpolation of band-limited auovariance sequences. Robust linear and nonlinear precoding for the multi-user downlink with multiple antennas which is based on incomplete channel state information or channel correlations (performance measures, duality, robust Tomlinson-Harashima precoding, robust vector precoding, nonlinear beamforming).

Product Details

ISBN-13:	9783540742463
Publisher:	Springer Berlin Heidelberg
Publication date:	12/14/2007
Series:	Foundations in Signal Processing, Communications and Networking , #2
Edition description:	2008
Pages:	280
Product dimensions:	6.10(w) x 9.25(h) x 0.03(d)

Table of Contents

Channel Estimation and Prediction.- Estimation of Channel and Noise Covariance Matrices.- Linear Precoding with Partial Channel State Information.- Nonlinear Precoding with Partial Channel State Information.