Dietl Linear Estimation and Detection in Krylov Subspaces

1. Auflage 2007
ISBN: 978-3-540-68479-4
Verlag: Springer Berlin Heidelberg
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 1, 232 Seiten

Reihe: Foundations in Signal Processing, Communications and Networking

This book focuses linear estimation theory, which is essential for effective signal processing. The first section offers a comprehensive overview of key methods like reduced-rank signal processing and Krylov subspace methods of numerical mathematics. Also, the relationship between statistical signal processing and numerical mathematics is presented. In the second part, the theory is applied to iterative multiuser detection receivers (Turbo equalization) which are typically desired in wireless communications systems.

Autoren/Hrsg.

Dietl, Guido K. E.

Inhaltsverzeichnis

1;Preface;7
2;Contents;9
3;List of Algorithms;12
4;List of Figures;13
5;List of Tables;16
6;1 Introduction;17
6.1;1.1 Overview and Contributions;20
6.2;1.2 Notation;23
7;Theory: Linear Estimation in Krylov Subspaces;27
7.1;2 Efficient Matrix Wiener Filter Implementations;28
7.1.1;2.1 Matrix Wiener Filter;29
7.1.2;2.2 Reduced-Complexity Matrix Wiener Filters;32
7.1.3;2.3 Reduced-Rank Matrix Wiener Filters;41
7.2;3 Block Krylov Methods;47
7.2.1;3.1 Principles and Application Areas;49
7.2.2;3.2 Block Arnoldi Algorithm;52
7.2.3;3.3 Block Lanczos Algorithm;55
7.2.4;3.4 Block Conjugate Gradient Algorithm;60
7.3;4 Reduced-Rank Matrix Wiener Filters in Krylov Subspaces;84
7.3.1;4.1 Multistage Matrix Wiener Filter;86
7.3.2;4.2 Relationship Between Multistage Matrix Wiener Filter and Krylov Subspace Methods;104
7.3.3;4.3 Krylov Subspace Based Multistage Matrix Wiener Filter Implementations;111
7.3.4;4.4 Computational Complexity Considerations;116
8;Application: Iterative Multiuser Detection;123
8.1;5 System Model for Iterative Multiuser Detection;124
8.1.1;5.1 Transmitter Structure;126
8.1.2;5.2 Channel Model;135
8.1.3;5.3 Iterative Receiver Structure;137
8.2;6 System Performance;151
8.2.1;6.1 Extrinsic Information Transfer Charts;152
8.2.2;6.2 Analysis Based on Extrinsic Information Transfer Charts;162
8.2.3;6.3 Bit Error Rate Performance Analysis;175
8.3;7 Conclusions;184
9;A Mathematical Basics;186
9.1;A.1 Inverses of Structured Matrices;186
9.2;A.2 Matrix Norms;187
9.3;A.3 Chebyshev Polynomials;188
9.4;A.4 Regularization;189
9.5;A.5 Vector Valued Function of a Matrix and Kronecker Product;192
9.6;A.6 Square Root Matrices;193
9.7;A.7 Binary Galois Field;194
10;B Derivations and Proofs;195
10.1;B.1 Inversion of Hermitian and Positive Definite Matrices;195
10.2;B.2 Real-Valued Coefficients of Krylov Subspace Polynomials;198
10.3;B.3 QR Factorization;199
10.4;B.4 Proof of Proposition 3.3;200
10.5;B.5 Correlated Subspace;203
11;C Abbreviations and Acronyms;208
12;References;211
13;Index;229