Partial-Update Adaptive Signal Processing

by Dogancay

• ISBN13: 9780123741967
• ISBN10: 0123741963
• Format: Hardcover
• Copyright: 2008-11-11
• Publisher: Elsevier Science

Summary

Partial-update adaptive signal processing algorithms not only permit significant complexity reduction in adaptive filter implementations, but can also improve adaptive filter performance in telecommunications applications. This book gives state-of-the-art methods for the design and development of partial-update adaptive signal processing algorithms for use in systems development. Partial-Update Adaptive Signal Processing provides a comprehensive coverage of key partial updating schemes, giving detailed information on the theory and applications of acoustic and network echo cancellation, channel equalization and multiuser detection. It also examines convergence and stability issues for partial update algorithms, providing detailed complexity analysis and a unifying treatment of partial-update techniques. Features: . Advanced analysis and design tools . Application examples illustrating the use of partial-update adaptive signal processing . MATLAB codes for developed algorithms This unique reference will be of interest to signal processing and communications engineers, researchers, R&D engineers and graduate students. "This is a very systematic and methodical treatment of an adaptive signal processing topic, of particular significance in power limited applications such as in wireless communication systems and smart ad hoc sensor networks. I am very happy to have this book on my shelf, not to gather dust, but to be consulted and used in my own research and teaching activities" - Professor A. G. Constantinides, Imperial College, London About the author: Kutluyýl Doðançay is an associate professor of Electrical Engineering at the University of South Australia. His research interests span statistical and adaptive signal processing and he serves as a consultant to defence and private industry. He was the Signal Processing and Communications Program Chair of IDC Conference 2007, and is currently chair of the IEEE South Australia Communications and Signal Processing Chapter. * Advanced analysis and design tools * Algorithm summaries in tabular format * Case studies illustrate the application of partial update adaptive signal processing * MATLAB code listings on an accompanying website

Table of Contents

Acknowledgements	p. 4
Preface	p. xi
Introduction	p. 1
Adaptive signal processing	p. 1
Examples of adaptive filtering	p. 1
Adaptive system identification	p. 2
Adaptive inverse system identification	p. 4
Raison d'etre for partial coefficient updates	p. 6
Resource constraints	p. 6
Convergence performance	p. 9
System identification with white input signal	p. 10
System identification with correlated input signal	p. 17
Approaches to partial coefficient updates	p. 25
Introduction	p. 25
Periodic partial updates	p. 26
Example 1: Convergence performance	p. 30
Example 2: Convergence difficulties	p. 31
Sequential partial updates	p. 33
Example 1: Convergence performance	p. 37
Example 2: Cyclostationary inputs	p. 38
Example 3: Instability	p. 39
Stochastic partial updates	p. 43
System identification example	p. 45
M-max updates	p. 47
Example 1: Eigenvalue spread of R[subscript M]	p. 52
Example 2: Convergence performance	p. 52
Example 3: Convergence rate and eigenvalues of R[subscript M]	p. 55
Example 4: Convergence difficulties	p. 58
Example 5: Instability	p. 59
Selective partial updates	p. 61
Constrained optimization	p. 61
Instantaneous approximation of Newton's method	p. 65
q-Norm constrained optimization	p. 67
Averaged system	p. 70
Example 1: Eigenanalysis	p. 71
Example 2: Convergence performance	p. 71
Example 3: Instability	p. 72
Set membership partial updates	p. 74
Example 1: Convergence performance	p. 78
Example 2: Instability	p. 78
Block partial updates	p. 78
Complexity considerations	p. 82
Convergence and stability analysis	p. 83
Introduction	p. 83
Convergence performance	p. 83
Steady-state analysis	p. 86
Partial-update LMS algorithms	p. 87
Partial-update NLMS algorithms	p. 92
Simulation examples for steady-state analysis	p. 97
Convergence analysis	p. 102
Partial-update LMS algorithms	p. 109
Partial-update NLMS algorithms	p. 125
Simulation examples for convergence analysis	p. 131
Partial-update adaptive filters	p. 143
Introduction	p. 143
Least-mean-square algorithm	p. 144
Partial-update LMS algorithms	p. 145
Periodic-partial-update LMS algorithm	p. 145
Sequential-partial-update LMS algorithm	p. 145
Stochastic-partial-update LMS algorithm	p. 146
M-max LMS algorithm	p. 147
Computational complexity	p. 147
Normalized least-mean-square algorithm	p. 149
Partial-update NLMS algorithms	p. 151
Periodic-partial-update NLMS algorithm	p. 151
Sequential-partial-update NLMS algorithm	p. 151
Stochastic-partial-update NLMS algorithm	p. 152
M-max NLMS algorithm	p. 152
Selective-partial-update NLMS algorithm	p. 152
Set-membership partial-update NLMS algorithm	p. 153
Computational complexity	p. 153
Affine projection algorithm	p. 156
Partial-update affine projection algorithms	p. 159
Periodic-partial-update APA	p. 159
Sequential-partial-update APA	p. 159
Stochastic-partial-update APA	p. 160
M-max APA	p. 160
Selective-partial-update APA	p. 161
Set-membership partial-update APA	p. 163
Selective-regressor APA	p. 165
Computational complexity	p. 167
Recursive least square algorithm	p. 171
Partial-update RLS algorithms	p. 176
Periodic-partial-update RLS algorithm	p. 178
Sequential-partial-update RLS algorithm	p. 179
Stochastic-partial-update RLS algorithm	p. 179
Selective-partial-update RLS algorithm	p. 179
Set-membership partial-update RLS algorithm	p. 181
Partial-update RLS simulations	p. 182
Computational complexity	p. 183
Transform-domain least-mean-square algorithm	p. 187
Power normalization	p. 195
Comparison of power normalization algorithms	p. 199
Partial-update transform-domain LMS algorithms	p. 201
Periodic-partial-update transform-domain LMS algorithm	p. 201
Sequential-partial-update transform-domain LMS algorithm	p. 201
Stochastic-partial-update transform-domain LMS algorithm	p. 202
M-max transform-domain LMS algorithm	p. 203
Computational complexity	p. 204
Generalized-subband-decomposition least-mean-square algorithm	p. 207
Relationship between GSD-LMS coefficients and equivalent time-domain response	p. 211
Eigenvalue spread of GSD input correlation matrix	p. 213
Partial-update GSD-LMS algorithms	p. 216
Periodic-partial-update GSD-LMS algorithm	p. 216
Sequential-partial-update GSD-LMS algorithm	p. 217
Stochastic-partial-update GSD-LMS algorithm	p. 218
M-max GSD-LMS algorithm	p. 219
Computational complexity	p. 220
Simulation examples: Channel equalization	p. 222
Selected applications	p. 233
Introduction	p. 233
Acoustic echo cancellation	p. 233
Network echo cancellation	p. 236
PNLMS and [mu]-law PNLMS with selective partial updates	p. 239
Blind channel equalization	p. 245
Normalized CMA	p. 249
Selective-partial-update NCMA	p. 249
Simulation examples	p. 251
Blind adaptive linear multiuser detection	p. 253
MUD in synchronous DS-CDMA	p. 256
Blind multiuser NLMS algorithm	p. 259
Selective-partial-update NLMS for blind multiuser detection	p. 260
Simulation examples	p. 262
Overview of fast sorting algorithms	p. 265
Introduction	p. 265
Running min/max and sorting algorithms	p. 265
Divide-and-conquer approaches	p. 265
Maxline algorithm	p. 268
The Gil-Werman algorithm	p. 268
Sortline algorithm	p. 270
Heapsort algorithm	p. 270
References	p. 273
Index	p. 279
Table of Contents provided by Ingram. All Rights Reserved.