Design Analysis and Implementation To order this title, and for more information, click here
By Kutluyil Dogancay, University of South Australia
Description 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:
Kutluyil Dogan ay
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.
Audience
Signal processing systems engineers,R&D engineers,university applied researchers and postgraduate students.
Contents TABLE OF CONTENTS
1. Introduction
1.1 Adaptive Signal Processing
1.2 Examples of Adaptive Filtering
1.2.1 Adaptive System Identification
1.2.2 Adaptive Inverse System Identification
1.3 Raison D'etre for Partial Coefficient Updates
1.3.1 Resource Constraints
1.3.2 Convergence
Performance
1.3.3 System Identification with White Input Signal
1.3.4 System Identification with Correlated Input Signal
2. Approaches
to Partial Coefficient Updates
2.1 Introduction
2.2 Periodic Partial Updates
2.2.1 Example 1: Convergence Performance
2.2.2 Example
2: Convergence Difficulties
2.3 Sequential Partial Updates
2.3.1 Example 1: Convergence Performance
2.3.2 Example 2: Cyclostationary
Inputs
2.3.3 Example 3: Instability
2.4 Stochastic Partial Updates
2.4.1 System Identification Example
2.5 M-Max Updates
2.5.1 Example
1: Eigenvalue Spread of R_M
2.5.2 Example 2: Convergence Performance
2.5.3 Example 3: Convergence Rate and Eigenvalues of R_M
2.5.4
Example 4: Convergence Difficulties
2.5.5 Example 5: Instability
2.6 Selective Partial Updates
2.6.1 Constrained Optimization
2.6.2
Instantaneous Approximation of Newton's Method
2.6.3 q-Norm Constrained Optimization
2.6.4 Averaged System
2.6.5 Example 1: Eigenanalysis
2.6.6 Example 2: Convergence Performance
2.6.7 Example 3: Instability
2.7 Set Membership Partial Updates
2.7.1 Example 1: Convergence
Performance
2.7.2 Example 2: Instability
2.8 Block Partial Updates
2.9 Complexity Considerations
3. Convergence and Stability Analysis
3.1 Introduction
3.2 Convergence Performance
3.3 Steady-State Analysis
3.3.1 Partial-Update LMS Algorithms
Periodic partial updates
Sequential partial updates
Stochastic partial updates
M-max partial updates
3.3.2 Partial-Update NLMS Algorithms
3.3.3 Simulation
Examples for Steady-State Analysis
3.4 Convergence Analysis
3.4.1 Partial-Update LMS Algorithms
Vectorization
Steady-State Analysis
Mean-Square Stability
Mean Stability
3.4.2 Partial-Update NLMS Algorithms
3.4.3 Simulation Examples for Convergence Analysis
4.
Partial-Update Adaptive Filters
4.1 Introduction
4.2 Least-Mean-Square Algorithm
4.3 Partial-Update LMS Algorithms
4.3.1 Periodic-Partial-Update
LMS Algorithm
4.3.2 Sequential-Partial-Update LMS Algorithm
4.3.3 Stochastic-Partial-Update LMS Algorithm
4.3.4 M-max LMS Algorithm
4.3.5 Computational Complexity
4.4 Normalized Least-Mean-Square Algorithm
4.5 Partial-Update NLMS Algorithms
4.5.1 Periodic-Partial-Update
NLMS Algorithm
4.5.2 Sequential-Partial-Update NLMS Algorithm
4.5.3 Stochastic-Partial-Update NLMS Algorithm
4.5.4 M-max NLMS Algorithm
4.5.5 Selective-Partial-Update NLMS Algorithm
4.5.6 Set-Membership Partial-Update NLMS Algorithm
4.5.7 Computational Complexity
4.6
Affine Projection Algorithm
4.7 Partial-Update Affine Projection Algorithms
4.7.1 Periodic-Partial-Update APA
4.7.2 Sequential-Partial-Update
APA
4.7.3 Stochastic-Partial-Update APA
4.7.4 M-max APA
4.7.5 Selective-Partial-Update APA
4.7.6 Set-Membership Partial-Update APA
4.7.7 Selective-Regressor APA
4.7.8 Computational Complexity
4.8 Recursive Least Squares Algorithm
4.9 Partial-Update RLS Algorithms
4.9.1 Periodic-Partial-Update RLS Algorithm
4.9.2 Sequential-Partial-Update RLS Algorithm
4.9.3 Stochastic-Partial-Update RLS Algorithm
4.9.4 Selective-Partial-Update RLS Algorithm
4.9.5 Set-Membership Partial-Update RLS Algorithm
4.9.6 Partial-Update RLS Simulations
4.9.7 Computational Complexity
4.10 Transform-Domain Least-Mean-Square Algorithm
4.10.1 Power Normalization
4.10.2 Comparison of
Power Normalization Algorithms
4.11 Partial-Update Transform-Domain LMS Algorithms
4.11.1 Periodic-Partial-Update Transform-Domain
LMS Algorithm
4.11.2 Sequential-Partial-Update Transform-Domain LMS Algorithm
4.11.3 Stochastic-Partial-Update Transform-Domain LMS
Algorithm
4.11.4 M-max Transform-Domain LMS Algorithm
4.11.5 Computational Complexity
4.12 Generalized-Subband-Decomposition Least-Mean-Square
Algorithm
4.12.1 Relationship Between GSD-LMS Coefficients and Equivalent Time Domain Response
4.12.2 Eigenvalue Spread of GSD Input
Correlation Matrix
4.13 Partial-Update GSD-LMS Algorithms
4.13.1 Periodic-Partial-Update GSD-LMS Algorithm
4.13.2 Sequential-Partial-Update
GSD-LMS Algorithm
4.13.3 Stochastic-Partial-Update GSD-LMS Algorithm
4.13.4 M-max GSD-LMS Algorithm
4.13.5 Computational Complexity
4.14 Simulation Examples: Channel Equalization
5. Selected Applications
5.1 Introduction
5.2 Acoustic Echo Cancellation
5.3 Network
Echo Cancellation
5.3.1 PNLMS and mu-Law PNLMS with Selective Partial Updates
PNLMS
mu-Law PNLMS
Selective Partial Updates
Simulation
Examples
5.4 Blind Channel Equalization
5.4.1 Normalized CMA
5.4.2 Selective-Partial-Update NCMA
5.4.3 Simulation Examples
5.5 Blind
Adaptive Linear Multiuser Detection
5.5.1 MUD in Synchronous DS-CDMA
5.5.2 Blind Multiuser NLMS Algorithm
5.5.3 Selective-Partial-Update
NLMS for Blind Multiuser Detection
5.5.4 Simulation Examples
A. Overview of Fast Sorting Algorithms
A.1 Introduction
A.2 Running
Min/Max and Sorting Algorithms
A.2.1 Divide-and-Conquer Approaches
A.2.2 Maxline Algorithm
A.2.3 The Gil-Werman Algorithm
A.2.4 Sortline
Algorithm
A.3 Heapsort Algorithm
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