Partial update least-square adaptive filtering /

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Adaptive filters play an important role in the fields related to digital signal processing and communication, such as system identification, noise cancellation, channel equalization, and beamforming. In practical applications, the computational complexity of an adaptive filter is an important consid...

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Bibliographic Details
Main Authors: Xie, Bei (Author), Bose, Tamal (Author)
Format: eBook
Language:English
Published: San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, 2014.
Series:Synthesis digital library of engineering and computer science.
Synthesis lectures on communications ; # 10.
Subjects:
Adaptive filters > Design and construction.
Least squares.
adaptive filter
CG
EDS
LSCMA
partial update
RLS
Online Access:Abstract with links to full text
Table of Contents:
  • 1. Introduction
  • 1.1 Motivation
  • 1.2 Problem statement
  • 1.3 Organization of the monograph
  • 2. Background
  • 2.1 Basic adaptive filter models
  • 2.2 Adaptive filter models
  • 2.2.1 System identification
  • 2.2.2 Channel equalization
  • 2.3 Existing work on partial update adaptive filters
  • 2.4 Basic partial update methods
  • 2.4.1 Periodic partial update method
  • 2.4.2 Sequential partial update method
  • 2.4.3 Stochastic partial update method
  • 2.4.4 MMax method
  • 3. Partial update CMA-based algorithms for adaptive filtering
  • 3.1 Motivation
  • 3.2 Review of constant modulus algorithms
  • 3.3 Partial update constant modulus algorithms
  • 3.3.1 Partial update CMA
  • 3.3.2 Partial update NCMA
  • 3.3.3 Partial update LSCMA
  • 3.4 Algorithm analysis for a time-invariant system
  • 3.4.1 Steady-state performance of partial update SDCMA
  • 3.4.2 Steady-state performance of partial update dynamic LSCMA
  • 3.4.3 Complexity of the PU SDCMA and LSCMA
  • 3.5 Simulation, a simple FIR channel
  • 3.5.1 Convergence performance
  • 3.5.2 Steady-state performance
  • 3.5.3 Complexity
  • 3.6 Algorithm analysis for a time-varying system
  • 3.6.1 Algorithm analysis of CMA1-2 and NCMA for a time-varying system
  • 3.6.2 Algorithm analysis of LSCMA for a time-varying system
  • 3.6.3 Simulation
  • 3.7 Conclusion
  • 4. Partial-update CG algorithms for adaptive filtering
  • 4.1 Review of conjugate gradient algorithm
  • 4.2 Partial-update CG
  • 4.3 Steady-state performance of partial-update CG for a time-invariant system
  • 4.4 Steady-state performance of partial-update CG for a time-varying system
  • 4.5 Simulations
  • 4.5.1 Performance of different PU CG algorithms
  • 4.5.2 Tracking performance of the PU CG using the first-order Markov model
  • 4.6 Conclusion
  • 5. Partial-update EDS algorithms for adaptive filtering
  • 5.1 Motivation
  • 5.2 Review of Euclidean direction search algorithm
  • 5.3 Partial update EDS
  • 5.4 Performance of the partial-update EDS in a time-invariant system
  • 5.5 Performance of the partial-update EDS in a time-varying system
  • 5.6 Simulations
  • 5.6.1 Performance of the PU EDS in a time-invariant system
  • 5.6.2 Tracking performance of the PU EDS using the first-order Markov model
  • 5.6.3 Performance comparison of the PU EDS with EDS, PU RLS, RLS, PU CG, and CG
  • 5.7 Conclusion
  • 6. Special applications of partial-update adaptive filters
  • 6.1 Application in detecting GSM signals in a local GSM system
  • 6.2 Application in image compression and classification
  • 6.2.1 Simulations
  • 6.3 Conclusion
  • Bibliography
  • Authors' biographies.

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