Proportionate adaptive algorithm for nonsparse systems based on krylov subspace and constrained optimization

Masahiro Yukawa, Wolfgang Utschick

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

In this paper, we propose an efficient design of proportionality factors in the recently established algorithm named Krylovproportionate normalized least mean-square (KPNLMS), which is an extention of the PNLMS algorithm to nonsparse (or dispersive) unknown systems by means of a Krylov subspace. The designing task takes a form of minimizing the number of iterations that is needed for an upper bound of the system mismatch to reach a specified target value. The minimization is performed under several constraints related to numerical stability, computational requirements, and nonnegativity, and its closed-form solution is derived. Numerical examples demonstrate that the proposed design significantly reduces the number of iterations needed to achieve target values of system mismatch especially when a low level of system mismatch is required.

Original languageEnglish
Title of host publication2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009
Pages3121-3124
Number of pages4
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 - Taipei, Taiwan, Province of China
Duration: 2009 Apr 192009 Apr 24

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009
Country/TerritoryTaiwan, Province of China
CityTaipei
Period09/4/1909/4/24

Keywords

  • Constrained optimization
  • Krylov subspace
  • Proportionate adaptive algorithm

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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