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 contributionpeer-review

8 Scopus citations

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
StatePublished - 2009
Event2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 - Taipei, Taiwan, Province of China
Duration: 19 Apr 200924 Apr 2009

Publication series

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

Conference

Conference2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009
Country/TerritoryTaiwan, Province of China
CityTaipei
Period19/04/0924/04/09

Keywords

  • Constrained optimization
  • Krylov subspace
  • Proportionate adaptive algorithm

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