Sparse LMS with segment zero attractors for adaptive estimation of sparse signals

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

10 Scopus citations

Abstract

Adaptive sparse signal estimation is needed for obtaining accurate channel knowledge in communication systems where the system response can be assumed to contain many near-zero coefficients. For sparse filter design, the zero-attracting LMS (ZA-LMS) incorporates the l1 norm penalty function into the quadratic LMS cost function to promote the sparseness during the adaptation process. The reweighted ZA-LMS (RZA-LMS) is developed using reweighted zero attractors with better performance. In this paper, we propose two new sparse LMS algorithms with segment zero attractors, referred as Segment RZA-LMS and Discrete Segment RZA-LMS. The Segment RZA-LMS outperforms RZA-LMS by using a piece-wise approximation of the reciprocal in the iterative algorithm of RZA-LMS. The Discrete Segment RZA-LMS is further developed to achieve faster convergence speed and lower steady state error performance than Segment RZA-LMS.

Original languageEnglish (US)
Title of host publicationProceedings of the 2010 Asia Pacific Conference on Circuit and System, APCCAS 2010
Pages422-425
Number of pages4
DOIs
StatePublished - Dec 1 2010
Event2010 Asia Pacific Conference on Circuit and System, APCCAS 2010 - Kuala Lumpur, Malaysia
Duration: Dec 6 2010Dec 9 2010

Publication series

NameIEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCAS

Other

Other2010 Asia Pacific Conference on Circuit and System, APCCAS 2010
Country/TerritoryMalaysia
CityKuala Lumpur
Period12/6/1012/9/10

Keywords

  • Adaptive filters
  • Least Mean Square (LMS)
  • compressive sensing
  • l norm
  • sparse signals
  • system identification

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