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

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

9 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
CountryMalaysia
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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