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

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 l 1 norm penalty function into th...

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Bibliographic Details
Published inAPCCAS 2010-2010 IEEE Asia Pacific Conference on Circuits and Systems pp. 422 - 425
Main Authors Jie Yang, Sobelman, G E
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2010
Subjects
Adaptive filters
compressive sensing
Convergence
Filtering algorithms
l 1 norm
Least Mean Square (LMS)
Least squares approximation
Signal processing algorithms
sparse signals
Speech processing
Steady-state
system identification
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Summary: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 l 1 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.
ISBN:142447454X
9781424474547
DOI:10.1109/APCCAS.2010.5774742