Zero-attracting variable-step-size least mean square algorithms for adaptive sparse channel estimation

Summary Recently, sparsity‐aware least mean square (LMS) algorithms have been proposed to improve the performance of the standard LMS algorithm for various sparse signals, such as the well‐known zero‐attracting LMS (ZA‐LMS) algorithm and its reweighted ZA‐LMS (RZA‐LMS) algorithm. To utilize the spar...

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Bibliographic Details
Published in:International Journal of Adaptive Control and Signal Processing Vol. 29; no. 9; pp. 1189 - 1206
Main Authors: Li, Yingsong, Hamamura, Masanori
Format: Journal Article
Language:English
Published: Bognor Regis Blackwell Publishing Ltd 01.09.2015
Wiley
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Subjects:
Adaptive algorithms
adaptive filtering
Algorithms
Channels
compressed sensing
Convergence
Dealing
l1-penalized least mean square
least mean square
Least mean squares algorithm
sparse channel estimation
variable-step-size
Wireless communication
zero-point attracting
ISSN:0890-6327, 1099-1115
Online Access:Get full text
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Summary:Summary Recently, sparsity‐aware least mean square (LMS) algorithms have been proposed to improve the performance of the standard LMS algorithm for various sparse signals, such as the well‐known zero‐attracting LMS (ZA‐LMS) algorithm and its reweighted ZA‐LMS (RZA‐LMS) algorithm. To utilize the sparsity of the channels in wireless communication and one of the inherent advantages of the RZA‐LMS algorithm, we propose an adaptive reweighted zero‐attracting sigmoid functioned variable‐step‐size LMS (ARZA‐SVSS‐LMS) algorithm by the use of variable‐step‐size techniques and parameter adjustment method. As a result, the proposed ARZA‐SVSS‐LMS algorithm can achieve faster convergence speed and better steady‐state performance, which are verified in a sparse channel and compared with those of other popular LMS algorithms. The simulation results show that the proposed ARZA‐SVSS‐LMS algorithm outperforms the standard LMS algorithm and the previously proposed sparsity‐aware algorithms for dealing with sparse signals. Copyright © 2015 John Wiley & Sons, Ltd.
Bibliography:istex:032242C561BC75477986702B824BC251AC6556BA
ArticleID:ACS2536
ark:/67375/WNG-N6TS216F-K
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0890-6327
1099-1115
DOI:10.1002/acs.2536