Regularization selection method for LMS-type sparse multipath channel estimation

Zhengxing Huang, Guan Gui, Anmin Huang, Dong Xiang, Fumiyki Adachi

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

11 Citations (Scopus)

Abstract

Least mean square (LMS)-type adaptive sparse algorithms have been attracting much attention on sparse multipath channel estimation (SMPC) due to their two advantages: low computational complexity and reliability. By introducing ℓ1 -norm sparse constraint function into LMS algorithm, both zero-attracting least mean square (ZA-LMS) and reweighted zero-attracting least mean square (RZA-LMS) have been proposed for SMPC. It is well known that the performance of the SMPC is decided by regularization parameter which balances channel estimation error and sparse penalty strength. However, optimal regularization parameter selection has not yet considered in the two proposed algorithms. Based on the compressive sensing theory, in this paper, we explain the mathematical relationship between Lasso and LMS-type adaptive sparse algorithms. Later, an approximate optimal regulation parameter selection method is proposed for ZA-LMS and RZA-LMS, respectively. Monte Carlo based computer simulations are presented to show the effectiveness of our propose method.

Original languageEnglish
Title of host publication2013 19th Asia-Pacific Conference on Communications, APCC 2013
PublisherIEEE Computer Society
Pages649-654
Number of pages6
ISBN (Print)9781467360500
DOIs
Publication statusPublished - 2013 Jan 1
Event2013 19th Asia-Pacific Conference on Communications, APCC 2013 - Denpasar, Indonesia
Duration: 2013 Aug 292013 Aug 31

Publication series

Name2013 19th Asia-Pacific Conference on Communications, APCC 2013

Other

Other2013 19th Asia-Pacific Conference on Communications, APCC 2013
CountryIndonesia
CityDenpasar
Period13/8/2913/8/31

Keywords

  • adaptive sparse channel estimation
  • least mean square (LMS)
  • regularization parameter selection
  • reweighted zero-attracting least mean square (RZA-LMS)
  • zero-attracting least mean square (ZA-LMS)

ASJC Scopus subject areas

  • Computer Networks and Communications

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