Improved adaptive sparse channel estimation based on the least mean square algorithm

Guan Gui, Wei Peng, Fumiyuki Adachi

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

73 Citations (Scopus)

Abstract

Least mean square (LMS) based adaptive algorithms have been attracted much attention since their low computational complexity and robust recovery capability. To exploit the channel sparsity, LMS-based adaptive sparse channel estimation methods, e.g., zero-attracting LMS (ZA-LMS), reweighted zero-attracting LMS (RZA-LMS) and Lp -norm sparse LMS (LP-LMS), have also been proposed. To take full advantage of channel sparsity, in this paper, we propose several improved adaptive sparse channel estimation methods using Lp -norm normalized LMS (LP-NLMS) and L0 -norm normalized LMS (L0-NLMS). Comparing with previous methods, effectiveness of the proposed methods is confirmed by computer simulations.

Original languageEnglish
Title of host publication2013 IEEE Wireless Communications and Networking Conference, WCNC 2013
Pages3105-3109
Number of pages5
DOIs
Publication statusPublished - 2013 Aug 21
Event2013 IEEE Wireless Communications and Networking Conference, WCNC 2013 - Shanghai, China
Duration: 2013 Apr 72013 Apr 10

Publication series

NameIEEE Wireless Communications and Networking Conference, WCNC
ISSN (Print)1525-3511

Other

Other2013 IEEE Wireless Communications and Networking Conference, WCNC 2013
CountryChina
CityShanghai
Period13/4/713/4/10

Keywords

  • adaptive sparse channel estimation
  • compressive sensing (CS)
  • least mean square (LMS)
  • normalized LMS (NLMS)
  • sparse penalty

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Gui, G., Peng, W., & Adachi, F. (2013). Improved adaptive sparse channel estimation based on the least mean square algorithm. In 2013 IEEE Wireless Communications and Networking Conference, WCNC 2013 (pp. 3105-3109). [6555058] (IEEE Wireless Communications and Networking Conference, WCNC). https://doi.org/10.1109/WCNC.2013.6555058