Least mean square/fourth algorithm for adaptive sparse channel estimation

Guan Gui, Abolfazl Mehbod Niya, Fumiyuki Adachi

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

48 Citations (Scopus)

Abstract

Broadband signal transmission over frequency-selective fading channel often requires accurate channel state information at receiver. One of the most attracting adaptive channel estimation (ACE) methods is least mean square (LMS) algorithm. However, its performance is often degraded by random scaling of input training signal. To overcome this degradation, in this paper we consider the use of standard least mean square/fourth (LMS/F) algorithm. Since the broadband channel is often described by sparse channel model, such sparsity could be exploited as prior information. First, we propose an adaptive sparse channel estimation (ASCE) method with zero-attracting LMS/F (ZA-LMS/F) algorithm by introducing an ℓ1-norm sparse constraint into the cost function. Then, to exploit the sparsity more effectively, an improved ASCE with reweighted zero-attracting LMS/F (RZA-LMS/F) algorithm is proposed. For different channel sparsity, we propose a Monte Carlo method for a regularization parameter selection in RA-LMS/F and RZA-LMS/F to achieve better steady-state estimation performance. Simulation results show that the proposed ASCE methods achieve better estimation performance than the conventional one.

Original languageEnglish
Title of host publication2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, PIMRC 2013
Pages296-300
Number of pages5
DOIs
Publication statusPublished - 2013 Dec 1
Event2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, PIMRC 2013 - London, United Kingdom
Duration: 2013 Sep 82013 Sep 11

Publication series

NameIEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC

Other

Other2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, PIMRC 2013
CountryUnited Kingdom
CityLondon
Period13/9/813/9/11

Keywords

  • Adaptive sparse channel estimation (ASCE)
  • Least mean square fourth (LMS/F)
  • Re-weighted zero-attracting least mean square/fourth (RZA-LMS/F)
  • Zero-attracting least mean square/fourth (ZA-LMS/F)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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