Adaptive sparse channel estimation using re-weighted zero-attracting normalized least mean fourth

Guan Gui, Abolfazl Mehbodniya, Fumiyuki Adachi

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

12 Citations (Scopus)

Abstract

Accurate channel estimation problem is one of the key technical issues in broadband wireless communications. Standard normalized least mean fourth (NLMF) algorithm was applied to adaptive channel estimation (ACE). Since the channel is often described by sparse channel model, such sparsity could be exploited and then estimation performance could be improved by adaptive sparse channel estimation (ASCE) methods using zero-attracting normalized least mean fourth (ZA-NLMF) algorithm. However, this algorithm cannot exploit channel sparsity efficiently. By virtual of geometrical figures, we explain the reason why ℓ1-norm sparse constraint penalizes channel coefficients uniformly. In this paper, we propose a novel ASCE method using re-weighted zero-attracting NLMF (RZA-NLMF) algorithm. Simulation results show that the proposed ASCE method achieves better estimation performance than the conventional one.

Original languageEnglish
Title of host publication2013 IEEE/CIC International Conference on Communications in China, ICCC 2013
Pages368-373
Number of pages6
DOIs
Publication statusPublished - 2013 Dec 1
Event2013 IEEE/CIC International Conference on Communications in China, ICCC 2013 - Xi'an, China
Duration: 2013 Aug 122013 Aug 14

Publication series

Name2013 IEEE/CIC International Conference on Communications in China, ICCC 2013

Other

Other2013 IEEE/CIC International Conference on Communications in China, ICCC 2013
CountryChina
CityXi'an
Period13/8/1213/8/14

Keywords

  • adaptive sparse channel estimation (ASCE)
  • normalized LMF (NLMF)
  • re-weighted zero-attracting NLMF (RZA-NLMF)

ASJC Scopus subject areas

  • Computer Networks and Communications

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  • Cite this

    Gui, G., Mehbodniya, A., & Adachi, F. (2013). Adaptive sparse channel estimation using re-weighted zero-attracting normalized least mean fourth. In 2013 IEEE/CIC International Conference on Communications in China, ICCC 2013 (pp. 368-373). [6671144] (2013 IEEE/CIC International Conference on Communications in China, ICCC 2013). https://doi.org/10.1109/ICCChina.2013.6671144