Extra gain: Improved sparse channel estimation using reweighted ℓ1-norm penalized LMS/F algorithm

Guan Gui, Li Xu, Fumiyuki Adachi

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

5 Citations (Scopus)

Abstract

The channel estimation is one of important techniques to ensure reliable broadband signal transmission. Broadband channels are often modeled as a sparse channel. Comparing with traditional dense-assumption based linear channel estimation methods, e.g., least mean square/fourth (LMS/F) algorithm, exploiting sparse structure information can get extra performance gain. By introducing ℓ1-norm penalty, two sparse LMS/F algorithms, (zero-attracting LMSF, ZA-LMS/F and reweighted ZA-LMSF, RZA-LMSF), have been proposed [1]. Motivated by existing reweighted ℓ1-norm (RL1) sparse algorithm in compressive sensing [2], we propose an improved channel estimation method using RL1 sparse penalized LMS/F (RL1-LMS/F) algorithm to exploit more efficient sparse structure information. First, updating equation of RL1-LMS/F is derived. Second, we compare their sparse penalize strength via figure example. Finally, computer simulation results are given to validate the superiority of proposed method over than conventional two methods.

Original languageEnglish
Title of host publication2014 IEEE/CIC International Conference on Communications in China, ICCC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages370-374
Number of pages5
ISBN (Electronic)9781479941469
DOIs
Publication statusPublished - 2015 Jan 12
Event2014 IEEE/CIC International Conference on Communications in China, ICCC 2014 - Shanghai, China
Duration: 2014 Oct 132014 Oct 15

Publication series

Name2014 IEEE/CIC International Conference on Communications in China, ICCC 2014

Other

Other2014 IEEE/CIC International Conference on Communications in China, ICCC 2014
Country/TerritoryChina
CityShanghai
Period14/10/1314/10/15

Keywords

  • Adaptive sparse channel estimation
  • Compressive sensing
  • Reweighted ℓ-norm sparse penalty
  • Zero-attracting least mean square/fourth (ZA-LMS/F)

ASJC Scopus subject areas

  • Computer Networks and Communications

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