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

Guan Gui, Li Xu, Fumiyuki Adachi

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2014 IEEE/CIC International Conference on Communications in China, ICCC 2014
出版社Institute of Electrical and Electronics Engineers Inc.
ページ370-374
ページ数5
ISBN(電子版)9781479941469
DOI
出版ステータスPublished - 2015 1 12
イベント2014 IEEE/CIC International Conference on Communications in China, ICCC 2014 - Shanghai, China
継続期間: 2014 10 132014 10 15

出版物シリーズ

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

Other

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

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信

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