NDLI: Regularization selection method for LMS-type sparse multipath channel estimation

Content Provider	IEEE Xplore Digital Library
Author	Zhengxing Huang Guan Gui Anmin Huang Dong Xiang Adachi, F.
Copyright Year	2013
Description	Author affiliation: Dept. of Software Eng., Tsinghua Univ., Beijing, China (Zhengxing Huang; Dong Xiang) \|\| Dept. of Commun. Eng., Tohoku Univ., Sendai, Japan (Guan Gui; Adachi, F.) \|\| Dept. of Electron. & Inf. Eng., Jinggangshan Univ., Jian, China (Anmin Huang)
Abstract	Least mean square (LMS)-type adaptive sparse algorithms have been attracting much attention on sparse multipath channel estimation (SMPC) due to their two advantages: low computational complexity and reliability. By introducing $ℓ_{1}$ -norm sparse constraint function into LMS algorithm, both zero-attracting least mean square (ZA-LMS) and reweighted zero-attracting least mean square (RZA-LMS) have been proposed for SMPC. It is well known that the performance of the SMPC is decided by regularization parameter which balances channel estimation error and sparse penalty strength. However, optimal regularization parameter selection has not yet considered in the two proposed algorithms. Based on the compressive sensing theory, in this paper, we explain the mathematical relationship between Lasso and LMS-type adaptive sparse algorithms. Later, an approximate optimal regulation parameter selection method is proposed for ZA-LMS and RZA-LMS, respectively. Monte Carlo based computer simulations are presented to show the effectiveness of our propose method.
Sponsorship	Indonesia Sect. COM Chapt.
Starting Page	649
Ending Page	654
File Size	2487383
Page Count	6
File Format	PDF
e-ISBN	9781467360500
DOI	10.1109/APCC.2013.6766029
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-29
Publisher Place	Indonesia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Estimation Multipath channels reweighted zero-attracting least mean square (RZA-LMS) regularization parameter selection least mean square (LMS) adaptive sparse channel estimation zero-attracting least mean square (ZA-LMS)
Content Type	Text
Resource Type	Article

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