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Paper Abstract and Keywords
Presentation 2013-03-01 11:30
Least Mean Square Algorithm with Application to Improved Adaptive Sparse Channel Estimation
Guan Gui, Wei Peng, Fumiyuki Adachi (Tohoku Univ.) RCS2012-359
Abstract (in Japanese) (See Japanese page) 
(in English) Least mean square (LMS) based adaptive algorithms have been attracted much attention since their low computational complexity and robust recovery capability. To exploit the channel sparsity, LMS-based adaptive sparse channel estimation methods, e.g., L1-norm LMS or zero-attracting LMS (sparse LMS or ZA-LMS), reweighted zero attracting LMS (RZA-LMS) and Lp-norm LMS (LP-LMS), have been proposed based on Lp-norm constraint. However, the above methods cannot exploit channel sparse structure information fully. To further improve estimation performance, in this paper, we introduce a L0-norm LMS (L0-LMS) algorithm with application sparse channel estimation to full take advantage of the sparsity. In addition, due to LMS-based channel estimation methods have a common drawback which is sensitive to the scaling of random training signal. Therefore, it is very hard to choose a proper learning rate to achieve robust estimation performance. To solve this problem, we propose several improved adaptive sparse channel estimation methods by using normalized LMS algorithm (NLMS), which normalizes the power of input signal, with different sparse penalties, e.g., Lp-norm and L0-norm. Computer simulation results demonstrate the advantage of the proposed channel estimation methods in estimation performance.
Keyword (in Japanese) (See Japanese page) 
(in English) Least Mean Square (LMS) / Adaptive Sparse Channel Estimation / Normalized LMS (NLMS) / Sparse Penalty / Compressive Sensing (CS) / / /  
Reference Info. IEICE Tech. Rep., vol. 112, no. 443, RCS2012-359, pp. 447-452, Feb. 2013.
Paper # RCS2012-359 
Date of Issue 2013-02-20 (RCS) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF RCS2012-359

Conference Information
Committee RCS SR SRW  
Conference Date 2013-02-27 - 2013-03-01 
Place (in Japanese) (See Japanese page) 
Place (in English) Waseda Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Mobile Communication Workshop 
Paper Information
Registration To RCS 
Conference Code 2013-02-RCS-SR-SRW 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Least Mean Square Algorithm with Application to Improved Adaptive Sparse Channel Estimation 
Sub Title (in English)  
Keyword(1) Least Mean Square (LMS)  
Keyword(2) Adaptive Sparse Channel Estimation  
Keyword(3) Normalized LMS (NLMS)  
Keyword(4) Sparse Penalty  
Keyword(5) Compressive Sensing (CS)  
Keyword(6)  
Keyword(7)  
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1st Author's Name Guan Gui  
1st Author's Affiliation Tohoku University (Tohoku Univ.)
2nd Author's Name Wei Peng  
2nd Author's Affiliation Tohoku University (Tohoku Univ.)
3rd Author's Name Fumiyuki Adachi  
3rd Author's Affiliation Tohoku University (Tohoku Univ.)
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Speaker Author-1 
Date Time 2013-03-01 11:30:00 
Presentation Time 20 minutes 
Registration for RCS 
Paper # RCS2012-359 
Volume (vol) vol.112 
Number (no) no.443 
Page pp.447-452 
#Pages 6 
Date of Issue 2013-02-20 (RCS) 

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