NDLI: Least mean square/fourth algorithm for adaptive sparse channel estimation

Content Provider	IEEE Xplore Digital Library
Author	Guan Gui Mehbodniya, A. Adachi, F.
Copyright Year	2013
Description	Author affiliation: Dept. of Commun. Eng., Tohoku Univ., Sendai, Japan (Guan Gui; Mehbodniya, A.; Adachi, F.)
Abstract	Broadband signal transmission over frequency-selective fading channel often requires accurate channel state information at receiver. One of the most attracting adaptive channel estimation (ACE) methods is least mean square (LMS) algorithm. However, its performance is often degraded by random scaling of input training signal. To overcome this degradation, in this paper we consider the use of standard least mean square/fourth (LMS/F) algorithm. Since the broadband channel is often described by sparse channel model, such sparsity could be exploited as prior information. First, we propose an adaptive sparse channel estimation (ASCE) method with zero-attracting LMS/F (ZA-LMS/F) algorithm by introducing an $ℓ_{1}-norm$ sparse constraint into the cost function. Then, to exploit the sparsity more effectively, an improved ASCE with reweighted zero-attracting LMS/F (RZA-LMS/F) algorithm is proposed. For different channel sparsity, we propose a Monte Carlo method for a regularization parameter selection in RA-LMS/F and RZA-LMS/F to achieve better steady-state estimation performance. Simulation results show that the proposed ASCE methods achieve better estimation performance than the conventional one.
Starting Page	296
Ending Page	300
File Size	363302
Page Count	5
File Format	PDF
ISBN	9781467362351
ISSN	21669570
DOI	10.1109/PIMRC.2013.6666149
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-08
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Least squares approximations Channel estimation Estimation Signal processing algorithms Standards Cost function Approximation algorithms re-weighted zero-attracting least mean square/fourth (RZA-LMS/F) least mean square fourth (LMS/F) adaptive sparse channel estimation (ASCE) zero-attracting least mean square/fourth (ZA-LMS/F)
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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