NDLI: Sparsity-information-aided least mean squares method for sparse channel estimation

Content Provider	IEEE Xplore Digital Library
Author	Wenying Lei Yansong Meng Yingwei Wu Su Zhe Xiaoliang Wang
Copyright Year	2015
Description	Author affiliation: Inst. of Navig. & Intra Satellite Link Technol., Acad. of Space Electron. Inf. Technol., Xi'an, China (Wenying Lei; Yansong Meng; Yingwei Wu; Su Zhe; Xiaoliang Wang)
Abstract	A novel least mean squares (LMS) method that exploits sparsity level information for sparse channel estimation is presented and studied in this paper. This method utilizes the channel sparsity level information by incorporating a penalty term into the cost function and has better performance than the compared methods which do not take into account the sparsity level information. The convergence analysis of the proposed method is provided. Both the transient and the steady-state advantages of the proposed method are confirmed numerically. Simulation results indicate that the sparsity-information-aided LMS method has faster convergence and higher accuracy than the compared approaches when the channel sparsity level information is known.
Starting Page	1
Ending Page	5
File Size	850889
Page Count	5
File Format	PDF
e-ISBN	9781467376877
DOI	10.1109/WCSP.2015.7340984
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Sparse channel Least squares approximations LMS Sparsity level Channel estimation Cost function Covariance matrices Standards Convergence
Content Type	Text
Resource Type	Article

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