NDLI: L 0-norm penalised shrinkage linear and widely linear LMS algorithms for sparse system identification

Content Provider	IET Digital Library
Author	Zhang, Youwen Xiao, Shuang Huang, Defeng ( David) Sun, Dajun Liu, Lu Cui, Hongyu
Abstract	In this study, the authors propose an l 0-norm penalised shrinkage linear least mean squares (l 0-SH-LMS) algorithm and an l 0-norm penalised shrinkage widely linear least mean squares (l 0-SH-WL-LMS) algorithm for sparse system identification. The proposed algorithms exploit the priori and the posteriori errors to calculate the varying step-size, thus they can adapt to the time-varying channel. Meanwhile, in the cost function they introduce a penalty term that favours sparsity to enable the applicability for sparse condition. Moreover, the l 0-SH-WL-LMS algorithm also makes full use of the non-circular properties of the signals of interest to improve the tracking capability and estimation performance. Quantitative analysis of the convergence behaviour for the l 0-SH-WL-LMS algorithm verifies the capabilities of the proposed algorithms. Simulation results show that compared with the existing least mean squares-type algorithms, the proposed algorithms perform better in the sparse channels with a faster convergence rate and a lower steady-state error. When channel changes suddenly, a filter with the proposed algorithms can adapt to the variation of the channel quickly.
Starting Page	86
Ending Page	94
Page Count	9
ISSN	17519675
Volume Number	11
e-ISSN	17519683
Issue Number	Issue 1, Feb (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/11/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2015.0218
Journal	IET Signal Processing
Publisher Date	2016-08-11
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Filter Channel Variation Convergence Rate Cost Function Estimation Performance Improvement Estimation Theory Filtering Method in Signal Processing Identification Interpolation And Function Approximation L0-norm Penalised Shrinkage Linear LMS Algorithm L0-norm Penalised Shrinkage Widely Linear LMS Algorithm L0-SH-LMS Algorithm L0-SH-WL-LMS Algorithm Least Mean Squares Algorithm Least Squares Approximation Modelling And Identification Noncircular Signal Property Numerical Analysis Posteriori Error Priori Error Signal Processing Theory Simulation Sparse System Identification Statistics Steady-state Error Time-varying Channel Tracking Capability Improvement Varying Step-size Calculation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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