NDLI: New Steady-State Analysis Results of Variable Step-Size LMS Algorithm With Different Noise Distributions

Content Provider	IEEE Xplore Digital Library
Author	Sheng Zhang Jiashu Zhang
Copyright Year	1994
Abstract	The step-size in well-known variable step-size least mean square (VSSLMS) is updated as μ_n+1 = αμ_n + γe_n² with , γ > 0, and μ_n+1 is set to μ_min or μ_max when it falls below or above these lower and upper bounds, respectively. It provides fast convergence at early stages of adaptation while ensuring small steady-state misalignment. This paper considers the steady-state performance of the VSSLMS in non-Gaussian noise environments. The contribution of the paper to the VSSLMS is threefold; (1) when γ ≪ 1 - α, the VSSLMS has low steady-state misalignment. (2) when α ≪ 1, the VSSLMS achieves different steady-state misalignments for different noise distributions. (3) In theory, there are different optimal values α for different noise distributions, i.e., 0.17 (Gaussian distribution), 0.21 (Student distribution), 0.38 (Laplace distribution), 0 (Binary and Uniform distributions). Analytical results are compared with simulations and are shown to agree well.
Sponsorship	IEEE Signal Processing Society
Starting Page	653
Ending Page	657
Page Count	5
File Size	1400799
File Format	PDF
ISSN	10709908
Volume Number	21
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Steady-state Noise Least squares approximations Signal processing algorithms Algorithm design and analysis Convergence Vectors variable step-size LMS Adaptive filter steady-state analysis
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Signal Processing Electrical and Electronic Engineering

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