Performance of soft limiters in the LMS algorithm for cyclostationary white Gaussian inputs

The analysis of saturation-type nonlinearities on the input and the error in the weight update equation for LMS adaptation were obtained for a stationary white Gaussian data model in [28] for system identification. Here the input signal is modeled by a cyclostationary white Gaussian random process w...

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Bibliographic Details
Published inSignal processing Vol. 152; pp. 197 - 205
Main Authors Bershad, Neil J., Eweda, Eweda, Bermudez, Jose C.M.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.11.2018
Subjects
Adaptive filters
Analysis
Signed LMS algorithms
Stochastic algorithms
Stochastic algorithms
Signed LMS algorithms
Adaptive filters
Analysis
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ISSN0165-1684
1872-7557
DOI10.1016/j.sigpro.2018.05.023

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Summary:The analysis of saturation-type nonlinearities on the input and the error in the weight update equation for LMS adaptation were obtained for a stationary white Gaussian data model in [28] for system identification. Here the input signal is modeled by a cyclostationary white Gaussian random process with periodically time-varying power. The system parameters vary according to a random-walk. Using the previous analysis results, nonlinear recursions are presented for the transient and steady-state weight first and second moments that include the effect of the soft limiters. Monte Carlo simulations of the algorithms provide strong support for the theory.
ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2018.05.023