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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Signal processing Ročník 152; s. 197 - 205
Hlavní autoři: Bershad, Neil J., Eweda, Eweda, Bermudez, Jose C.M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.11.2018
Témata:
Adaptive filters
Analysis
Signed LMS algorithms
Stochastic algorithms
Stochastic algorithms
Signed LMS algorithms
Adaptive filters
Analysis
ISSN:0165-1684, 1872-7557
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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