Stochastic analysis of the LMS algorithm for cyclostationary colored Gaussian and non-Gaussian inputs
| Autor: | Jose C. M. Bermudez, Neil J. Bershad, Eweda Eweda |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Cyclostationary process
Stochastic process Applied Mathematics Gaussian Monte Carlo method System identification 020206 networking & telecommunications 02 engineering and technology Filter (signal processing) Function (mathematics) Least mean squares filter symbols.namesake Computational Theory and Mathematics Artificial Intelligence Signal Processing 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Electrical and Electronic Engineering Statistics Probability and Uncertainty Algorithm Mathematics |
| Zdroj: | Digital Signal Processing. 88:149-159 |
| ISSN: | 1051-2004 |
| DOI: | 10.1016/j.dsp.2019.02.011 |
| Popis: | This paper studies the stochastic behavior of the LMS algorithm in a system identification framework for a cyclostationary colored input without assuming a Gaussian distribution for the input. The input cyclostationary signal is modeled by a colored random process with periodically time-varying power. The generation of the colored non-Gaussian random process is parametrized in novel manner by passing a Gaussian random process through a coloring filter followed by a zero memory nonlinearity. The unknown system parameters are fixed in most of the cases studied here. Mathematical models are derived for the behavior of the mean and mean-square-deviation (MSD) and the excess mean-square error (EMSE) of the adaptive weights as a function of the input cyclostationarity. The models display the dependence of the algorithm upon the input nonlinearity and coloration. Three nonlinearities that are studied in detail with Monte Carlo simulations provide strong support for the theory. |
| Databáze: | OpenAIRE |
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