Stochastic Analysis Of The LMS And NLMS Algorithms For Cyclostationary White Gaussian Inputs

Abstract

This paper studies the stochastic behavior of the LMS and NLMS algorithms for a system identification framework when the input signal is a Cyclostationary white Gaussian process. The input Cyclostationary signal is modeled by a white Gaussian random process with periodically time-varying power. Mathematical models are derived for the mean and mean-square-deviation (MSD) behavior of the adaptive weights with the input Cyclostationary. These models are also applied to the non-stationary system with a random walk variation of the optimal weights. Finally, the performance of the two algorithms is compared for a variety of scenarios.