Performance Analysis of Diffusion LMS for Cyclostationary White Non-Gaussian Inputs

This paper studies the transient behavior of the diffusion least-mean-square (LMS) algorithm over the single-task network for the non-stationary system using diverse types of cyclostationary white non-Gaussian inputs for an individual node. The analytical models of the recursive mean-weight-error vector and mean-square-deviation are derived for the system with random walk varying parameters and the white random process with periodically deterministic time-varying input variance. In addition, the approximated steady-state mean-square-deviation of the diffusion LMS is presented for the slow varying input variance. Monte Carlo simulations show excellent agreement with the theoretical prediction of mean-square-deviation validating the accuracy of derived analytical models and the tracking ability for non-stationary system and cyclostationary inputs simultaneously.