Stochastic Analysis Of The LMS And NLMS Algorithms For Cyclostationary White Gaussian Inputs (International Journal of Engineering and Computer Science, 2015)
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.