Stochastic Analysis of the LMS and NLMS Algorithms for Cyclo-stationary White Gaussian Inputs PROJECT TITLE : Stochastic Analysis of the LMS and NLMS Algorithms for Cyclo-stationary White Gaussian Inputs (2014) 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 cyclostationarity. These models are also applied to the non-stationary system with a random walk variation of the optimal weights. Monte Carlo simulations of the two algorithms provide strong support for the theory. Finally, the performance of the two algorithms is compared for a variety of scenarios. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Low-Complexity DFT-Based Channel Estimation with Leakage Nulling for OFDM Systems Estimation of Space-Time Varying Parameters Using a Diffusion LMS Algorithm