A new analytical model for the NLMS algorithm

This paper presents a new analytical model for the normalized least mean square (NLMS) adaptive algorithm. The new model is derived using a stochastic differential equation (SDE) approach. An accurate estimate of the steady-state weight-error correlations is also derived, which leads to an improved model performance for medium and large step sizes. Numerical simulations compare the new model with existing models and show better agreement with Monte Carlo simulations.