Stochastic Model for the Generalized Subband Decomposition ε NLMS Algorithm with Gaussian Data

This paper proposes a stochastic model for the generalized subband decomposition normalized LMS (NLMS) algorithm. This algorithm is used as an alternative to the standard NLMS one, aiming to improve the convergence speed under correlated input data. Analytical models for the first and second moments of the filter weights are derived taking into account the time-varying nature of normalized step size. Moreover, in the model expressions a positive regularization parameter epsi is added to the power estimates, preventing division by zero during the power normalization process. Through simulation results, the accuracy of the proposed analytical model can be verified