A Variable Step-Size Diffusion Normalized Least-Mean-Square Algorithm with a Combination Method Based on Mean-Square Deviation

Abstract

A novel diffusion normalized least-mean-square algorithm is proposed for distributed network. For the adaptation step, the upper bound of the mean-square deviation (MSD) is derived instead of the exact MSD value, and then, the variable step size is obtained by minimizing it to achieve fast convergence rate and small steady-state error. For the diffusion step, the individual estimate at each node is constructed via the weighted sum of the intermediate estimates at its neighbor nodes, where the weights are designed by using a proposed combination method based on the MSD at each node. The proposed MSD-based combination method provides effective weights by using the MSD at each node as a reliability indicator. Simulations in a system identification context show that the proposed algorithm outperforms other algorithms in the literatures.