Normalized Least Mean-Square Algorithm with Variable Step Size Based on Diffusion Strategy

摘要

In this paper, the problem of distributed estimation over adaptive networks is studied. A new diffusion normalized least mean-square (DifNLMS) algorithm is developed to tackle the considered problem. The main idea of the developed variable multi-step-size DifNLMS (VMSSDifNLMS) algorithm is to assign an individual time-varying step-size for each delivered signal in the weight adaptation step. As such, the developed algorithm is able to achieve a good tradeoff between fast convergence rate and low misadjustment. Numerical simulations are presented to show that the VMSSDifNLMS algorithm outperforms the conventional DifNLMS, VSSDifLMS, and DifLMS with optimal adaptive combination in terms of both convergence rate and steady-state error.