A Variable Step-Size Diffusion LMS Algorithm for Distributed Estimation

We propose a new variable step-size diffusion least mean square algorithm for distributed estimation that adaptively adjusts the step-size in every iteration. For a network application, the proposed method determines a suboptimal step-size at each node to minimize the mean square deviation for the intermediate estimate. The algorithm thus adapts the different node environments and profiles across the networks, and requires relatively less user interaction than existing algorithms. In experiments, the algorithm achieves both fast convergence speed and low misadjustment by remarkable improvement in an adaptation stage. We analyze the mean square performance of the proposed algorithm. Also, the proposed algorithm works well even in non-stationary environments.