This document proposes a new variable step-size diffusion least mean square algorithm for distributed estimation that adaptively adjusts the step-size in every iteration to minimize the mean square deviation for the intermediate estimate at each node. The algorithm adapts to different node environments and profiles across networks with relatively less user interaction than existing algorithms. Experiments show the algorithm achieves both fast convergence speed and low misadjustment through remarkable improvement in an adaptation stage, and it works well even in non-stationary environments.

1. A Variable Step-Size Diffusion LMS Algorithm for Distributed
Estimation
Abstract:
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.