Robust Multitask Diffusion Bias-Compensated LMS Algorithm for Distributed Estimation with Noisy Link and Input

This paper presents a robust multitask diffusion average bias compensation least mean square (RM-DABC-LMS) algorithm for distributed estimation in noisy input and communication link noise. The algorithm utilizes a robust cost function based on the maximum Versoria criterion, incorporates bias compensation, and applies adaptive combination coefficients to reduce noise impacts. Theoretical analysis demonstrates the stability of the algorithm, providing closed-form expressions for the steady-state mean square deviation (MSD). A compression diffusion strategy is introduced to reduce communication cost of the RM-DABC-LMS algorithm, ensuring fast convergence and accurate estimation. Simulation results indicate that the proposed algorithm outperforms existing methods in noisy environments, achieving faster convergence and lower steady-state error.