Study of Robust Distributed Diffusion RLS Algorithms with Side Information for Adaptive Networks
This work addresses robustness in distributed adaptive networks for applications like sensor networks, but it is incremental as it builds on existing diffusion RLS methods.
The authors tackled performance degradation in adaptive networks under impulsive noise by developing two robust diffusion recursive least squares algorithms, one using side information for improved robustness and the other reducing complexity with dichotomous coordinate descent, showing superiority over existing techniques in simulations.
This work develops robust diffusion recursive least squares algorithms to mitigate the performance degradation often experienced in networks of agents in the presence of impulsive noise. The first algorithm minimizes an exponentially weighted least-squares cost function subject to a time-dependent constraint on the squared norm of the intermediate update at each node. A recursive strategy for computing the constraint is proposed using side information from the neighboring nodes to further improve the robustness. We also analyze the mean-square convergence behavior of the proposed algorithm. The second proposed algorithm is a modification of the first one based on the dichotomous coordinate descent iterations. It has a performance similar to that of the former, however its complexity is significantly lower especially when input regressors of agents have a shift structure and it is well suited to practical implementation. Simulations show the superiority of the proposed algorithms over previously reported techniques in various impulsive noise scenarios.