Robust Incremental Least Mean Square Algorithm With Dynamic Combiner

Distributed networks, incremental least mean squares algorithm, decentralized estimation, steady-state analysis, noisy link

In distributed wireless networks, the adaptation process depends on the information being shared between various nodes. The global minimum, is therefore, likely to be affected when the information shared between the nodes gets corrupted. This could happen due to several reasons namely link failure, noisy environment and erroneous data etc. In this research, we propose a computationally efficient robust incremental least mean square (RILMS) algorithm to resolve the aforementioned issues. Essentially, a fusion step is introduced in the framework of the incremental least mean square (ILMS). Prior to adaptation at a node, the information shared by the neighbouring node is fused with the temporally preceding information of the node using an efficient combiner. An adaptive fusion strategy is proposed resulting in dynamic weight assignment for the fusion step. Closed form expression for the steady-state excess mean square error (EMSE) is derived and the performance of the proposed algorithm is evaluated for the noisy link environments and compared to the existing algorithms. Extensive experiments show the efficacy of the proposed approach compared to the contemporary methods. The proposed algorithm is found to be robust against the link failure and local node divergence problems. The improved performance of the proposed RILMS algorithm comes with a significant reduction in computational complexity compared to the convex combination based ILMS (CILMS) approach.