Data-selective diffusion LMS for reducing communication overhead

The diffusion strategies have been widely studied for distributed estimation over adaptive networks. In the structure, communication resources are assigned to every node in order to share its processed data with predefined neighbors. Although the performance improves through the information exchange, it entails a communication cost. We present a dynamic diffusion method that shares only reliable information with neighbors. Each node has the ability to evaluate its updated estimate by the contribution of the new measurements to minimizing mean-square deviation (MSD). In only case of decrease of MSD, the node is allowed to transmit its estimate to neighbors. Accordingly, the proposed algorithm has a reduced amount of communication while keeping the performance as much as possible. Experimental results show that the proposed algorithm achieves more efficient reduction of communication and better performance compared to the other related algorithms.