Improved least-squares-based combiners for diffusion networks

Adaptive networks have received great attention during recent years. In diffusion strategies, nodes diffuse their estimations to neighbors, and construct improved estimates by combining all information received by other nodes. When nodes work in heterogeneous conditions, it is reasonable to assign combination weights that take into account the performance of each node; thus, different schemes that implement adaptive combiners have been recently proposed. In this paper, we propose a novel scheme for adaptive combiners which attempts to minimize a least-squares cost function. The novelty in our proposal relies on making the adaptive combiners convex, by projection onto the standard simplex, what result in a numerically more stable implementation. The convergence and steady-state properties of the new scheme are analyzed theoretically, and its performance is experimentally evaluated with respect to existing methods.