Binary consensus with soft information processing in cooperative networks

In this paper we consider reaching binary consensus over a network with AWGN channels. We consider the case where knowledge of the corresponding link qualities is available at every receiving node. We propose novel soft information processing approaches to improve the performance in the presence of noisy links. We characterize the performance and derive an expression for the second largest eigenvalue. We show that soft information processing can improve the performance drastically. We furthermore show that, by statistically learning the voting patterns, we can solve the undesirable asymptotic behavior of binary consensus.