Cooperative blind equalization of frequency-selective channels in sensor networks using decentralized particle filtering

We introduce in this paper new decentralized particle filtering algorithms suitable for blind equalization of frequency-selective communication channels. The proposed methods rely on novel distributed sequential importance sampling techniques that spread the computational load across a network of processing nodes, which cooperate in turn to produce a global consensus estimate of the transmitted data stream. As we verify via numerical simulations, the new decentralized schemes approach the performance of the optimal centralized MAP receiver, exhibiting clear performance improvements compared to previous data-blind methods.