A new minimum-consensus distributed particle filter for blind equalization in receiver networks

We describe in this paper a novel distributed particle filtering algorithm that performs blind equalization of frequency-selective channels in a setup with a single transmitter and multiple receivers. The algorithm employs parallel minimum consensus iterations to determine some a posteriori probability functions, providing equal approximations on all network nodes in a finite, deterministic, network-dependent number of steps. We verify via computer simulations that the new algorithm exhibits a bit error rate (BER) performance similar to that of the centralized particle-filter estimator with communication requirements milder than that of previous approaches, as the new method drops the need to evaluate quantities via average consensus.