Consensus-based distributed particle filtering algorithms for cooperative blind equalization in receiver networks

We describe in this paper novel consensus-based distributed particle filtering algorithms which are applied to cooperative blind equalization of frequency-selective channels in a network with one transmitter and multiple receivers. The proposed algorithms employ parallel consensus averaging iterations to evaluate the product of some node-dependent quantities across the receiver network, thus eliminating the need for message broadcasts beyond each receiver's local neighborhood. Additionally, parallel minimum consensus iterations are used to assess the convergence of the quantized consensus averages and ensure accordingly the coherence of particle sets across the different network nodes. We verify via computer simulations that the consensus-based schemes exhibit a small performance gap compared to both centralized and communication-intensive broadcast solutions.