Channel Tracking in Relay Systems via Particle MCMC

We present a new approach for joint channel tracking and parameter estimation in cooperative wireless relay networks, based on a particle Markov chain Monte Carlo (PMCMC) method. We consider a system with multiple relay nodes operating under an amplify and forward relay function. In particular, it first involves developing a non-liner Bayesian state space model, then estimating the associated high dimensional posterior using an adaptive Markov chain Monte Carlo (MCMC) sampler relying on a proposal built using a Rao-Blackwellised Sequential Monte Carlo (SMC) filter. Simulation results demonstrate the effectiveness of the proposed algorithm, requiring only a fraction of the computational complexity of standard MCMC approaches.