Markov Chain Monte Carlo-Based Separation of Paired Carrier Multiple Access Signals

Abstract

Paired carrier multiple access (PCMA) signal is one of the most common single-channel mixtures. It is still a great challenge to recover the component signals from non-cooperative received PCMA signals due to the high complexity of existing single-channel blind source separation algorithms. Hence, a Markov chain Monte Carlo (MCMC) procedure, called the QR Decomposition with M algorithm-based Gibbs sampler, is employed to realize the separation of PCMA signals. The method is robust against modulation parameter estimation error, since a data-aided adaptive filter that can update the channel response is embedded into the QRD-M-based Gibbs sampler. Simulation results show that the MCMC estimator efficiently achieves approximately joint likelihood sequence estimation.