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Markov Chain Monte Carlo-Based Separation of Paired Carrier Multiple Access Signals | IEEE Journals & Magazine | IEEE Xplore

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-co...Show More

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.
Published in: IEEE Communications Letters ( Volume: 20, Issue: 11, November 2016)
Page(s): 2209 - 2212
Date of Publication: 12 August 2016

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