A bayesian approach to blind separation of mixed discrete sources by gibbs sampling

Authors:
Hang Zhang;Fanglin Gu;Yi Xiao
Affiliations:
Institute of Communication Engineering, PLA University of Science and Technology, Nanjing, China;Institute of Communication Engineering, PLA University of Science and Technology, Nanjing, China;School of Engineering & Information Technology, the University of New South Wales, Australian, Defence Force Academy, Canberra, Australia
Venue:
UIC'11 Proceedings of the 8th international conference on Ubiquitous intelligence and computing
Year:
2011

Citing 4
Cited 1

Multi-user pdf estimation based criteria for adaptive blind separation of discrete sources

Signal Processing - Special issue: Information theoretic signal processing
Blind MIMO-AR system identification and source separation with finite-alphabet

IEEE Transactions on Signal Processing
Equivariant adaptive source separation

IEEE Transactions on Signal Processing
Blind restoration of linearly degraded discrete signals by Gibbssampling

IEEE Transactions on Signal Processing

Blind separation of non-stationary sources using continuous density hidden Markov models

Digital Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Blind source separation (BSS) is the process of separating the original signals from their mixtures without the knowledge of neither the signals nor the mixing process. In this paper, a Bayesian modeling approach for the separation of instantaneous mixture of linear modulation signals with memory in communication systems is developed, in which the finite alphabet (FA) property of the source signals, together with the correlation contained in the source signals are used for the purpose of accurate signal separation. And the Gibbs sampling algorithm is employed to estimate discrete source signals and mixing coefficients. Moreover, the approach takes into account noise levels in the model in order to provide precise estimations of the signals. The simulation results under determined mixture condition show that this new algorithm gives precise estimation of sources and coefficients of mixture. Furthermore, the efficiency of this proposed approach under underdetermined mixture condition is attested by a numerical simulation experiment.