Discrete-time signal processing
Discrete-time signal processing
Model selection by MCMC computation
Signal Processing - Special section on Markov Chain Monte Carlo (MCMC) methods for signal processing
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Strategies in Scientific Computing
Monte Carlo Strategies in Scientific Computing
IEEE Transactions on Signal Processing
Blind equalization and identification of nonlinear and IIRsystems-a least squares approach
IEEE Transactions on Signal Processing
Blind identification of LTI-ZMNL-LTI nonlinear channel models
IEEE Transactions on Signal Processing
Blind identification of linear subsystems of LTI-ZMNL-LTI modelswith cyclostationary inputs
IEEE Transactions on Signal Processing
Linear multichannel blind equalizers of nonlinear FIR Volterrachannels
IEEE Transactions on Signal Processing
Particle filtering equalization method for a satellite communication channel
EURASIP Journal on Applied Signal Processing
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This paper proposes the use of Markov Chain Monte-Carlo (MCMC) simulation methods for equalizing a satellite communication system. The main difficulties encountered are the nonlinear distorsions caused by the amplifier stage in the satellite. Several processing methods manage to take into account the nonlinearity of the system but they require the knowledge of a training/learning input sequence for updating the parameters of the equalizer. Blind equalization methods also exist but they require a Volterra modelization of the system. The aim of the paper is also to blindly restore the emitted message. To reach the goal, we adopt a Bayesian point of view. We jointly use the prior knowledge on the emitted symbols, and the information available from the received signal. This is done by considering the posterior distribution of the input sequence and the parameters of the model. Such a distribution is very difficult to study and thus motivates the implementation of MCMC methods. The presentation of the method is cut into two parts. The first part solves the problem for a simplified model; the second part deals with the complete model, and a part of the solution uses the algorithm developed for the simplified model. The algorithms are illustrated and their performance is evaluated using bit error rate versus signal-to-noise ratio curves.