Particle Filtering Equalization Method for a Satellite Communication Channel

• Published in: EURASIP journal on advances in signal processing Vol. 2004; no. 15; p. 591429
• Main Authors: Sénécal, Stéphane, Amblard, Pierre-Olivier, Cavazzana, Laurent
• Format: Journal Article
• Language: English
• Published: BioMed Central Ltd 07.11.2004; SpringerOpen
• Subjects: Bayesian inference; Monte Carlo estimation method; particle filtering; sequential simulation; traveling-wave-tube amplifier
• ISSN: 1687-6180; 1687-6172; 1687-6180
• DOI: 10.1186/1687-6180-2004-591429

: We propose the use of particle filtering techniques and Monte Carlo methods to tackle the in-line and blind equalization of a satellite communication channel. The main difficulties encountered are the nonlinear distortions caused by the amplifier stage in the satellite. Several processing methods manage to take into account these nonlinearities but they require the knowledge of a training input sequence for updating the equalizer parameters. Blind equalization methods also exist but they require a Volterra modelization of the system which is not suited for equalization purpose for the present model. The aim of the method proposed in the paper is also to blindly restore the emitted message. To reach this goal, a Bayesian point of view is adopted. Prior knowledge of the emitted symbols and of the nonlinear amplification model, as well as the information available from the received signal, is jointly used by considering the posterior distribution of the input sequence. Such a probability distribution is very difficult to study and thus motivates the implementation of Monte Carlo simulation methods. The presentation of the equalization method is cut into two parts. The first part solves the problem for a simplified model, focusing on the nonlinearities of the model. The second part deals with the complete model, using sampling approaches previously developed. The algorithms are illustrated and their performance is evaluated using bit error rate versus signal-to-noise ratio curves.