Title :
Nonlinear channel equalizer using Gaussian sum approximations
Author :
Grohan, Patrick ; Marcos, Sylvie
Author_Institution :
Lab. des Signaux et Syst., CNRS, Gif-sur-Yvette, France
Abstract :
The aim of this paper is to revisit the problem of nonlinear channel equalization. The equalization is here viewed as the estimation, from the observation of the channel output, of the state vector of the channel consisting of the last transmitted symbols. If the probability density function of the state vector given all the available observations (the a posteriori density function) were known, an estimate of the state vector for any performance criterion could be determined. Alspach and Sorenson (1972) proposed an approximation, by a weighted sum of Gaussian probability density functions, that permits the explicit calculation of the a posteriori density from the Bayesian recursion relations. The application of these results to the minimum mean square error solution of the nonlinear channel equalization problem provides a new scheme which consists of the convex combination of the output of several extended Kalman filters operating in parallel
Keywords :
Bayes methods; Gaussian distribution; Kalman filters; equalisers; least mean squares methods; telecommunication channels; Bayesian recursion relations; Gaussian probability density functions; Gaussian sum approximations; a posteriori density function; channel output observation; extended Kalman filters; minimum mean square error solution; nonlinear channel equalization; probability density function; state vector estimation; Bayesian methods; Density functional theory; Dispersion; Equalizers; Filters; Intersymbol interference; Mean square error methods; Nonlinear equations; Probability density function; State estimation;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on
Conference_Location :
Munich
Print_ISBN :
0-8186-7919-0
DOI :
10.1109/ICASSP.1997.599584