Title :
A simplified network of LMS filters for Bayesian equalization based on a state model
Author :
Rim, Boujemcia Amuru ; Sylvie, Murcos
Author_Institution :
Unite Signaux et Syst., Tunis, Tunisia
Abstract :
The network of Kalman filter (NKF) structure was proposed to perform optimal Bayesian symbol-by-symbol estimation in a SISO equalization context [(P. Grohan et al., September 1997)(R. Amara et al., 2002)]. By approximating the error filtering covariance matrix of each branch of the network by a diagonal one, we show in this paper that the NKF can be simplified into a particular network of normalized LMS filters (NLMSF) minimizing the error on the predicted channel output, so that reducing the corresponding complexity. We also propose an adjusting procedure of the approximating matrix which is still related to the second order statistics of the symbol state estimation error. Simulations show the good performance of the NLMSF based equalizer compared to the NKF version for both short and long memory linear channels.
Keywords :
Bayes methods; Kalman filters; covariance matrices; filtering theory; least mean squares methods; state estimation; Bayesian equalization; Bayesian symbol-by-symbol estimation; Kalman filter network; SISO equalization; channel output; error filtering covariance matrix; memory linear channels; normalized LMS filters; second order statistics; symbol state estimation error; Bayesian methods; Covariance matrix; Equalizers; Equations; Filtering; Gaussian noise; Infinite horizon; Kalman filters; Least squares approximation; State estimation;
Conference_Titel :
Control, Communications and Signal Processing, 2004. First International Symposium on
Print_ISBN :
0-7803-8379-6
DOI :
10.1109/ISCCSP.2004.1296503
