NDLI: A simplified network of LMS filters for Bayesian equalization based on a state model

Content Provider	IEEE Xplore Digital Library
Author	Rim, B.A. Sylvie, M.
Copyright Year	2004
Description	Author affiliation: Unite Signaux et Syst., Tunis, Tunisia (Rim, B.A.)
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.
Sponsorship	IEEE IEEE Finland Section IEEE SP/CAS Chapter, Finland EURASIP UCSB Tampere Univ. of Technol. Ecole Superieure des Sciences et Techniques de Tunis, ESSTT, Univ. of Manouba
Starting Page	689
Ending Page	692
File Size	357002
Page Count	4
File Format	PDF
ISBN	0780383796
DOI	10.1109/ISCCSP.2004.1296503
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-03-21
Publisher Place	Tunisia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Equalizers Filtering Bayesian methods Gaussian noise Covariance matrix Infinite horizon Kalman filters Least squares approximation State estimation Equations
Content Type	Text
Resource Type	Article

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