Particle filter algorithms for joint blind equalization/decoding of convolutionally coded signals

This work introduces the use of particle filters for joint blind equalization/decoding of convolutionally coded signals transmitted over frequency selective channels. As in the equalization-only case, we show how to evaluate the optimal importance function recursively via a bank of Kalman filters. N...

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Main Authors: Bordin, C.J., Baccala, L.A.
Format: Conference Proceeding
Language:English
Subjects:
Additive noise
Bayesian methods
Binary phase shift keying
Blind equalizers
Convolution
Convolutional codes
Decoding
Electronic mail
Frequency
Particle filters
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Baccala, L.A.
description This work introduces the use of particle filters for joint blind equalization/decoding of convolutionally coded signals transmitted over frequency selective channels. As in the equalization-only case, we show how to evaluate the optimal importance function recursively via a bank of Kalman filters. Numerical simulation investigations using both stochastic and deterministic particle selection strategies show the outstanding superiority of the deterministic joint equalization/decoding method over approaches that perform blind equalization using particle filters prior to optimal decoding.
doi_str_mv 10.1109/ICASSP.2005.1415755
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ispartof Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005, 2005, Vol.3, p.iii/497-iii/500 Vol. 3
issn 1520-6149
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Additive noise
Bayesian methods
Binary phase shift keying
Blind equalizers
Convolution
Convolutional codes
Decoding
Electronic mail
Frequency
Particle filters
title Particle filter algorithms for joint blind equalization/decoding of convolutionally coded signals
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