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...

Full description

Saved in:
Bibliographic Details
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
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2005.1415755