Blind channel estimation and data detection using hidden Markov models

We propose applying the hidden Markov models (HMM) theory to the problem of blind channel estimation and data detection. The Baum-Welch (BW) algorithm, which is able to estimate all the parameters of the model, is enriched by introducing some linear constraints emerging from a linear FIR hypothesis...

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Bibliographic Details
Published in:IEEE transactions on signal processing 1997-01, Vol.45 (1), p.241-247
Main Authors: Anton-Haro, C., Fonollosa, J.A.R., Fonollosa, J.R.
Format: Article
Language:English
Subjects:
AWGN
Blind equalizers
Finite impulse response filter
GSM
Hidden Markov models
Maximum likelihood detection
Maximum likelihood estimation
Parameter estimation
Performance analysis
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Summary:We propose applying the hidden Markov models (HMM) theory to the problem of blind channel estimation and data detection. The Baum-Welch (BW) algorithm, which is able to estimate all the parameters of the model, is enriched by introducing some linear constraints emerging from a linear FIR hypothesis on the channel. Additionally, a version of the algorithm that is suitable for time-varying channels is also presented. Performance is analyzed in a GSM environment using standard test channels and is found to be close to that obtained with a nonblind receiver.
ISSN:1053-587X
1941-0476
DOI:10.1109/78.552223