Blind channel estimation and data detection using hidden Markov models

Author

Antón-Haro, Carles ; Fonollosa, José A R ; Fonollosa, Javier R.

Author_Institution

Dept. de Teoria del Senyal i Comunicacions, Univ. Politecnica de Catalunya, Barcelona, Spain

Volume

45

Issue

1

fYear

1997

fDate

1/1/1997 12:00:00 AM

Firstpage

241

Lastpage

247

Abstract

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

Keywords

cellular radio; equalisers; hidden Markov models; parameter estimation; signal detection; time-varying channels; Baum-Welch algorithm; GSM environment; HMM theory; blind channel estimation; data detection; hidden Markov models; linear FIR hypothesis; linear constraints; nonblind receiver; parameter estimation; performance analysis; standard test channels; time-varying channels; AWGN; Blind equalizers; Finite impulse response filter; GSM; Hidden Markov models; Maximum likelihood detection; Maximum likelihood estimation; Parameter estimation; Performance analysis; Transmitters;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.552223

Filename

552223