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On the inclusion of channel's time dependence in a hidden Markov model for blind channel estimation

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4 Author(s)
Anton-Haro, C. ; Dept. of Signal Theory & Commun., Univ. Politecnica de Catalunya, Barcelona, Spain ; Fonollosa, J.A.R. ; Fauli, C. ; Fonollosa, J.R.

The theory of hidden Markov models (HMM) is applied to the problem of blind (without training sequences) channel estimation and data detection. Within a HMM framework, the Baum-Welch (1970) identification algorithm is frequently used to find out maximum-likelihood (ML) estimates of the corresponding model. However, such a procedure assumes the model (i.e., the channel response) to be static throughout the observation sequence. By means of introducing a parametric model for time-varying channel responses, a version of the algorithm, which is more appropriate for mobile channels [time-dependent Baum-Welch (TDBW)] is derived. Aiming to compare algorithm behavior, a set of computer simulations for a GSM scenario is provided. Results indicate that, in comparison to other Baum-Welch (BW) versions of the algorithm, the TDBW approach attains a remarkable enhancement in performance. For that purpose, only a moderate increase in computational complexity is needed

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Vehicular Technology, IEEE Transactions on  (Volume:50 ,  Issue: 3 )