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

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Document typeArticle
Defense date2001-05-31
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Rights accessOpen Access
Abstract
In this paper, 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(BW) 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.
CitationAntón Haro, C.; Rodríguez Fonollosa, J. A.; Fauli, C.; Rodríguez Fonollosa, J. On the inclusion of channel's time dependence in a hidden Markov model for blind channel estimation. IEEE Transactions on Vehicular Technology, 2001, vol. 50, núm. 3, p. 867-873.
ISSN0018-9545
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