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.
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