Semi-blind block channel estimation and signal detection using hidden Markov models

We propose two maximum likelihood based semi-blind block channel estimation and signal detection algorithms for multipath channels with additive Gaussian noise. The algorithms are based on the Baum-Welch (1972) algorithm and the segmental k-means algorithm for hidden Markov models (HMMs). By making use of a training signal, the algorithms are applied block-wise to sequential disjoint subintervals of the whole observation interval. We study the effects of block length in terms of the bit error rate (BER), the mean square error (MSE) of the estimated channel impulse response, and its Cramer-Rao lower bound. Our simulation results show that the BER performance does not suffer even for a short block length when a good initial estimate is available.