Application of hidden Markov models to blind channel characterization and data detection

The paper applies the theory of hidden Markov models (HMM) in digital communications to obtain a complete characterization of the channel, convolutional code, and transmitted constellation in a blind environment, i.e., without the help of a training sequence. The HMM formulation leads to a joint maximum likelihood estimation of both the channel and the transmitted sequence. The Baum-Welch (BW) identification algorithm is able to estimate all the parameters of the model, including the constellation and the probability of each symbol. Minor modifications to the algorithm allow one to consider restrictions or known parameters to improve the estimation of the rest of parameters with methods of lower complexity. An LMS-type adaptive version of the BW algorithm is also developed and tested. The simulation results show the fast convergence of the proposed optimal approaches. They also demonstrate that the performance of the decoder is comparable to that obtained in a known environment