Blind channel estimation and data detection using hidden Markovmodels

We propose applying the hidden Markov models (HMM) theory to the problem of blind channel estimation and data detection. The Baum-Welch (BW) algorithm, which is able to estimate all the parameters of the model, is enriched by introducing some linear constraints emerging from a linear FIR hypothesis on the channel. Additionally, a version of the algorithm that is suitable for time-varying channels is also presented. Performance is analyzed in a GSM environment using standard test channels and is found to be close to that obtained with a nonblind receiver