  Accurate channel models are of high importance for the design of upcoming
mobile satellite systems. Nowadays most of the models for the LMSC are based on
Markov chains and rely on measurement data, rather than on pure theoretical
considerations. A key problem lies in the determination of the model parameters
out of the observed data. In this work we face the issue of state
identification of the underlying Markov model whose model parameters are a
priori unknown. This can be seen as a HMM problem. For finding the ML estimates
of such model parameters the BW algorithm is adapted to the context of channel
modeling. Numerical results on test data sequences reveal the capabilities of
the proposed algorithm. Results on real measurement data are finally presented.
