Improvement of Hidden Markov model evaluation of the mobile satellite channel by resorting to a transition localisation method

The mobile satellite channel has underlying Markovian properties and can then be represented by a Hidden Markov model (HMM). A challenging problem consists in estimating the model parameters from experimental data, especially when these parameters are not easily identifiable. In these cases, classification methods like k-means or scalable clustering, which are considered in this paper, show poor results when applied to the channel signal directly. We show that the detection of change-points of the signal, i.e. the detection of transitions between the model states, in a preliminary step, improves the estimation of the model parameters. We thus propose a method of model estimation including the detection of change-points that enables a better modelling of the satellite channel.