Classification of the Mobile Radio Propagation Environments Using Hidden Markov Models

The performance of wireless communication systems is primarily influenced by the mobile radio channel and its propagation mechanisms. This paper proposes an approach for characterizing wideband mobile radio channels based on hidden Markov model, in which the Viterbi algorithm is employed to provide a computationally efficient method for obtaining the likelihood sequence of hidden states (representing the radio propagation environments) from a set of observable states (channel temporal dispersion parameters). This approach enables the mobile radio channel classification into distinct transmission environments. The technique has proven to be a suitable option for statistically describing and classifying the wideband radio channel.