ON A LOGISTIC-HIDDEN MARKOV MODEL FOR COMMUNICATION SYSTEMS

This article proposes an extension of hidden Markov models for communication systems by allowing the Markovian transitions between the channel's states to be influenced by an external “catalyzer” (i.e., environmental or experimental conditions). The stochastic influence of the catalyzer is expressed by logistic link functions. The model can be useful for modeling communication channels, for example, in predicting single-ion channel behavior for cell signaling. A simulation study is provided in order to explore how trainable the model is. We prove that the Logistic-Hidden Markov model can be very well trained by means of the maximum likelihood method, assisted by a modified Baum–Welch algorithm.