Double Markov Process blind estimation: Application to communication in a long memory channel

摘要

Also called a Markov switching process, a Double Markov Process (DMP) is an extension of the Hidden Markov Chain (HMC) where the observed process conditionally to the hidden one is modelled by a Markov process. This paper focuses on the estimation of a DMP model, the goal being twofold. First we provide the Cramer-Rao bound of the covariance matrix of any unbiased estimator in order to assess the accuracy limitations. Then we develop a DMP blind estimator for communication through a long memory channel. The results show the benefit of such a modelling in terms of both performance and complexity.