  Finite-state Markov models are widely used for modeling wireless channels
affected by a variety of non-idealities, ranging from shadowing to
interference. In an industrial environment, the derivation of a Markov model
based on the wireless communication physics can be prohibitive as it requires a
complete knowledge of both the communication dynamics parameters and of the
disturbances/interferers. In this work, a novel methodology is proposed to
learn a Markov model of a fading channel via historical data of the
signal-to-interference-plus-noise-ratio (SINR). Such methodology can be used to
derive a Markov jump model of a wireless control network, and thus to design a
stochastic optimal controller that takes into account the interdependence
between the plant and the wireless channel dynamics. The proposed method is
validated by comparing its prediction accuracy and control performance with
those of a stationary finite-state Markov chain derived assuming perfect
knowledge of the physical channel model and parameters of a WirelessHART
point-to-point communication based on the IEEE-802.15.4 standard.
