Learning Markov models of fading channels in wireless control networks: a regression trees based approach

TL;DR

This paper introduces a data-driven approach using regression trees to learn Markov models of fading wireless channels from historical SINR data, enabling better control in wireless networks without detailed physical models.

Contribution

A novel methodology for learning Markov channel models from data using regression trees, applicable to wireless control networks lacking complete physical channel information.

Findings

The proposed method accurately predicts channel behavior.

It improves control performance over stationary Markov models.

Validation against WirelessHART shows enhanced modeling accuracy.

Abstract

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.