Rate Adaptation by Reinforcement Learning for Wi-Fi Industrial Networks
Giovanni Peserico, Tommaso Fedullo, Alberto Morato, Stefano Vitturi, Federico Tramarin
Abstract
Wireless technologies play a key role in the Industrial Internet of Things (IIoT) scenario, for the development of increasingly flexible and interconnected factory systems. Wi-Fi remains particularly attracting due to its pervasiveness and high achievable data rates. Furthermore, its Rate Adaptation (RA) capabilities make it suitable to the harsh industrial environments, provided that specifically designed RA algorithms are deployed. To this aim, this paper proposes to exploit Reinforcement Learning (RL) techniques to design an industry-specific RA algorithm. The RL is spreading in many fields since it allows to design intelligent systems by means of a stochastic discrete-time system based approach. In this work we propose to enhance the Robust Rate Adaptation Algorithm (RRAA) by means of a RL approach. The preliminary assessment of the designed RA algorithm is carried out through meaningful OMNeT++ simulations, that allow to recognize the beneficial impact of the introduction of RL with respect to several industry-specific performance indicators.