SNR-based Reinforcement Learning Rate Adaptation for Time Critical Wi-Fi Networks: Assessment through a Calibrated Simulator

Nowadays, the Internet of Things is spreading in several different research fields, such as factory automation, instrumentation and measurement, and process control, where it is referred to as Industrial Internet of Things. In these scenarios, wireless communication represents a key aspect to guarantee the required pervasive connectivity required. In particular, Wi-Fi networks are revealing ever more attractive also in time- and mission-critical applications, such as distributed measurement systems. Also, the multi-rate support feature of Wi-Fi, which is implemented by rate adaptation (RA) algorithms, demonstrated its effectiveness to improve reliability and timeliness. In this paper, we propose an enhancement of RSIN, which is a RA algorithm specifically conceived for industrial real-time applications. The new algorithm starts from the assumption that an SNR measure has been demonstrated to be effective to perform RA, and bases on Reinforcement Learning techniques. In detail, we start from the design of the algorithm and its implementation on the OmNet++ simulator. Then, the simulation model is adequately calibrated exploiting the results of a measurement campaign, to reflect the channel behavior typical of industrial environments. Finally, we present the results of an extensive performance assessment that demonstrate the effectiveness of the proposed technique