An RL-based Joint Diversity and Power Control Optimization for Reliable Factory Automation

Communication systems supporting cyber-physical production applications should satisfy stringent delay and reliability requirements. Violation of these requirements may result in faulty behavior of the system and cause significant economic losses. Although wireless communications enable mobility and easy maintenance to industrial networks, it introduces many challenges to high-performance control systems due to interference and harsh environments (e.g., vibrations and many metallic objects). Diversity techniques and power control are powerful approaches to reduce latency and enhance reliability at the expense of excessive resource usage due to redundant transmissions. In this paper, we adopt fundamental metrics from reliability literature to wireless communications and provide critical indicators to measure reliability key performance indicators (KPIs) of cyber-physical systems. Then, we design a deep reinforcement learning orchestrator for power control and hybrid automatic repeat request retransmissions to optimize our reliability KPIs. Our orchestrator enables near real-time control and can be implemented on the edge cloud. We implement our framework on 3GPP compliant simulator on a factory automation scenario. Our comprehensive experiments show that, compared to the state-of-the-art, our solution can substantially improve the performance, especially for 5th percentile availability.