Dynamic Spatial Diversity via Reinforcement Learning for Ultra-Reliable Low Latency Communications

Digital transformation within smart manufacturing presents new challenges for wireless communication, demanding stringent reliability and latency. One prominent approach to meet these requirements in 5G technology is to leverage spatial diversity techniques, such as the transmission of duplicated packets via independent user plane paths. While spatial diversity and hardware redundancy ensure high availability and reduced latency, they increase wireless resource utilization significantly. In this paper, we investigate a scenario where large industrial devices can access multiple user plane paths via multiple user equipment. To manage this effectively, we propose a deep Q-network-based reinforcement learning control framework that optimizes spatial diversity use to maximize communication service availability with minimized wireless resource usage. We implement our solution on a 3GPP-compliant simulator for a factory automation scenario. Our results show that our framework can adapt to varying delay bounds and greatly enhance communication service availability compared to the baselines. Remarkably, our method achieves these results more resource-efficiently, evading the baseline's need for double the bandwidth for comparable availability levels.