Reinforcement Learning Optimized Throughput for 5G Enhanced Swarm UAS Networking

The ubiquitous of 5G New Radio (5G NR) accelerates the massive implementations in many fields including swarm Unmanned Aircraft System (UAS) networking. The ultra capacities of 5G NR can provide more sufficient networking services for the swarm UAS networking which can enable swarm UAS to deploy in more complex and challenging scenarios to achieve missions. However, the conventional swarm UAS networking are mainly centralized or hierarchical which is vulnerable to the dynamics and the deployment of swarm UAS networking on a large scale. In this paper, we formulate a cell wall communications for the heterogeneous swarm UAS networking with the inspiration of biological cell wall communication. Fueled by reinforcement learning, we resolve the edge-coloring problem of cell wall communication scheduling to achieve the maximum throughput between the heterogeneous swarm UAS networking globally. The evaluation shows our proposed reinforcement learning enabled algorithm can surpass the conventional scheduling algorithms over 90% when the time piece is less than 0.01s and achieve the optimal throughput for the heterogeneous swarm UAS networking.