PPOSWC: Deep Reinforcement Learning Recharging Scheduling for Effective Service in Multi-UAV Aided Networks

In the era of 5G and beyond, Unmanned Aerial Vehicles (UAVs) have emerged as a promising technology to extend and improve wireless communication infrastructure. This is thanks to their flexibility, low cost, and high probability of establishing line-of-sight connections. As the UAV s' missions increase in length and complexity, battery life becomes the main bottleneck for deploying fully autonomous UAV systems at larger scales. In practice, battery replenishment operations are inevitable to avoid increased costs. However, carefully scheduling these operations is crucial to prevent hurting the mission's perfor- mance. In this work, we propose a fully autonomous scheduling solution based on Proximal Policy Optimization (PPO) that aims to reach the required mission duration without losing a critical number of UAVs and maximizing the aggregate throughput in a free path loss scenario. Extensive simulations have shown that the proposed algorithm outperforms baselines by 119.55% and 84.66% in maximizing the lifetime metric of UAVs and the minimum throughput for users, respectively.