Robust UAV-Oriented Wireless Communications via Multi-Agent Deep Reinforcement Learning to Optimize User Coverage

In this study, we deploy drones as dynamic base stations to address the issue of optimizing user coverage in areas without fixed base station infrastructure. To optimize drone placement, we employ Deep Q-Learning, beginning with a centralized approach due to its simplicity and ease of training. In this centralized approach, all drones are trained simultaneously. We also employ a decentralized technique in which each drone acts autonomously while sharing a common neural network, allowing for individualized learning. In addition, we explore the impacts of jamming on UAVs and provide a reliable approach for mitigating this interference. To boost robustness, we employ stochastic user distributions, which train our policy to successfully respond to a wide range of user situations.