Joint Channel Selection and Power Control for Multi-UAV-Enabled Anti-Jamming Communications Based on Game Guided Reinforcement Learning

Unmanned aerial vehicles (UAVs) have been widely employed as airborne base stations to enhance terrestrial communications. However, the growing demand for communications, spectrum and energy resources are increasingly in short supply, while malicious jamming from jammers threatens the communications reliability. To address these challenges, we propose a joint reliable channel selection and power control approach for multi-UAV-enabled communications networks under malicious jamming attacks, with the goal of maximizing user communication capacity under limited energy constraint and avoiding malicious jamming from jammers. Due to the dynamic and time-varying nature of communication environments, we propose an intelligent resource optimization algorithm based on game theory guided reinforcement learning. To be specific, we employ a hierarchical learning algorithm based on the Stackelberg game to help users in the follower layer cooperatively select channels to against co-channel interference and jamming, and develop a deep reinforcement learning-based algorithm for dynamic power control to maintain communication efficiency. Simulation results demonstrate that our proposed approach can significantly improve the user communication rate and achieves faster convergence compared with existing algorithms.