Energy Harvesting UAV-RIS-Assisted Maritime Communications Based on Deep Reinforcement Learning Against Jamming

With the rapid development of maritime activities, efficient and reliable maritime communications have attracted ever-increasing attention, and mounting reconfigurable intelligent surface (RIS) on unmanned aerial vehicle (UAV), called UAV-RIS, can provide flexible and adaptable services for maritime communications. In this paper, we investigate a UAV-RIS-assisted maritime communication system under a malicious jammer, where a UAV-RIS is deployed to jointly adjust its placement and RIS surface elements to maximize the system energy efficiency (EE) and guarantee quality of service requirements against jamming attacks. In addition, an adaptive energy harvesting scheme is developed for information transmission (IT) and energy harvesting (EH) simultaneously to enhance the endurance of the UAV by deploying different IT times for each RIS element. Considering the non-convex optimization problem and highly complex maritime environments, an intelligent resource management approach based on deep reinforcement learning is proposed to jointly optimize the base station's transmit power, placement of UAV-RIS, and RISs reflecting beamforming. Furthermore, hindsight experience replay is adopted to improve the learning efficiency and performance. The simulation results demonstrate that the proposed approach achieves the better EE and EH performances under different real-world settings compared with existing popular approaches.