Joint 3D trajectory and phase shift optimization via deep reinforcement learning for RIS-assisted UAV communication systems

Unmanned aerial vehicle (UAV) can be deployed as aerial base station to provide communication services for the user equipments (UEs). However, in urban environments, the links between UAV and UEs might be frequently blocked by obstacles, leading to severely adverse effects on the quality of service (QoS) of UEs. Additionally, due to the limited energy of the UAV, it might not always be feasible to re-establish the line-of- sight (LoS) links by frequently adjusting the positions of the UAV. In this context, the reconfigurable intelligent surface (RIS) is utilized to enhance the transmission range of UAV-UE links by reflecting incident signals to UEs. In this paper, we investigate the RIS-assisted UAV communication systems with the goal of maximizing the energy efficiency of the UAV through a joint optimization of the UAV's trajectory and the RIS's phase shift. The formulated optimization problem is non-convex, and challenging to solve in a polynomial time. Therefore, an effective deep reinforcement learning (DRL)-based solution, named Dueling DQN-PER is proposed, which combines the Dueling DQN algorithm with the prioritized experience replay (PER) technique. To ensure the fairness among all UEs, we design a service fairness index, and integrate it into the reward function when designing the proposed algorithm. Numerical results demonstrate that: 1) the proposed Dueling DQN-PER algorithm is capable of improving the system energy efficiency and has a better training performance than benchmark schemes; 2) by devising the service fairness index, the fairness among all UEs is ensured while enhancing the system performance in energy efficiency; 3) the RIS-assisted UAV communication systems benefit from significant energy efficiency gain over the systems without RIS.