基于深度强化学习的智能反射面辅助无人机无线能量传输网络能效优化

Lower production costs have inspired studies on unmanned aerial vehicles (UAV) for wireless communication. However, limited transmission power and size of the UAV make it challenging to use advanced communication models while meeting the growing need for high data rates and energy efficiency (EE). In this paper, we study an energy-efficient UAV network enhanced by an intelligent reflecting surface (IRS) with simultaneous wireless information and power transfer (SWIPT), where the IRS is employed to improve the EE of ground user equipment (GUE). The goal is to maximize the average EE by jointly controlling the UAV's flying route, IRS phase steer, UAV transmission power, and power splitting (PS) ratio of the energy transfer technology. The formulated problem of maximizing the average EE is non-convex and thus challenging to be solved. To address this problem, we propose a deep reinforcement learning (DRL) approach. The modified reward function is implemented to enhance the efficiency of the DRL agent, which is formulated based on the expected signal-to-interference-plus-noise ratio (SINR) map. Simulation results demonstrate that the proposed DRL algorithm achieves lower energy consumption, higher data rate, and improved EE compared to the comparison algorithm.