Salih, Nameer MufeedAldababsa, MahmoudYahya, Khalid2024-09-112024-09-1120231434-84111618-0399https://doi.org/10.1016/j.aeue.2023.154933https://hdl.handle.net/11363/7666Reconfigurable intelligent surfaces (RIS) have emerged as a prominent and widely debated solution to enhance the energy efficiency of wireless communications. This article explores the potential of combining RIS with unmanned aerial vehicles (UAVs)-RIS, to provide on-demand deployment services in dynamic environments. However, the energy limitations of battery-powered UAVs can curtail the advantages of UAV-RIS systems. To address this challenge and enhance the durability of UAV-RIS deployments, we introduce an energy harvesting technique for simultaneous wireless information and power transfer (SWIPT) coupled with optimized resource allocation and energy harvesting from incoming radio frequency (RF) signals. In contrast to previous research, our approach involves the division of passive reflected arrays across geometric space, facilitating simultaneous information transfer and energy harvesting. Additionally, we are developing deep Q-network (DQN) and deep deterministic policy gradient (DDPG) techniques to dynamically allocate UAV-RIS resources in both temporal and spatial dimensions. This allocation maximizes the overall harvested energy while upholding communication quality for every user. Our simulation results conclusively demonstrate the substantial superiority of the pro-posed UAV-RIS SWIPT system over the benchmark.eninfo:eu-repo/semantics/closedAccessReconfigurable intelligent surface (RIS)unmanned aerial vehicle (UAV)simultaneous wireless information and power transfer (SWIPT)deep Q -network (DQN)deep deterministic policy gradient (DDPG)Article17110.1016/j.aeue.2023.1549332-s2.0-85173048618WOS:001092887800001Q2