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Unmanned aerial vehicle (UAV) has demonstrated significant potential for applications in the new era of mobile network communications. However, the limitations of the resources carried by the UAV pose significant challenges to load capacity and operational sustainability. This paper presents a trajectory planning algorithm for UAVs, enhanced by a reconfigurable intelligent surface (RIS) and based on deep reinforcement learning. Initially, a system model for the RIS-assisted UAV communication network is developed. Subsequently, the study explores an optimization problem aimed at maximizing energy efficiency, with the UAV trajectory, transmission power, and phase shift as decision variables. Then, we propose a parallel TD3-based UAV trajectory planning algorithm assisted by RIS with optimally controllable phase shift (PTD3-RO). Both UAV and RIS are regarded as independent agents, which continuously optimize their behavior strategies through observation and learning. Finally, simulation results indicate that the PTD3-RO algorithm enables effective collaboration between UAV and RIS, and significantly enhances the energy efficiency of RIS assisted UAV communication system.

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RIS Assisted UAV Trajectory Planning Based on Deep Reinforcement Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Shuo Chen,
  • Zhongda Liu,
  • Jianwei Wang,
  • Xuehua Li

摘要

Unmanned aerial vehicle (UAV) has demonstrated significant potential for applications in the new era of mobile network communications. However, the limitations of the resources carried by the UAV pose significant challenges to load capacity and operational sustainability. This paper presents a trajectory planning algorithm for UAVs, enhanced by a reconfigurable intelligent surface (RIS) and based on deep reinforcement learning. Initially, a system model for the RIS-assisted UAV communication network is developed. Subsequently, the study explores an optimization problem aimed at maximizing energy efficiency, with the UAV trajectory, transmission power, and phase shift as decision variables. Then, we propose a parallel TD3-based UAV trajectory planning algorithm assisted by RIS with optimally controllable phase shift (PTD3-RO). Both UAV and RIS are regarded as independent agents, which continuously optimize their behavior strategies through observation and learning. Finally, simulation results indicate that the PTD3-RO algorithm enables effective collaboration between UAV and RIS, and significantly enhances the energy efficiency of RIS assisted UAV communication system.