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<p>With the rapidly evolving landscape of the Internet of Things (IoT) and the ubiquitous utilization of mobile devices, unmanned aerial vehicles (UAVs) play a crucial role in data collection. To address this challenging problem of limited onboard energy and flight time, we propose a novel approach leveraging reconfigurable intelligent surfaces (RIS) to enhance energy efficiency and optimize uplink transmission rates. Our system aims to determine optimal UAV paths in complex 3D urban environments, enabling rapid information collection while maximizing energy efficiency. We introduce the deep reinforcement learning (DRL) called RIS-prioritized twin delayed deep deterministic policy gradient (RIS-PTD3) algorithm, which maximizes communication rates while effectively exploring surroundings. Experimental validation across various user distributions and environmental conditions demonstrates the robustness and stability of our approach.</p>

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RIS-Assisted Energy-Efficient UAV Data Collection Method Based on Deep Reinforcement Learning

  • 期刊论文

  • 发表日期：

    2025年4月9日

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  • Lu Dong,
  • Mengjiao Lu,
  • Yang Wu,
  • Xiaomeng Li,
  • Yongbao Wu,
  • Xin Yuan

摘要

With the rapidly evolving landscape of the Internet of Things (IoT) and the ubiquitous utilization of mobile devices, unmanned aerial vehicles (UAVs) play a crucial role in data collection. To address this challenging problem of limited onboard energy and flight time, we propose a novel approach leveraging reconfigurable intelligent surfaces (RIS) to enhance energy efficiency and optimize uplink transmission rates. Our system aims to determine optimal UAV paths in complex 3D urban environments, enabling rapid information collection while maximizing energy efficiency. We introduce the deep reinforcement learning (DRL) called RIS-prioritized twin delayed deep deterministic policy gradient (RIS-PTD3) algorithm, which maximizes communication rates while effectively exploring surroundings. Experimental validation across various user distributions and environmental conditions demonstrates the robustness and stability of our approach.