Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach

@article{Wang2021TrajectoryDF,
  title={Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach},
  author={Yang Wang and Zhen Gao and Jun Zhang and Xianbin Cao and Dezhi Zheng and Yue Gao and Derrick Wing Kwan Ng and Marco di Renzo},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.11015}
}
  • Yang Wang, Zhen Gao, +5 authors M. Renzo
  • Published 23 July 2021
  • Computer Science, Engineering, Mathematics
  • ArXiv
In this paper, we investigate an unmanned aerial vehicle (UAV)-assisted Internet-of-Things (IoT) system in a sophisticated three-dimensional (3D) environment, where the UAV’s trajectory is optimized to efficiently collect data from multiple IoT ground nodes. Unlike existing approaches focusing only on a simplified two-dimensional scenario and the availability of perfect channel state information (CSI), this paper considers a practical 3D urban environment with imperfect CSI, where the UAV’s… Expand

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