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<p>Urban air mobility (UAM) demands reliable sensing and communication to ensure safe and efficient electric vertical take-off and landing (eVTOL) operations in complex urban environments. This paper proposes a multitier tower base station (TBS) and tethered unmanned aerial vehicle (TUAV)-assisted integrated sensing and communication (ISAC) framework for the eVTOL approach and landing, where the rigid shapes of eVTOLs are represented by the millimeter wave (mmWave) radar point clouds rather than simplifying them as a single point, which is closer to the real world. Then, an integrated radar–communication fair optimization problem is formulated to jointly maximize radar and channel capacities while satisfying tether, safety, and terminal constraints. To address the randomness of radar point clouds, a point-cloud-aware deep reinforcement learning (PCDRL) method is developed to solve the above optimization problem. The proposed framework extracts radar point clouds features and learns adaptive TUAV 3D trajectory control and dynamic power ratio allocation across multiple eVTOLs. Simulation results demonstrate that PCDRL improves both radar and communication performance, achieving challenging performance among all the benchmarks.</p>

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A multitier vertiport and tethered UAV cooperative ISAC scheme for eVTOL operation enabled by radar point clouds

  • 期刊论文

  • 发表日期：

    2026年7月23日

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  • Leyan Chen,
  • Shulu Chen,
  • Yang Fei,
  • Yuhao Wang,
  • Kai Wang,
  • Xiaobo Qu

摘要

Urban air mobility (UAM) demands reliable sensing and communication to ensure safe and efficient electric vertical take-off and landing (eVTOL) operations in complex urban environments. This paper proposes a multitier tower base station (TBS) and tethered unmanned aerial vehicle (TUAV)-assisted integrated sensing and communication (ISAC) framework for the eVTOL approach and landing, where the rigid shapes of eVTOLs are represented by the millimeter wave (mmWave) radar point clouds rather than simplifying them as a single point, which is closer to the real world. Then, an integrated radar–communication fair optimization problem is formulated to jointly maximize radar and channel capacities while satisfying tether, safety, and terminal constraints. To address the randomness of radar point clouds, a point-cloud-aware deep reinforcement learning (PCDRL) method is developed to solve the above optimization problem. The proposed framework extracts radar point clouds features and learns adaptive TUAV 3D trajectory control and dynamic power ratio allocation across multiple eVTOLs. Simulation results demonstrate that PCDRL improves both radar and communication performance, achieving challenging performance among all the benchmarks.