In this work, we present an unmanned aerial vehicle (UAV) wireless dataset collected as part of the AERPAW Autonomous Aerial Data Mule (AADM) challenge, organized by the NSF Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW) project. The AADM challenge was the second competition in which an autonomous UAV acted as a data mule, where the UAV downloaded data from multiple base stations (BSs) in a dynamic wireless environment. Participating teams designed flight control and decision-making algorithms for choosing which BSs to communicate with and how to plan flight trajectories to maximize data download within a mission completion time. The competition was conducted in two stages: Stage 1 involved development and experimentation using a digital twin (DT) environment, and in Stage 2, the final test run was conducted on the outdoor testbed. The total score for each team was compiled from both stages. The resulting dataset includes link quality and data download measurements, both in DT and physical environments. Along with the USRP measurements used in the contest, the dataset also includes UAV telemetry, Keysight RF sensors position estimates, link quality measurements from LoRa receivers, and Fortem radar measurements. It supports reproducible research on autonomous UAV networking, multi-cell association and scheduling, air-to-ground propagation modeling, DT-to-real-world transfer learning, and integrated sensing and communication, which serves as a benchmark for future autonomous wireless experimentation.

翻译：本研究提出了一个无人机无线数据集，该数据集采集自美国国家科学基金会（NSF）先进无线空中实验与研究平台（AERPAW）项目组织的AERPAW自主空中数据中继（AADM）挑战赛。AADM挑战赛是第二次以自主无人机作为数据中继的竞赛，无人机在动态无线环境中从多个基站下载数据。参赛团队设计了飞行控制与决策算法，用于选择通信基站并规划飞行轨迹，以在任务完成时间内最大化数据下载量。竞赛分为两个阶段：第一阶段使用数字孪生环境进行开发与实验；第二阶段在户外测试平台进行最终测试运行。各团队的总分由两个阶段的成绩综合评定。最终数据集包含数字孪生环境与物理环境中的链路质量和数据下载测量结果。除竞赛中使用的USRP测量数据外，该数据集还包含无人机遥测数据、Keysight射频传感器位置估计、LoRa接收机的链路质量测量结果以及Fortem雷达测量数据。该数据集支持自主无人机组网、多小区关联与调度、空对地传播建模、数字孪生到真实世界的迁移学习以及集成感知与通信等可重复研究，为未来自主无线实验提供了基准。