arXiv 2602.161632026-02-19cs.NI

基于AERPAW自主数据中继无人机的无线多模测量收集：数字孪生与现实环境中的AADM挑战

Collection: UAV-Based Wireless Multi-modal Measurements from AERPAW Autonomous Data Mule (AADM) Challenge in Digital Twin and Real-World Environments

North Carolina State University（北卡罗来纳州立大学）；Southern Methodist University（南方卫理公会大学）；National University of Sciences and Technology（国立科学与技术大学）；University of Utah（犹他大学）；Florida International University（佛罗里达国际大学）；Columbia University（哥伦比亚大学）；The University of Georgia（佐治亚大学）；Florida Atlantic University（佛罗里达大西洋大学）；The University of North Texas（北德克萨斯大学）；University of Minnesota（明尼苏达大学）

Md Sharif Hossen, Cole Dickerson, Ozgur Ozdemir, Anil Gurses, Mohamed Rabeek Sarbudeen, Thomas Zajkowski, Ahmed Manavi Alam, Everett Tucker, William Bjorndahl, … 展开作者

Md Sharif Hossen, Cole Dickerson, Ozgur Ozdemir, Anil Gurses, Mohamed Rabeek Sarbudeen, Thomas Zajkowski, Ahmed Manavi Alam, Everett Tucker, William Bjorndahl, Fred Solis, Sadaf Javed, Anirudh Kamath, Xiangyao Tang, Joarder Jafor Sadique, Kevin Liu Hermstein, Kaies Al Mahmud, Jose Angel Sanchez Viloria, Skyler Hawkins, Yuqing Cui, Annoy Dey, Yuchen Liu, Ali Gurbuz, Joseph Camp, Rizwan Ahmad, Jacobus van der Merwe, Ahmed Ibrahim Mohamed, Gil Zussman, Mehmet Kurum, Namuduri Kamesh, Zhangyu Guan, Dimitris Pados, George Sklivanitis, Ismail Guvenc, Mihail Sichitiu, Magreth Mushi, Rudra Dutta

备注：: 10 pages, 12 figures

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

本文提出基于AERPAW自主数据中继无人机的无线多模测量数据集，用于数字孪生与现实环境中的自主无线实验基准。

摘要：

在本工作中，我们介绍了一个由AERPAW自主空中数据中继（AADM）挑战组织的无人机无线数据集。该挑战由NSF高级无线（AERPAW）项目组织，是第二次比赛，其中自主无人机作为数据中继机，从多个基站（BSs）下载数据，以在动态无线环境中最大化数据下载量。参赛团队设计了飞行控制和决策算法，以选择通信的基站以及规划飞行轨迹以在任务完成时间内最大化数据下载。比赛分为两个阶段：第一阶段涉及在数字孪生（DT）环境中开发和实验，第二阶段则在户外测试场进行最终测试。每个团队的总得分由两个阶段组成。所得到的数据集包括链路质量和数据下载测量，分别在数字孪生和物理环境中。除了比赛中使用的USRP测量外，该数据集还包括无人机 telemetry、Keysight RF传感器位置估计、LoRa接收器的链路质量测量以及Fortem雷达测量。它支持可重复的研究，包括自主无人机网络、多小区关联和调度、空地传播建模、DT到现实世界的迁移学习以及集成传感与通信，为未来自主无线实验提供基准。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。