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

TL;DR

This paper introduces a comprehensive UAV wireless dataset from the AERPAW AADM challenge, enabling research in autonomous UAV networking, propagation modeling, and digital twin transfer learning in real-world and simulated environments.

Contribution

It provides a novel, multi-modal dataset combining link quality, telemetry, and sensor data from UAVs in both digital twin and physical settings for wireless research.

Findings

Dataset includes link quality and download measurements in DT and real environments.

Supports research on autonomous UAV networking and air-to-ground propagation.

Serves as a benchmark for future wireless experimentation with UAVs.

Abstract

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.