UAV-based wireless multi-modal measurements from AERPAW autonomous data mule (AADM) challenge in digital twin and real-world environments

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 AERPAW student competition, in which an autonomous UAV acted as
a data mule and 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.