Wireless Datasets for Aerial Networks

TL;DR

This work addresses the lack of open, high-quality aerial wireless datasets by surveying publicly available datasets collected on the AERPAW platform. It documents hardware/software pipelines, data formats (SigMF-compatible), representative results, and post-processing tools across I/Q, spectrum, KPI, LoRa, multipath, localization, and RT-comparison datasets. The paper emphasizes reproducibility and provides guidance on using these datasets to validate propagation models, train machine learning algorithms, and benchmark UAV-assisted wireless systems. It demonstrates the breadth of 3D propagation datasets and practical use cases, highlighting the datasets’ potential to accelerate development toward 5G-Advanced and future 6G aerial networks. Overall, the work offers a FAIR-aligned, multi-technology resource that supports model validation, data-driven design, and simulation-to-reality transfers for airborne wireless research.

Abstract

The integration of unmanned aerial vehicles (UAVs) into 5G-Advanced and future 6G networks presents a transformative opportunity for wireless connectivity, enabling agile deployment and improved LoS communications. However, the effective design and optimization of these aerial networks depend critically on high-quality, empirical data. This paper provides a comprehensive survey of publicly available wireless datasets collected from an airborne platform called Aerial Experimentation and Research Platform on Advanced Wireless (AERPAW). We highlight the unique challenges associated with generating reproducible aerial wireless datasets, and review the existing related works in the literature. Subsequently, for each dataset considered, we explain the hardware and software used, present the dataset format, provide representative results, and discuss how these datasets can be used to conduct additional research. The specific aerial wireless datasets presented include raw I/Q samples from a cellular network over different UAV trajectories, spectrum measurements at different altitudes, flying 4G base station (BS), a 5G-NSA Ericsson network, a LoRaWAN network, an radio frequency (RF) sensor network for source localization, wireless propagation data for various scenarios, and comparison of ray tracing and real-world propagation scenarios. References to all datasets and post-processing scripts are provided to enable full reproducibility of the results. Ultimately, we aim to guide the community toward effective dataset utilization for validating propagation models, developing machine learning algorithms, and advancing the next generation of aerial wireless systems.

Figures (37)

• Figure 1: Illustration of the AERPAW large multirotor-type UAV setup for the experiment, where the UAV carries a portable node funderburk2022aerpaw.
• Figure 2: Campaign environment and UAV trajectory for the I/Q measurement dataset: (a) Google Earth view of the site, (b) BS or transmitter, (c) pre-planned UAV trajectory, and (d) AERPAW UAV for I/Q signal reception.
• Figure 3: Snapshot of I/Q samples and GPS logs for I/Q Measurement Dataset.
• Figure 4: Representative results from the Wireless I/Q dataset: (a) LTE resource grid, (b) estimated channel, (c) RSRP along UAV trajectory, (d) time vs. 3D distance (UAV altitude: $70\,\mathrm{m}$), (e) time vs. UAV speed (UAV altitude: $70\,\mathrm{m}$), and (f) 3D distance vs. RSRP with path loss fitting (UAV altitude: $70\,\mathrm{m}$).
• Figure 5: Measurement setup and procedure for spectrum data collection using the helikite-mounted portable node. (a) Experimental setup of the portable node on the tethered helikite. (b) Spectrum sweep procedure.