Wireless Datasets for Aerial Networks

2025-10-09

AI Summary

The lack of high-quality, reproducible, publicly available over-the-air wireless datasets hinders research on 5G-Advanced/6G air-ground integrated networks. Method: Leveraging the AERPAW experimental platform, this work systematically constructs and open-sources the first multidimensional real-world dataset, acquired via software-defined radios mounted on unmanned aerial vehicles (UAVs). The dataset spans RF sensing, LoRaWAN, and 5G non-standalone (NSA) air-to-ground communications across diverse altitudes, flight trajectories, and environmental conditions. It integrates spectrum sensing, flying base station deployment, ray-tracing simulations, and empirical measurements for high-fidelity aerial channel characterization. Contribution/Results: The released dataset includes raw I/Q samples, propagation parameters, and standardized preprocessing scripts. It enables rigorous validation of propagation models, machine learning–driven air-interface optimization, and foundational research toward 6G space-air-ground integrated networks.

Technology Category

• Application Domains: Mobility, Driving & Flight
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data generation and simulation

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.

Problem

• Surveying wireless datasets from aerial platforms for network optimization
• Addressing challenges in generating reproducible aerial wireless measurement data
• Providing datasets to validate propagation models and develop ML algorithms

Innovation

• AERPAW platform collects aerial wireless datasets
• Datasets include cellular I/Q samples and spectrum measurements
• Provides reproducible data for model validation and ML