2023 journal article
LTE I/Q Data Set for UAV Propagation Modeling, Communication, and Navigation Research
IEEE COMMUNICATIONS MAGAZINE, 61(9), 90–96.
author keywords: Wireless communication; Visualization; Codes; Radio navigation; Metadata; Autonomous aerial vehicles; Long Term Evolution
TL;DR:
This article introduces raw LTE in-phase and quadrature sample data sets obtained from physical field experiments of the NSF AERPAW experimentation platform and implements a standardized metadata format that can be used to replicate the results obtained from the collected datasets.
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open access via arxiv.org (repository)
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(Web of Science)
Source: Web Of Science
Added: October 30, 2023
Unmanned aerial vehicles (UAVs) have recently been gaining considerable attention due to their vast range of potential applications. To facilitate UAV use cases involving beyond visual line of sight (BVLOS), cellular networks have emerged as ground connectivity points, enabling remote control and payload communication for UAV links. However, the availability of limited datasets obstructs the study of cellular technology coverage for UAV flights at different altitudes and the development of machine learning (ML) techniques for improving UAV communication and navigation. In this article, we introduce raw LTE in-phase and quadrature (I/Q) sample data sets obtained from physical field experiments of the NSF AERPAW experimentation platform. A UAV equipped with a software-defined radio (SDR) was flown at altitudes ranging from 30 m to 110 m, collecting raw I/Q samples from an SDR-based LTE base station operating at 3.51 GHz. We have implemented a standardized metadata format that can be used to replicate the results obtained from the collected datasets. The post-processing of raw I/Q samples is described and representative results are provided. In the end, we give examples of potential uses of the provided dataset, post-processing sample code, and I/Q collection sample experiment code by other ML, wireless, and UAV researchers.