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LTE I/Q Data Set for UAV Propagation Modeling, Communication, and Navigation Research

Published: 03 October 2023 Publication History

Abstract

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.

References

[1]
S. Hayat, E. Yanmaz, and R. Muzaffar, “Survey on Unmanned Aerial Vehicle Networks for Civil Applications: A Communications Viewpoint,” IEEE Commun. Surveys Tuts., vol. 18, no. 4, Apr. 2016, pp. 2624–61.
[2]
J. Sae et al., “Public LTE Network Measurements with Drones in Rural Environment,” Proc. IEEE Veh. Technol. Conf., Kuala Lumpur, Malaysia, Apr. 2019, pp. 1–5.
[3]
R. Amorim et al., “Measured Uplink Interference Caused by Aerial Vehicles in LTE Cellular Networks,” IEEE Wireless Commun. Lett., vol. 7, no. 6, Dec. 2018, pp. 958–61.
[4]
M. M. U. Chowdhury et al., “3-D Trajectory Optimization in UAV-Assisted Cellular Networks Considering Antenna Radiation Pattern and Backhaul Constraint,” IEEE Trans. Aerospace Electronic Syst., vol. 56, no. 5, 2020, pp. 3735–50.
[5]
Y. Zeng et al., “Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV With Deep Reinforcement Learning,” IEEE Trans. Wireless Commun., vol. 20, no. 7, 2021, pp. 4205–20.
[6]
S. J. Maeng et al., “LTE I/Q Measurement by AER- PAW Platform for Air-to-Ground Propagation Modeling,” IEEE Dataport, 2022; available: https://doi.org/10.21227/0p43%E2%80%930d72; https://doi.org/10.24433/CO.6586124.v1
[7]
Signal Metadata Format Specification v1.0.0,” available: https://github.com/gnuradio/SigMF/blob/sigmf-v1.x/sigmf-spec.md
[8]
S. J. Maeng et al., “AERIQ: SDR-Based LTE I/Q Measurement and Analysis Framework for Air-to-Ground Propagation Modeling,” Proc. IEEE Aerospace Conf., Big Sky, MT, USA, Mar. 2023.
[9]
M. M. U. Chowdhury et al., “A Taxonomy and Survey on Experimentation Scenarios for Aerial Advanced Wireless Testbed Platforms,” Proc. IEEE Aerospace Conf., Big Sky, MT, USA, Mar. 2021, pp. 1–20.
[11]
MathWorks, ‘'LTE Toolbox Documentation,” available: https://www.mathworks.com/help/lte/index.html
[12]
S. Duangsuwan, P. Juengkittikul, and M. Myint Maw, “Path Loss Characterization Using Machine Learning Models for GS-to-UAV-Enabled Communication in Smart Farming Scenarios,” Int'l. J. Antennas and Propagation, vol. 2021, 2021.
[13]
S. Ureten, A. Yongacoglu, and E. Petriu, “A comparison of Interference Cartography Generation Techniques in Cognitive Radio Networks,” Proc. IEEE Int'l. Conf. Commun., Ottawa, ON, Canada, June 2012, pp. 1879–83.
[14]
S. J. Maeng et al., “SDR-Based 5G NR C-Band I/Q Monitoring and Surveillance in Urban Area Using a Helikite,” Proc. IEEE Int'l. Conf. Ind. Technol., Orlando, FL, USA, Apr. 2023.
[15]
A. Panicker et al., “AERPAW Emulation Overview and Preliminary Performance Evaluation,” Computer Networks, vol. 194, Apr. 2021, p. 108083.

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      cover image IEEE Communications Magazine
      IEEE Communications Magazine  Volume 61, Issue 9
      September 2023
      168 pages

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      Published: 03 October 2023

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