Dataset-3: April 2022: LTE I/Q Measurements at Multiple UAV Heights

2023

2022, 3.3 GHz, 4G LTE, Drone, I/Q Dataset, Localization, Propagation Measurements, Rural, Trajectory

Canonical Experiments, UHD Python-API, UHD2:IQ Collection

LTE I/Q

Lead Experimenter

Ozgur Ozdemir, North Carolina State University

Link to Dataset

Dataset Link:
The dataset includes I/Q samples from an srsRAN 4G LTE eNB at 3.5 GHz and GPS logs. The data is captured at a UAV flying at Lake Wheeler Field Labs at 5 different altitudes. SigMF format dataset including format conversion codes can be accessed here [IEEE DataPort]
Post Processing Code:
Post-processing codes for the data to generate representative results can be accessed here [download].
Data Summary:
- I/Q samples at drone height 30 m [download]
- I/Q samples at drone height 50 m [download]
- I/Q samples at drone height 70 m [download]
- I/Q samples at drone height 90 m [download]
- I/Q samples at drone height 110 m [download]

Publications Using This Dataset

G. Reddy, I. Guvenc, M. L. Sichitiu, A. Bhuyan, B. Petersen, J. Abrahamson, “TransfoREM: Transformer aided 3D Radio Environment Mapping,” Proc. IEEE Int. Conf. Commun, May 2026.
M. Rahman, S. J. Maeng, I. Guvenc, C.-W. Wong, M. L. Sichitiu, J. A. Abrahamson, A. Bhuyan, “UAV-Based 3D Spectrum Sensing: Insights on Altitude, Bandwidth, Trajectory, and Effective Antenna Patterns on REM Reconstruction,” IEEE Sensors Journal, January 2026.
S. J. Maeng, H. Kwon, O. Ozdemir, I. Guvenc, “Impact of 3D Antenna Radiation Pattern in UAV Air-to-Ground Path Loss Modeling and RSRP-based Localization in Rural Area,” IEEE Open J. Antennas and Propag, October 2023.
S. J. Maeng, O. Ozdemir, I. Guvenc, M. L. Sichitiu, M. Mushi, R. Dutta, “LTE I/Q Data Set for UAV Propagation Modeling, Communication, and Navigation Research,” IEEE Commun. Mag, September 2023.
S. J. Maeng, O. Ozdemir, I. Guvenc, M. L. Sichitiu, “Kriging-Based 3-D Spectrum Awareness for Radio Dynamic Zones Using Aerial Spectrum Sensors,” IEEE Sensors, February 2023.
M. Rahman, I. Guvenc, D. Matolak, “GS-SBL: Bridging Greedy Pursuit and Sparse Bayesian Learning for Efficient 3D Wireless Channel Modeling,” Proc. IEEE Int. Sym. Antennas and Propagation and USNC-URSI Radio Science Meeting, July 2026.
S. J. Maeng, O. Ozdemir, I. Guvenc, M. Sichitiu, R. Dutta, M. Mushi, “AERIQ: SDR-Based LTE I/Q Measurement and Analysis Framework for Air-to-Ground Propagation Modeling,” Proc. IEEE Aerospace Conference, March 2023.

Equipment and Software Used

LAM carrying LPN, USRP B205mini, UHD2 IQ collection software, SE1 LTE SISO software

Description

These experiments collect IQ samples at different drone heights to measure RSRP. This dataset is used to investigate national radio dynamic zone (NRDZ) research. Experiments are executed at AERPAW 's Lake Wheeler Road Field Lab using the LW1 fixed node. The photos of the LW1 fixed, drone carrying a portable node, and some representative results are provided below.

Representative Results

Pictures in the Experiment: a drone flies Lake Wheeler Site

Result figures

(Left) a drone trajctory and signal strength in 3D over Google, (Center) drone trajectories with different heights in 3D view, and (Right) the change of UAV speed during flights

(Left) RSRP as a function of time with different heights, (Center) RSRP as a function of distance, and (Right) an estimated channel in the frequency domain.

Potential use cases for this dataset include:

Air-to-Ground Propagation Modeling
LTE Network Extension
Enhancements to LTE Receiver Processing Chain
Decoding of LTE Control and Data Channels
Channel Estimation and Prediction in Time and Frequency