Accelerating development in UAV network digital twins with a flexible simulation framework
Data files
Jul 17, 2025 version files 701.60 KB
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fixed_autonomous_trajectory_dataset_and_post_processing.zip
696.53 KB
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README.md
5.07 KB
Abstract
Unmanned aerial vehicles (UAVs) enhance coverage and provide flexible deployment in 5G and next-generation wireless networks. The performance of such wireless networks can be improved by developing new navigation and wireless adaptation approaches in digital twins (DTs). However, challenges such as complex propagation conditions and hardware complexities in real-world scenarios introduce a realism gap with the DTs. Moreover, while using real-time full-stack protocols in DTs enables subsequent deployment and testing in a real-world environment, development in DTs requires high computational complexity and involves a long development time. In this paper, we develop a measurement-calibrated Matlab-based simulation framework to accelerate the development cycle to replicate performance in a full-stack UAV wireless network DT. In particular, we use the DT from the NSF AERPAW platform and compare its reports with those generated by our developed simulation framework in wireless networks with similar settings. In both environments, we observe comparable results in terms of RSRP measurement, hence motivating iterative use of the developed simulation environment with the DT.
We have submitted our emulated and simulated UAV trajectory measurement data, post-processing scripts (MATLAB and Python), and high-quality static figures in PDF format. These data support wireless link analysis and UAV mobility modeling. In this dataset, “trajectory” refers to the latitude, longitude, and altitude of the UAV recorded over time.
List of Included Files
fixed_autonomous_trajectory_dataset_and_post_processing.zip\
A compressed archive containing all datasets for:- fixed trajectory (emulation and simulation approaches)
- autonomous trajectory (simulation)\
The archive includes CSV files for all four base stations, figures in PDF format, and all associated MATLAB and Python post-processing scripts.\
See detailed file and variable descriptions in the sections below.
File Descriptions
fixed_trajectory_rsrp_emulation
Included files:
LW1_fixed_trajectory.csvLW2_fixed_trajectory.csvLW3_fixed_trajectory.csvLW4_fixed_trajectory.csvplot_rsrp_emulation.pdfplot_rsrp_distance_emulation.pdf- MATLAB post-processing scripts
Each CSV contains raw UAV measurement data from the NSF AERPAW digital twin platform for one of four LTE base stations along a fixed trajectory. PDF files provide high-quality figures showing RSRP over time and versus distance.
CSV variables:
- time: Timestamp of measurement (YYYY-MM-DD HH:MM:SS.ssssss)
- Altitude: UAV altitude above ground (meters, m)
- BatteryVolts: UAV battery voltage (volts, V)
- GPSFix: Number of valid GPS fixes (count)
- Latitude: UAV latitude (decimal degrees)
- Longitude: UAV longitude (decimal degrees)
- NumberOfSatellites: Number of GPS satellites (count)
- Pitch: UAV pitch angle (radians)
- Roll: UAV roll angle (radians)
- VelocityX: UAV velocity, X-axis (meters/second, m/s)
- VelocityY: UAV velocity, Y-axis (meters/second, m/s)
- VelocityZ: UAV velocity, Z-axis (meters/second, m/s)
- Yaw: UAV yaw angle (radians)
- blerDl: Downlink block error rate (fraction 0–1)
- blerUl: Uplink block error rate (fraction 0–1)
- brateDl: Downlink bitrate (kilobits/second, kbps)
- brateUl: Uplink bitrate (kilobits/second, kbps)
- buff: Buffer size (internal, diagnostic)
- cc: Component carrier index
- cfo: Carrier frequency offset (kilohertz, kHz)
- mcsDl: Downlink modulation and coding scheme index
- mcsUl: Uplink modulation and coding scheme index
- num: Frame number (diagnostic)
- pci: Physical Cell ID of serving base station
- pl: Path loss (decibels, dB)
- rsrp: Reference Signal Received Power (dBm)
- snr: Signal-to-Noise Ratio (dB)
- ta_us: Timing advance (microseconds, µs)
- turbo: Turbo decoder iterations (diagnostic)
fixed_trajectory_rsrp_simulation
Included files:
LW1_log.csvLW2_log.csvLW3_log.csvLW4_log.csvplot_rsrp_simulation.pdfplot_rsrp_distance_simulation.pdf- MATLAB post-processing scripts
Each CSV contains simulated measurement data for a fixed UAV trajectory at one base station. PDF files are static plots of simulation results.
CSV variables:
- time: Timestamp (YYYY-MM-DD HH:MM:SS.sss)
- Longitude: UAV longitude (decimal degrees)
- Latitude: UAV latitude (decimal degrees)
- Altitude: UAV altitude (meters, m)
- rsrp: Reference Signal Received Power (dBm)
- snr: Signal-to-Noise Ratio (dB)
- datarate: Downlink data rate (megabits/second, Mbps)
autonomous_trajectory_rsrp_simulation
Included files:
LW1_log.csvLW2_log.csvLW3_log.csvLW4_log.csvplot_rsrp_simulation.pdfplot_throughput_distance_vs_time_lw12.pdfplot_throughput_distance_vs_time_lw34.pdf- MATLAB and Python post-processing scripts
Each CSV contains simulated measurement data for an autonomous (algorithm-driven) UAV trajectory at one base station. PDF files provide static plots for time-varying RSRP and throughput.
CSV variables:
- time: Timestamp (YYYY-MM-DD HH:MM:SS.sss)
- longitude: UAV longitude (decimal degrees)
- latitude: UAV latitude (decimal degrees)
- altitude: UAV altitude (meters, m)
- speed: UAV speed (meters/second, m/s); may be blank in some rows
- rsrp: Reference Signal Received Power (dBm)
- snr: Signal-to-Noise Ratio (dB)
- datarate: Downlink data rate (megabits/second, Mbps)
- Prx: Received power at receiver (dBm)
Recommended Software
- CSV files: Open with LibreOffice Calc, Microsoft Excel, or Google Sheets.
- PDF files: View with Adobe Acrobat Reader.
- Scripts: View with Notepad, TextEdit, VS Code, or any text editor.
- ZIP file: Unzip with built-in OS utilities, 7-Zip, or WinRAR.
Further Information
For methodology, simulation framework, or support, see:\
https://github.com/mhossenece/UAVFlexSimFramework\
For questions, contact the corresponding author.