UAV-Based Volumetric Measurements toward Radio Environment Map Construction and Analysis

Area of Science:

• Electrical Engineering
• Aerospace Engineering
• Computer Science

Background:

• Unmanned aerial vehicle (UAV) communications offer agile coverage for mobile nodes in 3D space, necessitating efficient spectrum utilization.
• Radio environment maps (REMs) are crucial for managing spectrum in dense aerial networks, but real-world data collection is challenging.
• Existing research on REMs is limited due to difficulties in acquiring radio frequency (RF) data from actual measurement campaigns.

Purpose of the Study:

• To present a novel experimental setup for constructing 3D radio environment maps using UAVs.
• To analyze the temporal, spatial, and frequency characteristics of RF signals in UAV-based communication networks.
• To investigate spectrum utilization and its variation with altitude within the 2.4 GHz band.

Main Methods:

• A novel experimental setup involving a constellation of three UAVs transmitting signals, measured by a software-defined radio (SDR) on a fourth UAV.
• The SDR-equipped UAV followed a defined trajectory to cover a 2D area at four different altitudes, enabling 3D REM construction.
• Collected RF data was processed to analyze features like received mean power level, average difference of mean power, and percentage of meaningful correlations.

Main Results:

• Analysis of signal features in temporal, spatial, and frequency domains revealed spectrum utilization patterns within a 20 MHz band at 2.4 GHz.
• Measurements demonstrated variations in spectrum utilization and signal characteristics with changes in altitude.
• Identified regions of high and low interest for measurement reduction and provided a basis for spectrum occupancy prediction.

Conclusions:

• The developed experimental setup and analysis provide a foundation for efficient spectrum management in UAV communication networks.
• The findings support research into reducing measurement overhead for REM construction by targeting key areas.
• This work contributes to enabling robust spectrum occupancy prediction and coexistence strategies for UAV-based communication systems.