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

Abstract

Unmanned aerial vehicle (UAV)-empowered communications have gained significant attention in recent years due to the promise of agile coverage provision for a large number of various mobile nodes on the ground and in three-dimensional (3D) space. Consequently, there is a need for efficient spectrum utilization in these dense aerial networks, which is characterized through radio environment maps (REMs), the construction of which is an important research area. Nevertheless, due to the difficult collection of radio frequency (RF) data, there are limited works that are based on real-world measurement campaigns. This paper presents a novel experimental setup that includes a constellation of three UAVs, the communication signals of which are measured by a software-defined radio (SDR) mounted on a separate UAV. It follows a trajectory that defines the REM’s two-dimensional (2D) area on a plane, executed at four altitudes, to extend the REM to 3D. The measurements are then processed and their features (received mean power level, average difference of the mean power, percentage of meaningful correlations) are analyzed in the temporal, spatial, and frequency domains to determine the utilization of a 20 MHz band in the 2.4 GHz spectrum, as well as their variation with altitude. This analysis provides a base for research in reducing the amount of measurements (by identifying the regions of low and of high interest) and spectrum occupancy prediction for UAV-based communication coexistence.

Deep Learning for Reduced Sampling Spatial 3-D REM Reconstruction

“…Development of interpolation methods for REM has seen remarkable progress in the recent years, including multidomain REM reconstruction and measurement collection approaches, as well as in applications for vehicular and UAV-based communications, spectrum sharing, and terrestrial networks coverage mapping [4], [5], [18], [25], [26]. This Section describes the main features and limitations of relevant works in the field of reduced sampling interpolation for spatial REM reconstruction, to outline the contributions of this paper.…”

“…Thus, it allows for dividing the coverage region into clusters where CR nodes can communicate under the interference constraints of the incumbent users. These applications emphasize the value of accurate REM reconstruction from reduced sets of measurements, which becomes a more difficult problem in spatial 3D due to the RSS variations in height [18].…”

“…Consequently, the dimension of space affects the model's complexity depending on the spatial grid resolution, and as described in Section V.D, the volume of data grows exponentially in the 3D case. In addition, the change in the RSS varies more significantly with height than on a single plane, for the reason of which, a higher resolution will lead to a better spectrum occupancy characterization, especially for unmanned aerial vehicle (UAV) based communications [18]. Therefore, employing a reduced set of measurements will decrease the computational intensity of the processing.…”

“…2) RW measurements -more rarely used due to the need for the deployment of expensive equipment in various scenarios, which are difficult to implement. This is true especially when it comes to UAV-based sensors, as they are usually limited to operate in very specific areas used for drone flights [18], [20], [21]. 3) PL models -classical method for obtaining the channel gain between a TX and a RX, via statistical models fitted by RW measurements [17], [22], [23].…”

Rapid Geometric Evaluation of Transportation Infrastructure Based on a Proposed Low-Cost Portable Mobile Laser Scanning System

“…Unfortunately, the measuring distance induces high measurement error, making it hard for quantitative evaluation of small objects [ 10 ]. UAVs can quickly cover a broad area from all angles and obtain high-resolution photographs from close range, realizing the object’s three-dimensional measurement [ 11 ]. However, data accuracy depends on the reliability of the positioning system and IMU [ 12 ], and the mass image demand of tilt photography modeling greatly increases flight time [ 13 ], reducing the efficiency advantage.…”