Optimizing Coverage in Convex Quadrilateral Regions with a Single UAV

TL;DR

This paper investigates optimal UAV deployment strategies for covering convex quadrilateral regions using directional antennas, balancing coverage, signal quality, and energy consumption through an optimization framework and simulations.

Contribution

It introduces a novel optimization framework for UAV altitude and antenna configuration to maximize coverage and signal quality while minimizing energy use in convex quadrilateral areas.

Findings

Optimal UAV altitude minimizes path loss.

Antenna directivity enhances minimum SNR.

Trade-offs between coverage, quality, and energy are quantified.

Abstract

The integration of unmanned aerial vehicles (UAVs) into next-generation wireless networks is a promising solution for providing flexible, efficient coverage. This paper explores the optimal deployment of a single UAV to cover an arbitrary convex quadrilateral region, utilizing a directional antenna with a tiltable beam that produces an elliptical coverage footprint. We examine two distinct coverage scenarios: (i) the largest inscribed ellipse, which maximizes coverage within the quadrilateral while excluding the boundary, and (ii) the smallest circumscribed ellipse, ensuring complete coverage of the entire area. The study formulates an optimization framework that accounts for path loss, signal-to-noise ratio (SNR), and energy consumption to determine the optimal altitude of the UAV. By employing a simplified path loss model, we derive the altitude that minimizes maximum path loss, while also analyzing the impact of antenna directivity on maximizing the minimum SNR at the coverage boundary. Additionally, the UAV's energy consumption is evaluated, considering the power demands during hovering, forward flight, and vertical takeoff. Numerical simulations are presented to illustrate the trade-offs between coverage effectiveness, communication performance, and energy efficiency across various environmental conditions and antenna configurations.