Aerial Base Station Placement Leveraging Radio Tomographic Maps

TL;DR

This paper introduces a novel method for deploying aerial base stations using radio tomographic maps and convex optimization to minimize UAVs needed for coverage, outperforming existing approaches.

Contribution

It develops a new scheme that exploits actual channel information via radio tomographic maps for optimal UAV placement, unlike prior average-based methods.

Findings

Proposed algorithm significantly outperforms competitors in simulations.

Radio tomographic maps enable more accurate UAV placement.

Convex optimization ensures minimal UAV deployment while respecting no-fly zones.

Abstract

Mobile base stations on board unmanned aerial vehicles (UAVs) promise to deliver connectivity to those areas where the terrestrial infrastructure is overloaded, damaged, or absent. A fundamental problem in this context involves determining a minimal set of locations in 3D space where such aerial base stations (ABSs) must be deployed to provide coverage to a set of users. While nearly all existing approaches rely on average characterizations of the propagation medium, this work develops a scheme where the actual channel information is exploited by means of a radio tomographic map. A convex optimization approach is presented to minimize the number of required ABSs while ensuring that the UAVs do not enter no-fly regions. A simulation study reveals that the proposed algorithm markedly outperforms its competitors.