Optimizing Number, Placement, and Backhaul Connectivity of Multi-UAV Networks

TL;DR

This paper addresses the complex problem of optimally deploying multiple UAV base stations for rural wireless coverage, ensuring minimal UAV use, backhaul connectivity, and complete ground user coverage, through a novel low-complexity algorithm.

Contribution

The paper formulates the joint UAV placement, backhaul, and coverage problem as an ILP, proves its NP-hardness, and proposes an efficient algorithm with performance guarantees.

Findings

The proposed algorithm closely matches optimal solutions in small scenarios.

It significantly outperforms baseline methods in large scenarios.

Up to 17% fewer UAVs needed for coverage and connectivity.

Abstract

Multi-Unmanned Aerial Vehicle (UAV) Networks is a promising solution to providing wireless coverage to ground users in challenging rural areas (such as Internet of Things (IoT) devices in farmlands), where the traditional cellular networks are sparse or unavailable. A key challenge in such networks is the 3D placement of all UAV base stations such that the formed Multi-UAV Network (i) utilizes a minimum number of UAVs while ensuring -- (ii) backhaul connectivity directly (or via other UAVs) to the nearby terrestrial base station, and (iii) wireless coverage to all ground users in the area of operation. This joint Backhaul-and-coverage-aware Drone Deployment (BoaRD) problem is largely unaddressed in the literature, and, thus, is the focus of the paper. We first formulate the BoaRD problem as Integer Linear Programming (ILP). However, the problem is NP-hard, and therefore, we propose a low complexity algorithm with a provable performance guarantee to solve the problem efficiently. Our simulation study shows that the Proposed algorithm performs very close to that of the Optimal algorithm (solved using ILP solver) for smaller scenarios, where the area size and the number of users are relatively small. For larger scenarios, where the area size and the number of users are relatively large, the proposed algorithm greatly outperforms the baseline approaches -- backhaul-aware greedy and random algorithm, respectively by up to 17% and 95% in utilizing fewer UAVs while ensuring 100% ground user coverage and backhaul connectivity for all deployed UAVs across all considered simulation setting.