On-Demand Deployment of Multiple Aerial Base Stations for Traffic Offloading and Network Recovery

TL;DR

This paper proposes efficient algorithms for deploying multiple UAVs in 3D space to provide rapid wireless coverage during network disruptions, combining optimization and unsupervised learning techniques.

Contribution

It introduces a novel low complexity algorithm based on electrostatics for 3D UAV deployment, improving upon existing methods in speed and effectiveness.

Findings

The electrostatics-based algorithm outperforms greedy approaches in coverage efficiency.

The proposed method is effective in various system scenarios.

It demonstrates superiority over recent related work.

Abstract

Unmanned aerial vehicles (UAVs) are being utilized for a wide spectrum of applications in wireless networks leading to attractive business opportunities. In the case of abrupt disruption to existing cellular network operation or infrastructure, e.g., due to an unexpected surge in user demand or a natural disaster, UAVs can be deployed to provide instant recovery via temporary wireless coverage in designated areas. A major challenge is to determine efficiently how many UAVs are needed and where to position them in a relatively large 3D search space. To this end, we formulate the problem of 3D deployment of a fleet of UAVs as a mixed integer linear program, and present a greedy approach that mimics the optimal behavior assuming a grid composed of a finite set of possible UAV locations. In addition, we propose and evaluate a novel low complexity algorithm for multiple UAV deployment in a continuous 3D space, based on an unsupervised learning technique that relies on the notion of electrostatics with repulsion and attraction forces. We present performance results for the proposed algorithm as a function of various system parameters and demonstrate its effectiveness compared to the close-to-optimal greedy approach and its superiority compared to recent related work from the literature.