Backhaul-aware Drone Base Station Placement and Resource Management for FSO-based Drone-assisted Mobile Networks

TL;DR

This paper proposes a joint optimization framework for drone base station placement and bandwidth allocation in FSO-based drone-assisted networks, considering backhaul capacity constraints to improve coverage and capacity in challenging scenarios.

Contribution

It introduces a novel joint optimization problem and the BROAD algorithm that accounts for FSO backhaul constraints, which was overlooked in prior DBS placement solutions.

Findings

BROAD effectively improves network coverage and capacity.

Joint optimization outperforms traditional placement methods.

Simulation results validate the algorithm's efficiency and effectiveness.

Abstract

In drone-assisted mobile networks, Drone-mounted Base Stations (DBSs) are responsively and flexibly deployed over any Places of Interest (PoI), such as sporadic hotspots and disaster-struck areas, where the existing mobile network infrastructure is unable to provide wireless coverage. In this paper, a DBS is an aerial base station to relay traffic between a nearby Macro Base Station (MBS) and the users. In addition, Free Space Optics (FSO) is applied as the backhauling solution to significantly increase the capacity of the backhaul link between an MBS and a DBS. Most of the existing DBS placement solutions assume the FSO-based backhaul link provides sufficient link capacity, which may not be true, especially when a DBS is placed far away from an MBS (e.g., > 10 km in disaster-struck areas) or in a bad weather condition. In this paper, we formulate a problem to jointly optimize bandwidth allocation and DBS placement by considering the FSO-based backhaul link capacity constraint. A Backhaul awaRe bandwidth allOcAtion and DBS placement (BROAD) algorithm is designed to efficiently solve the problem, and the performance of the algorithm is demonstrated via extensive simulations.