Joint Mobile Charging and Coverage-Time Extension for Unmanned Aerial Vehicles

TL;DR

This paper proposes a novel energy-efficient framework for drone-based networks, utilizing charging drones and towers, with optimized scheduling and power allocation to extend coverage time.

Contribution

It introduces a new energy-efficiency maximization problem with a two-stage charging matching and Lyapunov-based power optimization for MBS drones.

Findings

Extended drone network coverage time achieved.

Effective scheduling algorithms for charging and data transmission.

Convex reformulation of non-convex optimization problems.

Abstract

In modern networks, the use of drones as mobile base stations (MBSs) has been discussed for coverage flexibility. However, the realization of drone-based networks raises several issues. One of the critical issues is drones are extremely power-hungry. To overcome this, we need to characterize a new type of drones, so-called charging drones, which can deliver energy to MBS drones. Motivated by the fact that the charging drones also need to be charged, we deploy ground-mounted charging towers for delivering energy to the charging drones. We introduce a new energy-efficiency maximization problem, which is partitioned into two independently separable tasks. More specifically, as our first optimization task, two-stage charging matching is proposed due to the inherent nature of our network model, where the first matching aims to schedule between charging towers and charging drones while the second matching solves the scheduling between charging drones and MBS drones. We analyze how to convert the formulation containing non-convex terms to another one only with convex terms. As our second optimization task, each MBS drone conducts energy-aware time-average transmit power allocation minimization subject to stability via Lyapunov optimization. Our solutions enable the MBS drones to extend their lifetimes; in turn, network coverage-time can be extended.