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 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.

Highlights

  • In modern communication systems, the concept of mobile base stations (MBSs) has been widely and actively discussed in order to establish flexible wireless and cellular networking connections [1]–[7]

  • The energy-efficient operation is definitely helpful for extending the MBS drone operation times; in turn, it is useful for extending the network coverage and service time

  • As discussed in [14], [15], the concept of coverage-time is defined as the time until one MBS drone totally exhausts its own energy

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Summary

INTRODUCTION

The concept of mobile base stations (MBSs) has been widely and actively discussed in order to establish flexible wireless and cellular networking connections [1]–[7]. We propose a new energy-efficiency maximization problem, which is partitioned into two independently separable optimization tasks, under our network model. Toward this end, as our first optimization task, we design two-stage charging matching/scheduling, i.e., (i) charging matching between charging towers and charging drones and (ii) charging matching between charging drones and MBS drones. We first prove that the problem is non-convex, thereby converting the original non-convex formulation into a convex setting that can guarantee optimal solutions After conducting this two-stage charging matching/scheduling, as our second optimization task we design energy-aware data transmission in each MBS drone due to the fact that the drones are power-hungry.

SYSTEM MODEL
PERFORMANCE EVALUATION
EVALUATION RESULTS
CONCLUSIONS AND FUTURE WORK
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