Resource Allocation for Machine-to-Machine Communications with Unmanned Aerial Vehicles

TL;DR

This paper introduces a framework for power-efficient M2M communications using UAVs as aerial base stations, optimizing scheduling and resource allocation to reduce power consumption while meeting device rate requirements.

Contribution

It presents a novel optimal scheduling and resource allocation mechanism for UAV-assisted M2M communications, minimizing power use and ensuring queue stability.

Findings

UAVs significantly reduce CHs' transmit power by 68% compared to terrestrial base stations.

The number of UAVs needed increases with packet transmission probability.

Optimal resource allocation ensures M2M device rate requirements with minimal power.

Abstract

In this paper, a novel framework for power-efficient, cluster-based machine-to-machine (M2M) communications is proposed. In the studied model, a number of unmanned aerial vehicles (UAVs) are used as aerial base stations to collect data from the cluster heads (CHs) of a set of M2M clusters. To minimize the CHs' transmit power while satisfying the rate requirements of M2M devices, an optimal scheduling and resource allocation mechanism for CH-UAV communications is proposed. First, using the queue rate stability concept, the minimum number of UAVs as well as the dwelling time that each UAV must spend for servicing the CHs are computed. Next, the optimal resource allocation for the CH-UAV communication links is determined such that M2M devices rate requirements are satisfied with a minimum transmit power. Simulation results show that, as the packet transmission probability of machines increases, the minimum number of UAVs required to guarantee the queue rate stability of CHs will also significantly increase. Our results also show that, compared to a case with pre-deployed terrestrial base stations, the average transmit power of CHs will decrease by 68% when UAVs are used.