Research On Performance Optimization Of UAV Assisted MTC Networks

With the rapid development of the Internet of Things(IoT),it is expected that billions of devices will be connected to each other in the near future,and Machine-Type Communications(MTC)is the basic underlying technology to support such IoT applications.When the distance between Base Station(BS)and Machine-Type Communications Devices(MTCDs)is too far,Unmanned Aerial Vehicle(UAV)with high mobility can communicate with MTCDs as a mobile BS.However,both UAV and MTCDs have limited energy storage.Therefore,energy efficiency optimization on UAV-assisted MTC networks has research significance.This thesis focuses on the energy efficiency of UAV-assisted MTC networks,considering two different scenarios:UAV-assisted massive Machine-Type Communications(mMTC)networks and multi-UAVsassisted time-varying MTC networks.Firstly,considering the varying altitudes of actual buildings,the altitudes of MTCDs distributed in different positions may be varying,and therefore a UAV-assisted mMTC network with different altitudes is proposed.Next,a Hovering Positions Selection Algorithm based on Threedimensional Clustering(3D-HPSA)is proposed to optimize MTCDs’energy efficiency and determine the hovering positions of UAV.Meanwhile,a Discrete Cuckoo Search Algorithm with Genetic Mutation Operators(GMO-DCSA)is proposed to optimize UAV’energy efficiency.Simulation results show that compared with the selection algorithm based on two-dimensional clustering,the proposed 3D-HPSA improves more than 10%in the energy efficiency of MTCDs.Compared with Discrete Cuckoo Search(DCS)algorithm,the proposed GMO-DCSA reduces computational complexity by more than 10%when the UAV’ energy efficiency performance is comparable.Secondly,considering that the location of MTCDs may be time-varying in actual scenarios,and MTCDs’ computing capability is limited,a multiUAVs-assisted time-varying MTC network with Mobile Edge Computing(MEC)is proposed.In order to optimize the weighted sum of energy consumption of MTCDs and UAVs,an iterative optimization scheme,based on a proposed improved Particle Swarm Optimization(PSO)algorithm,is proposed by jointly optimizing the device association,the bit allocation,the computational frequency allocation and UAVs’ trajectory.Simulation results show that the proposed scheme reduces the weighted sum of energy consumption compared with other schemes,and devices’mobility does affect the UAVs’ trajectory.