Research On Energy Optimazition In UAV-assited MMTC Networks

According to the International Telecommunication Union report,in the massive machine type communication(mMTC)scenario,the fifth-generation mobile communication technology is expected to support a large number of machine-type devices for communication and play an important role.These mMTC scenarios include,but are not limited to,environmental awareness,smart grid and e-health.However,machine type communication is fundamentally different from current person-to-person communication.A large number of devices,small data packet transmission,burst access in a short time,limited energy consumption and diverse quality of service(QoS)bring great challenges to the design of high-data-rate human-to-human communication systems.Because of these unique characteristics,when a large number of MTC devices(MTCD)try to access the network in a short time,the radio access network.(RAN)will cause an overload problem.MTC places restrictions on the energy efficiency,computational efficiency,and low latency of low-complexity battery-powered equipment.The energy efficiency of battery-powered MTCD is an unavoidable constraint for mMTC network optimization.The existing MTCD computing storage resources are limited and cannot meet the needs of MTCD computing storage capacity for network upgradesAn effective solution is to deploy unmanned aerial vehicles(UAV)with communication and computing equipment as mobile base stations to provide information collection services and computing services for mMTC to reduce the energy consumption of machine-type communication equipment and enhance Its computing power.However,battery power is critical to the UAV’s flight and mission.Therefore,the optimization of energy efficiency of UAV is also an important part of the optimization of machine-type communication networks assisted by UAV.In this thesis,the UAV-assisted large-scale ground MTCD for communication is considered,and two working modes of the UAV and MTCD in the UAV-assisted mMTC network are proposed:hovering service mode and stand-by mode.For hovering service mode,we propose a non-service tolerance mechanism and a hybrid hovering position selection(HHPS)algorithm to determine the hovering position of the UAV,which can minimize the total power consumption of MTCD.A new path planning algorithm based on the cuckoo search algorithm was further proposed to optimize the MTCD task delay,energy efficiency and throughput of the UAV.We have also set different priorities for the information collection service and calculation service of the UAV and optimized the information collection efficiency and calculation efficiency.Simulation results show that the proposed HHPS algorithm for hovering position selection has better performance than the K-Means algorithm.In addition,compared with the step-by-step minimization algorithm,the proposed UAV path planning algorithm has better performance for different optimization goals,such as minimizing the energy consumption and maximizing the throughput of UAV.In view of the stand-by mode,we have introduced an adaptive and energy-efficient framework for UAV-assisted mMTC network.Based on this framework,strategies are proposed to improve energy efficiency and extend the life cycle of the network.Furthermore,an energy-efficient routing algorithm GEAU based on ant colony algorithm was further proposed,which reduced the energy consumption of MTCD and prolonged the life cycle of UAV-assisted mMTC network.We also propose a single-path,energy-efficient ACO routing algorithm(SEAU),which greatly reduces the computational complexity of GEAU while ensuring a basically flat life cycle.Simulation results show that compared with existing algorithms,the proposed algorithm can significantly reduce the energy consumption of MTCD and extend the life cycle of UAV-assisted mMTC networks,and has lower computational complexity.We analyzed and compared the energy efficiency of the above two working modes,and defined an overall energy efficiency evaluation index for the UAV-assisted mMTC network.Energy efficiency evaluation and selection of the two working modes can be performed for different scenarios.In summary,this thesis studies the UAV-assisted ground MTCD communication mode and its performance optimization in UAV-assisted mMTC networks.It also looks forward to the future research directions.