Research On Unmanned Aerial Vehicle Assisted Green Data Transmission Scheme

With the increasingly mature of the Internet of Things(IoT),various applications such as smart cities and smart agriculture will become reality in the future.It is expected that in 2030,hundreds of billions of devices equipped with sensors will access to the IoT and generate data exponentially.In the case of large-scale access,the energy consumption and channel resource occupation of IoT devices cannot be ignored.Therefore,higher requirements are put forward for the networks,including higher data rate and wider coverage.Unmanned Aerial Vehicle(UAV)communication has attracted much attention because of its flexibility and mobility.UAV can be used to collect data generated by remote IoT terminals beyond wireless coverage.Moreover,because of the flexibility of the UAV,it can move to the vicinity of the terminals and connect with the terminals,thus achieving high throughput and low energy consumption green communication.Based on the advantages in energy supply of UAV and IoT terminals,wireless energy transfer(WET)technology as a green communication solution has attracted wide attention from researchers.The radio frequency(RF)signals enable a large number of devices distributed in a wide area,which can be used for energy collection,conversion and storage of wireless devices,which have been proposed to further improve the energy efficiency of the IoT terminal,promote the low-cost of the IoT terminals,and prolong the terminal lifetime.On the other hand,the existing spectrum resources are not enough to meet the massive access in the future.The non-orthogonal multiple access(NOMA)is the key technology to improve the spectrum efficiency.Therefore,in order to enhance the coverage and improve the energy efficiency of the IoT,under the premise of limited spectrum resources,this paper constructs a hybrid-powered UAV-assisted NOMA-IoT data collection architecture,and proposes a joint UAV 3-D trajectory design and time allocation to maximize the total fair throughput.The scheme considers the energy limitation,QoS requirements and flight conditions of UAV.The simulation results show that the proposed algorithm can effectively improve the network utility.In addition,to solve the problem of communication and computing services for dynamic user of the IoT,this paper further deploys edge server(ES)on UAV to achieve flexible and timely dynamic user access.Considering that the UAV need to simultaneously perform communication,task computation and flight,we proposed a dual mode energy harvesting-based UAV-assisted NOMA-IoV edge computing architecture.To maximize the amount of data offloaded to the UAV and meet the requirements of UAV consumption,an optimization problem of joint computing resource allocation,SWIPT power splitting and UAV speed is constructed.Then,we propose a deep reinforcement learning(DRL)algorithm based on Actor-Critic architecture to solve it,and the simulation results show that the proposed algorithm can maximize the amount of data offloaded to the UAV for computation under the task atomicity requirements.