Research On Optimization Strategy Of UAV Mobile Edge Computing System

With the continuous development of innovative applications,,the traditional 4G network architecture has been difficult to meet the exponential growth of data flow,diversified terminal equipment and various complex service scenarios.Therefore,wireless communication puts forward higher requirements in mobile performance,security,network delay and communication reliability.In addition,mobile edge computing system is the key technology foundation of 5G.By combining wireless network technology with internet key technology,the wireless communication module adds computing,storage,CPU and so on,which effectively solves the problems of delay and storage space shortage in wireless communication network.Computation offloading in mobile edge computing networks has the characteristics of low response time for computing tasks and good network scalability.Adding Unmanned Aerial Vehicles(UAV)to mobile edge computing systems,utilize the flexible deployment of UAV,through joint computing and communication collaboration methods,enables users to cooperate in computing and communication to improve Mobile Edge Computing(MEC)performance.However,in view of the fast mobility of the UAV,the relationship between its flight trajectory control strategy and the capacity and power consumption of traditional communication systems is not clear.Adding UAV to mobile edge computing systems,using the flexible deployment of UAV through joint computing and communication collaboration methods,enables users to cooperate in computing and communication to improve the Mobile Edge Computing(MEC)performance.However,in view of the characteristics of the fast mobility of the UAV,the relationship between its flight trajectory control strategy and the capacity and power consumption of the traditional communication system is not yet clear.It is necessary to combine the classic air-to-ground line of sight according to the mobility characteristics of the UAV communication.Line of Sight(LoS)channel model,establish the UAV-GBS(Terrestrial Base Station)uplink transmission model,qualitatively and quantitatively analyze the offloading time of the UAV edge computing system,important performance indicators such as system power consumption,and analyze unmanned The UAV's flight trajectory optimization and resource optimization strategies improve the energy efficiency level of the UAV's edge computing system,and realize an efficient edge computing network with multi-objective optimization such as UAV energy efficiency,flight trajectory,and bit allocation.Based on the above requirements,this thesis intends to analyze the optimization strategies of UAV energy consumption and offload time based on the single UAV-multi-GBS and multi-UAV-multi-GBS task offloading models.The main research contents and scenarios can be summarized as:1.Research on Energy Consumption Optimization Strategy of UAV Mobile Edge ComputingBased on the mobile edge computing system model of a single UAV-multi-GBS,we propose a new mobile edge computing model in which the UAV is served by a cellular Ground Base Station(GBS)for computational offloading.The UAV flies between a given initial and final position,during which it needs to perform certain computing tasks by offloading them along certain trajectories to certain selected GBSs,while ensuring the computing tasks Under the premise of completion,we propose a flight hover communication design,using the Traveling Salesman Problem(TSP)and convex optimization technology to find an optional hover position,Then a path discretization method was found to convert the original infinite variable problem into a finite discrete variable optimization problem,and then use the convex approximation method to convert the non-convex problem to a convex problem,and finally use the CVX tool to obtain the energy consumption of the drone The smallest general solution.The numerical results show that compared with the benchmark scheme,the proposed scheme has a significant performance improvement in terms of saving UAV energy consumption.2.Multi-UAV mobile edge computing system minimizes task completion timeA mobile edge computing system based on multi-UAV cooperative cellular base stations uses multiple drones to offload tasks to ground base stations.Compared with single drones,multiple drones can make better use of their flexible deployment to achieve edge network coverage.In this system,using multiple drones to execute tasks in parallel can greatly reduce the task completion time.However,many drones are prone to collision risks during flight,and it is important to consider the safe flight between drones.In this article,we have considered the safety mechanism of the UAV during flight,and offloaded tasks to the ground base station through the slotted orthogonal access method.In the scenario where the computing power of the ground base station is limited,the task offload time is minimized by jointly optimizing slot scheduling,power allocation,and flight trajectory reuse convex optimization techniques.