Research On Performance Optimization Of UAV-Assisted Mobile Edge Computing System

Mobile edge computing(MEC)system,as a driving technology in 5G and Internet of Things(IoT),provides computing offload at the edge of the network by sinking computing capacity.But the traditional deployment form lacks flexibility and is vulnerable to scenario factors,which makes the communication link unsatisfactory.With its high flexibility,low cost and high probability of air-to-ground(A2G)line-of-sight(LoS)links,unmanned aerial vehicle(UAV)can be integrated into mobile edge computing as a promising solution,in which UAV equipped with computing resources and communication abilities can be employed as a supplement to ground terminals in extending computing capabilities.It is important to utilize the mobility of UAV and the scheduling of computing abilities and communication resources in the system to provide higher bearable computing capacity.However,current research still lacks in terms of user fairness and computational energy efficiency.This thesis investigates the fair bearable computing capacity and computational energy efficiency in UAV-assisted MEC system.The main innovations of this thesis are as follows:A fairness-based computational capacity optimization scheme for UAV-assisted mobile edge computing system is proposed.The system model of UAV as offloading service provider in an edge computing scenario is constructed,where ground terminals at different locations offload their latency-critical and computation-intensive data to UAV onboard server,and UAV adjusts its scheduling of computation capacity to perform computation for multiple users.In order to improve the fairnessbased terminal bearable computing capacity,the trajectory of the UAV,terminal transmit power,and on-board computing frequency allocation are jointly optimized,and under time division multiple access(TDMA)and frequency division multiple access(FDMA),time slot allocation and frequency band division are optimized respectively.A hierarchical iterative algorithm based on block coordinate descent(BCD)and successive convex approximation(SCA)method is employed to solve the two non-convex problems under different access schemes separately.The simulation results show that the proposed scheme can effectively improve the computational capacity of the terminal under the condition of guaranteed fairness.A computational energy efficiency optimization scheme for UAV relay-assisted MEC system is proposed.The model of an edge computing scenario in which the UAV has both relaying and offloading computing functions is established.While performing computations for ground terminals,the UAV can also transmit part of the terminals’ task to the ground base station to complete computation.To further study the system computational capacity problem in the energy-constrained environment,the trade-off between various types of energy consumption and computational capacity in the system is analyzed with the goal of maximizing computational energy efficiency,and the problem includes UAV trajectory optimization,offloading sub-time slot division,bandwidth allocation,power control and scheduling of on-board and native computing resources.A two-layer iterative algorithm based on Dinkelbach’s algorithm,SCA technique and BCD method is employed to tackle the complex optimization problem.Simulation results show that the proposed scheme has a significant gain in computational energy efficiency of the system.