Research On User Mobile Edge Computing Offloading Based On NOMA

With the rapid development of mobile communications,a variety of emerging intelligent applications are emerging,most of which are computationally intensive and consume a lot of energy to process them directly on the user’s device.In this situation,mobile edge computing(MEC)has been proposed as an emerging technology to reduce costs for users by offloading computing tasks to servers.Specifically,users can offload all or part of their computing tasks to MEC servers over wireless channels,thereby improving users’ quality of service(Qo S)and reducing network latency and energy consumption.In addition,due to limited spectrum resources,traditional orthogonal multiple access(OMA)technology can no longer meet the requirements of MEC for low latency and low energy consumption.In order to solve the shortcomings of OMA,non-orthogonal multiple access(NOMA)is proposed to solve the limitation of spectrum resources.Compared with OMA,NOMA can allocate the same resources to multiple users,effectively improving the spectral efficiency of the system.However,the introduction of NOMA also couples user grouping,power allocation,and computing resource allocation,which brings great challenges to the modeling and solving of task offloading problems in MEC systems.Therefore,this paper investigates user grouping,computing resource allocation,and task offloading in the NOMAMEC system.Chapter 3 of this dissertation examines scenarios where two Io T users offload their computing workloads to edge servers via orthogonal multiple access(OMA)and non-orthogonal multiple access(NOMA)transports,respectively.In this scenario,multiple transport includes three offloading modes,namely pure OMA,pure NOMA and hybrid NOMA,this chapter studies the system model and mathematical model under the three offloading modes,and explains the advantages and disadvantages of different offloading strategies by solving the closed solution of user power under the principle of minimizing energy consumption,and finally combines the constraints and the principle of minimizing energy consumption to select the appropriate computing offloading strategy for users.Chapter 4 of this dissertation builds on Chapter 3 to further study the NOMA-based multi-user single MEC server system,which can minimize the energy consumption of the system by optimizing user clustering,computing resource allocation,and power control.Ho wever,because the solution process of the optimization method contains integer variables and continuous variables,it is a non-convex problem and is difficult to solve.In order to solve this problem,this chapter analyzes the system model and mathematical model of the system,and proposes a heuristic algorithm for user grouping and computing resource allocation and a joint optimization scheme for PSO power control algorithm.The simulation results show the effectiveness of the proposed NOMA scheme in redu cing energy consumption.