Research On User Task Processing Technology Of Beyond 5G Mobile Edge Computing Systems

With the advent of the beyond 5G era and the rapid development of communication devices,network data traffic is growing explosively,and wireless communication systems are facing unprecedented challenges in terms of communication and computing capabilities.Emerging network applications in the beyond 5G era have more stringent requirements for mobile communications.However,traditional cloud computing cannot meet some delay-sensitive business needs because the cloud server is far away from the terminal device.Based on this,with the help of Mobile Edge Computing(MEC)technology,by sinking the service to the user side,the computing resources of the edge device are used to provide users with high-quality services.In view of the optimization of task offloading and resource allocation in this scenario,the user task offloading technology for beyond 5G mobile edge computing systems is studied.The main contributions of this thesis are as follows:First,a cellular network communication system composed of multiple MEC servers and multiple users is studied.The system includes multiple base stations equipped with MEC servers and multiple mobile user devices,where the MEC server and user devices work together as a data processing center.Aiming at the user capacity problem of multi-user mobile edge computing system,a priority-based user scheduling method is proposed.Under the limitation of MEC server computing resources,the edge computing resource allocation strategy is optimized with the goal of maximizing the users that the system can support.The simulation results show that when the total number of users in the system is large,compared with the traditional user Round-Robin scheduling scheme,the proposed scheme effectively increases the number of users that the system can support.Secondly,a cellular heterogeneous network communication system consisting of multiple MEC servers and multiple users is studied,which includes a Macro Base Station(MBS)equipped with MEC server,a Small Base Station(SBS)equipped with MEC server,and multiple mobile user devices,where each device has a computationally intensive task that needs to be handled by offloading computation.Aiming at the problem of data transmission and computing delay of multi-user tasks,a joint optimization method of offloading task allocation and computing resource allocation is proposed.With MEC server computing resources as constraints,the goal is to minimize the offloading computing delay of multi-user tasks.By adopting the alternate optimization method designs an iterative algorithm for the joint allocation of offloading tasks and edge computing resources.Simulation results show that,compared with the uniform task and computing resources allocation scheme,the proposed scheme can effectively reduce the total system delay.Finally,a cellular network communication system consisting of a single MEC server and multiple users is studied.The system includes a single base station equipped with MEC server and multiple mobile user devices,where the user device and the MEC server work together as a data processing center.Consider compressing task data before offloading the task on the user device to reduce the size of the transmitted data.Aiming at the problems of compression delay,offloading delay and computing delay of multi-user tasks,a joint optimization method of task offloading strategy and computing resource allocation is proposed,with the constraints of edge computing resources and the goal of minimizing the processing delay of multi-user tasks.An iterative algorithm for the joint optimization of task allocation,data compression,and edge computing resource allocation is designed by adopting an alternate optimization method.Simulation results show that,compared with the uniform task and computing resources allocation without data compression scheme,the proposed scheme can effectively reduce the total system delay.