The Energy Consumption Optimization Of The Mobile Edge Computing Network Based On NOMA

Driven by the new generation of network technology,users can obtain a variety of diversified services,but the numerous applications also bring great computing pressure to user equipment.As one of the key technologies of 5G,Mobile Edge Computing(MEC)can assist users to complete computing tasks through computing offloading,and makes full use of other idle computing resources to reduce and to transfer computing pressure,which effectively solves the problems that users face computation-intensive application tasks.In addition,Non-Orthogonal Multiple Access(NOMA)is no longer limited by the number of orthogonal resources,which improves the access potential of more users and has obvious advantages in terms of delay and energy consumption.This thesis focuses on performance optimization in the MEC system based on NOMA in different scenarios.The main work and research of this thesis are as follows:(1)For the offloading scenario of single user based on NOMA downlink,a resource allocation scheme that jointly optimizes transmit power and task offloading is proposed in combination with the cache queue.The system considers one user with computing task requirements and two wireless Access Points(AP)attached to small MEC servers.When the user has computing task requirements,the user will divide the task into three parts,which will be computed locally and offloaded to two MEC servers.Specifically,under the premise of ensuring the delay requirement,the problem of minimizing the energy consumption of the system is constructed.First,the probabilistic problem is transformed into a non-probability problem by performing an equivalent transformation on the problem.Since the resulting problem is non-convex,the problem is then split into two sub-problems.To solve the problem,the optimal allocation expression of the power allocation sub-problem is derived through formula derivation,and the transformed convex optimization problem is solved by using lagrange multiplier method and one-dimensional search algorithm to solve the task allocation subproblem.The simulation results show that the proposed scheme can effectively reduce the energy consumption compared with the schemes based on the Orthogonal Multiple Access(OMA)and no caches offloading.(2)For the multi-user offload scenario based on NOMA uplink,Dynamic Voltage Scaling(DVS)technology is introduced on the user side,and a resource allocation scheme that jointly optimizes communication resources and computing resources is proposed.Multiple even numbers of users and one MEC server with sufficient computing resources are considered in the system.Users are grouped in pairs into multiple groups,groups of users will offload the computing tasks on the same channel,in the form of NOMA.Specifically,under the constraint of maximum delay,the problem of building a system to minimize energy consumption.The constructed problem is non-convex.The problem is divided into several sub-problems to solve.Based on the derived calculation frequency and transmit power allocation expressions,this thesis combines the channel allocation algorithm based on matching theory and the particle swarm algorithm to iteratively obtain the results of the scheme.The simulation results show that,the proposed method can effectively reduce the power consumption compared to the method without using DVS.