Research On Performance Optimization For Mobile Edge Computing Based On CR-NOMA Network

With the rapid development of technology,the new generation of IT information technology and the communication technology based on 5G continues to break through and innovate.Internet of Things,Cloud Computing,Big Data,Artificial Intelligence,etc.,have become the driving force of a new round of technological revolution and industrial development.In this era of technological convergence,the integration of Information and Communications Technology(ICT)has great value.As a representative of emerging IT technologies,Mobile Edge Computing(MEC)integrates mobile networks and Internet technologies,and adds functions such as computing,storage,and processing on the mobile network side to upgrade traditional base stations to smart base stations.MEC is the key technology to support the digital transformation of various industries,because MEC provides customized,differentiated services for them,and it will also be one of the core key businesses of 5G and future mobile communications.MEC has a wide range of applications in enhancing the capabilities of wireless network equipment.However,the offloading from users to edge cloud center will occupy a large amount of spectrum resources for transmission,which may cause the problem of spectrum scarcity and hinder the sustainable development of MEC.In order to solve this problem,Cognitive Radio(CR)and Non-Orthogonal Multiple Access(NOMA)are used to improve spectrum utilization.Mobile Edge Computing based on CR-NOMA has become a research hotspot in recent years.For Mobile Edge Computing systems based on CR-NOMA,it is necessary to consider the size of edge server computing power,offloading policy from users,offloading transmission and computing of computation tasks.Therefore,resource allocation issues of transmit power and computing resources become very complicated.In the face of high requirements such as large-capacity,high-reliability,and low-latency communications in the future,how to optimize the performance of Mobile Edge Computing systems based on CR-NOMA has important research significance.Based on this research background,the main work and innovations of this article are summarized as follows:(1)In the Mobile Edge Computing system based on CR-NOMA,the computing resource of edge servers,offloading policy of cognitive users,and allocation of transmission power will affect the system delay.In order to reduce the system delay,this thesis proposes a system delay minimization scheme from the user.This problem is non-convex and difficult to solve directly.This thesis uses characteristics of the system delay minimization problem to decompose it into four sub-problems,which can be solved independently.An iterative optimization algorithm is designed to jointly optimize the cognitive user offloading policy,transmit power allocation,and computing resource allocation to obtain a sub-optimal solution which is very close to the optimal solution with low complexity.The simulation results show that the algorithm proposed in this thesis is better than the average allocation scheme in reducing the system delay.Compared with the branch and bound method,the algorithm proposed in this thesis can approach the upper limit of system delay performance with lower complexity.(2)In order to further improve the performance of the Mobile Edge Computing system based on CR-NOMA,this thesis adds the optimization of energy consumption to the research scope,and proposes a problem of minimizing the user system overhead.The overhead consists of two parts——delay overhead and energy consumption overhead.The delay overhead is composed of transmission delay overhead from users to edge center and calculation delay overhead of edge center,and the energy consumption overhead consists of transmit power consumption overhead,computing power consumption overhead,and edge server operating overhead.The cost minimization problem is a NP-hard problem,and it is difficult to find the optimal solution in a limited time.Therefore,this thesis proposes a joint scheduling strategy based on tabu search algorithm.The result obtained by the greedy algorithm is used as the initial solution of the tabu search algorithm,and the neighborhood movement operation is performed based on the initial solution.The simulation results show that the tabu search algorithm can quickly reach the state of convergence,and the minimum cost of the secondary user system during convergence is much smaller than the initial solution.Therefore,the tabu search algorithm has faster convergence speed and better convergence performance.Compared with the greedy algorithm,the tabu search algorithm has better performance in reducing system overhead.