Research On Resource Scheduling Strategy Of Customized Radio Access Network Slicing

5G has brought new vertical applications and business scenarios.Diversified business demands have posed greater challenges for the network.To meet the differentiated needs,customized slices provide customized services for different businesses by separating multiple virtual networks isolated from each other on the same physical foundation network.Access network,as a bridge connecting intelligent devices and core network,needs flexible segmentation to ensure the customization and isolation of slices,and achieve service access and resource guarantee.However,in the case of limited resources,large-scale service access and dynamic changing requirements on the access network side,as well as the mobility of intelligent devices,bring problems to the resource scheduling and reliability guarantee of customized slices.Therefore,in view of large-scale access and high reliability guarantee problems in customized access network slices,how to effectively schedule access network resources to improve resource utilization and guarantee service quality is the main research content of this paper.For large-scale wide access to equipment and demand dynamic change,given the massive access oriented customized access network section is widely used in the Internet of things(IoT),this article puts forward the dynamic access slicing model of IoT for IoT business types are classified according to the changes of the slice demand of real-time adjustment of access network resource allocation,analyzed using continuous allocation of resources and the continuous distribution of resources advantages and disadvantages of the two kinds of control mode.In order to improve the resource utilization and reduce the comprehensive load such as service degradation and computing delay,this paper proposes a dynamic resource adjustment strategy based on genetic algorithm,which selects the adjustment mode according to the total resource demand and approximates the optimal allocation mode.By comparing with the benchmark algorithm,the advantages of the proposed algorithm in terms of slice acceptance rate,resource utilization rate,total revenue and reduction of comprehensive load are shown.For high reliability,considering the car network is an important application in the field of high reliable customized access slicing scenario,based on the car in car networking service quality of the problems of high mobility,car networking access slicing collaboration model is put forward,and analyzes the actual car networking business types and the business section of the reasonable classification,for different types of section gives different priorities,business section can access different roadside units,multiple roadside unit collaboration for the same vehicle to provide services.Then,based on reinforcement learning,a slice cooperative selection model was established in this paper.In the process of vehicle driving,the appropriate RSU access was selected for the vehicle business slice by comprehensively considering the slice type,channel quality,RSU resources and other factors.After training,the optimal mapping between environmental state and cooperation strategy was obtained.Simulation results show that compared with the original RWSU switching scheme,the proposed slicing cooperative selection strategy can achieve better channel quality and resource guarantee for vehicle traffic slicing.