Research On Resource Allocation Srategy For 5G Service Coexistence

The three services of 5G scenarios are widely used and present various differentiated services.Among them,m MTC is designed to support a large number of Io T devices and only occasionally sends small data during the active phase.Hence,this paper mainly considers eMBB and uRLLC traffic transmission.Therefore,the coexistence scenario of eMBB service and uRLLC service will become a classic application scenario.In addition,the eMBB service has many users,large traffic,and ultra-high transmission data rate,so the eMBB service is the main business of 5G communication;the uRLLC service has a small amount of data and requires high reliability and low latency services with a reliability of 99.9999%.The uRLLC service also requires a delay of1 millisecond,which requires more radio resources.Therefore,considering the demand of reliability and delay,the uRLLC service also needs the support of the ultra-large bandwidth of the eMBB service.Thus,from the perspective of demand,there is a certain conflict of resources between the two services.So it is necessary to allocate resources for these two services.The research content of this paper is as follows:(1)Considering the multiplexing of uRLLC service and eMBB service is very important for high data rate and low delay service.In this case,resource allocation needs to be optimized through efficient scheduling.Therefore,this paper proposes a dynamic resource allocation scheme for service multiplexing of uRLLC and eMBB.Considering the rate requirements of different services,a Qo E-aware utility function is introduced.According to the introduced utility function model,the base station allocates resources reasonably to different users.The algorithm can guarantee business requirements and maximize the overall utility of the system.Finally,the simulation results are obtained that the proposed algorithm can improve the overall utility of the system on the premise of meeting the requirements of uRLLC traffic delay and reliability.(2)Aiming at the resource allocation problem of uRLLC and eMBB services under non-orthogonal channels,this paper uses deep reinforcement learning to design a reasonable resource allocation scheme.First,consider the multiplexing of uRLLC and eMBB services under non-orthogonal channels,and establish a corresponding system model.Then an effective capacity(EC)model is introduced to measure the performance of the scheme.Then the multiplexing problem of eMBB and uRLLC is defined as theMDP problem.The problem is solved by using deep reinforcement learning,the state space,action space and reward of the whole process are designed,and the proposed optimization objective is defined as the reward function;global information is introduced to obtain global resources Assign the optimal strategy.Finally,the algorithm of this paper is tested with simulation software.The experimental results of simulation analysis show that the algorithm proposed in this paper not only meets the delay and reliability requirements of uRLLC,but also reasonably allocates system resources and power,effectively improving eMBB.business throughput.