Research On Resource Orchestration Techniques For Coexisting URLLC And EMBB Services In 5G Networks

At the 22nd meeting of the International Telecommunication Union(ITU),Massive Machine Type Communication(m MTC),Enhanced Mobile Broadband(e MBB)and Ultra Reliable Low Latency Communication(uRLLC)are identified as the three major application scenarios of 5G.Among them,the e MBB service has many accesses and large traffic,and requires a high data transmission rate;the uRLLC service requires ultra-high reliability and low latency.In the initial stage of 5G deployment,the multiplexing of uRLLC services and e MBB services will become a typical application scenario.In addition,the achievement of low latency and high reliability indicators of uRLLC services also requires the support of ultra-high bandwidth of e MBB services.Therefore,in the scenario where two services coexist,reasonable resource allocation is a severe challenge.The research content of this thesis can be divided into the following two questions:(1)Aiming at the problem of resource slicing in ultra-reliable low-latency communication and enhanced mobile broadband dynamic multiplexing scenarios.e MBB services focus on high data rates,and uRLLC has stringent requirements in terms of latency and reliability.In view of this,the resource slicing problem is formulated as an e MBB/uRLLC joint resource allocation optimization problem,which aims to consider the variance of e MBB data rate to reduce the impact of immediately scheduled uRLLC traffic on e MBB reliability.This thesis proposes a risk-sensitive formula to allocate resources for incoming uRLLC traffic while minimizing the risk of e MBB transmission and ensuring uRLLC transmission reliability.And the optimization problem is decomposed into three sub-problems,and then the non-convex sub-problems are transformed into convex optimization problems to obtain an approximate solution for resource allocation.Simulation results show that the transmission scheme allocates resources for incoming uRLLC traffic while ensuring the transmission reliability of e MBB and uRLLC services.(2)In order to improve the throughput of e MBB services and ensure the reliability of uRLLC service transmission,this thesis proposes a Deep Reinforcement Learning(DRL)algorithm for the uRLLC scheduling problem in the joint resource allocation optimization of e MBB/uRLLC.The algorithm maximizes the e MBB data rate under uRLLC reliability constraints.The scheme is used to divide uRLLC and e MBB traffic into available physical layer resources.In this thesis scheme,the time-frequency resource grid is completely occupied by e MBB traffic.The thesis uses one of the state-of-the-art DRL algorithms,Proximal Policy Optimization(PPO),to train the DRL agent,which dynamically assigns incoming uRLLC data and transmits data by punching e MBB codewords.Simulation results show that the proposed scheme can meet the strict ultra-high reliability requirements of uRLLC while maximizing the data transmission rate of e MBB.Through the resource allocation scheme and scheduling algorithm proposed in this thesis,the multiplexing problem of uRLLC and e MBB services in the 5G network can be better solved,and efficient transmission of services and reasonable allocation of resources can be realized.