电力信息与通信技术2024，Vol.22Issue(12)：40-48,9.DOI:10.16543/j.2095-641x.electric.power.ict.2024.12.06
基于强化学习的异构业务资源分配方法
Reinforcement Learning-based Heterogeneous Business Resource Allocation Method
摘要
Abstract
In 5G and beyond wireless networks,enhanced mobile broadband(eMBB)and ultra-reliable low latency communications(URLLC)are two core services with different quality of service requirements.Achieving the heterogeneous coexistence of these two services using limited radio resources is a challenging problem.This paper proposes an intelligent resource allocation framework based on the perforation technique introduced by 3GPP,modeling the eMBB/URLLC resource allocation problem as an optimization problem that aims to maximize the eMBB user data rate while satisfying the URLLC reliability constraints.Additionally,considering the uncertainty of wireless channels and the impact of URLLC random perforation,a proportional fairness(PF)algorithm is introduced to balance the trade-off between total throughput and user fairness.To address this issue,this paper proposes a proportional fairness-based deep reinforcement learning algorithm,the proportional fairness-twin delayed deep deterministic policy gradient(PF-TD3A),to intelligently allocate resources for the two services.Experimental results show that the proposed algorithm can further increase the eMBB user data rate while meeting the eMBB reliability requirements,achieving an average improvement of approximately 7.4%.关键词
5G NR/eMBB/URLLC资源分配/深度强化学习/比例公平/穿孔调度Key words
5G new radio/eMBB/URLLC resource allocation/deep reinforcement learning/proportional fairness/perforation scheduling分类
信息技术与安全科学引用本文复制引用
李维,李秋生,李天一,陆忞,刘海璇..基于强化学习的异构业务资源分配方法[J].电力信息与通信技术,2024,22(12):40-48,9.基金项目
国网江苏省电力有限公司科技项目"支撑分布式电源聚合调控的5G应用关键技术"(J2023005). （J2023005）
