期刊
2019 IEEE 25TH INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED SYSTEMS (ICPADS)
卷 -, 期 -, 页码 625-632出版社
IEEE COMPUTER SOC
DOI: 10.1109/ICPADS47876.2019.00094
关键词
5G RAN; Network slicing; Markov decision process(MDP); Resource allocation; Deep reinforcement learning
资金
- National Natural Science Foundation of China [61872044, 61902029]
- Key Research and Cultivation Projects at Beijing Information Science and Technology University [5211910958]
- Supplementary and Supportive Project for Teachers at Beijing Information Science and Technology University [5111911128]
With the rapid growth of Internet of Things (IoT), network slicing is regarded as an important technology to support the multi-users' needs for 5G mobile network. Network slicing allows network operators to provide services to different users, which can improve the rational utilization of network and hardware resources. In order to ensure the quality of service and build low-cost network infrastructure services, it is a challenging problem to find an appropriate resource allocation mechanism. In this paper, we discuss resource allocation in 5G radio access network (RAN). Considering the real-time resource request of the slice user, we propose a semi-Markov decision system model, which enables the virtual network provider to effectively satisfy the different user demands in real time. Then, we propose a resource slicing algorithm based on deep reinforcement learning (RS-DRL), which aims to improve the long-term benefits of virtual network providers and the utilization of slicing resources. We evaluate the performance of the RS-DRL through evaluations and comparisons. The results show that the proposed RS-DRL algorithm can effectively improve the performance and achieve the long-term benefits quickly.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据