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Dynamic handoff policy for RAN slicing by exploiting deep reinforcement learning
EURASIP Journal on Wireless Communications and Networking ( IF 2.6 ) Pub Date : 2021-03-19 , DOI: 10.1186/s13638-021-01939-x
Yuansheng Wu , Guanqun Zhao , Dadong Ni , Junyi Du

It has been widely acknowledged that network slicing is a key architectural technology to accommodate diversified services for the next generation network (5G). By partitioning the underlying network into multiple dedicated logical networks, 5G can support a variety of extreme business service needs. As network slicing is implemented in radio access networks (RAN), user handoff becomes much more complicated than that in traditional mobile networks. As both physical resource constraints of base stations and logical connection constraints of network slices should be considered in handoff decision, an intelligent handoff policy becomes imperative. In this paper, we model the handoff in RAN slicing as a Markov decision process and resort to deep reinforcement learning to pursue long-term performance improvement in terms of user quality of service and network throughput. The effectiveness of our proposed handoff policy is validated via simulation experiments.



中文翻译:

利用深度强化学习的RAN切片动态切换策略

众所周知,网络切片是一种关键的体系结构技术,可为下一代网络(5G)提供多样化的服务。通过将基础网络划分为多个专用逻辑网络,5G可以支持各种极端的业务服务需求。随着在无线接入网络(RAN)中实现网络切片,用户切换变得比传统移动网络复杂得多。由于在切换决策中应同时考虑基站的物理资源限制和网络切片的逻辑连接限制,因此智能切换策略变得势在必行。在本文中,我们将RAN切片中的切换建模为马尔可夫决策过程,并借助深度强化学习来从用户服务质量和网络吞吐量方面寻求长期的性能改进。通过仿真实验验证了我们提出的切换策略的有效性。

更新日期:2021-03-19
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