Intelligent Network Management and Control Architecture and Key Technologies for Future 6G Networks

doi:10.11871/jfdc.issn.2096-742X.2020.03.003

Abstract

Abstract:

[Objective] 6G is considered as the next-generation network communication technology to network 2030. This paper proposes an intelligent management and control architecture for 6G networks in the term for network demands. [Application background] The architecture will integrate different types of artificial intelligence algorithm models, to solve the “network on demand” problem for highly resilient and dynamic 6G networks. [Methods] This paper first investigates the relevant literatures and the standardization progress in recent years, analyzes the requirements for 6G intelligent management and control, and the challenges faced by existing management and control technologies, and finally proposes corresponding solutions. [Results] Based on the concept of the 6G intelligent endogenous network, an intelligent management and control architecture of the 6G network is proposed, and the intelligent management and control function of the 6G network constructed with key technologies are described as well. [Limitations] The control architecture and key technologies proposed in this paper require further systematic verification with a prototype. [Conclusions] Endogenous intelligence is one of the important characteristics of 6G networks. Artificial intelligence technology will also play a core supporting role in the management and control of 6G networks, which provides ubiquitous and reliable guarantees for resource on-demand scheduling and network accompanying services.

Key words: 6G networks, artificial intelligence, management and control requirement, management and control architecture, management functions

Yu Peng,Li Wenjing,Feng Lei,Zhou Fanqin,Yang Yang,Qiu Xuesong. Intelligent Network Management and Control Architecture and Key Technologies for Future 6G Networks[J]. Frontiers of Data and Computing, 2020, 2(3): 32-44.

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功能名称	功能实例	功能描述	解决算法
配置管理	配置合理性分析	针对无线侧的终端、接入点,核心侧的网络设备,自主分析链路、端口、频谱等参数的正确性	规则类模型
配置管理	网络配置	依据设备接入、网络扩容等场景,自主完成网络的各项参数的合理配置	决策类模型
性能管理	性能门限管理	针对各项性能参数,动态完成性能门限的训练更新,降低网络频繁优化的影响	规则类模型
	性能劣化分析	通过分析网络状态参数,确定网络性能的状态,在性能下降时,为性能优化提供输入	规则类模型
	性能优化	依据当前网络的优化目标,自主选择合适的人工智能模型,完成性能参数的优化配置参数,并发送给指定的网络控制单元处理	决策类模型
计费管理	计费合理性分析	针对业务的资源需求的动态变化,自主分析设定的计费方式的公平性和有效性	规则类模型
计费管理	计费调整	针对不合理的计费方式,通过资源重配,完成对应的策略调整	决策类模型
故障管理	异常检测	通过数据分析训练,自主检测出当前网络中的异常和故障	规则类模型
	故障定位	通过历史的规则关联规则库,分析出故障的根原因	规则类模型
	故障修复	依据当前网络中存在的问题,自主选择合适的模型,完成故障的修复,保障网络的业务质量	决策类模型
安全管理	风险预测	通过数据分析训练,获取当前网络中的安全风险点	规则类模型
	网络攻击检测	通过流量分析训练,获取当前网络存在的内在攻击和外在攻击	规则类模型
	网络安全防护	依据安全需求,自主完成安全防护策略的选择	决策类模型