第五代移动通信增强技术(5G-Advanced,5G-A)网络中业务场景的多样化与差异化对传统静态网络服务体系提出了新的挑战。针对传统静态服务质量(Quality of Service,QoS)保障机制难以适应业务差异化诉求的问题,聚焦“人工智能+”行动的战略...第五代移动通信增强技术(5G-Advanced,5G-A)网络中业务场景的多样化与差异化对传统静态网络服务体系提出了新的挑战。针对传统静态服务质量(Quality of Service,QoS)保障机制难以适应业务差异化诉求的问题,聚焦“人工智能+”行动的战略规划,提出基于网络数据分析功能(Network Data Analytics Function,NWDAF)的5G-A网络智能化QoS保障方案,构建并实验验证了从业务识别、体验感知、质差分析到动态资源调度的完整闭环流程。实验结果表明,该方案可实现业务级和用户级的QoS保障,在提升用户体验的同时可优化网络资源利用率,为5G-A网络从被动签约向主动智能的服务模式演进提供了可行路径。展开更多
Artificial intelligence(AI)-native communication is considered one of the key technologies for the development of 6G mobile communication networks.This paper investigates the architecture for developing the network da...Artificial intelligence(AI)-native communication is considered one of the key technologies for the development of 6G mobile communication networks.This paper investigates the architecture for developing the network data analytics function(NWDAF)in 6G AI-native networks.The architecture integrates two key components:data collection and management,and model training and management.It achieves real-time data collection and management,establishing a complete workflow encompassing AI model training,deployment,and intelligent decision-making.The architecture workflow is evaluated through a vertical scaling use case by constructing an AI-native network testbed on Kubernetes.Within this proposed NWDAF,several machine learning(ML)models are trained to make vertical scaling decisions for user plane function(UPF)instances based on data collected from various network functions(NFs).These decisions are executed through the Ku-bernetes API,which dynamically allocates appropriate resources to UPF instances.The experimental results show that all implemented models demonstrate satisfactory predictive capabilities.Moreover,compared with the threshold-based method in Kubernetes,all models show a significant advantage in response time.This study not only introduces a novel AI-native NWDAF architecture but also demonstrates the potential of AI models to significantly improve network management and resource scaling in 6G networks.展开更多
移动通信系统在5G阶段首次引入网络智能,5G网络智能主要依赖于网络数据分析功能网元(Network Data Analytics Function,NWDAF)的定义,及其与其他核心网网元的协同。也正因是首次引入,5G网络智能具有如下技术局限性。一是外挂式AI,缺乏全...移动通信系统在5G阶段首次引入网络智能,5G网络智能主要依赖于网络数据分析功能网元(Network Data Analytics Function,NWDAF)的定义,及其与其他核心网网元的协同。也正因是首次引入,5G网络智能具有如下技术局限性。一是外挂式AI,缺乏全网AI能力协同机制。5G核心网网元功能(Network Function,NF)不具备AI能力,仅有新引入的NWDAF具备AI能力,属于典型的外挂式AI架构。展开更多
文摘第五代移动通信增强技术(5G-Advanced,5G-A)网络中业务场景的多样化与差异化对传统静态网络服务体系提出了新的挑战。针对传统静态服务质量(Quality of Service,QoS)保障机制难以适应业务差异化诉求的问题,聚焦“人工智能+”行动的战略规划,提出基于网络数据分析功能(Network Data Analytics Function,NWDAF)的5G-A网络智能化QoS保障方案,构建并实验验证了从业务识别、体验感知、质差分析到动态资源调度的完整闭环流程。实验结果表明,该方案可实现业务级和用户级的QoS保障,在提升用户体验的同时可优化网络资源利用率,为5G-A网络从被动签约向主动智能的服务模式演进提供了可行路径。
基金supported by the National Key Research and Development Program of China under Grant No.2023YFE0200700National Natural Science Foundation of China under Grant No.62171474ZTE Industry University-Institute Cooperation Funds under Grant No.IA20241014013。
文摘Artificial intelligence(AI)-native communication is considered one of the key technologies for the development of 6G mobile communication networks.This paper investigates the architecture for developing the network data analytics function(NWDAF)in 6G AI-native networks.The architecture integrates two key components:data collection and management,and model training and management.It achieves real-time data collection and management,establishing a complete workflow encompassing AI model training,deployment,and intelligent decision-making.The architecture workflow is evaluated through a vertical scaling use case by constructing an AI-native network testbed on Kubernetes.Within this proposed NWDAF,several machine learning(ML)models are trained to make vertical scaling decisions for user plane function(UPF)instances based on data collected from various network functions(NFs).These decisions are executed through the Ku-bernetes API,which dynamically allocates appropriate resources to UPF instances.The experimental results show that all implemented models demonstrate satisfactory predictive capabilities.Moreover,compared with the threshold-based method in Kubernetes,all models show a significant advantage in response time.This study not only introduces a novel AI-native NWDAF architecture but also demonstrates the potential of AI models to significantly improve network management and resource scaling in 6G networks.
文摘移动通信系统在5G阶段首次引入网络智能,5G网络智能主要依赖于网络数据分析功能网元(Network Data Analytics Function,NWDAF)的定义,及其与其他核心网网元的协同。也正因是首次引入,5G网络智能具有如下技术局限性。一是外挂式AI,缺乏全网AI能力协同机制。5G核心网网元功能(Network Function,NF)不具备AI能力,仅有新引入的NWDAF具备AI能力,属于典型的外挂式AI架构。
