As investigated by 3GPP,support of UPF(user plane function)onboard satellite can reduce the latency of communications via satellite,and then it becomes a key enhancement in 5G network integrating with satellite commun...As investigated by 3GPP,support of UPF(user plane function)onboard satellite can reduce the latency of communications via satellite,and then it becomes a key enhancement in 5G network integrating with satellite communication.However,current 5G system cannot support UPF onboard LEO(low earth orbit)satellites,as it would face challenges like UPF mobility handling,synchronization between mobile network and satellite network,and condition of activating local data switching.To solve such challenges,this paper proposes a solution to support UPF onboard LEO satellite,which consists of enhanced network architecture,I-UPF(intermediate UPF)based local data switching scheme and communication latency based data path selection.We subsequently develop analytic models for performance evaluation and conduct simulations using the constellation configuration of iridium II.The simulation results show that the data switching via I-UPF onboard LEO satellite can reduce E2E(end to end)packet delivery latency and E2E packet loss ratio significantly compared with that of routing the data back to 5GC on the ground.The proposed scheme yet has increased signaling cost for handling UPF mobility.els,compared with existing similar companding algorithms.展开更多
随着工业企业数字化转型进程的加快,面向互联网的新型应用层出不穷,5G专网作为支撑这一趋势的新型基础设施,其建设开始步入部署规模化、需求定制化的阶段。5G专网组网方案的规划设计应充分考虑不同工业场景的需求,灵活利用切片、用户端...随着工业企业数字化转型进程的加快,面向互联网的新型应用层出不穷,5G专网作为支撑这一趋势的新型基础设施,其建设开始步入部署规模化、需求定制化的阶段。5G专网组网方案的规划设计应充分考虑不同工业场景的需求,灵活利用切片、用户端口功能(User Port Function,UPF)分流、5G局域网(Local Area Network,LAN)等关键技术,结合虚拟专网、混合专网、物理专网等部署方式来综合制定,为5G进一步融入行业,成为赋能工业生产的关键基础设施提供支持。展开更多
近年来,健康医疗大数据产业被列入国家大数据战略布局,带来了健康医疗模式的深刻变化,但医疗行业整体数据安全和个人信息保护也面临着更大的威胁。本研究通过对传统远程医疗行业痛点的分析,提出了一种基于5G移动边缘计算的远程云诊疗平...近年来,健康医疗大数据产业被列入国家大数据战略布局,带来了健康医疗模式的深刻变化,但医疗行业整体数据安全和个人信息保护也面临着更大的威胁。本研究通过对传统远程医疗行业痛点的分析,提出了一种基于5G移动边缘计算的远程云诊疗平台设计。该设计采用5G端到端网络切片结合多重服务质量(Quality of Service,QoS)技术,实现了业务差异化服务水平协议(Service Level Agreement,SLA)保障,构筑了端到端安全、可靠,以及带宽和时延满足业务要求的虚拟专网,持续提高了智慧医疗行业的便捷化、自动化和智能化,从多方面保障了人们健康,显著提升了医疗服务水平。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
基金supported by the national key research and development program of China under Grant 2020YFB1807901the National Science Foundation Project in China under grant 61931005.
文摘As investigated by 3GPP,support of UPF(user plane function)onboard satellite can reduce the latency of communications via satellite,and then it becomes a key enhancement in 5G network integrating with satellite communication.However,current 5G system cannot support UPF onboard LEO(low earth orbit)satellites,as it would face challenges like UPF mobility handling,synchronization between mobile network and satellite network,and condition of activating local data switching.To solve such challenges,this paper proposes a solution to support UPF onboard LEO satellite,which consists of enhanced network architecture,I-UPF(intermediate UPF)based local data switching scheme and communication latency based data path selection.We subsequently develop analytic models for performance evaluation and conduct simulations using the constellation configuration of iridium II.The simulation results show that the data switching via I-UPF onboard LEO satellite can reduce E2E(end to end)packet delivery latency and E2E packet loss ratio significantly compared with that of routing the data back to 5GC on the ground.The proposed scheme yet has increased signaling cost for handling UPF mobility.els,compared with existing similar companding algorithms.
文摘随着工业企业数字化转型进程的加快,面向互联网的新型应用层出不穷,5G专网作为支撑这一趋势的新型基础设施,其建设开始步入部署规模化、需求定制化的阶段。5G专网组网方案的规划设计应充分考虑不同工业场景的需求,灵活利用切片、用户端口功能(User Port Function,UPF)分流、5G局域网(Local Area Network,LAN)等关键技术,结合虚拟专网、混合专网、物理专网等部署方式来综合制定,为5G进一步融入行业,成为赋能工业生产的关键基础设施提供支持。
文摘近年来,健康医疗大数据产业被列入国家大数据战略布局,带来了健康医疗模式的深刻变化,但医疗行业整体数据安全和个人信息保护也面临着更大的威胁。本研究通过对传统远程医疗行业痛点的分析,提出了一种基于5G移动边缘计算的远程云诊疗平台设计。该设计采用5G端到端网络切片结合多重服务质量(Quality of Service,QoS)技术,实现了业务差异化服务水平协议(Service Level Agreement,SLA)保障,构筑了端到端安全、可靠,以及带宽和时延满足业务要求的虚拟专网,持续提高了智慧医疗行业的便捷化、自动化和智能化,从多方面保障了人们健康,显著提升了医疗服务水平。
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.
