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
Although 6G networks combined with artificial intelligence present revolutionary prospects for healthcare delivery,resource management in dense medical device networks stays a basic issue.Reliable communication direct...Although 6G networks combined with artificial intelligence present revolutionary prospects for healthcare delivery,resource management in dense medical device networks stays a basic issue.Reliable communication directly affects patient outcomes in these settings;nonetheless,current resource allocation techniques struggle with complicated interference patterns and different service needs of AI-native healthcare systems.In dense installations where conventional approaches fail,this paper tackles the challenge of combining network efficiency with medical care priority.Thus,we offer a Dueling Deep Q-Network(DDQN)-based resource allocation approach for AI-native healthcare systems in 6G dense networks.First,we create a point-line graph coloringbased interference model to capture the unique characteristics of medical device communications.Building on this foundation,we suggest a DDQN approach to optimal resource allocation over multiple medical services by combining advantage estimate with healthcare-aware state evaluation.Unlike traditional graph-based models,this one correctly depicts the overlapping coverage areas common in hospital environments.Building on this basis,our DDQN design allows the system to prioritize medical needs while distributing resources by separating healthcare state assessment from advantage estimation.Experimental findings show that the suggested DDQN outperforms state-of-the-art techniques in dense healthcare installations by 14.6%greater network throughput and 13.7%better resource use.The solution shows particularly strong in maintaining service quality under vital conditions with 5.5%greater Qo S satisfaction for emergency services and 8.2%quicker recovery from interruptions.展开更多
The rapid evolution of radio access networks(RANs)highlights the pressing need for sustainable and programmable resource management strategies.This paper introduces the energy saving RAN application(ES-rApp),an AI-dri...The rapid evolution of radio access networks(RANs)highlights the pressing need for sustainable and programmable resource management strategies.This paper introduces the energy saving RAN application(ES-rApp),an AI-driven solution designed for intelligent resource orchestration within the open RAN(O-RAN)architecture.ES-rApp integrates traffic-aware prediction models and closed-loop automation to dynamically optimize network operations,enabling adaptive cell deactivation,intelligent transmit-power adjustment,and proactive resource allocation based on predicted traffic demands while preserving service quality.Unlike prior studies that rely primarily on simulations or small-scale prototypes,we present a deployable and O-RAN-compliant implementation validated on a real testbed.Experimental evaluations under diverse traffic profiles demonstrate that ES-rApp achieves up to 19.5%energy savings without degrading quality of service(QoS).These results provide a real-world evidence of AI-native energy optimization in live RAN environments,establishing ES-rApp as a scalable and practical solution for green RAN management.This work contributes a concrete pathway toward transforming conventional RANs into sustainable,intelligent infrastructures that advance both operational efficiency and environmental responsibility in next-generation wireless networks.展开更多
The issues of wireless communication network autonomy,the definition of capability level and the concept of AI-native solution based on the integration of the information communication data technology(ICDT)are first i...The issues of wireless communication network autonomy,the definition of capability level and the concept of AI-native solution based on the integration of the information communication data technology(ICDT)are first introduced in this paper.A series of innovative technologies proposed by ZTE Corporation,such as an autonomous evolution network and intelligent orchestration network,are then analyzed.These technologies are developed to realize the evolution of wireless networks to Level-4 and Level-5 intelligent networks.It is expected that the future AI-native intelligent network system will be built based on innovative technologies such as digital twins,intent-based networking,and the data plane and intelligent plane.These new technical paradigms will promote the development of intelligent B5G and 6G networks.展开更多
Artificial intelligence(AI)and wireless communications are catalyzing each other’s advancement as we approach 6G networks and beyond.This article presents a perspective on the two-way interplay between AI and communi...Artificial intelligence(AI)and wireless communications are catalyzing each other’s advancement as we approach 6G networks and beyond.This article presents a perspective on the two-way interplay between AI and communications-how AI techniques are revolutionizing communication network design(AI4Comm)and how emerging communication technologies are enabling and accelerating AI(Comm4AI).We discuss recent advances and outline the challenges in realizing an AI-native wireless ecosystem and propose a roadmap for integrating AI and communications,offering insights into a future where wireless communications and AI evolve together.展开更多
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.
基金supported by National Natural Science Foundation of China under Granted No.62202247。
文摘Although 6G networks combined with artificial intelligence present revolutionary prospects for healthcare delivery,resource management in dense medical device networks stays a basic issue.Reliable communication directly affects patient outcomes in these settings;nonetheless,current resource allocation techniques struggle with complicated interference patterns and different service needs of AI-native healthcare systems.In dense installations where conventional approaches fail,this paper tackles the challenge of combining network efficiency with medical care priority.Thus,we offer a Dueling Deep Q-Network(DDQN)-based resource allocation approach for AI-native healthcare systems in 6G dense networks.First,we create a point-line graph coloringbased interference model to capture the unique characteristics of medical device communications.Building on this foundation,we suggest a DDQN approach to optimal resource allocation over multiple medical services by combining advantage estimate with healthcare-aware state evaluation.Unlike traditional graph-based models,this one correctly depicts the overlapping coverage areas common in hospital environments.Building on this basis,our DDQN design allows the system to prioritize medical needs while distributing resources by separating healthcare state assessment from advantage estimation.Experimental findings show that the suggested DDQN outperforms state-of-the-art techniques in dense healthcare installations by 14.6%greater network throughput and 13.7%better resource use.The solution shows particularly strong in maintaining service quality under vital conditions with 5.5%greater Qo S satisfaction for emergency services and 8.2%quicker recovery from interruptions.
基金funded in part by Start-up Funding for the 2025 National High-End Talent Frontier Project(01)with Grant 70012501A01in part by the National Natural Science Foundation of China under Grant 62301328+1 种基金by the National Research Foundation,Singapore,and Infocomm Media Development Authority under its Future Communications Research&Development Programmein part by the SNS JU project 6G-GOALS under the EU's Horizon program Grant Agreement 101139232.
文摘The rapid evolution of radio access networks(RANs)highlights the pressing need for sustainable and programmable resource management strategies.This paper introduces the energy saving RAN application(ES-rApp),an AI-driven solution designed for intelligent resource orchestration within the open RAN(O-RAN)architecture.ES-rApp integrates traffic-aware prediction models and closed-loop automation to dynamically optimize network operations,enabling adaptive cell deactivation,intelligent transmit-power adjustment,and proactive resource allocation based on predicted traffic demands while preserving service quality.Unlike prior studies that rely primarily on simulations or small-scale prototypes,we present a deployable and O-RAN-compliant implementation validated on a real testbed.Experimental evaluations under diverse traffic profiles demonstrate that ES-rApp achieves up to 19.5%energy savings without degrading quality of service(QoS).These results provide a real-world evidence of AI-native energy optimization in live RAN environments,establishing ES-rApp as a scalable and practical solution for green RAN management.This work contributes a concrete pathway toward transforming conventional RANs into sustainable,intelligent infrastructures that advance both operational efficiency and environmental responsibility in next-generation wireless networks.
文摘The issues of wireless communication network autonomy,the definition of capability level and the concept of AI-native solution based on the integration of the information communication data technology(ICDT)are first introduced in this paper.A series of innovative technologies proposed by ZTE Corporation,such as an autonomous evolution network and intelligent orchestration network,are then analyzed.These technologies are developed to realize the evolution of wireless networks to Level-4 and Level-5 intelligent networks.It is expected that the future AI-native intelligent network system will be built based on innovative technologies such as digital twins,intent-based networking,and the data plane and intelligent plane.These new technical paradigms will promote the development of intelligent B5G and 6G networks.
基金supported in part by the National Natural Science Foundation of China under Grants 62341101,62125101,and 62301011in part by Beijing Natural Science Foundation under Grant L257016in part by the New Cornerstone Science Foundation through the Xplorer Prize.
文摘Artificial intelligence(AI)and wireless communications are catalyzing each other’s advancement as we approach 6G networks and beyond.This article presents a perspective on the two-way interplay between AI and communications-how AI techniques are revolutionizing communication network design(AI4Comm)and how emerging communication technologies are enabling and accelerating AI(Comm4AI).We discuss recent advances and outline the challenges in realizing an AI-native wireless ecosystem and propose a roadmap for integrating AI and communications,offering insights into a future where wireless communications and AI evolve together.
