Intent-Based Networks(IBNs),which are originally proposed to introduce Artificial Intelligence(AI)into the sixth-generation(6G)wireless networks,can effectively solve the challenges of traditional networks in terms of...Intent-Based Networks(IBNs),which are originally proposed to introduce Artificial Intelligence(AI)into the sixth-generation(6G)wireless networks,can effectively solve the challenges of traditional networks in terms of efficiency,flexibility,and security.IBNs are mainly used to transform users’business intent into network configuration,operation,and maintenance strategies,which are prominent for designing the AI-enabled 6G networks.In particular,in order to meet the massive,intelligent service demands and overcome the time-varying radio propagation,IBNs can continuously learn and adapt to the time-varying network environment based on the massive collected network data in real-time.From the aspects of both the core network and radio access network,this article comprehensively surveys the architectures and key techniques of IBNs for 6G.In particular,the demonstration platforms of IBNs,such as the Apstra Operating System,Forward Networks Verification Platform,and One Convergence Service Interaction Platform,are presented.Moreover,the industrial development of IBNs is elaborated,including the emerging new products and startups to solve the problems of open data platforms,automated network operations,and preemptive network fault diagnosis.Finally,several open issues and challenges are identified as well to spur future researches.展开更多
L.David Marquet,a decorated Navy Captain,transformed an under performing submarine crew by empowering his subordinates to be leaders and reach their full potential.He called this intent-based leadership(IBL).What woul...L.David Marquet,a decorated Navy Captain,transformed an under performing submarine crew by empowering his subordinates to be leaders and reach their full potential.He called this intent-based leadership(IBL).What would happen if Marquet’s model were implemented in Graduate Medical Education(GME)?In this letter to the editor,we summarize the potential of the IBL model in graduate medical education as opposed to the traditional leaderfollower method.IBL harnesses human productivity toward the shared goals of GME,which are patient care and trainee learning.This shift in mindset could lead both teachers and trainees to focus more on the real reason that we undertake GME and change behaviors for the better.We suggest that IBL can and should be adopted in GME and propose that both patients and providers will benefit from this action.展开更多
Telecommunication has undergone significant transformations due to the continuous advancements in internet technology,mobile devices,competitive pricing,and changing customer preferences.Specifically,the most recent i...Telecommunication has undergone significant transformations due to the continuous advancements in internet technology,mobile devices,competitive pricing,and changing customer preferences.Specifically,the most recent iteration of OpenAI’s large language model chat generative pre-trained transformer(ChatGPT)has the potential to propel innovation and bolster operational performance in the telecommunications sector.Nowadays,the exploration of network resource management,control,and operation is still in the initial stage.In this paper,we propose a novel network artificial intelligence architecture named language model for network traffic(NetLM),a large language model based on a transformer designed to understand sequence structures in the network packet data and capture their underlying dynamics.The continual convergence of knowledge space and artificial intelligence(AI)technologies constitutes the core of intelligent network management and control.Multi-modal representation learning is used to unify the multi-modal information of network indicator data,traffic data,and text data into the same feature space.Furthermore,a NetLM-based control policy generation framework is proposed to refine intent incrementally through different abstraction levels.Finally,some potential cases are provided that NetLM can benefit the telecom industry.展开更多
It has been an exciting journey since the mobile communications and artificial intelligence(AI)were conceived in 1983 and 1956.While both fields evolved independently and profoundly changed communications and computin...It has been an exciting journey since the mobile communications and artificial intelligence(AI)were conceived in 1983 and 1956.While both fields evolved independently and profoundly changed communications and computing industries,the rapid convergence of 5th generation mobile communication technology(5G)and AI is beginning to significantly transform the core communication infrastructure,network management,and vertical applications.The paper first outlined the individual roadmaps of mobile communications and AI in the early stage,with a concentration to review the era from 3rd generation mobile communication technology(3G)to 5G when AI and mobile communications started to converge.With regard to telecommunications AI,the progress of AI in the ecosystem of mobile communications was further introduced in detail,including network infrastructure,network operation and management,business operation and management,intelligent applications towards business supporting system(BSS)&operation supporting system(OSS)convergence,verticals and private networks,etc.Then the classifications of AI in telecommunication ecosystems were summarized along with its evolution paths specified by various international telecommunications standardization organizations.Towards the next decade,the prospective roadmap of telecommunications AI was forecasted.In line with 3rd generation partnership project(3GPP)and International Telecommunication Union Radiocommunication Sector(ITU-R)timeline of 5G&6th generation mobile communication technology(6G),the network intelligence following 3GPP and open radio access network(O-RAN)routes,experience and intent-based network management and operation,network AI signaling system,intelligent middle-office based BSS,intelligent customer experience management and policy control driven by BSS&OSS convergence,evolution from service level agreement(SLA)to experience level agreement(ELA),and intelligent private network for verticals were further explored.The paper is concluded with the vision that AI will reshape the future beyond 5G(B5G)/6G landscape,and we need pivot our research and development(R&D),standardizations,and ecosystem to fully take the unprecedented opportunities.展开更多
基金This work was supported in part by the State Major Science and Technology Special Project(Grant No.2018ZX03001002-004 and 2018ZX03001023)the National Natural Science Foundation of China under No.61921003,61925101,61831002,and 61901044+1 种基金the Beijing Natural Science Foundation under No.JQ18016and the National Program for Special Support of Eminent Professionals.
文摘Intent-Based Networks(IBNs),which are originally proposed to introduce Artificial Intelligence(AI)into the sixth-generation(6G)wireless networks,can effectively solve the challenges of traditional networks in terms of efficiency,flexibility,and security.IBNs are mainly used to transform users’business intent into network configuration,operation,and maintenance strategies,which are prominent for designing the AI-enabled 6G networks.In particular,in order to meet the massive,intelligent service demands and overcome the time-varying radio propagation,IBNs can continuously learn and adapt to the time-varying network environment based on the massive collected network data in real-time.From the aspects of both the core network and radio access network,this article comprehensively surveys the architectures and key techniques of IBNs for 6G.In particular,the demonstration platforms of IBNs,such as the Apstra Operating System,Forward Networks Verification Platform,and One Convergence Service Interaction Platform,are presented.Moreover,the industrial development of IBNs is elaborated,including the emerging new products and startups to solve the problems of open data platforms,automated network operations,and preemptive network fault diagnosis.Finally,several open issues and challenges are identified as well to spur future researches.
文摘L.David Marquet,a decorated Navy Captain,transformed an under performing submarine crew by empowering his subordinates to be leaders and reach their full potential.He called this intent-based leadership(IBL).What would happen if Marquet’s model were implemented in Graduate Medical Education(GME)?In this letter to the editor,we summarize the potential of the IBL model in graduate medical education as opposed to the traditional leaderfollower method.IBL harnesses human productivity toward the shared goals of GME,which are patient care and trainee learning.This shift in mindset could lead both teachers and trainees to focus more on the real reason that we undertake GME and change behaviors for the better.We suggest that IBL can and should be adopted in GME and propose that both patients and providers will benefit from this action.
基金This work was supported by the National Natural Science Foundation of China under Grants of 62071067,62101064,62201072,62171057,and 62001054,Beijing University of Posts and Telecommunications-China Mobile Research Institute Joint Innovation Center。
文摘Telecommunication has undergone significant transformations due to the continuous advancements in internet technology,mobile devices,competitive pricing,and changing customer preferences.Specifically,the most recent iteration of OpenAI’s large language model chat generative pre-trained transformer(ChatGPT)has the potential to propel innovation and bolster operational performance in the telecommunications sector.Nowadays,the exploration of network resource management,control,and operation is still in the initial stage.In this paper,we propose a novel network artificial intelligence architecture named language model for network traffic(NetLM),a large language model based on a transformer designed to understand sequence structures in the network packet data and capture their underlying dynamics.The continual convergence of knowledge space and artificial intelligence(AI)technologies constitutes the core of intelligent network management and control.Multi-modal representation learning is used to unify the multi-modal information of network indicator data,traffic data,and text data into the same feature space.Furthermore,a NetLM-based control policy generation framework is proposed to refine intent incrementally through different abstraction levels.Finally,some potential cases are provided that NetLM can benefit the telecom industry.
文摘It has been an exciting journey since the mobile communications and artificial intelligence(AI)were conceived in 1983 and 1956.While both fields evolved independently and profoundly changed communications and computing industries,the rapid convergence of 5th generation mobile communication technology(5G)and AI is beginning to significantly transform the core communication infrastructure,network management,and vertical applications.The paper first outlined the individual roadmaps of mobile communications and AI in the early stage,with a concentration to review the era from 3rd generation mobile communication technology(3G)to 5G when AI and mobile communications started to converge.With regard to telecommunications AI,the progress of AI in the ecosystem of mobile communications was further introduced in detail,including network infrastructure,network operation and management,business operation and management,intelligent applications towards business supporting system(BSS)&operation supporting system(OSS)convergence,verticals and private networks,etc.Then the classifications of AI in telecommunication ecosystems were summarized along with its evolution paths specified by various international telecommunications standardization organizations.Towards the next decade,the prospective roadmap of telecommunications AI was forecasted.In line with 3rd generation partnership project(3GPP)and International Telecommunication Union Radiocommunication Sector(ITU-R)timeline of 5G&6th generation mobile communication technology(6G),the network intelligence following 3GPP and open radio access network(O-RAN)routes,experience and intent-based network management and operation,network AI signaling system,intelligent middle-office based BSS,intelligent customer experience management and policy control driven by BSS&OSS convergence,evolution from service level agreement(SLA)to experience level agreement(ELA),and intelligent private network for verticals were further explored.The paper is concluded with the vision that AI will reshape the future beyond 5G(B5G)/6G landscape,and we need pivot our research and development(R&D),standardizations,and ecosystem to fully take the unprecedented opportunities.
