浏览全部资源
扫码关注微信
[ "李琴(1977− ),女,中国移动通信有限公司研究院研究员,主要研究方向为网络智能化" ]
[ "李唯源(1988− ),女,博士,中国移动通信有限公司研究院研究员,主要研究方向为网络智能化" ]
[ "孙晓文(1989− ),女,中国移动通信有限公司研究院研究员,主要研究方向为 5G 网络切片、网络自动化管理、数字孪生技术等" ]
[ "胡玉双(1993− ),女,中国移动通信有限公司研究院研究员,主要研究方向为5G网络切片、6G架构、空天地一体化等" ]
[ "孙滔(1981− ),男,中国移动通信有限公司研究院主任研究员、网络创新实验室技术经理,主要研究方向为移动网新架构、网络智能化" ]
网络出版日期:2021-09,
纸质出版日期:2021-09-20
移动端阅览
李琴, 李唯源, 孙晓文, 等. 6G网络智能内生的思考[J]. 电信科学, 2021,37(9):20-29.
Qin LI, Weiyuan LI, Xiaowen SUN, et al. Thinking of native artificial intelligence in 6G networks[J]. Telecommunications science, 2021, 37(9): 20-29.
李琴, 李唯源, 孙晓文, 等. 6G网络智能内生的思考[J]. 电信科学, 2021,37(9):20-29. DOI: 10.11959/j.issn.1000-0801.2021219.
Qin LI, Weiyuan LI, Xiaowen SUN, et al. Thinking of native artificial intelligence in 6G networks[J]. Telecommunications science, 2021, 37(9): 20-29. DOI: 10.11959/j.issn.1000-0801.2021219.
面向6G时代,网络将迎来新的应用场景和新的性能需求,多样化的应用和通信场景、超异构的网络连接以及极致性能的服务需求,都对移动通信网络提出了更高的要求。在总结阐述 5G 和 5G-Advanced 网络智能化的基础上,面向6G网络给出了6G网络智能内生的定义,提出了6G网络智能内生架构的四大特征,并分析了6G网络智能内生的潜在关键技术,最后结合两个应用场景进一步探讨了智能内生架构理念。
Facing the 6G era
the network will usher in new application scenarios and new performance requirements.Diversified application and communication scenarios
extremely heterogeneous communication networks
and extreme performance service requirements will all put forward higher requirements for mobile communication networks.On the basis of summarizing network intelligence in 5G and 5G-Advanced networks
the definition of intelligent-endogenesisin 6G networks and four major characteristics of 6G network architecture for intelligent-endogenesis were proposed
and the potential key technologies of intelligent-endogenesisin 6G network was analyzed
and finally combined with two application scenarios to further explore the concept of network architecture for intelligent-endogenesis.
3GPP . Architecture enhancements for 5G System (5GS) to support network data analytics services:TS23.288 [S ] . 2021 .
3GPP . Study on enhancement of management data analytics (MDA):TR28.809-040 [S ] . 2020 .
O-RAN . O-RAN minimum viable plan and acceleration towards commercialization (white paper) [R ] . 2021 .
AAZHANG B , AHOKANGAS P , ALVES H , et al . Key drivers and research challenges for 6G ubiquitous wireless intelligence(white paper) [EB ] . 2019 .
6GANA . Network AI white paper [R ] . 2021 .
6G Flagship,Finnish 6G flagship white papers [R ] . 2021 .
IMT-2030(6G)推进组 . 6G总体愿景与潜在关键技术 [R ] . 2021 .
IMT-2030(6G)Promotion Gtoup . Overall 6G vision and poten-tial key technologies [R ] . 2021 .
SHYY D , WATSON C , WOODS K . TW:6G and artificial intelligence & machine learning [R ] . 2021 .
YOU X H , WANG C X , HUANG J , et al . Towards 6G wireless communication networks:vision,enabling technologies,and new paradigm shifts [J ] . Science China Information Sciences , 2021 , 64 ( 1 ): 110301 .
TOMKOS I , KLONIDIS D , PIKASIS E , et al . Toward the 6G network era:opportunities and challenges [J ] . IT Professional , 2020 , 22 ( 1 ): 34 - 38 .
中国移动通信有限公司研究院 . 2030+网络架构展望 [R ] . 2021 .
China Mobile Research Institute . 2030+network architecture outlook [R ] . 2021 .
张平 , 牛凯 , 田辉 , 等 . 6G 移动通信技术展望 [J ] . 通信学报 , 2019 , 40 ( 1 ): 141 - 148 .
ZHANG P , NIU K , TIAN H , et al . Technology prospect of 6G mobile communications [J ] . Journal on Communications , 2019 , 40 ( 1 ): 141 - 148 .
WU J J , LI R P , AN X L , et al . Toward native artificial intelligence in 6G networks:system design,architectures,and paradigms [EB ] . 2021 .
WANG T Y , WANG S W , ZHOU Z H . Machine learning for 5G and beyond:from model-based to data-driven mobile wireless networks [J ] . China Communications , 2019 , 16 ( 1 ): 165 - 175 .
张钹 , 朱军 , 苏航 . 迈向第三代人工智能 [J ] . 中国科学:信息科学 , 2020 , 50 ( 9 ): 1281 - 1302 .
ZHANG B , ZHU J , SU H . Toward the third generation of artifi-cial intelligence [J ] . Scientia Sinica (Informationis) , 2020 , 50 ( 9 ): 1281 - 1302 .
MCMAHAN H B , MOORE E , RAMAGE D , et al . Communication-efficient learning of deep networks from decentralized data [J ] . 2016:arXiv:1602.05629 [cs.LG ] .
WARNAT-HERRESTHAL S , SCHULTZE H , SHASTRY K L , et al . Swarm Learning for decentralized and confidential clinical machine learning [J ] . Nature , 2021 , 594 ( 7862 ): 265 - 270 .
孙滔 , 周铖 , 段晓东 , 等 . 数字孪生网络(DTN):概念、架构及关键技术 [J ] . 自动化学报 , 2021 , 47 ( 3 ): 569 - 582 .
SUN T , ZHOU C , DUAN X D , et al . Digital twin network (DTN):concepts,architecture,and key technologies [J ] . Acta Automatica Sinica , 2021 , 47 ( 3 ): 569 - 582 .
ZOPH B , LE Q V . Neural architecture search with reinforcement learning [J ] . arXiv:1611.01578 , 2016 .
HE X , ZHAO K Y , CHU X W . AutoML:a survey of the stateof-the-art [J ] . Knowledge-Based Systems , 2021 ( 212 ):106622.
YAO Q M , WANG M S , HUGO J E , et al . Taking human out of learning applications:a survey on automated machine learning [EB ] . 2018 .
张彤 , 任奕璟 , 闫实 , 等 . 人工智能驱动的6G网络:智慧内生 [J ] . 电信科学 , 2020 , 36 ( 9 ): 14 - 22 .
ZHANG T , REN Y J , YAN S , et al . Artificial intelligence driven 6G networks:endogenous intelligence [J ] . Telecommunications Science , 2020 , 36 ( 9 ): 14 - 22 .
AMIT S , not strings . Introducing the knowledge graph:things.Google Blog [R ] . 2012 .
AIDAN H , EVA B , MICHAEL C , et al . Knowledge Graphs [J ] . arXiv:2003.02320 , 2021 .
DANG S P , AMIN O , SHIHADA B , et al . What should 6G Be? [J ] . Nature Electronics , 2020 , 3 ( 1 ): 20 - 29 .
0
浏览量
973
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构