意图抽象与知识联合驱动的6G内生智能网络架构

doi:10.11959/j.issn.1000-436x.2023016

通信学报 ›› 2023, Vol. 44 ›› Issue (2): 12-26.doi: 10.11959/j.issn.1000-436x.2023016

意图抽象与知识联合驱动的6G内生智能网络架构

杨静雅¹^,², 唐晓刚³, 周一青¹^,², 刘玲¹^,², Jiangzhou Wang⁴

¹ 中国科学院计算技术研究所处理器芯片全国重点实验室，北京 100190
² 中国科学院大学计算机科学与技术学院，北京 100049
³ 航天工程大学航天信息学院，北京 101400
⁴ 肯特大学工学院，坎特伯雷 CT2 7NZ

修回日期:2022-12-02 出版日期:2023-02-25 发布日期:2023-02-01
作者简介:杨静雅（1995− ），女，河南商丘人，中国科学院计算技术研究所博士生，主要研究方向为通信与计算融合、无线通信、意图驱动网络等
唐晓刚（1977− ），男，吉林桦甸人，博士，航天工程大学副教授，主要研究方向为空间信号智能处理、电磁频谱管控、人工智能航天应用
周一青（1975− ），女，浙江杭州人，博士，中国科学院计算技术研究所研究员，主要研究方向为通信与计算融合、移动边缘计算、存储通信、干扰管控等
刘玲（1990− ），女，北京人，博士，中国科学院计算技术研究所副研究员，主要研究方向为通信与计算融合等
Jiangzhou Wang（1961− ），男，博士，肯特大学教授，主要研究方向为移动通信、智能超表面、通信感知融合等
基金资助:
国家重点研发计划基金资助项目(2020YFB1807800)

6G native intelligence network architecture enabled by intent abstraction and knowledge

Jingya YANG¹^,², Xiaogang TANG³, Yiqing ZHOU¹^,², Ling LIU¹^,², Wang Jiangzhou⁴

¹ State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
² School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
³ School of Space Information, Space Engineering University, Beijing 101400, China
⁴ School of Engineering, University of Kent, Canterbury CT2 7NZ, UK

Revised:2022-12-02 Online:2023-02-25 Published:2023-02-01
Supported by:
The National Key Research and Development Program of China(2020YFB1807800)

摘要/Abstract

摘要：

6G 将以智能网络为演进形式，具备内生智能、开放性的特征。智能网络标准化研究中强调了意图驱动网络对实现网络智能化的必要性。但目前基于意图的网络将意图理解为“What to do”而非“What you want”，利用知识定义网络（KDN）可在一定程度上根据“What to do”完成“How to configure the network”。基于此，提出了意图抽象与知识联合驱动的 6G 内生智能网络架构，旨在根据“What you want”实现“How to configure the network”。首先，设计了意图抽象模块，通过意图获取、意图转译、意图映射和意图建模，从“What you want”获取“What to do”。其次，提出了认知模块，利用机器学习和逻辑推理联合动态优化获取网络知识，从而根据“What to do”完成“How to configure the network”。最后，介绍了支撑6G内生智能实现的意图映射、网络信息测量、网络策略生成、网络策略验证等关键技术及未来挑战。

关键词: 6G, 内生智能, 意图抽象, 网络知识

Abstract:

6G will evolve to an intelligent network, featured by native intelligence and openness.The study of standardization of intelligent network emphasizes the importance of intent-driven network for network intelligence.However, current intent-based networking takes the intent as “What to do” rather than “What you want”.Moreover, current knowledge defined network (KDN) can partially fulfill “How to configure the network” according to “What to do”.Therefore, a 6G native intelligent network architecture based on the intent abstraction and knowledge was proposed, aiming to achieve“How to configure the network” according to “What you want”.Firstly, an intent abstraction module was designed to obtain “What to do” from “What you want”, composed of intent acquisition, intent translation, intent mapping, and intent modeling.Secondly, the cognitive module was proposed to achieve “How to configure the network” according to “What to do”, which got network knowledge through joint optimization of machine learning and logical reasoning.Finally, enabling key technologies such as intent mapping, network information measurement, network policy generation, and network policy verification were introduced to support the implementation of 6G native intelligence.

Key words: 6G, native intelligence, intent abstraction, network knowledge

中图分类号:

TN92

杨静雅, 唐晓刚, 周一青, 刘玲, Jiangzhou Wang. 意图抽象与知识联合驱动的6G内生智能网络架构[J]. 通信学报, 2023, 44(2): 12-26.

Jingya YANG, Xiaogang TANG, Yiqing ZHOU, Ling LIU, Wang Jiangzhou. 6G native intelligence network architecture enabled by intent abstraction and knowledge[J]. Journal on Communications, 2023, 44(2): 12-26.

图/表 5

参考文献［66］

［1］	GONG S Q , XING C W , ZHAO X ,et al． Unified IRS-aided MIMO transceiver designs via majorization theory［J］． IEEE Transactions on Signal Processing, 2021,69: 3016-3032．
［2］	HUSSAIN B , DU Q H , IMRAN A ,et al． Artificial intelligence-powered mobile edge computing-based anomaly detection in cellular networks［J］． IEEE Transactions on Industrial Informatics, 2020,16(8): 4986-4996．
［3］	LIU L , ZHOU Y Q , YUAN J H ,et al． Economically optimal MS association for multimedia content delivery in cache-enabled heterogeneous cloud radio access networks［J］． IEEE Journal on Selected Areas in Communications, 2019,37(7): 1584-1593．
［4］	XIA B , WANG J L , XIAO K X ,et al． Outage performance analysis for the advanced SIC receiver in wireless NOMA systems［J］． IEEE Transactions on Vehicular Technology, 2018,67(7): 6711-6715．
［5］	ZHOU Y Q , LIU H , PAN Z G ,et al． Cooperative multicast with location aware distributed mobile relay selection:performance analysis and optimized design［J］． IEEE Transactions on Vehicular Technology, 2017,66(9): 8291-8302．
［6］	QI Y L , ZHOU Y Q , LIU Y F ,et al． Traffic-aware task offloading based on convergence of communication and sensing in vehicular edge computing［J］． IEEE Internet of Things Journal, 2021,8(24): 17762-17777．
［7］	ZHANG Z Q , XIAO Y , MA Z ,et al． 6G wireless networks:vision,requirements,architecture,and key technologies［J］． IEEE Vehicular Technology Magazine, 2019,14(3): 28-41．
［8］	ZHOU Y Q , LIU L , WANG L ,et al． Service-aware 6G:an intelligent and open network based on the convergence of communication,computing and caching［J］． Digital Communications and Networks, 2020,6(3): 253-260．
［9］	LETAIEF K B , CHEN W , SHI Y M ,et al． The roadmap to 6G:AI empowered wireless networks［J］． IEEE Communications Magazine, 2019,57(8): 84-90．
［10］	赛迪智库无线电管理研究所． 6G概念及愿景白皮书［R］． 2020．
	CCID Group． 6G concept and vision white paper［R］． 2020．
［11］	张平, 许晓东, 韩书君 ,等．智简无线网络赋能行业应用［J］．北京邮电大学学报, 2020,43(6): 1-9．
	ZHANG P , XU X D , HAN S J ,et al． Entropy reduced mobile networks empowering industrial applications［J］． Journal of Beijing University of Posts and Telecommunications, 2020,43(6): 1-9．
［12］	KATO N , MAO B M , TANG F X ,et al． Ten challenges in advancing machine learning technologies toward 6G［J］． IEEE Wireless Communications, 2020,27(3): 96-103．
［13］	YU Q , REN J , ZHOU H B ,et al． A cybertwin based network architecture for 6G［C］// Proceedings of 2020 2nd 6G Wireless Summit (6G SUMMIT)． Piscataway:IEEE Press, 2020: 1-5．
［14］	SHEN X M , GAO J , WU W ,et al． AI-assisted network-slicing based next-generation wireless networks［J］． IEEE Open Journal of Vehicular Technology, 2020,1: 45-66．
［15］	LIN M T , ZHAO Y P ． Artificial intelligence-empowered resource management for future wireless communications:a survey［J］． China Communications, 2020,17(3): 58-77．
［16］	BARIAH L , MOHJAZI L , MUHAIDAT S ,et al． A prospective look:key enabling technologies,applications and open research topics in 6G networks［J］． IEEE Access, 2020,8: 174792-174820．
［17］	WIJETHILAKA S , LIYANAGE M ． Survey on network slicing for Internet of Things realization in 5G networks［J］． IEEE Communications Surveys ＆ tutorials, 2021,23(2): 957-994．
［18］	ETSI． Improved operator experience through experiential net-worked intelligence (ENI)［R］． 2017．
［19］	ITU-T FG-ML5G． Unified architecture for machine learning in 5G and future networks［R］． 2019．
［20］	3GPP． Telecommunication management; study on scenarios for intent driven management services for mobile networks:TR 28．812［S］． 2020．
［21］	DAVID L ． Intent:don’t tell me what to do! (tell me what you want)[EB]. 2015．
［22］	ONF． Intent NBI - definition and principles［R］． 2016．
［23］	ANDREW L ． Intent-based networking[EB]. 2017．
［24］	开放数据中心标准推进委员会．意图网络技术与应用白皮书［R］． 2019．
	Open Data Center Committee． Intent based networking technology and applicaion［R］． 2019．
［25］	AKLAMANU F , RANDRIAMASY S , RENAULT E ． Demo:intent-based 5G IoT application network slice deployment［C］// Proceedings of 2019 10th International Conference on Networks of the Future (NoF)． Piscataway:IEEE Press, 2019: 141-143．
［26］	ABBAS K , KHAN T A , AFAQ M ,et al． Network slice lifecycle management for 5G mobile networks:an intent-based networking approach［J］． IEEE Access, 2021,9: 80128-80146．
［27］	SUBRAMANYA T , RIGGIO R , RASHEED T ． Intent-based mobile backhauling for 5G networks［C］// Proceedings of 2016 12th International Conference on Network and Service Management (CNSM)． Piscataway:IEEE Press, 2016: 348-352．
［28］	DAI W Z , XU Q L , YU Y ,et al． Tunneling neural perception and logic reasoning through abductive learning［J］． arXiv Preprint,arXiv:1802．01173, 2018．
［29］	CLARK D D , PARTRIDGE C , RAMMING J C ,et al． A knowledge plane for the Internet［C］// Proceedings of the 2003 Conference on Applications,Technologies,Architectures,and Protocols for Computer Communications． New York:ACM Press, 2003: 3-10．
［30］	MESTRES A , NATAL A R , CANCER J ,et al． Knowledge-defined networking［C］// Proceedings of 2017 ACM International Conference on the Applications,Technologies,Architectures,and Protocols for Computer Communication (2017 SIGCOMM)． New York:ACM Press, 2017: 2-10．
［31］	LU W , LIANG L P , KONG B X ,et al． AI-assisted knowledge-defined network orchestration for energy-efficient data center networks［J］． IEEE Communications Magazine, 2020,58(1): 86-92．
［32］	PHAM Q T A , AOUL Y H , OUTTAGARTS A ． Deep reinforcement learning based QoS-aware routing in knowledge-defined networking［C］// Proceedings of International Conference on Heterogeneous Networking for Quality,Reliability,Security and Robustness． Berlin:Springer, 2018: 14-26．
［33］	朱近康, 柴名扬, 周武旸．面向 B5G/6G 的三三三网络体系架构和优化学习机制［J］．通信学报, 2021,42(4): 62-75．
	ZHU J K , CHAI M Y , ZHOU W Y ． Three-three-three network architecture and learning optimization mechanism for B5G/6G［J］． Journal on Communications, 2021,42(4): 62-75．
［34］	廖建新, 付霄元, 戚琦 ,等． 6G-ADM：基于知识空间的6G网络管控体系［J］．通信学报, 2022,43(6): 3-15．
	LIAO J X , FU X Y , QI Q ,et al． 6G-ADM:knowledge based 6G network management and control architecture［J］． Journal on Communications, 2022,43(6): 3-15．
［35］	ZHOU Z H ． Abductive learning:towards bridging machine learning and logical reasoning［J］． Science China Information Sciences, 2019,62(7): 1-3．
［36］	周洋程, 闫实, 彭木根．意图驱动的 6G 无线接入网络［J］．物联网学报, 2020,4(1): 72-79．
	ZHOU Y C , YAN S , PENG M G ． Intent-driven 6G radio access network［J］． Chinese Journal on Internet of Things, 2020,4(1): 72-79．
［37］	ZEYDAN E , TURK Y ． Recent advances in intent-based networking:a survey［C］// Proceedings of 2020 IEEE 91st Vehicular Technology Conference． Piscataway:IEEE Press, 2020: 1-5．
［38］	VAN D H J , VEGA M T , TIMMERER C ,et al． Objective and subjective QoE evaluation for adaptive point cloud streaming［C］// Proceedings of 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX)． Piscataway:IEEE Press, 2020: 1-6．
［39］	ABAR T , BEN L A , EL A S ． Objective and subjective measurement QoE in SDN networks［C］// Proceedings of 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC)． Piscataway:IEEE Press, 2017: 1401-1406．
［40］	ALRESHOODI M , WOODS J ． Survey on QoE/QoS correlation models formultimedia services［J］． International Journal of Distributed and Parallel Systems, 2013,4(3): 53-72．
［41］	GRAMAGLIA M , DIGON I , FRIDERIKOS V ,et al． Flexible connectivity and QoE/QoS management for 5G networks:the 5G NORMA view［C］// Proceedings of 2016 IEEE International Conference on Communications Workshops． Piscataway:IEEE Press, 2016: 373-379．
［42］	WANG T Y , PERVEZ A , ZOU H ． VQM-based QoS/QoE mapping for streaming video［C］// Proceedings of 2010 3rd IEEE International Conference on Broadband Network and Multimedia Technology． Piscataway:IEEE Press, 2010: 807-812．
［43］	REICHL P , EGGER S , SCHATZ R ,et al． The logarithmic nature of QoE and the role of the Weber-Fechner law in QoE assessment［C］// Proceedings of 2010 IEEE International Conference on Communications． Piscataway:IEEE Press, 2010: 1-5．
［44］	中国通信学会．通感算一体化网络前沿报告［R］． 2022．
	China Institute of Communications． Frontier report of communication,perception and computation integration network［R］． 2022．
［45］	汪泽焱．一种基于多目标优化的 QoS 路由交互式算法［J］．国防科技大学学报, 2002,24(4): 37-41．
	WANG Z Y ． An interactive multiobject optimization QoS routing algorithm［J］． Journal of National University of Defense Technology, 2002,24(4): 37-41．
［46］	MOLDOVAN A N , GHERGULESCU I , MUNTEAN C H ． VQAMap:a novel mechanism for mapping objective video quality metrics to subjective MOS scale［J］． IEEE Transactions on Broadcasting, 2016,62(3): 610-627．
［47］	AMOUR L , BOULABIAR M I , SOUIHI S ,et al． An improved QoE estimation method based on QoS and affective computing［C］// Pro ceedings of 2018 International Symposium on Programming and Systems (ISPS)． Piscataway:IEEE Press, 2018: 1-6．
［48］	XU B , XIA W C , WEN W L ,et al． Adaptive hierarchical federated learning over wireless networks［J］． IEEE Transactions on Vehicular Technology, 2022,71(2): 2070-2083．
［49］	TAN L Z , SU W , ZHANG W ,et al． In-band network telemetry:a survey［J］． Computer Networks, 2021,186， 107763.
［50］	KIM C , BHIDE P , DOE E ,et al． In-band network telemetry (INT)［R］． 2016．
［51］	GULENKO A , WALLSCHL?GER M ,, KAO O ． A practical implementation of in-band network telemetry in open vSwitch［C］// Proceedings of 2018 IEEE 7th International Conference on Cloud Networking (CloudNet)． Piscataway:IEEE Press, 2018: 1-4．
［52］	KARAAGAC A , POORTER E D , HOEBEKE J ． In-band network telemetry in industrial wireless sensor networks［J］． IEEE Transactions on Network and Service Management, 2020,17(1): 517-531．
［53］	HAXHIBEQIRI J , ISOLANI P H , MARQUEZ-BARJA J M , ,et al． In-band network monitoring technique to support SDN-based wireless networks［J］． IEEE Transactions on Network and Service Management, 2021,18(1): 627-641．
［54］	QU K G , ZHUANG W H , SHEN X M ,et al． Dynamic resource scaling for VNF over nonstationary traffic:a learning approach［J］． IEEE Transactions on Cognitive Communications and Networking, 2021,7(2): 648-662．
［55］	WANG J , HU J , MIN G Y ,et al． Computation offloading in multi-access edge computing using a deep sequential model based on reinforcement learning［J］． IEEE Communications Magazine, 2019,57(5): 64-69．
［56］	PENG Y , LIU L , ZHOU Y Q ,et al． Deep reinforcement learning-based dynamic service migration in vehicular networks［C］// Proceedings of 2019 IEEE Global Communications Conference． Piscataway:IEEE Press, 2019: 1-6．
［57］	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．
［58］	LI Y H , YIN X , WANG Z L ,et al． A survey on network verification and testing with formal methods:approaches and challenges［J］． IEEE Communications Surveys ＆ Tutorials, 2019,21(1): 940-969．
［59］	LIU Y , HE C ． A heuristics-based incremental probabilistic model checking at runtime［C］// Proceedings of 2020 IEEE 11th International Conference on Software Engineering and Service Science． Piscataway:IEEE Press, 2020: 355-358．
［60］	HU H X , AHN G J , KULKARNI K ． Detecting and resolving firewall policy anomalies［J］． IEEE Transactions on Dependable and Secure Computing, 2012,9(3): 318-331．
［61］	VALENZA F , BASILE C , CANAVESE D ,et al． Classification and analysis of communication protection policy anomalies［J］． IEEE/ACM Transactions on Networking, 2017,25(5): 2601-2614．
［62］	PANG L , YANG C G , CHEN D Y ,et al． A survey on intent-driven networks［J］． IEEE Access, 2020,8: 22862-22873．
［63］	ZHU W J , FENG P , DENG M L ． An approximate CTL model checking approach［C］// Proceedings of 2019 IEEE 10th International Conference on Software Engineering and Service Science． Piscataway:IEEE Press, 2019: 646-648．
［64］	WANG P Z , HUANG L S , XU H L ,et al． Rule anomalies detecting and resolving for software defined networks［C］// Proceedings of 2015 IEEE Global Communications Conference． Piscataway:IEEE Press, 2015: 1-6．
［65］	COMER D , RASTEGATNIA A ． OSDF:an intent-based software defined network programming framework［C］// Proceedings of 2018 IEEE 43rd Conference on Local Computer Networks． Piscataway:IEEE Press, 2018: 527-535．
［66］	张海君, 陈安琪, 李亚博 ,等． 6G移动网络关键技术［J］．通信学报, 2022,43(7): 189-202．
	ZHANG H J , CHEN A Q , LI Y B ,et al． Key technologies of 6G mobile network［J］． Journal on Communications, 2022,43(7): 189-202．

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[2]	王晓云, 张小舟, 马良, 王亚娟, 楼梦婷, 姜涛, 金婧, 王启星, 刘光毅. 6G通信感知一体化网络的感知算法研究与优化[J]. 通信学报, 2023, 44(2): 219-230.
[3]	张海君, 陈安琪, 李亚博, 隆克平. 6G移动网络关键技术[J]. 通信学报, 2022, 43(7): 189-202.
[4]	廖建新, 付霄元, 戚琦, 王敬宇, 孙海峰. 6G-ADM：基于知识空间的6G网络管控体系[J]. 通信学报, 2022, 43(6): 3-15.
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[10]	周振宇, 贾泽晗, 廖海君, 赵雄文, 张磊. 基于上下文学习的电力物联网接入控制方法[J]. 通信学报, 2021, 42(3): 150-159.
[11]	张思成,林云,涂涯,ShiwenMao. 基于轻量级深度神经网络的电磁信号调制识别技术[J]. 通信学报, 2020, 41(11): 12-21.
[12]	张平, 牛凯, 田辉, 聂高峰, 秦晓琦, 戚琦, 张娇. 6G移动通信技术展望[J]. 通信学报, 2019, 40(1): 141-148.
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意图抽象与知识联合驱动的6G内生智能网络架构

6G native intelligence network architecture enabled by intent abstraction and knowledge

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意图抽象与知识联合驱动的6G内生智能网络架构

6G native intelligence network architecture enabled by intent abstraction and knowledge

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