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

Abstract

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

CLC Number:

TN92

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.

Figures/Tables 5

References ［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．

Metrics

Recommended 0

No Suggested Reading articles found!

[1]	Feibo JIANG, Yubo PENG, Li DONG. Deep image semantic communication model for 6G [J]. Journal on Communications, 2023, 44(3): 198-208.
[2]	Xiaoyun WANG, Xiaozhou ZHANG, Liang MA, Yajuan WANG, Mengting LOU, Tao JIANG, Jing JIN, Qixing WANG, Guangyi LIU. Research and optimization on the sensing algorithm for 6G integrated sensing and communication network [J]. Journal on Communications, 2023, 44(2): 219-230.
[3]	Haijun ZHANG, Anqi CHEN, Yabo LI, Keping LONG. Key technologies of 6G mobile network [J]. Journal on Communications, 2022, 43(7): 189-202.
[4]	Jianxin LIAO, Xiaoyuan FU, Qi QI, Jingyu WANG, Haifeng SUN. 6G-ADM: knowledge based 6G network management and control architecture [J]. Journal on Communications, 2022, 43(6): 3-15.
[5]	Zhiqin WANG, Jiamo JIANG, Peixi LIU, Xiaowen CAO, Yang LI, Kaifeng HAN, Ying DU, Guangxu ZHU. New design paradigm for federated edge learning towards 6G:task-oriented resource management strategies [J]. Journal on Communications, 2022, 43(6): 16-27.
[6]	Ang LI, Jianxin CHEN, Xin WEI, Liang ZHOU. 6G-oriented cross-modal signal reconstruction technology [J]. Journal on Communications, 2022, 43(6): 28-40.
[7]	Chuanhong LIU, Caili GUO, Yang YANG, Jiujiu CHEN, Meiyi ZHU, Lu’nan SUN. Intelligent task-oriented semantic communications:theory, technology and challenges [J]. Journal on Communications, 2022, 43(6): 41-57.
[8]	Pan TANG, Jiaxin LIN, Jianhua ZHANG, Lei TIAN, Zhaowei CHANG, Liang XIA, Qixing WANG. Research on reflection characteristics of the terahertz channel for 6G [J]. Journal on Communications, 2022, 43(5): 102-109.
[9]	Xiaoxi ZHANG, Yongjun XU. Survey on backscatter communication for zero-power IoT [J]. Journal on Communications, 2022, 43(11): 199-212.
[10]	Zhenyu ZHOU, Zehan JIA, Haijun LIAO, Xiongwen ZHAO, Lei ZHANG. Context-aware learning-based access control method for power IoT [J]. Journal on Communications, 2021, 42(3): 150-159.
[11]	Sha XIE,Haoran LI,Lingxiang LI,Zhi CHEN,Shaoqian LI. Survey of terahertz communication technology [J]. Journal on Communications, 2020, 41(5): 168-186.
[12]	Sicheng ZHANG,Yun LIN,Ya TU,Shiwen Mao. Electromagnetic signal modulation recognition technology based on lightweight deep neural network [J]. Journal on Communications, 2020, 41(11): 12-21.

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

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 5

References ［66］

Related Articles 12

Metrics

Recommended 0