数据与模型协同驱动的智能光网络架构与关键技术

doi:10.11959/j.issn.1000-0801.2022163

Abstract

Abstract:

With the expanding scale of the networks and the emerging applications of ultra-massive connection, ultra-high bandwidth and ultra-low delay, higher requirements are proposed for the utilization of optical transmission network resources and network differentiated services, which makes the network and configuration mode driven by the traditional approaches face challenges.Based on the idea of data and model collaborative driving, the intelligent optical network technologies of three-layerthree-cycle architecture and three functions were proposed, and the implementation technology was studied.The performance of the proposed algorithms was evaluated based on the designed intelligent transmission network platform.The validation results show that the transmission performance of intelligent optical network driven by collaborative data and model has been effectively improved, which provides theoretical and technical support for the realization of network intelligence.

Key words: intelligent optical network, collaborative data and model, state perception of physical layer, resource coordination of network layer, slice customization of application layer

CLC Number:

TP393

Zhiqun GU, Jiawei ZHANG, Yuefeng JI, Hao YU, Taleb Tarik. Network architecture and key technologies of intelligent optical networks driven by data and model[J]. Telecommunications Science, 2022, 38(7): 18-30.

Figures/Tables 16

References 29

[1]	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.
[2]	ZHU K Y , MUKHERJEE B . Traffic grooming in an optical WDM mesh network[J]. IEEE Journal on Selected Areas in Communications, 2002,20(1): 122-133.
[3]	蒲志强, 易建强, 刘振 ,等. 知识和数据协同驱动的群体智能决策方法研究综述[J]. 自动化学报, 2022,48(3): 627-643.
	PU Z Q , YI J Q , LIU Z ,et al. Knowledge-based and data-driven integrating methodologies for collective intelligence decision making:a survey[J]. Acta AutomaticaSinica, 2022,48(3): 627-643.
[4]	GAO Z G , YAN S Y , ZHANG J W ,et al. Deep reinforcement learning-based policy for baseband function placement and routing of RAN in 5G and beyond[J]. Journal of Lightwave Technology, 2022,40(2): 470-480.
[5]	CHEN Z , ZHANG J W , ZHANG B J ,et al. ADMIRE:demonstration of collaborative data-driven and model-driven intelligent routing engine for IP/optical cross-layer optimization in X-haul networks[C]// Proceedings of 2022 Optical Fiber Communications Conference and Exhibition (OFC). Piscataway:IEEE Press, 2022: 1-3.
[6]	欧阳晔, 王立磊, 杨爱东 ,等. 通信人工智能的下一个十年[J]. 电信科学, 2021,37(3): 1-36.
	OUYANG Y , WANG L L , YANG A D ,et al. Next decade of telecommunications artificial intelligence[J]. Telecommunications Science, 2021,37(3): 1-36.
[7]	MA H X , ZHANG J W , JI Y F . Graph sequence attention network-enabled reinforcement learning for time-aware robust routing in OSU-based OTN[C]// Proceedings of 2022 Optical Fiber Communications Conference and Exhibition (OFC). Piscataway:IEEE Press, 2022: 1-3.
[8]	POGGIOLINI P . The GN model of non-linear propagation in uncompensated coherent optical systems[J]. Journal of Lightwave Technology, 2012,30(24): 3857-3879.
[9]	ROTTONDI C , BARLETTA L , GIUSTI A ,et al. Machine-learning method for quality of transmission prediction of unestablished lightpaths[J]. Journal of Optical Communications and Networking, 2018,10(2): A286-A297.
[10]	CHO H J , VARUGHESE S , LIPPIATT D ,et al. Convolutional recurrent machine learning for osnr and launch power estimation:a critical assessment[C]// Optical Fiber Communication Conference.[S.l.:s.n.]: 2020.
[11]	PESIC J , LONARDI M , ROSSI N ,et al. How uncertainty on the fiber span lengths influences QoT estimation using machine learning in WDM networks[C]// Proceedings of 2020 Optical Fiber Communications Conference and Exhibition (OFC). Piscataway:IEEE Press, 2020: 1-3.
[12]	MORAIS R M , PEDRO J . Machine learning models for estimating quality of transmission in DWDM networks[J]. IEEE/OSA Journal of Optical Communications ＆ Networking, 2018,10(10): D84D99.
[13]	LIU X M , LUN H Z , FU M F ,et al. A three-stage training framework for customizing link models for optical networks[C]// Proceedings of 2020 Optical Fiber Communications Conference and Exhibition (OFC). Piscataway:IEEE Press, 2020.
[14]	LIU C Y , CHEN X L , PROIETTI R ,et al. Evol-TL:evolutionary transfer learning for QoT estimation in multi-domain networks[C]// Proceedings of 2020 Optical Fiber Communications Conference and Exhibition (OFC). Piscataway:IEEE Press, 2020: 1-3.
[15]	DI MARINO R , ROTTONDI C , GIUSTI A ,et al. Assessment of domain adaptation approaches for QoT estimation in optical networks[C]// Proceedings of Optical Fiber Communication Conference (OFC) 2020.Washington,D.C. :OSA, 2020.
[16]	ROTTONDI C , DI MARINO R , NAVA M ,et al. On the benefits of domain adaptation techniques for quality of transmission estimation in optical networks[J]. Journal of Optical Communications and Networking, 2021,13(1): A34A43.
[17]	AZZIMONTI D , ROTTONDI C , GIUSTI A ,et al. Active vs transfer learning approaches for QoT estimation with small training datasets[C]// Proceedings of Optical Fiber Communication Conference (OFC) 2020. Washington,D.C.:OSA, 2020.
[18]	MAHAJAN A , CHRISTODOULOPOULOS K , MARTINEZ R ,et al. Machine learning assisted EDFA gain ripple modelling for accurate QoT estimation[C]// Proceedings of 45th European Conference on Optical Communication (ECOC 2019). IET2019: 1-4.
[19]	SARTZETAKIS I , CHRISTODOULOPOULOS K K , VARVARIGOS E M . Accurate quality of transmission estimation with machine learning[J]. Journal of Optical Communications and Networking, 2019,11(3): 140-150.
[20]	GAO Z G , YAN S Y , ZHANG J W ,et al. ANN-based multi-channel QoT-prediction over a 563.4-km field-trial testbed[J]. Journal of Lightwave Technology, 2020,38(9): 2646-2655.
[21]	ZHANG J W , ZHAO Y L , YU X S ,et al. Energy-efficient traffic grooming in sliceable-transponder-equipped IP-over-elastic optical networks[J]. Journal of Optical Communications and Networking, 2015,7(1): A142--A152.
[22]	SUáREZ-VARELA J , MESTRES A , YU J L ,et al. Routing based on deep reinforcement learning in optical transport networks[C]// Proceedings of 2019 Optical Fiber Communications Conference and Exhibition (OFC). Piscataway:IEEE Press, 2019: 1-3.
[23]	XIAO Y M , ZHANG J W , JI Y F . Resource-efficient slicing with topology-level protection in optical access/aggregation networks for 5G and beyond[C]// Proceedings of 2021 Optical Fiber Communications Conference and Exhibition (OFC). Piscataway:IEEE Press, 2021: 1-3.
[24]	GOUR R , ISHIGAKI G , KONG J ,et al. Availability-guaranteed slice composition for service function chains in 5G transport networks[J]. Journal of Optical Communications and Networking, 2021,13(3): 14-24.
[25]	ZHENG D Y , PENG C Z , LIAO X T ,et al. Towards latency optimization in hybrid service function chain composition and embedding[C]// Proceedings of IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. Piscataway:IEEE Press, 2020: 1539-1548.
[26]	YU H , MUSUMECI F , ZHANG J W ,et al. Isolation-aware 5G RAN slice mapping over WDM metro-aggregation networks[J]. Journal of Lightwave Technology, 2020,38(6): 1125-1137.
[27]	YU H , TALEB T , ZHANG J W . Deterministic latency/jitter-aware service function chaining over beyond 5G edge fabric[J]. IEEE Transactions on Network and Service Management, 2022,1431(99): 1.
[28]	YU H , TALEB T , ZHANG J W ,et al. Deterministic latency bounded network slice deployment in IP-over-WDM based me tro-aggregation networks[J]. IEEE Transactions on Network Science and Engineering, 2022,9(2): 596-607.
[29]	ADDAD R A , DUTRA D L C , TALEB T ,et al. Toward using reinforcement learning for trigger selection in network slice mobility[J]. IEEE Journal on Selected Areas in Communications, 2021,39(7): 2241-2253.

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