{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T12:10:56Z","timestamp":1771330256297,"version":"3.50.1"},"reference-count":102,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2020,5,13]],"date-time":"2020-05-13T00:00:00Z","timestamp":1589328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,5,13]],"date-time":"2020-05-13T00:00:00Z","timestamp":1589328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci. China Inf. Sci."],"published-print":{"date-parts":[[2020,6]]},"DOI":"10.1007\/s11432-020-2871-2","type":"journal-article","created":{"date-parts":[[2020,5,19]],"date-time":"2020-05-19T12:03:44Z","timestamp":1589889824000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Artificial intelligence-driven autonomous optical networks: 3S architecture and key technologies"],"prefix":"10.1007","volume":"63","author":[{"given":"Yuefeng","family":"Ji","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rentao","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeyuan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,5,13]]},"reference":[{"key":"2871_CR1","doi-asserted-by":"publisher","first-page":"121301","DOI":"10.1007\/s11432-018-9551-8","volume":"61","author":"Y F Ji","year":"2018","unstructured":"Ji Y F, Zhang J W, Wang X, et al. Towards converged, collaborative and co-automatic (3C) optical networks. Sci China Inf Sci, 2018, 61: 121301","journal-title":"Sci China Inf Sci"},{"key":"2871_CR2","doi-asserted-by":"publisher","first-page":"19","DOI":"10.23919\/j.cc.2019.05.002","volume":"16","author":"Y F Ji","year":"2019","unstructured":"Ji Y F, Zhang J W, Xiao Y M, et al. 5G flexible optical transport networks with large-capacity, low-latency and high-efficiency. China Commun, 2019, 16: 19\u201332","journal-title":"China Commun"},{"key":"2871_CR3","volume-title":"Optical Networks: A Practical Perspective","author":"R Ramaswami","year":"2002","unstructured":"Ramaswami R, Sivarajan N K. Optical Networks: A Practical Perspective. 2nd ed. San Francisco: Morgan Kaufmann, 2002","edition":"2nd ed"},{"key":"2871_CR4","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1364\/JOCN.10.00D126","volume":"10","author":"D Rafique","year":"2018","unstructured":"Rafique D, Velasco L. Machine learning for network automation: overview, architecture, and applications. J Opt Commun Netw, 2018, 10: 126","journal-title":"J Opt Commun Netw"},{"key":"2871_CR5","doi-asserted-by":"publisher","first-page":"1206","DOI":"10.1109\/ACCESS.2015.2461602","volume":"3","author":"A Gupta","year":"2015","unstructured":"Gupta A, Jha R K. A survey of 5G network: architecture and emerging technologies. IEEE Access, 2015, 3: 1206\u20131232","journal-title":"IEEE Access"},{"key":"2871_CR6","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1109\/JLT.2015.2480798","volume":"34","author":"Z H Dong","year":"2016","unstructured":"Dong Z H, Khan F N, Sui Q, et al. Optical performance monitoring: a review of current and future technologies. J Lightw Technol, 2016, 34: 2","journal-title":"J Lightw Technol"},{"key":"2871_CR7","unstructured":"Musumeci G, Rottondi C, Nag S, et al. A survey on application of machine learning techniques in optical networks. 2018. ArXiv: 1803.07976"},{"key":"2871_CR8","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw, 2015, 61: 85\u2013117","journal-title":"Neural Netw"},{"key":"2871_CR9","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521: 436\u2013444","journal-title":"Nature"},{"key":"2871_CR10","unstructured":"Barboza R, Bastos-Filho J C, Martins-Filho F J, et al. Self-adaptive erbium-doped fiber amplifiers using machine learning. In: Proceedings of IEEE Microwave and Optoelectronics Conference (IMOC), 2013"},{"key":"2871_CR11","unstructured":"Huang Y S, Samoud W, Gutterman C L, et al. A machine learning approach for dynamic optical channel add\/drop strategies that minimize EDFA power excursions. In: Proceedings of the 42nd European Conference on Optical Communication, 2016. 1\u20133"},{"key":"2871_CR12","doi-asserted-by":"crossref","unstructured":"Huang Y S, Cho P B, Samadi P, et al. Dynamic power pre-adjustments with machine learning that mitigate EDFA excursions during defragmentation. In: Proceedings of Optical Fiber Communications Conference and Exposition (OFC), 2017. Th1J-2","DOI":"10.1364\/OFC.2017.Th1J.2"},{"key":"2871_CR13","doi-asserted-by":"publisher","first-page":"2570","DOI":"10.1109\/JLT.2011.2160933","volume":"29","author":"Z N Tao","year":"2011","unstructured":"Tao Z N, Dou L, Yan W Z, et al. Multiplier-free intrachannel nonlinearity compensating algorithm operating at symbol rate. J Lightw Technol, 2011, 29: 2570\u20132576","journal-title":"J Lightw Technol"},{"key":"2871_CR14","doi-asserted-by":"crossref","unstructured":"Wang D S, Zhang M, Li Z, et al. Nonlinear decision boundary created by a machine learning-based classifier to mitigate nonlinear phase noise. In: Proceedings of European Conference on Optical Communication, 2015","DOI":"10.1109\/ECOC.2015.7341753"},{"key":"2871_CR15","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1109\/LPT.2016.2555857","volume":"28","author":"D S Wang","year":"2016","unstructured":"Wang D S, Zhang M, Fu M, et al. Nonlinearity mitigation using a machine learning detector based on k-nearest neighbors. IEEE Photon Technol Lett, 2016, 28: 19","journal-title":"IEEE Photon Technol Lett"},{"key":"2871_CR16","doi-asserted-by":"publisher","first-page":"30337","DOI":"10.1364\/OE.23.030337","volume":"23","author":"F N Khan","year":"2015","unstructured":"Khan F N, Yu Y, Tan M C, et al. Experimental demonstration of joint OSNR monitoring and modulation format identification using asynchronous single channel sampling. Opt Express, 2015, 23: 30337\u201330346","journal-title":"Opt Express"},{"key":"2871_CR17","doi-asserted-by":"publisher","first-page":"9403","DOI":"10.1364\/OE.27.009403","volume":"27","author":"D S Wang","year":"2019","unstructured":"Wang D S, Wang M Y, Zhang M, et al. Cost-effective and data size-adaptive OPM at intermediated node using convolutional neural network-based image processor. Opt Express, 2019, 27: 9403\u20139419","journal-title":"Opt Express"},{"key":"2871_CR18","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1109\/LPT.2017.2742553","volume":"29","author":"D S Wang","year":"2017","unstructured":"Wang D S, Zhang M, Li Z, et al. Modulation format recognition and OSNR estimation using CNN-based deep learning. IEEE Photon Technol Lett, 2017, 29: 19","journal-title":"IEEE Photon Technol Lett"},{"key":"2871_CR19","unstructured":"Tanimura T, Hoshida T, Kato T, et al. Deep learningbased OSNR monitoring independent of modulation format, symbol rate and chromatic dispersion. In: Proceedings of European Conference on Optical Communication, 2016"},{"key":"2871_CR20","doi-asserted-by":"publisher","first-page":"17150","DOI":"10.1364\/OE.25.017150","volume":"25","author":"D S Wang","year":"2017","unstructured":"Wang D S, Zhang M, Li J, et al. Intelligent constellation diagram analyzer using convolutional neural network-based deep learning. Opt Express, 2017, 25: 17150\u201317166","journal-title":"Opt Express"},{"key":"2871_CR21","doi-asserted-by":"publisher","first-page":"10494","DOI":"10.1364\/OE.26.010494","volume":"26","author":"J Li","year":"2018","unstructured":"Li J, Zhang M, Wang D S, et al. Joint atmospheric turbulence detection and adaptive demodulation technique using the CNN for the OAM-FSO communication. Opt Express, 2018, 26: 10494\u201310508","journal-title":"Opt Express"},{"key":"2871_CR22","first-page":"1665","volume":"22","author":"S R T Shen","year":"2010","unstructured":"Shen S R T, Meng K, Lau P T A, et al. Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms. IEEE Photon Technol Lett, 2010, 22: 1665\u20131667","journal-title":"IEEE Photon Technol Lett"},{"key":"2871_CR23","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1364\/JOCN.10.000D84","volume":"10","author":"R M Morais","year":"2018","unstructured":"Morais R M, Pedro J. Machine learning models for estimating quality of transmission in DWDM networks. J Opt Commun Netw, 2018, 10: 84\u201399","journal-title":"J Opt Commun Netw"},{"key":"2871_CR24","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1364\/JOCN.10.00A286","volume":"10","author":"C Rottondi","year":"2018","unstructured":"Rottondi C, Barletta L, Giusti A, et al. Machine-learning method for quality of transmission prediction of unestablished lightpaths. J Opt Commun Netw, 2018, 10: 286\u2013297","journal-title":"J Opt Commun Netw"},{"key":"2871_CR25","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1364\/JOCN.9.000098","volume":"9","author":"T Panayiotou","year":"2017","unstructured":"Panayiotou T, Chatzis S P, Ellinas G. Performance analysis of a data-driven quality-of-transmission decision approach on a dynamic multicast-capable metro optical network. J Opt Commun Netw, 2017, 9: 98\u2013108","journal-title":"J Opt Commun Netw"},{"key":"2871_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1364\/JOCN.11.0000A1","volume":"11","author":"R Proietti","year":"2019","unstructured":"Proietti R, Chen X L, Zhang K Q, et al. Experimental demonstration of machine-learning-aided QoT estimation in multi-domain elastic optical networks with alien wavelengths. J Opt Commun Netw, 2019, 11: 1\u201310","journal-title":"J Opt Commun Netw"},{"key":"2871_CR27","doi-asserted-by":"publisher","first-page":"18553","DOI":"10.1364\/OE.25.018553","volume":"25","author":"Z L Wang","year":"2017","unstructured":"Wang Z L, Zhang M, Wang D S, et al. Failure prediction using machine learning and time series in optical network. Opt Express, 2017, 25: 18553\u201318565","journal-title":"Opt Express"},{"key":"2871_CR28","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1109\/JLT.2017.2781540","volume":"36","author":"D Rafique","year":"2018","unstructured":"Rafique D, Szyrkowiec T, Grieser H, et al. Cognitive assurance architecture for optical network fault management. J Lightw Technol, 2018, 36: 1443\u20131450","journal-title":"J Lightw Technol"},{"key":"2871_CR29","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1109\/JLT.2018.2859199","volume":"37","author":"B Shariati","year":"2019","unstructured":"Shariati B, Ruiz M, Comellas J, et al. Learning from the optical spectrum: failure detection and identification. J Lightw Technol, 2019, 37: 433\u2013440","journal-title":"J Lightw Technol"},{"key":"2871_CR30","unstructured":"Zhang X, Hou W G, Guo L, et al. Failure recovery solutions using cognitive mechanisms for software defined optical networks. In: Processing of the 15th International Conference on Optical Communications and Networks (ICOCN), 2016"},{"key":"2871_CR31","unstructured":"Ruiz M, Fresi F, Vela P A, et al. Service-triggered failure identification\/localization through monitoring of multiple parameters. In: Proceedings of European Conference on Optical Communication, 2016"},{"key":"2871_CR32","doi-asserted-by":"publisher","first-page":"97760","DOI":"10.1109\/ACCESS.2019.2929872","volume":"7","author":"D S Wang","year":"2019","unstructured":"Wang D S, Lou L Q, Zhang M, et al. Dealing with alarms in optical networks using an intelligent system. IEEE Access, 2019, 7: 97760\u201397770","journal-title":"IEEE Access"},{"key":"2871_CR33","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1364\/JOCN.10.00D100","volume":"10","author":"D Cote","year":"2018","unstructured":"Cote D. Using machine learning in communication networks. J Opt Commun Netw, 2018, 10: 100\u2013109","journal-title":"J Opt Commun Netw"},{"key":"2871_CR34","doi-asserted-by":"crossref","unstructured":"Bensalem M, Singh K S, Jukan A. On detecting and preventing Jamming attacks with machine learning in optical networks. 2019. ArXiv: 1902.07537v2","DOI":"10.20944\/preprints201901.0311.v2"},{"key":"2871_CR35","doi-asserted-by":"crossref","unstructured":"Furdek M, Natalino C, Schiano M, et al. Experiment-based detection of service disruption attacks in optical networks using data analytics and unsupervised learning. In: Proceedings of SPIE, 2019","DOI":"10.1117\/12.2509613"},{"key":"2871_CR36","unstructured":"Ruiz M, Fresi F, Vela P A, et al. Service-triggered failure identification\/localization through monitoring of multiple parameters. In: Proceedings of European Conference on Optical Communication, 2016"},{"key":"2871_CR37","doi-asserted-by":"crossref","unstructured":"Singh K S, Bziuk W, Jukan A. A combined optical spectrum scrambling and defragmentation in multi-core fiber networks. In: Proceedings of IEEE International Conference on Communications (ICC), 2017","DOI":"10.1109\/ICC.2017.7997408"},{"key":"2871_CR38","doi-asserted-by":"crossref","unstructured":"Lu W, Liang L, Kong B, et al. Leveraging predictive analytics to achieve knowledge-defined orchestration in a hybrid optical\/electrical DC network: collaborative forecasting and decision making. In: Proceedings of Optical Fiber Communications Conference and Exposition (OFC), 2018","DOI":"10.1364\/OFC.2018.Th3F.2"},{"key":"2871_CR39","doi-asserted-by":"publisher","first-page":"2598","DOI":"10.1109\/JLT.2005.854039","volume":"23","author":"B Wen","year":"2005","unstructured":"Wen B, Shenai R, Sivalingam K. Routing, wavelength and time-slot-assignment algorithms for wavelength-routed optical WDM\/TDM networks. J Lightw Technol, 2005, 23: 2598\u20132609","journal-title":"J Lightw Technol"},{"key":"2871_CR40","doi-asserted-by":"publisher","first-page":"1557","DOI":"10.1109\/TNET.2010.2044585","volume":"18","author":"K Christodoulopoulos","year":"2010","unstructured":"Christodoulopoulos K, Manousakis K, Varvarigos E. Offline routing and wavelength assignment in transparent WDM networks. IEEE\/ACM Trans Netw, 2010, 18: 1557\u20131570","journal-title":"IEEE\/ACM Trans Netw"},{"key":"2871_CR41","doi-asserted-by":"publisher","first-page":"2791","DOI":"10.1109\/JLT.2003.819584","volume":"21","author":"C Cavazzoni","year":"2003","unstructured":"Cavazzoni C, Barosco V, D\u2019Alessandro A, et al. The IP\/MPLS over ASON\/GMPLS test bed of the IST project LION. J Lightw Technol, 2003, 21: 2791\u20132803","journal-title":"J Lightw Technol"},{"key":"2871_CR42","doi-asserted-by":"publisher","first-page":"2738","DOI":"10.1109\/COMST.2016.2586999","volume":"18","author":"A S Thyagaturu","year":"2016","unstructured":"Thyagaturu A S, Mercian A, McGarry M P, et al. Software defined optical networks (SDONs): a comprehensive survey. IEEE Commun Surv Tut, 2016, 18: 2738\u20132786","journal-title":"IEEE Commun Surv Tut"},{"key":"2871_CR43","doi-asserted-by":"publisher","first-page":"1130","DOI":"10.1109\/JPROC.2012.2186213","volume":"100","author":"J E Berthold","year":"2012","unstructured":"Berthold J E, Ong L Y. Next-generation optical network architecture and multidomain issues. Proc IEEE, 2012, 100: 1130\u20131139","journal-title":"Proc IEEE"},{"key":"2871_CR44","doi-asserted-by":"crossref","unstructured":"Salani M, Rottondi C, Tornatore M. Routing and spectrum assignment integrating machine-learning-based QoT estimation in elastic optical networks. In: Proceedings of IEEE Conference on Computer Communications, Paris, 2019. 1738\u20131746","DOI":"10.1109\/INFOCOM.2019.8737413"},{"key":"2871_CR45","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.osn.2017.12.006","volume":"28","author":"J Mata","year":"2018","unstructured":"Mata J, de Miguel I, Dur\u00e1n R J, et al. Artificial intelligence (AI) methods in optical networks: a comprehensive survey. Opt Switch Netw, 2018, 28: 43\u201357","journal-title":"Opt Switch Netw"},{"key":"2871_CR46","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1364\/JOCN.6.000441","volume":"6","author":"M C Tan","year":"2014","unstructured":"Tan M C, Khan F N, Al-Arashi W H, et al. Simultaneous optical performance monitoring and modulation format\/bitrate identification using principal component analysis. J Opt Commun Netw, 2014, 6: 441\u2013448","journal-title":"J Opt Commun Netw"},{"key":"2871_CR47","doi-asserted-by":"publisher","first-page":"3008","DOI":"10.1109\/JLT.2007.905217","volume":"25","author":"A P T Lau","year":"2007","unstructured":"Lau A P T, Kahn J M. Signal design and detection in presence of nonlinear phase noise. J Lightw Technol, 2007, 25: 3008\u20133016","journal-title":"J Lightw Technol"},{"key":"2871_CR48","doi-asserted-by":"publisher","first-page":"3416","DOI":"10.1109\/JLT.2008.927791","volume":"26","author":"E Ip","year":"2008","unstructured":"Ip E, Kahn J M. Compensation of dispersion and nonlinear impairments using digital backpropagation. J Lightw Technol, 2008, 26: 3416\u20133425","journal-title":"J Lightw Technol"},{"key":"2871_CR49","doi-asserted-by":"crossref","unstructured":"Stojanovic N, Huang Y, Hauske N F, et al. Mlse-based nonlinearity mitigation for wdm 112 Gbit\/s pdm-qpsk transmissions with digital coherent receiver. In: Proceedings of of Optical Fiber Communications Conference and Exposition (OFC), 2011","DOI":"10.1364\/OFC.2011.OWW6"},{"key":"2871_CR50","doi-asserted-by":"publisher","first-page":"1815","DOI":"10.1587\/transcom.E94.B.1815","volume":"94","author":"D Rafique","year":"2011","unstructured":"Rafique D, Zhao J, Ellis A D. Compensation of nonlinear fibre impairments in coherent systems employing spectrally efficient modulation formats. IEICE Trans Commun, 2011, 94: 1815\u20131822","journal-title":"IEICE Trans Commun"},{"key":"2871_CR51","doi-asserted-by":"publisher","first-page":"7800312","DOI":"10.1109\/JPHOT.2013.2287565","volume":"5","author":"M L Li","year":"2013","unstructured":"Li M L, Yu S, Yang J, et al. Nonparameter nonlinear phase noise mitigation by using M-ary support vector machine for coherent optical systems. IEEE Photon J, 2013, 5: 7800312","journal-title":"IEEE Photon J"},{"key":"2871_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JPHOT.2016.2528886","volume":"8","author":"T Nguyen","year":"2016","unstructured":"Nguyen T, Mhatli S, Giacoumidis E, et al. Fiber nonlinearity equalizer based on support vector classification for coherent optical OFDM. IEEE Photon J, 2016, 8: 1\u20139","journal-title":"IEEE Photon J"},{"key":"2871_CR53","doi-asserted-by":"publisher","first-page":"2391","DOI":"10.1109\/JLT.2017.2678511","volume":"35","author":"E Giacoumidis","year":"2017","unstructured":"Giacoumidis E, Mhatli S, Stephens M F C, et al. Reduction of nonlinear intersubcarrier intermixing in coherent optical OFDM by a fast newton-based support vector machine nonlinear equalizer. J Lightw Technol, 2017, 35: 2391\u20132397","journal-title":"J Lightw Technol"},{"key":"2871_CR54","doi-asserted-by":"publisher","first-page":"B181","DOI":"10.1364\/OE.20.00B181","volume":"20","author":"D Zibar","year":"2012","unstructured":"Zibar D, Winther O, Franceschi N, et al. Nonlinear impairment compensation using expectation maximization for dispersion managed and unmanaged PDM 16-QAM transmission. Opt Express, 2012, 20: B181","journal-title":"Opt Express"},{"key":"2871_CR55","unstructured":"Shen T S R, Lau A P T. Fiber nonlinearity compensation using extreme learning machine for DSP-based coherent communication systems. In: Proceedings of OECC, Kaohsiung, 2011. 816\u2013817"},{"key":"2871_CR56","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"G B Huang","year":"2006","unstructured":"Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: theory and applications. Neurocomputing, 2006, 70: 489\u2013501","journal-title":"Neurocomputing"},{"key":"2871_CR57","volume-title":"Planning Fiber Optic Networks","author":"B Chomcyz","year":"2009","unstructured":"Chomcyz B. Planning Fiber Optic Networks. New York: McGraw-Hill, 2009"},{"key":"2871_CR58","doi-asserted-by":"crossref","unstructured":"Dods S D, Anderson T B. Optical performance monitoring technique using delay tap asynchronous waveform sampling. In: Proceedings of Optical Fiber Communications Conference and Exposition (OFC), 2007","DOI":"10.1109\/OFC.2006.215890"},{"key":"2871_CR59","doi-asserted-by":"publisher","first-page":"1164","DOI":"10.1109\/JSTQE.2010.2044751","volume":"16","author":"S J Savory","year":"2010","unstructured":"Savory S J. Digital coherent optical receivers: algorithms and subsystems. IEEE J Sel Top Quantum Electron, 2010, 16: 1164\u20131179","journal-title":"IEEE J Sel Top Quantum Electron"},{"key":"2871_CR60","doi-asserted-by":"publisher","first-page":"17767","DOI":"10.1364\/OE.25.017767","volume":"25","author":"F N Khan","year":"2017","unstructured":"Khan F N, Zhong K, Zhou X, et al. Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks. Opt Express, 2017, 25: 17767\u201317776","journal-title":"Opt Express"},{"key":"2871_CR61","unstructured":"Tanimura T, Hoshida T, Kato T, et al. Deep learningbased OSNR monitoring independent of modulation format, symbol rate and chromatic dispersion. In: Proceedings of European Conference on Optical Communication, 2016. Tu2C.2"},{"key":"2871_CR62","doi-asserted-by":"crossref","unstructured":"Jones T R, Diniz C M J, Yankov P M, et al. Prediction of second-order moments of inter-channel interference with principal component analysis and neural networks. In: Proceedings of European Conference on Optical Communication, 2017","DOI":"10.1109\/ECOC.2017.8346176"},{"key":"2871_CR63","doi-asserted-by":"crossref","unstructured":"Kashi S A, Zhuge Q B, Cartledge C J, et al. Fiber nonlinear noise-to-signal ratio monitoring using artificial neural networks. In: Proceedings of European Conference on Optical Communication, 2017","DOI":"10.1109\/ECOC.2017.8345880"},{"key":"2871_CR64","doi-asserted-by":"publisher","first-page":"3055","DOI":"10.1109\/JLT.2019.2910143","volume":"37","author":"Q B Zhuge","year":"2019","unstructured":"Zhuge Q B, Zeng X B, Lun H Z, et al. Application of machine learning in fiber nonlinearity modeling and monitoring for elastic optical networks. J Lightw Technol, 2019, 37: 3055\u20133063","journal-title":"J Lightw Technol"},{"key":"2871_CR65","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1364\/JOCN.10.000D42","volume":"10","author":"F J V Caballero","year":"2018","unstructured":"Caballero F J V, Ives D J, Laperle C, et al. Machine learning based linear and nonlinear noise estimation. J Opt Commun Netw, 2018, 10: 42\u201351","journal-title":"J Opt Commun Netw"},{"key":"2871_CR66","doi-asserted-by":"crossref","unstructured":"Willner E A, Hoanca B. Fixed and tunable management of fiber chromatic dispersion. In: Proceedings of Optical Fiber Telecommunications, 2002. 642\u2013724","DOI":"10.1016\/B978-012395173-1\/50014-1"},{"key":"2871_CR67","doi-asserted-by":"crossref","unstructured":"Kogelnik H, Jopson M R, Nelson E L. Polarization mode dispersion. In: Proceedings of Optical Fiber Telecommunications, 2002","DOI":"10.1016\/B978-012395173-1\/50015-3"},{"key":"2871_CR68","doi-asserted-by":"crossref","unstructured":"Dong Z, Sui Q, Lau P T A, et al. Optical performance monitoring in DSP-based coherent optical systems. In: Proceedings of Optical Fiber Communications Conference and Exposition (OFC), 2015","DOI":"10.1364\/OFC.2015.W4D.1"},{"key":"2871_CR69","doi-asserted-by":"publisher","first-page":"1353","DOI":"10.1109\/JLT.2008.917374","volume":"26","author":"B Kozicki","year":"2008","unstructured":"Kozicki B, Takuya O, Hidehiko T. Optical performance monitoring of phase-modulated signals using asynchronous amplitude histogram analysis. J Lightw Technol, 2008, 26: 1353\u20131361","journal-title":"J Lightw Technol"},{"key":"2871_CR70","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1109\/JLT.2008.926959","volume":"27","author":"R S Luis","year":"2009","unstructured":"Luis R S, Teixeira A, Monteiro P. Optical signal-to-noise ratio estimation using reference asynchronous histograms. J Lightw Technol, 2009, 27: 731\u2013743","journal-title":"J Lightw Technol"},{"key":"2871_CR71","doi-asserted-by":"publisher","first-page":"3149","DOI":"10.1364\/OE.18.003149","volume":"18","author":"Z Li","year":"2010","unstructured":"Li Z, Jian Z, Cheng L, et al. Signed chromatic dispersion monitoring of 100Gbit\/s CS-RZ DQPSK signal by evaluating the asymmetry ratio of delay tap sampling. Opt Express, 2010, 18: 3149\u20133157","journal-title":"Opt Express"},{"key":"2871_CR72","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1109\/LPT.2009.2037130","volume":"22","author":"F N Khan","year":"2010","unstructured":"Khan F N, Lau A P T, Li Z, et al. Statistical analysis of optical signal-to-noise ratio monitoring using delay-tap sampling. IEEE Photon Technol Lett, 2010, 22: 149\u2013151","journal-title":"IEEE Photon Technol Lett"},{"key":"2871_CR73","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1364\/JON.6.001257","volume":"6","author":"B Kozicki","year":"2007","unstructured":"Kozicki B, Maruta A, Kitayama K. Transparent performance monitoring of RZ-DQPSK systems employing delay-tap sampling. J Opt Netw, 2007, 6: 1257\u20131269","journal-title":"J Opt Netw"},{"key":"2871_CR74","doi-asserted-by":"publisher","first-page":"23953","DOI":"10.1364\/OE.17.023953","volume":"17","author":"H Y Choi","year":"2009","unstructured":"Choi H Y, Takushima Y, Chung Y C. Optical performance monitoring technique using asynchronous amplitude and phase histograms. Opt Express, 2009, 17: 23953\u201323958","journal-title":"Opt Express"},{"key":"2871_CR75","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1109\/LPT.2010.2046319","volume":"22","author":"F N Khan","year":"2010","unstructured":"Khan F N, Lau A P T, Li Z H, et al. OSNR monitoring for RZ-DQPSK systems using half-symbol delay-tap sampling technique. IEEE Photon Technol Lett, 2010, 22: 823\u2013825","journal-title":"IEEE Photon Technol Lett"},{"key":"2871_CR76","first-page":"1","volume":"11","author":"J Li","year":"2019","unstructured":"Li J, Wang D, Zhang M. Low-complexity adaptive chromatic dispersion estimation scheme using machine learning for coherent long-reach passive optical networks. IEEE Photon J, 2019, 11: 1\u201311","journal-title":"IEEE Photon J"},{"key":"2871_CR77","doi-asserted-by":"publisher","first-page":"942","DOI":"10.1109\/JLT.2013.2240257","volume":"31","author":"T Jimenez","year":"2013","unstructured":"Jimenez T, Aguado J C, de Miguel I, et al. A cognitive quality of transmission estimator for core optical networks. J Lightw Technol, 2013, 31: 942\u2013951","journal-title":"J Lightw Technol"},{"key":"2871_CR78","doi-asserted-by":"publisher","first-page":"682","DOI":"10.1364\/JOCN.9.000682","volume":"9","author":"H C Leung","year":"2017","unstructured":"Leung H C, Leung C S, Wong E W M, et al. Extreme learning machine for estimating blocking probability of bufferless OBS\/OPS networks. J Opt Commun Netw, 2017, 9: 682\u2013692","journal-title":"J Opt Commun Netw"},{"key":"2871_CR79","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1364\/JOCN.11.000140","volume":"11","author":"I Sartzetakis","year":"2019","unstructured":"Sartzetakis I, Christodoulopoulos K K, Varvarigos E M. Accurate quality of transmission estimation with machine learning. J Opt Commun Netw, 2019, 11: 140\u2013150","journal-title":"J Opt Commun Netw"},{"key":"2871_CR80","doi-asserted-by":"crossref","unstructured":"Mo W Y, Huang Y K, Zhang S L, et al. ANN-based transfer learning for QoT prediction in real-time mixed line-rate systems. In: Proceedings of Optical Fiber Communications Conference and Exposition (OFC), San Diego, 2018. 1\u20133","DOI":"10.1364\/OFC.2018.W4F.3"},{"key":"2871_CR81","doi-asserted-by":"crossref","unstructured":"Gu R T, Qu Y Y, Lian M, et al. Flexible optical network enabled proactive cross-layer restructuring for 5G\/B5G backhaul network with machine learning engine. In: Proceeding of Optical Fiber Communication Conference and Exhibition (OFC), San Diego, 2020","DOI":"10.1364\/OFC.2020.M3Z.18"},{"key":"2871_CR82","doi-asserted-by":"crossref","unstructured":"Guo Q Z, Gu R T, Wang Z H, et al. Proactive dynamic network slicing with deep learning based short-term traffic prediction for 5G transport network. In: Proceeding of Optical Fiber Communication Conference and Exhibition (OFC), San Diego, 2019","DOI":"10.1364\/OFC.2019.W3J.3"},{"key":"2871_CR83","doi-asserted-by":"publisher","first-page":"4761","DOI":"10.1109\/JLT.2018.2864676","volume":"36","author":"J N Guo","year":"2018","unstructured":"Guo J N, Zhu Z Q. When deep learning meets inter-datacenter optical network management: advantages and vulnerabilities. J Lightw Technol, 2018, 36: 4761\u20134773","journal-title":"J Lightw Technol"},{"key":"2871_CR84","doi-asserted-by":"crossref","unstructured":"Balanici M, Pachnicke S. Machine learning-based traffic prediction for optical switching resource allocation in hybrid intra-data center networks. In: Proceedings of Optical Fiber Communications Conference and Exhibition (OFC), San Diego, 2019. 1\u20133","DOI":"10.1364\/OFC.2019.Th1H.4"},{"key":"2871_CR85","doi-asserted-by":"publisher","first-page":"D12","DOI":"10.1364\/JOCN.10.000D12","volume":"10","author":"S K Singh","year":"2018","unstructured":"Singh S K, Jukan A. Machine-learning-based prediction for resource (re)allocation in optical data center networks. J Opt Commun Netw, 2018, 10: D12","journal-title":"J Opt Commun Netw"},{"key":"2871_CR86","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/MCOM.001.1900151","volume":"57","author":"X L Chen","year":"2019","unstructured":"Chen X L, Proietti R, Ben Yoo S J. Building autonomic elastic optical networks with deep reinforcement learning. IEEE Commun Magaz, 2019, 57: 20\u201326","journal-title":"IEEE Commun Magaz"},{"key":"2871_CR87","doi-asserted-by":"crossref","unstructured":"Troia S, Rodriguez A, Martin I, et al. Machine-learning-assisted routing in SDN-based optical networks. In: Proceedings of 2018 European Conference on Optical Communication, Rome, 2018. 1\u20133","DOI":"10.1109\/ECOC.2018.8535437"},{"key":"2871_CR88","doi-asserted-by":"crossref","unstructured":"Zhong Z Z, Hua N, Yuan Z G, et al. Routing without routing algorithms: an AI-based routing paradigm for multidomain optical networks. In: Proceedings of Optical Fiber Communications Conference and Exhibition (OFC), San Diego, 2019. 1\u20133","DOI":"10.1364\/OFC.2019.Th2A.24"},{"key":"2871_CR89","doi-asserted-by":"publisher","first-page":"2091","DOI":"10.1016\/j.comnet.2009.03.014","volume":"53","author":"A Belbekkouche","year":"2009","unstructured":"Belbekkouche A, Hafid A, Gendreau M. Novel reinforcement learning-based approaches to reduce loss probability in buffer-less OBS networks. Comput Netw, 2009, 53: 2091\u20132105","journal-title":"Comput Netw"},{"key":"2871_CR90","doi-asserted-by":"publisher","first-page":"105030","DOI":"10.1109\/ACCESS.2019.2931931","volume":"7","author":"W Q Jin","year":"2019","unstructured":"Jin W Q, Gu R T, Tan Y X, et al. Proactive grooming with delay optimization in sliceable elastic optical network. IEEE Access, 2019, 7: 105030\u2013105040","journal-title":"IEEE Access"},{"key":"2871_CR91","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.vehcom.2018.01.003","volume":"11","author":"R T Gu","year":"2018","unstructured":"Gu R T, Zhang S Z, Ji Y F, et al. Network slicing and efficient ONU migration for reliable communications in converged vehicular and fixed access network. Vehicular Commun, 2018, 11: 57\u201367","journal-title":"Vehicular Commun"},{"key":"2871_CR92","doi-asserted-by":"crossref","unstructured":"Gu R T, Cen M Y, Wang L H, et al. Integrated optical-wireless resource slicing management for 5G service-based architecture and multi-level RAN. In: Proceeding of Optical Fiber Communication Conference and Exhibition (OFC), San Diego, 2018","DOI":"10.1364\/OFC.2018.Tu3D.1"},{"key":"2871_CR93","doi-asserted-by":"publisher","first-page":"20893","DOI":"10.1109\/ACCESS.2018.2821179","volume":"6","author":"J E Dvalos","year":"2018","unstructured":"Dvalos J E, Barn B. A survey on algorithmic aspects of virtual optical network embedding for cloud networks. IEEE Access, 2018, 6: 20893\u201320906","journal-title":"IEEE Access"},{"key":"2871_CR94","doi-asserted-by":"crossref","unstructured":"Wang Y, Cao X J, Pan Y. A study of the routing and spectrum allocation in spectrum-sliced elastic optical path networks. In: Proceedings IEEE INFOCOM, Shanghai, 2011. 1503\u20131511","DOI":"10.1109\/INFCOM.2011.5934939"},{"key":"2871_CR95","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1109\/TNSM.2019.2927867","volume":"16","author":"I Martin","year":"2019","unstructured":"Martin I, Troia S, Hernandez J A, et al. Machine learning-based routing and wavelength assignment in software-defined optical networks. IEEE Trans Netw Serv Manage, 2019, 16: 871\u2013883","journal-title":"IEEE Trans Netw Serv Manage"},{"key":"2871_CR96","doi-asserted-by":"crossref","unstructured":"Pointurier Y, Heidari F. Reinforcement learning based routing in all-optical networks. In: Proceedings of 2007 4th International Conference on Broadband Communications, Networks and Systems (BROADNETS\u201907), Raleigh, 2007. 919\u2013921","DOI":"10.1109\/BROADNETS.2007.4550533"},{"key":"2871_CR97","doi-asserted-by":"publisher","first-page":"4155","DOI":"10.1109\/JLT.2019.2923615","volume":"37","author":"X L Chen","year":"2019","unstructured":"Chen X L, Li B J, Proietti R, et al. DeepRMSA: a deep reinforcement learning framework for routing, modulation and spectrum assignment in elastic optical networks. J Lightw Technol, 2019, 37: 4155\u20134163","journal-title":"J Lightw Technol"},{"key":"2871_CR98","doi-asserted-by":"publisher","first-page":"A18","DOI":"10.1364\/JOCN.12.000A18","volume":"12","author":"B J Li","year":"2020","unstructured":"Li B J, Lu W, Zhu Z Q. Deep-NFVOrch: leveraging deep reinforcement learning to achieve adaptive vNF service chaining in DCI-EONs. J Opt Commun Netw, 2020, 12: A18","journal-title":"J Opt Commun Netw"},{"key":"2871_CR99","doi-asserted-by":"crossref","unstructured":"Lian M, Gu R T, Qu Y Y, et al. Flexible optical network enabled hybrid recovery for edge network with reinforcement learning. In: Proceeding of Optical Fiber Communication Conference and Exhibition (OFC), San Diego, 2020","DOI":"10.1364\/OFC.2020.M1A.2"},{"key":"2871_CR100","doi-asserted-by":"crossref","unstructured":"Zhao X D, Yang H, Guo H F, et al. Accurate fault location based on deep neural evolution network in optical networks for 5G and beyond. In: Proceedings of Optical Fiber Communications Conference and Exhibition (OFC), San Diego, 2019. 1\u20133","DOI":"10.1364\/OFC.2019.M3J.5"},{"key":"2871_CR101","doi-asserted-by":"publisher","first-page":"1218","DOI":"10.1109\/TCCN.2019.2946312","volume":"5","author":"H Yang","year":"2019","unstructured":"Yang H, Wang B H, Yao Q Y, et al. Efficient hybrid multi-faults location based on hopfield neural network in 5G coexisting radio and optical wireless networks. IEEE Trans Cogn Commun Netw, 2019, 5: 1218\u20131228","journal-title":"IEEE Trans Cogn Commun Netw"},{"key":"2871_CR102","doi-asserted-by":"crossref","unstructured":"Vela P A, Ruiz M, Velasco L. Applying data visualization for failure localization. In: Proceedings of Optical Fiber Communications Conference and Exposition (OFC), San Diego, 2018. 1\u20133","DOI":"10.1364\/OFC.2018.W1D.5"}],"container-title":["Science China Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-020-2871-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11432-020-2871-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-020-2871-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T15:20:56Z","timestamp":1681312856000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11432-020-2871-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,13]]},"references-count":102,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2020,6]]}},"alternative-id":["2871"],"URL":"https:\/\/doi.org\/10.1007\/s11432-020-2871-2","relation":{},"ISSN":["1674-733X","1869-1919"],"issn-type":[{"value":"1674-733X","type":"print"},{"value":"1869-1919","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,13]]},"assertion":[{"value":"10 January 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 April 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"160301"}}