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Artificial intelligence-driven autonomous optical networks: 3S architecture and key technologies

Abstract

In the optical networks, the dynamicity, the complexity and the heterogeneity have dramatically increased owing to the deployment of advanced coherent techniques, and the optical cross-connect technologies and diverse network infrastructures pose great challenges in the optical network management and maintenance for the network operators. In this review, we propose a “3S” architecture for AI-driven autonomous optical network, which can aid the optical networks operated in “self-aware” of network status, “self-adaptive” of network control, and “self-managed” of network operations. To support these functions, a number of artificial intelligence (AI)-driven techniques have been investigated to improve the flexibility and the reliability from the device aspect to network aspect. Adaptative erbium-doped fiber amplifier (EDFA) controlling is an example for the device aspect, which provides a power self-adaptive capability according to the network condition. From the link aspect, adaptive fiber nonlinearity compensation, optical monitoring performance and quality of transmission estimation are developed to monitor and alleviate the link-dependent signal impairments in an automatic way. From the network aspect, traffic prediction and network state analysis methods provide the self-awareness, while automatic resource allocation and network fault management powered by AI enhance the self-adaptiveness and self-management capabilities. Benefit from the sufficient network management data, powerful data-mining capability and matured computation units, these AI techniques have great potentials to provide autonomous features for optical networks, including the network resource scheduling and the network customization.

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References

  1. 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

    Article  Google Scholar 

  2. 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: 19–32

    Article  Google Scholar 

  3. Ramaswami R, Sivarajan N K. Optical Networks: A Practical Perspective. 2nd ed. San Francisco: Morgan Kaufmann, 2002

    Google Scholar 

  4. Rafique D, Velasco L. Machine learning for network automation: overview, architecture, and applications. J Opt Commun Netw, 2018, 10: 126

    Article  Google Scholar 

  5. Gupta A, Jha R K. A survey of 5G network: architecture and emerging technologies. IEEE Access, 2015, 3: 1206–1232

    Article  Google Scholar 

  6. 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

    Article  Google Scholar 

  7. Musumeci G, Rottondi C, Nag S, et al. A survey on application of machine learning techniques in optical networks. 2018. ArXiv: 1803.07976

  8. Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw, 2015, 61: 85–117

    Article  Google Scholar 

  9. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521: 436–444

    Article  Google Scholar 

  10. 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

  11. 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. 1–3

  12. 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

  13. 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: 2570–2576

    Article  Google Scholar 

  14. 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

  15. 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

    Article  Google Scholar 

  16. 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: 30337–30346

    Article  Google Scholar 

  17. 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: 9403–9419

    Article  Google Scholar 

  18. 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

    Article  Google Scholar 

  19. 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

  20. Wang D S, Zhang M, Li J, et al. Intelligent constellation diagram analyzer using convolutional neural network-based deep learning. Opt Express, 2017, 25: 17150–17166

    Article  Google Scholar 

  21. 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: 10494–10508

    Article  Google Scholar 

  22. 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: 1665–1667

    Google Scholar 

  23. Morais R M, Pedro J. Machine learning models for estimating quality of transmission in DWDM networks. J Opt Commun Netw, 2018, 10: 84–99

    Article  Google Scholar 

  24. 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: 286–297

    Article  Google Scholar 

  25. 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: 98–108

    Article  Google Scholar 

  26. 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: 1–10

    Article  Google Scholar 

  27. 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: 18553–18565

    Article  Google Scholar 

  28. Rafique D, Szyrkowiec T, Grieser H, et al. Cognitive assurance architecture for optical network fault management. J Lightw Technol, 2018, 36: 1443–1450

    Article  Google Scholar 

  29. Shariati B, Ruiz M, Comellas J, et al. Learning from the optical spectrum: failure detection and identification. J Lightw Technol, 2019, 37: 433–440

    Article  Google Scholar 

  30. 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

  31. 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

  32. Wang D S, Lou L Q, Zhang M, et al. Dealing with alarms in optical networks using an intelligent system. IEEE Access, 2019, 7: 97760–97770

    Article  Google Scholar 

  33. Cote D. Using machine learning in communication networks. J Opt Commun Netw, 2018, 10: 100–109

    Article  Google Scholar 

  34. Bensalem M, Singh K S, Jukan A. On detecting and preventing Jamming attacks with machine learning in optical networks. 2019. ArXiv: 1902.07537v2

  35. 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

  36. 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

  37. 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

  38. 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

  39. 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: 2598–2609

    Article  Google Scholar 

  40. Christodoulopoulos K, Manousakis K, Varvarigos E. Offline routing and wavelength assignment in transparent WDM networks. IEEE/ACM Trans Netw, 2010, 18: 1557–1570

    Article  Google Scholar 

  41. Cavazzoni C, Barosco V, D’Alessandro A, et al. The IP/MPLS over ASON/GMPLS test bed of the IST project LION. J Lightw Technol, 2003, 21: 2791–2803

    Article  Google Scholar 

  42. Thyagaturu A S, Mercian A, McGarry M P, et al. Software defined optical networks (SDONs): a comprehensive survey. IEEE Commun Surv Tut, 2016, 18: 2738–2786

    Article  Google Scholar 

  43. Berthold J E, Ong L Y. Next-generation optical network architecture and multidomain issues. Proc IEEE, 2012, 100: 1130–1139

    Article  Google Scholar 

  44. 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. 1738–1746

  45. Mata J, de Miguel I, Durán R J, et al. Artificial intelligence (AI) methods in optical networks: a comprehensive survey. Opt Switch Netw, 2018, 28: 43–57

    Article  Google Scholar 

  46. 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: 441–448

    Article  Google Scholar 

  47. Lau A P T, Kahn J M. Signal design and detection in presence of nonlinear phase noise. J Lightw Technol, 2007, 25: 3008–3016

    Article  Google Scholar 

  48. Ip E, Kahn J M. Compensation of dispersion and nonlinear impairments using digital backpropagation. J Lightw Technol, 2008, 26: 3416–3425

    Article  Google Scholar 

  49. 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

  50. 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: 1815–1822

    Article  Google Scholar 

  51. 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

    Article  Google Scholar 

  52. 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: 1–9

    Article  Google Scholar 

  53. 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: 2391–2397

    Article  Google Scholar 

  54. 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

    Article  Google Scholar 

  55. 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. 816–817

  56. Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: theory and applications. Neurocomputing, 2006, 70: 489–501

    Article  Google Scholar 

  57. Chomcyz B. Planning Fiber Optic Networks. New York: McGraw-Hill, 2009

    Google Scholar 

  58. 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

  59. Savory S J. Digital coherent optical receivers: algorithms and subsystems. IEEE J Sel Top Quantum Electron, 2010, 16: 1164–1179

    Article  Google Scholar 

  60. 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: 17767–17776

    Article  Google Scholar 

  61. 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

  62. 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

  63. 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

  64. 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: 3055–3063

    Article  Google Scholar 

  65. 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: 42–51

    Article  Google Scholar 

  66. Willner E A, Hoanca B. Fixed and tunable management of fiber chromatic dispersion. In: Proceedings of Optical Fiber Telecommunications, 2002. 642–724

  67. Kogelnik H, Jopson M R, Nelson E L. Polarization mode dispersion. In: Proceedings of Optical Fiber Telecommunications, 2002

  68. 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

  69. Kozicki B, Takuya O, Hidehiko T. Optical performance monitoring of phase-modulated signals using asynchronous amplitude histogram analysis. J Lightw Technol, 2008, 26: 1353–1361

    Article  Google Scholar 

  70. Luis R S, Teixeira A, Monteiro P. Optical signal-to-noise ratio estimation using reference asynchronous histograms. J Lightw Technol, 2009, 27: 731–743

    Article  Google Scholar 

  71. 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: 3149–3157

    Article  Google Scholar 

  72. 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: 149–151

    Article  Google Scholar 

  73. Kozicki B, Maruta A, Kitayama K. Transparent performance monitoring of RZ-DQPSK systems employing delay-tap sampling. J Opt Netw, 2007, 6: 1257–1269

    Article  Google Scholar 

  74. Choi H Y, Takushima Y, Chung Y C. Optical performance monitoring technique using asynchronous amplitude and phase histograms. Opt Express, 2009, 17: 23953–23958

    Article  Google Scholar 

  75. 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: 823–825

    Article  Google Scholar 

  76. 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: 1–11

    Google Scholar 

  77. 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: 942–951

    Article  Google Scholar 

  78. 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: 682–692

    Article  Google Scholar 

  79. Sartzetakis I, Christodoulopoulos K K, Varvarigos E M. Accurate quality of transmission estimation with machine learning. J Opt Commun Netw, 2019, 11: 140–150

    Article  Google Scholar 

  80. 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. 1–3

  81. 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

  82. 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

  83. Guo J N, Zhu Z Q. When deep learning meets inter-datacenter optical network management: advantages and vulnerabilities. J Lightw Technol, 2018, 36: 4761–4773

    Article  Google Scholar 

  84. 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. 1–3

  85. 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

    Article  Google Scholar 

  86. Chen X L, Proietti R, Ben Yoo S J. Building autonomic elastic optical networks with deep reinforcement learning. IEEE Commun Magaz, 2019, 57: 20–26

    Article  Google Scholar 

  87. 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. 1–3

  88. 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. 1–3

  89. Belbekkouche A, Hafid A, Gendreau M. Novel reinforcement learning-based approaches to reduce loss probability in buffer-less OBS networks. Comput Netw, 2009, 53: 2091–2105

    MATH  Article  Google Scholar 

  90. 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: 105030–105040

    Article  Google Scholar 

  91. 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: 57–67

    Article  Google Scholar 

  92. 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

  93. Dvalos J E, Barn B. A survey on algorithmic aspects of virtual optical network embedding for cloud networks. IEEE Access, 2018, 6: 20893–20906

    Article  Google Scholar 

  94. 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. 1503–1511

  95. 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: 871–883

    Article  Google Scholar 

  96. 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’07), Raleigh, 2007. 919–921

  97. 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: 4155–4163

    Article  Google Scholar 

  98. 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

    Article  Google Scholar 

  99. 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

  100. 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. 1–3

  101. 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: 1218–1228

    Article  Google Scholar 

  102. 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. 1–3

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Acknowledgements

This work was supported by National Key R&D Program of China (Grant No. 2018YFB1800802) and National Natural Science Foundation of China (Grant No. 61871051).

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Correspondence to Yuefeng Ji.

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Ji, Y., Gu, R., Yang, Z. et al. Artificial intelligence-driven autonomous optical networks: 3S architecture and key technologies. Sci. China Inf. Sci. 63, 160301 (2020). https://doi.org/10.1007/s11432-020-2871-2

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  • DOI: https://doi.org/10.1007/s11432-020-2871-2

Keywords

  • artificial intelligence
  • optical networks
  • self-aware
  • self-adaptive
  • self-managed