【论文列表】AI in Optical Networks

综述文章

• Zibar D, Piels M, Jones R, Schäeffer CG. Machine learning techniques in optical communication. Journal of Lightwave Technology. 2015 Dec 17;34(6):1442-52. 比较早的一篇，偏重于ML方法在DSP各个步骤中的处理方面（贝叶斯非线性状态空间模型）
• Musumeci, F., Rottondi, C., Nag, A., Macaluso, I., Zibar, D., Ruffini, M. and Tornatore, M., 2018. An overview on application of machine learning techniques in optical networks. IEEE Communications Surveys & Tutorials, 21(2), pp.1383-1408. 也包含了基础的NN和RL，分类了不同任务，论述了ML方法在ON中的应用框架
• Musumeci, F., Rottondi, C., Corani, G., Shahkarami, S., Cugini, F. and Tornatore, M., 2019. A tutorial on machine learning for failure management in optical networks. Journal of Lightwave Technology, 37(16), pp.4125-4139. 故障管理应用，包括监测、识别、定位、预测等
• Pointurier, Y., 2021. Machine learning techniques for quality of transmission estimation in optical networks. Journal of Optical Communications and Networking, 13(4), pp.B60-B71. 好文，分析了QoT预测中的各种不确定性因素，对于入门QoT建模非常有帮助
• Khan FN, Fan Q, Lu C, Lau AP. An optical communication's perspective on machine learning and its applications. Journal of Lightwave Technology. 2019 Feb 3;37(2):493-516. 非常有条理的经典文章，图片非常丰富
• Khan FN. Non-technological barriers: the last frontier towards AI-powered intelligent optical networks. Nature Communications. 2024 Jul 17;15(1):5995. 为什么AI技术历经近十年的交叉研究仍然无法在商业光网络中大规模部署？本质上可能真是那么几个不是很技术的原因导致的；陈旧的系统和繁杂的流程、成本、数据、可解释性、责任问题。算是对一个关键问题开展了讨论，但是感觉还是缺乏一个主观上的本质因素，可能需要工业界人士来回答（本质原因可能是就没有需求）。
• Saif WS, Esmail MA, Ragheb AM, Alshawi TA, Alshebeili SA. Machine learning techniques for optical performance monitoring and modulation format identification: A survey. IEEE Communications Surveys & Tutorials. 2020 Sep 1;22(4):2839-82. 光性能监测OPM survey
• Panayiotou, T., Michalopoulou, M. and Ellinas, G., 2023. Survey on machine learning for traffic-driven service provisioning in optical networks. IEEE Communications Surveys & Tutorials, 25(2), pp.1412-1443. 光网络中的流量业务预测方面

非线性补偿/符号判决

• Layec P, Ghazisaeidi A, Charlet G, Antona JC, Bigo S. Generalized maximum likelihood for cross-polarization modulation effects compensation. Journal of Lightwave Technology. 2015 Apr 1;33(7):1300-7. 该团队有很多光网络实际问题的探讨
• Lau AP, Kahn JM. Signal design and detection in presence of nonlinear phase noise. Journal of Lightwave Technology. 2007 Oct 1;25(10):3008-16. 比较早的进行了ML星座图处理和判决的探讨
• Zibar D, Winther O, Franceschi N, Borkowski R, Caballero A, Arlunno V, Schmidt MN, Gonzales NG, Mao B, Ye Y, Larsen KJ. Nonlinear impairment compensation using expectation maximization for dispersion managed and unmanaged PDM 16-QAM transmission. Optics express. 2012 Dec 10;20(26):B181-96.

光网络故障管理

• Shahkarami S, Musumeci F, Cugini F, Tornatore M. Machine-learning-based soft-failure detection and identification in optical networks. In2018 Optical Fiber Communications Conference and Exposition (OFC) 2018 Mar 11 (pp. 1-3). IEEE.
• Vela AP, Shariati B, Ruiz M, Cugini F, Castro A, Lu H, Proietti R, Comellas J, Castoldi P, Yoo SB, Velasco L. Soft failure localization during commissioning testing and lightpath operation. Journal of Optical Communications and Networking. 2018 Jan 1;10(1):A27-36.
• Wang Z, Zhang M, Wang D, Song C, Liu M, Li J, Lou L, Liu Z. Failure prediction using machine learning and time series in optical network. Optics Express. 2017 Jul 24;25(16):18553-65.
• Panayiotou T, Chatzis SP, Ellinas G. Leveraging statistical machine learning to address failure localization in optical networks. Journal of Optical Communications and Networking. 2018 Mar 22;10(3):162-73.
• Ruiz M, Fresi F, Vela AP, Meloni G, Sambo N, Cugini F, Poti L, Velasco L, Castoldi P. Service-triggered failure identification/localization through monitoring of multiple parameters. InECOC 2016; 42nd European Conference on Optical Communication 2016 Sep 18 (pp. 1-3). VDE.
• Wang D, Zhang M, Fu M, Cai Z, Li Z, Han H, Cui Y, Luo B. Nonlinearity mitigation using a machine learning detector based on k-nearest neighbors. IEEE Photonics Technology Letters. 2016 Apr 21;28(19):2102-5.
• Rafique D, Szyrkowiec T, Grießer H, Autenrieth A, Elbers JP. Cognitive assurance architecture for optical network fault management. Journal of Lightwave Technology. 2018 Apr 1;36(7):1443-50.

EDFA动态增益建模

• You Y, Jiang Z, Janz C. Machine learning-based EDFA gain model. In2018 European Conference on Optical Communication (ECOC) 2018 Sep 23 (pp. 1-3). IEEE.
• Raj A, Wang Z, Slyne F, Chen T, Kilper D, Ruffini M. Self-normalizing neural network, enabling one shot transfer learning for modeling EDFA wavelength dependent gain. InIET Conference Proceedings CP839 2023 Oct 1 (Vol. 2023, No. 34, pp. 748-751). Stevenage, UK: The Institution of Engineering and Technology.

拉曼放大器相关建模

• Rosa Brusin AM, de Moura UC, Curri V, Zibar D, Carena A. Introducing load aware neural networks for accurate predictions of Raman amplifiers. Journal of Lightwave Technology. 2020 Dec 1;38(23):6481-91.
• de Moura UC, Zibar D, Brusin AM, Carena A, Da Ros F. Fiber-agnostic machine learning-based Raman amplifier models. Journal of Lightwave Technology. 2022 Sep 29;41(1):83-95.

光纤信道时域波形建模

• Wang D, Song Y, Li J, Qin J, Yang T, Zhang M, Chen X, Boucouvalas AC. Data-driven optical fiber channel modeling: A deep learning approach. Journal of Lightwave Technology. 2020 Sep 1;38(17):4730-43. 较早地使用BiLSTM进行光纤信道时序波形拟合
• Yang H, Niu Z, Xiao S, Fang J, Liu Z, Fainsin D, Yi L. Fast and accurate optical fiber channel modeling using generative adversarial network. Journal of Lightwave Technology. 2020 Nov 13;39(5):1322-33.
• Zhang N, Yang H, Niu Z, Zheng L, Chen C, Xiao S, Yi L. Transformer-based long distance fiber channel modeling for optical OFDM systems. Journal of Lightwave Technology. 2022 Sep 8;40(24):7779-89.
• Yang H, Niu Z, Xiao S, Fang J, Liu Z, Fainsin D, Yi L. Fast and accurate optical fiber channel modeling using generative adversarial network. Journal of Lightwave Technology. 2020 Nov 13;39(5):1322-33. 采用了线性和非线性损伤解耦处理的方式

光性能监测 OPM

• Shen TS, Meng K, Lau AP, Dong ZY. Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms. IEEE Photonics Technology Letters. 2010 Sep 27;22(22):1665-7.
• Wang D, Zhang M, Li Z, Li J, Fu M, Cui Y, Chen X. Modulation format recognition and OSNR estimation using CNN-based deep learning. IEEE Photonics Technology Letters. 2017 Aug 21;29(19):1667-70.
• Wang D, Zhang M, Li J, Li Z, Li J, Song C, Chen X. Intelligent constellation diagram analyzer using convolutional neural network-based deep learning. Optics express. 2017 Jul 10;25(15):17150-66.
• Wang Z, Zhang M, Wang D, Song C, Liu M, Li J, Lou L, Liu Z. Failure prediction using machine learning and time series in optical network. Optics Express. 2017 Jul 24;25(16):18553-65.

物理信息深度学习

• Karniadakis GE, Kevrekidis IG, Lu L, Perdikaris P, Wang S, Yang L. Physics-informed machine learning. Nature Reviews Physics. 2021 Jun;3(6):422-40. 物理数据混合驱动综述文章，有很多比较好的思想
• Jiang X, Wang D, Fan Q, Zhang M, Lu C, Lau AP. Physics‐informed neural network for nonlinear dynamics in fiber optics. Laser & Photonics Reviews. 2022 Sep;16(9):2100483.
• Zhang X, Zhang M, Song Y, Jiang X, Zhang F, Wang D. Deeponet-based waveform-level simulation for a wideband nonlinear wdm system. Journal of Lightwave Technology. 2023 Jul 26;41(22):6908-22. 深度神经算子的应用
• He X, Yan L, Jiang L, Yi A, Pu Z, Yu Y, Chen H, Pan W, Luo B. Fourier neural operator for accurate optical fiber modeling with low complexity. Journal of Lightwave Technology. 2022 Dec 27;41(8):2301-11. 傅里叶神经算子的应用

路由频谱分配RSA

• Chatterjee BC, Sarma N, Oki E. Routing and spectrum allocation in elastic optical networks: A tutorial. IEEE Communications Surveys & Tutorials. 2015 May 11;17(3):1776-800.
• Chen X, Li B, Proietti R, Lu H, Zhu Z, Yoo SB. DeepRMSA: A deep reinforcement learning framework for routing, modulation and spectrum assignment in elastic optical networks. Journal of Lightwave Technology. 2019 Aug 15;37(16):4155-63.
• Salani M, Rottondi C, Tornatore M. Routing and spectrum assignment integrating machine-learning-based QoT estimation in elastic optical networks. InIEEE INFOCOM 2019-IEEE Conference on Computer Communications 2019 Apr 29 (pp. 1738-1746). IEEE.
• Xu L, Huang YC, Xue Y, Hu X. Deep reinforcement learning-based routing and spectrum assignment of EONs by exploiting GCN and RNN for feature extraction. Journal of Lightwave Technology. 2022 May 19;40(15):4945-55.
• Vincent RJ, Ives DJ, Savory SJ. Scalable capacity estimation for nonlinear elastic all-optical core networks. Journal of Lightwave Technology. 2019 Nov 1;37(21):5380-91.
• He Q, Wang Y, Wang X, Xu W, Li F, Yang K, Ma L. Routing optimization with deep reinforcement learning in knowledge defined networking. IEEE Transactions on Mobile Computing. 2023 Jan 9;23(2):1444-55.
• Di Cicco N, Mercan EF, Karandin O, Ayoub O, Troia S, Musumeci F, Tornatore M. On deep reinforcement learning for static routing and wavelength assignment. IEEE Journal of Selected Topics in Quantum Electronics. 2022 Feb 14;28(4: Mach. Learn. in Photon. Commun. and Meas. Syst.):1-2.