Machine Learning Techniques in Optical Physical-Layer Monitoring

We review applications of machine learning to extract the physical-layer status of optical networks from their sensory information. After pointing out a representation process in learning, we demonstrates an end-to-end learning framework in optical monitoring by a convolutional neural network with asynchronously-sampled data right after intradyne coherent detection.

[1]  Marc Ruiz,et al.  Learning from the Optical Spectrum: Soft-Failure Identification and Localization [Invited] , 2018, 2018 Optical Fiber Communications Conference and Exposition (OFC).

[2]  Idelfonso Tafur Monroy,et al.  Stokes Space-Based Optical Modulation Format Recognition for Digital Coherent Receivers , 2013, IEEE Photonics Technology Letters.

[3]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[4]  Hyeon Yeong Choi,et al.  Optical performance monitoring of QPSK data channels by use of neural networks trained with parameters derived from asynchronous constellation diagrams. , 2010, Optics express.

[5]  Xiaoxia Wu,et al.  Applications of Artificial Neural Networks in Optical Performance Monitoring , 2009, Journal of Lightwave Technology.

[6]  Chao Lu,et al.  Optical Performance Monitoring Using Artificial Neural Networks Trained With Empirical Moments of Asynchronously Sampled Signal Amplitudes , 2012, IEEE Photonics Technology Letters.

[7]  J.C. Li,et al.  Multi Impairment Monitoring for Optical Networks , 2009, Journal of Lightwave Technology.

[8]  Takeshi Hoshida,et al.  Data-analytics-based Optical Performance Monitoring Technique for Optical Transport Networks , 2018, 2018 Optical Fiber Communications Conference and Exposition (OFC).

[9]  David J. Ives,et al.  Joint Estimation of Linear and Non-linear Signal-to-Noise Ratio based on Neural Networks , 2018, 2018 Optical Fiber Communications Conference and Exposition (OFC).

[10]  Darko Zibar,et al.  Machine Learning Techniques for Optical Performance Monitoring From Directly Detected PDM-QAM Signals , 2017, Journal of Lightwave Technology.

[11]  Changyuan Yu,et al.  Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks. , 2017, Optics express.

[12]  Takeshi Hoshida,et al.  OSNR monitoring by deep neural networks trained with asynchronously sampled data , 2016, 2016 21st OptoElectronics and Communications Conference (OECC) held jointly with 2016 International Conference on Photonics in Switching (PS).