Multiple-impairment monitoring for 40-Gbps RZ-OOK using artificial neural networks trained with reconstructed eye diagram parameters

A technique using artificial neural networks trained with parameters derived from reconstructed eye diagrams for multi-impairment monitoring in a 40-Gbps RZ-OOK system is demonstrated. The proposed monitoring scheme can accurately identify simultaneous impairments without requiring data clock recovery.

[1]  Sun Yu-nan.  Application of Chirp-z Transform to the Optical-SamplingOptical Performance Monitoring , 2011 .

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

[3]  P. Toliver,et al.  Automatic Identification of Impairments Using Support Vector Machine Pattern Classification on Eye Diagrams , 2006, IEEE Photonics Technology Letters.

[4]  K. Kikuchi,et al.  Real-time all-optical waveform sampling using a passively mode-locked fiber laser as the sampling pulse source , 2006, 2006 Optical Fiber Communication Conference and the National Fiber Optic Engineers Conference.

[5]  L. Rabiner,et al.  The chirp z-transform algorithm , 1969 .

[6]  A.E. Willner,et al.  Optical performance monitoring , 2004, Journal of Lightwave Technology.

[7]  C.M. Wang,et al.  A Robust Algorithm for Eye-Diagram Analysis , 2008, Journal of Lightwave Technology.

[8]  H. Sunnerud,et al.  Software-synchronized all-optical sampling for fiber communication systems , 2005, Journal of Lightwave Technology.

[9]  H. Sunnerud,et al.  All-Optical Waveform Sampling in High-Speed Optical Communication Systems Using Advanced Modulation Formats , 2009, Journal of Lightwave Technology.

[10]  Xiaoxia Wu,et al.  Experimental comparison of performance monitoring using neural networks trained with parameters derived from delay-tap plots and eye diagrams , 2010, 2010 Conference on Optical Fiber Communication (OFC/NFOEC), collocated National Fiber Optic Engineers Conference.

[11]  M.H. Hassoun,et al.  Fundamentals of Artificial Neural Networks , 1996, Proceedings of the IEEE.