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1 October 2018 Comprehensive analysis of the ability to monitor selected optical network parameters in the physical layer using convolutional neural networks
T. Mrozek, K. Perlicki
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Proceedings Volume 10808, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018; 108080G (2018) https://doi.org/10.1117/12.2501464
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018, 2018, Wilga, Poland
Abstract
The article presents the possibilities of using the Asynchronous Delay Tap Sampling and Convolutional Neural Network methods to simultaneously monitor the impairments of Chromatic Dispersion and Optical Signal to Noise Ratio. Using the ADTS method, which allows the presentation of distortions in the form of characteristics, a set of 10,000 images was generated simultaneously disturbed by the combination of CD and OSNR impairments. Next, using the convolutional algorithms of neural networks, the network learning process was carried out (using images obtained from the ADTS method) in order to obtain the best model for recognizing the occurring impairments and predicting their values. After a large number of tests, very good results were obtained ensuring a high adjustment of the models at the level of matching ratio R2 above 0.99 (and even above 0.999 for models for Chromatic Dispersion). Models with such a fit meet the requirements set for monitoring systems to recognize the value of occurring impairments within appropriate accuracy limits
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T. Mrozek and K. Perlicki "Comprehensive analysis of the ability to monitor selected optical network parameters in the physical layer using convolutional neural networks", Proc. SPIE 10808, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018, 108080G (1 October 2018); https://doi.org/10.1117/12.2501464
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KEYWORDS
Convolutional neural networks
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