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Optical performance monitoring using artificial neural networks trained with eye-diagram parameters

Published

Author(s)

Jeffrey A. Jargon, Xiaoxia Wu, Alan Willner

Abstract

We developed artificial neural network models to simultaneously identify three separate impairments that can degrade optical channels, namely optical signal-to-noise ratio, chromatic dispersion, and polarization mode dispersion. The neural networks were trained with parameters derived from eye diagrams to create models that can predict levels of concurrent impairments. This method provides a means of monitoring optical performance with diagnostic capabilities.
Citation
IEEE Photonics Technology Letters
Volume
21
Issue
1

Keywords

artificial neural network, chromatic dispersion, differential group delay, eye diagram, optical performance monitoring, optical signal-to-noise ratio

Citation

Jargon, J. , Wu, X. and Willner, A. (2009), Optical performance monitoring using artificial neural networks trained with eye-diagram parameters, IEEE Photonics Technology Letters, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=33016 (Accessed April 17, 2024)
Created January 1, 2009, Updated February 19, 2017