Optical Performance Monitoring Using Artificial Neural Networks Trained With Eye-Diagram Parameters

Author

Jargon, Jeffrey A. ; Wu, Xiaoxia ; Willner, Alan E.

Author_Institution

Nat. Inst. of Stand. & Technol., Boulder, CO

Volume

21

Issue

1

fYear

2009

Firstpage

54

Lastpage

56

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.

Keywords

monitoring; neural nets; optical fibre dispersion; optical fibre networks; optical fibre polarisation; telecommunication computing; telecommunication network management; artificial neural networks; chromatic dispersion; eye-diagram parameters; optical channel degradation; optical performance monitoring; optical signal-to-noise ratio; polarization-mode dispersion; Artificial neural network (ANN); chromatic dispersion (CD); eye diagram; optical performance monitoring (OPM); optical signal-to-noise ratio (OSNR); polarization-mode dispersion (PMD);

fLanguage

English

Journal_Title

Photonics Technology Letters, IEEE

Publisher

ieee

ISSN

1041-1135

Type

jour

DOI

10.1109/LPT.2008.2008447

Filename

4663494