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Optical performance monitoring of (Q)PSK data channels using artificial neural networks trained with parameters derived from delay-tap asynchronous diagrams

Published

Author(s)

Jeffrey A. Jargon, Xiaoxia Wu, Alan Willner, Loukas Paraschis

Abstract

We demonstrate a technique of using artificial neural networks trained with parameters derived from delay-tap asynchronous diagrams for optical performance monitoring of phase shift keying (PSK) data signals. We show that asynchronous diagrams from balanced detection give superior results compared to direct detection in a 40 Gb/s binary PSK system. Experimental demonstration in a 100 Gb/s quadrature PSK system verifies the effectiveness of the proposed technique.
Citation
IEEE Photonics Technology Letters

Keywords

neural networks, optical fiber communication, optical performance monitoring, phase modulation

Citation

Jargon, J. , Wu, X. , Willner, A. and Paraschis, L. (2011), Optical performance monitoring of (Q)PSK data channels using artificial neural networks trained with parameters derived from delay-tap asynchronous diagrams, IEEE Photonics Technology Letters (Accessed May 20, 2024)

Issues

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Created February 15, 2011, Updated February 19, 2017