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Journal of Lightwave Technology

Journal of Lightwave Technology

| A JOINT IEEE/OSA PUBLICATION

  • Vol. 27, Iss. 16 — Aug. 15, 2009
  • pp: 3580–3589

Applications of Artificial Neural Networks in Optical Performance Monitoring

Xiaoxia Wu, Jeffrey A. Jargon, Ronald A. Skoog, Loukas Paraschis, and Alan E. Willner

Journal of Lightwave Technology, Vol. 27, Issue 16, pp. 3580-3589 (2009)


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Abstract

Applications using artificial neural networks (ANNs) for optical performance monitoring (OPM) are proposed and demonstrated. Simultaneous identification of optical signal-to-noise-ratio (OSNR), chromatic dispersion (CD), and polarization-mode-dispersion (PMD) from eye-diagram parameters is shown via simulation in both 40 Gb/s on-off keying (OOK) and differential phase-shift-keying (DPSK) systems. Experimental verification is performed to simultaneously identify OSNR and CD. We then extend this technique to simultaneously identify accumulated fiber nonlinearity, OSNR, CD, and PMD from eye-diagram and eye-histogram parameters in a 3-channel 40 Gb/s DPSK wavelength-division multiplexing (WDM) system. Furthermore, we propose using this ANN approach to monitor impairment causing changes from a baseline. Simultaneous identification of accumulated fiber nonlinearity, OSNR, CD, and PMD causing changes from a baseline by use of the eye-diagram and eye-histogram parameters is obtained and high correlation coefficients are achieved with various baselines. Finally, the ANNs are also shown for simultaneous identification of in-phase/quadrature (I/Q) data misalignment and data/carver misalignment in return-to-zero differential quadrature phase shift keying (RZ-DQPSK) transmitters.

© 2009 USGov

Citation
Xiaoxia Wu, Jeffrey A. Jargon, Ronald A. Skoog, Loukas Paraschis, and Alan E. Willner, "Applications of Artificial Neural Networks in Optical Performance Monitoring," J. Lightwave Technol. 27, 3580-3589 (2009)
http://www.opticsinfobase.org/jlt/abstract.cfm?URI=jlt-27-16-3580

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