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Applications of artificial neural networks in optical performance monitoring



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


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.
Journal of Lightwave Technology


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


Jargon, J. , Wu, X. , Skoog, R. , Paraschis, L. and Willner, A. (2009), Applications of artificial neural networks in optical performance monitoring, Journal of Lightwave Technology, [online], (Accessed April 15, 2024)
Created August 15, 2009, Updated February 19, 2017