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|Author(s):||Jeffrey A. Jargon; Xiaoxia Wu; Ronald Skoog; Loukas Paraschis; Alan Willner;|
|Title:||Applications of artificial neural networks in optical performance monitoring|
|Published:||August 15, 2009|
|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.|
|Citation:||Journal of Lightwave Technology|
|Pages:||pp. 3580 - 3589|
|Keywords:||neural networks, optical fiber communication, optical performance monitoring, phase modulation|
|PDF version:||Click here to retrieve PDF version of paper (2MB)|