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
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