Abstract

OSNR monitoring techniques are reviewed and a use of machine learning to estimate the nonlinear noise and thereby monitor the OSNR is described. High monitoring accuracy is demonstrated for a wide range of link conditions using an artificial neural network.

© 2017 Optical Society of America

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