Abstract

Precise computation of the quality of transmission (QoT) of lightpaths (LPs) in transparent optical networks has techno-economic importance for any network operator. The QoT metric of LPs is defined by the generalized signal-to-noise ratio (GSNR), which includes the effects of both amplified spontaneous emission noise and nonlinear interference accumulation. Generally, the physical layer of a network is characterized by nominal values provided by vendors for the operational parameters of each network element (NE). Typically, NEs suffer a variation in the working point that implies an uncertainty from the nominal value, which creates uncertainty in the GSNR computation and requires the deployment of a system margin. We propose the use of a machine learning agent trained on a dataset from an in-service network to reduce the uncertainty in the GSNR computation on an unused sister network, based on the same optical transport equipment and thus following the transfer learning paradigm. We synthetically generate datasets for both networks using the open-source library GNPy and show how the proposed deep neural network based on TensorFlow may substantially reduce the GSNR uncertainty and, consequently, the needed margin. We also present a statistical analysis of the observed GSNR fluctuations, showing that the per-wavelength GSNR distribution is always well-approximated as Gaussian, enabling a statistical closed-form approach to the margin setting.

© 2021 Optical Society of America

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