Abstract

By associating machine learning and an analytical model (i.e., the Gaussian noise model), we reduce uncertainties on the output power profile and the noise figure of each amplifier in an optical network. We leverage the signal-to-noise ratio (SNR) of all the light paths of an optical network, monitored in all the coherent receivers. The learning process is based on a gradient-descent algorithm where all the uncertain input parameters of the analytical model are iteratively modified from their estimated values to match with the SNR of light paths in a European optical network. The design margin is then reduced to 0.1 dB for new traffic demands.

© 2021 Optical Society of America

Performance comparisons between machine learning and analytical models for quality of transmission estimation in wavelength-division-multiplexed systems [Invited]

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J. Opt. Commun. Netw. 11(3) 140-150 (2019)