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Exact component parameter agnostic QoT estimation using spectral data-driven LSTM in optical networks

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Abstract

We propose the use of spectral data-driven LSTM-based machine learning to improve generalized signal-to-noise ratio (gSNR) quality-of-transmission estimation in component parameter-agnostic network scenarios. We show gSNR estimation improvements up to 1.1 dB for unseen networks.

© 2022 The Author(s)

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