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Forecasting SNR margins has low-complexity

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Abstract

We use an extensive dataset from a production network to forecast signal-to-noise ratio (SNR) margin and show that a na¨ıve technique is able to forecast SNR margin better than a deep neural network (DNN).

© 2020 The Author(s)

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More Like This
Forecasting Lightpath QoT with Deep Neural Networks

Hussein Chouman, Petar Djukic, Christine Tremblay, and Christian Desrosiers
Th4J.5 Optical Fiber Communication Conference (OFC) 2021

Recurrent Neural Networks for Short-Term Forecast of Lightpath Performance

Sandra Aladin, Stéphanie Allogba, Anh Vu Stephan Tran, and Christine Tremblay
W2A.24 Optical Fiber Communication Conference (OFC) 2020

Deep Learning for Multi-Step Performance Prediction in Operational Optical Networks

Ameni Mezni, Douglas W. Charlton, Christine Tremblay, and Christian Desrosiers
STh4M.1 CLEO: Science and Innovations (CLEO_SI) 2020

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