Machine learning and data science for low-margin optical networks-Reference-Cited by-同舟云学术

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Machine learning and data science for low-margin optical networks

Published:2022 Issue: Volume: Page:281-315
ISSN:
Container-title:Machine Learning for Future Fiber-Optic Communication Systems
language:
Short-container-title:

Author:

Delezoide Camille,Ramantanis Petros,Layec Patricia

Publisher

Elsevier

Reference21 articles.

1. Integration of reliability and capacity in performance measure of a telecommunication network;Aggarwal;IEEE Transactions on Reliability,1985

2. Can we use flexible transponders to reduce margins?;Augé,2013

3. On the temperature-dependent gain and noise figure analysis of c-band high-concentration edfas with the effect of cooperative upconversion;Berkdemir;Journal of Lightwave Technology,2009

4. Modeling of the impact of nonlinear propagation effects in uncompensated optical coherent transmission links;Carena;Journal of Lightwave Technology,2012

5. Method for a comprehensive evaluation of margins in optical networks;Delezoide,2019

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1. Feedback-Based Channel Frequency Optimization in Superchannels;Journal of Lightwave Technology;2024-07-01

2. Enhancing Speech Privacy through Dynamic Chaos- Driven Scrambling with Nested Segmentation;2024-06-24

3. Design of low crosstalk homogeneous multicore few mode fiber for future high-capacity optical transmission;Computer Science and Engineering Research;2024-03-16

4. Streamlined Failure Localization Method and Application to Network Health Monitoring;Journal of Lightwave Technology;2023-10-01

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