Forecasting Lightpath Quality of Transmission and Implementing Uncertainty in the Forecast Models
Author:
Affiliation:
1. École de technologie supérieure, Montréal, QC, Canada
2. Ciena Corporation, Ottawa, ON, Canada
Funder
Mitacs
Ciena Corp.
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Atomic and Molecular Physics, and Optics
Link
http://xplorestaging.ieee.org/ielx7/50/10190599/10058153.pdf?arnumber=10058153
Reference53 articles.
1. Cross-feature trained machine learning models for QoT-estimation in optical networks
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3. Deep Learning for Multi-Step Performance Prediction in Operational Optical Networks
4. Machine-Learning-Based Lightpath QoT Estimation and Forecasting
5. On calibration of modern neural networks;guo;Proc 34th Int Conf Mach Learn,2017
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2. Non-technological barriers: the last frontier towards AI-powered intelligent optical networks;Nature Communications;2024-07-17
3. Using SHAP Values to Validate Model’s Uncertain Decision for ML-based Lightpath Quality-of-Transmission Estimation;2023 23rd International Conference on Transparent Optical Networks (ICTON);2023-07-02
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