Deep Neural Network-Based QoT Estimation for SMF and FMF Links
Author:
Affiliation:
1. School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
2. Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Italy
Funder
Universidad Carlos III de Madrid
Marie Skłodowska-Curie
Italian Government
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Atomic and Molecular Physics, and Optics
Link
http://xplorestaging.ieee.org/ielx7/50/10064221/09968100.pdf?arnumber=9968100
Reference34 articles.
1. Accurate Quality of Transmission Estimation With Machine Learning
2. Modeling EDFA Gain Ripple and Filter Penalties With Machine Learning for Accurate QoT Estimation
3. Convex Optimization
4. Quality of Transmission Estimation and Short-Term Performance Forecast of Lightpaths
5. Machine-Learning Method for Quality of Transmission Prediction of Unestablished Lightpaths
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2. Network-wide QoT Estimation Using SGD with Gradient Transfer Between Wavelengths;Optical Fiber Communication Conference (OFC) 2024;2024
3. DNN-based QoT Estimation Using Topological Inputs and Training with Synthetic-Physical Data;2023 IEEE Photonics Conference (IPC);2023-11-12
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