Survey on the Use of Machine Learning for Quality of Transmission Estimation in Optical Transport Networks-Reference-Cited by-同舟云学术

Survey on the Use of Machine Learning for Quality of Transmission Estimation in Optical Transport Networks

Published:2022-09-01 Issue:17 Volume:40 Page:5803-5815
ISSN:0733-8724
Container-title:Journal of Lightwave Technology
language:
Short-container-title:J. Lightwave Technol.

Author:

Ayassi Reda¹^ORCID,Triki Ahmed¹^ORCID,Crespi Noel²^ORCID,Minerva Roberto²^ORCID,Laye Maxime¹

Affiliation:

1. Orange Labs Lannion, Lannion, Bretagne, France

2. Wireless Networks and Multimedia Services Department of Institut Mines Telecom Telecom Sud Paris part of Institute Polytechnique de Paris, Paris, France

Funder

U.S. Department of Commerce

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atomic and Molecular Physics, and Optics

Link

http://xplorestaging.ieee.org/ielx7/50/9867899/09799746.pdf?arnumber=9799746

Reference78 articles.

1. The Perks of Using Machine Learning for QoT Estimation with Uncertain Network Parameters

2. Lightpath QoT computation in optical networks assisted by transfer learning

3. Performance studies of evolutionary transfer learning for end-to-end QoT estimation in multi-domain optical networks [Invited]

4. Leveraging ML-based QoT Tool Parameter Feeding for Accurate WDM Network Performance Prediction

5. Toward efficient, reliable, and autonomous optical networks: the ORCHESTRA solution [Invited]

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