CompQoTE: Generalizing QoT Estimation with Composable ML and End-to-End Learning-Reference-Cited by-同舟云学术

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CompQoTE: Generalizing QoT Estimation with Composable ML and End-to-End Learning

Published:2023-03 Issue: Volume: Page:
ISSN:
Container-title:2023 Optical Fiber Communications Conference and Exhibition (OFC)
language:
Short-container-title:

Author:

Gao Hanyu¹,Chen Xiaoliang¹,Sun Lu²,Li Zhaohui¹

Affiliation:

1. Sun Yat-sen University,Guangdong Provincial Key Labratory of Optoelectronic Information Processing Chips and Systems, School of Electronics and Information Technology,Guangzhou,China

2. State Grid Information & Telecommunication Group Co., Ltd,Beijing,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10115459/10115461/10117043.pdf?arnumber=10117043

Reference5 articles.

1. Hierarchical Learning for Cognitive End-to-End Service Provisioning in Multi-Domain Autonomous Optical Networks

2. Model transfer of QoT prediction in optical networks based on artificial neural networks

3. QoT Estimation for Unestablished Lighpaths using Machine Learning

4. An Optical Communication's Perspective on Machine Learning and Its Applications

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