Toward Deployments of ML Applications in Optical Networks

Abstract

To support the emerging 5G applications and the 5G bearer networks, optical networks, as the critical infrastructure, are continuously evolving to be more dynamic and automatic. The vision of future autonomous networks with low link margins requires precise estimation/prediction of the quality of transmission (QoT) of optical links. Machine learning (ML) technologies provide promising solutions to predict QoT of unestablished links. In this paper, we investigated hybrid modelling and transfer learning to address the key issues for deployment of ML applications in optical networks. The proposed approach for multiple-channel prediction reduces the training data requirement by 80% while obtaining the same MSE of 0.267dB compared with the model without transfer learning. The approach facilitates a streamlined ML life-cycle for data collection, training, and deployment.

Publication
IEEE Photonics Technology Letters
Paurakh Paudyal
Paurakh Paudyal
Previous Final-Year student (2019~ 2020)

Recent EEE Graduate with First Class Honours. Specialised in Machine Learning and Python programming.

Shen Sen
Shen Sen
PhD student (2020.10 ~ Now)

PhD student funded by Bristol-CSC joint scholarship.

Shuangyi Yan
Shuangyi Yan
Senior Lecturer in High Performance Networking & Optical Network

My research focuses on AI-driven automatic dynamic optical networks with flexible network functions and fast network reconfigurations.

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