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
Bibliographical noteFunding Information:
Manuscript received February 15, 2021; revised March 23, 2021; accepted April 15, 2021. Date of publication April 21, 2021; date of current version April 27, 2021. This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of U.K. through the Towards Ultimate Convergence of All Networks (TOUCAN) Project under Grant EP/L020009/1 and in part by the National Dark Fibre Facility National Dark Fibre Facility (NDFF) Project under Grant EP/S028854/1. (Corresponding author: Shuangyi Yan.) The authors are with the Smart Internet Laboratory, High Performance Networks Group (HPN), University of Bristol, Bristol BS8 1UB, U.K. (e-mail: firstname.lastname@example.org).
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- transfer learning
- optical networks
- Quality of Transmission estimation