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.

中文翻译：

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走向光网络中ML应用程序的部署

为了支持新兴的5G应用程序和5G承载网络，作为关键基础架构的光网络正在不断发展，以变得更加动态和自动化。未来具有低链路余量的自治网络的愿景要求对光链路的传输质量（QoT）进行精确的估计/预测。机器学习（ML）技术提供了有前途的解决方案，以预测未建立链接的QoT。在本文中，我们研究了混合建模和转移学习，以解决在光网络中部署ML应用程序的关键问题。与没有转移学习的模型相比，所提出的多通道预测方法将训练数据需求减少了80％，同时获得了0.267dB的相同MSE。该方法有助于简化ML生命周期以进行数据收集，