Neuron-level Transfer Learning for ANN-based QoT Estimation in Optical Networks

Quality of transmission (QoT) estimation of lightpaths is essential for their deployment in optical networks. Machine learning (ML) is a promising solution for QoT estimation, which needs a large set of training data to ensure the accuracy of ML models. However, due to the shortage of monitorable lightpaths and/or the absence of optical performance monitors, the acquisition of such a number of training data is unavailable. To solve this issue, we propose a neuron-level transfer learning (NL-TL) scheme for ANN-based QoT estimation, where a neuron importance-based transfer model search algorithm is proposed to determine the trainable neurons. Simulation results show that the NL-TL-based QoT estimation scheme improves estimation accuracy compared with conventional layer-level transfer learning-based QoT estimation schemes.