Evolutionary neuron-level transfer learning for QoT estimation in optical networks

Abstract

To ensure the reliability and efficiency of lightpath transmission in optical networks, it is essential to predict the quality of transmission (QoT) of the candidate lightpaths before their deployment. An artificial neural network (ANN) becomes a promising solution for QoT estimation of lightpaths, owing to its powerful data fitting capability and scalability. As a supervised learning model, an ANN requires a large set of training samples from lightpaths’ observations to ensure its accuracy. Unfortunately, the acquisition of the training samples is hindered by practical limitations, such as the shortage of monitorable lightpaths in the early stage of optical network deployment or/and the absence of optical performance monitors at partial network nodes. Therefore, how to obtain a high-precision QoT estimator with small-sized datasets is challenging. In this paper, we propose an evolutionary neuron-level transfer learning (ENTL) scheme for QoT estimation to improve the accuracy of the ANN model with small-sized datasets. In the ENTL-based QoT estimator, the minimal unit of knowledge transfer is the neuron of the ANN model, and the particle swarm optimization (PSO) algorithm is introduced to determine the trainable neurons and the frozen neurons, where the testing dataset generated by data augmentation assists the PSO algorithm to evaluate the feasible solutions. Simulation results show that the ENTL-based QoT estimator achieves higher accuracy than the traditional layer-level transfer learning (LTL)-based QoT estimator. And, when the ENTL-based QoT estimator is applied to optical network planning, it improves the reliability and throughput of optical networks compared with the LTL-based QoT estimator.