Invariant convolutional neural network for robust and generalizable QoT estimation in fiber-optic networks

Abstract

Accurately estimating the quality of transmission (QoT) in modern transport optical networks has been regarded as one of the most critical factors to reduce the design margins. In recent years, machine learning (ML) based models have exhibited a powerful capacity for various kinds of QoT estimation tasks. However, the existing ML-based QoT estimators suffer from two kinds of phenomena that are hard to bypass in real optical networks. The first conundrum is the variation of the number of parameters in transmission features introduced by the changeable link configurations. The second conundrum is the distribution drift of the transmission parameters relative to the training dataset. To mitigate the above two problems, we propose an invariant convolutional neural network predictor (ICNNP), which consists of a fixed-length encoder for encoding variable-length link features, and a robust neural network predictor, which can adapt to the changing transmission conditions with limited additional data. To alleviate the time dependence and link length dependence of the QoT estimator, we trained the model with a joint training algorithm. We validate our method experimentally by collecting datasets under different transmission configurations. The proposed ICNNP exhibits significant advantages in comparison with the four benchmark algorithms. When the span numbers vary from 9 to 12 and the evaluation period is expanded from 12 to 72 h, the standard deviation of the signal-to-noise ratio prediction error of our model holds below 0.4 dB and 0.25 dB, respectively. We also propose a continual learning workflow with an evaluation-update framework, with which our model can perform QoT estimation with the highest efficiency and the lowest training cost. The ensemble of components in this paper builds a deployment-oriented reliable QoT estimation tool.