Domain adversarial adaptation framework for few-shot QoT estimation in optical networks

The increasing complexity and dynamicity of future optical networks will necessitate accurate, fast, and low-cost quality-of-transmission (QoT) estimation. Machine learning-based QoT estimation models have shown promise in ensuring the reliability and efficiency of optical networks. However, the data-driven nature of these models impedes their application in practical settings. To address the problem of limited data availability in the target domain, known as the few-shot learning problem, we propose a domain adversarial adaptation method that aligns the distributions of representations from different source and target domains by minimizing the domain discrepancy quantified by the approximate Wasserstein distance. We demonstrate the method's effectiveness through a theoretical proof and two example adaptations, i.e., from simulation to experimental data and from experimental to real network data. Our method consistently outperforms commonly used artificial neural networks (ANNs) and more advanced transfer learning approaches for various target domain data sizes. More profoundly, we show two ways to further improve the prediction accuracy, i.e., incorporating unlabeled target domain data in the training stage and utilizing the learned representations after training to train a new ANN with a reweighting strategy. In the adaptation to actual field data, our model, trained with only eight labeled network data samples, outperforms an ANN trained with 300 samples, thus reducing the labeled target domain data burden by more than 97%. The proposed method's adaptability and generalizability make it a promising solution for accurate QoT estimation with low data requirements in future intelligent optical networks.