Network Data Based Transfer Learning Failure Prediction Agent Pre-Trained Using Digital Twin

This paper describes the use of Transfer Learning (TL) using experimental data and a Machine Learning (ML) model pre-trained with a Digital Twin (DT) for the prediction of amplifier failures in optical networks. Using GNPy, an open-source framework, amplifier failure conditions are simulated, creating the required training dataset for the ML model. Later, by implementing TL using optical transmission testbed network data, the model is able to capture realtime network fluctuations, thereby enabling it to distinguish network parameters variations due to any incoming failures from the regular network dynamics, thus enhancing the practical applicability of the model. The model is based on the Long Short-Term Memory (LSTM) ML Technique and is shown to achieve a TL accuracy of 99%, demonstrating the ability of the model to effectively predict failures. This method facilitates early identification and intervention, reduces service interruptions, and improves network reliability. Leveraging TL from network data provides a scalable and data-driven solution to enhance the resilience, efficiency, and ongoing operation of contemporary optical communication systems.