Adaptive Machine Learning Framework for QOT Approximation Using Link-Level Embeddings

The management of today’s optical networks is highly dependent on the correct estimation of Quality of Trans- mission (QoT). The current analytical approach requires exact physical values, which are often not available, resulting in inefficient management of the network. This paper proposes an Adaptive Machine Learning Framework that aims to address the analytical approach’s limitations using a new and innovative data-driven approach. The proposed framework combines link- level embeddings with an Artificial Neural Network (ANN) to process the unique sequence of fiber links in a lightpath, focusing on the fine-grained details of the sequence that are normally overlooked by the current analytical approach. Through dy- namic learning from the sequence data, the framework provides highly accurate signal quality estimates. These estimates enable intelligent and automated modulation format choices, greatly enhancing spectral efficiency and minimizing disconnections. This highly scalable solution is developed in Python and TensorFlow and is best suited for dynamic resource allocation and future- oriented network planning.