explainable AI framework

Machine-learning-assisted Quality of Transmission Estimation in Optical Networks

Andrea Bianco, Full Professor, Electronics and Telecommunications Department at Politecnico di Torino

Feb 8, 13:00 - 14:00

B9 L4 R4125

machine learning explainable AI framework artificial intelligence

Machine Learning (ML) tools have recently been adopted for a wide range of automated operations in optical networking, moving fundamental steps towards the paradigm of zero-touch infrastructures. One example of such tasks is estimating the Quality of transmission of a lightpath prior to its establishment, which is particularly challenging due to the non-linear characteristics of signal propagation in optical fibers and to the often-incomplete knowledge of equipment parameters. This talk provides an overview of the contribution of my research team in the field of ML-based lightpath QoT estimation, including transfer learning approaches for inter-domain model adaptation, active learning for model building with small-sized training dataset, quantification of prediction uncertainty, and adoption of Explainable AI framework to expose the internal decisional mechanisms of trained models.

Rihan Huang

M.S. Student, Computer Science

Graph Neural Networks explainable AI framework Reinforcement Learning