Failure management plays a significant role in optical networks. It ensures secure operation,
mitigates potential risks, and executes proactive protection. Machine learning (ML) is …

To support the development of intelligent optical networks, accurate modeling of the physical
layer is crucial. Digital twin (DT) modeling, which relies on continuous learning with real-time …

Optical network automation requires accurate physical layer models, not only for
provisioning but also for real-time analysis. In particular, in-phase (I) and quadrature (Q) …

In optical transport networks, failure localization is usually triggered as a response to alarms
and significant anomalous behaviors. However, the recent evolution of network control and …

The development of digital twins to represent the optical transport network might enable
multiple applications for network operation, including automation and fault management. In …

Soft-failure localization frameworks typically use if-else rules to localize failures based on
the received telemetry data. However, in certain cases, particularly in disaggregated …

Optical network automation and failure management require measuring the status and the
performance of the different network devices to anticipate any degradation and ensure the …

This work proposes a soft-failure evolution and localization framework to detect and localize
the root cause of future hard-failure incidents in a timely manner enabling repair actions to …

The intent-based networking (IBN) paradigm targets defining high-level abstractions so
network operators can define what their desired outcomes are without specifying how they …

Huge efforts have been paid lastly to study the application of Machine Learning techniques
to optical transport networks. Applications include Quality of Transmission (QoT) estimation …