Degradation Detection and Severity Estimation by Exploiting an Optical Time and Frequency
Digital Twin Page 1 Degradation Detection and Severity Estimation by Exploiting an Optical …

Integration of the machine learning (ML) technique in all‐optical networks can enhance the
effectiveness of resource utilization, quality of service assurances, and scalability in optical …

Machine learning is regarded as an attractive solution for soft failure management in optical
networks; however, the performance of trained models working on unseen data is of growing …

Detection and root cause analysis of performance degradation in optical networks using
machine learning Page 1 “Detection and Root Cause Analysis of Performance Degradation in …

Error vector magnitude (EVM) has proven to be one of the optical performance monitoring
metrics providing the quantitative estimation of error statistics. However, the EVM estimation …

In optical networks, reliable failure detection is essential for maintaining quality of service.
The methodology has evolved from traditional performance threshold-driven approaches to …

This paper presents a self-driving networking paradigm exploiting the state-of-the-art
reinforcement learning and transfer learning algorithms for highly resource-efficient …

Optical in-phase and quadrature (IQ) constellations enclose valuable information regarding
the optical elements traversed by the optical signal. Such information can be extracted and …

The uneven occurrence of certain types of failures in optical networks results in a scarcity of
data for less frequent failures, leading to imbalanced datasets for training machine learning …

A convolutional neural network (CNN) based soft failure identifier is proposed. Its superior
performance in identifying failure causes including filter shift, filter tightening, ASE noise and …