A review of machine learning-based failure management in optical networks
Failure management plays a significant role in optical networks. It ensures secure operation,
mitigates potential risks, and executes proactive protection. Machine learning (ML) is …
mitigates potential risks, and executes proactive protection. Machine learning (ML) is …
Building a digital twin for intelligent optical networks [Invited Tutorial]
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 …
layer is crucial. Digital twin (DT) modeling, which relies on continuous learning with real-time …
Demonstration of ML-assisted soft-failure localization based on network digital twins
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 …
and significant anomalous behaviors. However, the recent evolution of network control and …
Machine-learning-based soft-failure localization with partial software-defined networking telemetry
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 …
the received telemetry data. However, in certain cases, particularly in disaggregated …
Suspect fault screen assisted graph aggregation network for intra-/inter-node failure localization in ROADM-based optical networks
In optical networks, failure localization is essential to stable operation and service
restoration. Several approaches have been presented to achieve accurate failure …
restoration. Several approaches have been presented to achieve accurate failure …
A GaN based soft failure detection and identification framework for long-haul coherent optical communication systems
The rapid progress of 5G, the internet of things (IoT), high-definition online video, and cloud
computing have raised high requirements for the capacity of optical networks. To improve …
computing have raised high requirements for the capacity of optical networks. To improve …
Domain adaptation and transfer learning for failure detection and failure-cause identification in optical networks across different lightpaths
Optical network failure management (ONFM) is a promising application of machine learning
(ML) to optical networking. Typical ML-based ONFM approaches exploit historical monitored …
(ML) to optical networking. Typical ML-based ONFM approaches exploit historical monitored …
Machine-learning-based telemetry for monitoring long-haul optical transmission impairments: methodologies and challenges
Current management of optical communication systems is conservative, manual-based, and
time-consuming. To improve this situation, building an intelligent closed-loop control system …
time-consuming. To improve this situation, building an intelligent closed-loop control system …
Covert fault detection with imbalanced data using an improved autoencoder for optical networks
C Zhang, M Zhang, S Liu, Z Liu… - Journal of Optical …, 2023 - ieeexplore.ieee.org
Covert faults are characterized by the performance parameters falling within the normal
range, without any observable abnormalities. These types of faults pose a significant risk as …
range, without any observable abnormalities. These types of faults pose a significant risk as …
[PDF][PDF] Multi-Failure Localization in High-Degree ROADM-based Optical Networks using Rules-Informed Neural Networks
To accommodate ever-growing traffic, network operators are actively deploying high-degree
reconfigurable optical add/drop multiplexers (ROADMs) to build large-capacity optical …
reconfigurable optical add/drop multiplexers (ROADMs) to build large-capacity optical …