Soft-failure detection, localization, identification, and severity prediction by estimating QoT model input parameters
The performance of optical devices can degrade because of aging and external causes like,
for example, temperature variations. Such degradation might start with a low impact on the …
for example, temperature variations. Such degradation might start with a low impact on the …
Soft-failure localization and device working parameters estimation in disaggregated scenarios
Soft-Failure Localization and Device Working Parameters Estimation in Disaggregated
Scenarios Page 1 Th1F.2.pdf OFC 2020 © OSA 2020 Soft-Failure Localization and Device …
Scenarios Page 1 Th1F.2.pdf OFC 2020 © OSA 2020 Soft-Failure Localization and Device …
Protection against failure of machine-learning-based QoT prediction
N Guo, L Li, B Mukherjee… - Journal of Optical …, 2022 - ieeexplore.ieee.org
Machine learning (ML)-based methods are being widely explored to predict the quality of
transmission (QoT) of a lightpath. They are expected to reduce the signal-to-noise ratio …
transmission (QoT) of a lightpath. They are expected to reduce the signal-to-noise ratio …
Service-triggered failure identification/localization through monitoring of multiple parameters
Failures in the optical layer might impact the quality of supported services. We
experimentally characterize several failure causes and propose an effective machine …
experimentally characterize several failure causes and propose an effective machine …
Soft failure localization during commissioning testing and lightpath operation
In elastic optical networks (EONs), effective soft failure localization is of paramount
importance to early detection of service level agreement violations while anticipating …
importance to early detection of service level agreement violations while anticipating …
Machine learning models for alarm classification and failure localization in optical transport networks
The increase in computing capacity and the huge amount of available data have
significantly accelerated the use of artificial intelligence and machine learning algorithms to …
significantly accelerated the use of artificial intelligence and machine learning algorithms to …
[HTML][HTML] AI-based modeling and monitoring techniques for future intelligent elastic optical networks
With the development of 5G technology, high definition video and internet of things, the
capacity demand for optical networks has been increasing dramatically. To fulfill the capacity …
capacity demand for optical networks has been increasing dramatically. To fulfill the capacity …
Accurate QoT estimation by means of a reduction of EDFA characteristics uncertainties with machine learning
E Seve, J Pesic, Y Pointurier - 2020 International Conference …, 2020 - ieeexplore.ieee.org
Using machine learning and Signal-to-Noise Ratio (SNR) monitoring, we reduce
uncertainties on output power profile and noise figure (NF) of each EDFA in an optical …
uncertainties on output power profile and noise figure (NF) of each EDFA in an optical …
Domain adaptation and transfer learning for failure detection and failure-cause identification in optical networks across different lightpaths
F Musumeci, VG Venkata, Y Hirota, Y Awaji… - Journal of Optical …, 2022 - opg.optica.org
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 method for quality of transmission prediction of unestablished lightpaths
Predicting the quality of transmission (QoT) of a lightpath prior to its deployment is a step of
capital importance for an optimized design of optical networks. Due to the continuous …
capital importance for an optimized design of optical networks. Due to the continuous …