An overview on application of machine learning techniques in optical networks
Today's telecommunication networks have become sources of enormous amounts of widely
heterogeneous data. This information can be retrieved from network traffic traces, network …
heterogeneous data. This information can be retrieved from network traffic traces, network …
[HTML][HTML] Artificial intelligence (AI) methods in optical networks: A comprehensive survey
Artificial intelligence (AI) is an extensive scientific discipline which enables computer
systems to solve problems by emulating complex biological processes such as learning …
systems to solve problems by emulating complex biological processes such as learning …
Machine learning for network automation: overview, architecture, and applications [Invited Tutorial]
Networks are complex interacting systems involving cloud operations, core and metro
transport, and mobile connectivity all the way to video streaming and similar user …
transport, and mobile connectivity all the way to video streaming and similar user …
A tutorial on machine learning for failure management in optical networks
Failure management plays a role of capital importance in optical networks to avoid service
disruptions and to satisfy customers' service level agreements. Machine learning (ML) …
disruptions and to satisfy customers' service level agreements. Machine learning (ML) …
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 …
Learning from the optical spectrum: failure detection and identification
The availability of coarse-resolution cost-effective optical spectrum analyzers (OSAs) allows
their widespread deployment in operators' networks. In this paper, we explore several …
their widespread deployment in operators' networks. In this paper, we explore several …
Deep learning-based real-time analysis of lightpath optical constellations
M Ruiz, D Sequeira, L Velasco - Journal of Optical Communications …, 2022 - opg.optica.org
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) …
provisioning but also for real-time analysis. In particular, in-phase (I) and quadrature (Q) …
Monitoring and data analytics for optical networking: benefits, architectures, and use cases
Operators' network management continuously measures network health by collecting data
from the deployed network devices; data is used mainly for performance reporting and …
from the deployed network devices; data is used mainly for performance reporting and …
Self-taught anomaly detection with hybrid unsupervised/supervised machine learning in optical networks
This paper proposes a self-taught anomaly detection framework for optical networks. The
proposed framework makes use of a hybrid unsupervised and supervised machine learning …
proposed framework makes use of a hybrid unsupervised and supervised machine learning …
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 …