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 …
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 …
An optical communication's perspective on machine learning and its applications
Machine learning (ML) has disrupted a wide range of science and engineering disciplines in
recent years. ML applications in optical communications and networking are also gaining …
recent years. ML applications in optical communications and networking are also gaining …
A survey of networking applications applying the software defined networking concept based on machine learning
The main task of future networks is to build, as much as possible, intelligent networking
architectures for intellectualization, activation, and customization. Software-defined …
architectures for intellectualization, activation, and customization. Software-defined …
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 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 …
Data augmentation to improve performance of neural networks for failure management in optical networks
Despite the increased exploration of machine learning (ML) techniques for the realization of
autonomous optical networks, less attention has been paid to data quality, which is critical …
autonomous optical networks, less attention has been paid to data quality, which is critical …
Potential failure cause identification for optical networks using deep learning with an attention mechanism
C Zhang, D Wang, J Jia, L Wang, K Chen… - Journal of Optical …, 2022 - opg.optica.org
With a focus on failure management in optical networks, we propose a potential failure
cause identification scheme using an attention mechanism for optical transport network …
cause identification scheme using an attention mechanism for optical transport network …
Quantitative approaches for optimization of user experience based on network resilience for wireless service provider networks
D Kakadia, JE Ramirez-Marquez - Reliability Engineering & System Safety, 2020 - Elsevier
Since the 1980′ s and in particular 1996, telecom operators and recently mobile operators
have been facing increasingly fierce competition, combined with flat subscriber growth and …
have been facing increasingly fierce competition, combined with flat subscriber growth and …
Machine learning methods for optical communication systems and networks
Abstract Machine learning (ML) is being hailed as a new direction of innovation to transform
future optical communication systems. Signal processing paradigms based on ML are being …
future optical communication systems. Signal processing paradigms based on ML are being …