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 …
Digital Twin of Optical Networks: A Review of Recent Advances and Future Trends
Digital twin (DT) has revolutionized optical communication networks by enabling their full life-
cycle management, including planning, prediction, optimization, upgrade, and …
cycle management, including planning, prediction, optimization, upgrade, and …
Improved QoT estimations through refined signal power measurements and data-driven parameter optimizations in a disaggregated and partially loaded live …
Accurate quality of transmission (QoT) estimations are essential enablers for future low-
margin dynamic optical network operations. However, physical parameter measurement …
margin dynamic optical network operations. However, physical parameter measurement …
A survey on QoT prediction using machine learning in optical networks
In optical networks, a connection (eg, light-path and light-tree) is set up to carry data from its
source to destination (s). When the optical signal transmits through the fiber links and optical …
source to destination (s). When the optical signal transmits through the fiber links and optical …
Machine learning-enabled intelligent fiber-optic communications: major obstacles and the way forward
FN Khan - IEEE Communications Magazine, 2022 - ieeexplore.ieee.org
Machine learning (ML) has achieved phenomenal success in revolutionizing a number of
science and engineering disciplines over the last decade. Naturally, it is also being …
science and engineering disciplines over the last decade. Naturally, it is also being …
Machine learning-assisted nonlinear-impairment-aware proactive defragmentation for C+L band elastic optical networks
Efficient resource allocation and management can enhance the capacity of an optical
backbone network. In this context, spectrum retuning via hitless defragmentation has been …
backbone network. In this context, spectrum retuning via hitless defragmentation has been …
Quality-aware resource provisioning for multiband elastic optical networks: a deep-learning-assisted approach
Multiband elastic optical network (MB-EON) technology can help to sustain exponential
traffic growth in the optical backbone network. However, multiband operation creates high …
traffic growth in the optical backbone network. However, multiband operation creates high …
Automated training dataset collection system design for machine learning application in optical networks: an example of quality of transmission estimation
Applications of machine learning (ML) models in optical communications and networks have
been extensively investigated. For an optical wavelength-division-multiplexing (WDM) …
been extensively investigated. For an optical wavelength-division-multiplexing (WDM) …
Invariant convolutional neural network for robust and generalizable QoT estimation in fiber-optic networks
Accurately estimating the quality of transmission (QoT) in modern transport optical networks
has been regarded as one of the most critical factors to reduce the design margins. In recent …
has been regarded as one of the most critical factors to reduce the design margins. In recent …
Non-technological barriers: the last frontier towards AI-powered intelligent optical networks
FN Khan - Nature Communications, 2024 - nature.com
Abstract Machine learning (ML) has been remarkably successful in transforming numerous
scientific and technological fields in recent years including computer vision, natural …
scientific and technological fields in recent years including computer vision, natural …