Artificial neural networks for photonic applications—from algorithms to implementation: tutorial
This tutorial–review on applications of artificial neural networks in photonics targets a broad
audience, ranging from optical research and engineering communities to computer science …
audience, ranging from optical research and engineering communities to computer science …
Machine learning techniques for quality of transmission estimation in optical networks
Y Pointurier - Journal of Optical Communications and …, 2021 - ieeexplore.ieee.org
The estimation of the quality of transmission (QoT) in optical systems with machine learning
(ML) has recently been the focus of a large body of research. We discuss the sources of …
(ML) has recently been the focus of a large body of research. We discuss the sources of …
Machine learning for optical fiber communication systems: An introduction and overview
Optical networks generate a vast amount of diagnostic, control, and performance monitoring
data. When information is extracted from these data, reconfigurable network elements and …
data. When information is extracted from these data, reconfigurable network elements and …
Scalable and disaggregated GGN approximation applied to a C+ L+ S optical network
We investigate quality of transmission (QoT) estimation in a multi-band transmission
scenario, including a wideband description of the frequency-dependent physical layer …
scenario, including a wideband description of the frequency-dependent physical layer …
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 …
Survey on the use of machine learning for quality of transmission estimation in optical transport networks
Estimating the Quality of Transmission (QoT) of the optical signal from source to destination
nodes is the cornerstone of design engineering and service provisioning in optical transport …
nodes is the cornerstone of design engineering and service provisioning in optical transport …
Iterative supervised learning approach using transceiver bit-error-rate measurements for optical line system optimization
Defining the working points of optical amplifiers is a key factor when managing optical
networks, particularly for the quality of transmission (QoT) of deployed connections …
networks, particularly for the quality of transmission (QoT) of deployed connections …
Machine-learning-based EDFA gain estimation
Optical transmission systems with high spectral efficiency require accurate quality of
transmission estimation for optical channel provisioning. However, the wavelength …
transmission estimation for optical channel provisioning. However, the wavelength …
Lightpath QoT computation in optical networks assisted by transfer learning
Precise computation of the quality of transmission (QoT) of lightpaths (LPs) in transparent
optical networks has techno-economic importance for any network operator. The QoT metric …
optical networks has techno-economic importance for any network operator. The QoT metric …
Associating machine-learning and analytical models for quality of transmission estimation: combining the best of both worlds
By associating machine learning and an analytical model (ie, the Gaussian noise model),
we reduce uncertainties on the output power profile and the noise figure of each amplifier in …
we reduce uncertainties on the output power profile and the noise figure of each amplifier in …