We propose a technique for modeling erbium-doped fiber amplifiers (EDFAs) in optical fiber
networks, where the amplifier unit is located at a distant node outside the laboratory. We …

High-accuracy physical layer models enable intelligent, self-driving optical networks. The
dynamic wavelength-dependent gain characteristics of erbium-doped fiber amplifiers …

In this article, machine learning is used to create a differentiable model for the input-output
power spectral profile relations of C-band erbium-doped fiber amplifiers (EDFAs). The EDFA …

Optical transmission systems with high spectral efficiency require accurate quality of
transmission estimation for optical channel provisioning. However, the wavelength …

With the rapid development of virtual/augmented reality and cloud services, the capacity
demand for optical communication systems is ever-increasing. To further increase system …

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 …

Optical networks satisfy high bandwidth and low latency requirements for telecommunication
networks and data center interconnection. To improve network resource utilization, machine …

Machine learning based modelling of Erbium-Doped Fiber Amplifiers (EDFA) is used to
determine wavelength dependent gain for use in optical transmission systems, and achieves …

We report a neural-network based erbium-doped fiber amplifier (EDFA) gain model built
from experimental measurements. The model shows low gain-prediction error for both the …

We report on a Machine Learning approach based on artificial Neural Networks to predict
optical power excursion in Erbium Doped Fiber Amplifiers. Its flexibility and adaptability …