We propose a Convolutional Neural Network (CNN) to learn the mapping between the 2D power profiles, (distance and frequency), and the Raman pumps. Using the CNN, the pump powers and wavelengths for arbitrary 2D profiles can be determined with high accuracy
We introduce a load aware machine learning method for prediction of Raman gain profiles. It enables ...
In this work, we will give an overview of some of the most recent and successful applications of mac...
A machine learning technique was recently proposed to optimize the gain of a multi-pump single-mode...
We experimentally validate a real-time machine learning framework, capable of controlling the pump p...
We present a machine learning (ML) framework for designing desired signal power profiles over the sp...
It has been recently demonstrated that neural networks can learn the complex pump–signal relations i...
A machine learning framework predicting pump powers and noise figure profile for a target distribute...
Optical communication systems are always evolving to support the need for ever-increasing transmissi...
A wide range of highly-relevant problems in programmable and integrated photonics, optical amplifica...
A multi-layer neural network is employed to learn the mapping between Raman gain profile and pump po...
The paper presents recent advances in the design of controllable, highly accurate, and multi-band Ra...
A machine learning framework for Raman amplifier design is experimentally tested. Performance in ter...
Two artificial neural network (ANN) models are presented to predict power profiles over C+L–band in ...
Distributed Raman amplifier (DRA) has been widely studied in recent decades because of its low noise...
One of the most promising solutions to overcome the capacity limit of current optical fiber links i...
We introduce a load aware machine learning method for prediction of Raman gain profiles. It enables ...
In this work, we will give an overview of some of the most recent and successful applications of mac...
A machine learning technique was recently proposed to optimize the gain of a multi-pump single-mode...
We experimentally validate a real-time machine learning framework, capable of controlling the pump p...
We present a machine learning (ML) framework for designing desired signal power profiles over the sp...
It has been recently demonstrated that neural networks can learn the complex pump–signal relations i...
A machine learning framework predicting pump powers and noise figure profile for a target distribute...
Optical communication systems are always evolving to support the need for ever-increasing transmissi...
A wide range of highly-relevant problems in programmable and integrated photonics, optical amplifica...
A multi-layer neural network is employed to learn the mapping between Raman gain profile and pump po...
The paper presents recent advances in the design of controllable, highly accurate, and multi-band Ra...
A machine learning framework for Raman amplifier design is experimentally tested. Performance in ter...
Two artificial neural network (ANN) models are presented to predict power profiles over C+L–band in ...
Distributed Raman amplifier (DRA) has been widely studied in recent decades because of its low noise...
One of the most promising solutions to overcome the capacity limit of current optical fiber links i...
We introduce a load aware machine learning method for prediction of Raman gain profiles. It enables ...
In this work, we will give an overview of some of the most recent and successful applications of mac...
A machine learning technique was recently proposed to optimize the gain of a multi-pump single-mode...