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Optimization of Raman amplifiers: a comparison between black-, grey- and white-box modeling
Designing and optimizing optical amplifiers to maximize system performance is becoming increasingly important as optical communication systems strive to increase throughput.
Experimental validation of machine-learning based spectral-spatial power evolution shaping using Raman amplifiers
In this case, the experimental results assert that for 2D profiles with the target flat gain levels, the DE obtains less than 1 dB maximum gain deviation, when the setup is not physically limited in the pump power values.
Flexible Raman Amplifier Optimization Based on Machine Learning-aided Physical Stimulated Raman Scattering Model
The forward propagation model is combined with an experimentally-trained ML model of a backward-pumping Raman amplifier to jointly optimize the frequency and power of the forward amplifier's pumps and the powers of the backward amplifier's pumps.
Experimental Validation of Spectral-Spatial Power Evolution Design Using Raman Amplifiers
We experimentally validate a machine learning-enabled Raman amplification framework, capable of jointly shaping the signal power evolution in two domains: frequency and fiber distance.
Inverse design of Raman amplifier in frequency and distance domain using Convolutional Neural Networks
We present a Convolutional Neural Network (CNN) architecture for inverse Raman amplifier design.
Simultaneous gain profile design and noise figure prediction for Raman amplifiers using machine learning
A machine learning framework predicting pump powers and noise figure profile for a target distributed Raman amplifier gain profile is experimentally demonstrated.
Applied Physics Optics
