Search Results for author:
Found 6 papers, 2 papers with code
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.
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.
Deep Feature Selection using a Teacher-Student Network
In this paper, we present a novel teacher-student feature selection (TSFS) method in which a 'teacher' (a deep neural network or a complicated dimension reduction method) is first employed to learn the best representation of data in low dimension.
Deep Learning-Based Channel Estimation
This scheme considers the pilot values, altogether, as a low-resolution image and uses an SR network cascaded with a denoising IR network to estimate the channel.
