Zobrazeno 1 - 10
of 188
pro vyhledávání: '"Carena, Andrea"'
Designing and optimizing optical amplifiers to maximize system performance is becoming increasingly important as optical communication systems strive to increase throughput. Offline optimization of optical amplifiers relies on models ranging from whi
Externí odkaz:
http://arxiv.org/abs/2310.05954
An unprecedented comparison of closed-form incoherent GN (InGN) models is presented with heterogeneous spans and partially loaded links in elastic optical networks. Results reveal that with accumulated dispersion correction and modulation format term
Externí odkaz:
http://arxiv.org/abs/2210.09401
We experimentally validate a real-time machine learning framework, capable of controlling the pump power values of Raman amplifiers to shape the signal power evolution in two-dimensions (2D): frequency and fiber distance. In our setup, power values o
Externí odkaz:
http://arxiv.org/abs/2209.13401
Autor:
Yankov, Metodi Plamenov, Da Ros, Francesco, de Moura, Uiara Celine, Carena, Andrea, Zibar, Darko
The problem of Raman amplifier optimization is studied. A differentiable interpolation function is obtained for the Raman gain coefficient using machine learning (ML), which allows for the gradient descent optimization of forward-propagating Raman pu
Externí odkaz:
http://arxiv.org/abs/2206.07650
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. The proposed experiment addresses the amplification in the whole
Externí odkaz:
http://arxiv.org/abs/2206.07658
Publikováno v:
In Optical Fiber Technology January 2025 89
We present a machine learning (ML) framework for designing desired signal power profiles over the spectral and spatial domains in the fiber span. The proposed framework adjusts the Raman pump power values to obtain the desired two-dimensional (2D) pr
Externí odkaz:
http://arxiv.org/abs/2112.12637
We present a Convolutional Neural Network (CNN) architecture for inverse Raman amplifier design. This model aims at finding the pump powers and wavelengths required for a target signal power evolution, both in distance along the fiber and in frequenc
Externí odkaz:
http://arxiv.org/abs/2103.03837
Autor:
de Moura, Uiara Celine, Brusin, Ann Margareth Rosa, Carena, Andrea, Zibar, Darko, Da Ros, Francesco
Publikováno v:
Opt. Lett. 46, 1157-1160 (2021)
A machine learning framework predicting pump powers and noise figure profile for a target distributed Raman amplifier gain profile is experimentally demonstrated. We employ a single-layer neural network to learn the mapping from the gain profiles to
Externí odkaz:
http://arxiv.org/abs/2012.06050
Autor:
de Moura, Uiara Celine, Da Ros, Francesco, Brusin, Ann Margareth Rosa, Carena, Andrea, Zibar, Darko
Publikováno v:
Journal of Lightwave Technology, vol. 39, no. 4, pp. 1162-1170, 15 Feb.15, 2021
Optical communication systems are always evolving to support the need for ever-increasing transmission rates. This demand is supported by the growth in complexity of communication systems which are moving towards ultra-wideband transmission and space
Externí odkaz:
http://arxiv.org/abs/2010.12458