150 citations · 864 across the 60 of their papers we have counts for
5 papers · 1 filter
Experimental demonstration of arbitrary Raman gain-profile designs using machine learning
Uiara C. de Moura, Francesco Da Ros, A. Margareth Rosa Brusin +2
A machine learning framework for Raman amplifier design is experimentally tested. Performance in terms of maximum error over the gain profile is investigated for various fiber type…
End-to-end Learning for GMI Optimized Geometric Constellation Shape
Rasmus T. Jones, Metodi P. Yankov, Darko Zibar
Autoencoder-based geometric shaping is proposed that includes optimizing bit mappings. Up to 0.2 bits/QAM symbol gain in GMI is achieved for a variety of data rates and in the pres…
An ultra-fast method for gain and noise prediction of Raman amplifiers
Ann Margareth Rosa Brusin, Vittorio Curri, Darko Zibar +1
A machine learning method for prediction of Raman gain and noise spectra is presented: it guarantees high-accuracy (RMSE < 0.4 dB) and low computational complexity making it suitab…
Optical Frequency Comb Noise Characterization Using Machine Learning
Giovanni Brajato, Lars Lundberg, Victor Torres-Company +1
A novel tool, based on Bayesian filtering framework and expectation maximization algorithm, is numerically and experimentally demonstrated for accurate frequency comb noise charact…
Dual-polarization NFDM transmission with continuous and discrete spectral modulation
F. Da Ros, S. Civelli, S. Gaiarin +4
Nonlinear distortion experienced by signals during their propagation through optical fibers strongly limits the throughput of optical communication systems. Recently, a strong rese…