activity
20182020
collaborators

6 papers

eess.SP2020

Analysis of the Coherent Contributions to Nonlinear Interference Generation within Disaggregated Optical Line Systems

Elliot London, Emanuele Virgillito, Andrea D'Amico +2

Through a physical layer simulation study we highlight that the coherent accumulation of nonlinear interference becomes non-negligible for optical networks operating within high sy…

eess.SY2019

Quality of Transmission Estimation for Network Planning: how to handle single-channel nonlinear effects?

Andrea D'Amico, Elliot London, Emanuele Virgillito +2

We propose an analytical method to evaluate the equivalent SPM component of the NLI generated by each fiber span, to enabling a fully disaggregated evaluation of the GSNR degradati…

eess.SP2019

Observing the Effect of Polarization Mode Dispersion on Nonlinear Interference Generation in Wide-Band Optical Links

Dario Pilori, Mattia Cantono, Alessio Ferrari +2

With the extension of the spectral exploitation of optical fibers beyond the C-band, accurate modeling and simulation of nonlinear interference (NLI) generation is of the utmost pe…

eess.SP2019

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…

eess.SP2018

Observing the Effects of Legacy 10G on 100G Channels in Dispersion Managed Optical Systems

Emanuele Virgillito, Andrea Castoldi, Stefano Straullu +3

We show that 10G channels generate both amplitude and phase noise on 100G channels. Amplitude noise can be managed as the ASE and NLI noise, while the DSP robustness to the phase n…

physics.app-ph2018

Machine learning-based Raman amplifier design

D. Zibar, A. Ferrari, V. Curri +1

A multi-layer neural network is employed to learn the mapping between Raman gain profile and pump powers and wavelengths. The learned model predicts with high-accuracy, low-latency…