150 citations · 875 across the 67 of their papers we have counts for
9 papers · 1 filter
Gradient-free training of autoencoders for non-differentiable communication channels
Ognjen Jovanovic, Metodi Plamenov Yankov, Francesco Da Ros +1
Training of autoencoders using the back-propagation algorithm is challenging for non-differential channel models or in an experimental environment where gradients cannot be compute…
Simultaneous gain profile design and noise figure prediction for Raman amplifiers using machine learning
Uiara Celine de Moura, Ann Margareth Rosa Brusin, Andrea Carena +2
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 singl…
Experimental characterization of Raman amplifier optimization through inverse system design
Uiara Celine de Moura, Francesco Da Ros, Ann Margareth Rosa Brusin +2
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…
Experimental Demonstration of Optoelectronic Equalization for Short-reach Transmission with Reservoir Computing
Stenio M. Ranzini, Roman Dischler, Francesco da Ros +2
A receiver with shared complexity between optical and digital domains is experimentally demonstrated. Reservoir computing is used to equalize up to 4 directly-detected optically fi…
End-to-end optimization of coherent optical communications over the split-step Fourier method guided by the nonlinear Fourier transform theory
Simone Gaiarin, Francesco Da Ros, Rasmus T. Jones +1
Optimizing modulation and detection strategies for a given channel is critical to maximize the throughput of a communication system. Such an optimization can be easily carried out…
Power Evolution Prediction and Optimization in a Multi-span System Based on Component-wise System Modeling
Metodi P. Yankov, Uiara Celine de Moura, Francesco Da Ros
Cascades of a machine learning-based EDFA gain model trained on a single physical device and a fully differentiable stimulated Raman scattering fiber model are used to predict and…
Machine learning-based EDFA Gain Model Generalizable to Multiple Physical Devices
Francesco Da Ros, Uiara Celine de Moura, Metodi P. Yankov
We report a neural-network based erbium-doped fiber amplifier (EDFA) gain model built from experimental measurements. The model shows low gain-prediction error for both the same de…
Multi-band programmable gain Raman amplifier
Uiara Celine de Moura, Md Asif Iqbal, Morteza Kamalian +7
Optical communication systems, operating in C-band, are reaching their theoretically achievable capacity limits. An attractive and economically viable solution to satisfy the futur…
Probabilistically Shaped 4-PAM for Short-Reach IM/DD Links with a Peak Power Constraint
Thomas Wiegart, Francesco Da Ros, Metodi Plamenov Yankov +3
Probabilistic shaping for intensity modulation and direct detection (IM/DD) links is discussed and a peak power constraint determined by the limited modulation extinction ratio (ER…