activity
20152020
most citedSimultaneous gain profile design and noise figure prediction for Raman amplifiers using machine learning

18 citations · 31 across the 4 of their papers we have counts for

collaborators

10 papers

physics.app-ph202018 cited

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…

physics.app-ph2020

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…

physics.app-ph2020

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…

eess.SP20192 cited

A GN/EGN-Model Real-Time Closed-Form Formula Tested over 7,000 Virtual Links

Mahdi Ranjbar Zefreh, Andrea Carena, Fabrizio Forghieri +2

We derived a fully-closed-form GN-model formula and tested its accuracy of over 7,000 highly randomized Cband system scenarios. By further applying a correction that leverages the…

physics.app-ph2019

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…

eess.SP2019

High-Capacity and Rain-Resilient Free-Space Optics Link Enabled by Time-Adaptive Probabilistic Shaping

F. P. Guiomar, A. Lorences-Riesgo, D. Ranzal +6

Using time-adaptive probabilistic shaped 64QAM driven by a simple SNR prediction algorithm, we demonstrate 450 Gbps transmission over a 55-m free-space optics link with enhanced…