no code implementations • 30 Jan 2024 • Smaranika Swain, Christian Koefoed Schou, Metodi Plamenov Yankov, Michael Galili, Leif Katsuo Oxenløwe
To test the principle of the proposed method, we experimentally and numerically investigate a 6900-km long link with amplifier spacing of 50 and 150 km using a recirculating fiber transmission loop, and find that the result supports the analytical model and thus the proposed method.
no code implementations • 10 Nov 2023 • Metodi Plamenov Yankov, Smaranika Swain, Ognjen Jovanovic, Darko Zibar, Francesco Da Ros
The GCS scheme is experimentally demonstrated in a multi-span recirculating loop coherent optical fiber transmission system with a total distance of up to 3000 km.
no code implementations • 24 Feb 2023 • Edson Porto da Silva, Metodi Plamenov Yankov
Fiber nonlinearity compensation of probabilistically shaped constellations with adaptive turbo equalization is investigated for the first time.
no code implementations • 13 Jun 2022 • Metodi Plamenov Yankov, Francesco Da Ros, Uiara Celine de Moura, Andrea Carena, Darko Zibar
The forward propagation model is combined with an experimentally-trained ML model of a backward-pumping Raman amplifier to jointly optimize the frequency and power of the forward amplifier's pumps and the powers of the backward amplifier's pumps.
no code implementations • 5 Sep 2021 • Edson Porto da Silva, Metodi Plamenov Yankov
The estimated channel coefficients are used by a MIMO 2x2 soft-input soft-output (SISO) linear minimum mean square error (LMMSE) equalizer to compensate for the time-varying ISI.
no code implementations • 7 Jun 2021 • Metodi Plamenov Yankov, Pawel Marcin Kaminski, Henrik Enggaard Hansen, Francesco Da Ros
When the input power profile is optimized for flat and maximized received SNR per channel, the minimum performance in an arbitrary 3-span experimental system is improved by up to 8 dB w. r. t.
no code implementations • 21 Dec 2020 • Ognjen Jovanovic, Metodi Plamenov Yankov, Francesco Da Ros, Darko Zibar
Our results indicate that the autoencoder can be successfully optimized using the proposed training method to achieve better robustness to residual phase noise with respect to standard constellation schemes such as Quadrature Amplitude Modulation and Iterative Polar Modulation for the considered conditions.