38 citations · 38 across the 6 of their papers we have counts for
15 papers
Capacity and Achievable Rates of Fading Few-mode MIMO IM/DD Optical Fiber Channels
Metodi P. Yankov, Francesco Da Ros, Søren Forchhammer +1
The optical fiber multiple-input multiple-output (MIMO) channel with intensity modulation and direct detection (IM/DD) per spatial path is treated. The spatial dimensions represent…
Adaptive Turbo Equalization for Nonlinearity Compensation in WDM Systems
Edson Porto da Silva, Metodi Plamenov Yankov
In this paper, the performance of adaptive turbo equalization for nonlinearity compensation (NLC) is investigated. A turbo equalization scheme is proposed where a recursive least-s…
SNR optimization of multi-span fiber optic communication systems employing EDFAs with non-flat gain and noise figure
Metodi Plamenov Yankov, Pawel Marcin Kaminski, Henrik Enggaard Hansen +1
Throughput optimization of optical communication systems is a key challenge for current optical networks. The use of gain-flattening filters (GFFs) simplifies the problem at the co…
All-Optical Nonlinear Pre-Compensation of Long-Reach Unrepeatered Systems
Pawel M. Kaminski, Tiago Sutili, José Hélio da Cruz Júnior +9
We numerically demonstrate an all-optical nonlinearity pre-compensation module for state-of-the-art long-reach Raman-amplified unrepeatered links. The compensator design is optimiz…
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…
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…