70 citations · 305 across the 27 of their papers we have counts for
29 papers
Beyond white- and black-box modeling tools in optical communications and optical computing: physics-informed data-driven modeling
Isidora Teofilovic, Sergio Hernandez Fernandez, Metodi P. Yankov +3
Efficient optimization and control of photonic computing and communication systems increasingly rely on accurate surrogate models/digital twins. While data-driven models may achiev…
Experimental Analysis of a Self-Coherent M-QAM Receiver by Means of Recurrent Optical Spectrum Slicing and Direct Detection
Kostas Sozos, Francesco Da Ros, Senior Member Optica +6
High order modulation formats constitute the most prominent way for increasing spectral efficiency in transmission systems. Coherent transceivers that support such higher order for…
Experimental Investigation of a Recurrent Optical Spectrum Slicing Receiver for Intensity Modulation/Direct Detection systems using Programmable Photonics
Kostas Sozos, Francesco Da Ros, Metodi P. Yankov +4
In this paper, we experimentally validate our previous numerical works in recurrent optical spectrum slicing (ROSS) accelerators for dispersion compensation in high-speed IM/DD lin…
Spatial Distribution of Data Capacity for the Reduction of Number of Repeaters in Ultra Long-Haul Links
Smaranika Swain, Christian Koefoed Schou, Metodi Plamenov Yankov +2
We present a novel method to reduce the number of repeaters and amplifiers in trans-oceanic links by distributing a given data capacity in spatial channels. We analytically, numeri…
BICM-compatible Rate Adaptive Geometric Constellation Shaping Using Optimized Many-to-one Labeling
Metodi Plamenov Yankov, Smaranika Swain, Ognjen Jovanovic +2
In this paper, a rate adaptive geometric constellation shaping (GCS) scheme which is fully backward-compatible with existing state of the art bit-interleaved coded modulation (BICM…
A comparison between black-, grey- and white-box modeling for the bidirectional Raman amplifier optimization
Metodi P. Yankov, Mehran Soltani, Andrea Carena +2
Designing and optimizing optical amplifiers to maximize system performance is becoming increasingly important as optical communication systems strive to increase throughput. Offlin…
Rate Adaptive Geometric Constellation Shaping Using Autoencoders and Many-To-One Mapping
Metodi P. Yankov, Ognjen Jovanovic, Darko Zibar +1
A many-to-one mapping geometric constellation shaping scheme is proposed with a fixed modulation format, fixed FEC engine and rate adaptation with an arbitrarily small step. An aut…
Adaptive Turbo Equalization of Probabilistically Shaped Constellations
Edson Porto da Silva, Metodi Plamenov Yankov
Fiber nonlinearity compensation of probabilistically shaped constellations with adaptive turbo equalization is investigated for the first time. Potential for more than 100% transmi…
Rate Adaptive Autoencoder-based Geometric Constellation Shaping
Ognjen Jovanovic, Metodi P. Yankov, Francesco Da Ros +1
An autoencoder is used to optimize bit-to-symbol mappings for geometric constellation shaping. The mappings allow for net rate adaptivity without additional hardware complexity, wh…
Geometric Constellation Shaping for Fiber-Optic Channels via End-to-End Learning
Ognjen Jovanovic, Francesco Da Ros, Darko Zibar +1
End-to-end learning has become a popular method to optimize a constellation shape of a communication system. When the channel model is differentiable, end-to-end learning can be ap…
Spectral Power Profile Optimization of Field-Deployed WDM Network by Remote Link Modeling
Rasmus T. Jones, Kyle R. H. Bottrill, Natsupa Taengnoi +2
A digital twin model of a multi-node WDM network is obtained from a single access point. The model is used to predict and optimize the transmit power profile for each link in the n…
Flexible Raman Amplifier Optimization Based on Machine Learning-aided Physical Stimulated Raman Scattering Model
Metodi Plamenov Yankov, Francesco Da Ros, Uiara Celine de Moura +2
The problem of Raman amplifier optimization is studied. A differentiable interpolation function is obtained for the Raman gain coefficient using machine learning (ML), which allows…
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…
End-to-end Learning of a Constellation Shape Robust to Channel Condition Uncertainties
Ognjen Jovanovic, Metodi P. Yankov, Francesco Da Ros +1
Vendor interoperability is one of the desired future characteristics of optical networks. This means that the transmission system needs to support a variety of hardware with differ…
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…
End-to-end Learning of a Constellation Shape Robust to Variations in SNR and Laser Linewidth
Ognjen Jovanovic, Metodi P. Yankov, Francesco Da Ros +1
We propose an autoencoder-based geometric shaping that learns a constellation robust to SNR and laser linewidth estimation errors. This constellation maintains shaping gain in mutu…
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…
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…
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…
Multi-dimensional Joint Probabilistic and Geometric Shaping Strategies for Nonlinear Fiber Communications
Metodi P. Yankov
Constellation shaping is reviewed and revised for a WDM unrepeated system with high spectral efficiency. It is shown that for a constellation size-constrained system, previous opti…
End-to-end Learning for GMI Optimized Geometric Constellation Shape
Rasmus T. Jones, Metodi P. Yankov, Darko Zibar
Autoencoder-based geometric shaping is proposed that includes optimizing bit mappings. Up to 0.2 bits/QAM symbol gain in GMI is achieved for a variety of data rates and in the pres…