150 citations · 875 across the 67 of their papers we have counts for
25 papers · 1 filter
Experimental End-to-End Optimization of Directly Modulated Laser-based IM/DD Transmission
Sergio Hernandez, Christophe Peucheret, Francesco Da Ros +1
Directly modulated lasers (DMLs) are an attractive technology for short-reach intensity modulation and direct detection communication systems. However, their complex nonlinear dyna…
Quantum Noise Limited Temperature-Change Estimation for Phase-OTDR Employing Coherent Detection
Huwei Wang, Roman Ermakov, Francesco Da Ros +1
The quantum limit is a fundamental lower bound on the uncertainty when estimating a parameter in a system dominated by the minimum amount of noise (quantum noise). For the first ti…
Experimental Demonstration of End-to-End Optimization for Directly Modulated Laser-based IM/DD Systems
Sergio Hernandez, Christophe Peucheret, Francesco Da Ros +1
We experimentally demonstrate the joint optimization of transmitter and receiver parameters in directly modulated laser systems, showing superior performance compared to nonlinear…
End-to-End Learning of Transmitter and Receiver Filters in Bandwidth Limited Fiber Optic Communication Systems
Søren Føns Nielsen, Francesco Da Ros, Mikkel N. Schmidt +1
This paper investigates the application of end-to-end (E2E) learning for joint optimization of pulse-shaper and receiver filter to reduce intersymbol interference (ISI) in bandwidt…
End-to-End Learning of Pulse-Shaper and Receiver Filter in the Presence of Strong Intersymbol Interference
Søren Føns Nielsen, Francesco Da Ros, Mikkel N. Schmidt +1
We numerically demonstrate that joint optimization of FIR based pulse-shaper and receiver filter results in an improved system performance, and shorter filter lengths (lower comple…
End-to-End Optimization of Directly Modulated Laser Links using Chirp-Aware Modeling
Sergio Hernandez F., Christophe Peucheret, Francesco Da Ros +1
The rate and reach of directly-modulated laser links is often limited by the interplay between chirp and fiber chromatic dispersion. We address this by optimizing the transmitter,…
End-to-end Optimization of Optical Communication Systems based on Directly Modulated Lasers
Sergio Hernandez F., Christophe Peucheret, Francesco Da Ros +1
The use of directly modulated lasers (DMLs) is attractive in low-power, cost-constrained short-reach optical links. However, their limited modulation bandwidth can induce waveform…
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…
Differentiable Machine Learning-Based Modeling for Directly-Modulated Lasers
Sergio Hernandez, Ognjen Jovanovic, Christophe Peucheret +2
End-to-end learning has become a popular method for joint transmitter and receiver optimization in optical communication systems. Such approach may require a differentiable channel…
Low-complexity Samples versus Symbols-based Neural Network Receiver for Channel Equalization
Yevhenii Osadchuk, Ognjen Jovanovic, Stenio M. Ranzini +4
Low-complexity neural networks (NNs) have successfully been applied for digital signal processing (DSP) in short-reach intensity-modulated directly detected optical links, where ch…
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…
Data-Driven Modeling of Directly-Modulated Lasers
Sergio Hernandez Fernandez, Christophe Peucheret, Ognjen Jovanovic +2
The end-to-end optimization of links based on directly-modulated lasers may require an analytically differentiable channel. We overcome this problem by developing and comparing dif…
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…
Reservoir Computing-based Multi-Symbol Equalization for PAM 4 Short-reach Transmission
Yevhenii Osadchuk, Ognjen Jovanovic, Darko Zibar +1
We propose spectrum-sliced reservoir computer-based (RC) multi-symbol equalization for 32-GBd PAM4 transmission. RC with 17 symbols at the output achieves an order of magnitude red…
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…
Spectral and spatial power evolution design with machine learning-enabled Raman amplification
Mehran Soltani, Francesco Da Ros, Andrea Carena +1
We present a machine learning (ML) framework for designing desired signal power profiles over the spectral and spatial domains in the fiber span. The proposed framework adjusts the…
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…
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…
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…
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…
Dual-polarization NFDM transmission with continuous and discrete spectral modulation
F. Da Ros, S. Civelli, S. Gaiarin +4
Nonlinear distortion experienced by signals during their propagation through optical fibers strongly limits the throughput of optical communication systems. Recently, a strong rese…