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20042026
most citedNonlinear inverse synthesis for high spectral efficiency transmission in optical fibers

142 citations · 515 across the 42 of their papers we have counts for

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Showing 2021Show all

9 papers · 1 filter

eess.SP2021

Experimental Evaluation of Computational Complexity for Different Neural Network Equalizers in Optical Communications

Pedro J. Freire, Yevhenii Osadchuk, Antonio Napoli +5

Addressing the neural network-based optical channel equalizers, we quantify the trade-off between their performance and complexity by carrying out the comparative analysis of sever…

eess.SP2021

Neural Networks-based Equalizers for Coherent Optical Transmission: Caveats and Pitfalls

Pedro J. Freire, Antonio Napoli, Bernhard Spinnler +3

This paper performs a detailed, multi-faceted analysis of key challenges and common design caveats related to the development of efficient neural networks (NN) nonlinear channel eq…

eess.SP2021★ 1 cited

Deep Neural Network-aided Soft-Demapping in Optical Coherent Systems: Regression versus Classification

Pedro J. Freire, Jaroslaw E. Prilepsky, Yevhenii Osadchuk +2

We examine here what type of predictive modelling, classification, or regression, using neural networks (NN), fits better the task of soft-demapping based post-processing in cohere…

eess.SY2021

Experimental implementation of a neural network optical channel equalizer in restricted hardware using pruning and quantization

Diego R. Arguello, Pedro J. Freire, Jaroslaw E. Prilepsky +3

The deployment of artificial neural networks-based optical channel equalizers on edge-computing devices is critically important for the next generation of optical communication sys…

eess.SP2021

End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Model

Vladislav Neskorniuk, Andrea Carnio, Vinod Bajaj +4

We present a novel end-to-end autoencoder-based learning for coherent optical communications using a "parallelizable" perturbative channel model. We jointly optimized constellation…

eess.SP2021

Power and Modulation Format Transfer Learning for Neural Network Equalizers in Coherent Optical Transmission Systems

Pedro J. Freire, Daniel Abode, Jaroslaw E. Prilepsky +1

Transfer learning is proposed to adapt an NN-based nonlinear equalizer across different launch powers and modulation formats using a 450km TWC-fiber transmission. The result shows…