82 citations · 239 across the 26 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…
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…
Experimental Study of Deep Neural Network Equalizers Performance in Optical Links
Pedro J. Freire, Yevhenii Osadchuk, Bernhard Spinnler +5
We propose a convolutional-recurrent channel equalizer and experimentally demonstrate 1dB Q-factor improvement both in single-channel and 96 x WDM, DP-16QAM transmission over 450km…
Transfer Learning for Neural Networks-based Equalizers in Coherent Optical Systems
Pedro J. Freire, Daniel Abode, Jaroslaw E. Prilepsky +4
In this work, we address the question of the adaptability of artificial neural networks (NNs) used for impairments mitigation in optical transmission systems. We demonstrate that b…
Performance versus Complexity Study of Neural Network Equalizers in Coherent Optical Systems
Pedro J. Freire, Yevhenii Osadchuk, Bernhard Spinnler +5
We present the results of the comparative analysis of the performance versus complexity for several types of artificial neural networks (NNs) used for nonlinear channel equalizatio…