Search Results for author: Jaroslaw E. Prilepsky

Found 18 papers, 0 papers with code

Hardware Realization of Nonlinear Activation Functions for NN-based Optical Equalizers

no code implementations16 May 2023 Sasipim Srivallapanondh, Pedro J. Freire, Antonio Napoli, Sergei K. Turitsyn, Jaroslaw E. Prilepsky

To reduce the complexity of the hardware implementation of neural network-based optical channel equalizers, we demonstrate that the performance of the biLSTM equalizer with approximated activation functions is close to that of the original model.

Knowledge Distillation Applied to Optical Channel Equalization: Solving the Parallelization Problem of Recurrent Connection

no code implementations8 Dec 2022 Sasipim Srivallapanondh, Pedro J. Freire, Bernhard Spinnler, Nelson Costa, Antonio Napoli, Sergei K. Turitsyn, Jaroslaw E. Prilepsky

To circumvent the non-parallelizability of recurrent neural network-based equalizers, we propose knowledge distillation to recast the RNN into a parallelizable feedforward structure.

Knowledge Distillation

Reducing Computational Complexity of Neural Networks in Optical Channel Equalization: From Concepts to Implementation

no code implementations26 Aug 2022 Pedro J. Freire, Antonio Napoli, Diego Arguello Ron, Bernhard Spinnler, Michael Anderson, Wolfgang Schairer, Thomas Bex, Nelson Costa, Sergei K. Turitsyn, Jaroslaw E. Prilepsky

In this work, we propose and evaluate a Bayesian optimization-assisted compression, in which the hyperparameters of the compression are chosen to simultaneously reduce complexity and improve performance.

Bayesian Optimization Clustering +2

Computational Complexity Evaluation of Neural Network Applications in Signal Processing

no code implementations24 Jun 2022 Pedro Freire, Sasipim Srivallapanondh, Antonio Napoli, Jaroslaw E. Prilepsky, Sergei K. Turitsyn

We provide and link four software-to-hardware complexity measures, defining how the different complexity metrics relate to the layers' hyper-parameters.

Quantization

Towards FPGA Implementation of Neural Network-Based Nonlinearity Mitigation Equalizers in Coherent Optical Transmission Systems

no code implementations24 Jun 2022 Pedro J. Freire, Michael Anderson, Bernhard Spinnler, Thomas Bex, Jaroslaw E. Prilepsky, Tobias A. Eriksson, Nelson Costa, Wolfgang Schairer, Michaela Blott, Antonio Napoli, Sergei K. Turitsyn

For the first time, recurrent and feedforward neural network-based equalizers for nonlinearity compensation are implemented in an FPGA, with a level of complexity comparable to that of a dispersion equalizer.

Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication

no code implementations5 Apr 2022 Vladislav Neskorniuk, Andrea Carnio, Domenico Marsella, Sergei K. Turitsyn, Jaroslaw E. Prilepsky, Vahid Aref

Autoencoder-based deep learning is applied to jointly optimize geometric and probabilistic constellation shaping for optical coherent communication.

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

no code implementations30 Sep 2021 Pedro J. Freire, Antonio Napoli, Bernhard Spinnler, Nelson Costa, Sergei K. Turitsyn, Jaroslaw E. Prilepsky

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 equalizers in coherent optical communication systems.

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

no code implementations28 Sep 2021 Pedro J. Freire, Jaroslaw E. Prilepsky, Yevhenii Osadchuk, Sergei K. Turitsyn, Vahid Aref

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 coherent optical communications, where the transmission channel is nonlinear and dispersive.

regression

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

no code implementations17 Sep 2021 Pedro J. Freire, Yevhenii Osadchuk, Antonio Napoli, Bernhard Spinnler, Wolfgang Schairer, Nelson Costa, Jaroslaw E. Prilepsky, Sergei K. Turitsyn

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 several neural network architectures, presenting the results for TWC and SSMF set-ups.

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

no code implementations15 Sep 2021 Diego R. Arguello, Pedro J. Freire, Jaroslaw E. Prilepsky, Antonio Napoli, Morteza Kamalian-Kopae, Sergei K. Turitsyn

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

Edge-computing Model Compression +1

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

no code implementations26 Jul 2021 Vladislav Neskorniuk, Andrea Carnio, Vinod Bajaj, Domenico Marsella, Sergei K. Turitsyn, Jaroslaw E. Prilepsky, Vahid Aref

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

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

no code implementations24 Jun 2021 Pedro J. Freire, Daniel Abode, Jaroslaw E. Prilepsky, Sergei K. Turitsyn

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

Transfer Learning

Experimental Study of Deep Neural Network Equalizers Performance in Optical Links

no code implementations24 Jun 2021 Pedro J. Freire, Yevhenii Osadchuk, Bernhard Spinnler, Wolfgang Schairer, Antonio Napoli, Nelson Costa, Jaroslaw E. Prilepsky, Sergei K. Turitsyn

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 of TWC fiber.

Transfer Learning for Neural Networks-based Equalizers in Coherent Optical Systems

no code implementations11 Apr 2021 Pedro J. Freire, Daniel Abode, Jaroslaw E. Prilepsky, Nelson Costa, Bernhard Spinnler, Antonio Napoli, Sergei K. Turitsyn

We evaluate the capability of transfer learning to adapt the NN to changes in the launch power, modulation format, symbol rate, or even fiber plants (different fiber types and lengths).

Transfer Learning

Performance versus Complexity Study of Neural Network Equalizers in Coherent Optical Systems

no code implementations15 Mar 2021 Pedro J. Freire, Yevhenii Osadchuk, Bernhard Spinnler, Antonio Napoli, Wolfgang Schairer, Nelson Costa, Jaroslaw E. Prilepsky, Sergei K. Turitsyn

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 equalization in coherent optical communication systems.

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