Implementing Neural Network-Based Equalizers in a Coherent Optical Transmission System Using Field-Programmable Gate Arrays

no code implementations • 9 Dec 2022 • Pedro J. Freire, Sasipim Srivallapanondh, Michael Anderson, Bernhard Spinnler, Thomas Bex, Tobias A. Eriksson, Antonio Napoli, Wolfgang Schairer, Nelson Costa, Michaela Blott, Sergei K. Turitsyn, Jaroslaw E. Prilepsky

The main results are divided into three parts: a performance comparison, an analysis of how activation functions are implemented, and a report on the complexity of the hardware.

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

no code implementations • 26 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

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

no code implementations • 24 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.