no code implementations • 4 Jul 2023 • Sasipim Srivallapanondh, Pedro J. Freire, Ashraful Alam, Nelson Costa, Bernhard Spinnler, Antonio Napoli, Egor Sedov, Sergei K. Turitsyn, Jaroslaw E. Prilepsky
For the first time, multi-task learning is proposed to improve the flexibility of NN-based equalizers in coherent systems.
no code implementations • 26 May 2023 • Mohannad Abu-romoh, Nelson Costa, Yves Jaouën, Antonio Napoli, João Pedro, Bernhard Spinnler, Mansoor Yousefi
In this paper, we investigate the use of the learned digital back-propagation (LDBP) for equalizing dual-polarization fiber-optic transmission in dispersion-managed (DM) links.
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.
no code implementations • 8 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.
no code implementations • 11 Oct 2022 • Mohannad Abu-romoh, Nelson Costa, Antonio Napoli, João Pedro, Yves Jaouën, Mansoor Yousefi
A convolutional neural network is proposed to mitigate fiber transmission effects, achieving a five-fold reduction in trainable parameters compared to alternative equalizers, and 3. 5 dB improvement in MSE compared to DBP with comparable complexity.
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.
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.
no code implementations • 23 May 2022 • Mohannad Abu-romoh, Nelson Costa, Antonio Napoli, Bernhard Spinnler, Yves Jaouën, Mansoor Yousefi
Digital back-propagation (DBP) and learned DBP (LDBP) are proposed for nonlinearity mitigation in WDM dual-polarization dispersion-managed systems.
no code implementations • 23 May 2022 • Jamal Darweesh, Nelson Costa, Antonio Napoli, Bernhard Spinnler, Yves Jaouen, Mansoor Yousefi, .
A neural network is quantized for the mitigation of nonlinear and components distortions in a 16-QAM 9x50km dual-polarization fiber transmission experiment.
no code implementations • 25 Feb 2022 • Pedro J. Freire, Bernhard Spinnler, Daniel Abode, Jaroslaw E. Prilepsky, Abdallah A. I. Ali, Nelson Costa, Wolfgang Schairer, Antonio Napoli, Andrew D. Ellis, Sergei K. Turitsyn
We introduce the domain adaptation and randomization approach for calibrating neural network-based equalizers for real transmissions, using synthetic data.
no code implementations • 30 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.
no code implementations • 17 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.
no code implementations • 24 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.
no code implementations • 11 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).
no code implementations • 15 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.