no code implementations • 28 Aug 2023 • Yevhenii Osadchuk, Ognjen Jovanovic, Stenio M. Ranzini, Roman Dischler, Vahid Aref, Darko Zibar, Francesco Da Ros
In this work, we propose a low-complexity NN that performs samples-to-symbol equalization, meaning that the NN-based equalizer includes match filtering and downsampling.
no code implementations • 5 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.
no code implementations • 13 Dec 2021 • Vinod Bajaj, Mathieu Chagnon, Sander Wahls, Vahid Aref
We present a simple, efficient "direct learning" approach to train Volterra series-based digital pre-distortion filters using neural networks.
no code implementations • 9 Dec 2021 • Vahid Aref, Mathieu Chagnon
We present a novel autoencoder-based learning of joint geometric and probabilistic constellation shaping for coded-modulation systems.
no code implementations • 28 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.
no code implementations • 22 Sep 2021 • Kaoutar Benyahya, Amirhossein Ghazisaeidi, Vahid Aref, Mathieu Chagnon, Aymeric Arnould, Stenio Ranzini, Haik Mardoyan, Fred Buchali, Jeremie Renaudier
We report on theoretical and experimental investigations of the nonlinear tolerance of single carrier and digital multicarrier approaches with probabilistically shaped constellations.
no code implementations • 12 Sep 2021 • Son Thai Le, Vahid Aref, Junho Cho
One potential approach for solving this problem is to leverage the concept of single-ended coherent receiver (SER) where single-ended PDs are used instead of the balanced PDs.
no code implementations • 26 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.
no code implementations • 18 May 2020 • Boris Karanov, Mathieu Chagnon, Vahid Aref, Filipe Ferreira, Domanic Lavery, Polina Bayvel, Laurent Schmalen
The investigation of digital signal processing (DSP) optimized on experimental data is extended to pulse amplitude modulation with receivers performing sliding window sequence estimation using a feed-forward or a recurrent neural network as well as classical nonlinear Volterra equalization.
no code implementations • 18 May 2020 • Boris Karanov, Mathieu Chagnon, Vahid Aref, Domanic Lavery, Polina Bayvel, Laurent Schmalen
We investigate end-to-end optimized optical transmission systems based on feedforward or bidirectional recurrent neural networks (BRNN) and deep learning.
no code implementations • 11 Dec 2019 • Boris Karanov, Mathieu Chagnon, Vahid Aref, Domaniç Lavery, Polina Bayvel, Laurent Schmalen
We perform an experimental end-to-end transceiver optimization via deep learning using a generative adversarial network to approximate the test-bed channel.
no code implementations • 2 Oct 2019 • Boris Karanov, Gabriele Liga, Vahid Aref, Domaniç Lavery, Polina Bayvel, Laurent Schmalen
In this paper, we apply deep learning for communication over dispersive channels with power detection, as encountered in low-cost optical intensity modulation/direct detection (IM/DD) links.
Information Theory Signal Processing Information Theory
no code implementations • 2 Apr 2018 • Mohamad Dia, Vahid Aref, Laurent Schmalen
In this work, we formulate the fixed-length distribution matching as a Bayesian inference problem.
1 code implementation • 18 May 2016 • Vahid Aref
To generate an $N$-solitary waveform from desired discrete spectrum (eigenvalue and discrete spectral amplitudes), we use the Darboux Transform.
Numerical Analysis Optics