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 • 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 • 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 • 11 Apr 2018 • Boris Karanov, Mathieu Chagnon, Félix Thouin, Tobias A. Eriksson, Henning Bülow, Domaniç Lavery, Polina Bayvel, Laurent Schmalen
In this paper, we implement an optical fiber communication system as an end-to-end deep neural network, including the complete chain of transmitter, channel model, and receiver.