Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Karanov, Boris"'
Autor:
Fiorio, Luan Vinícius, Karanov, Boris, Defraene, Bruno, David, Johan, van Houtum, Wim, Widdershoven, Frans, Aarts, Ronald M.
We propose and analyze the use of an explicit time-context window for neural network-based spectral masking speech enhancement to leverage signal context dependencies between neighboring frames. In particular, we concentrate on soft masking and loss
Externí odkaz:
http://arxiv.org/abs/2408.15582
Recently, new types of interference in electric vehicles (EVs), such as converters switching and/or battery chargers, have been found to degrade the performance of wireless digital transmission systems. Measurements show that such an interference is
Externí odkaz:
http://arxiv.org/abs/2405.10828
We developed machine learning approaches for data-driven trellis-based soft symbol detection in coded transmission over intersymbol interference (ISI) channels in presence of bursty impulsive noise (IN), for example encountered in wireless digital br
Externí odkaz:
http://arxiv.org/abs/2405.10814
Recently, a data-driven Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm tailored to channels with intersymbol interference has been introduced. This so-called BCJRNet algorithm utilizes neural networks to calculate channel likelihoods. BCJRNet has demonstr
Externí odkaz:
http://arxiv.org/abs/2401.12645
Autor:
Oliari, Vinícius, Karanov, Boris, Goossens, Sebastiaan, Liga, Gabriele, Vassilieva, Olga, Kim, Inwoong, Palacharla, Paparao, Okonkwo, Chigo, Alvarado, Alex
In this paper we carry out a joint optimization of probabilistic (PS) and geometric shaping (GS) for four-dimensional (4D) modulation formats in long-haul coherent wavelength division multiplexed (WDM) optical fiber communications using an auto-encod
Externí odkaz:
http://arxiv.org/abs/2112.10471
Autor:
Karanov, Boris, Chagnon, Mathieu, Aref, Vahid, Ferreira, Filipe, Lavery, Domanic, Bayvel, Polina, Schmalen, Laurent
We investigate methods for experimental performance enhancement of auto-encoders based on a recurrent neural network (RNN) for communication over dispersive nonlinear channels. In particular, our focus is on the recently proposed sliding window bidir
Externí odkaz:
http://arxiv.org/abs/2005.08790
Autor:
Karanov, Boris, Chagnon, Mathieu, Aref, Vahid, Lavery, Domanic, Bayvel, Polina, Schmalen, Laurent
We investigate end-to-end optimized optical transmission systems based on feedforward or bidirectional recurrent neural networks (BRNN) and deep learning. In particular, we report the first experimental demonstration of a BRNN auto-encoder, highlight
Externí odkaz:
http://arxiv.org/abs/2005.08785
Autor:
Sillekens, Eric, Yi, Wenting, Semrau, Daniel, Ottino, Alessandro, Karanov, Boris, Zhou, Sujie, Law, Kevin, Chen, Jack, Lavery, Domanic, Galdino, Lidia, Bayvel, Polina, Killey, Robert I.
We present the first experimental demonstration of learned time-domain digital back-propagation (DBP), in 64-GBd dual-polarization 64-QAM signal transmission over 1014 km. Performance gains were comparable to those obtained with conventional, higher
Externí odkaz:
http://arxiv.org/abs/1912.12197
Autor:
Karanov, Boris, Chagnon, Mathieu, Aref, Vahid, Lavery, Domaniç, Bayvel, Polina, Schmalen, Laurent
We perform an experimental end-to-end transceiver optimization via deep learning using a generative adversarial network to approximate the test-bed channel. Previously, optimization was only possible through a prior assumption of an explicit simplifi
Externí odkaz:
http://arxiv.org/abs/1912.05146
Autor:
Karanov, Boris, Liga, Gabriele, Aref, Vahid, Lavery, Domaniç, Bayvel, Polina, Schmalen, Laurent
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. We consider an autoencoder based on the recently proposed s
Externí odkaz:
http://arxiv.org/abs/1910.01028