no code implementations • 31 Jan 2024 • Mindaugas Jarmolovičius, Daniel Semrau, Henrique Buglia, Mykyta Shevchenko, Filipe M. Ferreira, Eric Sillekens, Polina Bayvel, Robert I. Killey
We model the transmission of ultrawideband (UWB) signals, including wavelength-dependent fibre parameters: dispersion, nonlinear coefficient and effective fibre core area.
no code implementations • 27 Oct 2023 • Zichuan Zhou, Amany Kassem, James Seddon, Eric Sillekens, Izzat Darwazeh, Polina Bayvel, Zhixin Liu
We generate and transmit 75-GHz-bandwidth OFDM signals over the air using three mutually frequency-locked lasers, achieving minimal frequency gap between the wireless W and D bands using optical-assisted approaches, resulting in 173. 5 Gb/s detected capacity.
no code implementations • 7 Aug 2020 • Hui Yuan, Alessandro Ottino, Yunnuo Xu, Arsalan Saljoghei, Tetsuya Hayashi, Tetsuya Nakanishi, Eric Sillekens, Lidia Galdino, Polina Bayvel, Zhixin Liu, Georgios Zervas
Space division multiplexing using multi-core fiber (MCF) is a promising solution to cope with the capacity crunch in standard single-mode fiber based optical communication systems.
no code implementations • 17 Jun 2020 • Daniel Semrau, Eric Sillekens, Robert I. Killey, Polina Bayvel
ISRS is a nonlinear effect that redistributes optical power from high to lower frequencies during propagation.
no code implementations • 13 Jun 2020 • Daniel Semrau, Eric Sillekens, Robert I. Killey, Polina Bayvel
The throughput gains of extending the optical transmission bandwidth to the S+C+L-band are quantified using a Gaussian Noise model that accounts for inter-channel stimulated Raman scattering (ISRS).
no code implementations • 8 Jun 2020 • Daniel Semrau, Lidia Galdino, Eric Sillekens, Domanic Lavery, Robert I. Killey, Polina Bayvel
A closed-form formula for the nonlinear interference estimation of arbitrary modulation formats in ultra-wideband transmission systems is presented.
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 • 23 Dec 2019 • Eric Sillekens, Wenting Yi, Daniel Semrau, Alessandro Ottino, Boris Karanov, Sujie Zhou, Kevin Law, Jack Chen, Domanic Lavery, Lidia Galdino, Polina Bayvel, Robert I. Killey
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.
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 • 24 Jan 2019 • Boris Karanov, Domaniç Lavery, Polina Bayvel, Laurent Schmalen
Our novel SBRNN design aims at tailoring the end-to-end deep learning-based systems for communication over nonlinear channels with memory, such as the optical IM/DD fiber 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.