Search Results for author: Polina Bayvel

Found 13 papers, 0 papers with code

Optimising O-to-U Band Transmission Using Fast ISRS Gaussian Noise Numerical Integral Model

no code implementations31 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.

valid

Dual band wireless transmission over 75-150GHz millimeter wave carriers using frequency-locked laser pairs

no code implementations27 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.

Experimental Analysis on Variations and Accuracy of Crosstalk in Trench-Assisted Multi-core Fibers

no code implementations7 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.

Modelling the delayed nonlinear fiber response in coherent optical communications

no code implementations17 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.

The Benefits of Using the S-Band in Optical Fiber Communications and How to Get There

no code implementations13 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).

Modulation Format Dependent, Closed-Form Formula for Estimating Nonlinear Interference in S+C+L Band Systems

no code implementations8 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.

Experimental Investigation of Deep Learning for Digital Signal Processing in Short Reach Optical Fiber Communications

no code implementations18 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.

Optical Fiber Communication Systems Based on End-to-End Deep Learning

no code implementations18 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.

Experimental Demonstration of Learned Time-Domain Digital Back-Propagation

no code implementations23 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.

Concept and Experimental Demonstration of Optical IM/DD End-to-End System Optimization using a Generative Model

no code implementations11 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.

Generative Adversarial Network

Deep Learning for Communication over Dispersive Nonlinear Channels: Performance and Comparison with Classical Digital Signal Processing

no code implementations2 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

End-to-End Optimized Transmission over Dispersive Intensity-Modulated Channels Using Bidirectional Recurrent Neural Networks

no code implementations24 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.

End-to-end Deep Learning of Optical Fiber Communications

no code implementations11 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.

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