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Optical Fiber Communication Systems Based on End-to-End Deep Learning: (Invited Paper)

Karanov, B; Chagnon, M; Aref, V; Lavery, D; Bayvel, P; Schmalen, L; (2020) Optical Fiber Communication Systems Based on End-to-End Deep Learning: (Invited Paper). In: 2020 IEEE Photonics Conference, IPC 2020 - Proceedings. IEEE: Vancouver, BC, Canada. Green open access

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Abstract

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, highlighting the performance improvement achieved with recurrent processing for communication over dispersive nonlinear channels.

Type: Proceedings paper
Title: Optical Fiber Communication Systems Based on End-to-End Deep Learning: (Invited Paper)
Event: 2020 IEEE Photonics Conference (IPC)
ISBN-13: 9781728158914
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/IPC47351.2020.9252544
Publisher version: https://doi.org/10.1109/IPC47351.2020.9252544
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: optical communications, digital signal processing, deep learning, neural networks, modulation, detection
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10120662
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