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.
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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 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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