Implementation of Noise-Resistant Crowd Equalisation in Optical Communication Systems with Machine Learning DSP

Abstract

Abstract—We propose a solution to noisy neural networks employed in future optical communication systems. The proposed approach includes breaking down large networks into smaller ones and forming ”crowds” using these elementary networks.

Publication DOI: https://doi.org/10.1109/acp55869.2022.10088872
Divisions: College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
Additional Information: Copyright © 2023, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Funding: This work has received funding from: EU H2020 MSCA project No. 860360 and EPSRC project TRANSNET.
Event Title: 2022 Asia Communications and Photonics Conference (ACP) and International Conference on Information Photonics and Optical Communications (IPOC)
Event Type: Other
Event Dates: 2022-11-05 - 2022-11-08
Uncontrolled Keywords: Artificial Neural Networks,Equalisation,Computational Complexity,Noise Resilience
ISBN: 9781665481557
Last Modified: 21 Feb 2024 08:29
Date Deposited: 06 Jun 2023 15:39
Full Text Link:
Related URLs: https://ieeexpl ... cument/10088872 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2023-04-10
Authors: Nurlybayeva, Karina
Argüello Ron, Diego (ORCID Profile 0000-0002-6004-385X)
Kamalian-Kopae, Morteza (ORCID Profile 0000-0002-6278-976X)
Turitsyna, Elena (ORCID Profile 0000-0003-1756-362X)
Turitsyn, Sergei (ORCID Profile 0000-0003-0101-3834)

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Version: Accepted Version

Access Restriction: Restricted to Repository staff only until 10 April 2024.


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