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

Karina Nurlybayeva, Diego Argüello Ron*, Morteza Kamalian-Kopae, Elena Turitsyna, Sergei Turitsyn

*Corresponding author for this work

Research output: Chapter in Book/Published conference outputConference publication

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.
Original languageEnglish
Title of host publicationProceedings of the 2022 Asia Communications and Photonics Conference (ACP)
PublisherIEEE
Pages753-756
ISBN (Electronic)9781665481557
DOIs
Publication statusPublished - 10 Apr 2023
Event2022 Asia Communications and Photonics Conference (ACP) and International Conference on Information Photonics and Optical Communications (IPOC) - Shenzen, China
Duration: 5 Nov 20228 Nov 2022

Publication series

NameAsia Communications and Photonics Conference (ACP) Proceedings
PublisherIEEE

Conference

Conference2022 Asia Communications and Photonics Conference (ACP) and International Conference on Information Photonics and Optical Communications (IPOC)
Country/TerritoryChina
CityShenzen
Period5/11/228/11/22

Bibliographical note

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.

Keywords

  • Artificial Neural Networks
  • Equalisation
  • Computational Complexity
  • Noise Resilience

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