Recurrent neural network equalizer to extend input power dynamic range of SOA in 100Gb/s/λ PON

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dc.contributor.authorMurphy, Stephen
dc.contributor.authorTownsend, Paul D.
dc.contributor.authorAntony, Cleitus
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2022-11-29T12:10:07Z
dc.date.available2022-11-29T12:10:07Z
dc.date.issued2022-09-23
dc.date.updated2022-11-29T11:55:14Z
dc.description.abstractWe propose a novel equalization scheme for 100Gb/s/λ PAM4 PON based on Gated Recurrent Neural Network to increase SOA preamplifier input power dynamic range tolerance to 30 dB below hard-decision FEC BER limit of 3.8×10 −3.en
dc.description.sponsorshipScience Foundation Ireland (12/RC/2276P2)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleidSW4E.1en
dc.identifier.citationMurphy, S., Townsend, P. D. and Antony, C. (2022) 'Recurrent neural network equalizer to extend input power dynamic range of SOA in 100Gb/s/λ PON', 2022 Conference on Lasers and Electro-Optics (CLEO), San Jose, CA, USA, 15-20 May. Available at: https://ieeexplore.ieee.org/document/9889990 (Accessed: 29 November 2022)en
dc.identifier.endpage2en
dc.identifier.isbn978-1-957171-05-0
dc.identifier.isbn978-1-6654-6666-0
dc.identifier.issn2160-8989
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/13887
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urihttps://ieeexplore.ieee.org/document/9889990
dc.rights© 2022, the Authors. Published by IEEE. CLEO 2022 © Optica Publishing Group 2022.en
dc.subjectRecurrent neural networksen
dc.subjectEqualizersen
dc.subjectDynamic rangeen
dc.subjectLogic gatesen
dc.subjectLasers and electroopticsen
dc.subjectPreamplifiersen
dc.subjectElectrooptical waveguidesen
dc.titleRecurrent neural network equalizer to extend input power dynamic range of SOA in 100Gb/s/λ PONen
dc.typeConference itemen
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