High dynamic range 100G PON enabled by SOA preamplifier and recurrent neural networks

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dc.contributor.authorMurphy, Stephen L.en
dc.contributor.authorJamali, Faribaen
dc.contributor.authorTownsend, Paul D.en
dc.contributor.authorAntony, Cleitusen
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2023-09-12T11:27:44Z
dc.date.available2023-09-12T11:27:44Z
dc.date.issued2023-02-28en
dc.description.abstractIn recent years the PON research community has focused on future systems targeting 100 Gb/s/ $\lambda$ and beyond, with digital signal processing seen as a key enabling technology. Spectrally efficient 4-level pulse amplitude modulation (PAM4) is seen as a cost-effective solution that exploits the ready availability of cheaper, low-bandwidth devices, and Semiconductor Optical Amplifiers (SOA) are being investigated as receiver preamplifiers to compensate PAM4’s high signal-to-noise ratio requirements and meet the demanding 29 dB PON loss budget. However, SOA gain saturation-induced patterning distortion is a concern in the context of PON burst-mode signalling, and the 19.5 dB loud-soft packet dynamic range expected by the most recent ITU-T 50G standards. In this article we propose a recurrent neural network equalisation technique based on gated recurrent units (GRU-RNN) to not only mitigate SOA patterning affecting loud packet bursts, but to also exploit their remarkable effectiveness at compensating non-linear impairments to unlock the SOA gain saturated regime. Using such an equaliser we demonstrate $ > 28$ dB system dynamic range in 100 Gb/s PAM4 system by using SOA gain compression in conjunction with GRU-RNN equalisation. We find that our proposed GRU-RNN has similar equalisation capabilities as non-linear Volterra, fully connected neural network, and long short-term memory based equalisers, but observe that feedback-based RNN equalisers are more suited to the varying levels of impairment inherent to PON burst-mode signalling due to their low input tap requirements. Recognising issues surrounding hardware implementation of RNNs, we investigate a multi-symbol equalisation scheme to lower the feedback latency requirements of our proposed GRU-RNN. Finally, we compare equaliser complexities and performances according to trainable parameters and real valued multiplication operations, finding that the proposed GRU-RNN equaliser is more efficient than those based on Volterra, fully connected neural networks or long short-term memory units proposed elsewhere.en
dc.description.sponsorshipScience Foundation Ireland (12/RC/2276P2)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMurphy, S. L., Jamali, F., Townsend, P. D. and Antony, C. (2023) 'High dynamic range 100G PON enabled by SOA preamplifier and recurrent neural networks', Journal of Lightwave Technology, 41(11), pp. 3522-3532. doi: 10.1109/JLT.2023.3249046en
dc.identifier.doi10.1109/jlt.2023.3249046en
dc.identifier.eissn1558-2213en
dc.identifier.endpage3532en
dc.identifier.issn0733-8724en
dc.identifier.issued11en
dc.identifier.journaltitleJournal of Lightwave Technologyen
dc.identifier.startpage3522en
dc.identifier.urihttps://hdl.handle.net/10468/14940
dc.identifier.volume41en
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.rights© 2023, the Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectDigital signal processingen
dc.subjectFour-level pulse amplitude modulationen
dc.subjectMachine learningen
dc.subjectOptical fiber communicationsen
dc.subjectPassive optical networken
dc.subjectSemiconductor optical amplifieren
dc.titleHigh dynamic range 100G PON enabled by SOA preamplifier and recurrent neural networksen
dc.typeArticle (peer-reviewed)en
oaire.citation.issue11en
oaire.citation.volume41en
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