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dc.contributor.authorGiacoumidis, Elias
dc.contributor.authorYi-lin
dc.contributor.authorWei, Jinlong
dc.contributor.authorAldaya, Ivan
dc.contributor.authorTsokanos, Athanasios
dc.contributor.authorBarry, Liam
dc.date.accessioned2019-10-08T00:24:34Z
dc.date.available2019-10-08T00:24:34Z
dc.date.issued2018-12-20
dc.identifier.citationGiacoumidis , E , Yi-lin , Wei , J , Aldaya , I , Tsokanos , A & Barry , L 2018 , ' Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM ' , Future internet , vol. 11 , no. 1 , pp. 1-20 . https://doi.org/10.3390/fi11010002
dc.identifier.issn1999-5903
dc.identifier.otherPURE: 17481987
dc.identifier.otherPURE UUID: 21d8a9fe-0eea-4087-b2f4-f857bbc0f508
dc.identifier.otherScopus: 85060209121
dc.identifier.urihttp://hdl.handle.net/2299/21721
dc.description© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
dc.description.abstractCoherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.en
dc.format.extent20
dc.language.isoeng
dc.relation.ispartofFuture internet
dc.titleHarnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDMen
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionSchool of Engineering and Computer Science
dc.contributor.institutionDepartment of Computer Science
dc.description.statusPeer reviewed
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.3390/fi11010002
rioxxterms.typeJournal Article/Review


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