Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/106996
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dc.contributorPhotonics Research Centreen_US
dc.contributorDepartment of Electrical and Electronic Engineeringen_US
dc.creatorKhan, FNen_US
dc.creatorLu, Cen_US
dc.creatorLau, APTen_US
dc.date.accessioned2024-06-07T00:59:30Z-
dc.date.available2024-06-07T00:59:30Z-
dc.identifier.isbn978-1-943580-30-9en_US
dc.identifier.urihttp://hdl.handle.net/10397/106996-
dc.descriptionSignal Processing in Photonic Communications 2017, New Orleans, Louisiana United States, 24-27 July 2017en_US
dc.language.isoenen_US
dc.publisherOptica Publishing Groupen_US
dc.rights© 2017 Optical Society of Americaen_US
dc.rightsThe following publication F. N. Khan, C. Lu, and A. P. T. Lau, "Machine Learning Methods for Optical Communication Systems," in Advanced Photonics 2017 (IPR, NOMA, Sensors, Networks, SPPCom, PS), OSA Technical Digest (online) (Optica Publishing Group, 2017), paper SpW2F.3 is available at https://doi.org/10.1364/SPPCOM.2017.SpW2F.3.en_US
dc.titleMachine learning methods for optical communication systemsen_US
dc.typeConference Paperen_US
dc.identifier.doi10.1364/SPPCOM.2017.SpW2F.3en_US
dcterms.abstractWe review application of machine learning methods to tackle fiber linear/nonlinear impairments as well as to estimate crucial signal parameters in optical networks. Recent works involving hierarchical learning approaches are also discussed.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationAdvanced Photonics 2017 (IPR, NOMA, Sensors, Networks, SPPCom, PS), OSA Technical Digest (online) (Optica Publishing Group, 2017), paper SpW2F.3en_US
dcterms.issued2017-
dc.identifier.scopus2-s2.0-85027968441-
dc.relation.conferenceSignal Processing in Photonic Communications [SPPCom]en_US
dc.identifier.artnSpW2F.3en_US
dc.description.validate202405 bcchen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberEIE-0774-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextHong Kong Government General Research Fund; Hong Kong Polytechnic Universityen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS9601611-
dc.description.oaCategoryVoR alloweden_US
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