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dc.contributor.authorNevin, Josh W
dc.contributor.authorNallaperuma, Sam
dc.contributor.authorShevchenko, Nikita A
dc.contributor.authorLi, Xiang
dc.contributor.authorFaruk, Md Saifuddin
dc.contributor.authorSavory, Seb J
dc.date.accessioned2021-12-08T00:30:35Z
dc.date.available2021-12-08T00:30:35Z
dc.date.issued2021
dc.identifier.issn2378-0967
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/331264
dc.description.abstractOptical networks generate a vast amount of diagnostic, control and performance monitoring data. When information is extracted from this data, reconfigurable network elements and reconfigurable transceivers allow the network to adapt both to changes in the physical infrastructure but also changing traffic conditions. Machine learning is emerging as a disruptive technology for extracting useful information from this raw data to enable enhanced planning, monitoring and dynamic control. We provide a survey of the recent literature and highlight numerous promising avenues for machine learning applied to optical networks, including explainable machine learning, digital twins and approaches in which we embed our knowledge into the machine learning such as physics-informed machine learning for the physical layer and graph-based machine learning for the networking layer.
dc.publisherAIP Publishing
dc.rightsAll Rights Reserved
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserved
dc.titleMachine learning for optical fiber communication systems: An introduction and overview
dc.typeArticle
dc.publisher.departmentDepartment of Engineering
dc.date.updated2021-12-06T15:31:38Z
prism.publicationNameAPL PHOTONICS
dc.identifier.doi10.17863/CAM.78710
dcterms.dateAccepted2021-12-06
rioxxterms.versionofrecord10.1063/5.0070838
rioxxterms.versionAM
dc.contributor.orcidNevin, Joshua [0000-0002-6067-6892]
dc.contributor.orcidNallaperuma, Sam [0000-0002-4947-5870]
dc.contributor.orcidSavory, Seb [0000-0002-6803-718X]
dc.identifier.eissn2378-0967
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/R035342/1)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/L015455/1)
cam.issuedOnline2021-12-06
cam.orpheus.success2021-12-07 - Embargo set during processing via Fast-track
cam.depositDate2021-12-06
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
rioxxterms.freetoread.startdate2021-12-06


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