Deep neural network based precoding for wiretap channels with finite alphabet inputs

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buir.contributor.authorGümüş, Mücahit
buir.contributor.authorDuman, Tolga M.
buir.contributor.orcidGümüş, Mücahit|0000-0002-8289-1294
buir.contributor.orcidDuman, Tolga M.|0000-0002-5187-8660
dc.citation.epage1656en_US
dc.citation.issueNumber8en_US
dc.citation.spage1652en_US
dc.citation.volumeNumber10en_US
dc.contributor.authorGümüş, Mücahit
dc.contributor.authorDuman, Tolga M.
dc.date.accessioned2022-01-27T06:27:50Z
dc.date.available2022-01-27T06:27:50Z
dc.date.issued2021-04-28
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractWe consider secure transmission over multi-input multi-output multi-antenna eavesdropper (MIMOME) wiretap channels with finite alphabet inputs. We use a linear precoder to maximize the secrecy rate, which benefits from the generalized singular value decomposition to obtain independent streams and exploits the function approximation abilities of deep neural networks (DNNs) for solving the required power allocation problem. It is demonstrated that the DNN learns the optimal power allocation without any performance degradation compared to the conventional technique with a significant reduction in complexity.en_US
dc.identifier.doi10.1109/LWC.2021.3076398en_US
dc.identifier.eissn2162-2345
dc.identifier.issn2162-2337
dc.identifier.urihttp://hdl.handle.net/11693/76815
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttps://doi.org/10.1109/LWC.2021.3076398en_US
dc.source.titleIEEE Communications Lettersen_US
dc.subjectDeep neural networksen_US
dc.subjectPhysical layer securityen_US
dc.subjectMIMOME wiretap channelsen_US
dc.titleDeep neural network based precoding for wiretap channels with finite alphabet inputsen_US
dc.typeArticleen_US

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