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dc.contributor.authorXie, Zen_US
dc.contributor.authorLiu, Yen_US
dc.contributor.authorYi, Wen_US
dc.contributor.authorWu, Xen_US
dc.contributor.authorNallanathan, Aen_US
dc.date.accessioned2024-07-16T10:37:34Z
dc.date.issued2023-01-01en_US
dc.identifier.issn2334-0983en_US
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/98176
dc.description.abstractAn analytical framework for physical layer security in simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) transmissions is proposed, where legitimate users and eavesdroppers are randomly deployed. To characterize system performance, the channel statistics are first provided, and the Gamma approximation is adopted for general cascaded κ-μ fading. Afterwards, the energy splitting (ES) protocol is considered and closed-form expressions of average secrecy capacity are derived. To obtain further insights, the asymptotic secrecy slope is deduced. The theoretical results show that the secrecy slope of the ES protocol is one. The numerical results demonstrate that: 1) there is an optimal resource allocation ratio of STAR-RIS to maximize the system performance; 2) the STAR-RIS-aided NOMA significantly outperforms the STAR-RIS-aided orthogonal multiple access.en_US
dc.format.extent6759 - 6764en_US
dc.titleSecrecy Performance Analysis in STAR-RIS-Aided NOMA Networksen_US
dc.typeConference Proceeding
dc.rights.holder© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.identifier.doi10.1109/GLOBECOM54140.2023.10436902en_US
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US
rioxxterms.funder.projectb215eee3-195d-4c4f-a85d-169a4331c138en_US


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