Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/22809
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dc.contributor.authorBudati, AK-
dc.contributor.authorGhinea, G-
dc.contributor.authorGanesh, SNV-
dc.date.accessioned2021-06-07T13:17:41Z-
dc.date.available2021-01-01-
dc.date.available2021-06-07T13:17:41Z-
dc.date.issued2021-
dc.identifier.citationBudati, A.K., Ghinea, G. & Ganesh, S.N.V. Novel Aninath Computation Detection Algorithm to Identify the UAV Users in 5G Networks. Wireless Pers Commun (2021). https://doi.org/10.1007/s11277-021-08459-3en_US
dc.identifier.issn0929-6212-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/22809-
dc.description.abstractCognitive Radio (CR) Network is a backbone for the 5G cellular Networks and Unmanned Aerial Vehicle (UAV) user identification at low power levels is a biggest task CR. Detection of UAV user is more difficult than the stable or fixed user. In the available literature various authors proposed their research with single detection algorithms low power levels as well as concatenation of two or three detection methods. To estimate the user presence the existing detection methods proposed with covariance based approach at static or predefined threshold power levels. In this paper, the authors proposed a novel Aninath computation detection algorithm to estimate the threshold dynamically with inverse covariance approach to improve the Probability of Detection (P ) and mitigate the Probability of false alarm (P ) and Probability of miss detection (P ) at low power levels. D fa mden_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectHybrid Filter Detectionen_US
dc.subjectCyclostationary Feature Detectionen_US
dc.subjectCognitive Radioen_US
dc.subjectSpectrum Sensingen_US
dc.subjectDynamic Thresholden_US
dc.titleNovel Aninath Computation Detection Algorithm to Identify the UAV Users in 5G Networksen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1007/s11277-021-08459-3-
dc.relation.isPartOfWireless Personal Communications-
pubs.publication-statusPublished-
dc.identifier.eissn1572-834X-
Appears in Collections:Dept of Computer Science Embargoed Research Papers

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