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Optimizing Random Access Procedure to Support Massive and Delay-Critical MTCs

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dc.contributor.authorSong, Shilun-
dc.contributor.authorXie, Huiyang-
dc.contributor.authorJin, Hu-
dc.date.accessioned2024-03-28T03:01:45Z-
dc.date.available2024-03-28T03:01:45Z-
dc.date.issued2023-12-
dc.identifier.issn0000-0000-
dc.identifier.urihttps://scholarworks.bwise.kr/erica/handle/2021.sw.erica/118233-
dc.description.abstractIn 5G networks, random access plays a vital role, especially for Machine Type Communication (MTC) with a large number of access requirements. The 5G standard introduces two distinct random access mechanisms: 4-step Random Access Procedure (RAP) and 2-step RAP. The 4-step RAP provides a more stable access process, while the 2-step RAP significantly reduces access latency. In this paper, two types of MTC devices, using the corresponding RAP, coexist in the network and share the same resource pool. Based on the limited information that the base station possesses during the access process, algorithms for device quantity estimation, as well as resource allocation and ACB control based on this estimation, are designed. Finally, through simulation, we obtain observations on network performance, demonstrating the effectiveness of the proposed algorithm. © 2023 IEEE.-
dc.format.extent4-
dc.language영어-
dc.language.isoENG-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.titleOptimizing Random Access Procedure to Support Massive and Delay-Critical MTCs-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.doi10.1109/iCAST57874.2023.10359263-
dc.identifier.scopusid2-s2.0-85182737533-
dc.identifier.bibliographicCitation2023 12th International Conference on Awareness Science and Technology (iCAST), pp 169 - 172-
dc.citation.title2023 12th International Conference on Awareness Science and Technology (iCAST)-
dc.citation.startPage169-
dc.citation.endPage172-
dc.type.docTypeConference Paper-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordAuthor5G-
dc.subject.keywordAuthoraccess coexisting-
dc.subject.keywordAuthorBayesian learning-
dc.subject.keywordAuthoronline control-
dc.subject.keywordAuthorrandom access-
dc.identifier.urlhttps://ieeexplore.ieee.org/document/10359263-
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