Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 조성현 | - |
dc.date.accessioned | 2022-08-17T01:29:55Z | - |
dc.date.available | 2022-08-17T01:29:55Z | - |
dc.date.issued | 2021-08 | - |
dc.identifier.citation | 2021 IEEE Region 10 Symposium (TENSYMP) Region 10 Symposium (TENSYMP), 2021 IEEE. :1-4 Aug, 2021 | en_US |
dc.identifier.isbn | 978-1-6654-0026-8 | - |
dc.identifier.issn | 2642-6102 | - |
dc.identifier.uri | https://ieeexplore.ieee.org/document/9550999?arnumber=9550999&SID=EBSCO:edseee | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/172454 | - |
dc.description.abstract | Massive connection of machine-type communications devices is one of major issues for nextgeneration wireless communications technologies. In particular the contention-based random access method used in long-term evolution has many difficulties to support massive connection scenario of next-generation wireless communications technologies. This paper investigates the limitations in which the random access method currently used in cellular networks is difficult to apply to the massive connection scenario of the next-generation network. This paper firstly described the procedure of random access used in long-term evolution. This paper also describes the limitations of random access technology with respect to the collision probability and quality-of-service degradation due to retransmission. In addition, this paper introduces candidate solutions that can solve the described limitations of existing random access method and support massive connection requirements for the next-generation wireless communication technologies. | en_US |
dc.description.sponsorship | This work was supported by Institute for Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) (No. 2018-0-00969, Full duplex non-orthogonal multiple access (NOMA) optimization technologies using deep learning for 5G based autonomous vehicular networks) and (No. 2021-0-00368, Development of the 6G Service Targeted AI/ML-based autonomous-Regulating Medium Access Control (6G STAR-MAC)) | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.subject | Random Access | en_US |
dc.subject | Massive Machine-type Communications | en_US |
dc.subject | Non-orthogonal Multiple Access | en_US |
dc.subject | Grant-free Access | en_US |
dc.subject | Sixth-generation Wireless Communication Technologies | en_US |
dc.title | A Study of Random Access for Massive Machine-type Communications Limitations and Solutions | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/TENSYMP52854.2021.9550999 | - |
dc.relation.page | 1-4 | - |
dc.contributor.googleauthor | Youn, Jiseung | - |
dc.contributor.googleauthor | Park, Joohan | - |
dc.contributor.googleauthor | Kim, Soohyeong | - |
dc.contributor.googleauthor | You, Cheolwoo | - |
dc.contributor.googleauthor | Cho, Sunghyun | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF COMPUTING[E] | - |
dc.sector.department | SCHOOL OF COMPUTER SCIENCE | - |
dc.identifier.pid | chopro | - |
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