MARL-Based Access Control for Grant-Free Non-Orthogonal Random Access in UDN
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Youn, Jiseung | - |
dc.contributor.author | Park, Joohan | - |
dc.contributor.author | Kim, Soohyeong | - |
dc.contributor.author | Ahn, Seyoung | - |
dc.contributor.author | Kim, Yushin | - |
dc.contributor.author | Kim, Donghyun | - |
dc.contributor.author | Cho, Sunghyun | - |
dc.date.accessioned | 2024-06-17T04:30:21Z | - |
dc.date.available | 2024-06-17T04:30:21Z | - |
dc.date.issued | 2024-05 | - |
dc.identifier.issn | 2327-4662 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/119464 | - |
dc.description.abstract | This study addresses the challenge of high power collision rates in Grant-Free Non-Orthogonal Random Access (GF-NORA) for ultra-massive machine-type communication (umMTC) in ultra-dense networks (UDN). We analyze the impact of power collision and inter-cell interference, defining the key factors affecting successive interference cancellation (SIC) decoding failure. To tackle power collision problem, we propose a multi-agent reinforcement learning (MARL) framework, QMIX algorithm, with joint optimization of access control and power-level design. We evaluate the performance of the proposed scheme with extensive random access simulations in an umMTC environment. Our approach outperforms state-of-the-art schemes, achieving at most 10% increase in successful SIC decoding rate with lower access delay. | - |
dc.format.extent | 16 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | MARL-Based Access Control for Grant-Free Non-Orthogonal Random Access in UDN | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/JIOT.2024.3404418 | - |
dc.identifier.bibliographicCitation | IEEE Internet of Things Journal, pp 1 - 16 | - |
dc.citation.title | IEEE Internet of Things Journal | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 16 | - |
dc.type.docType | 정기학술지(Article(Perspective Article포함)) | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Internet of things (IoT) | - |
dc.subject.keywordAuthor | Grant-free random access | - |
dc.subject.keywordAuthor | umMTC | - |
dc.subject.keywordAuthor | NOMA | - |
dc.subject.keywordAuthor | UDN | - |
dc.subject.keywordAuthor | multi-agent reinforcement learning | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/10537046 | - |
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