Autonomous Power Allocation Based on Distributed Deep Learning for Device-to-Device Communication Underlaying Cellular Network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Jeehyeong | - |
dc.contributor.author | Park, Joohan | - |
dc.contributor.author | Noh, Jaewon | - |
dc.contributor.author | Cho, Sunghyun | - |
dc.date.accessioned | 2021-06-22T09:22:44Z | - |
dc.date.available | 2021-06-22T09:22:44Z | - |
dc.date.issued | 2020-06 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/1880 | - |
dc.description.abstract | For Device-to-device (D2D) communication of Internet-of-Things (IoT) enabled 5G system, there is a limit to allocating resources considering a complicated interference between different links in a centralized manner. If D2D link is controlled by an enhanced node base station (eNB), and thus, remains a burden on the eNB and it causes delayed latency. This paper proposes a fully autonomous power allocation method for IoT-D2D communication underlaying cellular networks using deep learning. In the proposed scheme, an IoT-D2D transmitter decides the transmit power independently from an eNB and other IoT-D2D devices. In addition, the power set can be nearly optimized by deep learning with distributed manner to achieve higher cell throughput. We present a distributed deep learning architecture in which the devices are trained as a group but operate independently. The deep learning can attain near optimal cell throughput while suppressing interference to eNB. | - |
dc.format.extent | 12 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Autonomous Power Allocation Based on Distributed Deep Learning for Device-to-Device Communication Underlaying Cellular Network | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/ACCESS.2020.3000350 | - |
dc.identifier.scopusid | 2-s2.0-85086998094 | - |
dc.identifier.wosid | 000544042700018 | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.8, pp 107853 - 107864 | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 8 | - |
dc.citation.startPage | 107853 | - |
dc.citation.endPage | 107864 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | RESOURCE-ALLOCATION | - |
dc.subject.keywordPlus | 5G | - |
dc.subject.keywordPlus | OPTIMIZATION | - |
dc.subject.keywordPlus | EFFICIENT | - |
dc.subject.keywordPlus | CHANNEL | - |
dc.subject.keywordPlus | IOT | - |
dc.subject.keywordAuthor | IoT-device-to-device communication | - |
dc.subject.keywordAuthor | autonomous power allocation | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | interference management | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9109349 | - |
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