Quantum Neural Network With Parallel Training for Wireless Resource Optimization
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Narottama, Bhaskara | - |
dc.contributor.author | Jamaluddin, Triwidyastuti | - |
dc.contributor.author | Shin, Soo Young | - |
dc.date.accessioned | 2024-08-09T06:30:21Z | - |
dc.date.available | 2024-08-09T06:30:21Z | - |
dc.date.issued | 2024-05 | - |
dc.identifier.issn | 1536-1233 | - |
dc.identifier.issn | 1558-0660 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/28854 | - |
dc.description.abstract | A quantum neural network with parallel training (called PS-QNN) is presented in this study to optimize wireless resource allocation. Instead of sending the whole dataset, each edge only requires to send the statistical parameters of the dataset; hence reducing the dimension of the training data. As a particular case, the proposed PS-QNN is utilized to optimize transmit precoding and power allocation in non-orthogonal multiple access with multiple-input and multiple-output antennas (MIMO-NOMA). Compared to the conventional training method, analysis shows that the proposed parallel training yields a lower complexity, while achieving a comparable sum rate compared to conventional method. | - |
dc.format.extent | 13 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE COMPUTER SOC | - |
dc.title | Quantum Neural Network With Parallel Training for Wireless Resource Optimization | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/TMC.2023.3321467 | - |
dc.identifier.scopusid | 2-s2.0-85174857456 | - |
dc.identifier.wosid | 001198016900166 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON MOBILE COMPUTING, v.23, no.5, pp 5835 - 5847 | - |
dc.citation.title | IEEE TRANSACTIONS ON MOBILE COMPUTING | - |
dc.citation.volume | 23 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 5835 | - |
dc.citation.endPage | 5847 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | MIMO-NOMA | - |
dc.subject.keywordPlus | SHALLOW | - |
dc.subject.keywordPlus | POWER | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Wireless communication | - |
dc.subject.keywordAuthor | Optimization | - |
dc.subject.keywordAuthor | NOMA | - |
dc.subject.keywordAuthor | Precoding | - |
dc.subject.keywordAuthor | Transmitting antennas | - |
dc.subject.keywordAuthor | Qubit | - |
dc.subject.keywordAuthor | Quantum neural networks | - |
dc.subject.keywordAuthor | unsupervised learning | - |
dc.subject.keywordAuthor | non-orthogonal multiple access | - |
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