Deep Learning-based Spectral Efficiency Maximization in Massive MIMO-NOMA Systems with STAR-RIS
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yoga, Perdana R.H. | - |
dc.contributor.author | Nguyen, T.-V. | - |
dc.contributor.author | Pramitarini, Y. | - |
dc.contributor.author | Shim, K. | - |
dc.contributor.author | An, B. | - |
dc.date.accessioned | 2023-04-24T03:40:25Z | - |
dc.date.available | 2023-04-24T03:40:25Z | - |
dc.date.issued | 2023-01-01 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/31097 | - |
dc.description.abstract | This paper studies a deep learning-based framework for spectral efficiency maximization problem in massive multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) systems with simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). We formulate the spectral efficiency maximization with a joint design of power allocation of the users, phase shift matrix of transmission and reflection element at the STAR-RIS. Since the problem is non-convex and power allocation of the users and reflector/transmitter elements at a STAR-RIS are coupled, it is very challenging to solve optimally. We propose a low-complexity iterative algorithm based on the inner approximation (IA) method to solve this problem with guaranteed convergence at a relatively optimal level. For real-time optimization, we design a deep learning (DL) framework to predict the optimal solution of power allocation of users, phase shift matrix of transmission and reflection elements at the STAR-RIS according to distances and channel gains from the base station (BS) to STAR-RIS and from STAR-RIS to users. Simulation results show that the suggested scheme improves the spectral efficiency (SE) compared to the massive MIMO system with direct link and without STAR-RIS. Besides, the DL framework can predict the optimal solution within a short time under the suggested scheme. © 2023 IEEE. | - |
dc.format.extent | 6 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Deep Learning-based Spectral Efficiency Maximization in Massive MIMO-NOMA Systems with STAR-RIS | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ICAIIC57133.2023.10067078 | - |
dc.identifier.scopusid | 2-s2.0-85152004355 | - |
dc.identifier.wosid | 001012997600123 | - |
dc.identifier.bibliographicCitation | 5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023, pp 644 - 649 | - |
dc.citation.title | 5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023 | - |
dc.citation.startPage | 644 | - |
dc.citation.endPage | 649 | - |
dc.type.docType | Proceedings Paper | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordAuthor | Deep learning neural networks | - |
dc.subject.keywordAuthor | massive MIMO | - |
dc.subject.keywordAuthor | NOMA | - |
dc.subject.keywordAuthor | non-convex optimization | - |
dc.subject.keywordAuthor | phase shift | - |
dc.subject.keywordAuthor | power allocation | - |
dc.subject.keywordAuthor | spectral efficiency | - |
dc.subject.keywordAuthor | STAR-RIS | - |
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