Machine Learning-based Reconfigurable Intelligent Surface-aided MIMO Systems
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Nhan Thanh Nguyen | - |
dc.contributor.author | Ly V Nguyen | - |
dc.contributor.author | Thien Huynh-The | - |
dc.contributor.author | Duy H N Nguyen | - |
dc.contributor.author | Swindlehurst, A. Lee | - |
dc.contributor.author | Juntti, Markku | - |
dc.date.accessioned | 2022-05-17T04:40:04Z | - |
dc.date.available | 2022-05-17T04:40:04Z | - |
dc.date.created | 2022-05-17 | - |
dc.date.issued | 2021-09 | - |
dc.identifier.issn | 2325-3789 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/21118 | - |
dc.description.abstract | Reconfigurable intelligent surface (RIS) technology has recently emerged as a spectral- and cost-efficient approach for wireless communications systems. However, existing hand-engineered schemes for passive beamforming design and optimization of RIS, such as the alternating optimization (AO) approaches, require a high computational complexity, especially for multiple-input-multiple-output (MIMO) systems. To overcome this challenge, we propose a low-complexity unsupervised learning scheme, referred to as learning-phase-shift neural network (LPSNet), to efficiently find the solution to the spectral efficiency maximization problem in RIS-aided MIMO systems. In particular, the proposed LPSNet has an optimized input structure and requires a small number of layers and nodes to produce efficient phase shifts for the RIS. Simulation results for a 16x2 MIMO system assisted by an RIS with 40 elements show that the LPSNet achieves 97.25% of the SE provided by the AO counterpart with more than a 95% reduction in complexity. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE | - |
dc.title | Machine Learning-based Reconfigurable Intelligent Surface-aided MIMO Systems | - |
dc.type | Conference | - |
dc.contributor.affiliatedAuthor | Thien Huynh-The | - |
dc.identifier.wosid | 000783745500021 | - |
dc.identifier.bibliographicCitation | 22nd IEEE International Workshop on Signal Processing Advances in Wireless Communications (IEEE SPAWC), pp.101 - 105 | - |
dc.relation.isPartOf | 22nd IEEE International Workshop on Signal Processing Advances in Wireless Communications (IEEE SPAWC) | - |
dc.relation.isPartOf | SPAWC 2021: 2021 IEEE 22ND INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS (IEEE SPAWC 2021) | - |
dc.citation.title | 22nd IEEE International Workshop on Signal Processing Advances in Wireless Communications (IEEE SPAWC) | - |
dc.citation.startPage | 101 | - |
dc.citation.endPage | 105 | - |
dc.citation.conferencePlace | US | - |
dc.citation.conferencePlace | Lucca, ITALY | - |
dc.citation.conferenceDate | 2021-09-27 | - |
dc.type.rims | CONF | - |
dc.description.journalClass | 1 | - |
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