Machine-Learning-Aided Massive Hybrid Analog and Digital MIMO DOA Estimation for Future Wireless Networks

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dc.contributor.authorZhao, X.
dc.contributor.authorShi, B.
dc.contributor.authorBai, J.
dc.contributor.authorShu, F.
dc.contributor.authorChen, Y.
dc.contributor.authorZhan, X.
dc.contributor.authorCai, W.
dc.contributor.authorHuang, M.
dc.contributor.authorJie, Q.
dc.contributor.authorLi, Y.
dc.contributor.authorWang, J.
dc.contributor.authorYou, X.
dc.coverage.issue4cs
dc.coverage.volume32cs
dc.date.accessioned2024-01-09T14:20:52Z
dc.date.available2024-01-09T14:20:52Z
dc.date.issued2023-12cs
dc.description.abstractDue to a high spatial angle resolution and low circuit cost of massive hybrid analog and digital (HAD) multiple-input multiple-output (MIMO), it is viewed as a valuable green communication technology for future wireless networks. Integrating the massive HAD-MIMO with direction of arrival (DOA) will provide an even ultra-high performance of DOA measurement, which can the fully-digital (FD) MIMO. However, phase ambiguity is a challenge issue for a massive HAD-MIMO DOA estimation. In this paper, we consider three parts: detection, estimation, and Cramer-Rao lower bound (CRLB). First, a multi-layer-neural-network (MLNN) detector is proposed to infer the existence of emitters. Then, a two-layer HAD (TLHAD) MIMO structure is proposed to estimate the DOA and eliminate phase ambiguity using only one time block. Simulation results show that the proposed MLNN detector is much better than both the existing generalized likelihood ratio test (GRLT) and the ratio of maximum eigen-value (Max-EV) to minimum eigen-value (R-MaxEV-MinEV) in terms of detection probability. Additionally, the proposed TLHAD structure can achieve the corresponding CRLB.en
dc.formattextcs
dc.format.extent634-642cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2023 vol. 32, č. 4, s. 634-642. ISSN 1210-2512cs
dc.identifier.doi10.13164/re.2023.0634en
dc.identifier.issn1210-2512
dc.identifier.urihttps://hdl.handle.net/11012/244207
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttps://www.radioeng.cz/fulltexts/2023/23_04_0634_0642.pdfcs
dc.rightsCreative Commons Attribution 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectDOAen
dc.subjecthybrid analog and digitalen
dc.subjectMIMOen
dc.subjectgreen technologiesen
dc.subjectCRLBen
dc.subjectmulti-layer-neural-networken
dc.titleMachine-Learning-Aided Massive Hybrid Analog and Digital MIMO DOA Estimation for Future Wireless Networksen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
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