A Novel Tree Model-based DNN to Achieve a~High-Resolution DOA Estimation via Massive MIMO Receive Array

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dc.contributor.authorLi, Y.
dc.contributor.authorShu, F.
dc.contributor.authorSong, Y.
dc.contributor.authorWang, J.
dc.coverage.issue4cs
dc.coverage.volume33cs
dc.date.accessioned2025-04-04T12:26:49Z
dc.date.available2025-04-04T12:26:49Z
dc.date.issued2024-12cs
dc.description.abstractTo satisfy the high-resolution requirements of direction-of-arrival (DOA) estimation, conventional deep neural network (DNN)-based methods using grid idea need to significantly increase the number of output classifications and also produce a huge high model complexity. To address this problem, a multi-level tree-based DNN model (TDNN) is proposed as an alternative , where each level takes small-scale multi-layer neural networks (MLNNs) as nodes to divide the target angular interval into multiple sub-intervals, and each output class is associated to a MLNN at the next level. Then the number of MLNNs is gradually increasing from the first level to the last level, and so increasing the depth of tree will dramatically raise the number of output classes to improve the estimation accuracy. More importantly, this network is extended to make a multi-emitter DOA estimation. Simulation results show that the proposed TDNN performs much better than conventional DNN and root multiple signal classification algorithm (root-MUSIC) at extremely low signal-to-noise ratio (SNR) with massive multiple input multiple output (MIMO) receive array, and can achieve Cramer-Rao lower bound (CRLB). Additionally, in the multi-emitter scenario, the proposed Q-TDNN has also made a substantial performance enhancement over DNN and Root-MUSIC, and this gain grows as the number of emitters increases.en
dc.formattextcs
dc.format.extent563-570cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2024 vol. 33, iss. 4, s. 563-570. ISSN 1210-2512cs
dc.identifier.doi10.13164/re.2024.0563en
dc.identifier.issn1210-2512
dc.identifier.urihttps://hdl.handle.net/11012/250830
dc.language.isoencs
dc.publisherRadioengineering societycs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttps://www.radioeng.cz/fulltexts/2024/24_04_0563_0570.pdfcs
dc.rightsCreative Commons Attribution 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectDOA estimationen
dc.subjectDNNen
dc.subjectmassive MIMOen
dc.subjectmulti-label learningen
dc.titleA Novel Tree Model-based DNN to Achieve a~High-Resolution DOA Estimation via Massive MIMO Receive Arrayen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta elektrotechniky a komunikačních technologiícs
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