Machine Learning-based Direction of Arrival Estimation
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da INATEL |
Texto Completo: | https://tede.inatel.br:8080/tede/handle/tede/252 |
Resumo: | Beamforming (BF) is expected to be one of the key technologies in Sixth Generation (6G) networks. BF improves the Signal-to-Noise Ratio (SNR) of received signals and focuses the radiation pattern to specific locations by weighting the amplitude and phase of individual antenna signals. This technique provides better coverage in an indoor environment and at the edge of a cell. To make the best use of this technology, it is essential to know the location of the device to direct the antenna beam of the radio Base Station (BS). Consequently, the Direction of Arrival (DOA) method becomes crucial and essential at this time. Therefore, this study addresses the problem of accurately predicting the azimuth and elevation angles of a signal impinging on an antenna array based on Machine Learning (ML) models. Simulation results show that ML models are competitive techniques to find the azimuth and elevation angles of a signal impinging on a receiving system. |
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Figueiredo, Felipe0188611850092267http://lattes.cnpq.br/0188611850092267Brito, Jos?? Marcos0370383210890132http://lattes.cnpq.br/0370383210890132Figueiredo, . Felipe0188611850092267http://lattes.cnpq.br/0188611850092267Dias, Cl??udio Ferreira6610319457893381http://lattes.cnpq.br/6610319457893381Mafra, Samuel9492423249629649http://lattes.cnpq.br/9492423249629649Carballeira, Anabel Reyes2024-03-26T17:44:18Z2023-02-09Carballeira, Anabel Reyes. Machine Learning-based Direction of Arrival Estimation. 2023. [115 p.]. disserta????o( Mestrado em Engenharia de Telecomunica????es) - Instituto Nacional de Telecomunica????es, [Santa Rita Do Sapuca??] .https://tede.inatel.br:8080/tede/handle/tede/252Beamforming (BF) is expected to be one of the key technologies in Sixth Generation (6G) networks. BF improves the Signal-to-Noise Ratio (SNR) of received signals and focuses the radiation pattern to specific locations by weighting the amplitude and phase of individual antenna signals. This technique provides better coverage in an indoor environment and at the edge of a cell. To make the best use of this technology, it is essential to know the location of the device to direct the antenna beam of the radio Base Station (BS). Consequently, the Direction of Arrival (DOA) method becomes crucial and essential at this time. Therefore, this study addresses the problem of accurately predicting the azimuth and elevation angles of a signal impinging on an antenna array based on Machine Learning (ML) models. Simulation results show that ML models are competitive techniques to find the azimuth and elevation angles of a signal impinging on a receiving system.Espera-se que o beamforming seja uma das principais tecnologias adotadas pelas redes de Sexta Gera????o (6G). O beamforming melhora a rela????o sinal-ru??do dos sinais recebidos e foca o padr??o de radia????o em uma dire????o espec??fica ponderando a amplitude e a fase dos sinais de antenas individuais. Esta t??cnica proporciona uma melhor cobertura em um ambiente interno e na borda de c??lulas. Para fazer o melhor uso desta tecnologia ?? importante conhecer a localiza????o do dispositivo para direcionar o feixe da antena da esta????o r??dio base. Consequentemente, o m??todo de estima????o de dire????o de chegada torna-se crucial e essencial neste momento. Portanto, este estudo aborda o problema de estimar com precis??o os ??ngulos de azimute e eleva????o de um sinal incidindo em um conjunto de antenas baseado em modelos de aprendizado de m??quina. Os resultados de simula????o mostram que os modelos de aprendizado de m??quina s??o uma solu????o competitiva para se encontrar os ??ngulos de azimute e de eleva????o de um sinal incidente em um sistema receptor.Submitted by Tede Dspace (tede@inatel.br) on 2024-03-26T17:44:18Z No. of bitstreams: 1 Disserta????o Anabel - vers??o final.pdf: 8096025 bytes, checksum: 9e34b904b27447bc588f2016454cd8ed (MD5)Made available in DSpace on 2024-03-26T17:44:18Z (GMT). No. of bitstreams: 1 Disserta????o Anabel - vers??o final.pdf: 8096025 bytes, checksum: 9e34b904b27447bc588f2016454cd8ed (MD5) Previous issue date: 2023-02-09application/pdfhttp://tede.inatel.br:8080/jspui/retrieve/1990/Disserta%c3%a7%c3%a3o%20Anabel%20-%20vers%c3%a3o%20final.pdf.jpgporInstituto Nacional de Telecomunica????esMestrado em Engenharia de Telecomunica????esINATELBrasilInstituto Nacional de Telecomunica????esDire????o de Chegada; Aprendizado de M??quina; Regress??o; Pr??-processamento de dados; Matriz de covari??ncia;Direction of Arrival; Machine Learning; Regression; Data preprocessing; Covariance matrix;Engenharia - Telecomunica????esMachine Learning-based Direction of Arrival Estimationinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da INATELinstname:Instituto Nacional de Telecomunicações (INATEL)instacron:INATELLICENSElicense.txtlicense.txttext/plain; charset=utf-850http://localhost:8080/tede/bitstream/tede/252/1/license.txtad97de64637545abb37de9243411913cMD51ORIGINALDisserta????o Anabel - vers??o final.pdfDisserta????o Anabel - vers??o final.pdfapplication/pdf8096025http://localhost:8080/tede/bitstream/tede/252/2/Disserta%C3%A7%C3%A3o+Anabel+-+vers%C3%A3o+final.pdf9e34b904b27447bc588f2016454cd8edMD52TEXTDisserta????o Anabel - vers??o final.pdf.txtDisserta????o Anabel - vers??o final.pdf.txttext/plain118635http://localhost:8080/tede/bitstream/tede/252/3/Disserta%C3%A7%C3%A3o+Anabel+-+vers%C3%A3o+final.pdf.txt139c2ccaa6fe0d76df770d258802ca40MD53THUMBNAILDisserta????o Anabel - vers??o final.pdf.jpgDisserta????o Anabel - vers??o final.pdf.jpgimage/jpeg3700http://localhost:8080/tede/bitstream/tede/252/4/Disserta%C3%A7%C3%A3o+Anabel+-+vers%C3%A3o+final.pdf.jpgcfb790b23be49af789c5d05dde5204d5MD54tede/2522024-04-18 11:38:36.681oai:localhost:tede/252aHR0cDovL2NyZWF0aXZlY29tbW9ucy5vcmcvbGljZW5zZXMvYnktbmMtbmQvNC4wLy4=Biblioteca Digital de Teses e Dissertaçõeshttp://tede.inatel.br:8080/jspui/PUBhttp://tede.inatel.br:8080/oai/requestbiblioteca@inatel.br || biblioteca.atendimento@inatel.bropendoar:2024-04-18T14:38:36Biblioteca Digital de Teses e Dissertações da INATEL - Instituto Nacional de Telecomunicações (INATEL)false |
dc.title.por.fl_str_mv |
Machine Learning-based Direction of Arrival Estimation |
title |
Machine Learning-based Direction of Arrival Estimation |
spellingShingle |
Machine Learning-based Direction of Arrival Estimation Carballeira, Anabel Reyes Dire????o de Chegada; Aprendizado de M??quina; Regress??o; Pr??-processamento de dados; Matriz de covari??ncia; Direction of Arrival; Machine Learning; Regression; Data preprocessing; Covariance matrix; Engenharia - Telecomunica????es |
title_short |
Machine Learning-based Direction of Arrival Estimation |
title_full |
Machine Learning-based Direction of Arrival Estimation |
title_fullStr |
Machine Learning-based Direction of Arrival Estimation |
title_full_unstemmed |
Machine Learning-based Direction of Arrival Estimation |
title_sort |
Machine Learning-based Direction of Arrival Estimation |
author |
Carballeira, Anabel Reyes |
author_facet |
Carballeira, Anabel Reyes |
author_role |
author |
dc.contributor.advisor2ID.por.fl_str_mv |
0370383210890132 |
dc.contributor.advisor1.fl_str_mv |
Figueiredo, Felipe |
dc.contributor.advisor1ID.fl_str_mv |
0188611850092267 |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/0188611850092267 |
dc.contributor.advisor2.fl_str_mv |
Brito, Jos?? Marcos |
dc.contributor.advisor2Lattes.fl_str_mv |
http://lattes.cnpq.br/0370383210890132 |
dc.contributor.referee1.fl_str_mv |
Figueiredo, . Felipe |
dc.contributor.referee1ID.fl_str_mv |
0188611850092267 |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/0188611850092267 |
dc.contributor.referee2.fl_str_mv |
Dias, Cl??udio Ferreira |
dc.contributor.referee2ID.fl_str_mv |
6610319457893381 |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/6610319457893381 |
dc.contributor.referee3.fl_str_mv |
Mafra, Samuel |
dc.contributor.referee3ID.fl_str_mv |
9492423249629649 |
dc.contributor.referee3Lattes.fl_str_mv |
http://lattes.cnpq.br/9492423249629649 |
dc.contributor.author.fl_str_mv |
Carballeira, Anabel Reyes |
contributor_str_mv |
Figueiredo, Felipe Brito, Jos?? Marcos Figueiredo, . Felipe Dias, Cl??udio Ferreira Mafra, Samuel |
dc.subject.por.fl_str_mv |
Dire????o de Chegada; Aprendizado de M??quina; Regress??o; Pr??-processamento de dados; Matriz de covari??ncia; |
topic |
Dire????o de Chegada; Aprendizado de M??quina; Regress??o; Pr??-processamento de dados; Matriz de covari??ncia; Direction of Arrival; Machine Learning; Regression; Data preprocessing; Covariance matrix; Engenharia - Telecomunica????es |
dc.subject.eng.fl_str_mv |
Direction of Arrival; Machine Learning; Regression; Data preprocessing; Covariance matrix; |
dc.subject.cnpq.fl_str_mv |
Engenharia - Telecomunica????es |
description |
Beamforming (BF) is expected to be one of the key technologies in Sixth Generation (6G) networks. BF improves the Signal-to-Noise Ratio (SNR) of received signals and focuses the radiation pattern to specific locations by weighting the amplitude and phase of individual antenna signals. This technique provides better coverage in an indoor environment and at the edge of a cell. To make the best use of this technology, it is essential to know the location of the device to direct the antenna beam of the radio Base Station (BS). Consequently, the Direction of Arrival (DOA) method becomes crucial and essential at this time. Therefore, this study addresses the problem of accurately predicting the azimuth and elevation angles of a signal impinging on an antenna array based on Machine Learning (ML) models. Simulation results show that ML models are competitive techniques to find the azimuth and elevation angles of a signal impinging on a receiving system. |
publishDate |
2023 |
dc.date.issued.fl_str_mv |
2023-02-09 |
dc.date.accessioned.fl_str_mv |
2024-03-26T17:44:18Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
Carballeira, Anabel Reyes. Machine Learning-based Direction of Arrival Estimation. 2023. [115 p.]. disserta????o( Mestrado em Engenharia de Telecomunica????es) - Instituto Nacional de Telecomunica????es, [Santa Rita Do Sapuca??] . |
dc.identifier.uri.fl_str_mv |
https://tede.inatel.br:8080/tede/handle/tede/252 |
identifier_str_mv |
Carballeira, Anabel Reyes. Machine Learning-based Direction of Arrival Estimation. 2023. [115 p.]. disserta????o( Mestrado em Engenharia de Telecomunica????es) - Instituto Nacional de Telecomunica????es, [Santa Rita Do Sapuca??] . |
url |
https://tede.inatel.br:8080/tede/handle/tede/252 |
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por |
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por |
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openAccess |
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Instituto Nacional de Telecomunica????es |
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Mestrado em Engenharia de Telecomunica????es |
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INATEL |
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Brasil |
dc.publisher.department.fl_str_mv |
Instituto Nacional de Telecomunica????es |
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Instituto Nacional de Telecomunica????es |
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