An??lise de mobilidade e um Autoencoder Robusto

Detalhes bibliográficos
Ano de defesa: 2022
Autor(a) principal: Pereira , Pedro M??rcio Raposo
Orientador(a): Souza , Rausley Adriano Amaral de lattes
Banca de defesa: Souza , Rausley Adriano Amaral de lattes, Bonfin, Roberto Cesar Dias Vilela lattes, Figueiredo, Felipe Augusto Pereira De lattes, Brito, Jos?? Marcos C??mara
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Instituto Nacional de Telecomunica????es
Programa de Pós-Graduação: Mestrado em Engenharia de Telecomunica????es
Departamento: Instituto Nacional de Telecomunica????es
País: Brasil
Palavras-chave em Português:
5G; 6G; mobilidade; sombreamento; IA; autoencoder; desvanecimento ??-??. xx
Palavras-chave em Inglês:
5G; 6G; mobility; shadowing; IA; autoencoder; ??-?? fading.
Área do conhecimento CNPq:
Engenharia - Telecomunica????es
Link de acesso: https://tede.inatel.br:8080/tede/handle/tede/234
Resumo: Statistical channel modeling plays an important role in the development of commu nication networks. With the advent of 5th generation of mobile networks (5G) and 6th generation of mobile networks (6G), it is necessary to use generalist models, since networks are expected to be increasingly diversified in terms of connected devices and with greater need for resources and efficiency. A promising paradigm for modern networks is artificial intelligence (AI), with the role of optimization, integration and management at various levels. This work seeks to evaluate a general ??-?? fading model affected by Gamma sha dowing in a random waypoint model (RWP) mobility scenario for different propa gation environments and physical network topologies. New expressions were ob tained for probability density function (PDF), cumulative distribution function (CDF), average symbol error probability (ASEP), outage probability (OP) and capacity. Then, the application of a communication system based on dense neural network (DNN) as an autoencoder (AE) in the proposed channel is investigated. With only the knowledge of the channel samples, the AE obtained a performance similar to traditional modulations and proved to be robust for channel variations.
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spelling Souza , Rausley Adriano Amaral de996.751.536-87http://lattes.cnpq.br/6238219709706103Souza , Rausley Adriano Amaral de996.751.536-87http://lattes.cnpq.br/6238219709706103Bonfin, Roberto Cesar Dias VilelaFigueiredo, Felipe Augusto Pereira De051.996.986-30http://lattes.cnpq.br/0188611850092267Brito, Jos?? Marcos C??mara495.450.866-53http://lattes.cnpq.br/0370383210890132087.460.176-23Pereira , Pedro M??rcio Raposo2022-08-23T13:53:05Z2022-07-19Pereira , Pedro M??rcio Raposo. An??lise de mobilidade e um Autoencoder Robusto. 2022. [96]. 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/234Statistical channel modeling plays an important role in the development of commu nication networks. With the advent of 5th generation of mobile networks (5G) and 6th generation of mobile networks (6G), it is necessary to use generalist models, since networks are expected to be increasingly diversified in terms of connected devices and with greater need for resources and efficiency. A promising paradigm for modern networks is artificial intelligence (AI), with the role of optimization, integration and management at various levels. This work seeks to evaluate a general ??-?? fading model affected by Gamma sha dowing in a random waypoint model (RWP) mobility scenario for different propa gation environments and physical network topologies. New expressions were ob tained for probability density function (PDF), cumulative distribution function (CDF), average symbol error probability (ASEP), outage probability (OP) and capacity. Then, the application of a communication system based on dense neural network (DNN) as an autoencoder (AE) in the proposed channel is investigated. With only the knowledge of the channel samples, the AE obtained a performance similar to traditional modulations and proved to be robust for channel variations.A modelagem estat??stica de canais desempenha um papel importante no desenvolvimento de redes de comunica????es, com o advento da quinta gera????o de redes m??veis (5G) e sexta gera????o de redes m??veis (6G), j?? que se espera redes cada vez mais diversificadas quanto aos dispositivos conectados e com maior necessidade de recursos e efici??ncia. Um paradigma promissor para redes modernas e a intelig??ncia artificial ( artificial intelligence, AI), com o papel de otimiza????o, integra????o e ger??ncia em v??rios n??veis. Este trabalho procura avaliar um modelo generalista de desvanecimento ??-?? afetado por um sombreamento Gama em um cen??rio de mobilidade do tipo modelo de paradas aleat??rias ( random waypoint model, RWP) para diferentes ambientes de propaga????o e topologias. Obtiveram-se novas express??es para fun????o densidade de probabilidade (FDP), fun????o de distribui????o cumulativa (FDC), probabilidade de erro de s??mbolo media ( average symbol error probability, ASEP), probabilidade de indisponibilidade (PI) e capacidade. Tamb??m, verificou-se a aplica????o?? ao de um sistema de comunica????o, baseado em rede neural densa (dense neural network, DNN), na forma de um autoencoder (AE) no canal proposto. Com o conhecimento apenas das amostras do canal, o AE obteve desempenho similar as modula????es tradicionais e se mostrou robusto para varia????es no canal.Submitted by Tede Dspace (tede@inatel.br) on 2022-08-23T13:52:23Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Disserta????o V.Final Pedro Marcio.pdf: 2382555 bytes, checksum: 57aedde8feeb3484c7f321c61ef6f53a (MD5)Made available in DSpace on 2022-08-23T13:53:05Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Disserta????o V.Final Pedro Marcio.pdf: 2382555 bytes, checksum: 57aedde8feeb3484c7f321c61ef6f53a (MD5) Previous issue date: 2022-07-19application/pdfhttp://tede.inatel.br:8080/jspui/retrieve/1869/Disserta%c3%a7%c3%a3o%20V.Final%20Pedro%20Marcio.pdf.jpgporInstituto Nacional de Telecomunica????esMestrado em Engenharia de Telecomunica????esINATELBrasilInstituto Nacional de Telecomunica????eshttp://creativecommons.org/licenses/by-nd/4.0/info:eu-repo/semantics/openAccess5G; 6G; mobilidade; sombreamento; IA; autoencoder; desvanecimento ??-??. xx5G; 6G; mobility; shadowing; IA; autoencoder; ??-?? fading.Engenharia - Telecomunica????esAn??lise de mobilidade e um Autoencoder Robustoinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisreponame: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/234/1/license.txtad97de64637545abb37de9243411913cMD51CC-LICENSElicense_urllicense_urltext/plain; charset=utf-846http://localhost:8080/tede/bitstream/tede/234/2/license_url587cd8ffae15c8598ed3c46d248a3f38MD52license_textlicense_texttext/html; charset=utf-80http://localhost:8080/tede/bitstream/tede/234/3/license_textd41d8cd98f00b204e9800998ecf8427eMD53license_rdflicense_rdfapplication/rdf+xml; charset=utf-80http://localhost:8080/tede/bitstream/tede/234/4/license_rdfd41d8cd98f00b204e9800998ecf8427eMD54ORIGINALDisserta????o V.Final Pedro Marcio.pdfDisserta????o V.Final Pedro Marcio.pdfapplication/pdf2382555http://localhost:8080/tede/bitstream/tede/234/5/Disserta%C3%A7%C3%A3o+V.Final+Pedro+Marcio.pdf57aedde8feeb3484c7f321c61ef6f53aMD55TEXTDisserta????o V.Final Pedro Marcio.pdf.txtDisserta????o V.Final Pedro Marcio.pdf.txttext/plain127701http://localhost:8080/tede/bitstream/tede/234/6/Disserta%C3%A7%C3%A3o+V.Final+Pedro+Marcio.pdf.txt3613fea4a25da49cf00135d3d14021bcMD56THUMBNAILDisserta????o V.Final Pedro Marcio.pdf.jpgDisserta????o V.Final Pedro Marcio.pdf.jpgimage/jpeg4005http://localhost:8080/tede/bitstream/tede/234/7/Disserta%C3%A7%C3%A3o+V.Final+Pedro+Marcio.pdf.jpg2adde8025a5c24dd0c9687d52824e6fcMD57tede/2342022-08-24 01:00:09.078oai:localhost:tede/234aHR0cDovL2NyZWF0aXZlY29tbW9ucy5vcmcvbGljZW5zZXMvYnktbmMtbmQvNC4wLy4=Biblioteca Digital de Teses e Dissertaçõeshttp://tede.inatel.br:8080/jspui/PUBhttp://tede.inatel.br:8080/oai/requestbiblioteca@inatel.br || biblioteca.atendimento@inatel.bropendoar:2022-08-24T04:00:09Biblioteca Digital de Teses e Dissertações da INATEL - Instituto Nacional de Telecomunicações (INATEL)false
dc.title.por.fl_str_mv An??lise de mobilidade e um Autoencoder Robusto
title An??lise de mobilidade e um Autoencoder Robusto
spellingShingle An??lise de mobilidade e um Autoencoder Robusto
Pereira , Pedro M??rcio Raposo
5G; 6G; mobilidade; sombreamento; IA; autoencoder; desvanecimento ??-??. xx
5G; 6G; mobility; shadowing; IA; autoencoder; ??-?? fading.
Engenharia - Telecomunica????es
title_short An??lise de mobilidade e um Autoencoder Robusto
title_full An??lise de mobilidade e um Autoencoder Robusto
title_fullStr An??lise de mobilidade e um Autoencoder Robusto
title_full_unstemmed An??lise de mobilidade e um Autoencoder Robusto
title_sort An??lise de mobilidade e um Autoencoder Robusto
author Pereira , Pedro M??rcio Raposo
author_facet Pereira , Pedro M??rcio Raposo
author_role author
dc.contributor.advisor1.fl_str_mv Souza , Rausley Adriano Amaral de
dc.contributor.advisor1ID.fl_str_mv 996.751.536-87
dc.contributor.advisor1Lattes.fl_str_mv http://lattes.cnpq.br/6238219709706103
dc.contributor.referee1.fl_str_mv Souza , Rausley Adriano Amaral de
dc.contributor.referee1ID.fl_str_mv 996.751.536-87
dc.contributor.referee1Lattes.fl_str_mv http://lattes.cnpq.br/6238219709706103
dc.contributor.referee2.fl_str_mv Bonfin, Roberto Cesar Dias Vilela
dc.contributor.referee3.fl_str_mv Figueiredo, Felipe Augusto Pereira De
dc.contributor.referee3ID.fl_str_mv 051.996.986-30
dc.contributor.referee3Lattes.fl_str_mv http://lattes.cnpq.br/0188611850092267
dc.contributor.referee4.fl_str_mv Brito, Jos?? Marcos C??mara
dc.contributor.referee4ID.fl_str_mv 495.450.866-53
dc.contributor.referee4Lattes.fl_str_mv http://lattes.cnpq.br/0370383210890132
dc.contributor.authorID.fl_str_mv 087.460.176-23
dc.contributor.author.fl_str_mv Pereira , Pedro M??rcio Raposo
contributor_str_mv Souza , Rausley Adriano Amaral de
Souza , Rausley Adriano Amaral de
Bonfin, Roberto Cesar Dias Vilela
Figueiredo, Felipe Augusto Pereira De
Brito, Jos?? Marcos C??mara
dc.subject.por.fl_str_mv 5G; 6G; mobilidade; sombreamento; IA; autoencoder; desvanecimento ??-??. xx
topic 5G; 6G; mobilidade; sombreamento; IA; autoencoder; desvanecimento ??-??. xx
5G; 6G; mobility; shadowing; IA; autoencoder; ??-?? fading.
Engenharia - Telecomunica????es
dc.subject.eng.fl_str_mv 5G; 6G; mobility; shadowing; IA; autoencoder; ??-?? fading.
dc.subject.cnpq.fl_str_mv Engenharia - Telecomunica????es
description Statistical channel modeling plays an important role in the development of commu nication networks. With the advent of 5th generation of mobile networks (5G) and 6th generation of mobile networks (6G), it is necessary to use generalist models, since networks are expected to be increasingly diversified in terms of connected devices and with greater need for resources and efficiency. A promising paradigm for modern networks is artificial intelligence (AI), with the role of optimization, integration and management at various levels. This work seeks to evaluate a general ??-?? fading model affected by Gamma sha dowing in a random waypoint model (RWP) mobility scenario for different propa gation environments and physical network topologies. New expressions were ob tained for probability density function (PDF), cumulative distribution function (CDF), average symbol error probability (ASEP), outage probability (OP) and capacity. Then, the application of a communication system based on dense neural network (DNN) as an autoencoder (AE) in the proposed channel is investigated. With only the knowledge of the channel samples, the AE obtained a performance similar to traditional modulations and proved to be robust for channel variations.
publishDate 2022
dc.date.accessioned.fl_str_mv 2022-08-23T13:53:05Z
dc.date.issued.fl_str_mv 2022-07-19
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dc.identifier.citation.fl_str_mv Pereira , Pedro M??rcio Raposo. An??lise de mobilidade e um Autoencoder Robusto. 2022. [96]. 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/234
identifier_str_mv Pereira , Pedro M??rcio Raposo. An??lise de mobilidade e um Autoencoder Robusto. 2022. [96]. disserta????o( Mestrado em Engenharia de Telecomunica????es) - Instituto Nacional de Telecomunica????es, [Santa Rita Do Sapuca??] .
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