Phase Drift Estimation and Symbol Detection in Digital Communications: A Stochastic Recursive Filtering Approach
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/11144/3694 |
Resumo: | This paper proposes a novel Bayesian stochastic filtering approach for the simultaneous phase drift estimation and symbol detection in digital communications. The posterior density of the phase drift is propagated in a recursive fashion by implementing a prediction and a filtering step in each iteration. The prediction step is supported on a random walk model playing the role of prior for the phase drift process; the filtering step is supported on a Gaussian sum approximation for the probability density of the current observation, i.e., the so-called sensor factor. The Gaussian sum approximation turns out to be the key element allowing to derive a fast and efficient stochastic filter, which otherwise would be very hard to compute. The detection of the digital symbols is then carried out based on the inferred statistics of the phase drift. The effectiveness of the proposed method is illustrated for BPSK signals in the presence of strong phase drift. |
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Phase Drift Estimation and Symbol Detection in Digital Communications: A Stochastic Recursive Filtering ApproachStochastic recursive filtering.Gaussian sum filterPhase driftState estimationBurst communicationsThis paper proposes a novel Bayesian stochastic filtering approach for the simultaneous phase drift estimation and symbol detection in digital communications. The posterior density of the phase drift is propagated in a recursive fashion by implementing a prediction and a filtering step in each iteration. The prediction step is supported on a random walk model playing the role of prior for the phase drift process; the filtering step is supported on a Gaussian sum approximation for the probability density of the current observation, i.e., the so-called sensor factor. The Gaussian sum approximation turns out to be the key element allowing to derive a fast and efficient stochastic filter, which otherwise would be very hard to compute. The detection of the digital symbols is then carried out based on the inferred statistics of the phase drift. The effectiveness of the proposed method is illustrated for BPSK signals in the presence of strong phase drift.IEEE2018-04-13T10:06:42Z2012-06-01T00:00:00Z2012-06info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/11144/3694eng1089-779810.1109/LCOMM.2012.042312.120314Bioucas-Dias, JoséDinis, RuiPedrosa, PedroNunes, Fernandoinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-11T02:18:31Zoai:repositorio.ual.pt:11144/3694Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:33:38.194598Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Phase Drift Estimation and Symbol Detection in Digital Communications: A Stochastic Recursive Filtering Approach |
title |
Phase Drift Estimation and Symbol Detection in Digital Communications: A Stochastic Recursive Filtering Approach |
spellingShingle |
Phase Drift Estimation and Symbol Detection in Digital Communications: A Stochastic Recursive Filtering Approach Bioucas-Dias, José Stochastic recursive filtering. Gaussian sum filter Phase drift State estimation Burst communications |
title_short |
Phase Drift Estimation and Symbol Detection in Digital Communications: A Stochastic Recursive Filtering Approach |
title_full |
Phase Drift Estimation and Symbol Detection in Digital Communications: A Stochastic Recursive Filtering Approach |
title_fullStr |
Phase Drift Estimation and Symbol Detection in Digital Communications: A Stochastic Recursive Filtering Approach |
title_full_unstemmed |
Phase Drift Estimation and Symbol Detection in Digital Communications: A Stochastic Recursive Filtering Approach |
title_sort |
Phase Drift Estimation and Symbol Detection in Digital Communications: A Stochastic Recursive Filtering Approach |
author |
Bioucas-Dias, José |
author_facet |
Bioucas-Dias, José Dinis, Rui Pedrosa, Pedro Nunes, Fernando |
author_role |
author |
author2 |
Dinis, Rui Pedrosa, Pedro Nunes, Fernando |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Bioucas-Dias, José Dinis, Rui Pedrosa, Pedro Nunes, Fernando |
dc.subject.por.fl_str_mv |
Stochastic recursive filtering. Gaussian sum filter Phase drift State estimation Burst communications |
topic |
Stochastic recursive filtering. Gaussian sum filter Phase drift State estimation Burst communications |
description |
This paper proposes a novel Bayesian stochastic filtering approach for the simultaneous phase drift estimation and symbol detection in digital communications. The posterior density of the phase drift is propagated in a recursive fashion by implementing a prediction and a filtering step in each iteration. The prediction step is supported on a random walk model playing the role of prior for the phase drift process; the filtering step is supported on a Gaussian sum approximation for the probability density of the current observation, i.e., the so-called sensor factor. The Gaussian sum approximation turns out to be the key element allowing to derive a fast and efficient stochastic filter, which otherwise would be very hard to compute. The detection of the digital symbols is then carried out based on the inferred statistics of the phase drift. The effectiveness of the proposed method is illustrated for BPSK signals in the presence of strong phase drift. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012-06-01T00:00:00Z 2012-06 2018-04-13T10:06:42Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/11144/3694 |
url |
http://hdl.handle.net/11144/3694 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1089-7798 10.1109/LCOMM.2012.042312.120314 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
IEEE |
publisher.none.fl_str_mv |
IEEE |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799136814587445248 |