Low-Complexity Channel Prediction Using Approximated Recursive DCT

Autores
Schmidt, Jorge Friedrich; Cousseau, Juan Edmundo; Wichman, Risto Ilari; Werner, Stefan
Año de publicación
2011
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We present a novel channel estimator/predictor for OFDM systems over time-varying channels using a recursive formulation of a basis expansion model (BEM) based on an approximated discrete cosine transform (DCT). We derive a recursive implementation of the approximated DCT-BEM for tracking time-varying channels based on a filter bank. The recursive approximated DCT-BEM structure is then used for long range channel prediction by proper scaling and time extrapolation of the filter bank. As the implicit BEM is time invariant we further simplify the implementation by employing a steady-state Kalman filter whose overall complexity is comparable to an LMS algorithm. The derived predictor outperforms, in terms of predictor range, previously proposed long range predictors that are based on autoregressive (AR) modeling of the time-varying channel. For a similar performance, in terms of MSE, the computational complexity of the proposed predictor is significantly lower than conventional sum-of-sinusoids (SOS) channel predictors as no channel delays nor Doppler frequencies need to be estimated.
Fil: Schmidt, Jorge Friedrich. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina
Fil: Cousseau, Juan Edmundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina
Fil: Wichman, Risto Ilari. Aalto University; Finlandia
Fil: Werner, Stefan. Aalto University; Finlandia
Materia
Channel prediction
Discrete cosine transform
Basis function approximation
Doppler spectrum
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/102250
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/102250
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Low-Complexity Channel Prediction Using Approximated Recursive DCTSchmidt, Jorge FriedrichCousseau, Juan EdmundoWichman, Risto IlariWerner, StefanChannel predictionDiscrete cosine transformBasis function approximationDoppler spectrumhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2We present a novel channel estimator/predictor for OFDM systems over time-varying channels using a recursive formulation of a basis expansion model (BEM) based on an approximated discrete cosine transform (DCT). We derive a recursive implementation of the approximated DCT-BEM for tracking time-varying channels based on a filter bank. The recursive approximated DCT-BEM structure is then used for long range channel prediction by proper scaling and time extrapolation of the filter bank. As the implicit BEM is time invariant we further simplify the implementation by employing a steady-state Kalman filter whose overall complexity is comparable to an LMS algorithm. The derived predictor outperforms, in terms of predictor range, previously proposed long range predictors that are based on autoregressive (AR) modeling of the time-varying channel. For a similar performance, in terms of MSE, the computational complexity of the proposed predictor is significantly lower than conventional sum-of-sinusoids (SOS) channel predictors as no channel delays nor Doppler frequencies need to be estimated.Fil: Schmidt, Jorge Friedrich. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Cousseau, Juan Edmundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Wichman, Risto Ilari. Aalto University; FinlandiaFil: Werner, Stefan. Aalto University; FinlandiaInstitute of Electrical and Electronics Engineers2011-07-14info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/102250Schmidt, Jorge Friedrich; Cousseau, Juan Edmundo; Wichman, Risto Ilari; Werner, Stefan; Low-Complexity Channel Prediction Using Approximated Recursive DCT; Institute of Electrical and Electronics Engineers; IEEE Transactions On Circuits And Systems I-regular Papers; 58; 10; 14-7-2011; 2520-25301549-8328CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/5951808info:eu-repo/semantics/altIdentifier/doi/10.1109/TCSI.2011.2158139info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:55:06Zoai:ri.conicet.gov.ar:11336/102250instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:55:07.264CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Low-Complexity Channel Prediction Using Approximated Recursive DCT
title Low-Complexity Channel Prediction Using Approximated Recursive DCT
spellingShingle Low-Complexity Channel Prediction Using Approximated Recursive DCT
Schmidt, Jorge Friedrich
Channel prediction
Discrete cosine transform
Basis function approximation
Doppler spectrum
title_short Low-Complexity Channel Prediction Using Approximated Recursive DCT
title_full Low-Complexity Channel Prediction Using Approximated Recursive DCT
title_fullStr Low-Complexity Channel Prediction Using Approximated Recursive DCT
title_full_unstemmed Low-Complexity Channel Prediction Using Approximated Recursive DCT
title_sort Low-Complexity Channel Prediction Using Approximated Recursive DCT
dc.creator.none.fl_str_mv Schmidt, Jorge Friedrich
Cousseau, Juan Edmundo
Wichman, Risto Ilari
Werner, Stefan
author Schmidt, Jorge Friedrich
author_facet Schmidt, Jorge Friedrich
Cousseau, Juan Edmundo
Wichman, Risto Ilari
Werner, Stefan
author_role author
author2 Cousseau, Juan Edmundo
Wichman, Risto Ilari
Werner, Stefan
author2_role author
author
author
dc.subject.none.fl_str_mv Channel prediction
Discrete cosine transform
Basis function approximation
Doppler spectrum
topic Channel prediction
Discrete cosine transform
Basis function approximation
Doppler spectrum
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv We present a novel channel estimator/predictor for OFDM systems over time-varying channels using a recursive formulation of a basis expansion model (BEM) based on an approximated discrete cosine transform (DCT). We derive a recursive implementation of the approximated DCT-BEM for tracking time-varying channels based on a filter bank. The recursive approximated DCT-BEM structure is then used for long range channel prediction by proper scaling and time extrapolation of the filter bank. As the implicit BEM is time invariant we further simplify the implementation by employing a steady-state Kalman filter whose overall complexity is comparable to an LMS algorithm. The derived predictor outperforms, in terms of predictor range, previously proposed long range predictors that are based on autoregressive (AR) modeling of the time-varying channel. For a similar performance, in terms of MSE, the computational complexity of the proposed predictor is significantly lower than conventional sum-of-sinusoids (SOS) channel predictors as no channel delays nor Doppler frequencies need to be estimated.
Fil: Schmidt, Jorge Friedrich. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina
Fil: Cousseau, Juan Edmundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina
Fil: Wichman, Risto Ilari. Aalto University; Finlandia
Fil: Werner, Stefan. Aalto University; Finlandia
description We present a novel channel estimator/predictor for OFDM systems over time-varying channels using a recursive formulation of a basis expansion model (BEM) based on an approximated discrete cosine transform (DCT). We derive a recursive implementation of the approximated DCT-BEM for tracking time-varying channels based on a filter bank. The recursive approximated DCT-BEM structure is then used for long range channel prediction by proper scaling and time extrapolation of the filter bank. As the implicit BEM is time invariant we further simplify the implementation by employing a steady-state Kalman filter whose overall complexity is comparable to an LMS algorithm. The derived predictor outperforms, in terms of predictor range, previously proposed long range predictors that are based on autoregressive (AR) modeling of the time-varying channel. For a similar performance, in terms of MSE, the computational complexity of the proposed predictor is significantly lower than conventional sum-of-sinusoids (SOS) channel predictors as no channel delays nor Doppler frequencies need to be estimated.
publishDate 2011
dc.date.none.fl_str_mv 2011-07-14
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/102250
Schmidt, Jorge Friedrich; Cousseau, Juan Edmundo; Wichman, Risto Ilari; Werner, Stefan; Low-Complexity Channel Prediction Using Approximated Recursive DCT; Institute of Electrical and Electronics Engineers; IEEE Transactions On Circuits And Systems I-regular Papers; 58; 10; 14-7-2011; 2520-2530
1549-8328
CONICET Digital
CONICET
url http://hdl.handle.net/11336/102250
identifier_str_mv Schmidt, Jorge Friedrich; Cousseau, Juan Edmundo; Wichman, Risto Ilari; Werner, Stefan; Low-Complexity Channel Prediction Using Approximated Recursive DCT; Institute of Electrical and Electronics Engineers; IEEE Transactions On Circuits And Systems I-regular Papers; 58; 10; 14-7-2011; 2520-2530
1549-8328
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/5951808
info:eu-repo/semantics/altIdentifier/doi/10.1109/TCSI.2011.2158139
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842269325419872256
score 13.13397

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