An Exponentially-Tight Approximate Factorization of the Joint PDF of Statistical Dependent Measurements in Wireless Sensor Networks

Autores
Maya, Juan Augusto; Rey Vega, Leonardo Javier; Lopez Tonellotto, Mariana Andrea
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We consider the distributed detection problem of a temporally correlated random radio source signal using a wireless sensor network capable of measuring the energy of the received signals. It is well-known that optimal tests in the Neyman-Pearson setting are based on likelihood ratio tests (LRT), which, in this set-up, evaluate the quotient between the probability density functions (PDF) of the measurements when the source signal is present and absent. When the source is present, the computation of the joint PDF of the energy measurements at the nodes is a challenging problem. This is due to the statistical dependence introduced to the received signals by the propagation through fading channels of the radio signal emitted by the source. We deal with this problem using the characteristic function of the (intractable) joint PDF, and proposing an approximation to it. We derive bounds for the approximation error in two wireless propagation scenarios, slow and fast fading, and show that the proposed approximation is exponentially tight with the number of nodes when the time-bandwidth product is sufficiently high. The approximation is used as a substitute of the exact joint PDF for building an approximate LRT, which performs better than other well-known detectors, as verified by Monte Carlo simulations.
Fil: Maya, Juan Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; Argentina
Fil: Rey Vega, Leonardo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Estudios Andinos "Don Pablo Groeber". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Estudios Andinos "Don Pablo Groeber"; Argentina
Fil: Lopez Tonellotto, Mariana Andrea. University of Klagenfurt; Austria
Materia
CORRELATION
DISTRIBUTED DETECTION
ENERGY MEASUREMENT
FADING CHANNELS
JOINT PDF FACTORIZATION
LIGHT RAIL SYSTEMS
PROBABILITY DENSITY FUNCTION
SENSORS
STATISTICALLY DEPENDENT OBSERVATIONS
WIRELESS SENSOR NETWORKS
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:11336/239943
Acceder
id CONICETDig_c38164dc33bf6b0e1a64dc6869bd367f
oai_identifier_str oai:ri.conicet.gov.ar:11336/239943
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling An Exponentially-Tight Approximate Factorization of the Joint PDF of Statistical Dependent Measurements in Wireless Sensor NetworksMaya, Juan AugustoRey Vega, Leonardo JavierLopez Tonellotto, Mariana AndreaCORRELATIONDISTRIBUTED DETECTIONENERGY MEASUREMENTFADING CHANNELSJOINT PDF FACTORIZATIONLIGHT RAIL SYSTEMSPROBABILITY DENSITY FUNCTIONSENSORSSTATISTICALLY DEPENDENT OBSERVATIONSWIRELESS SENSOR NETWORKShttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2We consider the distributed detection problem of a temporally correlated random radio source signal using a wireless sensor network capable of measuring the energy of the received signals. It is well-known that optimal tests in the Neyman-Pearson setting are based on likelihood ratio tests (LRT), which, in this set-up, evaluate the quotient between the probability density functions (PDF) of the measurements when the source signal is present and absent. When the source is present, the computation of the joint PDF of the energy measurements at the nodes is a challenging problem. This is due to the statistical dependence introduced to the received signals by the propagation through fading channels of the radio signal emitted by the source. We deal with this problem using the characteristic function of the (intractable) joint PDF, and proposing an approximation to it. We derive bounds for the approximation error in two wireless propagation scenarios, slow and fast fading, and show that the proposed approximation is exponentially tight with the number of nodes when the time-bandwidth product is sufficiently high. The approximation is used as a substitute of the exact joint PDF for building an approximate LRT, which performs better than other well-known detectors, as verified by Monte Carlo simulations.Fil: Maya, Juan Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; ArgentinaFil: Rey Vega, Leonardo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Estudios Andinos "Don Pablo Groeber". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Estudios Andinos "Don Pablo Groeber"; ArgentinaFil: Lopez Tonellotto, Mariana Andrea. University of Klagenfurt; AustriaInstitute of Electrical and Electronics Engineers2024-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/239943Maya, Juan Augusto; Rey Vega, Leonardo Javier; Lopez Tonellotto, Mariana Andrea; An Exponentially-Tight Approximate Factorization of the Joint PDF of Statistical Dependent Measurements in Wireless Sensor Networks; Institute of Electrical and Electronics Engineers; IEEE Open Journal of the Communications Society; 5; 1-2024; 221-2372644-125XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1109/OJCOMS.2023.3332259info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:13:06Zoai:ri.conicet.gov.ar:11336/239943instacron: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:34982025-10-22 11:13:06.58CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv An Exponentially-Tight Approximate Factorization of the Joint PDF of Statistical Dependent Measurements in Wireless Sensor Networks
title An Exponentially-Tight Approximate Factorization of the Joint PDF of Statistical Dependent Measurements in Wireless Sensor Networks
spellingShingle An Exponentially-Tight Approximate Factorization of the Joint PDF of Statistical Dependent Measurements in Wireless Sensor Networks
Maya, Juan Augusto
CORRELATION
DISTRIBUTED DETECTION
ENERGY MEASUREMENT
FADING CHANNELS
JOINT PDF FACTORIZATION
LIGHT RAIL SYSTEMS
PROBABILITY DENSITY FUNCTION
SENSORS
STATISTICALLY DEPENDENT OBSERVATIONS
WIRELESS SENSOR NETWORKS
title_short An Exponentially-Tight Approximate Factorization of the Joint PDF of Statistical Dependent Measurements in Wireless Sensor Networks
title_full An Exponentially-Tight Approximate Factorization of the Joint PDF of Statistical Dependent Measurements in Wireless Sensor Networks
title_fullStr An Exponentially-Tight Approximate Factorization of the Joint PDF of Statistical Dependent Measurements in Wireless Sensor Networks
title_full_unstemmed An Exponentially-Tight Approximate Factorization of the Joint PDF of Statistical Dependent Measurements in Wireless Sensor Networks
title_sort An Exponentially-Tight Approximate Factorization of the Joint PDF of Statistical Dependent Measurements in Wireless Sensor Networks
dc.creator.none.fl_str_mv Maya, Juan Augusto
Rey Vega, Leonardo Javier
Lopez Tonellotto, Mariana Andrea
author Maya, Juan Augusto
author_facet Maya, Juan Augusto
Rey Vega, Leonardo Javier
Lopez Tonellotto, Mariana Andrea
author_role author
author2 Rey Vega, Leonardo Javier
Lopez Tonellotto, Mariana Andrea
author2_role author
author
dc.subject.none.fl_str_mv CORRELATION
DISTRIBUTED DETECTION
ENERGY MEASUREMENT
FADING CHANNELS
JOINT PDF FACTORIZATION
LIGHT RAIL SYSTEMS
PROBABILITY DENSITY FUNCTION
SENSORS
STATISTICALLY DEPENDENT OBSERVATIONS
WIRELESS SENSOR NETWORKS
topic CORRELATION
DISTRIBUTED DETECTION
ENERGY MEASUREMENT
FADING CHANNELS
JOINT PDF FACTORIZATION
LIGHT RAIL SYSTEMS
PROBABILITY DENSITY FUNCTION
SENSORS
STATISTICALLY DEPENDENT OBSERVATIONS
WIRELESS SENSOR NETWORKS
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 consider the distributed detection problem of a temporally correlated random radio source signal using a wireless sensor network capable of measuring the energy of the received signals. It is well-known that optimal tests in the Neyman-Pearson setting are based on likelihood ratio tests (LRT), which, in this set-up, evaluate the quotient between the probability density functions (PDF) of the measurements when the source signal is present and absent. When the source is present, the computation of the joint PDF of the energy measurements at the nodes is a challenging problem. This is due to the statistical dependence introduced to the received signals by the propagation through fading channels of the radio signal emitted by the source. We deal with this problem using the characteristic function of the (intractable) joint PDF, and proposing an approximation to it. We derive bounds for the approximation error in two wireless propagation scenarios, slow and fast fading, and show that the proposed approximation is exponentially tight with the number of nodes when the time-bandwidth product is sufficiently high. The approximation is used as a substitute of the exact joint PDF for building an approximate LRT, which performs better than other well-known detectors, as verified by Monte Carlo simulations.
Fil: Maya, Juan Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; Argentina
Fil: Rey Vega, Leonardo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Estudios Andinos "Don Pablo Groeber". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Estudios Andinos "Don Pablo Groeber"; Argentina
Fil: Lopez Tonellotto, Mariana Andrea. University of Klagenfurt; Austria
description We consider the distributed detection problem of a temporally correlated random radio source signal using a wireless sensor network capable of measuring the energy of the received signals. It is well-known that optimal tests in the Neyman-Pearson setting are based on likelihood ratio tests (LRT), which, in this set-up, evaluate the quotient between the probability density functions (PDF) of the measurements when the source signal is present and absent. When the source is present, the computation of the joint PDF of the energy measurements at the nodes is a challenging problem. This is due to the statistical dependence introduced to the received signals by the propagation through fading channels of the radio signal emitted by the source. We deal with this problem using the characteristic function of the (intractable) joint PDF, and proposing an approximation to it. We derive bounds for the approximation error in two wireless propagation scenarios, slow and fast fading, and show that the proposed approximation is exponentially tight with the number of nodes when the time-bandwidth product is sufficiently high. The approximation is used as a substitute of the exact joint PDF for building an approximate LRT, which performs better than other well-known detectors, as verified by Monte Carlo simulations.
publishDate 2024
dc.date.none.fl_str_mv 2024-01
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/11336/239943
Maya, Juan Augusto; Rey Vega, Leonardo Javier; Lopez Tonellotto, Mariana Andrea; An Exponentially-Tight Approximate Factorization of the Joint PDF of Statistical Dependent Measurements in Wireless Sensor Networks; Institute of Electrical and Electronics Engineers; IEEE Open Journal of the Communications Society; 5; 1-2024; 221-237
2644-125X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/239943
identifier_str_mv Maya, Juan Augusto; Rey Vega, Leonardo Javier; Lopez Tonellotto, Mariana Andrea; An Exponentially-Tight Approximate Factorization of the Joint PDF of Statistical Dependent Measurements in Wireless Sensor Networks; Institute of Electrical and Electronics Engineers; IEEE Open Journal of the Communications Society; 5; 1-2024; 221-237
2644-125X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1109/OJCOMS.2023.3332259
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
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_ 1846781534030266368
score 12.982451

Publicaciones similares