Modelo de decisión espectral colaborativo para redes de radio cognitiva

La decisión espectral es un aspecto clave para mejorar el desempeño en las redes de radio cognitiva descentralizadas. Los usuarios secundarios deben tomar decisiones inteligentes en función de la variación del espectro y de las acciones adoptadas por otros usuarios secundarios. A partir de esta diná...

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Fecha de publicación:: 2020

Institución:: Universidad Distrital Francisco José de Caldas

Repositorio:: RIUD: repositorio U. Distrital

Idioma:: spa

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network_acronym_str	UDISTRITA2
network_name_str	RIUD: repositorio U. Distrital
repository_id_str
dc.title.spa.fl_str_mv	Modelo de decisión espectral colaborativo para redes de radio cognitiva
dc.title.titleenglish.spa.fl_str_mv	Collaborative spectral decision model for cognitive radio networks
title	Modelo de decisión espectral colaborativo para redes de radio cognitiva
spellingShingle	Modelo de decisión espectral colaborativo para redes de radio cognitiva Decisión espectral Redes de radio Redes de radio cognitiva Simulación Algoritmos colaborativo Redes de radio cognitiva Espectro radioeléctrico Spectral decision Radio networks Cognitive radio networks Simulation Collaborative algorithms
title_short	Modelo de decisión espectral colaborativo para redes de radio cognitiva
title_full	Modelo de decisión espectral colaborativo para redes de radio cognitiva
title_fullStr	Modelo de decisión espectral colaborativo para redes de radio cognitiva
title_full_unstemmed	Modelo de decisión espectral colaborativo para redes de radio cognitiva
title_sort	Modelo de decisión espectral colaborativo para redes de radio cognitiva
dc.contributor.orcid.spa.fl_str_mv	López Sarmiento, Danilo Alfonso [0000-0002-6148-3099] Giral Ramírez, Diego Armando [0000-0001-9983-4555]
dc.subject.spa.fl_str_mv	Decisión espectral Redes de radio Redes de radio cognitiva Simulación Algoritmos colaborativo
topic	Decisión espectral Redes de radio Redes de radio cognitiva Simulación Algoritmos colaborativo Redes de radio cognitiva Espectro radioeléctrico Spectral decision Radio networks Cognitive radio networks Simulation Collaborative algorithms
dc.subject.lemb.spa.fl_str_mv	Redes de radio cognitiva Espectro radioeléctrico
dc.subject.keyword.spa.fl_str_mv	Spectral decision Radio networks Cognitive radio networks Simulation Collaborative algorithms
description	La decisión espectral es un aspecto clave para mejorar el desempeño en las redes de radio cognitiva descentralizadas. Los usuarios secundarios deben tomar decisiones inteligentes en función de la variación del espectro y de las acciones adoptadas por otros usuarios secundarios. A partir de esta dinámica, la probabilidad de que dos o más usuarios secundarios elijan el mismo canal es alta, especialmente cuando el número de usuarios secundarios es mayor que el número de canales disponibles, debido a la externalidad negativa de la red; cuantos más usuarios secundarios seleccionen el mismo canal, menor será la utilidad que cada usuario secundario pueda obtener y el número de interferencias por el acceso simultáneo será mayor. Por esto, para modelar la red bajo parámetros de tráfico realistas, es necesario tener en cuenta la colaboración entre usuarios secundarios. Este libro de investigación presenta una propuesta para mejorar el proceso de toma de decisiones en una red de radio cognitiva descentralizada, y así dotar a los nodos con la capacidad de aprender del entorno, proponiendo estrategias que les permitan a los usuarios secundarios intercambiar información de forma cooperativa o competitiva.
publishDate	2020
dc.date.created.none.fl_str_mv	2020-11
dc.date.accessioned.none.fl_str_mv	2023-09-21T17:16:30Z
dc.date.available.none.fl_str_mv	2023-09-21T17:16:30Z
dc.type.spa.fl_str_mv	book
dc.type.coar.fl_str_mv	http://purl.org/coar/resource_type/c_2f33
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/book
dc.identifier.isbn.spa.fl_str_mv	978-958-787-245-3
dc.identifier.uri.none.fl_str_mv	http://hdl.handle.net/11349/32259
dc.identifier.editorial.spa.fl_str_mv	Universidad Distrital Francisco José de Caldas. Centro de Investigaciones y Desarrollo Científico
identifier_str_mv	978-958-787-245-3 Universidad Distrital Francisco José de Caldas. Centro de Investigaciones y Desarrollo Científico
url	http://hdl.handle.net/11349/32259
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.ispartofseries.spa.fl_str_mv	Espacios
dc.rights.*.fl_str_mv	Attribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_abf2
dc.rights.uri.*.fl_str_mv	http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.acceso.spa.fl_str_mv	Abierto (Texto Completo)
rights_invalid_str_mv	Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ Abierto (Texto Completo) http://purl.org/coar/access_right/c_abf2
dc.format.mimetype.spa.fl_str_mv	pdf
institution	Universidad Distrital Francisco José de Caldas
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spelling	will be generated::orcid::0000-0002-6148-3099600will be generated::orcid::0000-0001-9983-45556002023-09-21T17:16:30Z2023-09-21T17:16:30Z2020-11978-958-787-245-3http://hdl.handle.net/11349/32259Universidad Distrital Francisco José de Caldas. Centro de Investigaciones y Desarrollo CientíficoLa decisión espectral es un aspecto clave para mejorar el desempeño en las redes de radio cognitiva descentralizadas. Los usuarios secundarios deben tomar decisiones inteligentes en función de la variación del espectro y de las acciones adoptadas por otros usuarios secundarios. A partir de esta dinámica, la probabilidad de que dos o más usuarios secundarios elijan el mismo canal es alta, especialmente cuando el número de usuarios secundarios es mayor que el número de canales disponibles, debido a la externalidad negativa de la red; cuantos más usuarios secundarios seleccionen el mismo canal, menor será la utilidad que cada usuario secundario pueda obtener y el número de interferencias por el acceso simultáneo será mayor. Por esto, para modelar la red bajo parámetros de tráfico realistas, es necesario tener en cuenta la colaboración entre usuarios secundarios. Este libro de investigación presenta una propuesta para mejorar el proceso de toma de decisiones en una red de radio cognitiva descentralizada, y así dotar a los nodos con la capacidad de aprender del entorno, proponiendo estrategias que les permitan a los usuarios secundarios intercambiar información de forma cooperativa o competitiva.Spectral decision is a key aspect to improve performance in decentralized cognitive radio networks. Secondary users must make intelligent decisions based on spectrum variation and actions taken by other secondary users. From this dynamic, the probability that two or more secondary users choose the same channel is high, especially when the number of secondary users is greater than the number of available channels, due to the negative externality of the network; The more secondary users select the same channel, the less utility each secondary user can obtain and the greater the number of interferences due to simultaneous access. Therefore, to model the network under realistic traffic parameters, it is necessary to take into account the collaboration between secondary users. This research book presents a proposal to improve the decision-making process in a decentralized cognitive radio network, and thus provide the nodes with the ability to learn from the environment, proposing strategies that allow secondary users to exchange information cooperative or competitive.BogotápdfspaEspaciosAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Abierto (Texto Completo)http://purl.org/coar/access_right/c_abf2Decisión espectralRedes de radioRedes de radio cognitivaSimulaciónAlgoritmos colaborativoRedes de radio cognitivaEspectro radioeléctricoSpectral decisionRadio networksCognitive radio networksSimulationCollaborative algorithmsModelo de decisión espectral colaborativo para redes de radio cognitivaCollaborative spectral decision model for cognitive radio networksbookinfo:eu-repo/semantics/bookhttp://purl.org/coar/resource_type/c_2f333GPP. (2011). IEEE Approved Draft Standard For Information Technology. 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Broadband and Wireless Networking Laboratory technical report. Georgia Institute of Technology.Al-Amidie, M., Al-Asadi, A., Micheas, A. C. e Islam, N. E. (2019). Spectrum sensing based on Bayesian generalised likelihood ratio for cognitive radio systems with multiple antennas. IET Communications, 13(3), 305-311. http://doi. org/10.1049/iet-com.2018.5276Ali, A. y Hamouda, W. (2017). Advances on spectrum sensing for cognitive radio networks: Theory and applications. IEEE Communications Surveys & Tutorials, 19(2), 1277-1304. http://doi.org/10.1109/COMST.2016.2631080Alias, D. M. y Ragesh, G. k. (2016, 23-25 de marzo). Cognitive radio networks: A survey [presentación en conferencia]. 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, India. http://doi.org/10.1109/WiSPNET.2016.7566489Almasaeid, H. M. y kamal, A. E. (2010). Receiver-based channel allocation for wireless cognitive radio mesh networks. 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