Modelo de decisión espectral para redes de radio cognitiva

Este libro presenta el diseño de un modelo de decisión espectral dinámico para redes de radio cognitiva, que permite a los usuarios secundarios acceder al espectro de manera oportunista y utilizar el canal sin afectar el tráfico de los usuarios primarios. El objetivo de este trabajo es emplear el re...

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2019
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Universidad Distrital Francisco José de Caldas
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RIUD: repositorio U. Distrital
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OAI Identifier:
oai:repository.udistrital.edu.co:11349/32581
Acceso en línea:
http://hdl.handle.net/11349/32581
Palabra clave:
Ingeniería de sistemas
Redes
Transmisión de datos
Radio cognitiva
Ingeniería de sistemas
Redes inalámbricas
Sistemas de transmisión de datos
Redes de radio cognitiva
Systems engineer
Networks
Data transmission
Cognitive radio
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License
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
id UDISTRITA2_3f73393bbeddc0441a667d5f100874cd
oai_identifier_str oai:repository.udistrital.edu.co:11349/32581
network_acronym_str UDISTRITA2
network_name_str RIUD: repositorio U. Distrital
repository_id_str
dc.title.spa.fl_str_mv Modelo de decisión espectral para redes de radio cognitiva
dc.title.titleenglish.spa.fl_str_mv Spectral decision model for cognitive radio networks
title Modelo de decisión espectral para redes de radio cognitiva
spellingShingle Modelo de decisión espectral para redes de radio cognitiva
Ingeniería de sistemas
Redes
Transmisión de datos
Radio cognitiva
Ingeniería de sistemas
Redes inalámbricas
Sistemas de transmisión de datos
Redes de radio cognitiva
Systems engineer
Networks
Data transmission
Cognitive radio
title_short Modelo de decisión espectral para redes de radio cognitiva
title_full Modelo de decisión espectral para redes de radio cognitiva
title_fullStr Modelo de decisión espectral para redes de radio cognitiva
title_full_unstemmed Modelo de decisión espectral para redes de radio cognitiva
title_sort Modelo de decisión espectral para redes de radio cognitiva
dc.subject.spa.fl_str_mv Ingeniería de sistemas
Redes
Transmisión de datos
Radio cognitiva
topic Ingeniería de sistemas
Redes
Transmisión de datos
Radio cognitiva
Ingeniería de sistemas
Redes inalámbricas
Sistemas de transmisión de datos
Redes de radio cognitiva
Systems engineer
Networks
Data transmission
Cognitive radio
dc.subject.lemb.spa.fl_str_mv Ingeniería de sistemas
Redes inalámbricas
Sistemas de transmisión de datos
Redes de radio cognitiva
dc.subject.keyword.spa.fl_str_mv Systems engineer
Networks
Data transmission
Cognitive radio
description Este libro presenta el diseño de un modelo de decisión espectral dinámico para redes de radio cognitiva, que permite a los usuarios secundarios acceder al espectro de manera oportunista y utilizar el canal sin afectar el tráfico de los usuarios primarios. El objetivo de este trabajo es emplear el recurso del espectro de manera eficiente eligiendo el canal apropiado en un instante y reduciendo la cantidad de handoff para realizar por el usuario secundario. Los resultados del análisis de las técnicas de predicción indican que el algoritmo GRA, junto con SVM, presenta mejor desempeño, al elegir el canal menos utilizado, pues reducen interferencias a los usuarios primarios y disminuyen la cantidad de handoff necesarios para transmitir los servicios requeridos por el usuario.
publishDate 2019
dc.date.created.none.fl_str_mv 2019-11
dc.date.accessioned.none.fl_str_mv 2023-11-02T18:59:53Z
dc.date.available.none.fl_str_mv 2023-11-02T18:59:53Z
dc.type.spa.fl_str_mv book
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2f33
dc.identifier.isbn.spa.fl_str_mv 978-958-787-152-4
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/11349/32581
dc.identifier.editorial.spa.fl_str_mv Universidad Distrital Francisco José de Caldas. Centro de Investigaciones y Desarrollo Científico
identifier_str_mv 978-958-787-152-4
Universidad Distrital Francisco José de Caldas. Centro de Investigaciones y Desarrollo Científico
url http://hdl.handle.net/11349/32581
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
dc.source.bibliographicCitation.spa.fl_str_mv E. Tragos, S. Zeadally, A. Fragkiadakis y V. Siris, “Spectrum assignment in cognitve radio networks: a comprehensive survey”, IEEE Commun. Surv. Tutorials, vol. 15, n.° 3, pp. 1108-1135, 2013.
N. Abbas, Y. Nasser y K. El Ahmad, “Recent advances on artificial intelligence and learning techniques in cognitive radio networks”, EURASIP J. Wirel. Commun. Netw., vol. 174, 2011. [En línea] Disponible en: https://doi.org/10.1186/ s13638-015-0381-7.
M. T. Masonta, M. Mzyece y N. Ntlatlapa, “spectrum decision in cognitive radio networks: a survey”, IEEE Commun. Surv. Tutorials, vol. 15, n.° 3, pp. 1088-1107, 2013.
I. F. Akyildiz, W.-Y. Lee, M. C. Vuran y S. Mohanty, “A survey on Spectrum management in cognitive radio networks”, Commun. Mag. IEEE, vol. 46, n.° 4, pp. 40-48, 2008.
C. Hernández, Modelo adaptativo de handoff espectral para la mejora en el desempeño de la movilidad en redes móviles de radio cognitiva, Bogotá: Universidad Nacional de Colombia, 2017.
M. Lahby, S. Baghla y A. Sekkaki, “Survey and comparison of MADM methods for network selection access in heterogeneous networks”, en 2015 7th International Conference on New Technologies, Mobility and Security (NTMS), París, Francia, 2015.
J. Mitola y G. Q. Maguire, “Cognitive radio: making software radios more personal”, IEEE Pers. Commun., vol. 6, n.° 4, pp. 13-18, 1999.
M. Delgado y B. Rodríguez, “Opportunities for a more efficient use of the spectrum based in cognitive radio”, IEEE Lat. Am. Trans., vol. 14, n.° 2, pp. 610-616, 2016.
I. F. Akyildiz, W.-Y. Lee y K. R. Chowdhury, “Crahns: Cognitive Radio Ad Hoc Networks”, J. Ad Hoc Networks, vol. 7, pp. 810-836, 2009.
S. Ju y J. B. Evans, “Scalable cognitive routing protocol for mobile ad-hoc networks”, en 2010 IEEE Global Telecommunications Conference GLOBECOM 2010, Miami, Estados Unidos, 2010, pp. 1-6.
J. Marinho y E. Monteiro, “Cognitive radio: survey on communication protocols, spectrum decision issues, and future research directions”, Wirel. Networks, vol. 18, n.° 2, pp. 147-164, 2012.
Y. Chen, Q. Zhao y A. Swami, “Distributed spectrum sensing and access in cog nitive radio networks with energy constraint”, IEEE Trans. Signal Process., vol. 57, n.° 2, pp. 783-797, 2009.
P. Ren, Y. Wang, Q. Du y J. Xu, “A survey on dynamic spectrum access protocols for distributed cognitive wireless networks”, EURASIP J. Wirel. Commun. Netw., vol. 2012, n.° 1, p. 60, 2012.
E. Trigui, M. Esseghir y L. M. Boulahia, “Cognitive radio spectrum assignment and handoff decision”, en 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Londres, Reino Unido, 2013, pp. 2881-2886.
J. Wang, M. Ghosh y K. Challapali, “Emerging cognitive radio applications: A survey”, IEEE Commun. Mag., vol. 49, n.° 3, pp. 74-81, 2011.
W.-Y. L. I. F. Akyildiz, “A spectrum decision framework for cognitive radio networks”, IEEE Trans. Mob. Comput., vol. 10, n.° 2,
Standard for Wireless Regional Area Networks (WRAN)—Specific Requirements—Part 22: Cognitive Wireless RAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Policies and Procedures for Operation in the TV Bands, The Institute of Electrical and Electronics Engineering, IEEE Standard 802.22, 2011.
M. Amir, A. El-Keyi y M. Nafie, “Constrained interference alignment and the spatial degrees of freedom of mimo cognitive networks”, IEEE Trans. Inf. Theory, vol. 57, n.° 5, pp. 2994-3004, 2011
I. F. Akyildiz, W. Y. Lee, M. C. Vuran y S. Mohanty, “NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey”, Comput. Networks, vol. 50, n.° 13, pp. 2127-2159, 2006.
M. Ozger y O. B. Akan, “On the utilization of spectrum opportunity in cognitive radio networks”, IEEE Commun. Lett., vol. 20, n.° 1, pp. 157-160, 2016.
A. Azarfar, J.-F. Frigon y B. Sanso, “Improving the reliability of wireless networks using cognitive radios”, IEEE Commun. Surv. Tutorials, vol. 14, n.° 2, pp. 338-354, 2012
C. Devanarayana y A. S. Alfa, “Predictive channel access in cognitive radio networks based on variable order Markov models”, en 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011, Kathmandu, Nepal, 2011, pp. 1-6
C. Devanarayana y A. S. Alfa, “Proactive channel access in cognitive radio networks based on users statistics”, en 2014 1st International Workshop on Cognitive Cellular Systems (CCS), Alemania, 2014.
R. Aguilar-González et al., “Performance of MADM algorithms with real spectrum measurements for spectrum decision in cognitive radio networks”, en 2014 11th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), Campeche, México, 2014.
S. Pandit y G. Singh, “Spectrum sharing in cognitive radio using game theory”, en 2013 3rd IEEE International Advance Computing Conference (IACC), Ghaziabad, India, 2012, pp. 1503-1506.
Y. Wu, F. Hu, S. Kumar et al., “Apprenticeship learning based spectrum decision in multi-channel wireless mesh networks with multi-beam antennas”, IEEE T Mobile Comput, vol. 16, n.° 2, pp. 314-325, 2017.
I. Akbar y W. Tranter, “Dynamic spectrum allocation in cognitive radio using hidden Markov models: Poisson distributed case”, en Proceedings 2007 IEEE SoutheastCon, Richmond, VA, Estados Unidos, 2007, pp. 196-201.
P. S. Aizaz Zainab, “A survey of cognitive radio reconfigurable antenna design and proposed design using genetic algorithm”, en 2016 IEEE Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), Bhopal, India, 2016.
M. Matinmikko, J. Del Ser, T. Rauma y M. Mustonen, “Fuzzy-logic based framework for spectrum availability assessment in cognitive radio systems”, IEEE J. Sel. Areas Commun., vol. 31, n.° 11, pp. 2173-2184, 2013.
S. M. S, S. B. Mafra, G. S. Member, E. M. G. Fernández et al., “Power control and relay selection in cognitive radio ad hoc networks using game theory”, IEEE Syst J, vol. 12, n.°3, pp. 1-12, 2016.
F. Cai, Y. Gao, L. Cheng et al., “Spectrum sharing for LTE and WiFi coexistence using decision tree and game theory”, en 2016 IEEE Wireless Communications and Networking Conference, Doha, Qatar, 2016.
Y. Xu, A. Anpalagan, Q. Wu et al., “Decision- theoretic distributed channel se lection for opportunistic spectrum access: Strategies, challenges and solutions”, IEEE Commun. Surv. Tutorials, vol. 15, n.° 4, pp. 1689-1713, 2013.
X. Tan, H. Huang y L. Ma, “Frequency allocation with Artificial Neural Networks in cognitive radio system”, en IEEE 2013 Tencon - Spring, Sydney, NSW, Australia, 2013, pp. 366-370.
L. F. Pedraza, C. Hernández, K. Galeano, E. Rodríguez-Colina et al., Ocupación espectral y modelo de radio cognitiva para Bogotá, Bogotá: Editorial UD, 2016.
Y. Zhao, Z. Hong, Y. Luo, et al., “Prediction-Based Spectrum Management in Cognitive Radio Networks”, IEEE Syst J, vol. 12, n.° 4, pp. 3303-3314, dic. 2018.
X. Song, W. Liu, M. Zhang et al., “A network selection algorithm based on FAHP/GRA in heterogeneous wireless networks”, en 2016 2nd IEEE International Conference on Computer and Communications (ICCC) Chengdu, China, 2016, pp. 1445-1449.
M. Lahby y A. Adib, “Network selection mechanism by using M-AHP/GRA for heterogeneous networks”, en 6th Joint IFIP Wireless and Mobile Networking Conference (WMNC), Dubai, Emiratos Árabes Unidos, 2013, pp. 1-6.
M. Mansouri y C. Leghris, “A comparison between fuzzy TOPSIS and fuzzy GRA for the vertical handover decision making”, en 2017 Intelligent Systems and Computer Vision (ISCV), Fez, Marruecos, 2017, pp. 1-6.
G. Ding et al., “Spectrum inference in cognitive radio networks: algorithms and applications”, IEEE Commun. Surv. Tutorials, vol. 20, n.° 1, pp. 150-182, 2017.
N. Gupta, S. K. Dhurandher y I. Woungang, “On the probability of appearance of primary user in IEEE 802 . 22 WRAN using an artificial neural network learning technique”, en 2016 1st India International Conference on Information Processing (IICIP), Delhi, 2016, pp. 1-5.
M. Huk y J. Mizera-Pietraszko, “Contextual neural-network based spectrum prediction for cognitive radio”, en 2015 Fourth International Conference on Future Generation Communication Technology (FGCT), Luton, Reino Unido, 2015.
J. Guo, H. Ji, Y. Li y X. Li, “A novel spectrum handoff management scheme based on SVM in cognitive radio networks”, en A Novel Spectrum Handoff Management Scheme based on SVM in Cognitive Radio Networks, Harbin, China, 2011, pp. 645-649
A. Agarwal, S. Dubey, M. A. Khan et al., “Learning based primary user activity prediction in cognitive radio networks for efficient dynamic spectrum access”, en 2016 1st India International Conference on Information Processing (IICIP), Delhi, 2016, pp. 1-5.
M. Kyryk, N. Pleskanka y V. Yanyshyn, “Performance evaluation model for spectrum decision methods in cognitive radio”, en 2017 14th International Conference The Experience of Designing and Application of CAD Systems in Microelectronics (CADSM), Lviv, Ucrania, 2017, pp. 289-291.
Y. Liu, R. Yu y M. Pan, “SD-MAC : Spectrum Database-Driven MAC Protocol for Cognitive Machine-to-Machine Networks”, IEEE T Veh Technol, vol. 66, n.° 2, pp. 1456–1467, 2017.
L. Wang, J. Yang, and X. Song, “A QoE-Driven Spectrum Decision Scheme for Multimedia Transmissions over Cognitive Radio Networks,” en 2017 26th International Conference on Computer Communication and Networks (ICCCN), Vancouver, BC, Canadá, 2017.
A. Surampudi, K. Kalimuthu y B. Tech, “An adaptive decision threshold scheme for the matched filter method of spectrum sensing in cognitive radio using artificial neural networks”, en 2016 1st India International Conference on Information Processing (IICIP), Delhi, 2016, pp. 1-5.
F. Liu, J. Ma, R. Du y J. Wu, “ICSGC-based dynamic spectrum access algorithm for cognitive radio”, en 2017 29th Chinese Control And Decision Conference (CCDC), 2017, pp. 5692-5697.
C. Hernández, I. Páez y D. Giral, “Modelo AHP-VIKOR para handoff espectral en redes de radio cognitiva” , vol. 19, n.° 45, pp. 29-39,
A. F. Almutairi, “Weighting selection in GRA-based MADM for vertical handover in wireless networks”, en 2016 UKSim-AMSS 18th International Conference on Computer Modelling and Simulation (UKSim), Cambridge, 2016, pp. 331-336.
S. Iliya, E. Goodyer, J. Gow et al., “Application of artificial neural network and support vector regression in cognitive radio networks for RF power prediction using compact differential evolution algorithm”, en 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), Lodz, Polonia, 2015, pp. 55-56
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spelling 2023-11-02T18:59:53Z2023-11-02T18:59:53Z2019-11978-958-787-152-4http://hdl.handle.net/11349/32581Universidad Distrital Francisco José de Caldas. Centro de Investigaciones y Desarrollo CientíficoEste libro presenta el diseño de un modelo de decisión espectral dinámico para redes de radio cognitiva, que permite a los usuarios secundarios acceder al espectro de manera oportunista y utilizar el canal sin afectar el tráfico de los usuarios primarios. El objetivo de este trabajo es emplear el recurso del espectro de manera eficiente eligiendo el canal apropiado en un instante y reduciendo la cantidad de handoff para realizar por el usuario secundario. Los resultados del análisis de las técnicas de predicción indican que el algoritmo GRA, junto con SVM, presenta mejor desempeño, al elegir el canal menos utilizado, pues reducen interferencias a los usuarios primarios y disminuyen la cantidad de handoff necesarios para transmitir los servicios requeridos por el usuario.This book presents the design of a dynamic spectral decision model for cognitive radio networks, which allows secondary users to access the spectrum opportunistically and use the channel without affecting the traffic of primary users. The objective of this work is to use the spectrum resource efficiently by choosing the appropriate channel in an instant and reducing the amount of handoff to be performed by the secondary user. The results of the analysis of the Prediction techniques indicate that the GRA algorithm, together with SVM, presents better performance by choosing the least used channel, since it reduces interference to primary users and reduces the amount of handoff necessary to transmit the services required by the user.BogotápdfEspaciosAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Abierto (Texto Completo)http://purl.org/coar/access_right/c_abf2Ingeniería de sistemasRedesTransmisión de datosRadio cognitivaIngeniería de sistemasRedes inalámbricasSistemas de transmisión de datosRedes de radio cognitivaSystems engineerNetworksData transmissionCognitive radioModelo de decisión espectral para redes de radio cognitivaSpectral decision model for cognitive radio networksbookhttp://purl.org/coar/resource_type/c_2f33E. Tragos, S. Zeadally, A. Fragkiadakis y V. Siris, “Spectrum assignment in cognitve radio networks: a comprehensive survey”, IEEE Commun. Surv. Tutorials, vol. 15, n.° 3, pp. 1108-1135, 2013.N. Abbas, Y. Nasser y K. El Ahmad, “Recent advances on artificial intelligence and learning techniques in cognitive radio networks”, EURASIP J. Wirel. Commun. Netw., vol. 174, 2011. [En línea] Disponible en: https://doi.org/10.1186/ s13638-015-0381-7.M. T. Masonta, M. Mzyece y N. Ntlatlapa, “spectrum decision in cognitive radio networks: a survey”, IEEE Commun. Surv. Tutorials, vol. 15, n.° 3, pp. 1088-1107, 2013.I. F. Akyildiz, W.-Y. Lee, M. C. Vuran y S. Mohanty, “A survey on Spectrum management in cognitive radio networks”, Commun. Mag. IEEE, vol. 46, n.° 4, pp. 40-48, 2008.C. Hernández, Modelo adaptativo de handoff espectral para la mejora en el desempeño de la movilidad en redes móviles de radio cognitiva, Bogotá: Universidad Nacional de Colombia, 2017.M. Lahby, S. Baghla y A. Sekkaki, “Survey and comparison of MADM methods for network selection access in heterogeneous networks”, en 2015 7th International Conference on New Technologies, Mobility and Security (NTMS), París, Francia, 2015.J. Mitola y G. Q. Maguire, “Cognitive radio: making software radios more personal”, IEEE Pers. Commun., vol. 6, n.° 4, pp. 13-18, 1999.M. Delgado y B. Rodríguez, “Opportunities for a more efficient use of the spectrum based in cognitive radio”, IEEE Lat. Am. Trans., vol. 14, n.° 2, pp. 610-616, 2016.I. F. Akyildiz, W.-Y. Lee y K. R. Chowdhury, “Crahns: Cognitive Radio Ad Hoc Networks”, J. Ad Hoc Networks, vol. 7, pp. 810-836, 2009.S. Ju y J. B. Evans, “Scalable cognitive routing protocol for mobile ad-hoc networks”, en 2010 IEEE Global Telecommunications Conference GLOBECOM 2010, Miami, Estados Unidos, 2010, pp. 1-6.J. Marinho y E. Monteiro, “Cognitive radio: survey on communication protocols, spectrum decision issues, and future research directions”, Wirel. Networks, vol. 18, n.° 2, pp. 147-164, 2012.Y. Chen, Q. Zhao y A. Swami, “Distributed spectrum sensing and access in cog nitive radio networks with energy constraint”, IEEE Trans. Signal Process., vol. 57, n.° 2, pp. 783-797, 2009.P. Ren, Y. Wang, Q. Du y J. Xu, “A survey on dynamic spectrum access protocols for distributed cognitive wireless networks”, EURASIP J. Wirel. Commun. Netw., vol. 2012, n.° 1, p. 60, 2012.E. Trigui, M. Esseghir y L. M. Boulahia, “Cognitive radio spectrum assignment and handoff decision”, en 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Londres, Reino Unido, 2013, pp. 2881-2886.J. Wang, M. Ghosh y K. Challapali, “Emerging cognitive radio applications: A survey”, IEEE Commun. Mag., vol. 49, n.° 3, pp. 74-81, 2011.W.-Y. L. I. F. Akyildiz, “A spectrum decision framework for cognitive radio networks”, IEEE Trans. Mob. Comput., vol. 10, n.° 2,Standard for Wireless Regional Area Networks (WRAN)—Specific Requirements—Part 22: Cognitive Wireless RAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Policies and Procedures for Operation in the TV Bands, The Institute of Electrical and Electronics Engineering, IEEE Standard 802.22, 2011.M. Amir, A. El-Keyi y M. Nafie, “Constrained interference alignment and the spatial degrees of freedom of mimo cognitive networks”, IEEE Trans. Inf. Theory, vol. 57, n.° 5, pp. 2994-3004, 2011I. F. Akyildiz, W. Y. Lee, M. C. Vuran y S. Mohanty, “NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey”, Comput. Networks, vol. 50, n.° 13, pp. 2127-2159, 2006.M. Ozger y O. B. Akan, “On the utilization of spectrum opportunity in cognitive radio networks”, IEEE Commun. Lett., vol. 20, n.° 1, pp. 157-160, 2016.A. Azarfar, J.-F. Frigon y B. Sanso, “Improving the reliability of wireless networks using cognitive radios”, IEEE Commun. Surv. Tutorials, vol. 14, n.° 2, pp. 338-354, 2012C. Devanarayana y A. S. Alfa, “Predictive channel access in cognitive radio networks based on variable order Markov models”, en 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011, Kathmandu, Nepal, 2011, pp. 1-6C. Devanarayana y A. S. Alfa, “Proactive channel access in cognitive radio networks based on users statistics”, en 2014 1st International Workshop on Cognitive Cellular Systems (CCS), Alemania, 2014.R. Aguilar-González et al., “Performance of MADM algorithms with real spectrum measurements for spectrum decision in cognitive radio networks”, en 2014 11th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), Campeche, México, 2014.S. Pandit y G. Singh, “Spectrum sharing in cognitive radio using game theory”, en 2013 3rd IEEE International Advance Computing Conference (IACC), Ghaziabad, India, 2012, pp. 1503-1506.Y. Wu, F. Hu, S. Kumar et al., “Apprenticeship learning based spectrum decision in multi-channel wireless mesh networks with multi-beam antennas”, IEEE T Mobile Comput, vol. 16, n.° 2, pp. 314-325, 2017.I. Akbar y W. Tranter, “Dynamic spectrum allocation in cognitive radio using hidden Markov models: Poisson distributed case”, en Proceedings 2007 IEEE SoutheastCon, Richmond, VA, Estados Unidos, 2007, pp. 196-201.P. S. Aizaz Zainab, “A survey of cognitive radio reconfigurable antenna design and proposed design using genetic algorithm”, en 2016 IEEE Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), Bhopal, India, 2016.M. Matinmikko, J. Del Ser, T. Rauma y M. Mustonen, “Fuzzy-logic based framework for spectrum availability assessment in cognitive radio systems”, IEEE J. Sel. Areas Commun., vol. 31, n.° 11, pp. 2173-2184, 2013.S. M. S, S. B. Mafra, G. S. Member, E. M. G. Fernández et al., “Power control and relay selection in cognitive radio ad hoc networks using game theory”, IEEE Syst J, vol. 12, n.°3, pp. 1-12, 2016.F. Cai, Y. Gao, L. Cheng et al., “Spectrum sharing for LTE and WiFi coexistence using decision tree and game theory”, en 2016 IEEE Wireless Communications and Networking Conference, Doha, Qatar, 2016.Y. Xu, A. Anpalagan, Q. Wu et al., “Decision- theoretic distributed channel se lection for opportunistic spectrum access: Strategies, challenges and solutions”, IEEE Commun. Surv. Tutorials, vol. 15, n.° 4, pp. 1689-1713, 2013.X. Tan, H. Huang y L. Ma, “Frequency allocation with Artificial Neural Networks in cognitive radio system”, en IEEE 2013 Tencon - Spring, Sydney, NSW, Australia, 2013, pp. 366-370.L. F. Pedraza, C. Hernández, K. Galeano, E. Rodríguez-Colina et al., Ocupación espectral y modelo de radio cognitiva para Bogotá, Bogotá: Editorial UD, 2016.Y. Zhao, Z. Hong, Y. Luo, et al., “Prediction-Based Spectrum Management in Cognitive Radio Networks”, IEEE Syst J, vol. 12, n.° 4, pp. 3303-3314, dic. 2018.X. Song, W. Liu, M. Zhang et al., “A network selection algorithm based on FAHP/GRA in heterogeneous wireless networks”, en 2016 2nd IEEE International Conference on Computer and Communications (ICCC) Chengdu, China, 2016, pp. 1445-1449.M. Lahby y A. Adib, “Network selection mechanism by using M-AHP/GRA for heterogeneous networks”, en 6th Joint IFIP Wireless and Mobile Networking Conference (WMNC), Dubai, Emiratos Árabes Unidos, 2013, pp. 1-6.M. Mansouri y C. Leghris, “A comparison between fuzzy TOPSIS and fuzzy GRA for the vertical handover decision making”, en 2017 Intelligent Systems and Computer Vision (ISCV), Fez, Marruecos, 2017, pp. 1-6.G. Ding et al., “Spectrum inference in cognitive radio networks: algorithms and applications”, IEEE Commun. Surv. Tutorials, vol. 20, n.° 1, pp. 150-182, 2017.N. Gupta, S. K. Dhurandher y I. 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Gow et al., “Application of artificial neural network and support vector regression in cognitive radio networks for RF power prediction using compact differential evolution algorithm”, en 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), Lodz, Polonia, 2015, pp. 55-56Bernal Ariza, Cristian CamiloHernández Suárez, César AugustoLICENSElicense.txtlicense.txttext/plain; charset=utf-87167http://repository.udistrital.edu.co/bitstream/11349/32581/3/license.txt997daf6c648c962d566d7b082dac908dMD53open accessORIGINALModelo de inteligencia.pdfModelo de inteligencia.pdfLibroapplication/pdf4896202http://repository.udistrital.edu.co/bitstream/11349/32581/1/Modelo%20de%20inteligencia.pdff770d1984868650a58a9128a6fadc7baMD51open accessCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805http://repository.udistrital.edu.co/bitstream/11349/32581/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD52open accessTHUMBNAILCubierta Modelo de 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