Modelo de decisión espectral para redes de radio cognitiva

dc.contributor.authorBernal Ariza, Cristian Camilo
dc.contributor.authorHernández Suárez, César Augusto
dc.date.accessioned2023-11-02T18:59:53Z
dc.date.available2023-11-02T18:59:53Z
dc.date.created2019-11
dc.descriptionEste 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.spa
dc.description.abstractThis 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.spa
dc.description.cityBogotáspa
dc.format.mimetypepdfspa
dc.identifier.editorialUniversidad Distrital Francisco José de Caldas. Centro de Investigaciones y Desarrollo Científicospa
dc.identifier.isbn978-958-787-152-4spa
dc.identifier.urihttp://hdl.handle.net/11349/32581
dc.relation.ispartofseriesEspaciosspa
dc.relation.referencesE. 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.spa
dc.relation.referencesN. 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.spa
dc.relation.referencesM. 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.spa
dc.relation.referencesI. 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.spa
dc.relation.referencesC. 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.spa
dc.relation.referencesM. 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.spa
dc.relation.referencesJ. Mitola y G. Q. Maguire, “Cognitive radio: making software radios more personal”, IEEE Pers. Commun., vol. 6, n.° 4, pp. 13-18, 1999.spa
dc.relation.referencesM. 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.spa
dc.relation.referencesI. 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.spa
dc.relation.referencesS. 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.spa
dc.relation.referencesJ. 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.spa
dc.relation.referencesY. 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.spa
dc.relation.referencesP. 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.spa
dc.relation.referencesE. 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.spa
dc.relation.referencesJ. Wang, M. Ghosh y K. Challapali, “Emerging cognitive radio applications: A survey”, IEEE Commun. Mag., vol. 49, n.° 3, pp. 74-81, 2011.spa
dc.relation.referencesW.-Y. L. I. F. Akyildiz, “A spectrum decision framework for cognitive radio networks”, IEEE Trans. Mob. Comput., vol. 10, n.° 2,spa
dc.relation.referencesStandard 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.spa
dc.relation.referencesM. 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, 2011spa
dc.relation.referencesI. 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.spa
dc.relation.referencesM. 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.spa
dc.relation.referencesA. 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, 2012spa
dc.relation.referencesC. 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. 1-6spa
dc.relation.referencesC. 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.spa
dc.relation.referencesR. 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.spa
dc.relation.referencesS. 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.spa
dc.relation.referencesY. 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.spa
dc.relation.referencesI. 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.spa
dc.relation.referencesP. 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.spa
dc.relation.referencesM. 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.spa
dc.relation.referencesS. 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.spa
dc.relation.referencesF. 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.spa
dc.relation.referencesY. 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.spa
dc.relation.referencesX. 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.spa
dc.relation.referencesL. F. Pedraza, C. Hernández, K. Galeano, E. Rodríguez-Colina et al., Ocupación espectral y modelo de radio cognitiva para Bogotá, Bogotá: Editorial UD, 2016.spa
dc.relation.referencesY. 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.spa
dc.relation.referencesX. 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.spa
dc.relation.referencesM. 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.spa
dc.relation.referencesM. 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.spa
dc.relation.referencesG. Ding et al., “Spectrum inference in cognitive radio networks: algorithms and applications”, IEEE Commun. Surv. Tutorials, vol. 20, n.° 1, pp. 150-182, 2017.spa
dc.relation.referencesN. 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.spa
dc.relation.referencesM. 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.spa
dc.relation.referencesJ. 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. 645-649spa
dc.relation.referencesA. 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.spa
dc.relation.referencesM. 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.spa
dc.relation.referencesY. 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.spa
dc.relation.referencesL. 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.spa
dc.relation.referencesA. 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.spa
dc.relation.referencesF. 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.spa
dc.relation.referencesC. Hernández, I. Páez y D. Giral, “Modelo AHP-VIKOR para handoff espectral en redes de radio cognitiva” , vol. 19, n.° 45, pp. 29-39,spa
dc.relation.referencesA. 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.spa
dc.relation.referencesS. 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. 55-56spa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.accesoAbierto (Texto Completo)spa
dc.rights.accessrightsOpenAccessspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectIngeniería de sistemasspa
dc.subjectRedesspa
dc.subjectTransmisión de datosspa
dc.subjectRadio cognitivaspa
dc.subject.keywordSystems engineerspa
dc.subject.keywordNetworksspa
dc.subject.keywordData transmissionspa
dc.subject.keywordCognitive radiospa
dc.subject.lembIngeniería de sistemasspa
dc.subject.lembRedes inalámbricasspa
dc.subject.lembSistemas de transmisión de datosspa
dc.subject.lembRedes de radio cognitivaspa
dc.titleModelo de decisión espectral para redes de radio cognitivaspa
dc.title.titleenglishSpectral decision model for cognitive radio networksspa
dc.typebookspa
dc.type.coarhttp://purl.org/coar/resource_type/c_2f33
dc.type.driverinfo:eu-repo/semantics/book

Archivos

Bloque original

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
Modelo de inteligencia.pdf
Tamaño:
4.67 MB
Formato:
Adobe Portable Document Format
Descripción:
Libro

Bloque de licencias

Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
7 KB
Formato:
Item-specific license agreed upon to submission
Descripción: