Modelo de asignación espectral multiusuario para redes de radio cognitiva descentralizadas
dc.contributor.author | Hernández Suárez, César Augusto | |
dc.contributor.author | Giral Ramírez, Diego Armando | |
dc.contributor.author | Salgado Franco, Lizet Camila | |
dc.date.accessioned | 2023-11-02T22:26:47Z | |
dc.date.available | 2023-11-02T22:26:47Z | |
dc.date.created | 2021-12 | |
dc.description | El crecimiento de las aplicaciones inalámbricas plantea nuevos de safíos a los futuros sistemas de comunicación, como el uso ineficiente del espectro radioeléctrico. Las redes de radio cognitiva surgen como una solución a los problemas de escasez de espectro y uso ineficiente del recurso espectral, mediante el acceso dinámico al espectro. Estas redes están caracterizadas por percibir, aprender, planificar (toma de decisiones) y actuar de acuerdo con las condiciones actuales de la red. El objetivo general de una red de radio cognitiva consiste en que el usuario secundario acceda de manera oportuna a un canal de frecuencia disponible en una banda licenciada, sin generar interferencia al usuario primario, lo cual se puede lograr con una adecuada toma de decisión espectral. 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, y cuantos más usuarios secundarios seleccionen el mismo canal, menor será la utilidad que cada uno pueda obtener y el número de interferencias por acceso simultáneo será mayor. El desafío consiste entonces en dotar los nodos de una red descentralizada con la capacidad de aprender del entorno, proponiendo estrategias que les permita a los usuarios secundarios tomar decisiones e intercambiar información de forma cooperativa o competitiva, en un ambiente de acceso multiusuario al espectro. Asimismo, este libro busca resolver la pregunta: ¿cómo y en qué medida se puede reducir la tasa de handoff espectral en redes de radio cognitiva descentralizadas con un enfoque multiusuario y colaborativo | spa |
dc.description.abstract | The growth of wireless applications poses new challenges to future communication systems, such as the inefficient use of the radio spectrum. Cognitive radio networks emerge as a solution to the problems of spectrum scarcity and inefficient use of the spectral resource, through dynamic access to the spectrum. These networks are characterized by perceiving, learning, planning (decision making), and acting according to current network conditions. The general objective of a cognitive radio network is for the secondary user to timely access an available frequency channel in a licensed band, without generating interference to the primary user, which can be achieved with adequate spectral decision making. The probability that two or more secondary users will choose the same channel is high, especially when the number of secondary users is greater than the number of available channels, and the more secondary users select the same channel, the lower the utility each can obtain and the number of interferences due to simultaneous access will be greater. The challenge then consists of providing the nodes of a decentralized network with the ability to learn from the environment, proposing strategies that allow secondary users to make decisions and exchange information cooperatively or competitively, in an environment of multi-user access to the spectrum. Likewise, this book seeks to resolve the question: how and to what extent can the spectral handoff rate be reduced in decentralized cognitive radio networks with a multi-user and collaborative approach? | spa |
dc.description.city | Bogotá | spa |
dc.format.mimetype | spa | |
dc.identifier.editorial | Universidad Distrital Francisco José de Caldas. Centro de Investigaciones y Desarrollo Científico | spa |
dc.identifier.isbn | 9789587873108 | spa |
dc.identifier.isbn | 9587873106 | spa |
dc.identifier.uri | http://hdl.handle.net/11349/32604 | |
dc.relation.ispartofseries | Espacios | spa |
dc.relation.references | 3GPP. (2011). Part 22: Cognitive Wireless RAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Policies and Procedures for Operation in the TV Bands IEEE Computer Society (vol. 2015, Issue July). | spa |
dc.relation.references | Abass, A. A. A., Mandayam, N. B. y Gajic, Z. (2017). An evolutionary game model for threat revocation in ephemeral networks. 2017 51st Annual Conference on Information Sciences and Systems (CISS), 1-5. https://doi.org/10.1109/CISS.2017.7926128 | spa |
dc.relation.references | Abbas, N., Nasser, Y. y Ahmad, K. E. (2015). Recent advances on artificial intelligence and learning techniques in cognitive radio networks. EURASIP Journal on Wireless Communications and Networking, 1(2015), 174. https://doi.org/10.1186/s13638-015-0381-7 | spa |
dc.relation.references | Ahmed, A., Boulahia, L. M. y Gaïti, D. (2014). Enabling vertical handover decisions in heterogeneous wireless networks: A state-of-the-art and a classification. IEEE Communications Surveys and Tutorials, 16(2), 776-811. https://doi.org/10.1109/SURV.2013.082713.00141 | spa |
dc.relation.references | Ahmed, E., Gani, A., Abolfazli, S., Yao, L. J. y Khan, S. U. (2016). Channel assignment algorithms in cognitive radio networks: Taxonomy, open issues, and challenges. IEEE Communications Surveys & Tutorials, 18(1), 795-823. https://doi.org/10.1109/COMST.2014.2363082 | spa |
dc.relation.references | Akter, L., Natarajan, B. y Scoglio, C. (2008). Modeling and forecasting secondary user activity in cognitive radio networks. 17th International Conference on Computer Communications and Networks. https://doi.org/10.1109/ICCCN.2008.ECP.50 | spa |
dc.relation.references | Akyildiz, I. F. y Li, Y. (2006). OCRA: OFDM-based cognitive radio networks. En Broadband and Wireless Networking Laboratory Technical Report. | spa |
dc.relation.references | Akyildiz, I. F., Lee, W.-Y. y Chowdhury, K. R. (2009). CRAHNs: Cognitive radio ad hoc networks. Ad Hoc Networks, 7(5), 810-836. https://doi.org/10.1016/j.adhoc.2009.01.001 | spa |
dc.relation.references | Akyildiz, I. F., Lee, W.-Y., Vuran, M. C. y Mohanty, S. (2006). Next generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer Networks, 50(13), 2127-2159. https://doi.org/10.1016/j.comnet.2006.05.001 | spa |
dc.relation.references | Akyildiz, I. F., Lee, W.-Y., Vuran, M. C. y Mohanty, S. (2008). A survey on spectrum management in cognitive radio networks. Communications Magazine, IEEE, 46(4), 40-48. https://doi.org/10.1109/MCOM.2008.4481339 | spa |
dc.relation.references | Akyildiz, I. F., Lo, B. F. y Balakrishnan, R. (2011). Cooperative spectrum sensing in cognitive radio networks: A survey. Physical Communication, 4(1), 40- 62. https://doi.org/https://doi.org/10.1016/j.phycom.2010.12.003 | spa |
dc.relation.references | Al-Amidie, M., Al-Asadi, A., Micheas, A. C. y Islam, N. E. (2019). Spectrum sensing based on Bayesian generalized likelihood ratio for cognitive radio systems with multiple antennas. IET Communications, 13(3), 305- 311. https://doi.org/10.1049/iet-com.2018.5276 | spa |
dc.relation.references | Ali, A. y Hamouda, W. (2017). Advances on spectrum sensing for cognitive radio networks: Theory and applications. IEEE Communications Surveys and Tutorials, 19(2), 1277-1304. https://doi.org/10.1109/COMST.2016.2631080 | spa |
dc.relation.references | Alias, D. M. y Ragesh, G. K. (2016). Cognitive radio networks: A survey. Proceedings of the 2016 IEEE International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2016, 1981- 1986. https://doi.org/10.1109/WiSPNET.2016.7566489 | spa |
dc.relation.references | Almasaeid, H. M. y Kamal, A. E. (2010). Receiver-based channel allocation for wireless cognitive radio mesh networks. IEEE Symposium on New Frontiers in Dynamic Spectrum, 1-10. https://doi.org/10.1109/DYSPAN.2010.5457862 | spa |
dc.relation.references | Alnwaimi, G., Arshad, K. y Moessner, K. (2011). Dynamic spectrum allocation algorithm with interference management in co-existing networks. IEEE Communications Letters, 15(9), 932-934. https://doi.org/10.1109/LCOMM.2011.062911.110248 | spa |
dc.relation.references | Alsarhan, A. y Agarwal, A. (2009). Cluster-based spectrum management using cognitive radios in wireless mesh network. Internatonal Conference on Computer Communications and Networks, 1-6. | spa |
dc.relation.references | Amir, M., El-Keyi, A. y Nafie, M. (2011). Constrained interference alignment and the spatial degrees of freedom of mimo cognitive networks. IEEE Transactions on Information Theory, 57(5), 2994-3004. https://doi.org/10.1109/TIT.2011.2119770 | spa |
dc.relation.references | Amjad, M. F., Chatterjee, M. y Zou, C. C. (2016). Coexistence in heterogeneous spectrum through distributed correlated equilibrium in cognitive radio networks. Computer Networks, (98), 109-122. https://doi.org/10.1016/j.comnet.2016.01.016 | spa |
dc.relation.references | Azarfar, A., Frigon, J.-F. y Sanso, B. (2012). Improving the reliability of wireless networks using cognitive radios. IEEE Communications Surveys & Tutorials, 14(2, Second Quarter), 338-354. https://doi.org/10.1109/SURV.2011.021111.00064 | spa |
dc.relation.references | Baran, P. (1964). On distributed communications networks. IEEE Transactions on Communications, 12(1), 1-9. https://doi.org/10.1109/TCOM.1964.1088883 | spa |
dc.relation.references | Bhowmik, M. y Malathi, P. (2019). spectrum sensing in cognitive radio using actor-critic neural network with Krill Herd-Whale optimization algorithm. Wireless Personal Communications, 105(1), 335-354. https://doi.org/10.1007/s11277-018-6115-5 | spa |
dc.relation.references | Bkassiny, M., Li, Y. y Jayaweera, S. K. (2013). A survey on machine-learning techniques in cognitive radios. IEEE Communications Surveys and Tutorials. https://doi.org/10.1109/SURV.2012.100412.00017 | spa |
dc.relation.references | Bolstad, W. M. (2007). Introduction to Bayesian statistics. En Book. https://doi.org/10.1080/10543406.2011.589638 | spa |
dc.relation.references | Boorstin, J. (2016). An internet of things that will number ten billions. CNBS. | spa |
dc.relation.references | Brik, V., Rozner, E., Banerjee, S. y Bahl, P. (2005). DSAP: A protocol for coordinated spectrum access. 2005 1st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, DySPAN 2005, 611-614. https://doi.org/10.1109/DYSPAN.2005.1542680 | spa |
dc.relation.references | Bujari, A., Calafate, C. T., Cano, J.-C., Manzoni, P., Palazzi, C. E. y Ronzani, D. (2018). Flying adhoc network application scenarios and mobility models. International Journal of Distributed Sensor Networks, 13(10), 1550147717738192. https://doi.org/10.1177/1550147717738192 | spa |
dc.relation.references | Büyüközkan, G., Kahraman, C. y Ruan, D. (2004). A fuzzy multi-criteria decision approach for software development strategy selection. International Journal of General Systems, 33(2-3), 259-280. https://doi.org/10.1080/03081070310001633581 | spa |
dc.relation.references | Büyüközkan, G. y Çifçi, G. (2012). A combined fuzzy AHP and fuzzy TOPSIS based strategic analysis of electronic service quality in healthcare industry. Expert Systems with Applications, 39(3), 2341-2354. | spa |
dc.relation.references | Byun, S. S., Balasingham, I. y Liang, X. (2008). Dynamic spectrum allocation in wireless cognitive sensor networks: Improving fairness and energy efficiency. IEEE Vehicular Technology Conference. https://doi.org/10.1109/VETECF.2008.299 | spa |
dc.relation.references | Cao, L. y Zheng, H. (2005). Distributed spectrum allocation via local bargaining. 2005 Second Annual IEEE Communications Society Conference on Sensor and AdHoc Communications and Networks, SECON 2005, 2005, 475-486. https://doi.org/10.1109/SAHCN.2005.1557100 Cárdenas, M., Díaz, M., Pineda, U., Arce, A. y Stevens, E. (2016). On spectrum occupancy measurements at 2.4 GHz ISM band for cognitive radio applications. International Conference on Electronics, Communications and Computers, 25-31. https://doi.org/10.1109/CONIELECOMP.2016.7438547 | spa |
dc.relation.references | Chang, D.-Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649-655. https://doi.org/10.1016/0377-2217(95)00300-2 | spa |
dc.relation.references | Chen, Y. y Hee-Seok, O. (2016). A Survey of measurement-based spectrum occupancy modeling for cognitive radios. IEEE Communications Surveys & Tutorials, 18(1), 848-859. https://doi.org/10.1109/COMST.2014.2364316 | spa |
dc.relation.references | Chen, D., Zhang, Q. y Jia, W. (2008). Aggregation aware spectrum assignment in cognitive adhoc networks. International Conference on Cognitive Radio Oriented Wireless Networks and Communications. https://doi.org/10.1109/CROWNCOM.2008.4562548 | spa |
dc.relation.references | Chen, T., Zhang, H., Maggio, G. M. y Chlamtac, I. (2007). CogMesh: A cluster-based cognitive radio network. IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 168-178. https://doi.org/10.1109/DYSPAN.2007.29 | spa |
dc.relation.references | Cheng, X. y Jiang, M. (2011). Cognitive radio spectrum assignment based on artificial bee colony algorithm. IEEE International Conference on Communication Technology, 161-164. https://doi.org/10.1109/ICCT.2011.6157854 | spa |
dc.relation.references | Cheng, Y. C., Wu, E. H. y Chen, G. H. (2016). A decentralized MAC protocol for unfairness problems in coexistent heterogeneous cognitive radio networks scenarios with collision-based primary users. IEEE Systems Journal, 10(1), 346-357. https://doi.org/10.1109/JSYST.2015.2431715 | spa |
dc.relation.references | Cho, J. y Lee, J. (2013). Development of a new technology product evaluation model for assessing commercialization opportunities using Delphi method and fuzzy AHP approach. Expert Systems with Applications, 40(13), 5314-5330. | spa |
dc.relation.references | Chou, C. T., Shankar, S., Kim, H. y Shin, K. G. (2007). What and how much to gain by spectrum agility? IEEE Journal on Selected Areas in Communications, 25(3), 576-587. https://doi.org/10.1109/JSAC.2007.070408 | spa |
dc.relation.references | Choudhary, D. y Shankar, R. (2012). A STEEP-fuzzy AHP-TOPSIS framework for evaluation and selection of thermal power plant location: A case study from India. Energy, 42(1), 510-521. | spa |
dc.relation.references | Christian, I., Moh, S., Chung, I. y Lee, J. (2012). Spectrum mobility in cognitive radio networks. IEEE Communications Magazine, 50(6), 114-121. https://doi.org/10.1109/MCOM.2012.6211495 | spa |
dc.relation.references | CISCO. (2021). Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update. In CISCO. https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white-paper-c11-741490.html | spa |
dc.relation.references | Cortés, J. (2011). Metodología para la implementación de tecnologías de la información y las comunicaciones TIC’s para soportar una estrategia de cadena de suministro esbelta [Master’s Dissertation, Universidad Nacional de Colombia]. | spa |
dc.relation.references | Cruz-Pol, S., Van Zee, L., Kassim, N., Blackwell, W., Le Vine, D. y Scott, A. (2018). Spectrum management and the impact of RFI on science sensors. Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad), 1-5. https://doi.org/10.1109/MICRORAD.2018.8430720 | spa |
dc.relation.references | Csurgai-Horvath, L. y Bito, J. (2011). Primary and secondary user activity models for cognitive wireless network. International Conference on Telecommunications, 301-306. | spa |
dc.relation.references | Dadallage, S., Yi, C. y Cai, J. (2016). Joint beamforming, power and channel allocation in multi-user and multi-channel underlay MISO cognitive radio networks. IEEE Transactions on Vehicular Technology, 65(5), 3349-3359. https://doi.org/10.1109/TVT.2015.2440412 | spa |
dc.relation.references | Dadios, E. P. (2012). Fuzzy logic: Algorithms, techniques and implementations. TechOpen. | spa |
dc.relation.references | Darak, S. J., Zhang, H., Palicot, J. y Moy, C. (2014). Efficient decentralized dynamic spectrum learning and access policy for multi-standard multi-user cognitive radio networks. 2014 11th International Symposium on Wireless Communications Systems, ISWCS 2014–Proceedings, 271-275. https://doi.org/10.1109/ISWCS.2014.6933360 | spa |
dc.relation.references | Darak, Sumit J., Dhabu, S., Moy, C., Zhang, H., Palicot, J. y Vinod, A. P. (2015). Low complexity and efficient dynamic spectrum learning and tunable bandwidth access for heterogeneous decentralized Cognitive Radio Networks. Digital Signal Processing: A Review Journal, 37(1), 13-23. https://doi.org/10.1016/j.dsp.2014.12.001 Darak, Sumit J., Zhang, H., Palicot, J. y Moy, C. (2017). | spa |
dc.relation.references | Decision making policy for RF energy harvesting enabled cognitive radios in decentralized wireless networks. Digital Signal Processing, 60, 33-45. https://doi.org/10.1016/j.dsp.2016.08.014 | spa |
dc.relation.references | Del-Ser, J., Matinmikko, M., Gil-López, S. y Mustonen, M. (2010). A novel harmony search based spectrum allocation technique for cognitive radio networks. International Symposium on Wireless Communication Systems, 233-237. https://doi.org/10.1109/ISWCS.2010.5624341 | spa |
dc.relation.references | Delgado, M. y Rodríguez, B. (2016). Opportunities for a more Efficient Use of the Spectrum based in Cognitive Radio. IEEE Latin America Transactions, 14(2), 610-616. https://doi.org/10.1109/TLA.2016.7437200 | spa |
dc.relation.references | Deng, H., Huang, L., Yang, C. y Xu, H. (2018). Centralized spectrum leasing via cooperative SU assignment in cognitive radio networks. International Journal of Communication Systems, 31(13). https://doi.org/10.1002/ dac.3726 | spa |
dc.relation.references | Dhamodharavadhani, S. (2015). A survey on clustering based routing protocols in Mobile ad hoc networks. 2015 International Conference on Soft-Computing and Networks Security (ICSNS), 1-6. https://doi.org/10.1109/ICSNS.2015.7292426 | spa |
dc.relation.references | Digham, F. F., Alouini, M. y Simon, M. K. (2007). On the energy detection of unknown signals over fading channels. IEEE Transactions on Communications, 55(1), 21-24. https://doi.org/10.1109/TCOMM.2006.887483 | spa |
dc.relation.references | Ding, L., Melodia, T., Batalama, S. N., Matyjas, J. D. y Medley, M. J. (2010). Cross-layer routing and dynamic spectrum allocation in cognitive radio ad hoc networks. IEEE Transactions on Vehicular Technology, 59(4), 1969-1979. https://doi.org/10.1109/TVT.2010.2045403 | spa |
dc.relation.references | Duan, J. y Li, Y. (2011). An optimal spectrum handoff scheme for cognitive radio mobile Ad Hoc networks. Advances in Electrical and Computer Engineering, 11(3), 11-16. https://doi.org/10.4316/aece.2011.03002 | spa |
dc.relation.references | Federal Communications Commission. (2003). Notice of proposed rulemaking and order. Mexico DF: Report ET Docket No. 03, 332. | spa |
dc.relation.references | Ferber, J. (1999). Multi-agent systems: An introduction to distributed artificial intelligence. Addison-Wesley. | spa |
dc.relation.references | Fraser, A. M. (2008). Hidden Markov models and dynamical systems. SIAM. | spa |
dc.relation.references | Fudenberg, D. y Tirole, J. (1991). Game theory. MIT Press. | spa |
dc.relation.references | Gallardo, J. R., Pineda, U. y Stevens, E. (2009). VIKOR method for vertical handoff decision in beyond 3G wireless networks. International Conference on Electrical Engineering, Computing Science and Automatic Control. https://doi.org/10.1109/ICEEE.2009.5393320 | spa |
dc.relation.references | Gavrilovska, L., Atanasovski, V., Macaluso, I. y Dasilva, L. A. (2013). Learning and reasoning in cognitive radio networks. IEEE Communications Surveys and Tutorials, 15(4), 1761-1777. https://doi.org/10.1109/SURV.2013.030713.00113 | spa |
dc.relation.references | Ghanem, M., Sabaei, M. y Dehghan, M. (2017). A novel model for implicit cooperation between primary users and secondary users in cognitive radio-cooperative communication systems. International Journal of Communication Systems, e3524, 1-22. https://doi.org/10.1002/dac.3524 | spa |
dc.relation.references | Giupponi, L. y Pérez-Neira, A. I. (2008). Fuzzy-based spectrum handoff in cognitive radio networks. International Conference on Cognitive Radio Oriented Wireless Networks and Communications. https://doi.org/10.1109/CROWNCOM.2008.4562535 | spa |
dc.relation.references | Goldberg, D. E. y Holland, J. H. (1988). Genetic algorithms and machine learning. Machine Learning, 3(2), 95-99. https://doi.org/10.1023/A:1022602019183 | spa |
dc.relation.references | Goswami, M. M. (2017). AODV based adaptive distributed hybrid multipath routing for mobile AdHoc network. 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), 410- 414. https://doi.org/10.1109/ICICCT.2017.7975230 | spa |
dc.relation.references | Green, K. C., Armstrong, J. S. y Graefe, A. (2007). Methods to elicit forecasts from groups: Delphi and prediction markets compared. Social Science Research Network, (8), 17-20. | spa |
dc.relation.references | Han, J., Kamber, M. y Pei, J. (2012). Data mining: Concepts and techniques. Elsevier. | spa |
dc.relation.references | Hasegawa, M., Hirai, H., Nagano, K., Harada, H. y Aihara, K. (2014). Optimization for centralized and decentralized cognitive radio networks. Proceedings of the IEEE, 102(4), 574-584. https://doi.org/10.1109/JPROC.2014.2306255 | spa |
dc.relation.references | Haykin, S. (1998). Neural networks: A comprehensive foundation (2.ª ed.). Prentice Hall PTR. | spa |
dc.relation.references | Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201-220. | spa |
dc.relation.references | He, A., Bae, K. K., Newman, T. R., Gaeddert, J., Kim, K., Menon, R., Morales-Tirado, L., Neel, J., Zhao, Y., Reed, J. H. y Tranter, W. H. (2010). A survey of artificial intelligence for cognitive radios. IEEE Transactions on Vehicular Technology, 59(4), 1578-1592. https://doi.org/10.1109/TVT.2010.2043968 | spa |
dc.relation.references | Hernández-Guillén, J., Rodríguez-Colina, E., Marcelín-Jiménez, R. y Pascoe-Chalke, M. (2012). CRUAM-MAC: A novel cognitive radio MAC protocol for dynamic spectrum access. IEEE Latin-America Conference on Communications, 1-6. https://doi.org/10.1109/LATINCOM.2012.6505997 | spa |
dc.relation.references | Hernández-Sampieri, R., Fernández-Collado, C. y Baptista, P. (2006). Metodología de la investigación. McGraw-Hill. | spa |
dc.relation.references | Hernández, C., Giral, D. y Márquez, H. (2017). Evolutive algorithm for spectral handoff prediction in cognitive wireless networks. HIKARI Ltd, 10(14), 673-689. https://doi.org/10.12988/ces.2017.7766 | spa |
dc.relation.references | Hernández, C., Giral, D. y Páez, I. (2015a). Benchmarking of the performance of spectrum mobility models in cognitive radio networks. IJAER, 10(21), 42189-42197. | spa |
dc.relation.references | Hernández, C., Giral, D. y Páez, I. (2015b). Hybrid algorithm for frequency channel selection in Wi-Fi networks. World Academy of Science, Engineering and Technology, 9(12), 1212-1215. | spa |
dc.relation.references | Hernández, C., Giral, D. y Salgado, C. (2020). Failed handoffs in collaborative Wi-Fi networks. Telkomnika, 18(2), 669-675. | spa |
dc.relation.references | Hernández, C., Giral, D. y Santa, F. (2015c). MCDM Spectrum Handover Models for Cognitive Wireless Networks. World Academy of Science, Engineering and Technology, 9(10), 679-682. | spa |
dc.relation.references | Hernández, C., Márquez, H. y Giral, D. (2017). Comparative evaluation of prediction models for forecasting spectral opportunities. IJET, 9(5), 3775-3782. https://doi.org/10.21817/ijet/2017/v9i5/170905055 | spa |
dc.relation.references | Hernández, C., Pedraza, L. F. y Martínez, F. H. (2016a). Algoritmos para asignación de espectro en redes de radio cognitiva. Tecnura, 20(48), 69-88. https://doi.org/10.14483/udistrital.jour.tecnura.2016.2.a05 | spa |
dc.relation.references | Hernández, C., Pedraza, L. F., Páez, I. y Rodríguez, E. (2015d). Análisis de la movilidad espectral en redes de radio cognitiva. Información Tecnológica, 26(6), 169-186. | spa |
dc.relation.references | Hernández, C., Pedraza, L. F. y Rodríguez, E. (2016b). Fuzzy feedback algorithm for the spectral handoff in cognitive radio networks. Revista Facultad de Ingeniería de la Universidad de Antioquia. | spa |
dc.relation.references | Hernández, C., Salcedo, O. y Pedraza, L. F. (2009). An ARIMA model for forecasting Wi-Fi data network traffic values. Ingeniería e Investigación, 29(2), 65-69. | spa |
dc.relation.references | Hernández, C., Salgado, C., López, H. y Rodríguez, E. (2015e). Multivariable algorithm for dynamic channel selection in cognitive radio networks. EURASIP Journal on Wireless Communications and Networking, 2015(1), 216. https://doi.org/10.1186/s13638-015-0445-8 | spa |
dc.relation.references | Hernández, C., Salgado, C. y Salcedo, O. (2013). Performance of multivariable traffic model that allows estimating throughput mean values. Revista Facultad de Ingeniería Universidad de Antioquia, 67, 52-62. https://doi.org/http://doi.org/10.1186/s13638-015-0445-8 | spa |
dc.relation.references | Hernández, C., Vásquez, H. y Páez, I. (2015f). Proactive spectrum handoff model with time series prediction. International Journal of Applied Engineering Research (IJAER), 10(21), 42259-42264. | spa |
dc.relation.references | Hoven, N., Tandra, R. y Sahai, A. (2005). Some fundamental limits on cognitive radio. Wireless Foundations EECS, Univ. of California, Berkeley. | spa |
dc.relation.references | Höyhtyä, M., Mustonen, M., Sarvanko, H., Hekkala, A., Katz, M., Mämmelä, A., Kiviranta, M. y Kautio, A. (2008). Cognitive radio: An intelligent wireless communication system. In Research Report VTT-R-02219-08. | spa |
dc.relation.references | Hu, F., Chen, B., Zhai, X. y Zhu, C. (2016). Channel selection policy in MultiSU and Multi-PU cognitive radio networks with energy harvesting for internet of everything. Mobile Information Systems, 2016, 6024928. https://doi.org/10.1155/2016/6024928 | spa |
dc.relation.references | Huang, X., Han, T. y Ansari, N. (2014). On green energy powered cognitive radio networks. CoRR, abs/1405.5. http://arxiv.org/abs/1405.5747 | spa |
dc.relation.references | Hübner, R. (2007). Strategic supply chain management in process industries: An application to specialty chemicals production network design (vol. 594). Springer Science & Business Media. | spa |
dc.relation.references | IEEE. (2008). IEEE standard definitions and concepts for dynamic spectrum access: terminology relating to emerging wireless networks, system functionality, and spectrum management. En IEEE Std 1900.1-2008 (pp.1-62). https://doi.org/10.1109/IEEESTD.2008.4633734 | spa |
dc.relation.references | IEEE. (2008) Standards Coordinating Committee 41 on Dynamic Spectrum. IEEE standard definitions and concepts for dynamic spectrum access: terminology relating to emerging wireless networks, system functionality, and spectrum management. En IEEE Standard 1900.1-2008. https://doi.org/10.1109/IEEESTD.2008.4633734 | spa |
dc.relation.references | Iftikhar, A., Rauf, Z., Ahmed Khan, F., Shoaib Ali, M. y Kakar, M. (2019). Bayesian game-based user behavior analysis for spectrum mobility in cognitive radios. Physical Communication, 32, 200-208. https://doi.org/10.1016/j.phycom.2018.12.002 | spa |
dc.relation.references | Issariyakul, T., Pillutla, L. S. y Krishnamurthy, V. (2009). Tuning radio resource in an overlay cognitive radio network for TCP: Greed isn’t good. IEEE Communications Magazine, 47(7), 57-63. https://doi.org/10.1109/MCOM.2009.5183473 Jayaweera, S. y Christodoulou, C. (2011). Radiobots: Architecture, algorithms and realtime reconfigurable antenna designs for autonomous, self-learning future cognitive radios. | spa |
dc.relation.references | Ji, Z. y Liu, K. J. R. (2007). Cognitive radios for dynamic spectrum access–dynamic spectrum sharing: A game theoretical overview. IEEE Communications Magazine, 45(5), 88-94. https://doi.org/10.1109/MCOM.2007.358854 | spa |
dc.relation.references | Jiang, C, Chen, Y. y Liu, K. J. R. (2014a). Multi-channel sensing and access game: Bayesian social learning with negative network externality. IEEE Transactions on Wireless Communications, 13(4), 2176-2188. https://doi.org/10.1109/TWC.2014.022014.131209 | spa |
dc.relation.references | Jiang, C, Chen, Y. y Liu, K. J. R. (2014b). Sequential multi-channel access game in distributed cognitive radio networks. 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 1247-1251. https://doi.org/10.1109/GlobalSIP.2014.7032322 | spa |
dc.relation.references | Jiang, C., Chen, Y. y Liu, K. J. R. (2014). Multi-channel sensing and access game: Bayesian social learning with negative network externality. IEEE Transactions on Wireless Communications, 13(4), 2176-2188. https://doi.org/10.1109/TWC.2014.022014.131209 | spa |
dc.relation.references | Joda, R. y Zorzi, M. (2015). Decentralized heuristic access policy design for two cognitive secondary users under a primary type-I HARQ process. IEEE Transactions on Communications, 63(11), 4037-4049. https://doi.org/10.1109/TCOMM.2015.2480846 | spa |
dc.relation.references | Kanodia, V., Sabharwal, A. y Knightly, E. (2004). MOAR: A multi-channel opportunistic auto-rate media access protocol for ad hoc networks. International Conference on Broadband Networks, 600-610. | spa |
dc.relation.references | Kaur, A., Kaur, A. y Sharma, S. (2018a). Cognitive decision engine design for CR based IoTs using differential evolution and bat algorithm. 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN), 130-135. https://doi.org/10.1109/SPIN.2018.8474273 | spa |
dc.relation.references | Kaur, A., Kaur, A. y Sharma, S. (2018b). PSO based multiobjective optimization for parameter adaptation in CR based IoTs. 2018 4th International Conference on Computational Intelligence & Communication Technology (CICT), 1-7. https://doi.org/10.1109/CIACT.2018.8480298 | spa |
dc.relation.references | Kaya, T. y Kahraman, C. (2010). Multicriteria renewable energy planning using an integrated fuzzy VIKOR & AHP methodology: The case of Istanbul. Energy, 35(6), 2517-2527. | spa |
dc.relation.references | Kibria, M. R., Jamalipour, A. y Mirchandani, V. (2005). A location aware three-step vertical handoff scheme for 4G/B3G networks. Global Telecommunications Conference, 5, 2752-2756. https://doi.org/10.1109/GLOCOM.2005.1578260 | spa |
dc.relation.references | Kim, H. y Shin, K. G. (2008). Efficient discovery of spectrum opportunities with MAC-layer sensing in cognitive radio networks. IEEE Transactions on Mobile Computing, 7(5), 533-545. https://doi.org/10.1109/ TMC.2007.70751 | spa |
dc.relation.references | Kim, W., Kassler, A. J., Di Felice, M. y Gerla, M. (2010). Urban-X: Towards distributed channel assignment in cognitive multi-radio mesh networks. IFIP Wireless Days. https://doi.org/10.1109/WD.2010.5657733 | spa |
dc.relation.references | Kondareddy, Y. R., Agrawal, P. y Sivalingam, K. (2008). Cognitive radio network setup without a common control channel. IEEE Military Communications Conference. https://doi.org/10.1109/MILCOM.2008.4753398 | spa |
dc.relation.references | Kongsiriwattana, W. y Gardner-Stephen, P. (2017). Eliminating the high standby energy consumption of adhoc Wi-Fi. 2017-Janua, 1-7. https://doi.org/10.1109/GHTC.2017.8239229 | spa |
dc.relation.references | Krishnamurthy, S., Thoppian, M., Venkatesan, S. y Prakash, R. (2005). Control channel based MAC-layer configuration, routing and situation awareness for cognitive radio networks. Proceedings–IEEE Military Communications Conference MILCOM, 2005. https://doi.org/10.1109/MILCOM.2005.1605725 | spa |
dc.relation.references | Krizhevsky, A., Sutskever, I. y Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 1097-1105. | spa |
dc.relation.references | Kumar, K., Prakash, A. y Tripathi, R. (2016). Spectrum handoff in cognitive radio networks: A classification and comprehensive survey. Journal of Network and Computer Applications, 61(Supplement C), 161-188. https://doi.org/https://doi.org/10.1016/j.jnca.2015.10.008 | spa |
dc.relation.references | Lahby, M., Leghris, C. y Adib, A. (2011). A hybrid approach for network selection in heterogeneous multi-access environments. International Conference on New Technologies, Mobility and Security, 1-5. https://doi.org/10.1109/NTMS.2011.5720658 | spa |
dc.relation.references | Lee, W., y Akyildiz, I. F. (2008). Optimal spectrum sensing framework for cognitive radio networks. IEEE Transactions on Wireless Communications, 7(10), 3845-3857. https://doi.org/10.1109/T-WC.2008.070391 | spa |
dc.relation.references | Lee, W. y Akyildiz, I. F. (2011). A spectrum decision framework for cognitive radio networks. IEEE Transactions on Mobile Computing, 10(2). 161-174 https://doi: 10.1109/TMC.2010.147. | spa |
dc.relation.references | Lehtomaki, J. J., Juntti, M., Saarnisaari, H. y Koivu, S. (2005). Threshold setting strategies for a quantized total power radiometer. IEEE Signal Processing Letters, 12(11), 796-799. https://doi.org/10.1109/LSP.2005.855521 | spa |
dc.relation.references | Lertsinsrubtavee, A. y Malouch, N. (2016). Hybrid spectrum sharing through adaptive spectrum handoff and selection. IEEE Transactions on Mobile Computing, 15(11), 2781-2793. | spa |
dc.relation.references | Li, X. y Zekavat, S. A. (2008). Traffic pattern prediction and performance investigation for cognitive radio systems. IEEE Wireless Communications and Networking Conference, 894-899. https://doi.org/10.1109/WCNC.2008.163 | spa |
dc.relation.references | Li, Y., Shen, H. y Wang, M. (2016). Optimization spectrum decision parameters in CR using autonomously search algorithm. International Conference on Signal Processing (ICSP), 1146-1151. https://doi.org/10.1109/ICSP.2016.7878007 | spa |
dc.relation.references | López, D. A., Trujillo, E. R. y Gualdrón, O. E. (2015). Elementos fundamentales que componen la radio cognitiva y asignación de bandas espectrales. Información Tecnológica, 26(1), 23-40. https://doi.org/10.4067/S0718-07642015000100004 | spa |
dc.relation.references | López, D. L. (2017). Implementación de un modelo predictor para la toma de decisiones en redes inalámbricas de radio cognitiva [Universidad Distrital Francisco José de Caldas]. http://doctoradoingenieria.udistrital.edu.co/index.php/es/investigacion/publicaciones | spa |
dc.relation.references | Ma, L., Shen, C. C. y Ryu, B. (2007). Single-radio adaptive channel algorithm for spectrum agile wireless ad hoc networks. IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 547- 558. https://doi.org/10.1109/DYSPAN.2007.78 | spa |
dc.relation.references | Marinho, J. y Monteiro, E. (2012). Cognitive radio: Survey on communication protocols, spectrum decision issues, and future research directions. Wireless Networks, 18(2), 147-164. https://doi.org/10.1007/s11276-011-0392-1 | spa |
dc.relation.references | Márquez, H., Hernández, C. y Giral, D. (2017). Channel availability prediction in cognitive radio networks using naive bayes. HIKARI Ltd, 10(12), 593-605. https://doi.org/10.12988/ces.2017.7758 | spa |
dc.relation.references | Martins, L. R. y Andrade, L. H. (2018). Analysis of machine learning algorithms for spectrum decision in cognitive radios. 2018 15th International Symposium on Wireless Communication Systems (ISWCS), 1-6. https://doi.org/10.1109/ISWCS.2018.8491060 | spa |
dc.relation.references | Masonta, M. T., Mzyece, M. y Ntlatlapa, N. (2013). Spectrum decision in cognitive radio networks: a survey. IEEE Communications Surveys & Tutorials, 15(3), 1088-1107. https://doi.org/10.1109/SURV.2012.111412.00160 | spa |
dc.relation.references | Matinmikko, M., Del-Ser, J., Rauma, T. y Mustonen, M. (2013). Fuzzy-logic based framework for spectrum availability assessment in cognitive radio systems. IEEE Journal on Selected Areas in Communications, 31(11), 2173-2184. https://doi.org/10.1109/JSAC.2013.131117 | spa |
dc.relation.references | Matlab. (2015). Matlab getting started guide. Matlab. | spa |
dc.relation.references | Mehbodniya, A., Kaleem, F., Yen, K. K. y Adachi, F. (2012). A fuzzy MADM ranking approach for vertical mobility in next generation hybrid networks. International Congress on Ultra-Modern Telecommunications and Control Systems and Workshops, 262-267. https://doi.org/10.1109/ICUMT.2012.6459676 | spa |
dc.relation.references | Mir, U., Merghem-Boulahia, L., Esseghir, M. y Gaïti, D. (2011). Dynamic spectrum sharing for cognitive radio networks using multiagent system. IEEE Conference on Consumer Communications and Networking, 658-663. | spa |
dc.relation.references | Miranda, E. (2001). Improving subjective estimates using paired comparisons. IEEE Software, 18(1), 87-91. https://doi.org/10.1109/52.903173 | spa |
dc.relation.references | Mitola, J. y Maguire, G. Q. (1999). Cognitive radio: making software radios more personal. IEEE Personal Communications, 6(4), 13-18. https://doi.org/10.1109/98.788210 | spa |
dc.relation.references | Nisan, N., Roughgarden, T., Tardos, E. y Vazirani, V. V. (2007). Algorithmic game theory (vol. 1). Cambridge University Press Cambridge. | spa |
dc.relation.references | Ormond, O., Murphy, J. y Muntean, G. (2006). Utility-based intelligent network selection in beyond 3G systems. IEEE International Conference on Communications, 4, 1831-1836. https://doi.org/10.1109/ICC.2006.254986 | spa |
dc.relation.references | Oyewobi, S. S. y Hancke, G. P. (2017). A survey of cognitive radio handoff schemes, challenges and issues for industrial wireless sensor networks (CR-IWSN). Journal of Network and Computer Applications, 97, 140-156. https://doi.org/https://doi.org/10.1016/j.jnca.2017.08.016 | spa |
dc.relation.references | Ozger, M. y Akan, O. B. (2016). On the utilization of spectrum opportunity in cognitive radio networks. IEEE Communications Letters, 20(1), 157-160. https://doi.org/10.1109/LCOMM.2015.2504103 | spa |
dc.relation.references | Páez, I., Giral, D. y Hernández, C. (2015). Modelo AHP-VIKOR para handoff espectral en redes de radio cognitiva. Tecnura, 19(45), 29-39. | spa |
dc.relation.references | Páez, I., Hernández, C. y Giral, D. (2017). Modelo adaptativo multivariable de handoff espectral para incrementar el desempeño en redes móviles de radio cognitiva (1.ª ed.). Editorial UD. | spa |
dc.relation.references | Pankratev, D. A., Samsonov, A. A. y Stotckaia, A. D. (2019). Wireless data transfer technologies in a decentralized system. 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), 620-623. https://doi.org/10.1109/EIConRus.2019.8656671 | spa |
dc.relation.references | Patil, S. K. y Kant, R. (2014). A fuzzy AHP-TOPSIS framework for ranking the solutions of Knowledge Management adoption in Supply Chain to overcome its barriers. Expert Systems with Applications, 41(2), 679-693. https://doi.org/10.1016/j.eswa.2013.07.093 | spa |
dc.relation.references | Pedraza, L. F., Forero, F. y Páez, I. (2014). Evaluación de ocupación del espectro radioeléctrico en Bogotá-Colombia. Ingenieria y Ciencia, 10(19), 127-143. | spa |
dc.relation.references | Pedraza, L. F., Hernández, C., Galeano, K., Rodríguez, E. y Páez, I. (2016). Ocupación espectral y modelo de radio cognitiva para Bogotá (1.ª ed.). Universidad Distrital Francisco José de Caldas. | spa |
dc.relation.references | Petrova, M., Mahonen, P. y Osuna, A. (2010). Multi-class classification of analog and digital signals in cognitive radios using Support Vector Machines. International Symposium on Wireless Communication Systems, 986-990. https://doi.org/10.1109/ISWCS.2010.5624500 | spa |
dc.relation.references | Pham, C., Tran, N. H., Do, C. T., Moon, S. Il y Hong, C. S. (2014). Spectrum handoff model based on hidden Markov model in cognitive radio networks. International Conference on Information Networking, 406-411. | spa |
dc.relation.references | Pla, V., Vidal, J. R., Martínez-Bauset, J. y Guijarro, L. (2010). Modeling and characterization of spectrum white spaces for underlay cognitive radio networks. IEEE International Conference on Communications. https://doi.org/10.1109/ICC.2010.5501788 | spa |
dc.relation.references | Rahimian, N., Georghiades, C. N., Shakir, M. Z. y Qaraqe, K. A. (2014). On the probabilistic model for primary and secondary user activity for OFDMA-based cognitive radio systems: Spectrum occupancy and system throughput perspectives. IEEE Transactions on Wireless Communications, 13(1), 356-369. https://doi.org/10.1109/TWC.2013.120213.130658 | spa |
dc.relation.references | Ramírez, C. y Ramos, V. M. (2013). On the Effectiveness of Multi-criteria Decision Mechanisms for Vertical Handoff. International Conference on Advanced Information Networking and Applications, 1157-1164. https://doi.org/10.1109/AINA.2013.114 | spa |
dc.relation.references | Ramírez, C. y Ramos, V. M. (2010). Handover vertical: un problema de toma de decisión múltiple. Congreso Internacional sobre Innovación y Desarrollo Tecnológico. | spa |
dc.relation.references | Ramzan, M. R., Nawaz, N., Ahmed, A., Naeem, M., Iqbal, M. y Anpalagan, A. (2017). Multi-objective optimization for spectrum sharing in cognitive radio networks: A review. Pervasive and Mobile Computing, 41(Supplement C), 106-131. https://doi.org/https://doi.org/10.1016/j.pmcj.2017.07.010 | spa |
dc.relation.references | Rizk, Y., Awad, M. y Tunstel, E. W. (2018). Decision making in multiagent systems: A survey. IEEE Transactions on Cognitive and Developmental Systems, 10(3), 514-529. https://doi.org/10.1109/TCDS.2018.2840971 | spa |
dc.relation.references | Rodríguez, E., Ramírez, P., Carrillo, A. y Ernesto, C. (2011). Multiple attribute dynamic spectrum decision making for cognitive radio networks. International Conference on Wireless and Optical Communications Networks, 1-5. https://doi.org/10.1109/WOCN.2011.5872960 | spa |
dc.relation.references | Rodríguez, A. B., Ramírez, L. J. y Chahuan, J. (2015). Nueva generación de heurísticas para redes de fibra óptica WDM (Wavelength División Multiplexing) bajo tráfico dinámico. Información Tecnológica, 26(5), 135-142. | spa |
dc.relation.references | Roy, A., Midya, S., Majumder, K., Phadikar, S. y Dasgupta, A. (2017). Optimized secondary user selection for quality of service enhancement of Two-Tier multi-user Cognitive Radio Network: A game theoretic approach. Computer Networks, 123, 1-18. https://doi.org/10.1016/j.comnet.2017.05.002 | spa |
dc.relation.references | Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9-26. https://doi.org/10.1016/0377-2217(90)90057-I | spa |
dc.relation.references | Safavian, S. R. y Landgrebe, D. (1991). A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man and Cybernetics, 21(3), 660-674. https://doi.org/10.1109/21.97458 | spa |
dc.relation.references | Salgado, C., Márquez, H. y Gómez, V. (2016a). Técnicas inteligentes en la asignación de espectro dinámica para redes inalámbricas cognitivas. Revista Tecnura, 20(49), 133-151. https://doi.org/10.14483/udistrital.jour.tecnura.2016.3.a09 | spa |
dc.relation.references | Salgado, C., Mora, S. y Giral, D. (2016b). Collaborative algorithm for the spectrum allocation in distributed cognitive networks. IJET, 8(5), 2288- 2299. https://doi.org/10.21817/ijet/2016/v8i5/160805091 | spa |
dc.relation.references | Song, Q. y Jamalipour, A. (2005). A network selection mechanism for next generation networks. IEEE International Conference on Communications, 2, 1418-1422. https://doi.org/10.1109/ICC.2005.1494578 | spa |
dc.relation.references | Sriram, K. y Whitt, W. (1986). Characterizing superposition arrival processes in packet multiplexers for voice and data. IEEE Journal on Selected Areas in Communications, 4(6), 833-846. https://doi.org/10.1109/JSAC.1986.1146402 | spa |
dc.relation.references | Stevens, E., Martínez, J. D. y Pineda, U. (2012). Evaluation of vertical handoff decision algorithms based on MADM methods for heterogeneous wireless networks. Journal of Applied Research and Technology, 10(4), 534-548. | spa |
dc.relation.references | Stevens, E., Gallardo, R., Pineda, U. y Acosta, J. (2012). Application of MADM method VIKOR for vertical handoff in heterogeneous wireless networks. IEICE Transactions on Communications, 95(2), 599-602. https://doi.org/10.1587/transcom.E95.B.599 | spa |
dc.relation.references | Stevens, E., Lin, Y. y Wong, V. W. S. (2008). An MDP-based vertical handoff decision algorithm for heterogeneous wireless networks. IEEE Transactions on Vehicular Technology, 57(2), 1243-1254. https://doi.org/10.1109/TVT.2007.907072 | spa |
dc.relation.references | Stevens, E. y Wong, V. W. S. (2006). Comparison between vertical handoff decision algorithms for heterogeneous wireless networks. IEEE Vehicular Technology Conference, 2, 947-951. https://doi.org/10.1109/VETECS.2004.1388970 | spa |
dc.relation.references | Sutton, R. S. y Barto, A. G. (1998). Reinforcement learning: An introduction. IEEE Transactions on Neural Networks, 9(5), 1054. https://doi.org/10.1109/TNN.1998.712192 | spa |
dc.relation.references | Tabassam, A. A. y Suleman, M. U. (2012). Game theory in wireless and cognitive radio networks–Coexistence perspective. 2012 IEEE Symposium on Wireless Technology and Applications (ISWTA), 177-181. https://doi.org/10.1109/ISWTA.2012.6373837 | spa |
dc.relation.references | Tahir, M., Hadi Habaebi, M. e Islam, M. R. (2017). Novel distributed algorithm for coalition formation for enhanced spectrum sensing in cognitive radio networks. AEU–International Journal of Electronics and Communications, 77(Supplement C), 139-148. https://doi.org/https://doi.org/10.1016/j.aeue.2017.04.033 | spa |
dc.relation.references | Taj, M. I. y Akil, M. (2011). Cognitive radio spectrum evolution prediction using artificial neural networks based multivariate time series modelling. Wireless Conference Sustainable Wireless Technologies, 1-6. | spa |
dc.relation.references | Tanino, T., Tanaka, T. e Inuiguchi, M. (2003). Multi-objective programming and goal programming: Theory and applications (vol. 21). Springer Science & Business Media. | spa |
dc.relation.references | Thakur, P., Kumar, A., Pandit, S., Singh, G. y Satashia, S. N. (2017). Spectrum mobility in cognitive radio network using spectrum prediction and monitoring techniques. Physical Communication, (24), 1-8. https://doi.org/10.1016/j.phycom.2017.04.005 | spa |
dc.relation.references | Tragos, E., Zeadally, S., Fragkiadakis, A. y Siris, V. (2013). Spectrum assignment in cognitive radio networks: A comprehensive survey. IEEE Communications Surveys and Tutorials, 15(3), 1108-1135. https://doi.org/10.1109/SURV.2012.121112.00047 | spa |
dc.relation.references | Trigui, E., Esseghir, M. y Merghem-Boulahia, L. (2012). Multi-agent systems negotiation approach for handoff in mobile cognitive radio networks. International Conference on New Technologies, Mobility and Security, 1-5. https://doi.org/10.1109/NTMS.2012.6208687 | spa |
dc.relation.references | Tripathi, S., Upadhyay, A., Kotyan, S. y Yadav, S. (2019). Analysis and comparison of different fuzzy inference systems used in decision making for secondary users in cognitive radio network. Wireless Personal Communications, 104(3), 1175-1208. https://doi.org/10.1007/s11277-018-6075-9 | spa |
dc.relation.references | Tsiropoulos, G., Dobre, O., Ahmed, M. y Baddour, K. (2016). Radio resource allocation techniques for efficient spectrum access in cognitive radio networks. IEEE Communications Surveys & Tutorials, 18(1), 824-847. https://doi.org/10.1109/COMST.2014.2362796 | spa |
dc.relation.references | Valenta, V., Maršálek, R., Baudoin, G., Villegas, M., Suárez, M. y Robert, F. (2010). Survey on spectrum utilization in Europe: Measurements, analyses and observations. International Conference on Cognitive Radio Oriented Wireless Networks, 230126, 2-6. https://doi.org/10.4108/ICST.CROWNCOM2010.9220 | spa |
dc.relation.references | Vasudeva, A. y Sood, M. (2018). Survey on sybil attack defense mechanisms in wireless ad hoc networks. Journal of Network and Computer Applications, (120), 78-118. https://doi.org/https://doi.org/10.1016/j. jnca.2018.07.006 | spa |
dc.relation.references | Wang, B. y Liu, K. J. R. (2011). Advances in cognitive radio networks: A survey. IEEE Journal of Selected Topics in Signal Processing, 5(1), 5-23. https://doi.org/10.1109/JSTSP.2010.2093210 | spa |
dc.relation.references | Wang, C., Chen, Y. y Liu, K. J. R. (2017). Hidden Chinese restaurant game: Grand information extraction for stochastic network learning. IEEE Transactions on Signal and Information Processing over Networks, 3(2), 330- 345. https://doi.org/10.1109/TSIPN.2017.2682799 | spa |
dc.relation.references | Wang, J., Ghosh, M. y Challapali, K. (2011). Emerging cognitive radio applications: A survey. IEEE Communications Magazine. https://doi.org/10.1109/MCOM.2011.5723803 | spa |
dc.relation.references | Wang, P., Ansari, J., Petrova, M. y Mähönen, P. (2016). CogMAC+: A decentralized MAC protocol for opportunistic spectrum access in cognitive wireless networks. Computer Communications, 79(Supplement C), 22-36. https://doi.org/https://doi.org/10.1016/j.comcom.2015.09.016 | spa |
dc.relation.references | Wang, X., Wong, A. y Ho, P.-H. (2010). Dynamically optimized spatiotemporal prioritization for spectrum sensing in cooperative cognitive radio. Wireless Networks, 16(4), 889-901. https://doi.org/10.1007/s11276-009-0175-0 | spa |
dc.relation.references | Wei, Q., Farkas, K., Prehofer, C., Mendes, P. y Plattner, B. (2006). Contextaware handover using active network technology. Computer Networks, 50(15), 2855-2872. https://doi.org/10.1016/j.comnet.2005.11.002 | spa |
dc.relation.references | Wei, Y., Li, X., Song, M. y Song, J. (2008). Cooperation radio resource management and adaptive vertical handover in heterogeneous wireless networks. International Conference on Natural Computation, 5, 197-201. https://doi.org/10.1109/ICNC.2008.504 | spa |
dc.relation.references | Willkomm, D., Machiraju, S., Bolot, J. y Wolisz, A. (2008). Primary users in cellular networks: A large-scale measurement study. IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks, 401-411. https://doi.org/10.1109/DYSPAN.2008.48 | spa |
dc.relation.references | Woods, W. A. (1986). Important issues in knowledge representation. Proceedings of the IEEE, 74(10), 1322-1334. | spa |
dc.relation.references | Wooldridge, M. (2009). An introduction to multiagent systems. John Wiley & Sons. | spa |
dc.relation.references | Wu, Y., Yang, Q., Liu, X. y Kwak, K. (2016). Delay-Constrained optimal transmission with proactive spectrum handoff in cognitive radio networks. IEEE Transactions on Communications. https://doi.org/10.1109/TCOMM.2016.2561936 | spa |
dc.relation.references | Xenakis, D., Passas, N. y Merakos, L. (2014). Multi-parameter performance analysis for decentralized cognitive radio networks. Wireless Networks, 20(4), 787-803. https://doi.org/10.1007/s11276-013-0635-4 | spa |
dc.relation.references | Xu, G. y Lu, Y. (2006). Channel and modulation selection based on support vector machines for cognitive radio. International Conference on Wireless Communications, Networking and Mobile Computing, 4-7. https://doi.org/10.1109/WiCOM.2006.181 | spa |
dc.relation.references | Yang, S. F. y Wu, J. S. (2008). A IEEE 802.21 handover design with QoS provision across WLAN and WMAN. International Conference on Communications, Circuits and Systems Proceedings, 548-552. https://doi.org/10.1109/ICCCAS.2008.4657833 | spa |
dc.relation.references | Yang, S. J. y Tseng, W. C. (2013). Design novel weighted rating of multiple attributes scheme to enhance handoff efficiency in heterogeneous wireless networks. Computer Communications, 36(14), 1498-1514. https://doi.org/10.1016/j.comcom.2013.06.005 | spa |
dc.relation.references | Yifei, W., Yinglei, T., Li, W., Mei, S. y Xiaojun, W. (2013). QoS provisioning energy saving dynamic access policy for overlay cognitive radio networks with hidden Markov channels. China Communications, 10(12), 92-101. https://doi.org/10.1109/CC.2013.6723882 | spa |
dc.relation.references | Yonghui, C. (2010). Study of the bayesian networks. International Conference on E-Health Networking, Digital Ecosystems and Technologies, 1, 172-174. | spa |
dc.relation.references | Yoon, K. P. y Hwang, C.-L. (1995). Multiple attribute decision making: An introduction (vol. 104). Sage publications. | spa |
dc.relation.references | Youssef, M. E., Nasim, S., Wasi, S., Khisal, U. y Khan, A. (2018). Efficient cooperative spectrum detection in cognitive radio systems using wavelet fusion. International Conference on Computing, Electronic and Electrical Engineering. https://doi.org/10.1109/ICECUBE.2018.8610981 | spa |
dc.relation.references | Yu, X. y Xue, W. (2018). Joint Spectrum Allocation and Power Control for Cognitive Radio Networks Based on Potential Game BT–2018 International Symposium on Networks, Computers and Communications, ISNCC 2018, June 19, 2018– June 21, 2018. dbw Communication; iDirect; Nextant Applications a. https://doi.org/10.1109/ISNCC.2018.8530881 | spa |
dc.relation.references | Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X | spa |
dc.relation.references | Zapata, J. A., Arango, M. D. y Adarme, W. (2012). Applying fuzzy extended analytical hierarchy (FEAHP) for selecting logistics software. Ingeniería e Investigación, 32(1), 94-99. | spa |
dc.relation.references | Zhang, B., Chen, Y., Wang, C. y Liu, K. J. R. (2012). Learning and decision making with negative externality for opportunistic spectrum access. 2012 IEEE Global Communications Conference (GLOBECOM), 1404-1409. https://doi.org/10.1109/GLOCOM.2012.6503310 | spa |
dc.relation.references | Zhang, H., Nie, Y., Cheng, J., Leung, V. C. M. y Nallanathan, A. (2017). Sensing time optimization and power control for energy efficient cognitive small cell with imperfect hybrid spectrum sensing. IEEE Transactions on Wireless Communications, 16(2), 730-743. https://doi.org/10.1109/TWC.2016.2628821 | spa |
dc.relation.references | Zhang, W. (2004). Handover decision using fuzzy MADM in heterogeneous networks. IEEE Wireless Communications and Networking Conference, 2, 653-658. https://doi.org/10.1109/WCNC.2004.1311263 | spa |
dc.relation.references | Zhang, Y., Tay, W. P., Li, K. H., Esseghir, M. y Gaïti, D. (2016). Opportunistic spectrum access with temporal-spatial reuse in cognitive radio networks. IEEE International Conference on Acoustics, Speech and Signal Processing, 3661-3665. | spa |
dc.relation.references | Zhao, Y., Mao, S., Neel, J. O. y Reed, J. H. (2009). Performance evaluation of cognitive radios: Metrics, utility functions, and methodology. Proceedings of the IEEE, 97(4), 642-658. https://doi.org/10.1109/JPROC.2009.2013017 | spa |
dc.relation.references | Zheng, H. y Cao, L. (2005). Device-centric spectrum management. IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 56-65. https://doi.org/10.1109/DYSPAN.2005.1542617 | spa |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.acceso | Abierto (Texto Completo) | spa |
dc.rights.accessrights | OpenAccess | spa |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Espectro radioeléctrico | spa |
dc.subject | Redes de radio cognitiva | spa |
dc.subject | Acceso dinámico al espectro | spa |
dc.subject | Toma de decisión espectral | spa |
dc.subject.keyword | Radio spectrum | spa |
dc.subject.keyword | Cognitive radio networks | spa |
dc.subject.keyword | Dynamic spectrum access | spa |
dc.subject.keyword | Spectral decision making | spa |
dc.subject.lemb | Comunicaciones inalámbricas | spa |
dc.subject.lemb | Gestión del espectro | spa |
dc.subject.lemb | Interferencia de radiofrecuencia | spa |
dc.subject.lemb | Tecnologías de acceso al espectro | spa |
dc.subject.lemb | Espectro radioeléctrico | spa |
dc.subject.lemb | Redes de radio cognitivas | spa |
dc.title | Modelo de asignación espectral multiusuario para redes de radio cognitiva descentralizadas | spa |
dc.title.titleenglish | Multi-user spectral allocation model for decentralized cognitive radio networks | spa |
dc.type | book | spa |
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