Abstract
The predictive analysis for the spectral decision with automatic Learning is a task that is currently challenging. Some automatic Learning techniques are shown in order to predict the presence or absence of a primary user (PU) in Cognitive Radio. Four machine learning methods are examined including the K-nearest neighbors (KNN), the support vector machines (SVM), logistic regression (LR) and decision tree (DT) classifiers. These predictive models are built based on data and their performance is compared with the purpose of selecting the best classifier that can predict spectral occupancy.
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References
Abbas, N., Nasser, Y., El Ahmad, K.: Recent advances on artificial intelligence and learning techniques in cognitive radio networks. EURASIP J. Wirel. Commun. Netw. 2015(1), 174 (2015)
Elhachmi, J., Guennoun, Z.: Cognitive radio spectrum allocation using genetic algorithm. EURASIP J. Wirel. Commun. Netw. 2016(1), 133 (2016)
Senthilkumar, S., GeethaPriya, C.: A review of channel estimation and security techniques for CRNS. Autom. Control Comput. Sci. 50(3), 187–210 (2016)
Bkassiny, M., Li, Y., Jayaweera, S.K.: A survey on machine-learning techniques in cognitive radios. IEEE Commun. Surv. Tutor. 15(3), 1136–1159 (2013)
Wu, C., Yu, Q., Yi, K.: Least-squares support vector machine-based learning and decision making in cognitive radios, pp. 2855–2863, April 2012
Lu, Y., Zhu, P., Wang, D., Fattouche, M.: Machine learning techniques with probability vector for cooperative spectrum sensing in cognitive radio networks. In: WCNC, pp. 1–6 (2016)
Sharma, V.: Exploiting machine learning algorithms for cognitive radio, pp. 1554–1558 (2014)
Qiao, M., Zhao, H., Wang, S., Wei, J.: MAC Protocol selection based on machine learning in cognitive radio networks, pp. 453–458 (2016)
Grida, I., Yahia, B., Bendriss, J.: Cognitive 5G net works: comprehensive operator use cases with machine learning for management operations, pp. 252–259 (2017)
Alsarhan, A., Agarwal, A.: Profit optimization in multi-service cognitive mesh network using machine learning, pp. 1–14 (2011)
Liu, X., Li, B., Shen, D., Cao, J., Mao, B.: Analysis of grain storage loss based on decision tree algorithm. Procedia Comput. Sci. 122, 130–137 (2017)
Hosmer Jr., D.W., Lemeshow, S., Sturdivant, R.X.: Applied Logistic Regression. Wiley Series in Probability and Statistics, pp. 15–19 (2013)
Lingenfelter, D.J., Fessler, J.A., Scott, C.D., He, Z.: Predicting ROC curves for source detection under model mismatch, pp. 1092–1095. IEEExplorer (2010)
Ordoñez, J.: Caracterización de usuarios primarias para la implementación de un modelo predictor para la toma de decisiones en redes inalámbricas de radio cognitiva. In: 2016 Trabajo de Grado, Universidad Distrital Francisco José de Caldas (2016)
Pouriyeh, S.: A comprehensive investigation and comparison of machine learning techniques in the domain of Herat disease. In: 2017 IEEE International Conference Signal Processing, Informatics, Communication and Energy Systems (SPICES), February 2015
Charleonnan, A.: Predictive analytics for chronic kidney disease using machine learning techniques. In: 2014 International Conference on Advanced Computing and Communication Technologies (ICACACT 2014), pp. 1–5 (2014)
Mathworks: MatLab (2017), vol. 21, no. 8, pp. 680–693 (2010). https://es.mathworks.com/products/matlab.html
Teixera, H.: Contextual game design: from interface development to human activity recognition. Facultad de Ingeniería Universidad de Porto, Tesis maestría en Ingeniería Electrónica y de computadores (2017)
Bernal, C., Distrital, U., José, F., Hernández, C., Distrital, U., José, F.: Intelligent decision-making model for spectrum in cognitive wireless networks, vol. 10, no. 15, pp. 721–738 (2017)
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Soto, D.D.Z., Parra, O.J.S., Sarmiento, D.A.L. (2018). Detection of the Primary User’s Behavior for the Intervention of the Secondary User Using Machine Learning. In: Dang, T., Küng, J., Wagner, R., Thoai, N., Takizawa, M. (eds) Future Data and Security Engineering. FDSE 2018. Lecture Notes in Computer Science(), vol 11251. Springer, Cham. https://doi.org/10.1007/978-3-030-03192-3_15
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DOI: https://doi.org/10.1007/978-3-030-03192-3_15
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