Abstract
In this work, the performance of four Machine Learning Algorithms (MLAs) applied to Cognitive Mobile Radio Networks (CMRNs) are analyzed. These algorithms are Coalition Game Theory (CGT), Naive Bayesian Classifier (NBC), Support Vector Machine (SVM), and Decision Trees (DT). The numerical results of the performance analysis of these algorithms are presented based on two metrics. These metrics are commonly used in CMRNs which are Probability of Detection (\(P_d\)) and Probability of False Alarm (\(P_{fa}\)) against Signal-to-Noise Ratio (SNR). Furthermore, outcomes regarding the Classification Quality (CQ) and the simulation time are exposed. Theoretical and numerical results show that the SVM outperforms the rest of the algorithms in each of the metrics. The reasons behind this come from the SVM features, namely high precision, fast learning, and simplicity in the realization stage.
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Acknowledgment
This work was supported by ANID PFCHA/Beca de Doctorado Nacional/2019 21190489, SENESCYT “Convocatoria abierta 2014-primera fase Acta CIBAE-023-2014”, UDLA Telecommunications Engineering Degree, Project FONDECYT No. 11160517, and Grupo de Investigación en Inteligencia Artificial y Tecnologías de la Información (IA&TI).
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Játiva, P.P., Azurdia-Meza, C., Sánchez, I., Zabala-Blanco, D., Cañizares, M.R. (2021). Analysis of Spectrum Detection and Decision Using Machine Learning Algorithms in Cognitive Mobile Radio Networks. In: Wu, X., Wu, K., Wang, C. (eds) Quality, Reliability, Security and Robustness in Heterogeneous Systems. QShine 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 381. Springer, Cham. https://doi.org/10.1007/978-3-030-77569-8_11
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DOI: https://doi.org/10.1007/978-3-030-77569-8_11
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