Research Article
Analysis of Spectrum Detection and Decision Using Machine Learning Algorithms in Cognitive Mobile Radio Networks
@INPROCEEDINGS{10.1007/978-3-030-77569-8_11, author={Pablo Palacios J\^{a}tiva and Cesar Azurdia-Meza and Iv\^{a}n S\^{a}nchez and David Zabala-Blanco and Milton Rom\^{a}n Ca\`{o}izares}, title={Analysis of Spectrum Detection and Decision Using Machine Learning Algorithms in Cognitive Mobile Radio Networks}, proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Systems. 16th EAI International Conference, QShine 2020, Virtual Event, November 29--30, 2020, Proceedings}, proceedings_a={QSHINE}, year={2021}, month={6}, keywords={Cognitive mobile radio networks (CMRNs) Coalition game theory (CGT) Support vector machine (SVM) Decision tree (DT) Machine learning algorithms (MLAs) Naive bayesian classifier (NBC)}, doi={10.1007/978-3-030-77569-8_11} }- Pablo Palacios Játiva
Cesar Azurdia-Meza
Iván Sánchez
David Zabala-Blanco
Milton Román Cañizares
Year: 2021
Analysis of Spectrum Detection and Decision Using Machine Learning Algorithms in Cognitive Mobile Radio Networks
QSHINE
Springer
DOI: 10.1007/978-3-030-77569-8_11
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 ((Pd)) 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.
