Abstract
In this paper, the performance of several detection methods for primary user (PU) signals used to Cognitive Radio Networks (CRNs) are compared. Singular Value Decomposition Scheme (SVD), Eigen-value Decomposition Scheme (EVD), and Cyclo-stationary Detection Scheme (CD) are fairly compared based on Probability of Detection (\(P_d\)) as function of Signal-to-Noise ratio (SNR) in a CRN that coexists with a primary network based on Wireless Fidelity (WiFi) and Long Term Evolution (LTE) technologies. Results of the three methods implementation are obtained via numerical simulations. The Maximum Likelihood Estimator (MLE) is used to check the efficiency under established system measurement parameters such as the Standard Deviation (SD) and Standard Error (SE). Based on the results of the evaluation, it is concluded that the SVD scheme outperform the EVD and CD methods, according to the \(P_d\).
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This work was funded by CONICYT PFCHA/Beca de Doctorado Nacional/2019 21190489, SENESCYT “Convocatoria abierta 2014-primera fase, Acta CIBAE-023-2014”, and UDLA Telecommunications Engineering Degree.
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Palacios Játiva, P., Román-Cañizares, M., Saavedra, C., Freire, J.J. (2020). Signal Detection Methods in Cognitive Radio Networks: A Performance Comparison. In: Narváez, F., Vallejo, D., Morillo, P., Proaño, J. (eds) Smart Technologies, Systems and Applications. SmartTech-IC 2019. Communications in Computer and Information Science, vol 1154. Springer, Cham. https://doi.org/10.1007/978-3-030-46785-2_6
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