Machine Learning Techniques Applied to Multiband Spectrum Sensing in Cognitive Radios - Yanqueleth Molina-Tenorio; Alfonso Prieto-Guerrero; Rafael Aguilar-Gonzalez; Silvia Ruiz-Boqué (Sensors, 2019)
In this work, three specific machine learning techniques (neural networks, expectation maximization and k-means) are applied to a multiband spectrum sensing technique for cognitive radios. All of them have been used as a classifier using the approximation coefficients from a Multiresolution Analysis in order to detect presence of one or multiple primary users in a wideband spectrum. Methods were tested on simulated and real signals showing a good performance. The results presented of these three methods are effective options for detecting primary user transmission on the multiband spectrum. These methodologies work for 99% of cases under simulated signals of SNR higher than 0 dB and are feasible in the case of real signals.