Name
Title | ||
|---|---|---|
Machine Learning Techniques Applied to Multiband Spectrum Sensing in Cognitive Radios. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/s19214715 | SENSORS |
Keywords | Field | DocType |
cognitive radios,multiband spectrum sensing,machine learning,neural networks | Wideband,Expectation–maximization algorithm,Multiresolution analysis,Electronic engineering,Artificial intelligence,Engineering,Artificial neural network,Classifier (linguistics),Machine learning,Cognitive radio | Journal |
Volume | Issue | ISSN |
19 | 21.0 | 1424-8220 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanqueleth Molina-Tenorio | 1 | 0 | 0.68 |
| Alfonso Prieto-Guerrero | 2 | 0 | 4.39 |
| Rafael Aguilar-Gonzalez | 3 | 0 | 0.34 |
| Silvia Ruiz-Boqué | 4 | 0 | 0.34 |