Name
Title | ||
|---|---|---|
Eigenvalue and Support Vector Machine Techniques for Spectrum Sensing in Cognitive Radio Networks |
Abstract | ||
|---|---|---|
Cognitive radio has been described as the panacea to the problem of ever growing demand and scarcity of the radio spectrum. Fundamental to the successful implementation of cognitive radio is spectrum sensing. Here, we propose and investigate the performance of eigenvalue and support vector machine (SVM) based learning approach for spectrum sensing in multi-antenna cognitive radios. The simulation results show that the proposed technique is capable of yielding detection probability of ≥ 90 % at the signal-to-noise ratio (SNR) of-20 dB while maintaining the false alarm probability at ≤ 10%. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/.50 | Technologies and Applications of Artificial Intelligence |
Keywords | Field | DocType |
of-20 db,false alarm probability,cognitive radio,proposed technique,cognitive radio networks,simulation result,radio spectrum,signal-to-noise ratio,detection probability,support vector machine techniques,spectrum sensing,successful implementation,multi-antenna cognitive radio,learning artificial intelligence,support vector machines | False alarm,Computer science,Support vector machine,Speech recognition,Computer engineering,Telecommunication computing,Radio spectrum,Eigenvalues and eigenvectors,Radio spectrum management,Cognitive radio | Conference |
ISSN | ISBN | Citations |
1066-6192 | 978-1-4799-2528-5 | 4 |
PageRank | References | Authors |
0.44 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Olusegun Peter Awe | 1 | 6 | 1.83 |
| Ziming Zhu | 2 | 276 | 21.13 |
| Sangarapillai Lambotharan | 3 | 687 | 69.79 |