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- Title
Spectrum Sensing by Combining Cyclostationary PCA with RVM.
- Authors
WANG Xin; WANG Feng; SUN Jun; DU Kai; CHEN Jing-chuan
- Abstract
For the low accuracy rate of the primary user detection in the wireless channel environment, this paper proposes a method based on cyclostationary principal component analysis(PCA) and relevant vector machine(RVM) for spectrum sensing under the low signal to noise ratio( SNR) environment in cognitive radio. This method combines PCA with the RVM classification algorithm to solve spectrum sensing problem in cognitive network. A set of cyclic spectrum features are first calculated, and the PCA is applied to extract the most discriminate feature vector as training samples and testing samples for classification. The RVM is trained by training samples. Finally, the trained RVM is utilized to detect and decide the existence of the primary user. It is observed that the maximum increase of the detection probability of the proposed algorithm can be increased about 61. 6% in comparison with artificial neural network(ANN), support vector machine(SVM) and maximum-minimum eigenvalue (MME). Simulation results show the proposed algorithm can effectively detect primary user signals.
- Subjects
EIGENVALUES; VECTOR analysis; WIRELESS communications; SIGNAL-to-noise ratio; CYCLOSTATIONARY waves
- Publication
Telecommunication Engineering, 2014, Vol 54, Issue 7, p893
- ISSN
1001-893X
- Publication type
Article
- DOI
10.3969/j.issn.1001-893x.2014.07.006