Paper Info
Title
Signal Classification Based on Spectral Correlation Analysis and SVM in Cognitive Radio
Abstract
Spectrum sensing is one of the most challenging functions in cognitive radio system. Detection of the presence of signals and distinction of the type of signals in a particular frequency band are critical for cognitive radios to adapt to the radio environment. In this paper, a novel approach to signal classification combining spectral correlation analysis and support vector machine (SVM) is introduced. Four spectral coherence characteristic parameters are chosen via spectral correlation analysis. By utilizing a nonlinear SVM, a significant amount of calculation is performed offline, thus the computational complexity is reduced. Simulations indicate that the overall success rate is above 92.8% with data length of 1000 when SNR is equal to 4 dB. Compared to the existing methods including the classifiers based on binary decision tree (BDT) and multilayer linear perceptron network (MLPN), the proposed approach is more effective in the case of low SNR and limited training numbers.
Year
DOI
Venue
2008
10.1109/AINA.2008.27
AINA
Keywords
Field
DocType
cognitive radio system,low snr,binary decision tree,cognitive radio,spectral correlation analysis,radio environment,svm,nonlinear svm,telecommunication computing,spectral coherence characteristic parameter,support vector machine,signal classification,novel approach,support vector machines,correlation methods,frequency,rf signals,decision tree,signal analysis,computational complexity
Signal processing,Pattern recognition,Frequency band,Computer science,Coherence (signal processing),Support vector machine,Radio frequency,Artificial intelligence,Perceptron,Computational complexity theory,Cognitive radio
Conference
ISSN
ISBN
Citations 
1550-445X
978-0-7695-3095-6
15
PageRank 
References 
Authors
0.82
6
3
Name
Order
Citations
PageRank
Hao Hu1207.76
Yujing Wang2151.83
Song Junde317131.65