Blind signal-type classification using a novel robust feature subset selection method and neural network classifier
annals of telecommunications - annales des télécommunications (Impact Factor: 0.7). 05/2010; DOI: 10.1007/s12243-010-0180-4
Automatic modulation recognition plays an important role for many novel computer and communication technologies. Most of the proposed systems can only identify a few kinds of digital signal and/or low order of them. They usually require high levels of signal-to-noise ratio. In this paper, we present a novel hybrid intelligent system that automatically recognizes a variety of digital signals. In this recognizer, a multilayer perceptron neural network with resilient back propagation learning algorithm is proposed as the classifier. For the first time, a combination set of spectral features and higher order moments up to eighth and higher order cumulants up to eighth are proposed as the effective features. Then we have optimized the classifier design by bees algorithm (BA) for selection of the best features that are fed to the classifier. This optimization method is new for this area. Simulation results show that the proposed technique has very high recognition accuracy with seven features selected by BA.
- EJC Supplements 11/2010; 8(7):181-181. DOI:10.1016/S1359-6349(10)72280-1 · 9.39 Impact Factor
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ABSTRACT: Automatic recognition of the communication signals plays an important role for various applications. Most of the existing techniques require high levels of signal to noise ratio (SNR). In this paper, we propose a high efficient technique for classification of the digital modulations that requires a low level of SNRs. This technique includes two main modules: feature extraction module and the classifier module. In the feature extraction module we use the auto-regressive modeling together other useful features. These features are a combination set of the entropy and energy of the signal, variance of the coefficients wavelet packet transform, fourth order of moment and zero-crossing rate. In the classifier module we have used the two structures of the neural networks: multi-layer perceptron (MLP) neural network and radial basis neural networks. Simulation results show the proposed technique has very high recognition accuracy for identification of the considered digital modulations even at very low SNRs.Measurement 10/2011; 44(8):1475-1481. DOI:10.1016/j.measurement.2011.05.019 · 1.48 Impact Factor
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ABSTRACT: Digital modulation recognition has found important application both in military and civil areas. Many algorithms have been proposed to automatically identify and recognize the digitally modulated signals. This paper proposed a new algorithm to recognize automatically the type of the modulated signals. Firstly, we propose two novel features, and combine them with other existing parameters to construct a new feature set which needs no a priori information about the nature of the signal. Later we adopt a layered neural network classifier to complete the recognition process. Simulation results show that the average rate of accurate classification is over 97% at a signal-to-noise ratio (SNR) of 5dB.International Journal of Advancements in Computing Technology 09/2012; 4(15):311-318. DOI:10.4156/ijact.vol4.issue15.36
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