Conference Paper

Digital modulation recognition using support vector machine classifier

Dept. of Electr. & Comput. Eng., George Washington Univ., DC, USA
Conference: Signals, Systems and Computers, 2004. Conference Record of the Thirty-Eighth Asilomar Conference on, Volume: 2
Source: IEEE Xplore

ABSTRACT We propose four features to classify amplitude shift keying with two levels and four levels, binary phase shift keying, quadrature phase keying, frequency shift keying with two carriers and four carriers. After that we present a new method of classification based on support vector machine (SVM) that uses the four proposed features. We study the performance of SVM classifier and compare it to the previous work done in the literature on the digital modulation classification problem.

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    ABSTRACT: In this paper we study the quickness of a classifier based on simple feature that we have previously proposed to distinguish frequency from amplitude-phase digital modulations. The feature is based on the product of two consecutive signal values and on time averaging of the imaginary part of the product. First, the conditional probability density functions of the feature given the present modulation are determined. The central limit theorem for strictly stationary m-dependent sequences is used to obtain Gaussian approximations. Then thresholds are determined based on the minimization of the total probability of misclassification. Following that effects of carrier offsets, of fast fading, and of the symbol period and time delay being non-integer multiples of sampling period on the performance are studied. In the course of doing that, the proposed classifier is compared to the maximum likelihood classifier and the wavelet based classifier using support vector machine.
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    ABSTRACT: In this paper, we present a new classification method which aims to distinguish the contents of digital modulation signals without knowing about binary sequences. The differences of these binary sequences can be detected from the features extracted from their modulation signals directly. With the help of appropriately designed classifier, we can get the classification results in high accurate rates, and avoid the complex processing steps related with recovery of these binary sequences at the same time. We also provide an example and the method to deal with it in details. In numerical simulations, the mean probabilities of correct classification for signals we point out are more than 94%. And even in noisy conditions, the method is also effective.
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