Automatic modulation recognition using support vector machine in software radio applications

Most of the algorithms proposed in the literature deal with the problem of digital modulation classification. This paper discusses the modulation classifiers capable of classifying both analog and digital modulation signals in military and civilian communications applications. A total of 7 statistical signal features are extracted and used to classify 9 modulation signals. In this paper, we investigate the performance of the two types of SVM classifiers and compare the performance of these SVM classifiers with that of decision tree based and minimum distance based classifiers. In numerical simulations, SVM classifiers indicate good performance (i.e. Probability of correct classification > 95%) on an AWGN channel, even at signal-to-noise ratios as low as 5dB.