Abstract
<!--[if gte mso 11]><w:PermStart w:id="205147274"
 w:edGrp="everyone"/><![endif]--><p class="Abstract">In this article, an automatic Analog Modulation Classifier based on Empirical mode decomposition and Machine learning approaches (AMC-EM) is proposed. The AMC-EM operates without a priori information and can recognise typical analog modulation schemes: amplitude modulation, phase modulation, frequency modulation, and single sideband modulation. The AMC-EM uses Empirical Mode Decomposition (EMD) to evaluate the features of the signal for the successive classification by using Machine Learning (ML). In the design of the AMC-EM, the selection of the specific ML technique is performed by comparing, with numerical tests, the performance of the (i) Support Vector Machine (SVM), (ii) k-nearest neighbor classifier, and (iii) adaptive boosting, since they are commonly used in the field of signal classification. The tests have highlighted that the SVM, specifically the quadratic SVM, permits the best possible performance concerning classification accuracy, by considering different noise intensities superimposed on the signal. To assess the advantages of the proposal, a comparison with other classifiers available in the literature has been undertaken through numerical tests. Finally, the AMC-EM is experimentally evaluated, and the experimental results agree with those of the simulation.</p><p class="Abstract"><span lang="EN-US"><br /><!--[if gte mso 11]><w:PermEnd w:id="205147274"/><![endif]--></span></p>
Highlights
Selection of the specific Machine Learning (ML) technique is performed by comparing the performance of the most commonly used ones in the field of signal classification and the quadratic Support Vector Machine (SVM) has showed the best performance
The Analog Modulation Classifier (AMC)-EM has been characterised in simulation by considering signals corrupted by additive white Gaussian noise, and it shows very interesting results for low Signal-to-Noise Ratio (SNR) values
The usefulness of the proposed classifier with respect to the existing ones was assessed by comparing the AMC-EM with another classifier, available in the literature, based on Artificial Neural Network (ANN)
Summary
Communication systems typically use different analog and digital modulation techniques to send information between two or more apparatuses. The classification performance of the decision theoretic methods typically decreases along with the increasing of the amplitude of the noise superimposed on the signal. The automatic modulation scheme classification methods proposed in the literature present important limitations due to the requirement to have some previous knowledge about the detected signal, the dependence of the classifier effectiveness on specific operating conditions, and the influence of the amplitude of the noise superimposed on the signal on the classifier performance. A new Analog Modulation Classifier (AMC) relying on the joint utilisation of Empirical Mode Decomposition (EMD) and Machine Learning (ML) techniques is proposed to overcome the limitations of the existing classification methods. The operating conditions that are taken into account are (i) the inaccurate carrier frequency estimation of the signal under examination and (ii) different levels of superimposed noise.
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