Modulation identification of digital M-ary QAM signals by Hilbert-Huang Transform

Tanc, Yesim Hekim; Akan, Aydın

doi:10.1109/icecs.2013.6815505

Cited by 2 publications

(2 citation statements)

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“…In order to assess the advantages of the proposed classifier, AMC-EM is compared with another classifier available in the literature [15] that is capable of classifying the same modulation schemes as the proposed one. Other available classifiers are specific to single modulation and fail in the case examined in this article [24]. The comparison is performed by numerical tests executed in the aforementioned operating conditions.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

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Application of machine learning techniques and empirical mode decomposition for the classification of analog modulated signals

et al. 2020

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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.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

“…The features are extracted by the EMD [17]- [24] of the input signal. The EMD is chosen for the feature estimation because, differently to other decomposition methods based on Fourier or Wavelet, it does not need a basis defined a priori.…”

Section: Introductionmentioning

confidence: 99%