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New Automatic Modulation Classifier Using Cyclic-Spectrum Graphs With Optimal Training Features
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DOI：10.1109/LCOMM.2018.2819991.png)
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Abstract
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A new feature-extraction paradigm for graph-based automatic modulation classification is proposed in this letter. In the proposed new framework, the modulation features are optimally constructed using the Kullback-Leibler divergence of the dominant entries in the adjacency matrices associated with the graph presentation of the cyclic spectra. Then, the Hamming distance is invoked to measure the discrepancies between the features derived from the training and test data to determine the modulation type. The proposed algorithm, which can select the most distinguishable features, leads to the promising solution to automatic modulation classification (AMC). Compared with the existing AMC approach based on cyclic spectra, Monte Carlo simulation results demonstrate that the proposed AMC method using the new feature-extraction scheme is much more effective.
Keywords:
Automatic modulation classification (AMC)
cyclic spectrum
graph presentation
adjacency matrix
Kullback-Leibler divergence
Hamming distance
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