Abstract
A recent trend of automatic modulation classification is to automatically learn high-level abstraction of signals, instead of manually designing features for further classification. In this paper, we propose a new deep geometric convolutional network (DGCN) to hierarchically extract discriminative features from Wigner–Ville distribution map of signals. A group of geometric filters are constructed from a least square support vector machine, to capture the linear singularity existed in maps. The filters are cascaded to construct a deep network for extracting discriminative features and classifying signals with different modulation types. Some experiments are taken to investigate the performance of DGCN, and the results show that it can achieve high accuracy in classifying 15 types of modulation signals.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Nos. 61771380, 61906145, U1730109, 91438103, 61771376, 61703328, 91438201, U1701267, 61703328); the Equipment pre-research project of the 13th Five-Year Plan (Nos. 6140137050206, 414120101026, 6140312010103, 6141A020223, 6141B06160301, 6141B07090102), the Major Research Plan in Shaanxi Province of China (Nos. 2017ZDXM-GY-103, 017ZDCXL-GY-03-02), the Foundation of the State Key Laboratory of CEMEE (Nos. 2017K0202B, 2018K0101B, 2019K0203B, 2019Z0101B) and the Science Basis Research Program in Shaanxi Province of China (Nos. 16JK1823, 2017JM6086, 2019JQ-663).
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Li, R., Song, C., Song, Y. et al. Deep geometric convolutional network for automatic modulation classification. SIViP 14, 1199–1205 (2020). https://doi.org/10.1007/s11760-020-01641-3
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DOI: https://doi.org/10.1007/s11760-020-01641-3