Autors: Tonchev, K., Neshov, N. N., Ivanov, A. S., Manolova, A. H., Poulkov, V. K.
Title: Automatic Modulation Classification using Graph Convolutional Neural Networks for Time-frequency Representation
Keywords: Automatic modulation classification

Abstract: Recognition of the radio signal's modulating scheme is becoming increasingly important in civil and military applications. It can potentially alleviate the electromagnetic signal congestion in 5G networks by utilization of dynamic spectrum access or perform friend/foe identification in electronic military warfare as well as to support the detection of cyber-security related attacks. The recent advances in graph-convolutional networks (GCN) reveal a potential for usage in applications such as automatic modulation classification (AMC). Considering the structure of the modulated signal in time and frequency, this work proposes GCN architecture for AMC in various signal to noise (SNR) levels. The experimental results reveal that such approach delivers comparable results to other approaches published in the literature.

References

    Issue

    in Proceedings of International Symposium on Wireless Personal Multimedia Communications (WPMC), Herning, Denmark, 30 October-02 November 2022, pp. 75-79, 2022, Denmark, IEEE Computer Society, DOI 10.1109/WPMC55625.2022.10014833

    Copyright IEEE

    Цитирания (Citation/s):
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    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus и Web of Science