Enhanced Automatic Modulation Recognition Performance Using ResNet and GAF 


Vol. 50,  No. 4, pp. 595-602, Apr.  2025
10.7840/kics.2025.50.4.595


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  Abstract

Automatic Modulation Recognition (AMR) is a crucial technology for enhancing the reliability and efficiency of wireless communication. Recent AMR research has focused on improving performance by utilizing deep learning, with various models being proposed that use IQ constellations as training data. However, since constellations are tools for visualizing the signal state at a specific time, they fail to capture the temporal characteristics of the signal, which can lead to performance degradation. To address this issue, this paper proposes a novel visualization method called GAF that incorporates the temporal features of time-series data, along with the application of PCA for channel reduction and noise removal. The proposed GAF method effectively captures the temporal variations and patterns in the signal, providing more diverse information than traditional visualization techniques. Furthermore, applying this approach to a ResNet model resulted in 5% to 15% higher accuracy in the SNR range of -10dB to 5dB compared to other visualization techniques such as IQ constellations, GCC algorithms, and the traditional GAF method used in AMR.

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  Cite this article

[IEEE Style]

S. Lee and W. Lim, "Enhanced Automatic Modulation Recognition Performance Using ResNet and GAF," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 4, pp. 595-602, 2025. DOI: 10.7840/kics.2025.50.4.595.

[ACM Style]

Sangho Lee and Wansu Lim. 2025. Enhanced Automatic Modulation Recognition Performance Using ResNet and GAF. The Journal of Korean Institute of Communications and Information Sciences, 50, 4, (2025), 595-602. DOI: 10.7840/kics.2025.50.4.595.

[KICS Style]

Sangho Lee and Wansu Lim, "Enhanced Automatic Modulation Recognition Performance Using ResNet and GAF," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 4, pp. 595-602, 4. 2025. (https://doi.org/10.7840/kics.2025.50.4.595)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880