Multi-Modal Automatic Modulation Recognition Network Based on the Swin Transformer Architecture 


Vol. 50,  No. 10, pp. 1524-1531, Oct.  2025
10.7840/kics.2025.50.10.1524


PDF Full-Text
  Abstract

In modern electronic warfare, deep learning-based automatic modulation recognition (AMR) systems that provide high recognition accuracy across a wide range of communication signals have been actively studied. This trend stems from the growing complexity of signal environments in modern battlefields, resulting from the rapid evolution of communication technologies, which has significantly increased the difficulty of analyzing received signals. In this paper, we propose a multi-modal AMR neural network designed based on the 1D Swin Transformer architecture. The 1D Swin Transformer effectively extracts multi-resolution features from input sequences through a hierarchical feature representation and a shifted window-based mechanism. The proposed model employs a multi-modal structure that simultaneously utilizes IQ time-series signals in the time domain and spectrum information in the frequency domain. By extracting features from each modality and feeding them into the 1D Swin Transformer, the model performs modulation recognition by leveraging information from both the time and frequency domains. The proposed model achieved an average accuracy of 61.15% and outperformed conventional models. Furthermore, within the QAM family, the model achieved a high average recognition accuracy of 57.00% and demonstrated its ability to distinguish between structurally similar modulation schemes.

  Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


  Related Articles
  Cite this article

[IEEE Style]

D. Shin, M. Jeon, H. Kim, "Multi-Modal Automatic Modulation Recognition Network Based on the Swin Transformer Architecture," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 10, pp. 1524-1531, 2025. DOI: 10.7840/kics.2025.50.10.1524.

[ACM Style]

Da-Min Shin, Min-Wook Jeon, and Hyoung-Nam Kim. 2025. Multi-Modal Automatic Modulation Recognition Network Based on the Swin Transformer Architecture. The Journal of Korean Institute of Communications and Information Sciences, 50, 10, (2025), 1524-1531. DOI: 10.7840/kics.2025.50.10.1524.

[KICS Style]

Da-Min Shin, Min-Wook Jeon, Hyoung-Nam Kim, "Multi-Modal Automatic Modulation Recognition Network Based on the Swin Transformer Architecture," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 10, pp. 1524-1531, 10. 2025. (https://doi.org/10.7840/kics.2025.50.10.1524)
Vol. 50, No. 10 Index

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)






pISSN : 1226 - 4717
eISSN : 2287 - 3880