Application of Deep Learning to Automatic Modulation Recognition

Mengze Yu

doi:10.54254/2755-2721/2025.AST24988

Applied and Computational EngineeringOpen access

Application of Deep Learning to Automatic Modulation Recognition

Research Article

Open Access

Application of Deep Learning to Automatic Modulation Recognition

Mengze Yu ^1*

¹ College, Southwest Jiaotong University, Chengdu, Sichuan, China

^*Corresponding author: 2214709763@qq.com

Published on 11 July 2025

ACE Vol.175

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-237-9

ISBN (Online): 978-1-80590-238-6

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Abstract

With the rapid advancement of wireless communication technologies, the increasing diversity of modulation schemes poses significant challenges for traditional modulation recognition methods in complex communication environments. To address this, this research proposes a hybrid deep learning model that integrates Convolutional Neural Networks (CNN) and Transformers. The CNN module is employed to extract local time-frequency features from the modulated signals, enhancing the model's capacity to capture short-term dependencies. Meanwhile, the Transformer module leverages its self-attention mechanism to model global temporal dependencies, improving recognition accuracy for complex modulation patterns. The model is trained and validated using the publicly available DeepSig RadioML 2018.01A dataset across various Signal-to-Noise Ratio (SNR) conditions, ranging from -20 dB to 30 dB. Experimental results demonstrate that our hybrid model achieves a remarkable recognition accuracy of up to 91% in environments with SNRs above 10 dB, highlighting its robustness and effectiveness in modulation recognition tasks.

Keywords:

deep learning, transformer model, convolutional neural network, automatic modulation recognition

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References

[1]. M. Li, "Research on Automatic Modulation Recognition Technology of Digital Communication Signals, " Master's thesis, Harbin Engineering University, Harbin, China, 2012.

[2]. Q. A. Qiknow, "Automatic Modulation Recognition Based on Transformer (RML2018), " CSDN Blog, Aug. 2024. Available: https: //blog.csdn.net/QAQIknow/article/details/144815416.

[3]. I. Kang, "Automatic Modulation Recognition Based on Deep Learning, " CSDN Blog, Aug. 2018. Available: https: //blog.csdn.net/illikang/article/details/82019945.

[4]. C. Chen, N. Zhang, C. Yang, Y. Gao and Y. Sheng, "Multiple-Stream Model with CNN-LSTM for Automatic Modulation Recognition, " 2023 International Conference on Microwave and Millimeter Wave Technology (ICMMT), Qingdao, China, 2023, pp. 1-3, doi: 10.1109/ICMMT58241.2023.10277458.

[5]. S. Lin, Y. Zeng, and Y. Gong, "Learning of Time-Frequency Attention Mechanism for Automatic Modulation Recognition, " arXiv preprint arXiv: 2111.03258, 2021.

[6]. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17). Curran Associates Inc., Red Hook, NY, USA, 6000–6010.

[7]. R. Zhang, "Deep Learning-Based Automatic Modulation Recognition with Code, " CSDN Blog, Aug. 6, 2022. Available: https: //blog.csdn.net/qq_40935157/article/details/126190599

References

[1]. M. Li, "Research on Automatic Modulation Recognition Technology of Digital Communication Signals, " Master's thesis, Harbin Engineering University, Harbin, China, 2012.

[2]. Q. A. Qiknow, "Automatic Modulation Recognition Based on Transformer (RML2018), " CSDN Blog, Aug. 2024. Available: https: //blog.csdn.net/QAQIknow/article/details/144815416.

[3]. I. Kang, "Automatic Modulation Recognition Based on Deep Learning, " CSDN Blog, Aug. 2018. Available: https: //blog.csdn.net/illikang/article/details/82019945.

[5]. S. Lin, Y. Zeng, and Y. Gong, "Learning of Time-Frequency Attention Mechanism for Automatic Modulation Recognition, " arXiv preprint arXiv: 2111.03258, 2021.

[7]. R. Zhang, "Deep Learning-Based Automatic Modulation Recognition with Code, " CSDN Blog, Aug. 6, 2022. Available: https: //blog.csdn.net/qq_40935157/article/details/126190599

Cite this article

Yu,M. (2025). Application of Deep Learning to Automatic Modulation Recognition. Applied and Computational Engineering,175,18-29.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-CDS 2025 Symposium: Application of Machine Learning in Engineering

ISBN: 978-1-80590-237-9(Print) / 978-1-80590-238-6(Online)

Editor: Marwan Omar, Mian Umer Shafiq

Conference website: https://www.confcds.org

Conference date: 19 August 2025

Series: Applied and Computational Engineering

Volume number: Vol.175

ISSN: 2755-2721(Print) / 2755-273X(Online)

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