Download pdf
Research on CNN-Based Satellite Communication Modulation Mode Recognition Technology
- 1 School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China, 611731
* Author to whom correspondence should be addressed.
Abstract
The identification of modulation mode constitutes a pivotal element within satellite communication systems. Its utilization is pervasive, manifesting in domains such as signal demodulation, resource allocation, and communication quality assessment. However, traditional methods of modulation mode identification are dependent on manual feature extraction, which is both time-consuming and less adaptable in complex environments. Recent advancements in deep learning technology, particularly Convolutional Neural Network (CNN), have introduced novel approaches and methodologies for the identification of radio signal modulation modes. This paper focuses on how deep learning techniques, particularly CNN, can improve the accuracy and efficiency of modulation mode recognition during satellite communications. It summarize key advances in dataset construction, network model design, training methods and optimization techniques. This paper also explores the potential application prospects of CNN technology in 6G communications, emphasizing its critical role in enhancing communication efficiency and service quality. The findings indicate that CNN has significant advantages in satellite communication modulation mode recognition, especially in improving recognition accuracy and robustness.
Keywords
Convolutional Neural Network (CNN), Modulation mode recognition, Satellite communication, Deep learning, 6G communication
[1]. Yang, J., Qi, Y., Zhou, Y., et al. (2023). CNN-Transformer lightweight intelligent modulation recognition algorithm. Journal of Xi'an University of Electronic Science and Technology, 50(3), 40-49. https://doi.org/10.19665/j.issn1001-2400.2023.03.004
[2]. Shao, K., Zhu, M. M., & Wang, G. Y. (2022). A modulation recognition method based on generative adversarial and convolutional neural network. Systems Engineering and Electronics, 44(3), 1036-1043.
[3]. Ye, Y.-L., & Peng, C.-R. (2023). Automatic identification of common modulation modes for satellite communication. Wireless Interconnection Technology, 20(07), 23-25.
[4]. Li, Y., Bian, D., & Zhang, R. (2024). Satellite signal modulation mode identification based on multi-feature fusion. Signal Processing, 40(07), 1298-1306. https://doi.org/10.16798/j.issn.1003-0530.2024.07.011
[5]. Zhang, C. (2023). Research on satellite signal modulation mode recognition technology based on deep learning [Doctoral dissertation, Shenyang University of Technology]. https://doi.org/10.27323/d.cnki.gsgyc.2023.000681
[6]. Peng, M., Wang, C., Chen, T., & Liu, G. (2016). Nirfacenet: A convolutional neural network for near-infrared face identification. Information, 7(4), 61. https://doi.org/10.3390/info7040061
[7]. Luo, F., Song, Q., Qin, Y., et al. (2024). Research on the application of convolutional neural network in image recognition. Computer and Information Technology, 32(03), 51-54. https://doi.org/10.19414/j.cnki.1005-1228.2024.03.032
[8]. Zhang, L., & Lin, D. C. (2025). Research on the application of convolutional neural network in image classification. Computer Programming Skills and Maintenance, (01), 155-157. https://doi.org/10.16184/j.cnki.comprg.2025.01.049
[9]. Zayed, M. M., Mohsen, S., Alghuried, A., Hijry, H., & Shokair, M. (2024). Zayed, M. M., Mohsen, S., Alghuried, A., Hijry, H., & Shokair, M. (2024). IoUT-Oriented an efficient CNN model for modulation schemes recognition in optical wireless communication systems. IEEE Access, 12, 186836-186855. https://doi.org/10.1109/ACCESS.2024.3515895
[10]. Tian, X., Sun, X., Yu, X., & Li, X. (2019, October). Modulation pattern recognition of communication signals based on fractional low-order Choi-Williams distribution and convolutional neural network in impulsive noise environment. In 2019 IEEE 19th International Conference on Communication Technology (ICCT) (pp. 188-192). IEEE. https://doi.org/10.1109/ICCT46805.2019.8947208
[11]. Cui, H. (2023). Research on the classification and identification method of suppressed interference signal based on CNN model fusion [Doctoral dissertation, Jilin University]. https://doi.org/10.27162/d.cnki.gjlin.2023.004982
[12]. Li, C., Chen, M. H., Shi, Y. X., et al. (2025). Small-sample communication interference signal recognition based on DCGAN-CNN. Radio Communication Technology, 51(01), 70-79.
[13]. Yuan, Z., Chen, W., Liang, D., et al. (2024). Satellite communication signal recognition based on multi-scale recurrent convolutional neural network. Journal of Chinese Academy of Electronic Science, 19(03), 219-227.
[14]. Lodhwal, V., & Choudhary, G. (2023). Survey paper: automatic title generation for text with RNN and pre-trained transformer language model. International Journal for Research in Applied Science and Engineering Technology, 11, 2117-2124.
[15]. Hou, K.-Y., Lai, R.-G., Tong, Z., et al. (2020). Radar communication signal modulation identification based on convolutional neural network. Ship Electronic Countermeasures, 43(06), 69-75. https://doi.org/10.16426/j.cnki.jcdzdk.2020.06.016
[16]. Xu, M., Hou, J., Wu, P. J., et al. (2020). Convolutional neural network modulation identification based on time-frequency characteristics of communication signals. Computer Science, 47(02), 175-179.
Cite this article
Liu,Y. (2025). Research on CNN-Based Satellite Communication Modulation Mode Recognition Technology. Applied and Computational Engineering,154,7-13.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of CONF-SEML 2025 Symposium: Machine Learning Theory and Applications
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).