Convolutional neural network-based modulation recognition technique for communication signals

Research Article
Open access

Convolutional neural network-based modulation recognition technique for communication signals

Yunchao Zou 1*
  • 1 Beihang University    
  • *corresponding author 20373157@buaa.edu.cn
Published on 25 September 2023 | https://doi.org/10.54254/2755-2721/12/20230308
ACE Vol.12
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-013-4
ISBN (Online): 978-1-83558-014-1

Abstract

The last decade has seen breakthroughs in communication technology. The increasingly complex signal transmission environment has placed higher demands on signal modulation recognition. Traditional modulation recognition approaches cannot guarantee satisfactory recognition accuracy. Fortunately, with the continuous advancement of deep learning algorithms, convolutional neural network-based communication signal modulation recognition techniques have become the mainstream of current research. Therefore, this paper first reviews the development history of signal modulation recognition techniques and introduces the concepts of signal modulation theory. It includes ASK, PSK and FSK modulation methods, which are common today. Subsequently, I analyze the principles of signal modulation recognition and the implementation method of CNN in modulation recognition. To further explore the shortcomings of CNNs, I propose two optimized models, the residual network model and the CLDNN model. After comparing the performance, the former has higher performance, but its computational complexity is higher while the latter takes into account the high recognition accuracy while still reducing the network parameters as much as possible to keep the complexity at a low level.

Keywords:

signal modulation, deep earning, CNN, residual network, CLDNN.

Zou,Y. (2023). Convolutional neural network-based modulation recognition technique for communication signals. Applied and Computational Engineering,12,122-134.
Export citation

References

[1]. Shafi, M., et al. 2017. 5G: A tutorial overview of standards, trials, challenges, deployment, and practice. IEEE Journal on Selected Areas in Communications, 35(6), 1201-1221.

[2]. Li, R., et al. 2017. Intelligent 5G: When cellular networks meet artificial intelligence. IEEE Wireless Communications, 24(5), 175-183.

[3]. Simonyan, K., & Zisserman, A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

[4]. Szegedy, C., et al. 2015. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-9.

[5]. He, K., Zhang, X., Ren, S., & Sun, J. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770-778.

[6]. Krizhevsky, A., Sutskever, I., & Hinton, G. E. 2017. Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90.

[7]. Ying, K., et al. 2015. Optimization of signal-to-noise-plus-distortion ratio for dynamic-range-limited nonlinearities. Digital Signal Processing, 36, 104-114.

[8]. Nandi, A., & Azzouz, E. E. 1995. Automatic analogue modulation recognition. Signal Processing, 46(2), 211-222.

[9]. O’Shea, T. J., Corgan, J., & Clancy, T. C. 2016. Convolutional radio modulation recognition networks. In Engineering Applications of Neural Networks: 17th International Conference, EANN 2016, Aberdeen, UK, September 2-5, 2016, Proceedings 17 (pp. 213-226. Springer.

[10]. Liu, X., Yang, D., & El Gamal, A. 2017. Deep neural network architectures for modulation classification. In 2017 51st Asilomar Conference on Signals, Systems, and Computers (pp. 915-919. IEEE.

[11]. Guo, Y., & Wang, X. 2022. Modulation Signal Classification Algorithm Based on Denoising Residual Convolutional Neural Network. IEEE Access, 10, 121733-121740.

[12]. Peng, S., et al. 2018. Modulation classification based on signal constellation diagrams and deep learning. IEEE Transactions on Neural Networks and Learning Systems, 30(3), 718-727.

[13]. Zhang, Z., et al. 2019. Automatic modulation classification using convolutional neural network with features fusion of SPWVD and BJD. IEEE Transactions on Signal and Information Processing over Networks, 5(3), 469-478.

[14]. Qi, P., et al. 2020. Automatic modulation classification based on deep residual networks with multimodal information. IEEE Transactions on Cognitive Communications, 7(1), 21-33.

[15]. Zeng, Y., et al. 2019. Spectrum analysis and convolutional neural network for automatic modulation recognition. IEEE Wireless Communications Letters, 8(3), 929-932.

[16]. Zhu, Y., & Huang, C. 2012. An improved median filtering algorithm for image noise reduction. Physics Procedia, 25, 609-616

[17]. Zhang, Z., Wang, C., Gan, C., Sun, S., & Wang, M. 2019. Automatic modulation classification using convolutional neural network with features fusion of SPWVD and BJD. IEEE Transactions on Signal and Information Processing over Networks, 5(3), 469-478.

[18]. Hochreiter, S. 1998. The vanishing gradient problem during learning recurrent neural nets and problem solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 6(02), 107-116.

[19]. Sainath, T., Weiss, R. J., Wilson, K., Senior, A. W., & Vinyals, O. 2015. Learning the speech front-end with raw waveform CLDNNs.

Cite this article

Zou,Y. (2023). Convolutional neural network-based modulation recognition technique for communication signals. Applied and Computational Engineering,12,122-134.

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 the 2023 International Conference on Mechatronics and Smart Systems

ISBN：978-1-83558-013-4(Print) / 978-1-83558-014-1(Online)
Editor：Seyed Ghaffar, Alan Wang
Conference website: https://2023.confmss.org/
Conference date: 24 June 2023
Series: Applied and Computational Engineering
Volume number: Vol.12
ISSN：2755-2721(Print) / 2755-273X(Online)

© 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).

References

[1]. Shafi, M., et al. 2017. 5G: A tutorial overview of standards, trials, challenges, deployment, and practice. IEEE Journal on Selected Areas in Communications, 35(6), 1201-1221.

[2]. Li, R., et al. 2017. Intelligent 5G: When cellular networks meet artificial intelligence. IEEE Wireless Communications, 24(5), 175-183.

[3]. Simonyan, K., & Zisserman, A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

[4]. Szegedy, C., et al. 2015. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-9.

[5]. He, K., Zhang, X., Ren, S., & Sun, J. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770-778.

[6]. Krizhevsky, A., Sutskever, I., & Hinton, G. E. 2017. Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90.

[7]. Ying, K., et al. 2015. Optimization of signal-to-noise-plus-distortion ratio for dynamic-range-limited nonlinearities. Digital Signal Processing, 36, 104-114.

[8]. Nandi, A., & Azzouz, E. E. 1995. Automatic analogue modulation recognition. Signal Processing, 46(2), 211-222.

[9]. O’Shea, T. J., Corgan, J., & Clancy, T. C. 2016. Convolutional radio modulation recognition networks. In Engineering Applications of Neural Networks: 17th International Conference, EANN 2016, Aberdeen, UK, September 2-5, 2016, Proceedings 17 (pp. 213-226. Springer.

[10]. Liu, X., Yang, D., & El Gamal, A. 2017. Deep neural network architectures for modulation classification. In 2017 51st Asilomar Conference on Signals, Systems, and Computers (pp. 915-919. IEEE.

[11]. Guo, Y., & Wang, X. 2022. Modulation Signal Classification Algorithm Based on Denoising Residual Convolutional Neural Network. IEEE Access, 10, 121733-121740.

[12]. Peng, S., et al. 2018. Modulation classification based on signal constellation diagrams and deep learning. IEEE Transactions on Neural Networks and Learning Systems, 30(3), 718-727.

[13]. Zhang, Z., et al. 2019. Automatic modulation classification using convolutional neural network with features fusion of SPWVD and BJD. IEEE Transactions on Signal and Information Processing over Networks, 5(3), 469-478.

[14]. Qi, P., et al. 2020. Automatic modulation classification based on deep residual networks with multimodal information. IEEE Transactions on Cognitive Communications, 7(1), 21-33.

[15]. Zeng, Y., et al. 2019. Spectrum analysis and convolutional neural network for automatic modulation recognition. IEEE Wireless Communications Letters, 8(3), 929-932.

[16]. Zhu, Y., & Huang, C. 2012. An improved median filtering algorithm for image noise reduction. Physics Procedia, 25, 609-616

[17]. Zhang, Z., Wang, C., Gan, C., Sun, S., & Wang, M. 2019. Automatic modulation classification using convolutional neural network with features fusion of SPWVD and BJD. IEEE Transactions on Signal and Information Processing over Networks, 5(3), 469-478.

[18]. Hochreiter, S. 1998. The vanishing gradient problem during learning recurrent neural nets and problem solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 6(02), 107-116.

[19]. Sainath, T., Weiss, R. J., Wilson, K., Senior, A. W., & Vinyals, O. 2015. Learning the speech front-end with raw waveform CLDNNs.

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.