Classifying wireless signal modulation sorting using convolutional neural network

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.266801

Keywords:

wireless communications, digital modulation, deep learning convolutional neural network classifiers

Abstract

Deep learning has recently been used for this issue with superior results in automatic modulation classification. Previous studies state that it is challenging to categorize a variety of modulation formats using traditional approaches; however, modulation classification is a crucial component of non-cooperative communication in wireless communication. The deep learning network was applied to solve the issue and get decent outcomes. This work uses a deep learning convolutional neural network (DLCNN) to classify three analog and eight digital modulation techniques by generating channel-impaired and synthetic waveforms as training data. The obtained DLCNN is tested by over-the-air indicators and a Software Define Radio(SDR) platform. The trained DLCNN estimates the modulation kind of each frame by taking 1024 samples of channel-impaired signals. The method includes generating several frames of 4-arry pulse amplitude modulation (PAM4) that are impaired with sampling time drift, Additive white Gaussian noise (AWGN), center frequency, and Rician multipath fading. The DLCNN predicts real inputs when receiving a signal with complex samples of baseband. Before updating the network coefficients and on all iterations, the data store transforms data from files and records it. This network takes about 50 minutes to train using in-memory data and 110 minutes to train using disk data. The evaluation of the trained DLCNN is carried out by obtaining the classification accuracy for the test frames. The obtained outcome demonstrates that the developed network can achieve an accuracy of about 94.3 % in roughly 12 epochs for such types of waveforms, which elapsed about 26 minutes for training. This will increase the efficiency of spectrum usage and detect the modulation type of the wireless communication receivers

Supporting Agency

  • All authors are acknowledging the University of Technology-Iraq for their assistance and support.

Author Biographies

Ekhlas Hamza, University of Technology - Iraq

Doctor of Electric Engineering/Communications

DepartmentofControl and Systems Engineering

Sameir Aziez, University of Technology - Iraq

Doctor of Electric Engineering/Communications

Department of Electromechanical Engineering

Fadia Hummadi, Al-Khwarizmi College of Engineering, University of Baghdad

Master in Electric Engineering/Communications

Department of Communications

Ahmad Sabry, Al-Nahrain University

Doctor of Control and Automation Engineering

Department of Computer Engineering

References

  1. Huynh-The, T., Pham, Q.-V., Nguyen, T.-V., Nguyen, T. T., Ruby, R., Zeng, M., Kim, D.-S. (2021). Automatic Modulation Classification: A Deep Architecture Survey. IEEE Access, 9, 142950–142971. doi: https://doi.org/10.1109/access.2021.3120419
  2. Xu, Y., Li, D., Wang, Z., Guo, Q., Xiang, W. (2018). A deep learning method based on convolutional neural network for automatic modulation classification of wireless signals. Wireless Networks, 25 (7), 3735–3746. doi: https://doi.org/10.1007/s11276-018-1667-6
  3. Al-Shoukry, S., M. Jawad, B. J., Musa, Z., Sabry, A. H. (2022). Development of predictive modeling and deep learning classification of taxi trip tolls. Eastern-European Journal of Enterprise Technologies, 3 (3 (117)), 6–12. doi: https://doi.org/10.15587/1729-4061.2022.259242
  4. Jwaid, W. M., Al-Husseini, Z. S. M., Sabry, A. H. (2021). Development of brain tumor segmentation of magnetic resonance imaging (MRI) using U-Net deep learning. Eastern-European Journal of Enterprise Technologies, 4 (9(112)), 23–31. doi: https://doi.org/10.15587/1729-4061.2021.238957
  5. Shijer, S. S., Sabry, A. H. (2021). Analysis of performance parameters for wireless network using switching multiple access control method. Eastern-European Journal of Enterprise Technologies, 4 (9 (112)), 6–14. doi: https://doi.org/10.15587/1729-4061.2021.238457
  6. Zhang, H., Wang, Y., Xu, L., Aaron Gulliver, T., Cao, C. (2020). Automatic Modulation Classification Using a Deep Multi-Stream Neural Network. IEEE Access, 8, 43888–43897. doi: https://doi.org/10.1109/access.2020.2971698
  7. Zhou, Y., Lin, T., Zhu, Y. (2020). Automatic Modulation Classification in Time-Varying Channels Based on Deep Learning. IEEE Access, 8, 197508–197522. doi: https://doi.org/10.1109/access.2020.3034942
  8. Clement, J. C., Indira, N., Vijayakumar, P., Nandakumar, R. (2020). Deep learning based modulation classification for 5G and beyond wireless systems. Peer-to-Peer Networking and Applications, 14 (1), 319–332. doi: https://doi.org/10.1007/s12083-020-01003-3
  9. Perenda, E., Rajendran, S., Bovet, G., Pollin, S., Zheleva, M. (2022). Evolutionary Optimization of Residual Neural Network Architectures for Modulation Classification. IEEE Transactions on Cognitive Communications and Networking, 8 (2), 542–556. doi: https://doi.org/10.1109/tccn.2021.3137519
  10. Zhou, R., Liu, F., Gravelle, C. W. (2020). Deep Learning for Modulation Recognition: A Survey With a Demonstration. IEEE Access, 8, 67366–67376. doi: https://doi.org/10.1109/access.2020.2986330
  11. Ujan, S., Navidi, N., Landry, R. J. (2020). Hierarchical Classification Method for Radio Frequency Interference Recognition and Characterization in Satcom. Applied Sciences, 10 (13), 4608. doi: https://doi.org/10.3390/app10134608
  12. Zheng, S., Qi, P., Chen, S., Yang, X. (2019). Fusion Methods for CNN-Based Automatic Modulation Classification. IEEE Access, 7, 66496–66504. doi: https://doi.org/10.1109/access.2019.2918136
  13. Ji, K., Chang, S., Huang, S., Chen, H., Jia, S., Lu, H. (2021). Modulation Classification of Active Attack Signals for Internet of Things Using GP-CNN Network. 2021 IEEE International Conference on Communications Workshops (ICC Workshops). doi: https://doi.org/10.1109/iccworkshops50388.2021.9473800
  14. Al-Nuaimi, D. H., Akbar, M. F., Salman, L. B., Abidin, I. S. Z., Isa, N. A. M. (2021). AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks. Electronics, 10 (1), 76. doi: https://doi.org/10.3390/electronics10010076
Classifying wireless signal modulation sorting using convolutional neural network

Downloads

Published

2022-12-30

How to Cite

Hamza, E., Aziez, S., Hummadi, F., & Sabry, A. (2022). Classifying wireless signal modulation sorting using convolutional neural network. Eastern-European Journal of Enterprise Technologies, 6(9 (120), 70–79. https://doi.org/10.15587/1729-4061.2022.266801

Issue

Vol. 6 No. 9 (120) (2022): Information and controlling system

Section

Information and controlling system

License

Copyright (c) 2022 Ekhlas Hamza, Sameir Aziez, Fadia Hummadi, Ahmad Sabry

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.