Classifying wireless signal modulation sorting using convolutional neural network




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


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


  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:
  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:
  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:
  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:
  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:
  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:
  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:
  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:
  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:
  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:
  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:
  12. Zheng, S., Qi, P., Chen, S., Yang, X. (2019). Fusion Methods for CNN-Based Automatic Modulation Classification. IEEE Access, 7, 66496–66504. doi:
  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:
  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:
Classifying wireless signal modulation sorting using convolutional neural network




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.



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