Automatic Modulation Recognition using ResNet Architecture

Abstract

This work focuses on the application of deep learning models for the classification and identification of signals modulated using both analog and digital modulation techniques. The paper aims to explore the potential of advanced neural networks in accurately distinguishing between various modulation schemes, which play a critical role in modern communication systems. By leveraging large datasets and employing robust machine learning frameworks, the study evaluates the performance of these models in terms of accuracy, efficiency, and reliability under different signal-to-noise ratio (SNR) conditions. Furthermore, the paper provides a comparative analysis of the models, highlighting their strengths and limitations in handling diverse modulation types. The results of this work contribute to the development of intelligent communication systems capable of adapting to dynamic environments, ensuring robust and efficient signal processing.

Keywords

• Deep learning
• ResNet
• Modulation
• Classification

References

1. Al-Nuaimi, D. H., Hashim, I. A., Zainal Abidin, I. S., Salman, L. B., & Mat Isa, N. A. (2019). Performance of feature-based techniques for automatic digital modulation recognition and classification: A review. Electronics, 8(12), 1407. https://doi.org/10.3390/electronics8121407
2. Azzouz, E. E., & Nandi, A. K. (1996). Automatic modulation recognition of communication signals, Springer USA. https://doi.org/10.1007/978-1-4757-2469-1
3. Azzouz, E. E., & Nandi, A. K. (1997). Automatic modulation recognition – I. Journal of the Franklin Institute, 334(2), 241–273. https://doi.org/10.1016/S0016-0032(96)00069-5
4. Çamlıbel, A., Karakaya, B., & Tanç, Y. H. (2024). Automatic modulation recognition with deep learning algorithms. In 32nd Signal Processing and Communications Applications Conference (SIU) (pp. 1–4). Mersin, Türkiye.
5. Hsue, S. Z., & Soliman, S. S. (1989). Automatic modulation recognition of digitally modulated signals. In Proceedings of the IEEE Military Communications Conference (pp. 645–649). Boston, MA, USA.
6. Jdid, B., Hassan, K., Dayoub, I., Lim, W. H., & Mokayef, M. (2021). Machine learning based automatic modulation recognition for wireless communications: A comprehensive survey. IEEE Access, 9, 57851–57873. https://doi.org/10.1109/ACCESS.2021.3071801
7. Nandi, A. K., & Azzouz, E. E. (2002). Algorithms for automatic modulation recognition of communication signals. IEEE Transactions on Communications, 46(4), 431–436. https://doi.org/10.1109/26.664294
8. O’Shea, T. J., Roy, T., & Clancy, T. C. (2018). Over-the-air deep learning based radio signal classification. IEEE Journal on Selected Topics in Signal Processing, 12(1), 168–179. https://doi.org/10.1109/JSTSP.2018.2797022

9. Thameur, H. B., Dayoub, I., & Hamouda, W. (2022). USRP RIO-based testbed for real-time blind digital modulation recognition in MIMO systems. IEEE Communications Letters, 26(10), 2500–2504. https://doi.org/10.1109/LCOMM.2022.3191787
10. Xing, H., Zhang, X., Chang, S., Ren, J., Zhang, Z., Xu, J., & Cui, S. (2024). Joint signal detection and automatic modulation classification via deep learning. IEEE Transactions on Wireless Communications, 23(11), 17129–17142. https://doi.org/10.1109/TWC.2024.3450972
11. Xu, J. L., Su, W., & Zhou, M. (2010). Software-defined radio equipped with rapid modulation recognition. IEEE Transactions on Vehicular Technology, 59(4), 1659–1667. https://doi.org/10.1109/TVT.2010.2041805
12. Xu, T., & Ma, Y. (2023). Signal automatic modulation classification and recognition in view of deep learning. IEEE Access, 11, 114623–114637. https://doi.org/10.1109/ACCESS.2023.3324673
13. Yang, C., He, Z., Peng, Y., Wang, Y., & Yang, J. (2019). Deep learning aided method for automatic modulation recognition. IEEE Access, 7, 109063–109068. https://doi.org/10.1109/ACCESS.2019.2933448
14. Zeng, Y., Zhang, M., Han, F., Gong, Y., & Zhang, J. (2019). Spectrum analysis and convolutional neural network for automatic modulation recognition. IEEE Wireless Communications Letters, 8(3), 929–932. https://doi.org/10.1109/LWC.2019.2900247