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DERİN ÖĞRENME TABANLI MODÜLASYON TANIMA

Year 2023, Volume: 28 Issue: 1, 123 - 140, 30.04.2023

Mehmet Merih Leblebici Ali Çalhan Murtaza Cicioğlu

https://doi.org/10.17482/uumfd.1161509

Abstract

Haberleşme teknolojilerinde her geçen gün artan sinyal çeşitliliği, bu sinyallerin tanımlanması ve sınıflandırılması gerekliliğini ortaya çıkarmıştır. Beşinci nesil (fifth generation, 5G) ve ötesi kablosuz haberleşme teknolojileri, birçok uygulama için vazgeçilmez iletişim araçları haline gelmiştir. Otomatik modülasyon tanıma (automatic modulation recognition, AMR) tekniği, özellikle yeni nesil nesnelerin interneti, akıllı şehirler, otonom araçlar ve bilişsel radyo gibi birçok uygulama için temel bileşen haline gelmiştir. Bu çalışmada sekiz farklı modülasyon türü kullanılarak bir veri seti oluşturulmuş ve derin öğrenme (deep learning, DL) algoritmalarından olan evrişimli sinir ağları (convolutional neural network, CNN) kullanılarak farklı sinyal-gürültü oranlarında (signal-to-noise ratio, SNR) modülasyon türü sınıflandırılması yapılmıştır. Sonuç olarak SNR değerleri 10 dB, 20 dB ve 30 dB iken CNN ile sınıflandırma işleminde sırasıyla %80,76, %99,89 ve %100 doğruluk sağlanmıştır.

Keywords

Otomatik Modülasyon Tanıma Evrişimli Sinir Ağları Derin Öğrenme 5G ve Ötesi Haberleşme Teknolojileri

References

  • 1. Ansari, S., Alnajjar, K. A., Saad, M., Abdallah, S. ve El-Moursy, A. A. (2022) Automatic Digital Modulation Recognition Based on Genetic-Algorithm-Optimized Machine Learning Models, IEEE Access, 10, 50265–50277. doi:10.1109/ACCESS.2022.3171909.
  • 2. Dulek, B. (2017) Online Hybrid Likelihood Based Modulation Classification Using Multiple Sensors, IEEE Transactions on Wireless Communications, 16(8), 4984–5000. doi: 10.1109/TWC.2017.2704124.
  • 3. Hu, L., Jiang, H., Lu, R. ve Liu, C. (2021) Signal Classification in Real-time Based on SDR using Convolutional Neural Network, Proceedings of 2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA2021, (Iciba), 893–898. doi:10.1109/ICIBA52610.2021.9687958
  • 4. Lin, S., Zeng, Y., ve Gong, Y. (2022) Learning of Time-Frequency Attention Mechanism for Automatic Modulation Recognition, IEEE Wireless Communications Letters, 11(4), 707–711. doi: 10.1109/LWC.2022.3140828.
  • 5. O’Shea T. J., Corgan, J., ve Clancy, T. C. (2016) Convolutional radio modulation recognition networks, Communications in Computer and Information Science, 629, 213–216. doi: 10.1007/978-3-319-44188-7_16.
  • 6. Shi F. Y., Hu, Z. M., Yue, C. S. ve Chen, Z. C. (2022a) Combining neural networks for modulation recognition, Digital Signal Processing, 120, 103264. doi: 10.1016/J.DSP.2021.103264.
  • 7. Shi F. Y., Yue, C. S. ve Han, C. (2022b) A lightweight and efficient neural network for modulation recognition, Digital Signal Processing, 123, 103444. doi: 10.1016/J.DSP.2022.103444.
  • 8. Wang, Y., Liu, M., Yang, J. ve Gui, G. (2019) Data-Driven Deep Learning for Automatic Modulation Recognition in Cognitive Radios, IEEE Transactions on Vehicular Technology, 68(4), 4074–4077. doi:10.1109/TVT.2019.2900460
  • 9. Wang Y., Gui, J., Yin, Y., Wang, J., Sun, J., Gui, G., Gacanin, H., Sari, H. ve Adachi, F. (2020) Automatic Modulation Classification for MIMO Systems via Deep Learning and Zero-Forcing Equalization, IEEE Transactions on Vehicular Technology, 69(5), 5688–5692. doi: 10.1109/TVT.2020.2981995.
  • 10. Zeng, Y., Zhang, M., Han, F., Gong, Y. ve Zhang, J. (2019) Spectrum Analysis and Convolutional Neural Network for Automatic Modulation Recognition, IEEE Wireless Communications Letters, 8(3), 929–932. doi:10.1109/LWC.2019.2900247
  • 11. Zhang F., Luo, C., Xu, J., Luo, Y. ve Zheng, F. (2022) Deep learning based automatic modulation recognition: Models, datasets, and challenges, Digital Signal Processing, 129, 103650. doi: 10.1016/J.DSP.2022.103650.
  • 12. Zhou, R., Liu, F. ve Gravelle, C. W. (2020) Deep Learning for Modulation Recognition: A Survey with a Demonstration IEEE Access, 8, 67366–67376. doi:10.1109/ACCESS.2020.2986330

Deep Learning Based Modulation Recognition

Year 2023, Volume: 28 Issue: 1, 123 - 140, 30.04.2023

Mehmet Merih Leblebici Ali Çalhan Murtaza Cicioğlu

https://doi.org/10.17482/uumfd.1161509

Abstract

The increasing signal diversity of communication technologies has revealed the need that these signals to be defined and classified. Fifth-generation (5G) and beyond wireless communication technologies have become indispensable communication tools for many applications. The automatic modulation recognition (AMR) technique has become a key component for many applications, especially the next-generation internet of things, smart cities, autonomous vehicles, and cognitive radio. In this study, a data set was created using eight different modulation types and modulation classification was made at different signal-to-noise ratios (SNR) using convolutional neural networks (CNN) from deep learning (DL) algorithms. As a result, while the SNR values were 10 dB, 20 dB, and 30 dB, CNN provided 80.76%, 99.89%, and 100% accuracy in the classification process, respectively.

Keywords

Automatic Modulation Recognition Convolutional Neural Networks Deep Learning 5G and Beyond Communication Technologies

References

  • 1. Ansari, S., Alnajjar, K. A., Saad, M., Abdallah, S. ve El-Moursy, A. A. (2022) Automatic Digital Modulation Recognition Based on Genetic-Algorithm-Optimized Machine Learning Models, IEEE Access, 10, 50265–50277. doi:10.1109/ACCESS.2022.3171909.
  • 2. Dulek, B. (2017) Online Hybrid Likelihood Based Modulation Classification Using Multiple Sensors, IEEE Transactions on Wireless Communications, 16(8), 4984–5000. doi: 10.1109/TWC.2017.2704124.
  • 3. Hu, L., Jiang, H., Lu, R. ve Liu, C. (2021) Signal Classification in Real-time Based on SDR using Convolutional Neural Network, Proceedings of 2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA2021, (Iciba), 893–898. doi:10.1109/ICIBA52610.2021.9687958
  • 4. Lin, S., Zeng, Y., ve Gong, Y. (2022) Learning of Time-Frequency Attention Mechanism for Automatic Modulation Recognition, IEEE Wireless Communications Letters, 11(4), 707–711. doi: 10.1109/LWC.2022.3140828.
  • 5. O’Shea T. J., Corgan, J., ve Clancy, T. C. (2016) Convolutional radio modulation recognition networks, Communications in Computer and Information Science, 629, 213–216. doi: 10.1007/978-3-319-44188-7_16.
  • 6. Shi F. Y., Hu, Z. M., Yue, C. S. ve Chen, Z. C. (2022a) Combining neural networks for modulation recognition, Digital Signal Processing, 120, 103264. doi: 10.1016/J.DSP.2021.103264.
  • 7. Shi F. Y., Yue, C. S. ve Han, C. (2022b) A lightweight and efficient neural network for modulation recognition, Digital Signal Processing, 123, 103444. doi: 10.1016/J.DSP.2022.103444.
  • 8. Wang, Y., Liu, M., Yang, J. ve Gui, G. (2019) Data-Driven Deep Learning for Automatic Modulation Recognition in Cognitive Radios, IEEE Transactions on Vehicular Technology, 68(4), 4074–4077. doi:10.1109/TVT.2019.2900460
  • 9. Wang Y., Gui, J., Yin, Y., Wang, J., Sun, J., Gui, G., Gacanin, H., Sari, H. ve Adachi, F. (2020) Automatic Modulation Classification for MIMO Systems via Deep Learning and Zero-Forcing Equalization, IEEE Transactions on Vehicular Technology, 69(5), 5688–5692. doi: 10.1109/TVT.2020.2981995.
  • 10. Zeng, Y., Zhang, M., Han, F., Gong, Y. ve Zhang, J. (2019) Spectrum Analysis and Convolutional Neural Network for Automatic Modulation Recognition, IEEE Wireless Communications Letters, 8(3), 929–932. doi:10.1109/LWC.2019.2900247
  • 11. Zhang F., Luo, C., Xu, J., Luo, Y. ve Zheng, F. (2022) Deep learning based automatic modulation recognition: Models, datasets, and challenges, Digital Signal Processing, 129, 103650. doi: 10.1016/J.DSP.2022.103650.
  • 12. Zhou, R., Liu, F. ve Gravelle, C. W. (2020) Deep Learning for Modulation Recognition: A Survey with a Demonstration IEEE Access, 8, 67366–67376. doi:10.1109/ACCESS.2020.2986330
There are 12 citations in total.

Details

Primary Language Turkish
Subjects Electrical Engineering
Journal Section Research Articles
Authors

Mehmet Merih Leblebici 0000-0002-7709-2906

Ali Çalhan 0000-0002-5798-3103

Murtaza Cicioğlu 0000-0002-5657-7402

Publication Date April 30, 2023
Submission Date August 12, 2022
Acceptance Date January 2, 2023
Published in Issue Year 2023 Volume: 28 Issue: 1

Cite

APA Leblebici, M. M., Çalhan, A., & Cicioğlu, M. (2023). DERİN ÖĞRENME TABANLI MODÜLASYON TANIMA. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 28(1), 123-140. https://doi.org/10.17482/uumfd.1161509
AMA Leblebici MM, Çalhan A, Cicioğlu M. DERİN ÖĞRENME TABANLI MODÜLASYON TANIMA. UUJFE. April 2023;28(1):123-140. doi:10.17482/uumfd.1161509
Chicago Leblebici, Mehmet Merih, Ali Çalhan, and Murtaza Cicioğlu. “DERİN ÖĞRENME TABANLI MODÜLASYON TANIMA”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 28, no. 1 (April 2023): 123-40. https://doi.org/10.17482/uumfd.1161509.
EndNote Leblebici MM, Çalhan A, Cicioğlu M (April 1, 2023) DERİN ÖĞRENME TABANLI MODÜLASYON TANIMA. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 28 1 123–140.
IEEE M. M. Leblebici, A. Çalhan, and M. Cicioğlu, “DERİN ÖĞRENME TABANLI MODÜLASYON TANIMA”, UUJFE, vol. 28, no. 1, pp. 123–140, 2023, doi: 10.17482/uumfd.1161509.
ISNAD Leblebici, Mehmet Merih et al. “DERİN ÖĞRENME TABANLI MODÜLASYON TANIMA”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 28/1 (April 2023), 123-140. https://doi.org/10.17482/uumfd.1161509.
JAMA Leblebici MM, Çalhan A, Cicioğlu M. DERİN ÖĞRENME TABANLI MODÜLASYON TANIMA. UUJFE. 2023;28:123–140.
MLA Leblebici, Mehmet Merih et al. “DERİN ÖĞRENME TABANLI MODÜLASYON TANIMA”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, vol. 28, no. 1, 2023, pp. 123-40, doi:10.17482/uumfd.1161509.
Vancouver Leblebici MM, Çalhan A, Cicioğlu M. DERİN ÖĞRENME TABANLI MODÜLASYON TANIMA. UUJFE. 2023;28(1):123-40.

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