AUTOMATIC MODULATION CLASSIFICATION USING DEEP LEARNING POLAR FEATURE

Authors

  • Ali H. Shah Electrical Engineering Department, College of Engineering, Mustansiriyah University, Baghdad, Iraq Author
  • Abbas H. Miry Electrical Engineering Department, College of Engineering, Mustansiriyah University, Baghdad, Iraq Author
  • Tariq M. Salman Electrical Engineering Department, College of Engineering, Mustansiriya University, Baghdad, Iraq Author

DOI:

https://doi.org/10.31272/jeasd.27.4.5

Keywords:

Automatic Modulation Recognition, Cognitive Radio, Convolution Neural Network, Deep Learning

Abstract

The automatic modulation classification of signals is of great importance in modern communications, especially on cognitive radio. Several methods have been used in this field, the most important of which is the classification of modulation automatically using Deep Learning, where the methods depend on the convolution neural network, which is one of the Deep Learning networks, achieved high accuracy in classifying the modulation, so the proposed network depends on the type of deep learning CNN consisting of four blocks, each block contains a set of symmetric and asymmetric filters. The network also contains Max Pool. In this paper, the features extracted in phase-squaring and polar have been combined for the input, which helps in extending the input, that is, an increase in the features inside the network. It also contributes to improving the accuracy of classifying the higher-order modulation through the Polar plane. The dataset RadioML 2018.01A was adopted, which is used in the most recent research, where 11 types of modulation normal-class: (FM, GMSK, QPSK, BPSK, 0QPSK, AM-SSB-SC, 4ASK, AM-DSB-SC, 16QAM, 8PSK,00K) were taken. A simulation of which can be found in Matlab 2021. The proposed network achieved 100% classification accuracy when the signal-to-noise ratio is greater or equal to 2 dB for 11 types of modulation. The results of the paper were compared with modern networks Baseline network, Visual Geometry Group network, and Residual Neural network. The comparison showed the superiority of the proposed network over these networks, as the proposed network achieved an accuracy equal to   100% at SNR 2 dB while BL achieved an accuracy equal to 72% at SNR 2 dB, RN, and VGG almost reach 93% at SNR 2 dB.

References

W. Juan-ping, H. Ying-zheng, Z. Jin-mei, and W. Hua-kui. (2010) “Automatic modulation recognition of digital communication signals”. In 2010 First International Conference on Pervasive Computing, Signal Processing and Applications, pages 590–593. https://doi.org/10.1109/PCSPA.2010.148.

V. Sze, Y. Chen, T. Yang, and J. S. Emer (2017) “Efficient processing of deep neural networks: A tutorial and survey”. Proceedings of the IEEE, 105(12):2295–2329. https://doi.org/10.1109/JPROC.2017.2761740.

S.Huang, R. Dai, J. Huang, Y. Yao, Y. Gao, F. Ning, and Z. Feng, (2020) ‘‘Automatic modulation classification using gated recurrent residual network’’ IEEE Internet Things J., vol. 7, no. 8, pp. 7795–780. https://doi.org/10.1109/JIOT.2020.2991052.

Y. A. Eldemerdash, O. A. Dobre, and M. Öner, (2016) “Signal Identification for Multiple-Antenna Wireless Systems: Achievements and Challenges”, IEEE Communications Surveys Tutorials, vol. 18, no. 3, pp. 1524–1551. https://doi.org/10.1109/COMST.2016.2519148.

T. J. O’Shea, T. Roy, and T. C. Clancy, (2018)” Over-the-Air Deep Learning-Based Radio Signal Classification,” IEEE J. Sel. Topics Signal Process, vol. 12, no. 1, pp. 168-179. https://doi.org/10.1109/JSTSP.2018.2797022.

T. Huynh-The, C. Hua, Q. Pham, and D. -S. Kim,(2020) ”MCNet: An Efficient CNN Architecture for Robust Automatic Modulation Classification,” IEEE Commun. Lett., vol. 4, no. 2, pp. 811-815.https://doi.org/10.1109/LCOMM.2020.2968030.

A. Swami and B. M. Sadler (2000), ‘‘Hierarchical digital modulation classification using cumulants,’’ IEEE Trans. Commun., vol. 48, no. 3, pp. 416–429. https://doi.org/10.1109/26.837045.

S. Huang, Y. Yao, Z. Wei, Z. Feng, and P. Zhang,(2016) ‘‘Automatic modulation classification of overlapped sources using multiple cumulants,’’ IEEE Trans. Veh. Technol., vol. 66, no. 7, pp. 6089–6101. https://doi.org/10.1109/TVT.2016.2636324.

D.-C. Chang and P.-K. Shih,(2015) ‘‘Cumulants-based modulation classification technique in multipath fading channels,’’ IET Commun., vol. 9, no. 6, pp. 828–835. https://doi.org/10.1049/iet-com.2014.0773

K. C. Ho, W. Prokopiw, and Y. T. Chan,(2000) ‘‘Modulation identification of digital signals by the wavelet transform,’’ IEE Proc. Radar, Sonar Navigat., vol. 147, no. 4, pp. 169–176. http://dx.doi.org/10.1049/ip-rsn:20000492

O. A. Dobre, M. Oner, S. Rajan, and R. Inkol,(2012) ‘‘Cyclostationarity-based robust algorithms for QAM signal identification,’’ IEEE Commun. Lett., vol. 16, no. 1, pp. 12–15. https://doi.org/10.1109/LCOMM.2011.112311.112006.

G. B. Tunze, T. Huynh-The, J.-M. Lee, and D.-S. Kim,(2020) ‘‘Sparsely connected CNN for efficient automatic modulation recognition,’’ IEEE Trans. Veh. Technol., vol. 69, no. 12, pp. 15557–15568. https://doi.org/10.1109/TVT.2020.3042638.

S. Majhi, R. Gupta, W. Xiang, and S. Glisic,(2017) ‘‘Hierarchical hypothesis and feature-based blind modulation classification for linearly modulated signals,’’ IEEE Trans. Veh. Technol., vol. 66, no. 12, pp. 11057–11069. https://doi.org/10.1109/TVT.2017.2727858.

T. Huynh-The, C.-H. Hua, T.-T. Ngo, and D.-S. Kim,(2020) ‘‘Image representation of pose-transition feature for 3D skeleton-based action recognition,’’ Inf. Sci., vol. 513, pp. 112–126. https://doi.org/10.1016/j.ins.2019.10.047

H. Purwins, B. Li, T. Virtanen, J. Schlüter, S.-Y. Chang, and T. Sainath, (2019)‘‘Deep learning for audio signal processing,’’ IEEE J. Sel. Topics Signal Process., vol. 13, no. 2, pp. 206–219. https://doi.org/10.1109/JSTSP.2019.2908700.

A. Iqbal, M.-L. Tham, and Y. C. Chang,(2020) ‘‘Double deep Q-network for power allocation in cloud radio access network,’’ in Proc. IEEE 3rd Int. Conf. Comput. Commun. Eng. Technol. (CCET), pp.272–277. https://doi.org/10.1109/CCET50901.2020.9213138.

A. Iqbal, M.-L. Tham, and Y. C. Chang,(2021) ‘‘Double deep Q-network-based energy-efficient resource allocation in cloud radio access network,’’ IEEE Access, vol. 9, pp. 20440–20449. https://doi.org/10.1109/ACCESS.2021.3054909.

A. Maier, C. Syben, T. Lasser, and C. Riess, (2019)‘‘A gentle introduction to deep learning in medical image processing,’’ Zeitschrift für Medizinische Physik, vol. 29, no. 2, pp. 86–101. https://doi.org/10.1016/j.zemedi.2018.12.003

L. Jiao and J. Zhao,(2019) ‘‘A survey on the new generation of deep learning in image processing,’’ IEEE Access, vol. 7, pp.172231–172263: https://doi.org/10.1109/ACCESS.2019.2956508.

A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis,(2018) ‘‘Deep learning for computer vision: A brief review,’’ Comput. Intell. Neurosci., vol. 2018, pp. 1–13, Art. no. 7068349. https://doi.org/10.1155/2018/7068349

N. Akhtar and A. Mian,(2018) ‘‘Threat of adversarial attacks on deep learning in computer vision: A survey,’’ IEEE Access, vol. 6, pp. 14410–14430. https://doi.org/10.1109/ACCESS.2018.2807385

A. Krizhevsky, I. Sutskever, and G. E. Hinton, (2013)‘‘ImageNet classification with deep convolutional neural networks,’’ in Proc. Int. Conf. Adv. Neural Infor. Process. Syst. (NIPS), pp. 1097–1105. https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich,(2015) ‘‘Going deeper with convolutions,’’ in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Boston, MA, USA, pp. 1–9. https://doi.org/10.1109/CVPR.2015.7298594

K. Simonyan and A. Zisserman, (2014) ‘‘Very deep convolutional networks for large-scale image recognition,’’ CoRR, vol. abs/1409.1556, pp. 1–14. https://doi.org/10.48550/arXiv.1409.1556

K. He, X. Zhang, S. Ren, and J. Sun, (2016)‘‘Deep residual learning for image recognition,’’ in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Las Vegas, NV, USA pp. 770–778. https://doi.org/10.1109/ITNEC.2017.8284852.

T. Chen and C. Guestrin,(2016) “Xgboost: A scalable tree boosting system,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, pp. 785–794. Doi: https://doi.org/10.48550/arXiv.1603.02754

T. J. O’Shea and N. West, (2016)“Radio machine learning dataset generation with GNU radio,” in Proc. GNU Radio, vol. 1, no. 1. [Online]. Available: https://pubs. gnuradio.org/index.php/grcon/article/view/11.

Downloads

Key Dates

Published

2023-07-01

How to Cite

AUTOMATIC MODULATION CLASSIFICATION USING DEEP LEARNING POLAR FEATURE. (2023). Journal of Engineering and Sustainable Development, 27(4), 477-486. https://doi.org/10.31272/jeasd.27.4.5

Similar Articles

11-20 of 222

You may also start an advanced similarity search for this article.