A new algorithm to improve recognition accuracy of communication signals
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Abstract
This article proposes a new algorithm aimed at improving accuracy when identifying communication signals using artificial intelligence applications. The proposed research algorithm focuses on analog and digitally modulated signals commonly used in communication systems. The proposed algorithm includes two main steps: Step one is used to extract signal features using the analytic Wavelet transform. A deep learning artificial network is designed in step 2 to identify the above signals. The effectiveness of the proposed algorithm is evaluated on simulated signals in MATLAB and compared with common feature extraction methods: short-time Fourier transform, Wigner-Ville distribution, and other networks such as SqueezeNet, AlexNet. The simulation results show that the proposed algorithm achieves 92% recognition accuracy, which is higher than STFT and WVD methods. In comparison with current networks, the proposed method has a 1.5 time-reduced training time and equivalent performance.
References
-
[1] A. Poisel, Information Warfare and Electronic Warfare Systems, Artech House, London, UK, 2013. ISBN: 9781608077052.
[2] K. R. Yarlagadda, Analog and Digital Signal and Systems, Springer, New York, USA, 2010. ISBN: 9781441900333.
[3] Graham, Communications, Radar and Electronic Warfare, John Wiley and Sons, West Sussex, UK, 2011. ISBN: 9780470688717.
[4] N. Trefethen, Spectral Methods in MATLAB, SIAM, Philadelphia, USA, 2011. ISBN: 978-0-89871-465-4.
[5] M. Duong, J. Vesely, P. Hubacek, P. Janu, and X. L. Tran, “Detection and Parameter Estimation of Intra-pulse Modulated Radar Signals in Complex Interference Environment”, SN Applied Sciences, vol. 5, no. 184, 2023. https://doi.org/10.1007/s42452-023-05403-x.
[6] M. Duong, J. Vesely, P. Hubacek, P. Janu, and N. G. Phan, “Detection and Parameter Estimation Analysis of Binary Shift Keying Signals in High Noise Environments”, Sensors, vol. 22, no. 9, 2022. https://doi.org/10.3390/s22093203.
[7] J. O’Shea, T. Roy, and T. C. Clancy, "Over-the-Air Deep Learning Based Radio Signal Classification”, IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, pp. 168-179, Feb. 2018, doi: 10.1109/JSTSP.2018.2797022.
[8] A. Harper, M. A. Thornton, and E. C. Larson, “Automatic Modulation Classification with Deep Neural Networks”, Electronics, vol. 12, no. 3962, 2023, https://doi.org/10.3390/electronics12183962.
[9] Qiang, X. Yu, and G. Ruan, “LPI Radar Waveform Recognition Based on Deep Convolutional Neural Network Transfer Learning”, Symmetry, vol. 11, no. 4, pp. 540, 2019, https://doi.org/10.3390/sym11040540.
[10] Hao, Z. Chong, S. Li, and Z. Yamiao, “Research on Radar Signal Recognition Technology Based on Convolution Neural Network”, in Proceedings of the 4th International Conference on Advanced Information Science and System, New York, USA, November 2022, no. 39, pp. 1-6. https://doi.org/10.1145/3573834.357451.
[11] Xia, Z. Ma, and Z. Huang, “Radar Waveform Recognition Based on a Two‐stream Convolutional Network and Software Defined Radio”, IET Radar Sonar Navig. vol. 16, no. 5, pp. 837–851, 2022. https://doi. org/10.1049/rsn2.12224.
[12] Sklar, “Digital Communications Fundamentals and Applications”, 3rd edition, Pearson, New Jersey, USA, 2020. ISBN: 978-0134588568.
[13] M. Walenczykowska, A. Kawalec, and K. Krenc, “An Application of Analytic Wavelet Transform and Convolutional Neural Network for Radar Intrapulse Modulation Recognition”, Sensors, vol. 23, no. 4, 2023. https://doi.org/10.3390/s23041986.
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