Weak sinusoidal signal extraction from white noise using convolutional neural network
Description
A great number of analog and digital data communications schemes use the sinusoidal waveform as a basic elementary signal, including the spread spectrum data exchange techniques. Detection of the presence of the sinusoidal waveform in a mixture of signal and noise is a common task, regardless the specific modulation scheme. This paper presents the machine learning-based approach for detection of the sinusoidal wave. It presents the structure of the convolutional neural network, as well as the performance metrics for the sinusoidal signals detection. The paper provides an assessment of the overall accuracy for the binary signals. It reports the overall accuracy value of 0.93 for the sinusoidal signal detection in the presence of additive white Gaussian noise at the signal-to-noise ratio value of −20 dB for a balanced dataset.
Files
2023_ICISSE_paper_77_final.pdf
Files
(364.9 kB)
Name | Size | Download all |
---|---|---|
md5:a3d1d4e8f03c234a17c94e79bda5e0e8
|
364.9 kB | Preview Download |
Additional details
References
- H. Wu and X. Hua, "A review of digital signal modulation methods based on wavelet transform," 2020 IEEE International Conference on Mechatronics and Automation (ICMA), Beijing, China, 2020, pp. 1123-1128, doi: 10.1109/ICMA49215.2020.9233682
- S. Nagul, "A review on 5G modulation schemes and their comparisons for future wireless communications," 2018 Conference on Signal Processing And Communication Engineering Systems (SPACES), Vijayawada, India, 2018, pp. 72-76, doi: 10.1109/SPACES.2018.8316319
- Q. Shi and Y. Karasawa, "Maximum likelihood based modulation classification for unsynchronized QAMs," 2008 IEEE Global Telecommunications Conference, New Orleans, LA, USA, 2008, pp. 1-5, doi: 10.1109/GLOCOM.2008.ECP.664
- Kaiyan Zhu and Shuxun Wang, "Sinusoidal signal extraction from chaotic background using wavelet packet transform," Proceedings 7th International Conference on Signal Processing, 2004. Proceedings. ICSP '04. 2004., Beijing, China, 2004, pp. 176-179 vol.1, doi: 10.1109/ICOSP.2004.1452610
- S. Haykin and Xiao Bo Li, "Detection of signals in chaos," in Proceedings of the IEEE, vol. 83, no. 1, pp. 95-122, Jan. 1995, doi: 10.1109/5.362751
- A. K. Ziarani, I. M. Blumenfeld, and A. Konrad, "Experimental verification of a novel method of extraction of nonstationary sinusoids," The 2002 45th Midwest Symposium on Circuits and Systems, 2002. MWSCAS-2002., Tulsa, OK, USA, 2002, pp. I-455, doi: 10.1109/MWSCAS.2002.1187256
- C. Ozturk, B. Dulek and S. Gezici, "Convexity properties of detection probability for noncoherent detection of a modulated sinusoidal carrier," in IEEE Transactions on Vehicular Technology, vol. 67, no. 12, pp. 12410-12415, Dec. 2018, doi: 10.1109/TVT.2018.2876516
- M. Azizoglu, "Convexity properties in binary detection problems," in IEEE Transactions on Information Theory, vol. 42, no. 4, pp. 1316-1321, July 1996, doi: 10.1109/18.508867
- I. Lazarovych et al., "Software implemented enhanced efficiency BPSK demodulator based on perceptron model with randomization," 2021 IEEE 3rd Ukraine Conference on Electrical and Computer Engineering (UKRCON), 2021, pp. 221-225, doi: 10.1109/UKRCON53503.2021.9575458
- M. Kozlenko and A. Bosyi, "Performance of spread spectrum system with noise shift keying using entropy demodulation," 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET), Lviv-Slavske, Ukraine, 2018, pp. 330-333, doi: 10.1109/TCSET.2018.8336213
- Hai-Peng Ren, Hui-Ping Yin, Hong-Er Zhao, C. Bai, C. Grebogi, "Artificial intelligence enhances the performance of chaotic baseband wireless communication," IET Communication, vol. 15, 2021, pp. 1467–1479, doi: 10.1049/cmu2.12162
- J. Taylor, "The JT65 Communications Protocol," QEX, Sept.-Oct. 2005, pp. 3–12
- J. Taylor, "WSJT: New Software for VHF Meteor-Scatter Communication," QST, Dec. 2001, pp. 36–41
- V. Tkachuk and M. Kozlenko, "Improved quantum genetic algorithm on multilevel quantum systems for 0-1 knapsack problem," in Advances in Artificial Systems for Logistics Engineering (Lecture Notes on Data Engineering and Communications Technologies, vol. 135), Z. Hu, Q. Zhang, S. Petoukhov, and M. He, Eds., Kyiv, Ukraine, Feb. 20-22, 2022, pp. 51–70, doi: 10.1007/978-3-031-04809-8_5
- V. Tkachuk, M. Kozlenko, M. Kuz, I. Lazarovych, and M. Dutchak, "Function optimization based on higher-order quantum genetic algorithm," Electronic Modeling, vol. 41, no. 3, pp. 43–58, 2019, doi: 10.15407/emodel.41.03.043
- D. Boulinguez, C. Garnier, Y. Delignon, and L. Clavier, "Unsupervised characterization of digital modulations," 6th International Conference on Signal Processing, 2002., Beijing, China, 2002, pp. 1316-1319 vol.2, doi: 10.1109/ICOSP.2002.1180034
- R. Figueiredo, N. B. Carvalho, A. Piacibello and V. Camarchia, "Nonlinear dynamic RF system characterization: envelope intermodulation distortion profiles - a noise power ratio-based approach," in IEEE Transactions on Microwave Theory and Techniques, vol. 69, no. 9, pp. 4256-4271, Sept. 2021, doi: 10.1109/TMTT.2021.3092398