Выходит 12 номеров в год
ISSN Печать: 0040-2508
ISSN Онлайн: 1943-6009
Indexed in
RESEARCH ON NETWORK COMMUNICATION SIGNAL PROCESSING RECOGNITION BASED ON DEEP LEARNING
Краткое описание
With the popularization of wireless communication technology, the modulation of wireless signal not only improves the information transmission, but also can realize encryption and anti-interference processing. For the unknown signal, it is necessary to determine its modulation type before demodulating the real signal, so as to determine whether the signal is legal. This study introduced back-propagation (BP) neural network and convolutional neural network (CNN) and applied them to the modulation type recognition of wireless communication signals. In order to improve the recognition accuracy of CNN model for modulation signals, the steps of drawing signal constellation diagram were added on the basis of original CNN. Then the simulation experiments were carried out on the BP, traditional CNN and improved CNN models by using MATLAB software. The results showed that the constellation could effectively reflect the modulation type characteristics of the modulation signal; in the model training process, the improved CNN model had the fastest convergence and the smallest training loss when the convergence was stable, followed by the traditional CNN model, and the BP model had the slowest convergence and the most loss when the convergence was stable; with the increase of the signal-to-noise ratio of the detection signal, the average accuracy of the three recognition models showed a tendency of stable after increasing; under the same signal-to-noise ratio, the improved CNN model had the highest recognition accuracy, followed by the traditional CNN model and BP model.
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Li, C., Zhou, Q., Han, X., Yin, J., and Shao, M., (2017) Underwater non-cooperative communication signal recognition with deep learning, J. Acoust. Soc. Am., 142.
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Qin, J., Huang, Z., and Liu, C., (2015) Novel Blind Recognition Algorithm of Frame Synchronization Words Based on Soft-Decision in Digital Communication Systems, Plos One, 10.
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Gong, A.M., Wang, B.H., and Qu, Y., (2015) Modulation Type Recognition of OFDM Signals Based on EMD, Appl. Mech. Mater., 721, pp.670-673.
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Zhao, X., (2016) Mixed recognition algorithm for signal modulation schemes by high-order cumulants and cyclic spectrum, J. Electr. Inform. Tech.,38(3).
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Fan, X., Li, T., andSu, S., (2017) Intrapulse modulation type recognition for pulse compression radar signal, J. Appl. Remote Sens., 11(3), pp. 1.
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Long, X., Zhang, H., and Zhang, M., (2017) Recognition algorithm of wireless communication signal modulation based on harmonic mean fractal box dimension, J. Jiangsu Univ. (Nat. Sci. Ed.), 38(3), pp. 308-312.
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Dwyer, R.T., Spahr, T., Agrawal, S., Hetlinger, C., Holder, J.T., and Gifford, R.H., (2016) Participant-generated Cochlear Implant Programs: Speech Recognition, Sound Quality, and Satisfaction, Otol. Neurotol., 37(7), pp. e209-e216.
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Norouzi, S., Jamshidi, A., and Zolghadrasli, A.R., (2016) Adaptive modulation recognition based on the evolutionary algorithms, Appl. Soft Comput., 43, pp. 312-319.
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Zhang, Z., Li, Y.B., and Zhu, X.L., (2017) A Method for Modulation Recognition Based on Entropy Features and Random Forest, IEEE Int. Conf. on Software Quality, Reliability and Security Companion (QRS-C).
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Li, M.F., Xiao, X., and Yang, Q., (2017) 40 Gbaud binary phase shift keying signal modulation using a substrate removed silicon modulator, Mod. Phys. Lett. B,31(19-21), pp. 1740009.
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Zhang, D.N., Ding, W.R., and Zhang, B.C., (2018) Automatic Modulation Classification Based on Deep Learning for Unmanned Aerial Vehicles, Sensors, 18(3), pp. 924.
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Tian, B., Zhang, Q., and Ma, J.X., (2018) Proposal and performance analysis on the PDM microwave photonic link for the mm-wave signal with hybrid QAM-MPPM-RZ modulation, Opt. Commun.,419, pp. 59-66.
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Dong, G., Kalifa, R., Nath, P.R., Babichev, Y., Gelkop, S., and Isakov, N., (2017) Crk adaptor proteins regulate CD3Z chain phosphorylation and TCR/CD3 down-modulation in activated T cells, Cell Signal, 36, pp. 117-126.
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Chen, D., Chen, F., Murray, A., and Zheng, D., (2017) Phase Difference between Respiration Signal and Respiratory Modulation Signal from Oscillometric Cuff Pressure Pulses during Blood Pressure Measurement, Comput. Cardiol. Conf.
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Kermani, A. and Seyed, A.H.F., (2017) Scatter Signal Elimination by Localized Primary Modulation in Industrial Computed Radiography, J. Nondestruct. Eval.,36(4), pp. 71.