Abstract
In this paper, we propose a new method for modulation recognition of received digital signals using bispectrum and AlexNet. The bispectrum analysis is used to generate the feature images, AlexNet, as a widely used deep neural network (DNN), is used as the classifier. It is able to classify six common digital communication signals, including 2ASK, 4ASK, 2FSK, 4FSK, 2PSK and 4PSK. Compared to the traditional decision-theoretic methods, the proposed method needs no prior information for the received signals. The numerical results indicate that this method is more robust and effective than the classical decision theory and its improved algorithm, particularly when the signal-to-noise ratio (SNR) is low. It is shown that the success rate of 90% can be achieved when the SNR is greater than or equal to 3 dB.
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Acknowledgments
This work was supported in part by National Nature Science Foundation of China under Grants No. 61201192 and the National High Technology Research and Development Program of China under Grants No. 2015AA01A708.
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Yu, J., He, Z., Zhang, Y. (2020). A Modulation Recognition Method Based on Bispectrum and DNN. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_108
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DOI: https://doi.org/10.1007/978-981-13-6504-1_108
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