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28 April 2023 A CLSTM network algorithm for automatic modulation recognition
Yuying Wang, Shengliang Fang, Youchen Fan, Ziyang Wang
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Proceedings Volume 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022); 126261X (2023) https://doi.org/10.1117/12.2674408
Event: International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 2022, Zhuhai, China
Abstract
Aiming at the problems of low recognition accuracy and inexplicable features extracted by neural networks in signal modulation recognition algorithms based on deep learning, this paper proposes a CLSTM recognition algorithm based on IQ signal features. The algorithm first uses a 2×1 convolution kernel to extract the phase feature of the signal, then uses a multiple convolution layer to extract the temporal feature of the signal, and then outputs the result of the convolution layer to the LSTM module to further extract the temporal feature, finally, the recognition result is output by the fully connected layer and the recognition effect is improved by optimizing the network structure. The experimental results show that on the RML2016.10a public dataset, when the SNR is higher than 0 dB, the recognition accuracy of the algorithm reaches 89.21%, which is 15.02% and 9.85% higher than the classical CNN2 and CLDNN network algorithms.
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Yuying Wang, Shengliang Fang, Youchen Fan, and Ziyang Wang "A CLSTM network algorithm for automatic modulation recognition", Proc. SPIE 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 126261X (28 April 2023); https://doi.org/10.1117/12.2674408
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KEYWORDS
Convolution
Modulation
Detection and tracking algorithms
Feature extraction
Neural networks
Deep learning
Data modeling
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