A 2layer-BiLSTM-Attention-Based Method for Modulation Recognition of Communication Signals
Pages 146 - 152
Abstract
Aiming at the problems that feature extraction accuracy of feature-based modulation recognition methods is difficult to guarantee, the recognition accuracy of one-dimensional-feature-based deep learning methods is low, and the time and storage overhead of high-dimensional-feature-based deep learning methods is large, a recognition method based on the two-layer BiLSTM followed by the attention layer is proposed. The experiment results show that, based on the RadioML2016.10a dataset, the method achieves high recognition accuracy without increasing the training burden and extracting features from original signals. The recognition reaches 86.7% at 0 dB and 91.5% at 18 dB, outperforming other deep learning methods.
References
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Liu X, Yang D, El Gamal A. 2017. Deep neural network architectures for modulation classification. 51st Asilomar Conference on Signals, Systems, and Computers. IEEE, Pacific Grove, CA, USA, 915-919. https://doi.org/10.1109/ACSSC.2017.8335483
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Peng S, Jiang H, Wang H, 2018. Modulation classification based on signal constellation diagrams and deep learning. IEEE transactions on neural networks and learning systems, 30(3): 718-727. https://10.1109/TNNLS.2018.2850703
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Zhang Z, Wang C, Gan C, 2019. Automatic modulation classification using convolutional neural network with features fusion of SPWVD and BJD. IEEE Transactions on Signal and Information Processing over Networks, 5(3): 469-478. https://10.1109/TSIPN.2019.2900201
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Li Nan. 2019. Application of Bi-spectral Feature and Deep Learning in Signal Modulation Recognition. Journal of Projectiles, Rockets, Missiles and Guidance. 39(5): 81-84. http://10.15892/j.cnki.djzdxb.2019.05.019
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Published In
December 2022
385 pages
ISBN:9781450397438
DOI:10.1145/3582197
Copyright © 2022 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 30 March 2023
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