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Deep Neural Network Based on Convolution Factor Decomposition for Wireless Signal Modulation Recognition

Published: 15 March 2023 Publication History

Abstract

In recent years, deep neural network (DNN) technology is widely used in the radio signal modulation recognition task to achieve a high recognition accuracy. However, the performance of DNN depends on a large number of training samples to solve a large number of neural network model parameters. Thus, sufficient training data are required and solving DNN parameters is time-consuming. To alleviate these issues, we propose to use a special module - the Inception module combined with CNN to build a novel neural network for radio signal modulation recognition. In our approach, using the idea of convolution factorization (a key idea in the Inception module), we broaden the network to ensure the model learning ability while reducing the model parameters. Moreover, we use the data enhancement strategy to increase the number of training samples to improve recognition performance. The experiment results show the effectiveness of our approach.

References

[1]
Z. Ji, W. Wang, and C. Liu, 2012. Identification of radio signal pulse modulation characteristics. In 2012 IEEE 11th International Conference on Signal Processing. Vol. 3, pp. 1910-1913.
[2]
Q. Chen, X. Zhu, Z.H. Ling, S. Wei, H. Jiang, and D. Inkpen, 2017. Enhanced LSTM for natural language inference. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Vol. 1.
[3]
R. Luo, T. Hu, Z. Tang, C. Wang, X. Gong, and H. Tu, 2019. A radio signal modulation recognition algorithm based on residual networks and attention mechanisms.
[4]
T. O'Shea and N. West, 2016. Radio machine learning dataset generation with gnu radio. Proceedings of the GNU Radio Conference.
[5]
N. Sainath, A. Senio, O. Vinyalsr, and H. Sak, 2015. Convolutional, long short-term memory, fully connected deep neural networks. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Pp. 4580-4584.
[6]
X. Li, L. Ding, W. Li, and C. Fang, 2017. FPGA accelerates deep residual learning for image recognition. In 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference. Pp. 837-840.
[7]
N.E. West and T.J. O'Shea, 2017. Deep architectures for modulation recognition. In 2017 IEEE International Symposium on Dynamic Spectrum Access Networks. Pp. 1-6.
[8]
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, and D. Anguelov, 2015. Going deeper with convolutions. In 2015 IEEE Conference on Computer Vision and Pattern Recognition. Pp. 1-9.
[9]
S. Kouamo, C. Tangha, and O. Kouamo, 2020. Reduction of false rejection in an authentication system by fingerprint with deep neural networks. Journal of Software Engineering and Applications. Vol. 13, no. 1, pp. 1-13.
[10]
S. Ioffe and C. Szegedy, 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In F. Bach and D. Blei, editors, Proceedings of the 32nd International Conference on Machine Learning. Vol. 37, pp. 448-456, Lille, France.

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  1. Deep Neural Network Based on Convolution Factor Decomposition for Wireless Signal Modulation Recognition

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    EITCE '22: Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering
    October 2022
    1999 pages
    ISBN:9781450397148
    DOI:10.1145/3573428
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 15 March 2023

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    Author Tags

    1. Dataset enhancement
    2. Deep neural network
    3. Inception
    4. Radio signal modulation recognition

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