A modulation classification method based on deformable convolutional neural networks for broadband satellite communication systems

Qian Li, Qi Zhang, Xiangjun Xin, Qinghua Tian, Ying Tao, Yufei Shen, Guixing Cao, Naijing Liu, Jixi Qian

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In order to solve the problem of broadband satellite modulation signal with SNR fluctuation of complex channel and various modulation signal recognizing, we propose a Deformable Convolutional Neural Networks (DCNN) classification model based on broadband satellite communication systems. In our algorithm, we propose a deformable convolution kernel, which only need to calculate the 2/3 pixel convoluting. Our algorithm not only can be used to reduce the complexity and improve the robustness, but also used to improve the accuracy. We simulate the accuracy and the complexity of the algorithm among the four neural network models of DCNN, VGG, AlexNet and ResNet. The results show that the design of the DCNN model has high recognition rate and low algorithm complexity. Then we simulate the DCNN network in variable signal-to-noise of BPSK, QPSK, 8PSK, 16APSK, 32APSK, 16QAM, 32QAM and 64QAM commonly used satellite modulation signal classification and complex channel conditions, and training the four basic modulation signal used to identify other modulation signals. The results show that the DCNN model not only can be used to maintain a high recognition rate of the modulated signal, but also used to reduce the complexity of the algorithm and improves the robustness of the algorithm.

Original languageEnglish
Title of host publicationTenth International Conference on Information Optics and Photonics
EditorsYidong Huang
PublisherSPIE
ISBN (Electronic)9781510625792
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event10th International Conference on Information Optics and Photonics - Beijing, China
Duration: 8 Jul 201811 Jul 2018

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10964
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference10th International Conference on Information Optics and Photonics
Country/TerritoryChina
CityBeijing
Period8/07/1811/07/18

Keywords

  • DCNN
  • convolution kernel
  • modulation signal classification
  • robustness

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Li, Q., Zhang, Q., Xin, X., Tian, Q., Tao, Y., Shen, Y., Cao, G., Liu, N., & Qian, J. (2018). A modulation classification method based on deformable convolutional neural networks for broadband satellite communication systems. In Y. Huang (Ed.), Tenth International Conference on Information Optics and Photonics Article 109644I (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10964). SPIE. https://doi.org/10.1117/12.2506109