An Automatic Modulation Recognition Method with Low Parameter Estimation Dependence Based on Spatial Transformer Networks
PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(5), 1010; https://doi.org/10.3390/app9051010
Received: 7 February 2019
/
Revised: 5 March 2019
/
Accepted: 7 March 2019
/
Published: 11 March 2019
(This article belongs to the Special Issue Advances in Deep Learning)
Recently, automatic modulation recognition has been an important research topic in wireless communication. Due to the application of deep learning, it is prospective of using convolution neural networks on raw in-phase and quadrature signals in developing automatic modulation recognition methods. However, the errors introduced during signal reception and processing will greatly deteriorate the classification performance, which affects the practical application of such methods. Therefore, we first analyze and quantify the errors introduced by signal detection and isolation in noncooperative communication through a baseline convolution neural network. In response to these errors, we then design a signal spatial transformer module based on the attention model to eliminate errors by a priori learning of signal structure. By cascading a signal spatial transformer module in front of the baseline classification network, we propose a method that can adaptively resample the signal capture to adjust time drift, symbol rate, and clock recovery. Besides, it can also automatically add a perturbation on the signal carrier to correct frequency offset. By applying this improved model to automatic modulation recognition, we obtain a significant improvement in classification performance compared with several existing methods. Our method significantly improves the prospect of the application of automatic modulation recognition based on deep learning under nonideal synchronization.
View Full-Text
Keywords:
deep learning; automatic modulation recognition; spatial transformer networks; signal processing
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Li, M.; Li, O.; Liu, G.; Zhang, C. An Automatic Modulation Recognition Method with Low Parameter Estimation Dependence Based on Spatial Transformer Networks. Appl. Sci. 2019, 9, 1010. https://doi.org/10.3390/app9051010
AMA Style
Li M, Li O, Liu G, Zhang C. An Automatic Modulation Recognition Method with Low Parameter Estimation Dependence Based on Spatial Transformer Networks. Applied Sciences. 2019; 9(5):1010. https://doi.org/10.3390/app9051010
Chicago/Turabian StyleLi, Mingxuan, Ou Li, Guangyi Liu, and Ce Zhang. 2019. "An Automatic Modulation Recognition Method with Low Parameter Estimation Dependence Based on Spatial Transformer Networks" Applied Sciences 9, no. 5: 1010. https://doi.org/10.3390/app9051010
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
