基于多端卷积神经网络的调制识别方法

doi:10.11959/j.issn.1000-436x.2019206

Abstract

Abstract:

In order to identify the main modulation modes adopted in current satellite communication systems,a signal modulation recognition algorithm based on multi-inputs convolution neural network was proposed.With the prior information of the signals and knowledge of the network topological structure,the time-domain signal waveforms were converted into eye diagrams and vector diagrams to represent the shallow features of the signals.Meanwhile,the modulation recognition model based on multi-inputs convolution neural network was designed.Through the training of the network,the shallow features were deeply extracted and mapped.Finally,the signal modulation recognition task was completed.The simulation results show that compared with the traditional algorithms and deep learning algorithms,the proposed method has a better anti-noise performance,and the overall recognition rate of this algorithm can reach 95% when the signal-to-noise ratio is 5 dB.

Key words: modulation recognition, multi-inputs convolution neural network, eye diagram, vector diagram

CLC Number:

TN911.7

Xiong ZHA,Hua PENG,Xin QIN,Guang LI,Tianyun LI. Modulation recognition method based on multi-inputs convolution neural network[J]. Journal on Communications, 2019, 40(11): 30-37.

Figures/Tables 12

$\frac{E_{b}}{N_{0}} / d B$					时间复杂度/ms
			样本生成过程					信号识别过程
	文献[4]	文献[5]	文献[8]	文献[9]	本文算法	文献[4]	文献[5]	文献[8]	文献[9]	本文算法
5	2.2	4.1	0	15.2	13.2	2.5	2.7	9.5	16.2	9.9
10	2.1	4.1	0	15.8	13.4	1.3	1.4	9.5	16.1	9.7

References 14

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