基于TCNN-BiLSTM网络的调制识别算法

doi:10.3969/j.issn.1001-506X.2020.08.26

Abstract

Abstract:

For the traditional modulation recognition algorithm, the recognition rate is not high at low signal to noise ratio (SNR). The paper proposes a two-way convolutional neural network casaded bidirectional long short-term memory (TCNN-BiLSTM) network modulation recognition algorithm. Firstly, the algorithm parallelizes the convolutional layers with convolution kernels of different scales to extract features of different dimensions of the modulation signal. Then it cascades the BiLSTM layers to build LSTM time model for multi-dimensional features. Finally, a softmax classifier is used to complete the recognition. Simulation experiments show that the performance of the algorithm structure under additive Gaussian white noise and Rayleigh fading channels with specific channel parameters is better than the recognition algorithms based on traditional features and other network structures. When the SNR in the Rayleigh fading channel with specific channel parameters is as low as 6 dB, the recognition rate of the six digital modulation signals can still reach above 92%.

Key words: modulation recognition, parallel network, convolutional neural network, bidirectional long short-term memory network

CLC Number:

TN911.3

Kai LIU, Bin ZHANG, Qinghua HUANG. Modulation recognition algorithm based on TCNN-BiLSTM[J]. Systems Engineering and Electronics, 2020, 42(8): 1841-1849.

Figures/Tables 17

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调制信号处理平台	Matlab 2016b
深度学习仿真平台	Tensorflow 1.4.0
硬件加速平台	NVIDIA GTX1080Ti
调制方式	2PSK, 4PSK, 2FSK, 4FSK, 8QAM, 16QAM
符号速率/Mbps	1
每个符号采样点数	4
载波频率/MHz	8
SNR/dB	-6:2:20

BiLSTM层数	无	一层	两层	3层	4层
预测时间/s	0.58	2.09	4.15	4.95	6.15

层类型	激活函数	输出维度
输入层	/	(1, 2, 128)
批标准化层	/	(1, 2, 128)
卷积层L11	Relu	(128, 2, 125)
卷积层L21	Relu	(128, 2, 130)
卷积层L1	Relu	(16, 1, 126)
卷积层L2	Relu	(16, 1, 131)
融合层	/	(16, 1, 257)
3层BiLSTM层	/	(None, 64)
输出层	softmax	(None, 6)

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Modulation recognition algorithm based on TCNN-BiLSTM

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