基于Stacked-TCN的空间混叠信号单通道盲源分离方法

doi:10.12305/j.issn.1001-506X.2021.09.32

Abstract

Abstract:

Aiming at the problem of insufficient separation accuracy of aliased signals in space Internet satellite ground communication scene, a stacked time domain convolutional network (Stacked-TCN) separation method based on deep learning is proposed. Firstly, the coding feature representation is extracted from the mixed signal. Then, the deep feature mask of the source signal is trained through the time-domain convolution network. The mask of each signal source and the coding feature of the mixed signal are multiplied by Hadamard to obtain the coding feature representation of the source signal. Finally, 1-D convolution is used to decode the characteristics of the source signal to obtain the original waveform. In the experiment, the negative scale invariant source to noise ratio is used as the loss function of network training, that is, the evaluation index of single channel blind source separation performance. The results show that the Stacked-TCN method has better separation accuracy and noise robustness than the other four algorithms.

Key words: underdetermined blind source separation, co-frequency interference, single channel, time-domain convolutional network (TCN)

CLC Number:

TN911.7

Mengchen ZHAO, Xiujuan YAO, Jing WANG, Suhui DONG. Single-channel blind source separation method of spatial aliasing signal based on Stacked-TCN[J]. Systems Engineering and Electronics, 2021, 43(9): 2628-2636.

Figures/Tables 10

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序号	方法	信号种类	信噪比范围/dB	步长/dB
1	文献[43]	BPSK	5~30	5
2	文献[44]	BASK、BPSK	5~25	5
3	文献[45]	QPSK	12~22	2
4	文献[46]	雷达信号	10~30	0. 5
5	文献[7]	EEG、ECG	—	—
6	文献[47]	语音信号	-6~9	3
7	文献[48]	语音信号	—	—
8	文献[49]	语音信号	—	—
9	Stacked-TCN	BPSK、8PSK、16QAM、64QAM、PAM4	5~20	2.5

算法	参数	值
ICA	迭代次数	100
NMF	迭代阈值	1×10^－8
NMF	迭代次数	100
TasNet	基信号数N	128
	帧长L	64
	LSTM隐层单元数	128
	LSTM-block数X	2
Wave-U-Net	卷积核大小P	5
	block数X	5
	通道数	16-32-64-128-256
Stacked-TCN	编码器filter数N	512
	帧长L	16
	瓶颈层通道数B	128
	卷积核大小P	3
	1D-block通道数H	512
	1D-block数X	8
	Repeat数R	3

混合信号	算法
混合信号	Stacked-TCN	TasNet	Wave-U-Net	ICA	NMF
BPSK_8PSK	-22.51	-2.94	-17.22	5.36	9.92
BPSK_16QAM	-18.71	-4.06	-17.02	1.64	6.32
BPSK_64QAM	-22.83	-1.44	-19.08	6.02	8.72
BPSK_PAM4	-28.44	-2.35	-12.09	5.92	8.19
8PSK_16QAM	-13.00	-3.63	-13.55	4.87	9.82
8PSK_64QAM	-5.96	-1.98	-11.31	1.07	7.67
8PSK_PAM4	-15.64	-1.95	-18.67	2.68	6.13
16QAM_64QAM	-6.32	-2.51	-1.51	5.48	8.73
16QAM_PAM4	-21.61	-2.25	-17.31	5.32	7.79
64QAM_PAM4	-5.53	-2.36	-11.92	2.50	5.98
平均值	-16.05	-2.55	-13.97	4.09	7.93

算法	单帧时长	CPU所用时间
Stacked-TCN时域掩模法	0.032	7.90×10^－3
时-频域掩模法	0.256	1.24

算法	所用时间
NMF	0.38×10^－4
ICA	0.157 27
Wave-U-Net	0.202 59
TasNet	0.233 69
Stacked-TCN	0.334 49

Single-channel blind source separation method of spatial aliasing signal based on Stacked-TCN

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