Modulation Recognition Method of Mixed Signal Based on Intelligent Analysis of Cyclic Spectrum Section

doi:10.16182/j.issn1004731x.joss.22-0239

Abstract

Abstract:

Aiming at the problems of low intelligence and poor adaptability for the existing mixed signal recognition methods, an intelligent recognition method based on cyclic spectral cross section and deep learning is proposed. For common mixed communication signals, the characteristics of zero frequency cross section of cyclic spectrum are theoretically deduced and analyzed. Two new pre-processing methods, nonlinear segmental mapping and directional pseudo-clustering are proposed, which can effectively improve the adaptability and consistency of cross section features. The pre-processed feature graph is combined with the residual network (ResNet), and the deep learning network is used to mine and analyze the deep details of modulation information in the feature graph, and the effective recognition of mixed signals is realized. Simulation results show that the method is insensitive to noise, and the average recognition rate is more than 90% when the SNR is no less than -2dB. The proposed method has good adaptability to the variation of signal parameters and energy ratio between signals.

Key words: communication signal processing, deep learning, modulation recognition, cyclic spectrum, residual network

CLC Number:

TN911.72

Yu Du, Xinquan Yang, Jianhua Zhang, Suchun Yuan, Huachao Xiao, Jingjing Yuan. Modulation Recognition Method of Mixed Signal Based on Intelligent Analysis of Cyclic Spectrum Section[J]. Journal of System Simulation, 2023, 35(1): 146-157.

Figures/Tables 12

Fig. 1

Fig. 2

Table 1

Theoretical values of cyclic spectrum at different frequency points of mixed signals

序号	循环频率	类型	BPSK+QPSK			BPSK+OQPSK			QPSK+OQPSK
1	$α = n T$	理论值	0			0			0
		最大值归一化幅值	$E B : E Q$	1 $:$ 1	0	$E B : E O$	1:1	0	$E Q : E O$	1:1	0
				1:4	0		1:4	0		1:4	0
				4:1	0		4:1	0		4:1	0
2	$α = 2 f c$	理论值	$T E B / 4$			$T E B / 4$			0
		最大值归一化幅值	$E B : E Q$	1:1	1.00	$E B : E O$	1:1	1.00	$E Q : E O$	1:1	0
				1:4	1.00		1:4	0.49		1:4	0
				4:1	1.00		4:1	1.00		4:1	0
3	$α = 2 f c ± 1 T$	理论值	$T E B / π 2$			$T E B / π 2 + T E O / π 2$			$T E O / π 2$
		最大值归一化幅值	$E B : E Q$	1:1	0.41	$E B : E O$	1:1	0.81	$E Q : E O$	1:1	1.00
				1:4	0.41		1:4	1.00		1:4	1.00
				4:1	0.41		4:1	0.51		4:1	1.00
4	$α = 2 f c ± 2 T$	理论值	0			0			—
		最大值归一化幅值	$E B : E Q$	1:1	0	$E B : E O$	1:1	0	$E Q : E O$	1:1	—
				1:4	0		1:4	0		1:4	—
				4:1	0		4:1	0		4:1	—
5	$α = 2 f c ± 3 T$	理论值	$T E B / 9 π 2$			$T E B / 9 π 2 + T E O / 9 π 2$			$T E O / 9 π 2$
		最大值归一化幅值	$E B : E Q$	1:1	0.05	$E B : E O$	1:1	0.10	$E Q : E O$	1:1	0.11
				1:4	0.10		1:4	0.14		1:4	0.10
				4:1	0.05		4:1	0.06		4:1	0.11
6	$α ≥ 2 f c + 4 T$ $α ≤ 2 f c - 4 T$	理论值	0			0			0
		最大值归一化幅值	$E B : E Q$	1:1	0	$E B : E O$	1:1	0	$E Q : E O$	1:1	0
				1:4	0		1:4	0		1:4	0
				4:1	0		4:1	0		4:1	0

Table 1

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