面向空间认知通信的轻量化网络自动调制分类方法

doi:10.12305/j.issn.1001-506X.2023.07.33

Abstract

Abstract:

The deep learning models currently used for automatic modulation classification have the problems of numerous parameters and computation. According to the characteristics of continuous sampling in-phase and quadrature signals, a lightweight and efficient deep network structure is proposed. By constructing a directional filter, the phase features are first extracted, then the temporal features are extracted, and finally the mean values of every channel features are used for classification. After validated with communication signal classification dataset, when the signal-to-noise ratio(SNR) > 0 dB, the recognition accuracy exceeds 60%, and when the SNR ≥10 dB, the recognition accuracy exceeds 90%. Compared with mainstream deep models, when reaching the same accuracy, only about 20% of the model parameters and about 50% of the actual running time are used, which is more suitable for application in space cognitive communication systems.

Key words: automatic modulation classification, deep learning, lightweight network structure

CLC Number:

Tianshu CUI, Dong WANG, Zhen HUANG. Automatic modulation classification based on lightweight network for space cognitive communication[J]. Systems Engineering and Electronics, 2023, 45(7): 2220-2226.

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序号	名称	CCNet	VGG	CLDNN	ResNet
1	卷积层数	7	7	4	12
2	卷积核尺寸	(3, 1)	(8, 1)	(8, 1)	(8, 1)
3	卷积通道数	48	64	64	32
4	LSTM层数	0	0	2	0
5	LSTM单元	0	0	50	0
6	分类层层数	1	2	2	2
7	分类层单元数	48	128	128	128

数据集	RadioML
数据格式	1 024×2
SNR/dB	[-20:2:30]
调制类型数量	24
模拟调制	FM, AM-SSB-SC, AM-SSB-WC, AM-DSB-WC, AM-DSB-SC,
数字调制	OOK, OQPSK, GMSK, BPSK, QPSK, 8PSK, 16PSK, 4ASK, 8ASK, 16APSK, 32APSK, 64APSK, 128APSK, 32PSK, 16QAM, 32QAM, 64QAM, 128QAM, 256QAM
样本数据量	24×26×4 096=2 555 904

网络名称	参数数量	浮点计算量(×10⁶)	乘加操作(×10⁶)	内存使用/MB
IQCNet	43 416	25.42	12.85	2.58
IQCNet-N	43 368	50.65	25.60	4.98
CCNet	85 080	151.20	75.92	5.65
VGG	283 352	26.65	13.49	3.16
ResNet	205 848	40.38	20.39	5.65
CLDNN	168 920	-	-	-

网络结构	2080Ti	树莓派4B	Nano
IQCNet	21 330	17.83	5.86
IQCNet-N	27 490	33.55	9.58
CCNet	31 959	135.89	375.49
VGG	31 596	38.80	9.52
ResNet	28 168	47.08	18.30
CLDNN	32 486	48.44	12.89

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Automatic modulation classification based on lightweight network for space cognitive communication

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