基于生成对抗数据增强支持向量机的小样本信号调制识别算法

谢智东; 谭信; 袁昕旺; 杨刚; 韩裕

doi:10.11999/JEIT220624

基于生成对抗数据增强支持向量机的小样本信号调制识别算法

doi: 10.11999/JEIT220624

军事科学院国防科技创新研究院北京 100071

详细信息

作者简介:
谢智东：男，副研究员，研究方向为无人集群电磁对抗、无人集群通信、卫星通信

谭信：男，硕士生，研究方向为智能信息感知处理与传输

袁昕旺：男，硕士生，研究方向为网络通信安全

杨刚：男，硕士生，研究方向为信号处理

韩裕：男，硕士生，研究方向为信号处理

通讯作者:
谭信　tanxin2017@163.com

中图分类号: TP391.4; TN911.3
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- 被引次数: 0
出版历程
- 收稿日期: 2022-05-17
- 修回日期: 2022-10-07
- 网络出版日期: 2022-10-11
- 刊出日期: 2023-06-10

Small Sample Signal Modulation Recognition Algorithm Based on Support Vector Machine Enhanced by Generative Adversarial Networks Generated Data

College of National Defense Technology and Innovation, Academic of Military Science, Beijing 100071, China

摘要

摘要: 着眼于解决小样本信号调制识别问题，该文首先研究了利用支持向量机(SVM)进行分类识别的理论可行性；其次根据统计学习理论，对利用生成对抗网络(GAN)生成数据增强支持向量机分类识别能力进行了理论分析；最后通过构建包含层归一化的深度卷积生成对抗网络(LDCGAN)，与普通深度卷积生成对抗网络相比，其生成数据映射至高维空间后特征更加明显，更有利于支持向量机的分类，实验验证了该生成对抗网络生成数据可以在小样本条件下实现对支持向量机分类识别能力的有效增强。
- 调制识别 /
- 生成对抗网络 /
- 支持向量机 /
- 小样本
Abstract: Focusing on solving the problem of small sample signal modulation recognition, the theoretical feasibility of using Support Vector Machine (SVM) for modulation recognition is investigated firstly; Secondly, based on statistical learning theory, a theoretical analysis of using Generative Adversarial Networks (GAN) generated data to enhance the classification ability of SVM is conducted; And finally, a Deep Convolutional Generative Adversarial Network based on Layer normalization (LDCGAN) is constructed , whose generated data has more obvious features than Deep Convolutional Generative Adversarial Networks (DCGAN) after mapping to a high-dimensional space, so the generated data is more conducive to the classification of SVM. The experiments verify that LDGAN generated data can achieve an effective enhancement of the classification ability of SVM under the condition of small samples.
- Modulation recognition /
- Generative Adversarial Networks (GAN) /
- Support Vector Machine (SVM) /
- Small samples

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图 1 LDCGAN-SVM信号调制识别算法流程

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图 2 真实样本数量对传统机器学习算法识别效果的影响

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图 3 FM,QPSK真实样本与扩充样本时频图

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图 4 FM,QPSK真实样本与扩充样本频谱

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图 5 FM,QPSK真实样本与扩充样本同相正交信息时域图

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图 6 LDCGAN混合不同机器学习算法识别对比

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图 7 不同训练epoch次数对LDCGAN+SVM扩充识别影响

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图 8 层归一化对模型识别效果的影响

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图 9 WGAN-GP与WGAN损失函数识别效果对比

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图 10 WGAN-GP与WGAN损失函数收敛速度对比

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图 11 FM,QPCK不同损失函数频谱图

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图 12 FM,QPCK不同损失函数同相正交信息时域图

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图 13 LDCGAN+SVM在不同真实样本数量时识别准确率

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图 14 真实样本数量、扩充样本数量与信噪比对识别准确率的影响

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图 15 识别效果受信噪比影响

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表 1 LDCGAN整体网络结构

生成网络		鉴别网络
输入	输出维度	输入	输出维度
Noise = Input(shape=(100,))	[(None,100)]	Input(shape=(1024,1,1))	[(None,1024,2,1)]
Dense(256×2×128,activation='relu')	(None,65536)	Con2D(16,(2,2),padding='same',strides=2), LeakyReLU(alpha=0.2)	(None,512,1,16)
Reshape((256,2,128))	(None,256,2,128)	LayerNormalization	(None,512,1,16)
UpSampling2D((2,1))	(None,512,2,128)	Con2D(32,(2,1),padding='same',strides=(2,1)), LeakyReLU(alpha=0.2)	(None,256,1,32)
Conv2D(128,(2,1),strides=1,padding='same', activation='relu')	(None,512,2,128)	LayerNormalization	(None,256,1,32)
BatchNormalization	(None,512,2,128)	ZeroPadding2D(padding=((1,1),(1,1)))	(None,258,3,32)
UpSampling2D((2,1))	(None,1024,2,128)	Con2D(64,(2,2),padding='valid',strides=1), LeakyReLU(alpha=0.2)	(None,257,2,64)
Conv2D(64,(2,1),strides=1,padding='same', activation='relu')	(None,1024,2,64)	LayerNormalization	(None,257,2,64)
BatchNormalization	(None,1024,2,64)	Con2D(128,(2,2),padding='same',strides=2), LeakyReLU(alpha=0.2)	(None,129,1,128)
Conv2D(32,(2,1),strides=1,padding='same', activation='relu')	(None,1024,2,32)	LayerNormalization	(None,129,1,128)
BatchNormalization	(None,1024,2,32)	Con2D(256,(2,1),padding='same',strides=1), LeakyReLU(alpha=0.2)	(None,129,1,256)
Conv2D(1,(2,1),strides=1,padding='same', activation='tanh')	(None,1024,2,1)	GlobalAveragePooling2D()	(None,256)
		Dense(1)	(None,1)

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表 2 传统机器学习算法训练耗时(s)

	SVM	KNN	MLP	Random Forest	LightGBM	GBDT	XGBoost	AdaBoost
耗时	1.8	0.1	2.5	0.3	11.0	188.0	7.0	9.0

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表 3 LDCGAN训练耗时(h)

训练epoch	10000	20000	25000	30000	40000	60000	80000
耗时	0.6	1.2	1.5	1.8	2.4	3.6	4.8

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