Communication Interference Generation Technology Based on Lightweight DCGAN

doi:10.3969/j.issn.1009-086x.2026.03.013

Abstract

Abstract:

Traditional interference waveform generation methods require prior acquisition of parameter characteristics of the target signal， making it difficult to achieve favorable results when confronted with the complex and diverse communication signal patterns nowadays. To address this issue， this paper proposes a communication interference generation technology based on lightweight deep convolutional generative adversarial networks （DCGAN）. The DCGAN network is utilized to perform unsupervised learning on the features of the target signal without prior information， thereby generating interference waveforms that are highly similar to the target signal. Meanwhile， lightweight processing is applied to the network to reduce the number of network parameters. In this paper， simulation experiments are conducted on communication signals of various modulation types. The experimental results show that the lightweight DCGAN network reduces the number of parameters by approximately 38% without compromising the quality of the generated interference waveforms. When the jamming-to-signal ratio （JSR） is above 0 dB， the bit error ratio （BER） of the communication system affected by the generated interference waveforms rapidly approaches 0.5， which is far superior to the interference effect of traditional interference waveforms.

Key words: communication interference, interference generation, machine learning, deep convolutional generative adversarial network, lightweight network

摘要：

传统的干扰波形生成方法需要提前获取目标信号的参数特征，在面对如今复杂多样的通信信号样式时难以取得良好的效果。针对这个问题，提出一种基于轻量化深层卷积生成对抗网络（deep convolution generative adversarial networks，DCGAN）的通信干扰生成技术。利用DCGAN网络在无先验信息条件下无监督学习目标信号特征，生成与目标信号高度相似的干扰波形。同时对网络进行了轻量化处理，减少了网络参数量。针对多种调制类型的通信信号进行了仿真实验，实验结果表明：轻量化DCGAN网络在参数量减少了约38%的情况下，生成的干扰波形质量并未下降。在0 dB干信比（jamming-to-signal ratio, JSR）以上时，受生成干扰波形影响的通信系统的误比特率（bit error ratio, BER）快速逼近0.5，远优于传统干扰波形的干扰效果。

关键词: 通信干扰, 干扰生成, 机器学习, 深层卷积生成对抗网络, 轻量化网络

CLC Number:

TN975

Li PAN, Hang RUAN, Yifan FU, Dongping ZHOU, Lei LEI. Communication Interference Generation Technology Based on Lightweight DCGAN[J]. Modern Defense Technology, 2026, 54(3): 140-150.

潘黎, 阮航, 付亦凡, 周东平, 雷蕾. 基于轻量化DCGAN的通信干扰生成技术[J]. 现代防御技术, 2026, 54(3): 140-150.

Figures/Tables 14

Fig. 1 Constellation diagram of 16QAM signal

Fig. 2 Time-domain waveform of communication signals

Fig. 3 Basic structure of DCGAN

Fig. 4 DCGAN network architecture

Table 1 Convolutional layer parameters of the discriminator

卷积层序号	特征维度	卷积核宽度
Conv1	32	13
Conv2	64	11
Conv3	128	9
Conv4	256	7
Conv5	1	4

Table 2 Convolutional layer parameters of the generator

转置卷积层序号	特征维度	卷积核宽度
Tconv1	512	5
Tconv2	256	7
Tconv3	128	9
Tconv4	64	11
Tconv5	2	13

Table 3 Comparison of network parameters

网络类型	网络参数总量
轻量化DCGAN网络	1 739 497
全大核DCGAN网络	2 808 955

Fig. 6 Algorithm procedure

Fig. 7 DCGAN training loss function

Fig. 8 Constellation diagram comparison

Fig. 9 4FSK spectrogram comparison

Fig. 10 Modulation classification network training loss function and classification accuracy

Table 4 Modulation classification results

调制类型	分类为对应类型的比率/%
QPSK	97.40
16QAM	100
4FSK	100

Fig. 11 BER of communication systems interfered by five kinds of interference

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