基于生成对抗网络的信号调制方式的开集识别

为解决信号调制方式的开集识别问题,基于生成对抗网络提出了一种适用于一维信号数据的重构判别网络模型,该模型由重构网络和判别网络组成,分别用来重构和判别输入信号。两个网络在相互对抗的训练过程中,对已知调制方式信号的数据分布形式充分学习,使得重构后的输出不仅能够呈现已知调制方式信号更多有用的信息,而且能够扰乱未知调制方式的信号,从而增强判别网络对输入信号调制方式的判别能力。仿真结果表明,该模型能够实现信号调制方式的开集识别,而且在信噪比大于0 dB时,对已知调制方式和未知调制方式信号的识别率均大于93%。

Open-set recognition of signal modulation based on generative adversarial networks

To solve the problem of open-set recognition of signal modulation, the reconstruction and discrimination networks (RDN) model for one-dimensional signal data is proposed based on the generative adversarial networks. The model is composed of two networks which are used to separately reconstruct and discriminate the input signals. During the adversarial training procedure, the two networks fully learn the data distribution form of the known modulation signal. The model enables the output of the reconstruction network to not only present more useful information of the known modulation signal, but also distort the unknown modulation signal, thereby enhancing the ability of the network to discriminate the type of modulation of the input signals. The simulation results show that the model can realize the open-set recognition of signal modulation. When the signal-to-noise ratio is greater than 0 dB, the recognition rates of both the known modulation signal and the unknown modulation signal are greater than 93%.