## 2 Related Work
### The Evolution of GAN Model  
The GAN model includes a generative model \(G\) and a discriminative model \(D\), which uses an adversarial process to propose a new framework for estimating generative models [1]. We can improve the \(D\) model to classify the wireless signals collected from the UAVs. Since 2014, GAN has appeared in various forms, as shown in Figure 3. In 2015, [PERSON] et al. [3] found a Deep Convolutional GAN (DCGAN) model, which is more stable in training and produces higher quality samples. The DCGAN is competitive with a probabilistic generative data augmentation technique utilizing learned per class transformations [33] while being more general as it directly models the data instead of transformations of the data. However, the DCGAN model is unsuitable for classification. Information Maximizing GAN (InfoGAN) proposed by [PERSON] et al. [34] uses the information-theoretic extension to learn disentangled representations in a completely unsupervised manner. In contrast to previous approaches that require supervision, InfoGAN is completely unsupervised and learns interpretable and disentangled representations on challenging datasets. In addition, InfoGAN adds only negligible computation cost on top of GAN and is easy to train. However, the core idea is more suitable for learning hierarchical latent representations, improving semi-supervised learning, and using InfoGAN as a high-dimensional data discovery tool. In 2016, [PERSON] et al. [35] found that regular GANs hypothesize the discriminator as a classifier with the sigmoid cross entropy loss function. However, this loss function may lead to the vanishing gradients problem during the learning process. The Least Squares Generative Adversarial Networks (LSGANs) adopt the least squares loss function for the discriminator. Minimizing the objective function of LSGAN yields minimizing the Pearson \(\chi^{2}\) divergence. The LSGANs are able to generate higher quality images than regular GANs and are more stable during the learning process. However, there is little research of the LSGANs in complex datasets.  
Figure 2: **(a)** The data acquisition equipment USRP B210. (**b**) The center frequency of different UAVs including Phantom2: 2.447 GHz, and Phantom3: 2.457 GHz. (**c**) The wireless communication signals of different UAVs.  
Wasserstein GAN (WGAN) [36] can improve the stability of learning, get rid of problems such as mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches. Furthermore, the corresponding optimization problem is sound and provides extensive theoretical work, highlighting the deep connections to different distances between distributions [37]. The Auxiliary Classifier GANs (AC-GANs) proposed by [PERSON] et al. [38] can generate multiple samples, which expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models. Most of the GAN models are used for image sample generation rather than for classification and recognition detection. In our paper, we improve the discriminative \(D\) model of the AC-GANs model, which can be adapted to the multi-class identification and detection of wireless signals, and enhance the AC-GANs model according to the WGAN model. Therefore, a more stable AC-WGANs model can be obtained to effectively enhance the recognition rate. The framework of the AC-WGANs model is shown in Figure 4.