## 1 Introduction  
Since the theory of Generative Adversarial Networks (GAN) [1] was proposed in 2014, GAN has been widely used in various fields [2]. In signal synthesis, [PERSON] et al. [3] used the DCGAN to generate various image datasets, and [PERSON] et al. [4] used GAN for music generation. In signal translation, Cycle-GAN is used to perform image-to-image translation [5, 6]. GAN is also used for text-to-image generation [7], texture synthesis, style transfer, video stylization [8], pixel-level domain transfer [9], image inpainting [10] and so on. Since GAN has a generative model \(G\) and a discriminative model \(D\), we explore in this paper whether the discriminative model of GAN can effectively be used for signal classification problem as well. In signal classification, a typical challenge is that the labeled data are hard to collect and therefore limited. As a direct consequence, the training result is significantly affected. To solve this problem, we explore using the GAN model to improve the training datasets and recognition model to obtain better classification results in the wireless signal recognition of Unmanned Aerial Vehicles (UAVs). In recent years, with the rapid development of the UAV technology, civilian and consumer-grade UAVs have been put into use on a large scale, including disaster rescue [11], delivery service [12] and so on. However, reports of unmanned aircraft (UAS) sightings from pilots, citizens, and law enforcement also have increased dramatically over the past two years, the FAA now receives more than 100 such reports each month [13]. UAVs have crashed into the White House, a nuclear power station [14], an airport [15] and many other places, buildings and infrastructure have been destroyed and many innocent people have been hurt [16]. However, due to the extensive application of UAVs [17], the scene of multiple UAVs flying is inevitable [18]. As shown in Figure 1, it has become a necessity to distinguish illegal UAVs from legal ones when they are flying together. UAVs in operation are conventionally detected by radar, visual detection or acoustic sensors, as shown in Table 1. [PERSON] and [PERSON] [19] discussed the detection of UAVs with Frequency-Modulated Continuous-Wave (FMCW) Radar system, and the UAV signal was detected up to more than 500 m of distance in real-time with the error of less than 0.1%. However, the cost of radar detection is relatively high, and it is difficult to detect at a lower height. [PERSON] et al. [20] introduced a system for detecting the presence of UAVs by identifying unique signatures of a UAV's body vibration and body shifting in the WiFi signal. The recognition rate of this method approximately reached 80% at a distance of 600 m. [PERSON] et al. [21] presented a method capable of detecting the presence of commercial hobby UAVs as a binary classification problem based on sound event detection. This method used Recurrent Neural Network (RNN), which showed the best detection performance with an F-Score of 0.8009 with 240 ms of input audio with a short processing time, indicating the applicability to real-time detection systems. However, this system is easy to be deceived and cannot identify some types of UAVs. [PERSON] et al. [22] proposed a system that classifies payload carrying and non-payload carrying DJI Phantom II UAVs by presenting sound spectrum data to a simple Convolutional Neural Networks (CNN). These networks provide about 99.92% recognition rate for this problem without the need to violate minimal cost constraint, but video detection is easily affected by weather and lighting. [PERSON] et al. [23] used a two-dimensional scale, rotation and translation invariant Generic Fourier Descriptor (GFD) features and classified targets as a drone or a bird by a neural network. This system can achieve up to 85.3% overall correct classification rate. However, this method is unable to deal with the micro-Doppler effect.