# Method for Recognition of Communication Interference Signals under Small-Sample Conditions
## 2 System Model and Data Pre-Processing  
This section proposes a communication jamming signals recognition method based on WDCGAN-SA and C-ResNet, aiming to improve the recognition rate of interference signals under small-sample conditions. The model comprises three parts: the pre-processing module, the feature enhancement network (WDCGAN-SA), and the classification network (C-ResNet). 1. In the data pre-processing module, communication jamming signals are transformed into time-frequency plots using Short-Time Fourier Transform (STFT). This method primarily aims to convert one-dimensional time-domain signals into two-dimensional time-frequency plots, fully leveraging the advantages of deep learning to capture the characteristic patterns of frequency changes over time. 2. In the data enhancement network, we designed a training strategy that combines the Wasserstein distance and gradient penalty mechanism for stable training. After receiving the pre-processed two-dimensional time-frequency plots as input, a self-attention module is added to focus the model on the global key features of the time-frequency plots of communication jamming signals. Finally, changing the training strategy improves the quality of sample generation. 3. Finally, in the C-ResNet classification network, the original images and generated images are combined to form a new dataset, which is then fed into a classification module that integrates cross-channel and spatial information for recognition. This model can capture the key features of the time-frequency plots, generating richer and more discriminative feature representations, thereby improving recognition accuracy. The overall framework of the proposed method is illustrated in Figure 1.  
Pre-Processing ModuleWhen nodes 1 and 2 communicate with each other, the jamming device utilizes jamming signals to attack the communication channel. The jamming signals include Single-Tone Jamming (STI), Multi-Tone Jamming (MTJ), NarrowBand Jamming (NBJ), BroadBandJamming (BBJ), Comb-Frequency Jamming (CFJ), and Linear-Sweeping Frequency Jamming (LSFJ). After node 1 sends information, the signal received by node 2 can be expressed as:  
\[Y(n)=S(n)+J(n)+N(n) \tag{1}\]  
where \(S(n)\), \(J(n)\) and \(N(n)\) represent the transmitted signals, jamming signals and Gaussian white noise. Due to high-intensity jamming signals, users are incapable of receiving the acknowledgment character (ACK). Suppose that the receiver will not send a signal until it receives the ACK. Then, the signal received by node 2 can be expressed as:  
\[Y(n)=J(n)+N(n) \tag{2}\]  
Node 2 performs a time-frequency transformation on the received signal \(Y(n)\), converting the one-dimensional time-domain signal into a two-dimensional time-frequency image. The two-dimensional time-frequency image can analyze the frequency changes of communication jamming signals over time [15]. In this paper, the Short-Time Fourier Transform (STFT) is chosen as the time-frequency transformation method. The formula for STFT is:  
\[STFT(t,f)=\int_{-\infty}^{+\infty}[x(\tau)\omega(\tau-t)]e^{-j2\pi f\tau}d\tau \tag{3}\]  
where \(x(t)\) represents the original jamming signal, and \(\omega(t)\) is the window function. Figure 2 shows the time-frequency plots of jamming signals generated at a JNR of 0 dB, revealing distinct time-frequency distribution characteristics for different types of jamming signals. In (a), Single-Tone Jamming exhibits a single spectral line on the time-frequency plot due to its interference at a single frequency point. In (b), Multi-Tone Jamming results in multiple spectral lines due to interference at multiple frequency points. (c) Narrow Band Jamming and (d) Broad Band Jamming are distinguished on the time-frequency plot by the thickness and thinness of the spectral lines, respectively, corresponding to the bandwidth occupied. (e) Comb Frequency Jamming also shows multiple spectral lines but with dispersed energy. (f) Linear Sweeping Frequency Jamming exhibits spectral lines with varying slopes based on the sweep speed.