## 2 Related Work
[21] proposed a compression method to reduce the complexity of CVNNs, but the performance of the compressed network decreases drastically at low SNRs. [PERSON] et al. [22] conducted ablation experiments on real LoRa (Long Range) and WiFi datasets with different network layers, components of the network layers (convolutional filters or their outputs), and real and imaginary components of the network layer outputs. The experimental results confirm that, when the number of model parameters is approximately equal, the complex-valued deep neural networks (DNNs) utilize the joint features of the in-phase and quadrature components of the signal more effectively and the performance of the complex-valued DNNs are always better than the real-valued DNNs. The above-mentioned deep learning-based RFF identification methods have demonstrated good identification performance in stable environments with high SNRs, but the identification efficiency of the deep learning models decreases rapidly in high-dynamic-noise and low-SNR environments. To address these issues, recent studies have introduced GANs into the applications of RFF identification. The basic modules of GANs still consist of convolutional layers, fully connected layers, and activation functions, which are commonly used for data augmentation and denoising. [PERSON] et al. [23] proposed a preprocessing algorithm improving synchrosqueezed wavelet transforms by energy regularization (ISWTE) and an unsupervised neural-network-noise-feature-extracting GAN (NEGAN). ISWTE improves the robustness of RFF by simplifying the process of signal preprocessing. NEGAN extracts RFF features directly from the structure of signal waveform and obtains more stable RF fingerprint features by subtracting noise features learned. Experimental results on a dynamic test set with an SNR range of \(-20\)-\(10\) dB show that the method achieves \(96\%\) (SNR of \(10\) dB), \(85\%\) (SNR of \(0\) dB), and \(25\%\) (SNR of \(-20\) dB) identification accuracy, respectively. [PERSON] et al. [24] proposed a novel deep denoising network for TEM signals (TEM-NLnet) which is inspired by noise-learning. Specifically, the method first learns the noise present in real-world signals through GANs and then applies a denoiser based on DNNs to learn the mapping from the noisy TEM signals to the corresponding noise-free signals. Experiments on simulated and real-world geological datasets have shown that TEM-NLnet performs better both quantitatively and visually compared to other state-of-the-art denoising methods for TEM signals. [PERSON] et al. [25] proposed a generative adversarial denoising and classification network (GADNet) for denoising radar signals and identifying intra-pulse modulations. The network comprises three components: a generator, a discriminator, and a classifier. The generator has the structure of an encoder-decoder which extracts high-order feature vectors from the input radar signals through symmetric upsampling and downsampling operations and recovers clean signals from noise. The discriminator is used to determine the authenticity of the denoised output generated by the generator. Then, a cascading classifier with a generator and discriminator is used to ensure that the denoising results meet the semantic information required for classification. The experimental results show that GADNet possesses a high-quality signal denoising effect and high identification accuracy in dense noise environments, as well as a certain superiority in regard to the transferability of data with low SNRs. In summary, we consider making full use of the advantages of CVNNs in the feature extraction of complex signals and GANs in denoising noisy signals, combining the two networks to realize the denoising of RF signals and the extraction of RF fingerprints. The proposed CC-GAN is obtained by modifying the GAN.