## 4 Experiments and Results
### Performance Evaluation for CC-GAN  
To evaluate the performance of the CC-GAN, the reference signal set and the dynamic noisy signal set were fed into the discriminator network and the generator network for training, respectively. The training set and test set account for 90% and 10%, respectively, in the original dataset. The trends of the loss values for the generator network and the discriminator network during the training process are shown in Figure 6a, while the trend in the classification accuracy of the discriminator network is shown in Figure 6b. From Figure 6, we can see that the training loss values of the two networks continuously decrease and eventually are stabilized, indicating that both have learned effective feature representations and are better trained. Meanwhile, the classification accuracy of the discriminator network keeps increasing and eventually maintains a high level, indicating that the discriminator also possesses a strong classification capability. To evaluate the denoising effect of the proposed generator network, the structure of the discriminator network was kept unchanged, and the wavelet threshold denoising algorithm and the infinite impulse response (IIR) filter were used, respectively, to denoise the complex signals before the discriminator was used to realize the classification. The wavelet threshold denoising algorithm has become a popular filtering method in recent years that first utilizes wavelet decomposition to ensure that the magnitude of the wavelet coefficients of the signal exceeds that of the noise coefficients (coefficients with larger magnitudes represent the signal, while those with smaller magnitudes largely represent noise); it then removes the coefficients below the threshold as noise and, finally, reconstructs the signal through wavelet reconstruction to achieve denoising. An IIR filter is a type of filter with an infinite unit impulse response and a feedback loop. The output of the IIR filter depends not only on the current inputs but also on previous inputs and outputs. An IIR filter has advantages in terms of improving the filtering efficiency and reducing the consumption of computational resources. The changes in the amplitude of the sample after denoising the same complex-valued signal sample with different denoising algorithms are shown in Figure 7. After denoising the test set in the dynamic noisy signal set with different denoising methods, the discrimi  
Figure 6: The training process of the CC-GAN. **(a)** The changing trend of the loss of the generator network and the discriminator network with the number of iterations; **(b)** the changing trend of the accuracy of the discriminator network with the number of iterations. nator was then utilized for identification. Comparison results of identification accuracy are shown in Figure 8.  
Figure 7: The amplitude of the complex signal sample changes with different denoising algorithms. **(a)** The raw dynamic noisy sample; **(b)** the sample denoised with the generator network of the CC-GAN; **(c)** the sample denoised with the wavelet threshold denoising algorithm; **(d)** the sample denoised with the IIR filter. From Figures 7 and 8, we can see that the accuracy of individual identification is only 66.38% after processing the complex-valued signal samples with the wavelet threshold denoising algorithm, which indicates that the original signal samples were severely distorted during the process of denoising, resulting in the loss of signal features and decreased accuracy compared to scenarios where no denoising algorithm was applied. After processing the complex-valued signal samples with an IIR filter for denoising, the accuracy of individual identification is improved compared to that without using the denoising algorithm, but the improvement is not significant, which indicates that the IIR filter cannot effectively adapt to dynamic noise.