## 4 Experiments and Results
### Experimental Comparison with Other Deep Learning Models
\begin{table}
\begin{tabular}{c l} \hline \hline
**Model** & **Architecture** \\ \hline \multirow{8}{*}{CC-GAN} & Generator: \\  & (CConv1d, CBN, zReLU) \(\times\) 3 \\  & (CConvTranspose1d, CBN, zReLU) \(\times\) 2 \\  & (CConvTranspose1d, CTahn) \\  & Discriminator: \\  & (CConv1d, CBN, zReLU, CAdaptiveAvgPool1d) \(\times\) 6 \\  & Flatten \\  & CDense \(\times\) 3 (256, 128, 42) \\ \hline \multirow{8}{*}{RC-GAN} & Generator: \\  & (Conv2d, BN, ReLU) \(\times\) 3 \\  & (ConvTranspose2d, BN, ReLU) \(\times\) 2 \\  & (ConvTranspose2d, Tahn) \\  & Discriminator: \\  & (Conv2d, BN, ReLU, AdaptiveAvgPool2d) \(\times\) 6 \\  & Flatten \\  & Dense \(\times\) 3 (256, 128, 42) \\ \hline \multirow{8}{*}{RGADNet} & Generator: \\  & Residual Block A \(\times\) 2 \\  & (ConvTranspose2d, BN, ReLU) \\  & Residual Block A \\  & (ConvTranspose2d, BN, ReLU) \\  & (ConvTranspose2d, Tahn) \\  & Discriminator: \\  & (Conv2d, BN, ReLU, AdaptiveAvgPool2d) \(\times\) 6 \\  & Flatten \\  & Dense \(\times\) 3 (256, 128, 42) \\ \hline \multirow{2}{*}{RResNet} & Residual Block \(\times\) 6 \\  & Flatten \\  & Dense \(\times\) 3 (256, 128, 42) \\ \hline \multirow{2}{*}{RDRSN} & Residual Shrinkage Block \(\times\) 6 \\  & Flatten \\  & Dense \(\times\) 3 (256, 128, 42) \\ \hline \multirow{2}{*}{CDRSN} & Complex-valued Residual Shrinkage Block \(\times\) 6 \\  & Flatten \\  & CDense \(\times\) 3 (256, 128, 42) \\ \hline \multirow{4}{*}{DCUnet-GAN} & Generator: \\  & Modified DCUnet \\ \cline{1-1}  & Discriminator: \\ \cline{1-1}  & (CConv1d, CBN, zReLU, CAdaptiveAvgPool1d) \(\times\) 6 \\ \cline{1-1}  & Flatten \\ \cline{1-1}  & CDense \(\times\) 3 (256, 128, 42) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detailed architectures of the CC-GAN and other deep learning models. complex-valued counterparts. The DCUnet-GAN consists of two components: a generator and a discriminator. The generator employs a deep complex U-Net (DC-Unet) without the complex-valued ratio mask (cRM) layer [38]. The structure of the discriminator is consistent with the CC-GAN. The RVNNs are unable to directly process the complex-valued signal samples. To ensure the consistency of the input data, each complex-valued signal sample was extracted and recombined into real-valued 2D vectors comprising the components of in-phase and quadrature. The size of each reconstructed sample was \(1024\times 2\). The test set from the reconstructed dynamic noisy signals set and the original test set were input into the above models, respectively, and the results of the number of parameters, training time, and identification accuracy of the models compared with the CC-GAN are shown in Table 2.  
From Table 2, we can see that the RC-GAN and the RGADNet have better identification performance compared with other RVNNs and the CDRSN, indicating that the generator in their architectures can eliminate the random noise of the original 2D IQ vectors to a certain extent, which is conducive to completing the feature extraction of the signals and individual identification by the subsequent discriminator. However, the identification accuracy of the RGADNet is slightly superior to that of the RC-GAN, which indicates that the incorporation of residual units in the generator of the GADNet enhances its capability of feature extraction compared to the RC-GAN. The identification accuracies of the CVNNs are higher than the RVNNs in the case of consistent architectures, indicating that the phase-related features in the complex signals are better able to characterize satellite individuals. Thus, the key features can only be \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Parameters** & **Training Time** & **Accuracy (\%)** \\ \hline CC-GAN & 408,706 & 1 h 47 min 12 s & 93.16 \\ DCUnet-GAN & 563,925 & 2 h 28 min 07 s & 92.87 \\ RGADNet & 394,713 & 1 h 52 min 18 s & 90.61 \\ RC-GAN & 254,764 & 1 h 6 min 35 s & 89.03 \\ CDRSN & 373,578 & 1 h 41 min 26 s & 84.95 \\ RDRSN & 201,264 & 1 h 2 min 27 s & 82.72 \\ RResNet & 104,778 & 50 min 39 s & 79.34 \\ \hline \hline \end{tabular}
\end{table}