# Adversarial Sample Generation Method Based on Frequency Domain Transformation and Channel Awareness
## 7 Simulation Result
### Channel Estimation
Figure 9 shows the MSE comparison of the proposed method under the same number of pilots, the same channel sparsity, and different pilot interval schemes. For SDNet, as the pilot interval I increases, the MSE initially shows an upward trend (from I = 4 to I = 8), and then significantly increases at I = 12. This indicates that under a smaller pilot interval, SDNet can better utilize the pilot signal for channel estimation. However, when the pilot interval is too large, the performance will decline significantly. For SDRNet, MSE decreases slowly with the increase in the pilot interval I, but the overall change is not significant. This indicates that SDRNet is relatively insensitive to the change in pilot intervals and can maintain relatively stable performance under different pilot intervals. Under most pilot intervals, the MSE of SDRNet is generally lower than that of SDNet, especially at larger pilot intervals (such as I = 12), the performance advantage of SDRNet is more obvious. This indicates that SDRNet may have adopted more effective algorithms or structures in channel estimation and can provide better performance under \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Parameter Valve** \\ \hline Symbol period & \(6.4\times 10^{-6}\) \\ Multipath quantity & 3 \\ Sampling period & \(1\times 10^{-6}\) \\ Number of loop iterations & 100 \\ Delay time & \(0\), \(1\times 10^{-6}\), \(2\times 10^{-6}\) \\ Root mean square delay spread & \(4\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Default parameters of the channel simulation system. different pilot intervals. Moreover, the MSE curve of SDRNet is smoother, showing better stability and robustness. Figure 10 shows the comparison of MSE performance of different pilot quantity schemes under the same pilot interval and channel sparsity. For SDNet, as the number of pilots increases (Nc = 8 to 32), the MSE decreases significantly, mainly due to the cyclic prefix (CP) improving signal continuity and reducing time-domain aliasing. However, when the pilot is too much (Nc = 64), the MSE slightly rebounds, which might be due to an increase in pilot overhead or interference. The MSE of  
Figure 8: Comparison of BER results for different channel estimation methods. Figure 7: Comparison of MSE results for different channel estimation methods. smoothly decreases with the increase in pilot frequency, indicating that it can utilize pilot resources more efficiently. Overall, the variation range of MSE in the two methods is limited, indicating that they are not sensitive to the number of pilots, which is conducive to reducing the system overhead. Figure 11 compares the MSE performance changes in SDNet and SDRNet under different channel sparsity (K = 3, 6, 9). When the sparsity of SDNet increases from K = 3 to K = 6, the MSE slightly decreases, indicating that a moderate increase in sparsity is conducive to improving the accuracy of channel estimation. However, when the sparsity  
Figure 10: Comparison of MSE results for different cyclic prefix lengths (CP). Figure 9: Comparison of MSE results for different pilot spacing I.  
further increases to K = 9 and K = 12, the MSE rises significantly, indicating that an excessively high sparsity will make the channel overly complex and reduce the estimation performance. In contrast, the MSE of SDRNet slightly decreased from K = 3 to K = 6, and then remained stable, demonstrating strong robustness to changes in sparsity. This indicates that SDRNet can better adapt to channel environments with different sparsity, while the performance of SDNet depends more on the reasonable selection of sparsity.