## 6 Evaluation Metrics and Simulation Results
### Simulation Results  
The TDD system described in Section 2 was implemented using Matlab 2022b, working in 5G FR1 n78 band. The simulation model assigns independent channel waveforms to each UE, with a pre-coding method of ZF. A Clustered Delay Line (CDL) MIMO link-level fading channel was utilized, with AWGN as the noise. The simulation system parameters are listed in Table 3.  
To replicate the dynamic process of multi-user communication, in each experiment, the simulator first sets up a legitimate user at a fixed position. Then, malicious users are sequentially added to the system until the number of malicious users reaches the maximum. Antenna array size and location for malicious users are randomly generated within the numerical range provided in Table 3. For each step, all UEs perform 300 UL transmissions, resulting in the total number of samples being the product of the number of UEs, the number of users, and the number of UL transmissions. This method leads to an imbalance in the number of positive and negative samples, resulting in a significant increase in the MDR. The number of legitimate and malicious samples is balanced by keeping the number of legitimate samples constant and sampling among malicious samples according to the number of users, so that the ratio of positive and negative samples is kept at about 1:1. Furthermore, 20% of the dataset is allocated as the test dataset solely for performance evaluation. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Carrier frequency & 3.5 GHz \\ SCS & 15 kHz \\ Number of resource blocks & 106 \\ Channel bandwidth & 20 MHz \\ Antenna array size for BS & 32 \\ Antenna array size for users & 1, 2, 4 \\ CDL channel delay spread & \(10^{-7}\) \\ Range of Azimuth offsets & (\(-60\), \(60\)) degree \\ Range of distance between BS and users & (\(50\), \(600\)) m \\ Up-Link SNR & 3, 6, 9, 12, 15 dB \\ Number of malicious users & 1, 2, 3, 4, 5, 6, 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of Simulation. From Figure 6, it can be observed that DTRN is capable of handling multiple shapes of vectors. The training loss gradually decreases with the number of iterations, indicating that the network converges. However, after approximately 40 epochs, the network starts to overfit. The main purpose of the training process is to verify whether the network converges properly and to find suitable training hyperparameters. Adjustments to certain hyperparameters will be made to enhance the efficiency of network operation. Comparison of the ACC, MDR, and FAR between DTRN and Res-18 is shown in Figure 7. The results demonstrate that optimizing network parameters and inclusion of D-block not only improves the authentication accuracy but also achieves effective authentication results under the condition of limited data samples. Despite limited data samples, DTRN manages to maintain an MDR below 0.04. On the other hand, the network without D-block exhibits high MDR and FAR, with a significant disparity between MDR and FAR. This indicates that the network may have issues with fitting. The structure of Res-18 is consistent with the network structure proposed by [PERSON] et al. (2018), with the only difference lying in the network input. Due to the unchanged network structure, Res-18 cannot successfully perform the multi-morphology feature classification task. Therefore, in this experiment, a single-channel output \(1\times 1\) convolutional layer was added to the network input to modify the feature vectors. Figure 8 illustrates the comparison of the ACC under different SNR scenarios. DTRN was trained on two sample sets with UL SNR of 6 dB and 15 dB, respectively. The results indicate that a higher UL SNR leads to more accurate channel estimation, thereby improving the classification accuracy. The network generally achieves higher accuracy on the SNR of the training dataset, but the difference is limited, indicating that the model exhibits good generalization capability.