# Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning
## 4 Experimental Results
### Robustness and Sensitivity Analysis  
In this part, the robustness and sensitivity of the proposed schemes are analyzed, and the impact of interference from other devices is also discussed:  
1. **Robustness.** The measured SNR of our experimental system was about 25 dB. To test the robustness of the proposed schemes in a low-SNR regime, we added Gaussian noise to the collected signals. The SNR levels were adjusted to 10 dB, 15 dB and 20 dB. The corresponding DCTFs are presented in Figure 17, where the impact of the noise can be observed. The classification results are presented in Figure 18.
2. **Sensitivity.** The impact of the proportion of training and testing sets on the classification performance was tested, and the results are shown in Table 5. The training time is also presented. Taking into account both the training time and classification performance, we found that the proposed scheme was more efficient when the training and testing sets were at an 8:2 ratio.
3. **Impact of interference.** Interference from other devices had a significant impact on the performance of RFF identification. We simulated the scenario where the transmissions of two devices overlapped. Assuming that UE1 and UE2 both transmitted the PRACH preamble of index 1, the DCTFs of UE1 with different overlap ratios are presented in Figure 19, where the head of the signal of UE2 is superimposed onto the tail of the signal of UE1. It can be seen from Figure 19 that as the overlap ratio increased, the deformation of the DCTF became significant.  
Figure 16: Classification results of different methods in Scenario 1 and Scenario 2.  
Figure 17: DCTF for PRACH preamble of index 1 with SNR of (**a**) 25 dB, (**b**) 20 dB, (**c**) 15 dB and (**d**) 10 dB.  
The FDE method [15], which consists of using frequency domain-equalized I/Q samples with a rebuilt CFO, and the I/Q imbalance feature [27] were also simulated for comparison purposes. The I/Q samples of the FDE method are classified with a deep CNN [15]. The I/Q imbalance feature is extracted through a low-complexity algorithm that uses gain imbalance and phase imbalance as the indices of I/Q imbalance [27]. The I/Q imbalance features were classified using the k-nearest neighbors (KNN) algorithm.  
It can be seen from Figure 18 that the index-based multi-channel scheme outperformed the other methods for all the SNR values. The performance of the _group_-based single-channel scheme was similar to that of the FDE method at a low SNR. However, this scheme showed an accuracy higher than that of the FDE method in a high SNR regime. The experimental results in Figure 18 demonstrate the robustness of the proposed schemes.  
The classification performance of the overlapped signal is presented in Figure 20. It can be seen that as the overlap ratio increased, the classification accuracy decreased. However, the overlap case can be detected by computing the signal-to-interference ratio of the received signal.  
Figure 19: DCTF of UE1 for PRACH preamble of index 1 with an overlap ratio of (**a**) 1:16, (**b**) 1:8, (**c**) 1:4 and (**d**) 1:2.  
Figure 18: Classification results of different methods in function of SNR for Scenario 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Proportion of Training and Testing Sets** & **6:4** & **7:3** & **8:2** & **9:1** \\ \hline \multirow{2}{*}{Index-based DCTF-MC} & Training time & 413 s & 465 s & 524 s & 571 s \\ \cline{2-6}  & Accuracy & 89.38\% & 90.83\% & 92.51\% & 92.45\% \\ \hline \multirow{2}{*}{Index-based DCTF-SC} & Training time & 992 s & 1164 s & 1362 s & 1663 s \\ \cline{2-6}  & Accuracy & 88.12\% & 90.14\% & 92.19\% & 92.25\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification performance with different proportions of training and testing sets.