# Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning
## 4 Experimental Results
### Performance of the Group-Based DCTF Identification Scheme  
For Scenario 2, the gNB had only partial knowledge of the PRACH preambles used by the phone. Hence, the training set was composed of 27 types of preamble sequences, similar to Scenario 1, and the testing set was composed of 50 types of preamble sequences. The indexes of the sequences used for training and testing are listed in Table 3. The training set of Scenario 2 was the same as that in Scenario 1. The number of samples for each type of sequence of each device was fixed to 142 in the testing set. The CNN model was trained and tested using the sequences from the same _group_. Figure 12: Cross-correlation between (**a**) preamble sequences of the same index, (**b**) preamble sequences from the same _group_ and (**c**) preamble sequences from different _groups_. The performance of the group-based DCTF identification scheme using a single-channel CNN is presented in Figure 15. We note that the classification accuracy of each device was slightly lower than that of Scenario 1. However, the accuracies of UE2 and UE4 both exceed 96%, which demonstrates that the RFF features of different types of devices were more distinguishable. The overall classification accuracy was 89.59%. Moreover, the relevance between different types of preamble sequences within the same _group_ was tested. Table 4 presents the classification results when using one type of preamble sequence for training and another type of preamble sequence belonging to the same group for testing. The single-channel CNN model was used. The results show that the identification accuracy depended on the spacing of preamble indexes. The adjacent  
Figure 14: Performance of index-based DCTF identification scheme using a single-channel CNN. Figure 13: Performance of index-based DCTF identification scheme using a multi-channel CNN. \begin{table}
\begin{tabular}{c c} \hline \hline
**Set** & **Index** \\ \hline Train & 1 2 4 5 6 9 10 13 14 15 21 25 26 31 32 35 36 38 39 41 42 46 48 52 53 55 57 \\ \hline Test & 1 2 3 4 5 6 8 9 10 12 13 14 15 16 21 22 23 25 26 29 30 31 32 33 34 35 36 \\  & 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The indexes of sequences used for training and testing sets in Scenario 2.  
indexes led to higher accuracy, while the indexes with a difference of three resulted in lower accuracy. Hence, it can be concluded that when the indexes between sequences within the same _group_ were closer, their DCTF features were more similar. Finally, to summarize the performance of the two proposed RFF identification schemes and clarify the appropriate CNN model for each scenario, the overall classification results are presented in Figure 16. To ensure the robustness of the results, a box plot with 15 repetitions has been used. In each repetition, the training and testing sets were reformed from the collected data samples with the help of a normally distributed random generator. For Scenario 1, four identification methods were compared: the index-based single-channel, index-based multi-channel, _group_-based single-channel and _group_-based multi-channel schemes. We note that the index-based multi-channel method outperformed the other methods in Scenario 1. However, the performance differences among the four methods were within 1%. For the _group_-based approach, the number of CNN models used could be significantly reduced. For Scenario 2, with the _group_-based approach, we found that the single-channel CNN outperformed the multi-channel CNN because the stability of the transient features within the _group_ was not as good as that of the steady state features. Hence, emphasizing transient features leads to a degradation in classification performance.