# Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning
## 4 Experimental Results
### Experimental Set-up and Data Collection  
The experimental system included a gNB and 6 5G mobile phones, as illustrated in Figure 10. The gNB was built with a USRP B210 and the OAI platform [36; 37], and the specific parameters are listed in Table 1. The 5G core network was also established using OAI, with Docker containers and Docker Compose to facilitate the installation and management. The models of the 6 mobile phones are described in Table 2, including 4 Huawei Mate30 phones, 1 Huawei Mate40 phones, and 1 OnePlus 9 Pro phone. A test subscriber identity module (SIM) white card was used with the mobile phones to enable access to the gNB. The transmitted PRACH signals were captured using the USRP N210 device with a sampling rate of 20 MHz. The positions of the USRP N210, gNB and the 6 mobile phones remained fixed during the PRACH signal capture process. The distance between the USRP N210 and the gNB was 1.4 m, and the distance between the mobile phones and the USRP N210 was 0.35 m. The measured signal-to-noise ratio (SNR) level was about 25 dB. We kept toggling the airplane mode of the phone on and off to force the phone to send PRACH signals. We could collect about 3000 signal samples per day. However, to obtain sufficient samples for each PRACH preamble from randomly transmitted PRACH signals, a total of at least 40,000 samples needed to be collected for each phone. The signal collection process extended for 3 months. Hence, the collected signal dataset included variations in both channel fading and time. Examples of the estimated frequency domain channel fading for one PRACH preamble of one phone are presented in Figure 11. It can be seen that the channel varied during the process of signal acquisition. This was due to the movement of people and changes in the surrounding objects. We note that real-world transmission scenarios are more complex, and we will test more scenarios, including non-line-of-sight (NLOS) channels and outdoor scenarios, in our future work. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Description** \\ \hline
**frequencyBand** & 78 & Signals operating in the n78 frequency band \\
**subcarrierSpacing** & 1 & Subcarrier spacing is 30 kHz \\ carrierBandwidth & 106 & Carrier bandwidth is 106 resource blocks \\
**prach\_ConfigurationIndex** & 98 & The preamble format is A2, and the length of the ZC sequence is 139 \\
**zeroCorrelationZoneConfig** & 13 & The value of \(N_{CS}\) is 34, and every \(\lfloor 139/34\rfloor=4\) preamble sequences share a root value \(\mu\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The gNB critical parameters. Figure 9: Group-based DCTF identification scheme using a single-channel CNN. After the signal was collected, it was necessary to determine which of the 64 preamble sequences was received. Hence, the cross-correlations between the received preamble and the 64 standard preambles were computed such that  
\[R_{x}y=|\text{IFFT}(Y(f)X_{l}^{*}(f))|,l=0,\cdots,63 \tag{14}\]  
where \(Y(f)\) and \(X_{l}(f)\) are the Fourier transform of the received preamble after time synchronization and the \(l\)th standard preamble, respectively, while \(\text{IFFT}(\cdot)\) is the inverse fast Fourier transform. Examples of the cross-correlation results are presented in Figure 12. When the cross-correlation was computed based on the same preamble sequence, several significant peaks could be observed, and there was one peak at the beginning. When the cross-correlation was computed using preamble sequences of the same _group_, the peaks could be observed, but there was no peak at the beginning. When the cross-correlation was computed using preamble sequences from different _groups_, no peak could be observed. More than 40,000 PRACH signals were collected for each mobile phone. However, 10 types of preamble sequences were never observed, although