# Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning
## 1 Introduction  
The fifth generation (5G) of new radio (NR) has brought ultra-high-speed data transmission, extensive wireless coverage and low data latency. These advancements have driven the evolution of technologies such as the Internet of Things (IoT), autonomous driving and virtual reality (VR). However, the connectivity of massive devices makes network security even more critical, as 5G networks are vulnerable to physical layer threats including radio frequency (RF) jamming, spoofing and sniffing [1]. The claimed bit-level identities of a device (e.g., media access control (MAC) address, subscriber identity module (SIM) number and international mobile equipment identity (IMEI) number) can be easily spoofed [2] and are not secure enough for authentication. An effective device identification method is required to identify malicious attackers and enhance network security. Radio frequency fingerprint (RFF)-based device identification is an emerging physical layer (PHY) authentication technique for wireless communication systems [3; 4; 5; 6; 7; 8]. Due to the minor hardware manufacturing variations among electronic components, the transmitted signal waveform carries the inherent features of the device. These features are unique, persistent and resistant to cloning or tampering attempts, which can be considered the \"fingerprint\" of a device [2; 9]. The research on RFF identification has covered a number of wireless communication systems, such as GSM [10], Wi-Fi [11; 12; 13; 14; 15], LoRa [16; 17], ZigBee [2; 18; 19], RFID [20], LTE [21; 22] and IEEE 802.11 ad [23]. The source of RFF features includes crystal oscillator deviation [24; 25], mixer imbalance [26; 27], a transmission filter, amplifier nonlinearity [28; 29] and antenna coupling. RFFfeatures can be classified into two categories: transient features and steady state features. The transient features are extracted during the on-off transient period of the waveform, and the steady state features are extracted from the transmitted symbols. The steady state features include the carrier frequency offset (CFO) [25], in-phase/quadrature (I/Q) imbalance [26; 27], sampling offset and amplifier nonlinearity [28; 29]. With the application of machine learning algorithms in RFF identification, the overall signal can also be used as the RFF feature for classification. This approach can comprehensively consider the overall characteristics of the signal and preserve more signal details compared with the method of extracting a single feature. Raw I/Q samples with only CFO compensation were used in [30] for RFF identification of Zigbee's device. The differential constellation trace figure (DCTF) was proposed in [18], where raw I/Q samples could be transformed into a stable two-dimensional DCTF even with the presence of a CFO. The DCTF provides a visualization of the statistical distribution of the I/Q samples after differential operation, which reflects the influence of the RFF features [18]. A convolutional neural network (CNN) is designed to identify the DCTF's features. The DCTF's features have been studied with several kinds of wireless devices, exhibiting high identification accuracy. The performance of DCTF identification was studied using LoRa devices in [17]. The authors of [10] classified the DCTFs of global system for mobile communications (GSM) phones using a CNN. The DCTF of a long-term evolution (LTE) mobile terminal was studied in [21], where the DCTF of a physical random access channel (PRACH) preamble was identified. In this work, the DCTF is divided into transient parts and steady state parts, and then a multi-channel CNN is used with each channel treated a part of the DCTF. In this paper, the DCTF features of a 5G mobile phone are studied based on the PRACH preamble. The PRACH preamble is the first signal transmitted by the user terminal for random access procedures. Device identification through PRACH signals can effectively prevent malicious devices from accessing the network and can also detect distributed denial of service (DDOS) attacks.