## 2 Related Works  
The transmission information of the physical layer in the wireless communication system, which is at the bottom of the OSI network layer, contains some characteristics that cannot be reflected in the data transmission process from the upper layer. Therefore, the identification and authentication technology based on radio frequency fingerprints takes advantage of the unique hardware features of the devices which can be reflected in the physical layer to distinguish different users, aiming at improving the security level of information transmission. Radio frequency fingerprint is the hardware characteristic of the device, which can be understood as the DNA of the device itself. There are certain differences in the electronic components of the transmitting signal circuit between each wireless device, so that each wireless transmitter will produce different radio frequency response parameters when performing radio frequency work, and ultimately lead to different radio frequency fingerprints produced by different devices [15]. Radio frequency fingerprint mainly has five characteristics: universality, uniqueness, short-term invariance, independence, and robustness, which is not affected by channel and location variation. In the process of feature extraction and recognition, the hardware structure of the original system does not need to be changed [16], and its normal operation is not affected; thus, non-perceptual authentication and recognition can be realized. Moreover, the hardware characteristics of the devices are difficult to clone, which brings higher attack cost and better anti-attack performance. In the more than 20 years of development of radio frequency fingerprint identification technology, scholars have achieved a variety of RFF extraction and recognition methods through research. The research of RFF identification has experienced the transformation from using transient signals to using steady-state preamble field sequences, and then to using arbitrary data field information; from the use of single domain features to the use of multi-domain features, and from the waveform domain-based methods to the modulation domain-based methods; from the use of supervised learning to unsupervised learning, and then to deep learning methods [17]. Most of the early research on RFF focused on transient characteristics, which extract the instantaneous amplitude, instantaneous frequency, signal envelope, and phase information by detecting the transient signal [18]. In recent years, more researchers have turned to the feature extraction of steady-state signals, such as stable preamble signal, to achieve more recognizable steady-state RFF features. Initially, [PERSON] et al. [19] proposed that they converted the steady-state preamble signals to spectral features as radio frequency fingerprints based on frequency domain characteristics to identify user equipment in the universal mobile telecommunications system. Afterwards, [PERSON] et al. [20] extracted  
Figure 1: Flow chart of DSRC radio frequency fingerprint extraction and identification. the carrier frequency offset value from the preamble of the IEEE 802.11a Wi-Fi signal and studied the effect of using or removing the frequency offset on RFF recognition. In addition, RFF feature extraction and recognition can also be carried out in the waveform domain and modulation domain. In terms of waveform domain, [PERSON] et al. [21] have shown through experiments that the fingerprint recognition performance can be improved from the time domain to the wavelet domain and the spectral domain. While in the modulation domain, the feasible features include carrier offset, modulation offset, I/Q offset, power amplifier nonlinearity, constellation trace figure, and so on. [PERSON] et al. [22] proposed an RFF extraction and device classification scheme that mixes multiple modulation features.