## 2 Related Works
Within this hybrid method, extensive experiments were conducted to prove the effectiveness of classifying a large number of devices under different channel conditions including line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios and to verify that the extracted RFF features are still stable and invariable after one and a half years. In the past decade, many studies have applied classifiers to RFF identification. Designing classifiers for machine learning can improve generalization ability, which is the key processing step after extracting RFF characteristics. The existing mature methods for RFF classification include Bayesian classification algorithm, K-nearest neighbor algorithm, neural network classification algorithm, support vector machine classification algorithm, random forest algorithm, etc. In addition, deep learning methods that have demonstrated excellent performance in image recognition, voice recognition and other fields can also be applied to RFF recognition, such as using the framework of deep neural network to detect the extracted fingerprint features [23]. For different kinds of signals and modulation modes, different multipath channels and environmental interference, and different signal-to-noise ratios, the researchers also put forward some novel improved methods to optimize the RFF identification rate. For example, according to ZigBee signals, [PERSON] et al. [24] proposed an algorithm for long-term stacking of repeated symbols to convert the measurement noise into standard Gaussian distribution, and another algorithm for artificially adding noise in the training stage to improve the performance under time-varying channels. Their further study reveals that the scheme is feasible and robust in additive white Gaussian noise channel and multipath scenarios. For linear frequency modulation (LFM) radar signals, [PERSON] et al. [25] put forward a radio frequency fingerprint identification mechanism, which performs the signal derivation and envelope operations to extract both transient-based and modulation-based features after the interference pulses are eliminated through the piecewise curve fitting-based denoising algorithm. For GSM communication standard mobile phones, [PERSON] et al. [26] combine the physical layer RFF method of differential constellation trace figure with the classification scheme of convolution neural network, which can identify different devices with low complexity and high accuracy and can still possess strong robustness when the device locations and GSM parameters change. From the above analysis, it can be seen that the research on radio frequency fingerprint identification has made significant progress, which is still a hot topic. At the same time, there are some deficiencies and challenges in the current research, and the following issues remain to be further explored for practical application in the V2X intelligent transportation systems. First, the influence of time-varying wireless multipath channels on the stability of RFF has not been emphatically discussed in the past. Since the vehicles are in a high-speed moving state in the actual DSRC vehicle networking environment, the received signal strength indication (RSSI) and channel state information (CSI) values are constantly changing, which cannot be used as device fingerprint features. Previous studies on RFFs mostly considered the ideal static situation and did not take into account the diversity of transmission channel in the mobile environment and the interference caused by other obstacles. The extracted device fingerprints are often mixed with channel fingerprints, which will change with the location, and the RFF identification accuracy will decrease once in a mobile environment. Second, the 802.11p protocol signal in DSRC is a broadbandsignal with a bandwidth of 10 MHz, which carries abundant spectral information and is more susceptible to the multipath interference caused by people walking, wall and obstacle reflections, and other negative factors in complex indoor environment.