## 2 Related Work  
Here, we focus on radio frequency fingerprint identification methods using steady-state signals. The main methods can be divided into two categories: an identification method based on expert features and classifiers and an identification method based on deep learning. The feature engineering method is supported by domain-specific expertise to extract interpretable features, but hand-crafted features are time-consuming, labor-intensive, and rely on complex and protocol-specific knowledge, lacking flexibility [14]. The deep learning method is data-driven and is able to learn features automatically, adjusting quickly and flexibly with the task; however, the performance of deep learning models degrades rapidly when there is a large discrepancy between the distribution of training data and test data [15]. However, the deep learning method faces some difficulties, and extensive experiments have demonstrated that its performance in various identification tasks has surpassed the feature engineering method. Thus, we pay more attention to the research development of RFF identification methods based on deep learning. [PERSON] et al. [16] proposed a convolutional neural network structure based on IQ correlation features (IQCNet) on the basis of traditional convolutional neural networks (CNNs). The model uses 32 convolutional kernels with a size of 2 \(\times\) 1 to extract the correlated features of I/Q paths, effectively reducing the parameters and computation, making it more suitable for lightweight deployment. Experimental results demonstrated that specific transmitters can be identified by variable power signals from 21 transmitters, with an identification accuracy of 94% for fixed variable power packets and an accuracy of 86% for random variable power packets. [PERSON] et al. [17] proposed an adaptive radio frequency fingerprints fusion network (ARFNet), which is based on dual attention convolution (DA-Conv), to extract and adaptively fuse multiple radio frequency fingerprints in a data-driven manner. Experimental results demonstrated that combining ARFNet with data augmentation can achieve an identification accuracy of 99.5% on five USRP X310 devices and 95.7% on 56 ADS-B devices. [PERSON] et al. [18] proposed a classification network (FSig-Net) based on deep learning in the case of a small number of signal samples, which can reduce the dependence of the model on big data by adaptively extracting the distance metric between features. The FSig-Net achieves an average identification accuracy exceeding 90% with a signal-to-noise ratio (SNR) ranging from 20 to 32 dB and a sample size of only 10 for each class. [PERSON] et al. [19] used a multi-channel attentive feature fusion (McAFF) method to extract the RFF features of radio signals and utilized the convolution-based ResNeXt module to accomplish device identification. Experimental results on 72 commercial WiFi end devices show that the McAFF significantly outperforms the single-channel-based RFF method in terms of identification accuracy and robustness and is more robust in noisy environments. However, the RF signals collected directly are mostly complex-valued data in practical applications which cannot be directly processed by RVNNs. Thus, complex-valued neural networks (CVNNs) have been applied to the scenarios of RFF identification due to their excellent performance and ability to directly process complex-valued data. [PERSON] et al. [20] employed complex-valued deep neural networks to investigate the effects of various confounding factors, such as data collected with different times (trainingand testing on different days), clock drift, and wireless channel variations on RFF features. Experimental results demonstrated that the networks will tend to learn notable features reflecting mixed factors instead of the nonlinear device-specific characteristics of hardware without implementing some strategies, also showing that the use of data augmentation methods can be effective in improving the generalization performance of the networks. [PERSON] et al.