# Open-Set Specific Emitter Identification Based on Prototypical Networks and Extreme Value Theory
## 1 Introduction
The simulation results validated that the Hilbert spectrum image was a successful signal representation and demonstrated that the fingerprints extracted from raw images using deep learning were more effective and robust than the expert ones. Similarly, the differential constellation trace figure (DCTF) [13], bispectrum [14] and nonlinear features of the power amplifier, and modulator distortion features [15] of the signal are recognized by different convolutional neural networks (CNNs) for SEI, and all of these methods present substantial performance improvements over traditional methods. However, converting signals into two-dimensional forms such as images increases the model complexity and may cause the loss of some original information related to the signals. Therefore, many researchers have used neural networks to recognize the original sequence signals directly; for example, using one-dimensional CNNs to accomplish the feature extraction and classification of I/Q sequence signals [16; 17; 18]. Further, recognition methods based on hybrid networks and complex neural networks have also been proposed [19; 20], such as the fusion of CNNs and LSTM. In [19], deep bidirectional long short-term memory (DBi-LSTM) and a one-dimensional residual convolution network with dilated convolution and a squeeze-and-excitation block (Conv-OrdsNet) were devised to extract temporal structure features directly from baseband I/Q samples. Moreover, a data augmentation method was used to overcome the interference of unreliable features. The proposed model could effectively extract reliable RF fingerprinting features from I/Q samples, and the classification results were better than those of most existing methods. Meanwhile, ensemble neural networks were proposed for the recognition of the fusion features of graphs and the sequence features of signals [21]. These methods made use of powerful feature extraction techniques, the self-learning of deep learning, and corresponding data processing methods, which greatly improved their classification performance. In addition, a multi-channel model was established to reduce the effect of channel changes on individual recognition, which effectively improved the recognition accuracy and robustness of the models under the conditions of channel changes and noise [22]. Based on the communication of the physical layer and the support vector data description (SVDD) algorithm, [PERSON]. [23] established a radio frequency fingerprint authentication model for communication devices. [PERSON]. [24] proposed a light-weight radio frequency fingerprinting identification (RFFID) scheme combined with a two-layer model to realize authentication for a large number of resource-constrained terminals under a mobile edge computing (MEC) scenario. The results showed that the novel method could achieve a higher recognition rate than that of the traditional RFFID method by using the wavelet feature effectively, which demonstrated the efficiency of the proposed method. [PERSON]. [25] defined a device-specific unique fingerprint by analyzing solely the inter-arrival time of packets as a feature to identify a device. Thus, they obtained a superior identification model compared with ResNet 50-layer and basic CNN 5-layer architectures. Most previous research on SEI was based on closed-set scenes, i.e., the same classes of samples were used for both validation and training. However, recognition algorithms for closed-set scenes are difficult to apply to actual electromagnetic environments with open-set properties. In open-set scenes, the samples to be tested may contain new classes that do not appear in the training set, and models trained in a closed set will recognize the new samples as known classes, thus seriously affecting the recognition accuracy. Several researchers have started to research SEI in open-set scenes. [PERSON].