# Open-Set Specific Emitter Identification Based on Prototypical Networks and Extreme Value Theory
## 4 Experimental Results Analysis  
### Experimental Platform and Data Preprocessing  
In this section, the performance of the open-set model is evaluated. The dataset used in the experiments came from the signals of five ZigBee devices [13], with a sampling rate of 10 M samples/s, representing ten times the oversampling of the ZigBee 1M chip rate. The carrier frequency of the ZigBee device was set as 2505 MHz with offset quadrature phase-shift keying (OQPSK) modulation, following the IEEE 802.15.4 standard, and the Ettus Research N210 USRP device was used to capture RF waveforms from different ZigBee devices at 2505 MHz. Five segments of signals were available for each device, and each segment was divided into nine small sub-segments, each with about 40,000 sampling points. In the laboratory environment, affected by channel noise, the received signal contained the RF fingerprint characteristics of the devices and the transmission channel characteristics in the indoor environment. In the experiment, three of the five devices were selected as known classes. Four segments of signals from each known class were used as the training set, and the fifth segment of signals from all five devices was selected for the test set.  
Meanwhile, in order to verify the generalization of the model, we also used an open-source dataset from the literature [17]. The transmitter used was a USRP 310. The transmitted signal was processed by the MATLAB WLAN toolbox to generate a standard frame, which conformed to the IEEE 802.11a standard. The RF frequency was 2.45 GHz. The signal was received by the B210 radio receiver with a sampling rate of 5 M sample/s. Finally, the RF signal was converted into baseband I/Q data. The experiment used 10 types of signals in the dataset, of which seven were known for training, and the other three were unknown. There were 15,000 samples for each known class and 3750 samples for each unknown class. In order to simulate a more realistic electromagnetic environment, data of mixed SNRs were used for training and testing, and the SNR was evenly distributed between \(-4\) and 10 dB.  
All the signals were processed by matlabR2019a. Firstly, the signal power was normalized to eliminate the effect caused by different signal powers. Then, the signals were sliced and supplemented with Gaussian noise. Finally, the signals were processed into fixed-length sequence samples, and the data format of each I/Q sample was 2 \(\times\) 800. The performance comparison of the different parameters in the following experiments was conducted on the ZigBee dataset.  
The experimental platform contained an NVDIA GeForce RTX3070 GPU, AMD Ryzen 7 5800H CPU. The deep learning framework was PyTorch 1.9.1, with the programming language Python.  
Network training parameters: training times \(N_{epoch}=40\), batch size \(N_{batch}=64\). The optimizer was Adam [39], and the optimization objective was to minimize the joint loss \(L((x,y);\theta,A)\). Adam is an algorithm for the first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The initial learning rate was 0.0005, which was reduced by 50% after each 10 iterations. In Adam, beta1 = 0.9 and beta2 = 0.999, which are the exponential decay rates for the moment estimates, and Epsilon = \(1\times 10^{-8}\), which is a term added to the denominator to increase the stability of a numerical calculation. Finally, the weight decay = \(1\times 10^{-5}\) is a penalty item added to the parameter when it is updated.