# Intelligent Radio Frequency Identification for URLLC in Industrial IoT Networks
## 5 Results and Discussion
### RFISSL Experiments Setup and Performance  
To comprehensively evaluate the identification performance of our proposed RFISSL model, we established a practical LTE scenario dataset by collecting raw time-signal data from the 30 smart devices listed in Table 1. For simplicity but without loss of generality, those devices represent a typical application in an industrial scenario. In our system configuration, two different sounding reference signal (SRS) symbols were located in symbols 4 and 11 of a regular frame. Through the long-term static acquisition of all devices, we obtained nearly 40,000 frames of data for each mobile phone, with a total dataset size of about 50 GB, which could be used to evaluate the ability of various complex networks to extract device fingerprint features. After obtaining a signal, we conducted some preprocessing operations, mainly including the normalization and dimension transformation of the signal [30]. On this basis, the training and testing samples were established in a raw series. During the experimental process, we only selected few labeled samples, and the rest of the samples were selected to be unlabeled data to assist in the mutual learning of the two RFIResNets. We configured a 42 layer one-dimensional network with a ReLU activation function to construct the RFISSL framework. We used \((x_{l},y_{l})\) to represent the labeled data, where \(x_{l}\) denotes collected time signals, and \(y_{l}\) denotes the device label listed in Table 1. Unlabeled data \((x_{u,})\) only contain time signals without a device label. The key mechanism of RFISSL is that the teacher network learns the weight feedback of students under pseudolabels to further improve its performance and generate more accurate soft labels. As shown in Figure 7a, various percentages of labeled data were examined by RFISSL and compared with SL models such as ResNet18-1D, VGG16-1D, and AlexNet under different training samples [40]. This chart shows that RFISSL achieved almost 0.1 identification error with only 1% of labeled training samples, matching fully supervised learning with 100% of labeled instances. Lower error can also be exploited by RFISSL with an increase in the percentage of labeled instances. When trained with 4500 labeled samples, RFISSL achieved almost \(10^{-5}\) error. We also evaluated several SL models and found that VGG16-1D and ResNet18-1D, which were trained with 4500 labeled samples, only achieved performance that RFISSL could achieve only with 10% labeled samples (total slicenum equal to 2700). Furthermore, we transformed our one-dimensional signals into time-frequency data by short-time Fourier transform (STFT). Then, we analyzed identification performance under AlexNet. The result in Figure 7a shows that AlexNet achieved better performance than that of VGG16-1D due to its lager network parameters. These excellent properties confirmed the benefit of efficient student and teacher feedback learning. In the process of network training, two networks need to continuously learn from each other. Due to the existence of unlabeled samples, the training process has a huge cost, but the testing of network model needed about 0.52 ms on the server. This satisfies the requirement of practical URLLCs. Figure 7a shows that consistent gains confirmed the benefit of internal distribution characteristic deviation of device features. Figure 7b again confirms that our proposed RFISSL could truly realize almost \(10^{-5}\) RF identification error of a large-scale smart device with 4500 training samples. Our RFI error \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & **Phone** & **Number of UE** & **Label** \\ \hline
1 & Iphone6s & 5 & 1–5 \\
2 & Xaiomi6 & 5 & 6–10 \\
3 & Meizux8 & 5 & 11–15 \\
4 & Honor10 & 5 & 16–20 \\
5 & Huaweip9 & 5 & 21–25 \\
6 & Oppor11 & 5 & 26–30 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of smart devices in experiments. Figure 7: RFISSL performance. (**a**) Identification accuracy of RFISSL under different percentages of labeled data.