# Intelligent Radio Frequency Identification for URLLC in Industrial IoT Networks
## 3 RFI Based on Semisupervised Learning For URLLCs
### Novel Architecture Illustration of Semisupervised Learning Model  
Supervised learning requires massive numbers of labeled training samples to guarantee the promised performance [30]. Semisupervised learning shown in Figure 5b, i.e., joint learning from labeled and unlabeled samples, is an active research topic due to its key role on relaxing human supervision. Despite the strong performance of pseudolabel methods, there is still a main drawback: if pseudolabels are inaccurate, the student network must learn from an inaccurate parameter [36]. The student network may not achieve significantly better performance than that of the pretrained teacher network, which is called confirmation bias. To solve the above problems, the authors systematically designed a solution framework to reduce false label learning bias [34]. In this framework, we designed such a mechanism where the teacher network could obtain information from student predictions, which is used to further correct soft label decisions for unlabeled data. To elaborate the mutual learning process between the two networks, we rewrote the cross-entropy loss function on the basis of the student network:  
\[w_{s}^{ssl}=\operatorname*{arg\,min}_{w_{s}}\mathbb{E}_{x_{u}}[\mathcal{C}(T( x_{u};w_{t}),S(x_{u};w_{s}))] \tag{7}\]  
where \(w_{s}\) and \(w_{t}\) represent the weight parameters of the teacher and student networks. \((x_{u},x_{l},y_{l})\) refer to the batch of training data that consist of \(x_{u},x_{l}\) and corresponding labels \(y_{l}\), respectively. \(T(),S()\) denotes the soft prediction decisions of input data. We used \(\mathcal{C}()\) to describe the cross-entropy between different distributions. Furthermore, we used \(\mathbb{E}_{x_{l},y_{l}}\left[\mathcal{C}\left(y_{l},S\left(x_{l};w_{s}^{ssl} \right)\right)\right]\) to represent student loss on labeled samples \(L_{l}\left(w_{s}^{ssl}\right)\).The minimal value of student network loss function is the optimal solution in the network training process; the optimizing problem can be described as  
\[\min_{w_{t}}L_{l}(w_{s}^{ssl}(w_{t})) \tag{8}\]  
where \(w_{s}^{ssl}(w_{t})=\operatorname*{arg\,min}_{w_{s}}L_{u}(w_{t},w_{s})\), and due to the mutual promotion learning of two networks, accurate gradient descent leads to a huge amount of computation. The idea is to approximate \(w_{s}^{ssl}(w_{t})\) by using only a single training step to completely replace the inner optimization by training until convergence. Related techniques are used in metalearning, gradient-based hyperparameter tuning, and unrolled generative adversarial networks. Therefore, the approximate expansion is used to retain the maximal term and can be described as  
\[w_{s}^{ssl}(w_{t})\approx w_{s}-\eta_{s}\cdot\bigtriangledown_{w_{s}}L_{u}(w_{ t},w_{s}) \tag{9}\]  
Figure 4: Constellation corresponding to two different real mobile devices’ SRS frame in the same environment. (**a**) Nonlinear characteristics of deviel in constellation. (**b**) Nonlinear characteristics of device2 in constellation. Then, we can substitute (9) into (8) to obtain  
\[\min_{w_{t}}L_{l}(w_{s}-\eta_{s}\cdot\bigtriangledown_{w_{s}}L_{u}(w_{t},w_{s})) \tag{10}\]  
In order to solve the optimization problem above, we need to minimize the student network loss function by adjusting the weight of the teacher network.