# Intelligent Radio Frequency Identification for URLLC in Industrial IoT Networks
## 1 Introduction
### Related Works  
Recent studies related to URLLCs mainly include short-packet analysis [14], resource allocation [12], massive MIMO, and joint power and placement optimization [9]. Critical reliability and latency were recently investigated in [11] by utilizing Euclidean norm theory. Unlike the vast majority of studies on URLLCs, which focus on the aforementioned single central cell, the authors in [13] applied a cell-free framework into a URLLC system to improve the system sum rate by using a new path-following algorithm. Specifically, the authors in [16] considered a typical massive MIMO scenario that encompasses pilot contamination and imperfect channel state information, which was an early rigorous framework to evaluate uplink transmission error probability and network availability with finite block length. In [5], the authors proposed a one-stage approach radio access to support safety-related industrial applications without a complex high-layer protocol. To improve transmission efficiency, the joint optimization of pilot overhead and power was considered in [17], while the downlink performance of MIMO-NOMA was investigated in [28]. To  
Figure 1: Detailed illustration of a typical RFI-aided intelligent automated factory scenario.  
sum up, all those methods need additional overload to achieve authentication by higher protocols. There is a lack of systematic analysis for channel correlation impact in URLLC massive MIMO. Therefore, establishing a highly reliable and lightweight access method is a critical problem in URLLCs due to the huge communication payload of conventional protocols. A key guarantee for URLLCs to improve transmission efficiency and access reliability is radio frequency identification by machine learning.  
Different from traditional random access schemes, novel physical-layer access based on RFI refers to the accurate identification of different devices. The authors in [19] proposed a nonparametric Bayesian method to classify Zigbee devices. Device fingerprinting and the potential application of wireless security was systematically considered in [22]. The influence of wireless channels to RFI on the Defence Advanced Research Project Agency dataset was investigated in [23]. [PERSON] et al. [24] proposed a novel approach that detects a new device without retraining the network. [PERSON] et al. [25] researched the depth of the model structure and partial equalization. [PERSON] et al. [26] designed a hybrid and adaptive scheme that weighted different features. In [21, 29], the application of RFI in physical-layer authentication was discussed to improve efficiency and robustness. Although deep convolutional neural networks (DCNNs) have achieved many remarkable results, they still require massive numbers of labeled samples to guarantee the convergence of the DCNN model [30]. Semisupervised learning (SSL) based on meta pseudo labels is efficient for achieving remarkable identification accuracy. This framework contains two different networks from which to learn by generating soft labels among unlabeled samples. It achieves remarkable identification accuracy in practical applications. This is the first work, to the best of our knowledge, involving the RFI scheme into URLLCs to provide better transmission efficiency and robustness.