# Intelligent Radio Frequency Identification for URLLC in Industrial IoT Networks
## 2 System Model Description  
In this section, we introduce an RFI-aided URLLC architecture as shown in Figure 2, where a radio frequency identification model and URLLC system with massive MIMO are deployed to cooperatively serve large-scale smart devices [13]. Massive intelligent devices such as lightweight sensors, robots, and low-power communication devices transmit key information to distributed BSs through wireless channels. Then, received signals are processed by those BSs for high = level information extraction. Furthermore, valuable information is transferred to the cloud server for further computation, analysis, and decision making. Under classical URLLC scenarios, most of the information block is short packages that contain different transmission characteristics compared to typical Shannon capacity theory. We discuss those characteristics in the following section. All those radio frequency characteristics are created in the transmitter, and can be efficiently and completely detected at the receiver side. Once we obtain the device's raw radio frequency signals, authentication processing can be carried out for the access legitimacy of the device.  
In order to describe the generation mechanism of RF radio frequency characteristics, the typical structure of the transmitter is shown in Figure 2. All original bits are processed through modulation, upconversion, filtering, and high-power amplification at the transmitting end, and all those radio frequency signals are then transmitted to BSs through antennas [23]. For modulation, some IQ mismatches bring a special nonlinearity. Filtering and high-power amplification modules are key elements for RF signal noise reduction and transmission. The nonlinear characteristics of those different units are embedded in a wireless signal waveform [31; 32; 33]. After propagation through a wireless channel, the receiver can obtain raw large-scale RF time signals by directly sampling. When a transmission arrives at BS, device identification is applied to achieve efficient physical-layer access without any additional bit overhead by the RFI system. As shown in the lower right part of Figure 2, the RFI system mainly contains offline training and online evaluation. An offline training process guarantees remarkable identification accuracy with novel construction and a few labeled samples [34]. Supervised learning is the traditional method for model training. However, a number of labeled samples are required. Semisupervised learning is an emerging efficient framework for practical model training due to its self-feedback learning. Furthermore, model construction is a bottleneck for RFI. So, we propose novel one-dimensional model RFIResNet for practical applications. Training parameterssuch as batch size, learning ratio, and optimizer are important for training convergence. When trained model is obtained, it is deployed at BSs or edge nodes to achieve radio frequency identification.  
Then, online legitimacy testing is provided to check the information transmission block [35]. Once legitimacy is confirmed, it is transmitted to subsequent processing units for further demodulation and decoding. A central unit is deployed in the cloud sever for computing and controlling to which all BSs are connected by wired cables. Then, high-level decision making and analysis are realized through the central controller in the cloud sever. The considered URLLC system consists of \(N\) smart devices and \(B\) BSs. The number of antennas at the \(b\)-th BSs is \(M_{b}\). For simplicity but without loss of generality, we assumed that all those devices were equipped with a single antenna, a central BS with MIMO, and additive Gaussian channel noise.