# Intelligent Radio Frequency Identification for URLLC in Industrial IoT Networks
## 3 RFI Based on Semisupervised Learning For URLLCs  
### Basic Principles of Radio Frequency Identification  
Typical wireless communication processing architectures include a baseband digital part, digital-to-analog conversion, filters, an IQ modulator, HPAs, and antennas, as shown in the bottom left part of Figure 2. RF features are produced when a signal passes through nonlinear modules. Actual stable, unchanging, subtle hardware impairments are features that we want to acquire [26]. The offset error of an analog-to-digital converter (ADC) is the difference between the center of the least significant bit (LSB) and the ideal ADC with the same bits. We always treat converter quantization interval as LSB. We measure the first and last threshold of the converter to determine the precise location of the ideal center. The \(LSB\) is defined as  
\[V_{LSB}=\frac{V_{FSR}}{2^{N_{bits}}} \tag{1}\]  
where \(V_{FSR}\) is the full-scale range of the ADC. \(N_{bits}\) are the ADC bits that represent the precision of ADC, and \(2^{N_{bits}}\) denotes the number of voltage intervals. We can rewrite the error function with \(V\) as  
\[E_{offset}=2^{N_{bits}}\frac{V_{Error}}{V_{FSR}} \tag{2}\]  
we can define a normalized unit's linearity errors \(G_{ADC}\) as  
\[G_{ADC}=\frac{E_{offset}}{V_{FSR}} \tag{3}\]  
Figure 2: Typical architecture of industrial URLLC systems with intelligent RFI to allow for seamless integration of short-packet communication and reliable physical-layer access. The DC component of analog IQ signals is the main source of carrier leakage. As shown in Figure 3, IQ mismatch contains offset, imbalance, and error. \(I_{offset}\) and \(Q_{offset}\) represent the relative value of the respective signal amplitudes. All those impairments form partial unique fingerprint features. We can assess carrier rejection through factor \(\alpha\)  
\[\alpha=20 lg(\sqrt{I_{offset}^{2}+Q_{offset}^{2}}) \tag{4}\]  
we can evaluate practical \(I/Q\) imbalance (shown in Figure 4) through the following equation. \[G_{IQ}=\sqrt{\frac{1+\alpha^{2}-2\alpha cos(\varepsilon)}{1+\alpha^{2}+2\alpha cos (\varepsilon)}} \tag{5}\]  
The radio frequency filter is the key module in communication systems, and is mainly responsible for the suppression of noise and illegal signals outside the working frequency band. However, limited by material characteristics and implementation, high-frequency filters often cannot achieve ideal performance. \(G_{Filter}\) is always utilized to denote filter nonlinearity such as Q value, insertion loss, and phase jitter. The power amplifier (PA) is the last module before transmission. Although digital predistortion (DPD) can be utilized to compensate for nonlinear characteristics of PA, harmonic and intermodulation distortion still occur in a high-saturation state. Inevitable distortion caused by PA can be denoted by \(G_{Amp}\). In order to verify and evaluate whether the nonlinearity of the different modules above forms a radio fingerprint, we collected different mobile device signals in a routine environment on the basis of the system-processing flow in Figure 2. The collected signal is represented by  
\[x_{i}=G_{IQ}G_{ADC}G_{Filter}G_{Amp}s_{i}=Gs \tag{6}\]  
where \(s_{i}\) is the original binary information, and \(G\) is the nonlinear fingerprint. We carried out preliminary demodulation on the basis of collected samples. Figure 4 shows that two different devices indicated relatively obvious differences from the perspective of the constellation. Therefore, the radio fingerprint based on hardware impairment was stable while exiting, and could be further mined and processed. So, a deep-learning framework is the most suitable solution due to its massive nonlinear layer-to-layer neurons. Figure 3: Composition of \(IQ\) mismatch. (**a**) \(IQ\) offset causes carrier leakage, which is very obvious from a spectral point of view.