# Intelligent Radio Frequency Identification for URLLC in Industrial IoT Networks
## 1 Introduction  
In the era of Industry 4.0, the industrial Internet of Things (IIoT) has attracted extensive attention from industrial companies and academics [1; 2; 3; 4]. Compared to conventional wired control systems, the IIoT utilizes wireless networks to decentralize and improve efficiency during various industrial processes with massive sensors and other devices [5]. The standardization and commercialization of fifth-generation (5G) wireless networks produced various mission-critical IoT applications requiring ultrareliable and low-latency communications (URLLCs), such as autonomous driving, target tracking, and tactile Internet [6; 7; 8; 9; 10]. The third-generation partnership project (3 GPP) has standardized general URLLC requirements, with 99.999% reliability and about 1-10 ms latency with short packet transmission. In fact, there are several emerging URLLC applications in IIoT networks, such as time-sensitive networking (TSN), status reporting, and remote control [11; 12].  
Nonetheless, it is extremely challenging for URLLCs to simultaneously ensure latency and reliability due to their stringent and conflicting constraints such as the fading and propagation loss of channel [13; 14; 15]. Multiuser channel access, coexistence, and mutual interference with other systems reduce network availability. Finite block length is the main transmission type, which is different from classical Shannon capacity theory [16; 17]. In the existing technical route, multiple upper-layer transfers are often used to confirmthe legitimacy of device access. Unfortunately, traditional security methods rely on predefined protocols that require huge communication and computational payloads [5]. A profound security challenge in physical-layer authentication is how to keep it scalable and reliable with low overheads towards future URLLC use, such as industrial scenarios, autonomous vehicles, and edge computing [18; 19; 20; 21]. Intelligent radio frequency identification via semisupervised learning (RFISSL) is an emerging physical-layer authentication technique that employs unique device-specific hardware-level features, such as phase noise, inphase/quadrature (I/Q) imbalance, frequency offset, and harmonic distortions [22; 23; 24; 25; 26]. It is a potential solution to reduce system overhead under critical applications. Furthermore, how to significantly improve the transmission efficiency of RFI-aided URLLCs is a meaningful topic. Diversity technology is a remarkable technical scheme to improve the efficiency of wireless communication systems. However, the time diversity of uplink transmission is limited in low latency. Frequency diversity is unlikely achievable due to the limitation of terminal standardization. Massive multiple-input multiple-output (MIMO) is achieved by equipping base stations (BSs) with numerous antennas to support multitude user equipment (UE) [16; 17]. Massive MIMO can provide remarkable spectral efficiency and reliability benefiting from spatial diversity [13; 16]. Furthermore, the transmission block length and the propagation model of URLLCs are assumed to be infinity and an independent fading model, respectively [27; 28].  
Those assumptions above are not applicable to URLLC practical applications. There is a lack of a strong theoretical foundation of RFI-aided URLLCs with massive MIMO to achieve intelligent physical security and high reliability. On the basis of these assumptions and potential usage scenarios, we present a typical system for Industry 4.0, as shown in Figure 1, which integrates RFI and massive MIMO, where multiple distributed BSs are deployed to cooperatively serve all devices. A central controller is deployed for control and planning to which all BSs are connected by optical or wired cables.