Intelligent Radio Frequency Identification for URLLC in Industrial IoT Networks

Abstract

In the era of Industry 4.0, there are many emerging industrial Internet of Things (IIoT) applications that require ultrareliable and low-latency communications (URLLCs), such as time-sensitive networking (TSN), status reporting, and remote control. With the deployment of massive intelligent devices in IIoT networks, providing security for device physical-layer access is a challenging issue, especially for URLLC applications with strict latency and reliability constraints. We thus developed an intelligent radio frequency identification (RFI) framework to provide a lightweight and energy-efficient physical-layer access scheme for URLLCs via leveraging unique hardware-level imperfections of transmitter. We propose a novel semisupervised-learning (SSL) algorithm to realize intelligent RFI in URLLCs scenarios. One-dimensional network construction is also exploited to improve the accuracy of the proposed SSL algorithm. On the basis of the proposed RFI framework, we analyze the overall uplink transmission error probability and network availability of URLLCs with massive MIMO, which can achieve comparable symmetry performance with that of downlink, and experimental evaluation is also provided to gain comprehensive insight on RFI. Numerical and experimental results demonstrate the effectiveness of our proposed RFI framework and the impact of channel correlation, and provide design guidelines for supporting the radio frequency identification of URLLC applications in IIoT systems.

Keywords

• URLLCs
• industrial IoT
• symmetry
• intelligent radio frequency identification
• semisupervised learning
• short-packet communication