Blocklength Optimization and Power Allocation for Energy-Efficient and Secure URLLC in Industrial IoT

Ultrareliable low-latency communication (URLLC) is essential to facilitate mission-critical applications like the Industrial Internet of Things (IIoT). In order to improve communication efficiency, URLLC adopts short-packet signals to transmit the delay-sensitive control information among the Internet of Things (IoT) devices in IIoT. However, IoT adoption is hindered by the security vulnerability of URLLC due to the openness of wireless medium. Therefore, in this article, we focus on developing a secure and energy-efficient URLLC signal transmission scheme for mission-critical IIoT. In this regard, we formulate an optimization problem for maximizing the secure energy efficiency of the IIoT system under the constraints like URLLC Quality of Service (QoS), decoding error probability, and intercept probability of the eavesdroppers. For that, we adopt the physical-layer security (PLS) enhancement techniques in this work because it can provide low-complex security solutions for URLLC. Specifically, average secrecy throughput is used to measure the PLS performance of the URLLC-IIoT system. The optimization problem is solved iteratively by maximizing the secrecy throughput and minimizing the average power allocation per device. Meanwhile, a joint optimization of blocklength and pilot signal length is proposed to maximize the average secrecy throughput of the system. The nonasymptotic closed-form expression of the average secrecy throughput and the decoding error probability at the legitimate receiver have been derived to make the numerical evaluation tractable. Using the Lagrange multiplier technique with the Karush–Kuhn–Tucker (KKT) conditions we obtain the optimal power allocation for the maximization of the secure energy efficiency. The extensive simulation and numerical results validate the theoretical approximations presented in the work and demonstrate the effectiveness of the proposed method for improving the secure energy efficiency of IIoT.