Secure beamforming design for MISO URLLC networks in IoT applications

This paper investigates joint beamforming and artificial noise (AN) design for secure multiple-input single-output (MISO) ultra-reliable and low latency communication (URLLC) networks in Internet of Things (IoT) applications. In considered system, a base station (BS) transmits confidential information for individual IoT users using the short-packet communication technique under the wiretap of eavesdroppers. To enhance physical layer security, the BS injects additional dedicated AN symbols to degrade the information retrieval ability at eavesdroppers. In this paper, we aim to joint design the transmit beamforming and AN symbol to maximize the minimum URLLC secrecy rate of IoT user subject to the power budget of the BS. The optimization design problem is highly nonconvex due to coupled variables in the URLLC secrecy rate and channel dispersion expressions, and thus it is mathematically challenging to solve this problem directly. To overcome this issue, we first introduce various convex inner approximations to convexify the nonconvex terms, and then we develop an efficient iterative algorithm based on the sequential convex programming approach. Extensive numerical simulation results are conducted to investigate the URLLC secrecy rate region. In conclusion, the two new URLLC parameters, i.e., the transmit packet blocklength and block error probability, will cause the considerable degradation on the URLLC secrecy rate region when comparing to that of the traditional beamforming design based on the Shannon capacity.