Sum-rate maximization for downlink multiuser MISO URLLC system aided by IRS with discrete phase shifters

Intelligent reflecting surface (IRS) has recently been considered as a potential technology for realizing ultra-reliable and low-latency (URLLC) in wireless networks. This paper proposes a resource optimization scheme to maximize the sum-rate for an IRS-assisted downlink multiuser multi-input single-output (MISO) URLLC system. For the perfect CSI scenario, we jointly optimize each user's block-length and packet-error probability, the precoding vectors at the base station (BS), and the passive beamforming with discrete phase shifts at the IRS. Given the problem's complexity, we design a computationally efficient iterative algorithm using successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques to obtain a locally optimal solution. Specifically, for the imperfect CSI scenario, we construct a robust resource optimization problem model and incorporate the S-procedure to address the impact of channel uncertainty, proposing an iterative algorithm based on the alternating optimization (AO) method to achieve a locally optimal solution. Simulation results demonstrate that: 1) An IRS equipped with a 2-bit quantized resolution phase shifter is sufficient to achieve a system sum-rate comparable to that of an ideal phase shifter; 2) Compared to other Baseline schemes, Algorithm 2 exhibits better robustness and superior performance gains under imperfect CSI.