Resource Allocation for IRS-Assisted Uplink URLLC Systems
Zhicheng Li, Hong Shen, Wei Xu, Pengcheng Zhu, Chunming Zhao
Abstract
We study the resource allocation optimization in an intelligent reflecting surface (IRS)-assisted uplink ultra reliable and low-latency communication (URLLC) system. The transmit power of users, the receive beamformer at the base station (BS) and the IRS reflection phase shifts are jointly optimized to maximize the achievable sum finite blocklength (FBL) rate, which turns out to be a complicated non-convex problem. To tackle this problem, we develop a low-complexity minorization-maximization (MM)-based algorithm, which only involves calculating closed-form expressions in each iteration. Compared with an existing quadratic transform based scheme, the proposed design achieves better performance while with reduced complexity.