Resource Allocation for Intelligent Reflecting Surface-Assisted Cooperative NOMA-URLLC Networks in Smart Grid

Abstract

In this paper, we consider the resource allocation of mission-critical services in the smart grid, where we deploy an intelligent reflecting surface (IRS) during the transmission to alleviate the shortage of cooperative non-orthogonal multiple access (C-NOMA) in ultra-reliable and low-latency communications (URLLC). The purpose of this paper is to jointly optimize the power allocation, IRS phase shift, and the blocklength with finite blocklength information theory to minimize the total energy consumption subject to their delay and reliability constraints. Since the formulated optimization is non-convex, we first introduced two lemmas to simplify the constraints, and then we solve the optimization problem via the alternating optimization (AO). The transmit power and the blocklengths are optimized by using the techniques of successive convex approximation (SCA) and arithmetic geometry mean (AGM), and the reflective beamforming is optimized by using the techniques of semi-define relaxation (SDR) and sequential rank-one constraint relaxation (SROCR). Simulation results validate the advantage of IRS to C-NOMA in URLLC and the effectiveness of the resource allocation.