Maximizing the Rate of IRS-Assisted Downlink NOMA Systems

Abstract

Nonorthogonal multiple access (NOMA) techniques and intelligent reflecting surface (IRS) are being explored as potential essential technologies for future wireless communications. Accordingly, this paper provides a network framework for IRS-aided downlink NOMA transmission, in which IRS is employed to improve the NOMA system’s transmission performance, and optimization problems are raised to maximize the achievable rate. Given the fractional structure of multivariate coupling as presented in this study, the fractional problem first converts to a linear form; then, the semidefinite relaxation (SDR) algorithm is proposed to address nonconvex issues for a single-user scenario. As for a multiuser scenario, the alternating optimization (AO) algorithm is raised based on transmit beamforming and reflection phase shift matrix to settle relevant issues and mitigate computational complexity. The simulation results suggest that the algorithm described in this paper can significantly increase the signal’s achievable rate compared to the nondeployed IRS and IRS random phase-shifting schemes.