Low-Complexity Two-Step Optimization in Active-IRS-Assisted Uplink NOMA Communication

Recently, the active intelligent reflecting surface (IRS) has been proposed to adjust the phase and amplify the magnitude of the incident signal simultaneously. It has a more reasonable hardware overhead compared with the conventional relay. This letter aims to maximize the sum rate of multiple users in the active-IRS-assisted uplink non-orthogonal multiple access (NOMA) system. We propose a low-complexity two-step optimization algorithm to decompose the original non-convex problem into two sub-problems. First, the extreme low-complexity fixed point iteration (FPI) method is proposed to optimize the phase shifts. Then, two algorithms are proposed to solve the amplification optimization problem: the convergence-guaranteed quadratic transform (QT) and the low-complexity generalized eigenvalue decomposition (GEVD) algorithms. Simulation results show that the performance can be enhanced significantly compared with the orthogonal multiple access (OMA) and the passive-IRS scheme.