Joint Subcarrier Allocation and Beamforming Optimization for IRS-Assisted Multiuser MISO-OFDMA Systems

In this article, we propose a novel resource allocation strategy for multiuser multiple-input single-output orthogonal frequency division multiple access (MU-MISO-OFDMA) systems within internet of things networks, utilizing an intelligent reflecting surface (IRS) to enhance system performance. Our goal is to maximize the sum rate for all networks by jointly optimizing transmit beamforming, IRS reflection coefficients, and OFDMA subcarrier allocation (SA). The problem is characterized as a mixed-integer nonlinear programming problem, which is inherently complex. To efficiently tackle the problem, we introduce an innovative framework that employs an alternative optimization of the beamforming matrix, IRS reflection coefficients, and the SA matrix. Additionally, we utilize the inner approximation method to address the nonconvex sub-problems related to beamforming and IRS reflection coefficients. Numerical results demonstrate the efficacy of the proposed approach while satisfying quality of service constraints. Notably, the proposed SA scheme substantially outperforms the system without SA, closely approaching the performance of the exhaustive search method while significantly reducing computational complexity.