Sum-Rate Maximization for Partitioned-IRS-Assisted Hybrid NOMA Networks

Intelligent Reflecting Surfaces (IRS) have emerged as a revolutionary solution in next-generation (6G) wireless communication systems. Comprising numerous programmable reflecting units, IRS technology improves data transmission rates by dynamically adjusting the phase shifts of reflected signals. Non-orthogonal multiple access (NOMA) is a technique that can greatly enhance spectral efficiency and system sum-rate. In this paper, we consider a partitioned-IRS-assisted NOMA system in which a base station (BS) transmits superposed downlink signals to multiple users. Firstly, we establish the system sum-rate maximization problem of optimal joint IRS phase shifts and the partition sub-surfaces matching scheme by adapting combined channel strength(CSS)-based hierarchical pairing algorithm. However, since the formulated problem is non-convex, we use an alternating optimization (AO) technique to decompose the non-convex optimization problem into two subproblems for iterative solving. To further optimize the sum-rate, we also propose a NOMA user selection criteria based on the minimum signal-to-noise ratio (SNR) difference. The simulation results show a significant improvement in the performance of the IRS-assisted NOMA system compared to various traditional benchmarking schemes.