Abstract
This paper investigates the joint optimization of active and passive beamforming in simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted non-orthogonal multiple access (NOMA) systems, with the aim of maximizing system throughput and improving overall performance. To achieve this goal, we propose an iterative and efficient algorithmic framework. For active beamforming optimization, the fractional programming (FP) method is employed to reformulate the non-convex optimization problem into a convex problem, making it more tractable. Additionally, Nesterov's extrapolation technique is introduced to enhance the convergence rate and reduce computational overhead. For the phase optimization of the STAR-RIS, a binary phase design method is proposed, which reformulates the binary phase optimization problem as a segmentation problem on the unit circle. This approach enables a closed form solution that can be derived in linear time. Simulation results demonstrate that the proposed algorithmic framework outperforms existing benchmark algorithms in terms of both system throughput and computational efficiency, verifying its effectiveness and practicality in STAR-RIS-assisted NOMA systems.