Enhanced Resource Management for RIS-Assisted NOMA: A Fractional Programming Approach

This paper investigates the joint optimization of user clustering, power allocation, and beamforming in RIS-assisted downlink NOMA systems to maximize network capacity. The problem formulation, which incorporates multiple RIS, multiple users, and configurable rate constraints, is non-convex due to coupled optimization variables. To address this, we derive analytical bounds on power allocation factors, formulate a low-complexity clustering criterion, and apply fractional programming within an alternating optimization framework for beamforming. Using a 3GPP-based three-dimensional channel model for evaluation, we conduct extensive Monte Carlo simulations and demonstrate significant improvements in sum rate, convergence, and robustness over state-of-the-art methods.