Deficit-Aware Randomized User Grouping for Fair Near-Field Energy-Splitting STAR-RIS Systems

Abstract

Near-field simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) systems enable full-space coverage, but dense multiuser operation requires active-user grouping under practical stream or RF-chain constraints. Gain-based or proportional-fair scheduling may over-serve strong users, while random selection improves service regularity but ignores channel and region information. This work proposes a randomized deficit-aware user grouping (RD-FRUG) scheme for energy-splitting STAR-RIS-aided near-field multiuser systems. RD-FRUG jointly accounts for service deficit, long-term rate imbalance, transmission/reflection region balance, and inter-user channel correlation. Given the selected group, the STAR-RIS energy-splitting coefficients, passive phase profile, and BS precoder are updated with low computational overhead. Simulation results under near-field channels with distance-dependent pathloss show that the proposed heuristic provides a favorable sum-rate–fairness tradeoff under the considered settings. It approaches the Jain’s fairness index of random selection, achieves higher sum-rate than random selection, and improves the fifth-percentile user rate over gain-greedy, proportional-fair greedy, and region-balanced baselines.