A Hybrid Prediction-Axiom Dual-Driven Port Selection Algorithm for Fluid Antenna Systems in 6G High-Mobility Scenarios

A significant bottleneck for the practical deployment of fluid antenna systems (FAS) in 6G high-mobility scenarios is the conflicting demands of low outage probability and the high overhead of full port channel estimation. To resolve this problem, a novel "prediction-axiom" dual-driven paradigm is introduced ​ that fundamentally differs from pure data-driven approaches. The core innovation lies in using an enhanced unified adaptive modeling algorithm (UAMA) not for direct decision-making, but as a computational foundation to enable information-theoretic axioms under sparse observation conditions (30% of ports). The UAMA predictor, leveraging spatiotemporal correlations, accurately reconstructs the full channel state from limited measurements. This prediction then empowers an information-theoretic scoring mechanism, which synergizes Fisher information, curvature metrics, and port entropy to transform optimal port selection into a tractable maximization problem. Consequently, the system outage probability remains close to the ideal performance limit achievable under full observability. Tests on diverse antenna systems confirm the algorithm's high accuracy and robust adaptive capability. This work delivers a reliable, low-cost implementation strategy for 6G dynamic networks, effectively bridging the gap between mathematical theory and practical FAS deployment.