Efficient Port Selection for MIMO Fluid Antenna Systems: A Gaussian Process Bandit Approach

Fluid antenna system (FAS) offers a promising solution to enhance spatial diversity in space-constrained user equipment. However, realizing its full potential in multi-antenna communication systems is hindered by the prohibitive pilot signaling overhead required for exhaustive port selection. Furthermore, existing learning-based selection schemes are mostly designed for single-user or non-precoded multi-user FAS, which are not directly applicable to multi-user precoded scenarios. To address these limitations, this paper proposes a port selection framework for multi-user precoded FAS with a novel learning-based Gaussian process upper confidence bound (GP-UCB) approach. Two system-level reward functions: one that minimizes the Frobenius norm of the zero-forcing (ZF) precoder, and another that maximizes the minimum singular value of the effective channel matrix are designed. These functions are optimized in an efficient manner using a GP-UCB framework, which avoids the exhaustive search procedure by iteratively measuring one port at a time and training a predictive model to infer performance across all unmeasured ports. Simulation results validate the efficiency of our framework, which accurately predicts the performance of all unmeasured ports with only a few channel measurements. Furthermore, the proposed schemes achieve substantial symbol error rate (SER) gains over conventional random port selection scheme and the channel gain based port selection method.