Active Inference for Joint Port Selection and Pilot Allocation in Fluid Antenna Systems Under Partial CSI

Abstract

Fluid antenna systems (FAS) obtain spatial degrees of freedom by activating a few radiating ports from a dense grid of candidates, but deciding which ports to activate presupposes channel knowledge at ports that are never measured. Prior port-selection work assumes full or near-full channel state information (CSI) and treats the pilot overhead as a fixed cost. We instead formulate port selection and pilot allocation jointly as active inference. A spatio-temporal generative model -- sub-wavelength spatial correlation together with an order-p autoregressive temporal prior -- maintains a Gaussian belief over all N port channels, and the transmitter chooses both the ports it serves and the subset of them it pilots by minimizing an expected free energy that trades achievable rate against information gain and port-switching cost. Selection is greedy and submodular, at O(NM) per slot. On a 441-port grid with a hybrid front end at n_RF = 2K, the agent attains 91% of a full-CSI genie's sum rate while measuring only 2.3% of the candidate ports; pilots can then be withdrawn from ports that are still served, one fifth of them for 2% of the sum rate and two fifths for 8%. No training is required.