Learning Representative Port Subsets for Efficient Fluid Antenna Systems

Abstract:Fluid antenna systems (FASs) provide many candidate ports, but probing all ports for each channel realization incurs substantial online overhead. We study a complementary offline problem: compress the candidate-port space once and reuse the compressed set across future channel realizations. The method collects optimal ports from sampled training realizations, requiring only one scan of the candidate ports per realization. On a DeepMIMO 7-by-7 FAS with 49 candidate ports, 120 training realizations yield 27 representative ports on average while retaining 99.995% of the channel power gain achieved by exhaustive port selection.

Information Theory (cs.IT); Machine Learning (cs.LG)

arXiv:2610.33730 [cs.IT]

(or arXiv:2610.33730v1 [cs.IT] for this version)

https://doi.org/10.48550/arXiv.2610.33730 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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