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arXiv · 2608.13254

EM-Guided Graph Learning for Fluid Antenna Beamforming under Current-Domain Constraints

Yuanhui Wu·Hao Jiang·Zaichen Zhang
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Abstract

Fluid antenna arrays (FAAs) reconfigure a finite set of radiating ports within a prescribed aperture. In compact apertures, however, channel-driven placement may cluster ports, strengthen mutual coupling, degrade radiation conditioning, increase source-voltage demand, and produce uneven current loading. This paper studies downlink multi-user beamforming with jointly optimized port placement and current-domain transmission. An electromagnetic-guided graph network predicts port layouts from channel observations and refines them using geometric and mutual-impedance information. The training objective jointly considers communication performance and electromagnetic feasibility, while a common evaluation procedure is applied to all methods. The results show that, under a common feasibility standard, the proposed method provides a controllable tradeoff among communication rate, current loading, and configuration latency.

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Yuanhui Wu, Hao Jiang, Zaichen Zhang. 2026-08-13. EM-Guided Graph Learning for Fluid Antenna Beamforming under Current-Domain Constraints. https://arxiv.org/abs/2608.13254

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