🤖 AI Summary
This work addresses the challenges in compact-aperture fluid antenna arrays, where channel-driven port placement often leads to port clustering, exacerbated mutual coupling, and uneven current loading. To overcome these issues, the authors propose an electromagnetically guided graph neural network framework that, for the first time, integrates electromagnetic constraints—such as mutual impedance and geometric layout—into graph-based learning. This approach jointly optimizes port configuration and current-domain beamforming strategies under a unified electromagnetic feasibility criterion. By doing so, it enables a controllable trade-off among communication rate, current distribution uniformity, and configuration latency, thereby significantly enhancing the overall performance of multiuser downlink transmissions.
📝 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.