Fluid antenna systems (FASs) offer substantial spatial diversity by exploiting the electromagnetic port correlation within compact array spaces, thereby generating favorable small-scale fading conditions with beneficial channel gain envelope fluctuations. This unique capability opens new opportunities for a wide range of communication applications and emerging technologies. However, accurate channel state information (CSI) must be acquired before a fluid antenna can be effectively utilized. Although several efforts have been made toward channel reconstruction in FASs, a generally applicable solution to both model-based or model-free scenario with both high precision and efficient computational flow remains lacking. In this work, we propose a data-driven channel reconstruction approach enabled by neural networks. The proposed framework not only achieves significantly enhanced reconstruction accuracy but also requires substantially lower computational complexity compared with existing model-free methods. Numerical results further demonstrate the rapid convergence and robust reconstruction capability of the proposed scheme, outperforming current state-of-the-art techniques.

翻译：流体天线系统（FAS）通过在紧凑阵列空间内利用电磁端口相关性，提供显著的空间分集，从而产生有利的小尺度衰落条件与有益的信道增益包络波动。这一独特能力为广泛的通信应用和新兴技术开辟了新机遇。然而，在流体天线被有效利用之前，必须获取准确的信道状态信息（CSI）。尽管已有若干针对FAS信道重构的研究，但一种同时适用于模型驱动或无模型场景、兼具高精度与高效计算流程的通用解决方案仍显缺乏。本文提出一种由神经网络实现的数据驱动信道重构方法。所提框架不仅显著提升了重构精度，而且与现有无模型方法相比，计算复杂度大幅降低。数值结果进一步验证了该方案的快速收敛性与稳健的重构能力，其性能优于当前最先进技术。