Neural Networks-Enabled Channel Reconstruction for Fluid Antenna Systems: A Data-Driven Approach

TL;DR

This paper introduces a neural network-based data-driven method for accurate and computationally efficient channel reconstruction in fluid antenna systems, enhancing performance in diverse scenarios.

Contribution

It presents a novel neural network framework for fluid antenna channel reconstruction that outperforms existing methods in accuracy and efficiency.

Findings

Significantly improved reconstruction accuracy.

Lower computational complexity compared to existing methods.

Robust performance with rapid convergence.

Abstract

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.