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

TL;DR

The paper tackles the challenge of obtaining accurate CSI for fluid antenna systems by introducing a data-driven neural-network framework. It uses a two-hidden-layer multilayer perceptron to map pilot observations to the channel, avoiding sparsity priors and fixed statistical models. The approach demonstrates superior NMSE performance and substantially lower online complexity compared with model-free baselines, with rapid convergence and robust generalization across sparse and dense scattering environments. This work enables real-time, adaptive channel reconstruction in practical FAS deployments, and provides reproducible code to facilitate adoption and further research.

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.

Figures (5)

• Figure 1: An illustration of channel estimation for an FAS.
• Figure 2: Proposed MLP for FAS channel estimation
• Figure 3: Training and validation loss curves of the proposed network: Convergence behavior.
• Figure 4: NMSE performance under the strongly sparse CSC channel model with $(C, R) = (2,10)$. Benchmarks include the FAS-OMP greedy1, FAS-ML fas_beyes, SelMMSE ls_estimate, and S-BAR fas_beyes.
• Figure 5: NMSE performance under the the weakly sparse CSC channel model with $(C, R) = (4,40)$. Benchmarks include the FAS-OMP greedy1, FAS-ML fas_beyes, SelMMSE ls_estimate, and S-BAR fas_beyes.