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arXiv · 2507.04435

Context-Aware Deep Learning for Robust Channel Extrapolation in Fluid Antenna Systems

Yanliang Jin·Runze Yu·Yuan Gao·Shengli Liu·Xiaoli Chu·Kai-Kit Wong·Chan-Byoung Chae
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Abstract

Fluid antenna systems (FAS) offer remarkable spatial flexibility but face significant challenges in acquiring high-resolution channel state information (CSI), leading to considerable overhead. To address this issue, we propose CANet, a robust deep learning model for channel extrapolation in FAS. CANet combines context-adaptive modeling with a cross-scale attention mechanism and is built on a ConvNeXt v2 backbone to improve extrapolation accuracy for unobserved antenna ports. To further enhance robustness, we introduce a novel spatial amplitude perturbation strategy, inspired by frequency-domain augmentation techniques in image processing. This motivates the incorporation of a Fourier-domain loss function, capturing frequency-domain consistency, alongside a spectral structure consistency loss that reinforces learning stability under perturbations. Our simulation results demonstrate that CANet outperforms benchmark models across a wide range of signal-to-noise ratio (SNR) levels.

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Yanliang Jin, Runze Yu, Yuan Gao, Shengli Liu, Xiaoli Chu, Kai-Kit Wong, Chan-Byoung Chae. 2025-07-06. Context-Aware Deep Learning for Robust Channel Extrapolation in Fluid Antenna Systems. https://arxiv.org/abs/2507.04435

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