Context-Aware Deep Learning for Robust Channel Extrapolation in Fluid Antenna Systems
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917224234614784 |
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| author | Jin, Yanliang Yu, Runze Gao, Yuan Liu, Shengli Chu, Xiaoli Wong, Kai-Kit Chae, Chan-Byoung |
| author_facet | Jin, Yanliang Yu, Runze Gao, Yuan Liu, Shengli Chu, Xiaoli Wong, Kai-Kit Chae, Chan-Byoung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04435 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Context-Aware Deep Learning for Robust Channel Extrapolation in Fluid Antenna Systems Jin, Yanliang Yu, Runze Gao, Yuan Liu, Shengli Chu, Xiaoli Wong, Kai-Kit Chae, Chan-Byoung Signal Processing 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. |
| title | Context-Aware Deep Learning for Robust Channel Extrapolation in Fluid Antenna Systems |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2507.04435 |