Channel Extrapolation for Fluid Antenna Systems: Diffusion-Based Framework and CEUNet Approach

Abstract

Fluid antenna systems (FAS) have emerged as a promising paradigm for wireless communications, enabling channel reconfigurability that offers a novel spatial degree of freedom. Nevertheless, efficiently acquiring accurate and high-resolution channel state information (CSI) in FAS remains challenging, primarily due to its dynamic spatial structure and limited coherence time. This paper proposes a novel diffusion framework that takes the partially observed CSI matrix as the terminal state of the Markov chain and operates exclusively on the unobserved ports. Built upon this framework, we design a UNet-based architecture, termed the channel extrapolation UNet (CEUNet), that integrates modified MaxViT (mMaxViT) blocks and residual blocks (ResBlocks) to jointly capture local and global channel dependencies for accurate CSI extrapolation. Extensive experiments on the Jakes’ channel model are conducted to evaluate CEUNet. Numerical results show that CEUNet consistently outperforms state-of-the-art deep learning models in estimation accuracy across all signal-to-noise ratios and observation ratios, even with only two sampling steps. Furthermore, a comprehensive complexity analysis is conducted to compare the computational efficiency of CEUNet with that of the baseline models, while ablation studies are carried out to quantitatively evaluate the contribution of each component integrated into the proposed CEUNet.