标题： 精确快速的流体天线系统信道估计算法及其扩散模型 显示英文标题

标题： Accurate and Fast Channel Estimation for Fluid Antenna Systems with Diffusion Models

摘要： 流体天线系统（FAS）为下一代无线系统提供了增强的空间多样性。然而，由于可重构端口数量众多以及射频（RF）链的可用性有限——特别是在高维FAS场景中，获取准确的信道状态信息（CSI）仍然具有挑战性。 为了解决这一挑战，我们提出了一种基于高效后验采样的信道估计算法，该算法利用了一个简化U型网络架构的扩散模型（DM），以捕捉二维FAS信道的空间相关结构。DM最初以无监督方式离线训练，然后作为学习到的隐式先验在线应用，通过去噪扩散恢复模型（DDRM）通过后验采样重建CSI。 为了加速在线推理，我们引入了一种跳过采样策略，在采样过程中只更新潜在变量的一部分，从而在最小化精度损失的情况下降低了计算成本。 仿真结果显示，所提出的算法相比最先进的压缩感知方法，估计精度显著提高，并且速度快了20多倍，这突显了它在高维FAS中实际部署的潜力。 显示英文摘要

摘要： Fluid antenna systems (FAS) offer enhanced spatial diversity for next-generation wireless systems. However, acquiring accurate channel state information (CSI) remains challenging due to the large number of reconfigurable ports and the limited availability of radio-frequency (RF) chains -- particularly in high-dimensional FAS scenarios. To address this challenge, we propose an efficient posterior sampling-based channel estimator that leverages a diffusion model (DM) with a simplified U-Net architecture to capture the spatial correlation structure of two-dimensional FAS channels. The DM is initially trained offline in an unsupervised way and then applied online as a learned implicit prior to reconstruct CSI from partial observations via posterior sampling through a denoising diffusion restoration model (DDRM). To accelerate the online inference, we introduce a skipped sampling strategy that updates only a subset of latent variables during the sampling process, thereby reducing the computational cost with minimal accuracy degradation. Simulation results demonstrate that the proposed approach achieves significantly higher estimation accuracy and over 20x speedup compared to state-of-the-art compressed sensing-based methods, highlighting its potential for practical deployment in high-dimensional FAS.