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

TL;DR

This paper introduces a diffusion model-based channel estimator for fluid antenna systems that significantly improves accuracy and speed, enabling practical deployment in high-dimensional scenarios.

Contribution

It proposes a novel diffusion model approach with a simplified U-Net architecture for efficient CSI reconstruction in FAS, including a skipped sampling strategy for faster inference.

Findings

Achieves over 20x speedup compared to compressed sensing methods.

Demonstrates significantly higher estimation accuracy.

Effective in high-dimensional fluid antenna systems.

Abstract

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.