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

arXiv: https://arxiv.org/abs/2505.04930 · HTML: https://mlsanity.com/paper/2505.04930/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models.
- Builds on 11 of its 14 references that are in the map.
- First posted on arXiv 8 May 2025.

## Placed in

- Diffusion models / [Diffusion for compression and wireless](https://mlsanity.com/diffusion/diffusion-for-compression-and-wireless/)

## Builds on (most cited first, 11 of 11)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Denoising Diffusion Restoration Models](https://mlsanity.com/paper/2201.11793/) (2022)
- [Robust Compressed Sensing MRI with Deep Generative Priors](https://mlsanity.com/paper/2108.01368/) (2021)
- [MIMO Channel Estimation using Score-Based Generative Models](https://mlsanity.com/paper/2204.07122/) (2022)
- [Movable-Antenna Enhanced Multiuser Communication via Antenna Position Optimization](https://mlsanity.com/paper/2302.06978/) (2023)
- [Successive Bayesian Reconstructor for Channel Estimation in Fluid Antenna Systems](https://mlsanity.com/paper/2312.06551/) (2023)
- [Channel Estimation for Movable Antenna Communication Systems: A Framework Based on Compressed Sensing](https://mlsanity.com/paper/2312.06969/) (2023)
- [Joint Beamforming and Antenna Position Optimization for Fluid Antenna-Assisted MU-MIMO Networks](https://mlsanity.com/paper/2503.04040/) (2025)
- [Bayes-Optimal Unsupervised Learning for Channel Estimation in Near-Field Holographic MIMO](https://mlsanity.com/paper/2312.10438/) (2023)
- [Historical Review of Fluid Antenna and Movable Antenna](https://mlsanity.com/paper/2401.02362/) (2024)

## Built on by (most cited first, 2 of 2)

- [Proximal-Based Generative Modeling for Bayesian Inverse Problems](https://mlsanity.com/paper/2605.13278/) (2026)
- [A Unified Two-Stage Generative Diffusion Framework for Channel Estimation and Port Selection in Multiuser MIMO-FAS](https://mlsanity.com/paper/2605.29679/) (2026)

## Abstract (arXiv)

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.