# Flow matching-based generative models for MIMO channel estimation

arXiv: https://arxiv.org/abs/2511.10941 · HTML: https://mlsanity.com/paper/2511.10941/

## Key facts

- Cited by 3 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 0 of its 19 references that are in the map.
- First posted on arXiv 14 Nov 2025.

## Placed in

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

## Built on by (most cited first, 3 of 3)

- [Sampling-Free Diffusion Transformers for Low-Complexity MIMO Channel Estimation](https://mlsanity.com/paper/2602.02202/) (2026)
- [Null-Space Flow Matching for MIMO Channel Estimation in Latency-Constrained Systems](https://mlsanity.com/paper/2604.22005/) (2026)
- [Restoration Flow Matching-Based Channel Refinement and Equalization Correction for MIMO Semantic Communications](https://mlsanity.com/paper/2607.23615/) (2026)

## Abstract (arXiv)

Diffusion model (DM)-based channel estimation, which generates channel samples via a posteriori sampling stepwise with denoising process, has shown potential in high-precision channel state information (CSI) acquisition. However, slow sampling speed is an essential challenge for recent developed DM-based schemes. To alleviate this problem, we propose a novel flow matching (FM)-based generative model for multiple-input multiple-output (MIMO) channel estimation. We first formulate the channel estimation problem within FM framework, where the conditional probability path is constructed from the noisy channel distribution to the true channel distribution. In this case, the path evolves along the straight-line trajectory at a constant speed. Then, guided by this, we derive the velocity field that depends solely on the noise statistics to guide generative models training. Furthermore, during the sampling phase, we utilize the trained velocity field as prior information for channel estimation, which allows for quick and reliable noise channel enhancement via ordinary differential equation (ODE) Euler solver. Finally, numerical results demonstrate that the proposed FM-based channel estimation scheme can significantly reduce the sampling overhead compared to other popular DM-based schemes, such as the score matching (SM)-based scheme. Meanwhile, it achieves superior channel estimation accuracy under different channel conditions.