Channel Estimation in MIMO Systems Using Flow Matching Models

TL;DR

This work tackles the problem of accurate MIMO channel estimation with low pilot overhead by replacing slow diffusion/score-based priors with a deterministic flow-matching prior. A neural velocity field $v_t$ is learned to deterministically transform simple samples into realistic channels, and this flow is integrated into a plug-and-play proximal gradient descent (PnP-PGD) scheme as a denoiser. The approach is trained in an unsupervised, distribution-agnostic manner and demonstrates up to $49\times$ faster inference and $20\times$ memory reduction while maintaining or improving NMSE performance, with robustness to distribution shifts between CDL-C and CDL-D. The results indicate substantial practical potential for real-time, resource-constrained wireless systems and include public release of code and models for reproducibility.

Abstract

Multiple-input multiple-output (MIMO) systems require efficient and accurate channel estimation with low pilot overhead to unlock their full potential for high spectral and energy efficiency. While deep generative models have emerged as a powerful foundation for the channel estimation task, the existing approaches using diffusion-based and score-based models suffer from high computational runtime due to their stochastic and many-step iterative sampling. In this paper, we introduce a flow matching-based channel estimator to overcome this limitation. The proposed channel estimator is based on a deep neural network trained to learn the velocity field of wireless channels, which we then integrate into a plug-and-play proximal gradient descent (PnP-PGD) framework. Simulation results reveal that our formulated approach consistently outperforms existing state-of-the-art (SOTA) generative model-based estimators, achieves up to 49 times faster inference at test time, and reduces up to 20 times peak graphics processing unit (GPU) memory usage. Our code and models are publicly available to support reproducible research.

Figures (3)

• Figure 1: Performance comparison on CDL-C channels.
• Figure 2: Performance comparison on CDL-D channels.
• Figure 3: Impacts of pilot density $\alpha$ on NMSE performance.