Channel Estimation in MIMO Systems Using Flow Matching Models

TL;DR

This paper introduces a flow matching-based neural network for MIMO channel estimation, significantly improving speed and memory efficiency over existing generative models while maintaining high accuracy.

Contribution

It proposes a novel flow matching approach integrated into a PnP-PGD framework for efficient MIMO channel estimation, outperforming prior diffusion and score-based methods.

Findings

Up to 49 times faster inference than state-of-the-art methods

Reduces GPU memory usage by up to 20 times

Achieves superior estimation accuracy in simulations

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.