Recursive Flow: A Generative Framework for MIMO Channel Estimation

TL;DR

This paper introduces Recursive Flow, a novel generative framework for MIMO channel estimation that leverages flow matching priors, achieving robust, fast, and accurate channel recovery in noisy conditions.

Contribution

The paper proposes Recursive Flow, a closed-loop generative method with a novel restart mechanism and stability analysis, improving MIMO channel estimation over existing approaches.

Findings

Reduces inference latency by two orders of magnitude.

Achieves a 2.7 dB performance gain in low SNR regimes.

Demonstrates robustness across diverse noise levels.

Abstract

Channel estimation is a fundamental challenge in massive multiple-input multiple-output systems, where estimation accuracy governs the spectral efficiency and link reliability. In this work, we introduce Recursive Flow (RC-Flow), a novel solver that leverages pre-trained flow matching priors to robustly recover channel state information from noisy, under-determined measurements. Different from conventional open-loop generative models, our approach establishes a closed-loop refinement framework via a serial restart mechanism and anchored trajectory rectification. By synergizing flow-consistent prior directions with data-fidelity proximal projections, the proposed RC-Flow achieves robust channel reconstruction and delivers state-of-the-art performance across diverse noise levels, particularly in noise-dominated scenarios. The framework is further augmented by an adaptive dual-scheduling strategy, offering flexible management of the trade-off between convergence speed and reconstruction accuracy. Theoretically, we analyze the Jacobian spectral radius of the recursive operator to prove its global asymptotic stability. Numerical results demonstrate that RC-Flow reduces inference latency by two orders of magnitude while achieving a 2.7 dB performance gain in low signal-to-noise ratio regimes compared to the score-based baseline.