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.

翻译：信道估计是大规模多输入多输出系统中的基础性挑战，其估计精度直接决定了系统的频谱效率与链路可靠性。本研究提出递归流（RC-Flow），一种基于预训练流匹配先验的新型求解器，能够从含噪且欠定的测量数据中鲁棒地恢复信道状态信息。与传统开环生成模型不同，该方法通过串行重启机制与锚定轨迹校正构建了闭环优化框架。通过将流一致先验方向与数据保真近端投影相协同，所提出的RC-Flow实现了鲁棒的信道重建，并在不同噪声水平下（尤其在噪声主导场景中）取得了最先进的性能。该框架进一步通过自适应双调度策略增强，为收敛速度与重建精度之间的权衡提供了灵活的管理机制。理论上，我们通过分析递归算子的雅可比谱半径证明了其全局渐近稳定性。数值实验表明，与基于分数的基线方法相比，RC-Flow在低信噪比区域实现了2.7 dB的性能增益，同时将推理延迟降低了两个数量级。