Accurate yet low-latency channel state information (CSI) acquisition is essential for multiple-input multiple-output (MIMO) communication systems. While advanced deep generative models, such as score-based and diffusion models, enable high-fidelity CSI reconstruction from limited pilot observations, they often suffer from high inference latency. To achieve accurate CSI estimation under stringent latency constraints, this paper proposes a null-space flow matching (FM) framework that decomposes pilot-limited MIMO channel estimation into a range-space reconstruction problem and a null-space generation problem. Specifically, the range-space component of the channel is directly recovered from noisy pilot observations, while only the ambiguous null-space component is iteratively refined using an FM-based generative prior. To further improve the robustness of the proposed framework, we introduce a power-law time schedule to better allocate the limited number of refinement steps, along with a noise-aware adaptive correction strategy to suppress channel noise on the refinement trajectory. Experimental results demonstrate that our method achieves a competitive normalized mean square error (NMSE) even under a strict latency budget of around 3 ms, while delivering superior estimation accuracy and faster inference than both model-based and generative baselines.

翻译：精准且低延迟的信道状态信息(CSI)获取对多输入多输出(MIMO)通信系统至关重要。尽管基于分数和扩散模型等先进深度生成模型能够从有限的导频观测中实现高保真CSI重建，但这些方法通常面临高推理延迟问题。为在严格延迟约束下实现精确CSI估计，本文提出了一种零空间流匹配(FM)框架，将导频受限的MIMO信道估计分解为值域空间重建问题和零空间生成问题。具体而言，信道的值域空间分量直接从含噪导频观测中恢复，而仅有不确定性较高的零空间分量通过基于FM的生成先验进行迭代细化。为提升所提框架的鲁棒性，我们引入幂律时间调度以更优地分配有限细化步数，同时提出噪声感知自适应校正策略以抑制细化轨迹上的信道噪声。实验结果表明，即使在约3毫秒的严格延迟预算下，本方法仍能达到具有竞争力的归一化均方误差(NMSE)，同时相较于基于模型和生成式基线方法，展现出更优的估计精度和更快的推理速度。