Null-Space Flow Matching for MIMO Channel Estimation in Latency-Constrained Systems

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 leverages a range-null space decomposition to separate observation-informed and underdetermined channel components. Specifically, the pilot observations are used to regulate the observable range-space channel component, while an FM-based generative prior primarily resolves the ambiguous null-space degrees of freedom through iterative refinement. To further improve the robustness and efficiency of the proposed framework, we introduce a noise-aware adaptive correction strategy to suppress channel noise on the refinement trajectory, along with a power-law time schedule to better allocate the limited number of refinement steps. Experimental results demonstrate that our method achieves competitive normalized mean square error (NMSE) performance even under a strict latency budget of around 3 ms, while delivering a superior accuracy-latency tradeoff compared with both model-based and generative baselines.