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

• URL: http://arxiv.org/abs/2604.22005v1
• Date: Thu, 23 Apr 2026 18:49:06 GMT
• Title: Null-Space Flow Matching for MIMO Channel Estimation in Latency-Constrained Systems
• Abstract summary: This paper proposes a null-space flow matching (FM) framework that decomposes pilot-limited channel estimation into a range-space reconstruction problem and a null-space generation problem.<n> Experimental results demonstrate that our method achieves a competitive normalized mean square error (NMSE) even under a strict latency budget of around 3 ms.
• Score: 8.479186000343482
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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