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arXiv 2604.22005cs.ITcs.LGeess.SPmath.IT
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空域流匹配用于受限延迟系统的MIMO信道估计

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

College of Electronic Information and Optical Engineering(电子信息与光学工程学院);University of Glasgow(格拉斯哥大学);University of Aberdeen(阿伯丁大学)

Junjie Zhao, Guangming Liang, Xiaonan Liu, Dongzhu Liu

AI总结:

本文提出空域流匹配框架,将受限试点的MIMO信道估计分解为范围空间重建和空域生成问题,通过流匹配生成先验实现高效准确的信道估计,在严格延迟约束下实现优于基模型和生成模型的性能。

摘要:

准确且低延迟的信道状态信息(CSI)获取对于多输入多输出(MIMO)通信系统至关重要。尽管先进的深度生成模型,如基于分数的和扩散模型,能够从有限的试点观测中实现高保真度的CSI重建,但它们通常面临高推理延迟。为了在严格延迟约束下实现准确的CSI估计,本文提出了一种空域流匹配(FM)框架,将受限试点的MIMO信道估计分解为范围空间重建问题和空域生成问题。具体而言,通道的范围空间部分直接从噪声试点观测中恢复,而只有模糊的空域部分通过基于流匹配的生成先验进行迭代优化。为进一步提高所提框架的鲁棒性,我们引入了幂律时间调度以更好地分配有限的优化步骤,并引入了噪声感知的自适应校正策略以抑制通道噪声。实验结果表明,我们的方法在约3毫秒的严格延迟预算下实现了具有竞争力的归一化均方误差(NMSE),同时在估计精度和推理速度上优于基于模型和生成的基线。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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