Generative models have shown immense potential for wireless communication by learning complex channel data distributions. However, the iterative denoising process associated with these models imposes a significant challenge in latency-sensitive wireless communication scenarios, particularly in channel estimation. To address this challenge, we propose a novel solution for one-step generative channel estimation. Our approach bypasses the time-consuming iterative steps of conventional models by directly learning the average velocity field. Through extensive simulations, we validate the effectiveness of our proposed method over existing state-of-the-art diffusion-based approach. Specifically, our scheme achieves a normalized mean squared error up to 2.65 dB lower than the diffusion method and reduces latency by around 90%, demonstrating the potential of our method to enhance channel estimation performance.

翻译：生成式模型通过学习复杂的信道数据分布，在无线通信领域展现出巨大潜力。然而，这些模型所涉及的迭代去噪过程在时延敏感的无线通信场景（尤其是信道估计中）带来了显著挑战。为解决这一问题，我们提出了一种新颖的一步生成式信道估计方案。该方法通过直接学习平均速度场，绕过了传统模型耗时的迭代步骤。通过大量仿真实验，我们验证了所提方法相较于现有基于扩散的先进方案的有效性。具体而言，本方案实现了比扩散方法低至2.65 dB的归一化均方误差，并将时延降低了约90%，这证明了本方法在提升信道估计性能方面的潜力。