通过平均速度场实现一步生成式信道估计
One-Step Generative Channel Estimation via Average Velocity Field
AI总结：
本文提出通过平均速度场实现一步生成式信道估计，有效降低延迟并提升性能
摘要：
生成模型在无线通信中展现出巨大的潜力，通过学习复杂的信道数据分布。然而，这些模型相关的迭代去噪过程在对延迟敏感的无线通信场景中提出了显著挑战，特别是在信道估计中。为了解决这一挑战，我们提出了一种新的一步生成式信道估计方案。我们的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。通过广泛的仿真，我们验证了所提方法在现有最先进扩散方法上的有效性。具体而言，我们的方案在归一化均方误差方面比扩散方法低达2.65 dB，并将延迟降低了约90%，展示了该方法在提升信道估计性能方面的潜力。
英文摘要：
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.
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