One-Step Generative Channel Estimation via Average Velocity Field

TL;DR

This paper introduces a one-step generative channel estimation method that directly learns the average velocity field, significantly reducing latency and improving accuracy compared to traditional iterative diffusion models in wireless communication.

Contribution

The paper presents a novel one-step generative approach for channel estimation that bypasses iterative denoising, enabling faster and more accurate wireless channel estimation.

Findings

Achieves up to 2.65 dB lower normalized mean squared error than diffusion methods.

Reduces latency by approximately 90%.

Validated through extensive simulations showing improved performance.

Abstract

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.