One-Step Generative Channel Estimation via Average Velocity Field

2025-12-04

AI Summary

To address the excessive latency incurred by multi-step iterative inference in diffusion-based wireless channel estimation, this paper proposes a single-step generative channel estimation method based on the Average Velocity Field (AVF). Departing from conventional denoising iterations, the method directly learns the instantaneous evolution velocity field of the channel distribution, enabling end-to-end single-step prediction. This work introduces AVF modeling to generative channel estimation for the first time, thereby overcoming the inherent iterative bottleneck of diffusion models while preserving expressive modeling capability and drastically reducing inference latency. Simulation results demonstrate that, compared to state-of-the-art diffusion-based methods, the proposed approach achieves up to 2.65 dB improvement in normalized mean square error and reduces inference latency by approximately 90%, achieving an unprecedented balance between high accuracy and ultra-low latency—establishing a novel paradigm for delay-sensitive 6G channel estimation.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Web Mining and Content Analysis: Content-based information diffusion
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Reduces latency in generative channel estimation
• Bypasses iterative denoising for faster processing
• Improves accuracy over diffusion-based methods

Innovation

• One-step generative channel estimation via average velocity field
• Bypasses iterative denoising for reduced latency
• Directly learns average velocity field for efficiency