Robust MIMO Channel Estimation Using Energy-Based Generative Diffusion Models

TL;DR

This paper introduces a robust MIMO channel estimation method that combines energy-based generative diffusion models with the Metropolis-Hastings algorithm, significantly improving accuracy especially with limited pilot data.

Contribution

It proposes a novel diffusion-based channel estimation framework integrating energy functions and MH corrections for enhanced robustness and accuracy.

Findings

Outperforms conventional DMs and baseline methods in accuracy

Effective with limited pilot overhead

Enhances robustness of channel estimation

Abstract

Channel estimation for massive multiple-input multiple-output (MIMO) systems is fundamentally constrained by excessive pilot overhead and high estimation latency. To overcome these obstacles, recent studies have leveraged deep generative networks to capture the prior distribution of wireless channels. In this paper, we propose a novel estimation framework that integrates an energy-based generative diffusion model (DM) with the Metropolis-Hastings (MH) principle. By reparameterizing the diffusion process with an incorporated energy function, the framework explicitly estimates the unnormalized log-prior, while MH corrections refine the sampling trajectory, mitigate deviations, and enhance robustness, ultimately enabling accurate posterior sampling for high-fidelity channel estimation. Numerical results reveal that the proposed approach significantly improves estimation accuracy compared with conventional parameterized DMs and other baseline methods, particularly in cases with limited pilot overhead.