Plug-and-Play Consistency Models for MIMO Channel Estimation

TL;DR

This paper introduces a plug-and-play consistency model framework for low-latency MIMO channel estimation, leveraging generative models to improve stability and efficiency in high-dimensional inverse problems.

Contribution

It applies consistency models as priors in MIMO channel estimation, demonstrating their feasibility and highlighting the importance of adaptive parameters and robustness across scenarios.

Findings

Feasibility of using consistency models as channel priors.

Effective recovery of angular-domain channel vectors with few iterations.

Identified need for adaptive parameter tuning and robustness enhancements.

Abstract

Consistency models (CMs) learn a consistent mapping from multiple noise levels to the data endpoint and can therefore perform generative inference in one or a few steps. This property makes them attractive as learned priors for low-latency inverse problems. Multiple-input multiple-output (MIMO) channel estimation under limited pilot overhead can be formulated as a high-dimensional linear inverse problem with an explicit measurement matrix, where data consistency alone is often insufficient for stable angular-domain channel recovery. This paper applies the plug-and-play consistency model (PnP-CM) framework to pilot-aided MIMO channel estimation. The PnP-CM inference procedure enforces the pilot observation model in the data-consistency update and invokes a pretrained CM denoiser in the prior update, thereby recovering the angular-domain channel vector within a small number of outer iterations. Preliminary experiments validate the feasibility of using CMs as low-latency channel-estimation priors and show that adaptive parameter scheduling and cross-scenario robustness remain important directions for further improvement.