Improving Channel Estimation via Multimodal Diffusion Models with Flow Matching

TL;DR

This paper introduces MultiCE-Flow, a multimodal diffusion-based framework that leverages environmental sensing data to improve channel estimation accuracy and robustness in wireless communication systems.

Contribution

It proposes a novel multimodal perception module and employs flow matching with diffusion transformers to enhance channel estimation using environmental data.

Findings

Outperforms traditional and generative baselines in accuracy

Shows robustness to out-of-distribution scenarios

Effective with sparse pilot data

Abstract

Deep generative models offer a powerful alternative to conventional channel estimation by learning complex channel distributions. By integrating the rich environmental information available in modern sensing-aided networks, this paper proposes MultiCE-Flow, a multimodal channel estimation framework based on flow matching and diffusion transformer (DiT). We design a specialized multimodal perception module that fuses LiDAR, camera, and location data into a semantic condition, while treating sparse pilots as a structural condition. These conditions guide a DiT backbone to reconstruct high-fidelity channels. Unlike standard diffusion models, we employ flow matching to learn a linear trajectory from noise to data, enabling efficient one-step sampling. By leveraging environmental semantics, our method mitigates the ill-posed nature of estimation with sparse pilots. Extensive experiments demonstrate that MultiCE-Flow consistently outperforms traditional baselines and existing generative models. Notably, it exhibits superior robustness to out-of-distribution scenarios and varying pilot densities, making it suitable for environment-aware communication systems.