Improving Channel Estimation via Multimodal Diffusion Models with Flow Matching

• URL: http://arxiv.org/abs/2603.13440v1
• Date: Fri, 13 Mar 2026 11:26:10 GMT
• Title: Improving Channel Estimation via Multimodal Diffusion Models with Flow Matching
• Abstract summary: MultiCE-Flow is a multimodal channel estimation framework based on flow matching and diffusion transformer (DiT)<n>We design a specialized multimodal perception module that fuses LiDAR, camera, and location data into a semantic condition.<n>Unlike standard diffusion models, we employ flow matching to learn a linear trajectory from noise to data, enabling efficient one-step sampling.
• Score: 41.609067424764746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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