用环境感知信息提升稀疏导频下的信道估计精度。
Improving Channel Estimation via Multimodal Diffusion Models with Flow Matching
- 融合激光雷达、视觉与位置信息作为语义条件，导频作为结构条件。
- 在低导频密度下仍保持高精度，相较基线提升显著。
- 适合智能交通等动态环境中的自适应通信系统。
深度生成模型为学习复杂信道分布提供了强大替代方案。本文提出基于流匹配与扩散Transformer（DiT）的多模态信道估计框架MultiCE-Flow。设计专用多模态感知模块，将激光雷达、摄像头和位置数据融合为语义条件，同时将稀疏导频视为结构条件。这些条件引导DiT主干网络重建高保真信道。不同于标准扩散模型，采用流匹配学习从噪声到数据的线性轨迹，实现高效一步采样。通过利用环境语义信息，缓解了稀疏导频下估计的病态问题。大量实验表明，MultiCE-Flow持续优于传统基线和现有生成模型。尤其在分布外场景及不同导频密度下表现更鲁棒，适用于环境感知型通信系统。
原文摘要 · Abstract (English)
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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