Accurate channel state information (CSI) underpins reliable and efficient wireless communication. However, acquiring CSI via pilot estimation incurs substantial overhead, especially in massive multiple-input multiple-output (MIMO) systems operating in high-Doppler environments. By leveraging the growing availability of environmental sensing data, this treatise investigates pilot-free channel inference that estimates complete CSI directly from multimodal observations, including camera images, LiDAR point clouds, and GPS coordinates. In contrast to prior studies that rely on predefined channel models, we develop a data-driven framework that formulates the sensing-to-channel mapping as a cross-modal flow matching problem. The framework fuses multimodal features into a latent distribution within the channel domain, and learns a velocity field that continuously transforms the latent distribution toward the channel distribution. To make this formulation tractable and efficient, we reformulate the problem as an equivalent conditional flow matching objective and incorporate a modality alignment loss, while adopting low-latency inference mechanisms to enable real-time CSI estimation. In experiments, we build a procedural data generator based on Sionna and Blender to support realistic modeling of sensing scenes and wireless propagation. System-level evaluations demonstrate significant improvements over pilot- and sensing-based benchmarks in both channel estimation accuracy and spectral efficiency for the downstream beamforming task.

翻译：准确的信道状态信息（CSI）是实现可靠高效无线通信的基础。然而，通过导频估计获取CSI会带来显著的开销，尤其是在高多普勒环境下运行的大规模多输入多输出（MIMO）系统中。本文利用日益丰富的环境感知数据，研究了一种免导频的信道推断方法，该方法直接从多模态观测（包括相机图像、LiDAR点云和GPS坐标）中估计完整的CSI。与先前依赖预定义信道模型的研究不同，我们开发了一个数据驱动的框架，将感知到信道的映射表述为一个跨模态流匹配问题。该框架将多模态特征融合到信道域内的潜在分布中，并学习一个速度场，持续地将潜在分布向信道分布转换。为使这一表述易于处理且高效，我们将问题重新表述为等效的条件流匹配目标，并引入了模态对齐损失，同时采用低延迟推断机制以实现实时CSI估计。在实验中，我们基于Sionna和Blender构建了一个程序化数据生成器，以支持对感知场景和无线传播的逼真建模。系统级评估表明，在下游波束成形任务中，该方法在信道估计精度和频谱效率方面均显著优于基于导频和感知的基准方法。