Environment-Aware Channel Inference via Cross-Modal Flow: From Multimodal Sensing to Wireless Channels
This work addresses the challenge of efficient CSI acquisition in massive MIMO systems, particularly in high-Doppler environments, by leveraging multimodal sensing data, representing an incremental advancement over prior methods.
The paper tackles the problem of high overhead in acquiring channel state information (CSI) for wireless communication by proposing a pilot-free channel inference method that estimates CSI directly from multimodal environmental sensing data, achieving significant improvements in channel estimation accuracy and spectral efficiency for beamforming tasks.
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.