Environment-Aware Channel Inference via Cross-Modal Flow: From Multimodal Sensing to Wireless Channels

2025-12-04

AI Summary

To address the high pilot overhead and poor real-time performance of pilot-based channel state information (CSI) estimation in massive MIMO systems under high-Doppler conditions, this paper proposes a pilot-free, end-to-end CSI inference framework. Methodologically, it fuses multimodal environmental perception data—including camera images, LiDAR point clouds, and GPS coordinates—to construct a cross-modal flow matching model. The channel mapping is formulated as a continuous transformation of conditional distributions in a latent space, jointly optimized via modality alignment loss and a conditional flow matching objective, enabling efficient, low-latency inference. Evaluated on a programmable simulation platform integrating Sionna and Blender, the proposed method significantly outperforms pilot-based baselines and existing sensing-aided approaches in both CSI estimation accuracy and beamforming spectral efficiency. It represents the first data-driven, pilot-free, real-time mapping from multimodal environmental perception to full-dimensional CSI.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

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.

Problem

• Estimates CSI without pilots using multimodal sensing data
• Maps sensing data to wireless channels via cross-modal flow matching
• Enables real-time channel inference for massive MIMO systems

Innovation

• Multimodal sensing data replaces pilot estimation
• Cross-modal flow matching learns sensing-to-channel mapping
• Conditional flow matching enables real-time CSI inference