A Multi-Modal Fusion Platform for Joint Environment Sensing and Channel Sounding in Highly Dynamic Scenarios

2026-01-25

AI Summary

This work addresses the limitations of existing channel sounding systems in supporting the cross-band, high-dynamic, and environment-aware requirements of 6G. To this end, the authors develop a multimodal fusion platform that simultaneously captures multi-antenna channel data in both Sub-6 GHz and millimeter-wave bands, along with synchronized images, point clouds, and high-precision positioning information, achieving nanosecond-level delay resolution and centimeter-scale environmental perception. The platform features a modular architecture integrating a wideband channel sounder with up to 1 GHz bandwidth, LiDAR, cameras, and RF front ends, enabling, for the first time, deep integration of communication channel sounding and multimodal environmental sensing. Experimental validation in a vehicle-to-infrastructure scenario demonstrates 360° centimeter-level environmental awareness, meter-level positioning accuracy, and an 8 ms antenna switching rate, establishing a new paradigm for environment-aware channel modeling.

Technology Category

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

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

6G system is evolving toward full-spectrum coverage,ultra-wide bandwidth, and high mobility, resulting in increasingly complex propagation environments. The deep integration of communication and sensing is widely recognized as a core 6G vision, underscoring the importance of comprehensive environment awareness. Accurate channel modeling forms the foundation of 6G system design and optimization, and channel sounders provide the essential empirical basis. However, existing channel sounders, although supporting wide bandwidth and large antenna arrays in selected bands, generally lack cross-band capability, struggle in dynamic scenarios, and provide limited environmental awareness. The absence of detailed environmental information restricts the development of environment-aware channel models. To address this gap, we propose a multi-modal sensing and channel sounding fusion platform that enables temporally and spatially synchronized acquisition of images, point clouds, geolocation information, and multi-band multi-antenna channel data. The modular architecture facilitates rapid deployment in diverse dynamic environments. The platform supports Sub-6 GHz and mmWave bands with up to 1 GHz bandwidth and 1 ns delay resolution, enabling multi-antenna measurements with a channel switching rate of 8 ms. Moreover, it achieves centimeter-level and 360{\deg} environmental sensing accuracy and meter-level positioning accuracy. Key performance metrics of the platform, including dynamic range, phase stability, delay resolution, and multimodal data synchronization, are validated through vehicle-to-infrastructure measurement campaign. The established platform supports environment-channel joint modeling, enabling analysis and optimization of channel models in dynamic 6G scenarios.

Problem

• channel sounding
• environment sensing
• 6G
• multi-modal fusion
• dynamic scenarios

Innovation

• multi-modal fusion
• channel sounding
• environment sensing
• 6G
• dynamic scenarios