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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.17809 |
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| _version_ | 1866908786603589632 |
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| author | Zhang, Xuejian He, Ruisi Yang, Mi Zhang, Zhengyu Qi, Ziyi |
| author_facet | Zhang, Xuejian He, Ruisi Yang, Mi Zhang, Zhengyu Qi, Ziyi |
| contents | 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° 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17809 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Multi-Modal Fusion Platform for Joint Environment Sensing and Channel Sounding in Highly Dynamic Scenarios Zhang, Xuejian He, Ruisi Yang, Mi Zhang, Zhengyu Qi, Ziyi Information Theory 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° 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. |
| title | A Multi-Modal Fusion Platform for Joint Environment Sensing and Channel Sounding in Highly Dynamic Scenarios |
| topic | Information Theory |
| url | https://arxiv.org/abs/2601.17809 |