Multimodal-NF: A Wireless Dataset for Near-Field Low-Altitude Sensing and Communications
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917517784514560 |
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| author | Li, Mengyuan Lu, Qianfan Tian, Jiachen Hu, Hongjun Han, Yu Li, Xiao Wen, Chao-Kai Jin, Shi |
| author_facet | Li, Mengyuan Lu, Qianfan Tian, Jiachen Hu, Hongjun Han, Yu Li, Xiao Wen, Chao-Kai Jin, Shi |
| contents | Environment-aware 6G wireless networks demand the deep integration of multimodal and wireless data. However, most existing datasets are confined to 2D terrestrial far-field scenarios, lacking the 3D spatial context and near-field characteristics crucial for low-altitude extremely large-scale multiple-input multiple-output (XL-MIMO) systems. To bridge this gap, this letter introduces Multimodal-NF, a large-scale dataset and specialized generation framework. Operating in the upper midband, it synchronizes high-fidelity near-field channel state information (CSI) and precise wireless labels (e.g., Top-5 beam indices, LoS/NLoS) with comprehensive sensory modalities (RGB images, LiDAR point clouds, and GPS). Crucially, these multimodal priors provide spatial semantics that help reduce the near-field search space and thereby lower the overhead of wireless sensing and communication tasks. Finally, we validate the dataset through representative case studies, demonstrating its utility and effectiveness. The open-source generator and dataset are available at https://lmyxxn.github.io/6GXLMIMODatasets/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_28280 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multimodal-NF: A Wireless Dataset for Near-Field Low-Altitude Sensing and Communications Li, Mengyuan Lu, Qianfan Tian, Jiachen Hu, Hongjun Han, Yu Li, Xiao Wen, Chao-Kai Jin, Shi Signal Processing Environment-aware 6G wireless networks demand the deep integration of multimodal and wireless data. However, most existing datasets are confined to 2D terrestrial far-field scenarios, lacking the 3D spatial context and near-field characteristics crucial for low-altitude extremely large-scale multiple-input multiple-output (XL-MIMO) systems. To bridge this gap, this letter introduces Multimodal-NF, a large-scale dataset and specialized generation framework. Operating in the upper midband, it synchronizes high-fidelity near-field channel state information (CSI) and precise wireless labels (e.g., Top-5 beam indices, LoS/NLoS) with comprehensive sensory modalities (RGB images, LiDAR point clouds, and GPS). Crucially, these multimodal priors provide spatial semantics that help reduce the near-field search space and thereby lower the overhead of wireless sensing and communication tasks. Finally, we validate the dataset through representative case studies, demonstrating its utility and effectiveness. The open-source generator and dataset are available at https://lmyxxn.github.io/6GXLMIMODatasets/. |
| title | Multimodal-NF: A Wireless Dataset for Near-Field Low-Altitude Sensing and Communications |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2603.28280 |