Multimodal-NF: A Wireless Dataset for Near-Field Low-Altitude Sensing and Communications

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Abstract
Environment-aware 6G wireless networks demand the deep integration of wireless and multimodal 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 wireless-centric, large-scale multimodal dataset and its generation framework. Operating in the upper mid-band, it synchronizes high-fidelity near-field channel state information (CSI) and precise wireless labels (e.g., Top-5 beam indices and a binary LoS indicator) 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://github.com/Lmyxxn/Multimodal-NF.
Funding Information
  • National Natural Science Foundation of China (62422105)
  • National Natural Science Foundation of China (U25A20392)
  • National Natural Science Foundation of China (624B2038)
  • Key Technologies Research and Development Program of Jiangsu (BE2023022)
  • Key Technologies Research and Development Program of Jiangsu (BE2023022-1)
  • Key Technologies Research and Development Program of Jiangsu (BE2023022-2)
  • Southeast University Innovation Capability Enhancement Plan for Doctoral Students (CXJH_SEU 26093)
  • National Science and Technology Council (NSTC 114-2221-E-110-031-MY3)

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