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

TL;DR

Multimodal-NF is a comprehensive dataset combining near-field wireless channel data with multimodal sensory information, designed to enhance environment-aware 6G networks and reduce sensing overhead.

Contribution

It introduces a large-scale, multimodal dataset and generation framework for near-field 6G wireless scenarios, filling a gap in existing datasets.

Findings

Dataset enables spatial semantics to reduce search space.

Validation through case studies demonstrates utility.

Open-source generator and dataset available online.

Abstract

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/.