Low-altitude communications can promote the integration of aerial and terrestrial wireless resources, expand network coverage, and enhance transmission quality, thereby empowering the development of sixth-generation (6G) mobile communications. As an enabler for low-altitude transmission, 3D channel fingerprints (3D-CF), also referred to as the 3D radio map or 3D channel knowledge map, are expected to enhance the understanding of communication environments and assist in the acquisition of channel state information (CSI), thereby avoiding repeated estimations and reducing computational complexity. In this paper, we propose a modularized multimodal framework to construct 3D-CF. Specifically, we first establish the 3D-CF model as a collection of CSI-tuples based on Rician fading channels, with each tuple comprising the low-altitude vehicle's (LAV) positions and its corresponding statistical CSI. In consideration of the heterogeneous structures of different prior data, we formulate the 3D-CF construction problem as a multimodal regression task, where the target channel information in the CSI-tuple can be estimated directly by its corresponding LAV positions, together with communication measurements and geographic environment maps. Then, a high-efficiency multimodal framework is proposed accordingly, which includes a correlation-based multimodal fusion (Corr-MMF) module, a multimodal representation (MMR) module, and a CSI regression (CSI-R) module. Numerical results show that our proposed framework can efficiently construct 3D-CF and achieve at least 27.5% higher accuracy than the state-of-the-art algorithms under different communication scenarios, demonstrating its competitive performance and excellent generalization ability. We also analyze the computational complexity and illustrate its superiority in terms of the inference time.

翻译：低空通信可促进空地无线资源融合，扩展网络覆盖范围，提升传输质量，从而赋能第六代（6G）移动通信发展。作为低空传输的关键使能技术，三维信道指纹（3D-CF）（亦称三维无线电地图或三维信道知识图谱）有望增强对通信环境的理解，辅助获取信道状态信息（CSI），从而避免重复估计并降低计算复杂度。本文提出一种模块化多模态框架用于构建3D-CF。具体而言，我们首先基于莱斯衰落信道建立由CSI元组构成的3D-CF模型，每个元组包含低空飞行器（LAV）的位置及其对应的统计CSI。考虑到不同先验数据的异质性，我们将3D-CF构建问题转化为多模态回归任务，其中CSI元组中的目标信道信息可通过LAV位置、通信测量值及地理环境地图直接估计。据此，本文提出一种高效多模态框架，包含基于相关性的多模态融合（Corr-MMF）模块、多模态表征（MMR）模块和CSI回归（CSI-R）模块。数值结果表明，所提框架能够高效构建3D-CF，在多种通信场景下准确率较现有最优算法提升至少27.5%，展现出优异的竞争性能与泛化能力。我们还分析了计算复杂度，并论证了其在推理时间方面的优越性。