用全景视觉+大模型预测低空通信多径，精度更高且适应不同飞行高度。
Low-Altitude Channel Multipath Prediction via Panoramic Perception and Vision-Language Model
- 基于全景RGB-D图像和视觉语言模型，捕捉环境特征进行多径预测
- 在18,949条链路测试中，多径参数和统计指标均优于现有方法
- 适用于6G低空无人机通信，尤其适合复杂城市环境
无人机通信有望在6G移动网络中支持大量低空应用场景。然而，传统统计信道模型在特定环境中精度有限，而射线追踪等确定性方法通常依赖精确的三维环境建模，计算开销大。现有多模态信道预测方法主要关注路径损耗等宏观指标，对小尺度参数建模仍不足。为此，本文提出PanoLAMP框架——一种基于全景感知与视觉语言模型的低空多径预测方法。该方法采用预训练视觉语言模型作为主干网络，通过在发射端和接收端采集的全景RGB-D观测数据，预测相对于视距路径的时延、功率、方位角及仰角偏移。实验基于包含18,949条无人机-车辆链路的合成数据集，在七个无人机飞行高度上展开。结果表明，所提方法在多径参数与统计指标上持续优于代表性基线，并展现出更强的跨飞行高度泛化能力。
原文摘要 · Abstract (English)
Unmanned aerial vehicle (UAV) communication is expected to support a wide range of low-altitude applications in 6G mobile networks. However, traditional statistical channel models provide limited accuracy in specific environments, while deterministic methods such as ray tracing usually rely on accurate three-dimensional environment models and involve high computational complexity. Existing multimodal channel prediction approaches mainly focus on large-scale metrics such as path loss, and remain insufficient for modeling small-scale parameters. To address these limitations, this paper proposes PanoLAMP, a Panoramic perception and vision-language model-based Low-Altitude Multipath Prediction framework. It adopts a pretrained vision-language model as the backbone and captures the propagation environment features through panoramic RGB-D observations collected at both the transmitter and receiver to predict the delay, power, azimuth angle, and zenith angle offset relative to the line-of-sight path. Experiments are conducted on a synthetic dataset containing 18,949 UAV-vehicle links across seven UAV altitudes. Experimental results show that the proposed method consistently outperforms representative baselines in both multipath parameters and statistical metrics, and demonstrates stronger generalization across different flight heights.
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