AI 中文总结
针对无人机通信中传统信道预测方法的局限，提出基于全景感知和视觉语言模型的PanoLAMP框架，利用预训练模型及全景观测捕捉特征预测多径参数，实验显示其在多径参数、统计指标及泛化性上表现出色。
AI 中文摘要
无人机通信有望在6G移动网络中支持广泛的低空应用。传统统计信道模型在特定环境中准确性有限，射线追踪等确定性方法依赖精确三维环境模型且计算复杂度高。现有多模态信道预测方法主要关注路径损耗等大规模指标，对小规模参数建模不足。本文提出基于全景感知和视觉语言模型的低空多径预测框架PanoLAMP，采用预训练视觉语言模型，通过收发端全景RGB-D观测捕捉传播环境特征来预测相关参数。在含18949个无人机-车辆链路的合成数据集上实验，结果表明该方法在多径参数和统计指标上均优于基线，且跨不同飞行高度泛化性更强。
英文摘要
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.
