Accurate beam prediction is a key enabler for next-generation wireless communication systems. In this paper, we propose a multimodal large language model (LLM)-based beam prediction framework that effectively utilizes contextual information, provided by sensory data including RGB camera images and LiDAR point clouds. To effectively fuse heterogeneous modalities, we design specialized modality encoders together with a beam-guided attention masking mechanism and a high-frequency temporal alignment strategy, enabling robust cross-modal feature integration under dynamic environments. Furthermore, we construct a large-scale multimodal dataset for communication, named Multimodal-Wireless, which covers diverse weather and traffic conditions with high-fidelity ray-tracing labels. Extensive simulation results demonstrate that the proposed approach significantly reduces the reliance on oracle angle-of-departure knowledge and consistently outperforms state-of-the-art multimodal LLM-based beam prediction methods in terms of beam accuracy and communication performance, improving the average Top-1 accuracy to 80.8% and the average normalized gain to 89.1%.

翻译：精准的波束预测是下一代无线通信系统的关键使能技术。本文提出了一种基于多模态大语言模型（LLM）的波束预测框架，该框架有效利用RGB摄像头图像和激光雷达点云等感知数据提供的上下文信息。为有效融合异质模态，我们设计了专用模态编码器，并配合波束引导注意力掩蔽机制与高频时间对齐策略，实现了动态环境下鲁棒的跨模态特征融合。此外，我们构建了通信领域大规模多模态数据集Multimodal-Wireless，其覆盖多样化天气与交通场景，并配备高保真光线追踪标签。大量仿真结果表明，所提方法显著降低了对理想离开角知识的依赖，并在波束精度和通信性能上持续优于当前最先进的多模态LLM波束预测方法，将平均Top-1精度提升至80.8%，平均归一化增益提升至89.1%。