发表机构
University of Technology Sydney; The Hong Kong University of Science and Technology (Guangzhou)（悉尼科技大学; 香港科技大学（广州））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对V2X网络波束预测依赖射频感知不可靠的问题，提出基于Transformer的多模态融合框架BeamTransFuser，融合摄像头、激光雷达、雷达和GPS数据，并引入生成模块处理模态缺失，实验验证其优于基线且更具鲁棒性。
AI 中文摘要
集成感知与通信（ISAC）为未来车联网（V2X）网络中的波束预测提供了有前景的基础。然而，现有的感知辅助波束成形方法仍高度依赖射频感知，这在复杂车载环境中可能变得不可靠。与此同时，摄像头和激光雷达等异构传感器的日益普及，为通过更丰富的环境感知来改进波束预测提供了新的机遇。受此启发，本文提出了一种面向V2X网络的多模态波束预测框架。具体而言，我们开发了BeamTransFuser，这是一种基于Transformer的分层架构，能够逐步融合摄像头、激光雷达、雷达和GPS观测数据，以实现鲁棒的波束预测。此外，为应对实际部署中可能出现的模态缺失情况，我们引入了一个生成模块，该模块可从可用观测中重建缺失的模态特征。在真实世界多模态V2X数据集上的实验结果表明，所提出的框架持续优于代表性基线方法，同时生成模块进一步提升了在感知条件不完整情况下的鲁棒性。
英文摘要
Integrated sensing and communication (ISAC) provides a promising foundation for beam prediction in future vehicle-to-everything (V2X) networks. However, existing sensing-assisted beamforming methods still rely heavily on radio-frequency sensing, which may become unreliable in complex vehicular environments. Meanwhile, the growing availability of heterogeneous sensors, such as cameras and LiDAR, offers new opportunities to improve beam prediction through richer environmental perception. Motivated by this, this paper proposes a multi-modal beam prediction framework for V2X networks. Specifically, we develop BeamTransFuser, a hierarchical Transformer-based architecture that progressively fuses camera, LiDAR, radar, and GPS observations for robust beam prediction. In addition, to handle possible missing modalities in practical deployment, we introduce a generative module that reconstructs missing modality features from the available observations. Experimental results on a real-world multi-modal V2X dataset show that the proposed framework consistently outperforms representative baselines, while the generative module further improves robustness under incomplete sensing conditions.
Comments6 pages, 3 figures
Journal refGLOBECOM 2026
