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Life sciences · Preprint
arXiv · September 9, 2026
Posted before peer review. The findings may change or fail to hold.
This preprint describes BeamTransFuser, a Transformer-based multi-modal fusion architecture for beam prediction in V2X networks that combines camera, LiDAR, radar, and GPS data. The work includes a generative module to handle missing modalities and reports outperformance over baselines on a real-world V2X dataset, but the work is not peer reviewed and lacks detailed quantitative results in the abstract.
Preprint. Real-world multi-modal V2X network deployment with heterogeneous sensors.. Intervention: BeamTransFuser: hierarchical Transformer-based architecture fusing camera, LiDAR, radar, and GPS observations; generative module reconstructs missing modality features.. Compared with: Representative baselines (unspecified in abstract)..
Proposed framework consistently outperforms representative baselines on real-world multi-modal V2X dataset Generative module improves robustness under incomplete sensing conditions with missing modalities
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This is an unrefereed arXiv preprint presenting a machine learning architecture for beam prediction in vehicular networks, evaluated on a real-world dataset but lacking peer review and clinical relevance.
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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.
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