Robust Beam Prediction for V2X Networks with Multi-Modal Sensing

2026-09-09

AI Summary

本文针对复杂环境下V2X网络中的波束预测问题，提出了一种融合多模态传感器（如摄像头、激光雷达等）数据的框架BeamTransFuser，并通过生成模块增强在不完整感知条件下的鲁棒性。

Abstract

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.

Problem

• Beam Prediction
• V2X Networks
• Multi-Modal Sensing
• Heterogeneous Sensors

Innovation

• Multi-modal Beam Prediction
• Hierarchical Transformer
• Generative Module
• V2X Networks