用摄像头预判波束，实现车用毫米波通信的实时双向快速对准。
Look Once, Beam Twice: Camera-Primed Real-Time Double-Directional mmWave Beam Management for Vehicular Connectivity
- 结合摄像头视觉与闭环射频反馈，缩小波束搜索范围。
- 在真实场景中实现1.1%-1.4%的低中断率，优于现有方法。
- 适合对延迟敏感的车载通信，兼顾实时性与泛化能力。
毫米波（mmWave）频段为车联网（V2X）提供多吉比特连接，但面临严重路径损耗和移动导致的波束错位问题。可靠连接需快速完成双向波束对准，但现有方法存在训练开销高、泛化能力差的问题。本文提出基于视觉的波束成形（VIBE），一种融合机器学习、模型驱动推理与闭环射频反馈的混合架构，用于由摄像头感知驱动的实时双方向毫米波波束管理。VIBE利用摄像头观测减少波束搜索空间，避免全量训练，加速链路建立。轻量级波束精炼与偏移追踪机制可动态适应应用需求。在在线室内外测试平台、公共数据集及真实车载实验中评估，VIBE展现强泛化能力，相较5G NR分层波束成形，始终维持更低中断率；在公开数据集上优于现有端到端深度学习模型，中断率低至1.1%-1.4%。结果表明，混合模型驱动的闭环学习架构比纯端到端训练模型更适用于真实世界毫米波车载通信。代码已开源：https://github.com/UNL-CPN-Lab/Look-Once-Beam-Twice。
原文摘要 · Abstract (English)
Millimeter-wave (mmWave) frequencies promise multi-gigabit connectivity for vehicle-to-everything (V2X) networks, but face challenges in terms of severe path loss and mobility-related beam misalignment. Reliable V2X connectivity requires fast, double-directional beam alignment. However, existing methods suffer from high training overhead and limited generalization to unseen scenarios. This paper presents VIsion-based BEamforming(VIBE), a hybrid model-based, closed-loop, learning architecture for real-time double-directional mmWave beam management primed by camera sensing. VIBE fuses machine learning, model-based reasoning, and closed-loop RF feedback to balance beam-pair establishment latency with link quality. VIBE bypasses exhaustive training overhead and accelerates link establishment by leveraging camera observations to reduce the beam-search space. Lightweight beam refinement and offset tracking mechanisms adaptively refine beams in response to dynamic application requirements. VIBE is implemented and evaluated across online indoor/outdoor testbeds, public datasets, and real-time vehicular experiments, demonstrating strong generalization capabilities, making it suitable for real-time V2X communication. Comparisons with 5G NR hierarchical beamforming show that VIBE consistently maintains lower outage rates. Furthermore, VIBE outperforms state-of-the-art end-to-end ML models for beam selection when evaluated on public datasets and achieves outage rates as low as 1.1-1.4 %. The results show that a hybrid model-based, closed-loop learning architecture is better suited for real-world mmWave vehicular connectivity than end-to-end trained ML models. For reproducibility, we publish our code to https://github.com/UNL-CPN-Lab/Look-Once-Beam-Twice.
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