AI 中文总结
BeamGuard是面向6G毫米波V2I链路的多模态感知辅助波束管理框架，通过融合多模态传感与带内观测预测波束分布，实现自适应虚拟波束宽度控制，在DeepSense 6G数据集上较基线方法性能更优，可实现鲁棒的低开销波束管理。
AI 中文摘要
可靠的波束管理是6G毫米波（mmWave）车联网（V2I）链路的核心挑战，其中窄波束可提供高阵列增益，但易受移动性导致的失配、遮挡和域变化影响。BeamGuard是一种多模态感知辅助的波束管理框架，它结合外感知传感器与可选的部分带内毫米波功率观测值，以预测未来波束分布并选择自适应虚拟波束宽度动作，实现可靠的V2I控制。该框架通过时间多模态预测器融合相机、雷达、激光雷达（LiDAR）、全球定位系统（GPS）和毫米波功率观测值，再通过风险感知规划器将预测后验转换为波束中心和码本级虚拟波束宽度。此处的虚拟波束宽度指码本索引空间中的相邻波束覆盖范围，而非物理模拟宽波束合成。BeamGuard支持仅传感器操作以降低波束训练开销、带有限带操作（带掩码的波束功率条目），以及结合感知与通信侧测量的全混合操作。我们在DeepSense 6G的场景32和33上评估BeamGuard，额外在场景31和34上进行保留测试，涵盖昼夜训练、迁移、有限自适应、消融实验、预算扫描和轻量级基线。作为完整系统参考的全混合基准达到约0.393/0.778/0.897的Top-1/Top-3/Top-5准确率，而规划器实现约0.0060的阈值中断概率和约0.895的增益比。匹配预算基线进一步表明，在可比的带内观测设置下，BeamGuard比多层感知器、循环和时间卷积预测器表现更优。这些结果证明了通过多模态预测和风险感知虚拟波束宽度控制实现的鲁棒、开销感知的波束管理。
英文摘要
Reliable beam management is a central challenge for 6G millimeter-wave (mmWave) vehicle-to-infrastructure (V2I) links, where narrow beams provide high array gain but are vulnerable to mobility-induced misalignment, blockage, and domain variation. BeamGuard is a multimodal sensing-aided beam-management framework that combines exteroceptive sensing with optional partial in-band mmWave power observations to forecast future beam distributions and select adaptive virtual beamwidth actions for reliable V2I control. It fuses camera, radar, LiDAR, GPS, and mmWave power observations with a temporal multimodal forecaster, then converts the predicted posterior into a beam center and virtual codebook-level beamwidth through a risk-aware planner. Here, virtual beamwidth denotes adjacent-beam coverage in the codebook index space rather than physical analog wide-beam synthesis. BeamGuard supports sensor-only operation for beam-training overhead reduction, limited in-band operation with masked beam-power entries, and full hybrid operation with sensing and communication-side measurements. We evaluate BeamGuard on DeepSense 6G Scenarios 32 and 33, with additional held-out tests on Scenarios 31 and 34, covering day--night training, transfer, limited adaptation, ablations, budget sweeps, and lightweight baselines. The full-hybrid anchor, used as the complete-system reference, achieves Top-1/Top-3/Top-5 accuracies of approximately \(0.393/0.778/0.897\), while the planner attains a threshold-based outage probability of about \(0.0060\) with a gain ratio of about \(0.895\). Matched-budget baselines further show that BeamGuard improves over multilayer perceptron, recurrent, and temporal convolutional predictors under comparable in-band observation settings. These results demonstrate robust, overhead-aware beam management through multimodal forecasting and risk-aware virtual beamwidth control.
CommentsAccepted for Publication in IEEE Open Journal of the Communications Society . 18 pages, 5 figures, 11 Tables
