BeamGuard: Risk-Aware Multimodal Beam Forecasting and Adaptive Virtual Beamwidth Control for 6G mmWave V2I Links

Abstract: 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.