Broader cross-scenario generalization

Characterize and improve the cross-scenario generalization of the BeamGuard multimodal forecasting-and-control pipeline beyond the DeepSense 6G Scenarios 32 and 33 day--night evaluation, including substantially different road geometries and scenario distributions.

Background

The main experiments train and evaluate BeamGuard primarily on DeepSense 6G Scenarios 32 and 33, which represent paired daytime and nighttime conditions. Additional tests on Scenarios 31 and 34 reveal a substantial degradation in ranked beam-prediction accuracy and communication-level performance under broader scenario shift.

The held-out results indicate that the framework does not yet establish universal generalization across different road settings and scenario distributions. The authors therefore explicitly identify broader scenario generalization as unresolved.

References

These results strengthen the evaluation by extending BeamGuard beyond the original S32/S33 pair, while also showing that broader scenario generalization remains an open challenge.

— BeamGuard: Risk-Aware Multimodal Beam Forecasting and Adaptive Virtual Beamwidth Control for 6G mmWave V2I Links  (2608.25433 - Orimogunje et al., 26 Aug 2026) in Section V, Additional Held-out Scenario Generalization

However, stemming from results of this work, some questions still need to be answered for this topic. First, the proposed approach needs to be evaluated on more routes and devices with a wider range of data distribution, as the historic data or \ac{rem} are likely to be shared between different end devices.

— Mitigating Concept Drift in QoS Prediction for Teleoperation of Autonomous Vehicles Using Historic Data  (2610.08297 - Su et al., 6 Oct 2026) in Section CONCLUSION