Sensing-Assisted Adaptive Beam Probing with Calibrated Multimodal Priors and Uncertainty-Aware Scheduling

TL;DR

This paper introduces a sensing-assisted adaptive beam probing method that uses multimodal sensing data and uncertainty estimation to efficiently align mmWave/THz links, reducing training overhead and improving reliability.

Contribution

It proposes a novel adaptive probing policy combining multimodal priors, deep ensemble uncertainty, and a confidence-based scheduling mechanism for beam alignment.

Findings

Achieves Top-1 accuracy of 0.81 and Top-3 of 0.99 with minimal probes.

Reduces beam probing overhead significantly compared to exhaustive methods.

Improves link reliability and robustness using multimodal priors and uncertainty modeling.

Abstract

Highly directional mmWave/THz links require rapid beam alignment, yet exhaustive codebook sweeps incur prohibitive training overhead. This letter proposes a sensing-assisted adaptive probing policy that maps multimodal sensing (radar/LiDAR/camera) to a calibrated prior over beams, predicts per-beam reward with a deep Q-ensemble whose disagreement serves as a practical epistemic-uncertainty proxy, and schedules a small probe set using a Prior-Q upper-confidence score. The probing budget is adapted from prior entropy, explicitly coupling sensing confidence to communication overhead, while a margin-based safety rule prevents low signal-to-noise ratio (SNR) locks. Experiments on DeepSense-6G (train: scenarios 42 and 44; test:43) with a 21-beam discrete Fourier transform (DFT) codebook achieve Top-1/Top-3 of 0.81/0.99 with expected beam probe of 2 per sweep and zero observed outages at {\theta} = 0 dB with margin {\Delta} = 3 dB. The results show that multimodal priors with ensemble uncertainty match link quality and improve reliability compared to ablations while cutting overhead with better predictive model.