LiDAR-Derived Surface Priors for Multimodal Sensing-Assisted NLoS Beam Search in Indoor 60-GHz Networks

Abstract

Highly directional 60-GHz Internet-of-Things (IoT) links can exploit naturally occurring indoor surfaces to sustain connectivity under blockage. Identifying viable non-line-of-sight (NLoS) paths, however, can require extensive RF beam training. This paper investigates whether LiDAR can reduce this search overhead by providing a surface-aware prior without assuming a direct mapping between optical return and mmWave reflection. The proposed framework uses LiDAR-derived geometry and return statistics to rank candidate propagation directions, while RF measurements remain responsible for final beam selection. The experimental validation is organized in three stages to separate descriptor robustness, cross-modal association, and beam-search performance. Controlled LiDAR measurements first quantify how geometric and radiometric surface descriptors vary with acquisition geometry. Matched LiDAR and 60-GHz measurements in an L-shaped corridor then determine whether these descriptors are associated with the measured surface-mediated RF response under a prescribed NLoS interaction. Finally, a separate room-scale campaign evaluates the resulting prior using exhaustive TX-RX beam maps without prescribing the underlying propagation mechanism. The measurements show that descriptor reliability depends on acquisition geometry and point-cloud representation, and that LiDAR and RF surface responses exhibit cross-modal association without supporting deterministic RF-power prediction. In the room experiment, local three-ring 3-D planarity retains a beam within 3 dB of exhaustive search at 74.5% of the measured locations while reducing RF beam-pair probing by 72% relative to exhaustive probing over the candidate search region. These results establish LiDAR-derived local surface structure as a communication-oriented prior for concentrating RF probing and reducing mmWave beam-search uncertainty.