室内60GHz网络中多模态传感辅助非视距波束搜索的激光雷达衍生表面先验
LiDAR-Derived Surface Priors for Multimodal Sensing-Assisted NLoS Beam Search in Indoor 60-GHz Networks
浏览论文内容
中文总结 AI 辅助
该研究提出用激光雷达衍生的局部表面结构作为先验，结合射频测量，可将60GHz网络的波束对探测减少72%，同时保持74.5%位置的波束在详尽搜索的3dB范围内，降低毫米波波束搜索不确定性。
中文摘要 AI 辅助
高定向性60GHz物联网（IoT）链路可利用室内自然表面在遮挡下维持连接，但识别可行的非视距（NLoS）路径需大量射频（RF）波束训练。本文研究激光雷达（LiDAR）能否在不假设光学回波与毫米波反射直接映射的情况下，提供感知表面的先验以降低搜索开销。所提框架利用激光雷达衍生的几何结构与回波统计对候选传播方向排序，最终波束选择仍由射频测量负责。实验验证分三个阶段开展：第一阶段量化受控激光雷达测量中几何与辐射度表面描述符随采集几何的变化；第二阶段在L形走廊中进行匹配的激光雷达与60GHz测量，确定这些描述符是否与规定NLoS交互下测得的表面介导射频响应相关；第三阶段在独立的房间尺度实验中，利用详尽的发射机-接收机（TX-RX）波束图评估所得先验，且不规定潜在传播机制。测量结果显示，描述符可靠性取决于采集几何与点云表示，激光雷达与射频表面响应存在跨模态关联但无法支持确定性射频功率预测；在房间实验中，局部三环三维平面度可在74.5%的测量位置上将波束保持在详尽搜索的3dB范围内，同时相对于候选搜索区域的详尽探测减少72%的射频波束对探测。这些结果表明，激光雷达衍生的局部表面结构可作为面向通信的先验，用于集中射频探测并降低毫米波波束搜索的不确定性。
英文摘要
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.
