论文标题
LIDAR帮助现实世界中的未来光束预测V2I通信
LiDAR Aided Future Beam Prediction in Real-World Millimeter Wave V2I Communications
论文作者
论文摘要
本文介绍了使用LiDAR数据指导MMWave Beam预测任务的第一个大规模现实世界评估。开发了一种机器学习（ML）模型，该模型利用LiDAR感觉数据预测当前和未来光束。根据大型现实世界数据集DeepSense 6G，该模型在使用高度移动车辆的车辆到基础结构通信方案中进行了评估。实验结果表明，开发的LiDAREAD束预测和跟踪模型可以预测$ 95 \％$的最佳光束，并且在光束训练开销中减少了$ 90 \％$ $。 LIDAR-AID的光束跟踪与基线解决方案具有可比的精度性能，该解决方案具有完美的对先前最佳光束的了解，而无需了解先前的最佳光束信息，而无需任何梁校准。这突出了MMWave和Terahertz通信系统中临界光束对齐挑战的有前途的解决方案。
This paper presents the first large-scale real-world evaluation for using LiDAR data to guide the mmWave beam prediction task. A machine learning (ML) model that leverages the LiDAR sensory data to predict the current and future beams was developed. Based on the large-scale real-world dataset, DeepSense 6G, this model was evaluated in a vehicle-to-infrastructure communication scenario with highly-mobile vehicles. The experimental results show that the developed LiDAR-aided beam prediction and tracking model can predict the optimal beam in $95\%$ of the cases and with more than $90\%$ reduction in the beam training overhead. The LiDAR-aided beam tracking achieves comparable accuracy performance to a baseline solution that has perfect knowledge of the previous optimal beams, without requiring any knowledge about the previous optimal beam information and without any need for beam calibration. This highlights a promising solution for the critical beam alignment challenges in mmWave and terahertz communication systems.
