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arXiv · 2203.05548

LiDAR Aided Future Beam Prediction in Real-World Millimeter Wave V2I Communications

Shuaifeng Jiang·Gouranga Charan·Ahmed Alkhateeb
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Abstract

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.

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Shuaifeng Jiang, Gouranga Charan, Ahmed Alkhateeb. 2022-03-10. LiDAR Aided Future Beam Prediction in Real-World Millimeter Wave V2I Communications. https://arxiv.org/abs/2203.05548

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