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

arXiv2203.05548MaRDI QIDQ6393372

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Publication date: 10 March 2022

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 $95\mathrm{\%}$ of the cases and with more than $90\mathrm{\%}$ 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.

Has companion code repository: https://github.com/acyiobs/lidar_beam_tracking

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