Abstract
This letter 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 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 around 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.
Original language | English (US) |
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Pages (from-to) | 212-216 |
Number of pages | 5 |
Journal | IEEE Wireless Communications Letters |
Volume | 12 |
Issue number | 2 |
DOIs | |
State | Published - Feb 1 2023 |
Keywords
- Beam tracking
- DeepSense 6G
- LiDAR
- machine learning
- real-world data
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering