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

Shuaifeng Jiang, Gouranga Charan, Ahmed Alkhateeb

Research output: Contribution to journalArticlepeer-review


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 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 languageEnglish (US)
Pages (from-to)1
Number of pages1
JournalIEEE Wireless Communications Letters
StateAccepted/In press - 2022
Externally publishedYes


  • beam tracking
  • DeepSense 6G
  • Feature extraction
  • Indexes
  • Laser radar
  • LiDAR
  • machine learning
  • Millimeter wave communication
  • Predictive models
  • real-world data
  • Sensors
  • Task analysis

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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