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

arXiv: https://arxiv.org/abs/2203.05548 · HTML: https://mlsanity.com/paper/2203.05548/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2022.

## Built on by (most cited first, 22 of 22)

- [DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset](https://mlsanity.com/paper/2211.09769/) (2022)
- [Multimodal Transformers for Wireless Communications: A Case Study in Beam Prediction](https://mlsanity.com/paper/2309.11811/) (2023)
- [Camera Based mmWave Beam Prediction: Towards Multi-Candidate Real-World Scenarios](https://mlsanity.com/paper/2308.06868/) (2023)
- [A Multi-Modal Simulation Framework to Enable Digital Twin-based V2X Communications in Dynamic Environments](https://mlsanity.com/paper/2303.06947/) (2023)
- [BeamLLM: Vision-Empowered mmWave Beam Prediction with Large Language Models](https://mlsanity.com/paper/2503.10432/) (2025)
- [Resource-Efficient Beam Prediction in mmWave Communications with Multimodal Realistic Simulation Framework](https://mlsanity.com/paper/2504.05187/) (2025)
- [DeepSense-V2V: A Vehicle-to-Vehicle Multi-Modal Sensing, Localization, and Communications Dataset](https://mlsanity.com/paper/2406.17908/) (2024)
- [M2BeamLLM: Multimodal Sensing-empowered mmWave Beam Prediction with Large Language Models](https://mlsanity.com/paper/2506.14532/) (2025)
- [Vehicle Cameras Guide mmWave Beams: Approach and Real-World V2V Demonstration](https://mlsanity.com/paper/2308.10362/) (2023)
- [Generative AI Empowered LiDAR Point Cloud Generation with Multimodal Transformer](https://mlsanity.com/paper/2406.18542/) (2024)
- [Beam Selection in ISAC using Contextual Bandit with Multi-modal Transformer and Transfer Learning](https://mlsanity.com/paper/2503.08937/) (2025)
- [Multi-Modality Sensing in mmWave Beamforming for Connected Vehicles Using Deep Learning](https://mlsanity.com/paper/2504.06173/) (2025)
- [Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey](https://mlsanity.com/paper/2601.03181/) (2026)
- [Multimodal Mixture-of-Experts for ISAC in Low-Altitude Wireless Networks](https://mlsanity.com/paper/2512.01750/) (2025)
- [Millimeter Wave Drones with Cameras: Computer Vision Aided Wireless Beam Prediction](https://mlsanity.com/paper/2211.07569/) (2022)
- [Equivariant Multi-agent Reinforcement Learning for Multimodal Vehicle-to-Infrastructure Systems](https://mlsanity.com/paper/2604.06914/) (2026)
- [QuaMoE-DRF: Proactive Beam and Rate Adaptation via Multimodal Dynamic Radio Map Forecasting in ISAC Networks](https://mlsanity.com/paper/2607.00974/) (2026)
- [GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication](https://mlsanity.com/paper/2505.17530/) (2025)
- [Refined Bayesian Optimization for Efficient Beam Alignment in Intelligent Indoor Wireless Environments](https://mlsanity.com/paper/2512.00036/) (2025)
- [Structure-Aware Multimodal LLM Framework for Trustworthy Near-Field Beam Prediction](https://mlsanity.com/paper/2603.16143/) (2026)
- [Enwar 3.0: An Agentic Multi-Modal LLM Orchestrator for Situation-Aware Beamforming, Blockage Prediction, and Handover Management](https://mlsanity.com/paper/2605.03215/) (2026)
- [Look Once, Beam Twice: Camera-Primed Real-Time Double-Directional mmWave Beam Management for Vehicular Connectivity](https://mlsanity.com/paper/2605.05071/) (2026)

## Abstract (arXiv)

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.