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

2022/03/10 by Shuaifeng Jiang, Jiang, Shuaifeng, Gouranga Charan +3 · 13 citations
Engineering · #Millimeter-Wave Propagation and Modeling #Indoor and Outdoor Localization Technologies #Microwave Engineering and Waveguides

paper · pdf · doi:10.48550/arxiv.2203.05548

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.

Cited by

• AMBER: An Adaptive Multimodal Mask Transformer for Beam Prediction with Missing Modalities
• Multimodal Mixture-of-Experts for ISAC in Low-Altitude Wireless Networks
• Refined Bayesian Optimization for Efficient Beam Alignment in Intelligent Indoor Wireless Environments
• DL-Based Beam Management for mmWave Vehicular Networks Exploring Temporal Correlation
• Beam Index Map Prediction in Unseen Environments from Geospatial Data
• Digital Twin Aided Massive MIMO CSI Feedback: Exploring the Impact of Twinning Fidelity
• Radar-Aided Near-Field Beam Prediction via Beam Map Learning for XL-MIMO V2I Communications
• Deep Reinforcement Learning for Dynamic Sensing and Communications
• Data-Free Knowledge Distillation for LiDAR-Aided Beam Tracking in MmWave Systems
• Attention-Enhanced Learning for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications
• Knowledge Distillation for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications
• Polarization Reconfigurable Transmit-Receive Beam Alignment with Interpretable Transformer
• When Vision-Language Model (VLM) Meets Beam Prediction: A Multimodal Contrastive Learning Framework

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