# LIDAR Data for Deep Learning-Based mmWave Beam-Selection

arXiv: https://arxiv.org/abs/1908.07488 · HTML: https://mlsanity.com/paper/1908.07488/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2019.

## Built on by (most cited first, 15 of 15)

- [Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network](https://mlsanity.com/paper/2201.04712/) (2022)
- [Federated mmWave Beam Selection Utilizing LIDAR Data](https://mlsanity.com/paper/2102.02802/) (2021)
- [LIDAR and Position-Aided mmWave Beam Selection with Non-local CNNs and Curriculum Training](https://mlsanity.com/paper/2104.14579/) (2021)
- [Multiverse at the Edge: Interacting Real World and Digital Twins for Wireless Beamforming](https://mlsanity.com/paper/2305.10350/) (2023)
- [Point Cloud-based Proactive Link Quality Prediction for Millimeter-wave Communications](https://mlsanity.com/paper/2301.00752/) (2023)
- [VOMTC: Vision Objects for Millimeter and Terahertz Communications](https://mlsanity.com/paper/2409.09330/) (2024)
- [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)
- [Overhead-Free Blockage Detection and Precoding Through Physics-Based Graph Neural Networks: LIDAR Data Meets Ray Tracing](https://mlsanity.com/paper/2209.07350/) (2022)
- [QuaMoE-DRF: Proactive Beam and Rate Adaptation via Multimodal Dynamic Radio Map Forecasting in ISAC Networks](https://mlsanity.com/paper/2607.00974/) (2026)
- [Simulation of machine learning-based 6G systems in virtual worlds](https://mlsanity.com/paper/2204.09518/) (2022)
- [HawkRover: An Autonomous mmWave Vehicular Communication Testbed with Multi-sensor Fusion and Deep Learning](https://mlsanity.com/paper/2401.01822/) (2024)
- [AI-Driven Mobility Management for High-Speed Railway Communications: Compressed Measurements and Proactive Handover](https://mlsanity.com/paper/2407.04336/) (2024)
- [Machine Learning for LiDAR-Based Indoor Surface Classification in Intelligent Wireless Environments](https://mlsanity.com/paper/2509.03813/) (2025)
- [RFWeave: Learning Wireless Propagation Across Scenes and Transceiver Configurations](https://mlsanity.com/paper/2610.16656/) (2026)

## Abstract (arXiv)

Millimeter wave communication systems can leverage information from sensors to reduce the overhead associated with link configuration. LIDAR (light detection and ranging) is one sensor widely used in autonomous driving for high resolution mapping and positioning. This paper shows how LIDAR data can be used for line-of-sight detection and to reduce the overhead in millimeter wave beam-selection. In the proposed distributed architecture, the base station broadcasts its position. The connected vehicle leverages its LIDAR data to suggest a set of beams selected via a deep convolutional neural network. Co-simulation of communications and LIDAR in a vehicle-to-infrastructure (V2I) scenario confirm that LIDAR can help configuring mmWave V2I links.