# Computer Vision Aided mmWave Beam Alignment in V2X Communications

arXiv: https://arxiv.org/abs/2207.11409 · HTML: https://mlsanity.com/paper/2207.11409/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2022.

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

- [A Survey on Integrated Sensing, Communication, and Computation](https://mlsanity.com/paper/2408.08074/) (2024)
- [Physics-informed Generalizable Wireless Channel Modeling with Segmentation and Deep Learning: Fundamentals, Methodologies, and Challenges](https://mlsanity.com/paper/2401.01288/) (2024)
- [A Multi-Modal Simulation Framework to Enable Digital Twin-based V2X Communications in Dynamic Environments](https://mlsanity.com/paper/2303.06947/) (2023)
- [Position Aware 60 GHz mmWave Beamforming for V2V Communications Utilizing Deep Learning](https://mlsanity.com/paper/2402.01259/) (2024)
- [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)
- [Vision Aided Channel Prediction for Vehicular Communications: A Case Study of Received Power Prediction Using RGB Images](https://mlsanity.com/paper/2501.18618/) (2025)
- [Transformer-Based Rate Prediction for Multi-Band Cellular Handsets](https://mlsanity.com/paper/2509.25722/) (2025)
- [QuaMoE-DRF: Proactive Beam and Rate Adaptation via Multimodal Dynamic Radio Map Forecasting in ISAC Networks](https://mlsanity.com/paper/2607.00974/) (2026)
- [Timeliness-Fidelity Tradeoff in 3D Scene Representations](https://mlsanity.com/paper/2407.16575/) (2024)
- [Digital Twin Channel-Enabled Online Resource Allocation for 6G: Principle, Architecture and Application](https://mlsanity.com/paper/2507.19974/) (2025)
- [Task-Oriented Communications for 3D Scene Representation: Balancing Timeliness and Fidelity](https://mlsanity.com/paper/2509.17282/) (2025)
- [A Camera-Cooperative ISAC Framework for Multimodal Non-Cooperative UAVs Sensing](https://mlsanity.com/paper/2605.22090/) (2026)
- [Resolving Multi-Target Association in OFDM-based ISAC via Vision-aided Multi-Modal Learning](https://mlsanity.com/paper/2606.22195/) (2026)
- [Link Both Views: Cross-Image Routing Attention for Dual-Endpoint V2I Beam-Pair Power Prediction](https://mlsanity.com/paper/2610.18195/) (2026)

## Abstract (arXiv)

Visual information, captured for example by cameras, can effectively reflect the sizes and locations of the environmental scattering objects, and thereby can be used to infer communications parameters like propagation directions, receiver powers, as well as the blockage status. In this paper, we propose a novel beam alignment framework that leverages images taken by cameras installed at the mobile user. Specifically, we utilize 3D object detection techniques to extract the size and location information of the dynamic vehicles around the mobile user, and design a deep neural network (DNN) to infer the optimal beam pair for transceivers without any pilot signal overhead. Moreover, to avoid performing beam alignment too frequently or too slowly, a beam coherence time (BCT) prediction method is developed based on the vision information. This can effectively improve the transmission rate compared with the beam alignment approach with the fixed BCT. Simulation results show that the proposed vision based beam alignment methods outperform the existing LIDAR and vision based solutions, and demand for much lower hardware cost and communication overhead.