# Millimeter Wave Drones with Cameras: Computer Vision Aided Wireless Beam Prediction

arXiv: https://arxiv.org/abs/2211.07569 · HTML: https://mlsanity.com/paper/2211.07569/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Pruning Filters for Efficient ConvNets](https://mlsanity.com/paper/1608.08710/) (2016)
- [Vision-Position Multi-Modal Beam Prediction Using Real Millimeter Wave Datasets](https://mlsanity.com/paper/2111.07574/) (2021)
- [LiDAR Aided Future Beam Prediction in Real-World Millimeter Wave V2I Communications](https://mlsanity.com/paper/2203.05548/) (2022)
- [ViWi: A Deep Learning Dataset Framework for Vision-Aided Wireless Communications](https://mlsanity.com/paper/1911.06257/) (2019)
- [Radar Aided 6G Beam Prediction: Deep Learning Algorithms and Real-World Demonstration](https://mlsanity.com/paper/2111.09676/) (2021)
- [Position Aided Beam Prediction in the Real World: How Useful GPS Locations Actually Are?](https://mlsanity.com/paper/2205.09054/) (2022)
- [Integrated Sensing and Communication for 6G: Ten Key Machine Learning Roles](https://mlsanity.com/paper/2208.02157/) (2022)
- [Multi-Modal Beam Prediction Challenge 2022: Towards Generalization](https://mlsanity.com/paper/2209.07519/) (2022)
- [A Prospective Look: Key Enabling Technologies, Applications and Open Research Topics in 6G Networks](https://mlsanity.com/paper/2004.06049/) (2020)

## Built on by (most cited first, 1 of 1)

- [GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication](https://mlsanity.com/paper/2505.17530/) (2025)

## Abstract (arXiv)

Millimeter wave (mmWave) and terahertz (THz) drones have the potential to enable several futuristic applications such as coverage extension, enhanced security monitoring, and disaster management. However, these drones need to deploy large antenna arrays and use narrow directive beams to maintain a sufficient link budget. The large beam training overhead associated with these arrays makes adjusting these narrow beams challenging for highly-mobile drones. To address these challenges, this paper proposes a vision-aided machine learning-based approach that leverages visual data collected from cameras installed on the drones to enable fast and accurate beam prediction. Further, to facilitate the evaluation of the proposed solution, we build a synthetic drone communication dataset consisting of co-existing wireless and visual data. The proposed vision-aided solution achieves a top-1 beam prediction accuracy of ≈ 91% and close to 100% top-3 accuracy. These results highlight the efficacy of the proposed solution towards enabling highly mobile mmWave/THz drone communication.