# Applying Deep-Learning-Based Computer Vision to Wireless Communications: Methodologies, Opportunities, and Challenges

arXiv: https://arxiv.org/abs/2006.05782 · HTML: https://mlsanity.com/paper/2006.05782/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2020.

## Built on by (most cited first, 9 of 9)

- [Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network](https://mlsanity.com/paper/2201.04712/) (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)
- [VOMTC: Vision Objects for Millimeter and Terahertz Communications](https://mlsanity.com/paper/2409.09330/) (2024)
- [Vision-Aided Beam Tracking: Explore the Proper Use of Camera Images with Deep Learning](https://mlsanity.com/paper/2109.14686/) (2021)
- [Vision Aided Channel Prediction for Vehicular Communications: A Case Study of Received Power Prediction Using RGB Images](https://mlsanity.com/paper/2501.18618/) (2025)
- [Equivariant Multi-agent Reinforcement Learning for Multimodal Vehicle-to-Infrastructure Systems](https://mlsanity.com/paper/2604.06914/) (2026)
- [Semantic-Aware Visual Information Transmission With Key Information Extraction Over Wireless Networks](https://mlsanity.com/paper/2506.12786/) (2025)
- [Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework](https://mlsanity.com/paper/2604.02396/) (2026)

## Abstract (arXiv)

Deep learning (DL) has seen great success in the computer vision (CV) field, and related techniques have been used in security, healthcare, remote sensing, and many other fields. As a parallel development, visual data has become universal in daily life, easily generated by ubiquitous low-cost cameras. Therefore, exploring DL-based CV may yield useful information about objects, such as their number, locations, distribution, motion, etc. Intuitively, DL-based CV can also facilitate and improve the designs of wireless communications, especially in dynamic network scenarios. However, so far, such work is rare in the literature. The primary purpose of this article, then, is to introduce ideas about applying DL-based CV in wireless communications to bring some novel degrees of freedom to both theoretical research and engineering applications. To illustrate how DL-based CV can be applied in wireless communications, an example of using a DL-based CV with a millimeter-wave (mmWave) system is given to realize optimal mmWave multiple-input and multiple-output (MIMO) beamforming in mobile scenarios. In this example, we propose a framework to predict future beam indices from previously observed beam indices and images of street views using ResNet, 3-dimensional ResNext, and a long short-term memory network. The experimental results show that our frameworks achieve much higher accuracy than the baseline method, and that visual data can significantly improve the performance of the MIMO beamforming system. Finally, we discuss the opportunities and challenges of applying DL-based CV in wireless communications.