# ViT LoS V2X: Vision Transformers for Environment-aware LoS Blockage Prediction for 6G Vehicular Networks

arXiv: https://arxiv.org/abs/2407.15023 · HTML: https://mlsanity.com/paper/2407.15023/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 34 references that are in the map.
- First posted on arXiv 27 Jun 2024.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition](https://mlsanity.com/paper/1310.1531/) (2013)
- [V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer](https://mlsanity.com/paper/2203.10638/) (2022)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels](https://mlsanity.com/paper/1910.02900/) (2019)
- [The Road Towards 6G: A Comprehensive Survey](https://mlsanity.com/paper/2102.01420/) (2021)
- [6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities](https://mlsanity.com/paper/2008.03213/) (2020)
- [Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoff](https://mlsanity.com/paper/2102.09527/) (2021)
- [Millimeter Wave Base Stations with Cameras: Vision Aided Beam and Blockage Prediction](https://mlsanity.com/paper/1911.06255/) (2019)
- [ViWi: A Deep Learning Dataset Framework for Vision-Aided Wireless Communications](https://mlsanity.com/paper/1911.06257/) (2019)
- [Machine Learning for Reliable mmWave Systems: Blockage Prediction and Proactive Handoff](https://mlsanity.com/paper/1807.02723/) (2018)
- [Deep Learning Based NLOS Identification with Commodity WLAN Devices](https://mlsanity.com/paper/1710.07450/) (2017)
- [Multi-Connectivity in 5G mmWave Cellular Networks](https://mlsanity.com/paper/1605.00105/) (2016)

## Built on by (most cited first, 2 of 2)

- [Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey](https://mlsanity.com/paper/2601.03181/) (2026)
- [From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications](https://mlsanity.com/paper/2505.22311/) (2025)

## Abstract (arXiv)

As wireless communication technology progresses towards the sixth generation (6G), high-frequency millimeter-wave (mmWave) communication has emerged as a promising candidate for enabling vehicular networks. It offers high data rates and low-latency communication. However, obstacles such as buildings, trees, and other vehicles can cause signal attenuation and blockage, leading to communication failures that can result in fatal accidents or traffic congestion. Predicting blockages is crucial for ensuring reliable and efficient communications. Furthermore, the advent of 6G technology is anticipated to integrate advanced sensing capabilities, utilizing a variety of sensor types. These sensors, ranging from traditional RF sensors to cameras and Lidar sensors, are expected to provide access to rich multimodal data, thereby enriching communication systems with a wealth of additional contextual information. Leveraging this multimodal data becomes essential for making precise network management decisions, including the crucial task of blockage detection. In this paper, we propose a Deep Learning (DL)-based approach that combines Convolutional Neural Networks (CNNs) and customized Vision Transformers (ViTs) to effectively extract essential information from multimodal data and predict blockages in vehicular networks. Our method capitalizes on the synergistic strengths of CNNs and ViTs to extract features from time-series multimodal data, which include images and beam vectors. To capture temporal dependencies between the extracted features and the blockage state at future time steps, we employ a Gated Recurrent Unit (GRU)-based architecture. Our results show that the proposed approach achieves high accuracy and outperforms state-of-the-art solutions, achieving more than 95% accurate predictions.