# Vision Aided Channel Prediction for Vehicular Communications: A Case Study of Received Power Prediction Using RGB Images

arXiv: https://arxiv.org/abs/2501.18618 · HTML: https://mlsanity.com/paper/2501.18618/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 50 references that are in the map.
- First posted on arXiv 25 Jan 2025.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset](https://mlsanity.com/paper/2211.09769/) (2022)
- [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)
- [Vision-Position Multi-Modal Beam Prediction Using Real Millimeter Wave Datasets](https://mlsanity.com/paper/2111.07574/) (2021)
- [Communication-Efficient Multimodal Split Learning for mmWave Received Power Prediction](https://mlsanity.com/paper/2003.00645/) (2020)
- [Computer Vision Aided mmWave Beam Alignment in V2X Communications](https://mlsanity.com/paper/2207.11409/) (2022)
- [Artificial intelligence enabled radio propagation for communications-Part II: Scenario identification and channel modeling](https://mlsanity.com/paper/2111.12228/) (2021)
- [Proactive Received Power Prediction Using Machine Learning and Depth Images for mmWave Networks](https://mlsanity.com/paper/1803.09698/) (2018)
- [ViWi Vision-Aided mmWave Beam Tracking: Dataset, Task, and Baseline Solutions](https://mlsanity.com/paper/2002.02445/) (2020)
- [Applying Deep-Learning-Based Computer Vision to Wireless Communications: Methodologies, Opportunities, and Challenges](https://mlsanity.com/paper/2006.05782/) (2020)
- [When Wireless Communications Meet Computer Vision in Beyond 5G](https://mlsanity.com/paper/2010.06188/) (2020)
- [Computer Vision Aided Blockage Prediction in Real-World Millimeter Wave Deployments](https://mlsanity.com/paper/2203.01907/) (2022)
- [Deep Learning-based Link Configuration for Radar-aided Multiuser mmWave Vehicle-to-Infrastructure Communication](https://mlsanity.com/paper/2201.04657/) (2022)
- [MmWave vehicle-to-infrastructure communication: Analysis of urban microcellular networks](https://mlsanity.com/paper/1702.08122/) (2017)
- [Artificial intelligence enabled radio propagation for communications-Part I: Channel characterization and antenna-channel optimization](https://mlsanity.com/paper/2111.12227/) (2021)
- [Pervasive wireless channel modeling theory and applications to 6G GBSMs for all frequency bands and all scenarios](https://mlsanity.com/paper/2206.02442/) (2022)
- [Multi-User Matching and Resource Allocation in Vision Aided Communications](https://mlsanity.com/paper/2304.09075/) (2023)
- [COST CA20120 INTERACT Framework of Artificial Intelligence Based Channel Modeling](https://mlsanity.com/paper/2411.11798/) (2024)
- [Non-stationarity Characteristics in Dynamic Vehicular ISAC Channels at 28 GHz](https://mlsanity.com/paper/2403.00557/) (2024)
- [Orthogonal Delay-Doppler Division Multiplexing Modulation with Tomlinson-Harashima Precoding](https://mlsanity.com/paper/2405.08288/) (2024)

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

- [Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework](https://mlsanity.com/paper/2604.02396/) (2026)

## Abstract (arXiv)

The communication scenarios and channel characteristics of 6G will be more complex and difficult to characterize. Conventional methods for channel prediction face challenges in achieving an optimal balance between accuracy, practicality, and generalizability. Additionally, they often fail to effectively leverage environmental features. Within the framework of integration communication and artificial intelligence as a pivotal development vision for 6G, it is imperative to achieve intelligent prediction of channel characteristics. Vision-aided methods have been employed in various wireless communication tasks, excluding channel prediction, and have demonstrated enhanced efficiency and performance. In this paper, we propose a vision-aided two-stage model for channel prediction in millimeter wave vehicular communication scenarios, realizing accurate received power prediction utilizing solely RGB images. Firstly, we obtain original images of propagation environment through an RGB camera. Secondly, three typical computer vision methods including object detection, instance segmentation and binary mask are employed for environmental information extraction from original images in stage 1, and prediction of received power based on processed images is implemented in stage 2. Pre-trained YOLOv8 and ResNets are used in stages 1 and 2, respectively, and fine-tuned on datasets. Finally, we conduct five experiments to evaluate the performance of proposed model, demonstrating its feasibility, accuracy and generalization capabilities. The model proposed in this paper offers novel solutions for achieving intelligent channel prediction in vehicular communications.