# Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework

arXiv: https://arxiv.org/abs/2604.02396 · HTML: https://mlsanity.com/paper/2604.02396/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 34 references that are in the map.
- First posted on arXiv 2 Apr 2026.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [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)
- [M3SC: A Generic Dataset for Mixed Multi-Modal (MMM) Sensing and Communication Integration](https://mlsanity.com/paper/2306.14125/) (2023)
- [Proactive Received Power Prediction Using Machine Learning and Depth Images for mmWave Networks](https://mlsanity.com/paper/1803.09698/) (2018)
- [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)
- [Environment Semantics Aided Wireless Communications: A Case Study of mmWave Beam Prediction and Blockage Prediction](https://mlsanity.com/paper/2301.05837/) (2023)
- [Camera Based mmWave Beam Prediction: Towards Multi-Candidate Real-World Scenarios](https://mlsanity.com/paper/2308.06868/) (2023)
- [Environment Semantic Communication: Enabling Distributed Sensing Aided Networks](https://mlsanity.com/paper/2402.14766/) (2024)
- [Multi-Modal Intelligent Channel Modeling: A New Modeling Paradigm via Synesthesia of Machines](https://mlsanity.com/paper/2411.03711/) (2024)
- [Artificial intelligence enabled radio propagation for communications-Part I: Channel characterization and antenna-channel optimization](https://mlsanity.com/paper/2111.12227/) (2021)
- [COST CA20120 INTERACT Framework of Artificial Intelligence Based Channel Modeling](https://mlsanity.com/paper/2411.11798/) (2024)
- [Environment Sensing-aided Beam Prediction with Transfer Learning for Smart Factory](https://mlsanity.com/paper/2405.15339/) (2024)
- [Vision-Aided Channel Prediction Based on Image Segmentation at Street Intersection Scenarios](https://mlsanity.com/paper/2501.15726/) (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)
- [Artificial Intelligence Empowered Channel Prediction: A New Paradigm for Propagation Channel Modeling](https://mlsanity.com/paper/2601.09205/) (2026)

## Abstract (arXiv)

The deep integration of communication with intelligence and sensing, as a defining vision of 6G, renders environment-aware channel prediction a key enabling technology. As a representative 6G application, vehicular communications require accurate and forward-looking channel prediction under stringent reliability, latency, and adaptability demands. Traditional empirical and deterministic models remain limited in balancing accuracy, generalization, and deployability, while the growing availability of onboard and roadside sensing devices offers a promising source of environmental priors. This paper proposes an environment-aware channel prediction framework based on multimodal visual feature fusion. Using GPS data and vehicle-side panoramic RGB images, together with semantic segmentation and depth estimation, the framework extracts semantic, depth, and position features through a three-branch architecture and performs adaptive multimodal fusion via a squeeze-excitation attention gating module. For 360-dimensional angular power spectrum (APS) prediction, a dedicated regression head and a composite multi-constraint loss are further designed. As a result, joint prediction of path loss (PL), delay spread (DS), azimuth spread of arrival (ASA), azimuth spread of departure (ASD), and APS is achieved. Experiments on a synchronized urban V2I measurement dataset yield the best root mean square error (RMSE) of 3.26 dB for PL, RMSEs of 37.66 ns, 5.05 degrees, and 5.08 degrees for DS, ASA, and ASD, respectively, and mean/median APS cosine similarities of 0.9342/0.9571, demonstrating strong accuracy, generalization, and practical potential for intelligent channel prediction in 6G vehicular communications.