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

2025-01-25

AI Summary

To address the low prediction accuracy, poor generalization, and difficulty in fusing environmental visual information for millimeter-wave (mmWave) channel estimation in 6G vehicle-to-everything (V2X) communications, this paper proposes the first purely RGB image-driven, two-stage vision-aided channel prediction framework. In Stage I, YOLOv8 performs object detection and instance segmentation to generate binary environmental masks encoding propagation characteristics; in Stage II, a ResNet-based architecture performs end-to-end regression of received signal power. The method eliminates reliance on explicit channel models or prior environmental knowledge and supports transfer learning and fine-tuning. Evaluated on a custom mmWave channel dataset across five experimental settings, the framework achieves significantly lower prediction error than conventional model-based approaches and demonstrates strong cross-scenario generalization. This work establishes a deployable, vision-driven paradigm for integrated sensing, communication, and intelligence in 6G systems.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• 6G Communication
• Millimeter Wave Signal Prediction
• Visual Information Integration

Innovation

• Visual-Assisted Prediction
• AI-Communication Integration
• 6G Vehicular Communication