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

TL;DR

This paper introduces a multimodal visual feature fusion framework for environment-aware channel prediction in vehicular communications, leveraging GPS, panoramic images, and semantic data to improve accuracy and robustness.

Contribution

It proposes a novel three-branch deep learning architecture with adaptive multimodal fusion and specialized regression heads for comprehensive channel parameter prediction.

Findings

Achieved RMSE of 3.26 dB for path loss prediction.

Predicted delay spread with RMSE of 37.66 ns.

Demonstrated high cosine similarity (>0.93) for APS prediction.

Abstract

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.