A Multi-Modal Intelligent U2V Channel Model for 6G Sensing-Communication Integration

TL;DR

This paper introduces a novel 3D scatterer prediction model for UAV-to-Vehicle channels in 6G, utilizing LiDAR data to enhance sensing-communication integration accuracy and modeling of dynamic environments.

Contribution

It presents a new high-fidelity dataset and the 3D-SPADE algorithm for accurate scatterer prediction, improving modeling of U2V channels in dynamic scenarios.

Findings

3D-SPADE achieves over 93% recall and 96% precision in scatterer detection.

Simulation results align closely with ray-tracing, outperforming standard models.

The model effectively captures channel non-stationarity in dynamic environments.

Abstract

This paper proposes a novel UAV-to-Vehicle (U2V) channel model for sixth-generation (6G) intelligent sensing-communication integration, based on three-dimensional (3D) scatterer prediction. To explore the mapping relationship between physical environment and electromagnetic space, a new high-fidelity mixed sensing-communication integration U2V simulation dataset under wide-lane scenarios with different vehicular traffic densities (VTDs) and UAV heights is constructed. Based on the constructed dataset, a novel 3D Scatterer Prediction and Distribution Estimation (3D-SPADE) algorithm is proposed, which leverages LiDAR point clouds to accurately predict the spatial distribution of scatterers. Furthermore, the clustering of scatterers and the subsequent classification into dynamic and static types are meticulously designed for highly dynamic U2V scenarios, while reducing computational complexity and improving modeling accuracy. As LiDAR point clouds vary over time, dynamic and static clusters evolve via 3D-SPADE, enabling precise modeling of channel non-stationarity and consistency. Simulation results demonstrate that, in the wide-lane scenario with varying VTDs and UAV heights, the proposed 3D-SPADE consistently achieves high scatterer occupancy detection performance within the voxel grid. In particular, under favorable configurations, recall reaches 93.26%, and precision reaches 95.74%, highlighting the reliability of 3D-SPADE. Key channel statistical characteristics are simulated and analyzed. These characteristics from the simulation experiments are highly consistent with ray-tracing results and exhibit better agreement than with the standardized model and inconsistent model, validating the necessity of exploring the mapping relationship and the effectiveness of the proposed model.