A LiDAR-Aided Channel Model for UAV-to-Vehicle Communications in Urban Scenarios

In this paper, we propose a light detection and ranging (LiDAR)-aided channel modeling framework for un-manned aerial vehicle (UAV)-to-vehicle communications in urban environments. To address challenges of sparse point clouds caused by vehicle mobility and the difficulty in fitting planes, we develop a dynamic plane adaptive fitting method for robust environmental reconstruction. Our hierarchical reflection modeling algorithm integrates static building image points and dynamic vehicle-reflected beams to generate multi-dimensional propagation paths. Moreover, the non-stationarity in the time domain is characterized and analyzed. Finally, validation against ray tracing (RT) and the geometry-based stochastic model (GBSM) confirms the accuracy and computational efficiency in capturing statistical channel properties, particularly for predicting delay spread and angle spread across diverse urban layouts, offering a practical solution for environment-aware UAV-assisted Internet of Everything (IoE) systems in the sixth-generation (6G) scenarios.