A Novel Scenario Reconstruction Method Based on 3D Point Cloud Data and RT Channel Modeling for 6G Indoor Communications

With the rapid evolution of 6G wireless communication technology, the granular classification of communication scenarios becomes increasingly sophisticated. This necessitates a deeper exploration of the intrinsic relationships between environmental information and channel characteristics. Consequently, the development of efficient and accurate methods for communication scenario reconstruction emerges as a critical imperative. Leveraging comprehensive three-dimensional (3D) spatial information from point cloud data, we propose a two-stage workflow for processing massive unstructured point cloud data to generate triangular mesh models of large indoor communication environments. The first stage implements an enhanced RANdom SAmple Consensus (RANSAC) algorithm with adaptive thresholding for robust wall structure extraction. Subsequently, we employ a hybrid reconstruction method combining template-based deformation for furniture elements with a zero-shot semantic segmentation network for wall opening detection. The geometric information extracted through the aforementioned process is utilized to generate mesh models, and ray-tracing (RT) is adopted to simulate channel characteristics. Finally, the efficiency and accuracy of the proposed scenario reconstruction and channel modeling method is demonstrated by comparing its simulated channel characteristics with those of channel measurements.