公开(公告)号：US20250080996A1

公开(公告)日：2025-03-06

申请号：US18816784

申请日：2024-08-27

Applicant: Southeast University ,  PURPLE MOUNTAIN LABORATORIES

Inventor： Chengxiang WANG ,  Zheao LI ,  Chen HUANG ,  Long YU ,  Junling LI ,  Zhongyu QIAN

IPC: H04W16/18 ,  H04W24/00

Abstract: A novel scatterer density-based predictive channel modeling method includes: obtaining channel data with different scenarios scatterer densities through a channel measurement or a simulation; obtaining corresponding channel statistical characteristic parameters through a data preprocessing based on the channel data; constructing a graph dataset by taking scatterer density in different scenarios as main characteristics to enhance a space-time correlation of data; dividing the graph dataset according to a certain proportion, and then using a graph attention network and a gated recurrent unit network to extract correlated channel space-time characteristics and implementing a cross scenario channel prediction. The method can capture channel variations in different scenarios, and obtain channel characteristics under different scatterer densities through high space-time correlated channel characteristics, and has good performance in channel prediction based on scenario.