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.

公开(公告)号：US20240259121A1

公开(公告)日：2024-08-01

申请号：US18564273

申请日：2023-03-19

Applicant: Southeast University

Inventor： Chengxiang WANG ,  Zheao LI ,  Jie HUANG ,  Wenqi ZHOU ,  Chen HUANG

IPC: H04B17/391 ,  H04W24/06

CPC classification number: H04B17/3913 ,  H04B17/3912 ,  H04W24/06

Abstract: Disclosed in the present disclosure is a predictive channel modeling method based on a generative adversarial network and a long short-term memory artificial neural network, which method effectively achieves a channel prediction function in different frequency bands and scenarios, and generates a large number of channel data sets for simulation experiments. The method comprises: firstly, inputting channel measurement data for existing frequency bands and scenarios for training; then, learning true channel data using a long short-term memory artificial neural network, and acquiring a channel time sequence feature; by means of adversarial learning of a generative adversarial network, greatly eliminating redundant information of the channel data, and on the basis of the measurement data, generating accurate channel data, and acquiring massive channel information; and finally, achieving the balance between a generative model and a discriminative model during the continuous iteration of the generative adversarial network, and then outputting a trained predictive channel model. A statistical channel feature obtained by means of prediction by a model can clearly specify the predictive learning for a channel distribution feature in the present disclosure, and real-time and complex prediction problems in wireless communication can be solved.