Environment Information-Based Channel Prediction Method Assisted by Graph Neural Network  

作　　者：Yutong Sun Jianhua Zhang Yuxiang Zhang Li Yu Qixing Wang Guangyi Liu 

机构地区：[1]State Key Lab of Networking and Switching Technology,Beijing University of Posts and Telecommunications,Beijing 100876,China [2]Future Research Laboratory,China Mobile Research Institute,Beijing 100053,China

出　　处：《China Communications》2022年第11期1-15,共15页中国通信（英文版）

基　　金：supported by the National Science Fund for Distinguished Young Scholars(No.61925102);National Natural Science Foundation of China(No.62101069);National Natural Science Foundation of China(No.62031019);National Natural Science Foundation of China(No.92167202);BUPT-CMCC Joint Innovation Center.

摘　　要：Recently,whether the channel prediction can be achieved in diverse communication scenarios by directly utilizing the environment information gained lots of attention due to the environment impacting the propagation characteristics of the wireless channel.This paper presents an environment information-based channel prediction(EICP)method for connecting the environment with the channel assisted by the graph neural networks(GNN).Firstly,the effective scatterers(ESs)producing paths and the primary scatterers(PSs)generating single propagation paths are detected by building the scatterercentered communication environment graphs(SCCEGs),which can simultaneously preserve the structure information and highlight the pending scatterer.The GNN-based classification model is implemented to distinguish ESs and PSs from other scatterers.Secondly,large-scale parameters(LSP)and small-scale parameters(SSP)are predicted by employing the GNNs with multi-target architecture and the graphs of detected ESs and PSs.Simulation results show that the average normalized mean squared error(NMSE)of LSP and SSP predictions are 0.12 and 0.008,which outperforms the methods of linear data learning.

关 键 词：channel prediction propagation environment GRAPH scatterer detection GNN 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TN929.5[自动化与计算机技术—控制科学与工程]