Electromagneticwave property inspired radio environment knowledge construction and artificial intelligence based verification for6G digital twin channel  

作　　者：Jialin WANG Jianhua ZHANG Yutong SUN Yuxiang ZHANG Tao JIANG Liang XIA 

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

出　　处：《Frontiers of Information Technology & Electronic Engineering》2025年第2期260-277,共18页信息与电子工程前沿(英文版)

基　　金：supported by the National Key R&D Program of China(No.2023YFB2904803);the National Natural Science Foundation of China(Nos.62341128,62201087,and 62101069);the National Science Fund for Distinguished Young Scholars,China(No.61925102);the Beijing Natural Science Foundation,China(No.L243002);the Beijing University of Posts and Telecommunications-China Mobile Research Institute Joint Innovation Center。

摘　　要：As the underlying foundation of a digital twin network(DTN),digital twin channel(DTC)can accurately depict the electromagnetic wave propagation in the air interface to support the DTN-based 6G wireless network.Since electromagnetic wave propagation is affected by the environment,constructing the relationship between the environment and radio wave propagation is the key to implementing DTC.In the existing methods,the environmental information inputted into the neural network has many dimensions,and the correlation between the environment and the channel is unclear,resulting in a highly complex relationship construction process.To solve this issue,we propose a unified construction method of radio environment knowledge(REK)inspired by the electromagnetic wave property to quantify the propagation contribution based on easily obtainable location information.An effective scatterer determination scheme based on random geometry is proposed which reduces redundancy by 90%,87%,and 81%in scenarios with complete openness,impending blockage,and complete blockage,respectively.We also conduct a path loss prediction task based on a lightweight convolutional neural network(CNN)employing a simple two-layer convolutional structure to validate REK’s effectiveness.The results show that only 4 ms of testing time is needed with a prediction error of 0.3,effectively reducing the network complexity.

关 键 词：Digital twin channel Radio environment knowledge(REK)pool Wireless channel Environmental information Interpretable REK construction Artificial intelligence based knowledge verification 

分 类 号：TN929.5[电子电信—通信与信息系统]