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Electromagnetic wave property inspired radio environment knowledge construction and AI-based verification for 6G digital twin channel
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Jialin WANG, Jianhua ZHANG, Yutong SUN, Yuxiang ZHANG, Tao JIANG, Liang XIA. Electromagnetic wave property inspired radio environment knowledge construction and AI-based verification for 6G digital twin channel[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="Electromagnetic wave property inspired radio environment knowledge construction and AI-based verification for 6G digital twin channel",
author="Jialin WANG, Jianhua ZHANG, Yutong SUN, Yuxiang ZHANG, Tao JIANG, Liang XIA",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2400464"
}
%0 Journal Article
%T Electromagnetic wave property inspired radio environment knowledge construction and AI-based verification for 6G digital twin channel
%A Jialin WANG
%A Jianhua ZHANG
%A Yutong SUN
%A Yuxiang ZHANG
%A Tao JIANG
%A Liang XIA
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2400464
TY - JOUR
T1 - Electromagnetic wave property inspired radio environment knowledge construction and AI-based verification for 6G digital twin channel
A1 - Jialin WANG
A1 - Jianhua ZHANG
A1 - Yutong SUN
A1 - Yuxiang ZHANG
A1 - Tao JIANG
A1 - Liang XIA
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2400464
Abstract: As the underlying foundation of a digital twin network (DTN), a 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 input into the neural network has many dimensions, and the correlation between the environment and the channel relationship is unclear, resulting in a highly complex relationship construction process. To solve this issue, in this study, we proposed 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 conducted 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 the prediction error of 0.3 only needs 0.04 s of testing time, effectively reducing the network complexity.
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