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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2400464


Electromagnetic wave property inspired radio environment knowledge construction and AI-based verification for 6G digital twin channel


Author(s):  Jialin WANG, Jianhua ZHANG, Yutong SUN, Yuxiang ZHANG, Tao JIANG, Liang XIA

Affiliation(s):  State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; more

Corresponding email(s):   wangjialinbupt@bupt.edu.cn, jhzhang@bupt.edu.cn

Key Words:  Digital twin channel, Radio environment knowledge pool, Wireless channel, Environmental information, Interpretable REK construction, AI-based knowledge verification


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): .

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%A Yutong SUN
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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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