JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering 2025 Vol.26 No.2 P.260-277

Electromagnetic wave property inspired radio environment knowledge construction and artificial intelligence 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 (REK) pool, Wireless channel, Environmental information, Interpretable REK construction, Artificial intelligence based knowledge verification

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Abstract: 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.

电磁波特性启发的无线环境知识构建与基于人工智能的验证用于6G数字孪生信道

王嘉琳¹，张建华¹，孙语瞳¹，张宇翔¹，姜涛²，夏亮²
¹北京邮电大学网络与交换技术国家重点实验室，中国北京市，100876
²中国移动研究院，中国北京市，100053
摘要：数字孪生信道作为数字孪生网络的底层基础，能够准确描述空口传输中的电磁波传播，从而支持基于数字孪生网络的6G无线网络。电磁波传播受环境影响，因此建立环境与电波传播之间的关系是实现数字孪生信道的关键。在现有方法中，输入到神经网络的环境信息是多维的，环境与信道之间的关联关系不明确，导致关系构建过程高度复杂。为解决这一问题，本文提出一种基于电磁波特性启发的通用的无线环境知识（REK）构建方法，以利用容易获取的位置信息量化电磁波传播贡献。提出一种有效的基于随机几何的散射体确定方法，在完全空旷、即将遮挡和完全遮挡的情况下，分别减少90%、87%和81%的环境信息冗余度。此外，基于一个采用简单的两层卷积结构的轻量级卷积神经网络进行路径损耗预测，以验证REK的有效性。结果表明，在预测误差为0.3时，仅需4 ms测试时间，有效降低了网络复杂度。

关键词：数字孪生信道；无线环境知识库；无线信道；环境信息；可解释的无线环境知识构建；基于人工智能的知识验证

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电磁波特性启发的无线环境知识构建与基于人工智能的验证用于6G数字孪生信道

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