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.

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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