Towards 6G Digital Twin Channel: Wireless Environment Knowledge Online Generator as A Key Enabler

Digital twin channel (DTC) is expected to provide significant performance enhancements for the sixth generation (6 G). However, as an important component of the DTC, how to achieve high-precision and fast channel prediction remains a challenge. To address this, we propose a method called wireless environment knowledge online generator (WEK-OG), which generates environment knowledge online by constructing the mapping relationship between environmental information and wireless channels, thereby enabling fast channel prediction through knowledge. First, we present the workflow of WEK-OG. Then, we adopt communication task-oriented environmental feature extraction, data dimensionality reduction, and environment-tochannel matching mapping as key methods to implement WEKOG. Third, when facing new scenarios or tasks, we utilize knowledge to achieve fast prediction, thereby supporting DTC. We validate WEK-OG’s effectiveness using an electromagnetic wave property inspired wireless environment knowledge construction method. Experimental validation in outdoor street scenario and industrial internet of things (IIoT) scenario demonstrate its adaptability to complex scenarios, low computational complexity, and strong generalization.