Digital Twin-Enabled Channel Modeling: A New Approach to Ensuring Localization Performance for 6G Wireless Communications

Dynamic changes in radio propagation conditions within complex electromagnetic environments will lead to large localization errors or even discontinuous localization. To provide high-quality localization services for all users in 6 G wireless communication systems, it is fundamental to establish scenariosadaptive channel models. How to identify and extract the channel parameters required for localization accurately under dynamic changing environments and channel conditions is an overarching problem to be solved. To address this problem, we propose a novel digital twin channel modeling based on integrated sensing, localization, and communication (ISLAC), which can proactively adapt to changing scenarios, thereby enhancing localization performance. For instance, in a long indoor corridor containing various elements such as metal doors, the proposed localization enhancement method based on the generated radio environment map (REM) can achieve the root mean square localization error of 0.13 m along the user equipment movement trajectory under dynamically changing non-line-of-sight (NLOS) propagation conditions. Additionally, the Cramér-Rao lower bound (CRLB) for localization error in the NLOS scenario is preliminarily derived.