Generative AI-Driven Digital Twin for Mobile Networks

The sixth generation mobile network (6G) is evolving to provide ubiquitous connections, multidimensional perception, native intelligence, global coverage, etc. , which poses intense demands for network design to tackle the highly dynamic context and diverse service requirements. Digital Twin (DT) is envisioned as an efficient method for designing 6G that migrates the behaviors of physical nodes to the virtual space. However, in the high-dynamic 6G network, there still exist challenges in achieving accuracy and flexibility when constructing DT. In this article, we propose a Generative Artificial Intelligence (GAI) -driven mobile network digital twin paradigm, where the GAI is utilized as a key enabler to generate DT data. Specifically, GAI is capable of implicitly learning the complex distribution of network data, allowing it to sample from the distribution and obtain high-fidelity data. In addition, the construction of DT is closely related to various types of data, such as environmental, user, and service data. GAI can utilize these data as conditions to control the generation process under different scenarios, thereby enhancing flexibility. In practice, we develop a network digital twin prototype system to accurately model the behaviors of mobile network elements (i. e. , mobile users, base stations, and wireless environments) and to evaluate network performance. Evaluation results demonstrate that the proposed prototype system can generate high-fidelity DT data and provide practical network optimization solutions.