Large Generative Model-Enabled Digital Twin for 6G Networks
Yi Yang, Wen Sun, Jianhua He, Yaru Fu, Lexi Xu
Abstract
The next generation (6G) wireless networks are under intensive research and envisioned to realize the interconnection of everything and ubiquitous intelligence. One of the major challenges faced by 6G networks is to deliver fully intelligent, automated network control, and customized services, given the vast complexity, scale, and dynamics of the network environment. Digital Twin (DT) technology emerges as a promising solution. By replicating a virtual 6G network system, the DT can effectively monitor the system status, and provide robust planning and optimization functionalities to achieve efficient and intelligent control of 6G networks and applications. To enable intelligent and automated control of 6G networks, we leverage Large Generative Models (LGMs) and propose an innovative LGM-enabled DTs framework. In this framework, a real-time duplicate of the 6G network system is maintained within the DT, updated through continuous synchronization with the physical network. LGMs analyze network contexts and situations to generate intelligent control strategies for network optimization and automation. A case study on the LGM-enabled DT-supported connected autonomous driving is conducted with an incentive scheme designed for efficient DTs co-evolution. Preliminary results demonstrate the feasibility and superior performance of the proposed framework and schemes.