Digital Twins-Driven Green and Reliable Resource Allocation for High Dynamic 6G Edge Networks

Digital twin (DT), as a key enabling technology for 6G edge intelligence, can establish real-time connections between digital twin objects and physical devices, ensuring real-time synchronization and thereby enhancing the service performance and stability of edge networks. This paper considers the high dynamics of edge networks and combines digital twins with edge networks to construct a three-layer network architecture. Based on the demands for low-latency services and system energy efficiency, we design a network metric: system overhead, to minimize service latency and system energy consumption. To achieve these system objectives, we integrate digital twin technology with multiagent deep reinforcement learning (MADRL), proposing a digital twin-driven multi-agent scheme for green and reliable resource allocation. This approach effectively minimizes system overhead and can adapt well to dynamic changes in terminal devices. Compared with baseline algorithms, it reduces system overhead by at least 7 % while maintaining significant reliable.