Data Synchronization Optimization Algorithm for The Digital Twin with Grouped Load Balance and Mask-assisted Power Control

The digital twin acquires the environment states through device data synchronization, providing robust support for improving collaboration efficiency in edge-end collaborative networks. Grouping is an effective way to reduce competition and simplify scheduling, with multiple devices sharing spectrum resources within the group through power control to synchronize data. However, unreasonable grouping and power control will lead to uneven load and frequent collisions. We propose a data synchronization optimization algorithm for the digital twin based on grouped load balance and mask-assisted power control to handle this problem. First, we allocate periodic devices to groups that maximize the least common multiple of the period in the group to reduce collisions. Event-triggered devices are allocated to groups based on balancing each group’s cumulative average packet generation rate. Second, we apply multi-agent reinforcement learning for power control of grant-free devices. Masks are used to shield prohibited actions to reduce the impact on grant-based devices, and agents can utilize auxiliary experiences to learn the impact of prohibited actions without interaction. Finally, we design some experiments, and the results show that our schemes can reduce collisions while balancing the load and perform better than other schemes after convergence.