Adaptive Digital Twin and Communication-Efficient Federated Learning Network Slicing for 5G-enabled Internet of Things
This work addresses efficient resource management for industrial IoT networks with multiservice requirements, representing an incremental improvement by integrating digital twins and federated learning into network slicing.
The paper tackled the problem of resource allocation for network slices in 5G-enabled IoT by using graph-attention networks to build a digital twin for demand forecasting and federated multi-agent reinforcement learning for resource allocation, resulting in improved demand prediction accuracy and reduced communication overhead.
Network slicing enables industrial Internet of Things (IIoT) networks with multiservice and differentiated resource requirements to meet increasing demands through efficient use and management of network resources. Typically, the network slice orchestrator relies on demand forecasts for each slice to make informed decisions and maximize resource utilization. The new generation of Industry 4.0 has introduced digital twins to map physical systems to digital models for accurate decision-making. In our approach, we first use graph-attention networks to build a digital twin environment for network slices, enabling real-time traffic analysis, monitoring, and demand forecasting. Based on these predictions, we formulate the resource allocation problem as a federated multi-agent reinforcement learning problem and employ a deep deterministic policy gradient to determine the resource allocation policy while preserving the privacy of the slices. Our results demonstrate that the proposed approaches can improve the accuracy of demand prediction for network slices and reduce the communication overhead of dynamic network slicing.