Adaptive Digital Twin and Communication-Efficient Federated Learning Network Slicing for 5G-enabled Internet of Things

TL;DR

This paper presents a novel approach combining digital twins, graph-attention networks, and federated reinforcement learning to optimize network slicing in 5G-enabled IoT, enhancing demand prediction accuracy and reducing communication overhead.

Contribution

It introduces a digital twin environment using graph-attention networks and formulates resource allocation as a federated reinforcement learning problem for the first time in this context.

Findings

Improved demand prediction accuracy for network slices.

Reduced communication overhead in dynamic network slicing.

Effective resource allocation through federated reinforcement learning.

Abstract

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.