M3Net: A Multi-Metric Mixture of Experts Network Digital Twin with Graph Neural Networks

Abstract--The rise of 5G/6G network technologies promises to enable applications like autonomous vehicles and virtual reality, resulting in a significant increase in connected devices and necessarily complicating network management. Even worse, these applications often have strict, yet heterogeneous, performance requirements across metrics like latency and reliability. Much recent work has thus focused on developing the ability to predict network performance. However, traditional methods for network modeling, like discrete event simulators and emulation, often fail to balance accuracy and scalability. Network Digital Twins (NDTs), augmented by machine learning, present a viable solution by creating virtual replicas of physical networks for real-time simulation and analysis. State-of-the-art models, however, fall short of full-fledged NDTs, as they often focus only on a single performance metric or simulated network data. We introduce M3Net, a Multi-Metric Mixture-of-experts (MoE) NDT that uses a graph neural network architecture to estimate multiple performance metrics from an expanded set of network state data in a range of scenarios. We show that M3Net significantly enhances the accuracy of flow delay predictions by reducing the MAPE (Mean Absolute Percentage Error) from 20.06% to 17.39%, while also achieving 66.47% and 78.7% accuracy on jitter and packets dropped for each flow. Emerging 5G and 6G mobile network architectures aim to support new applications like autonomous vehicles and mixed reality [1], [2], both of which require significantly expanded network capabilities. These and other new applications envisioned as part of the 5G and 6G network ecosystem will lead to massive numbers of connected devices with heterogeneous performance expectations, which increases the complexity and cost of managing communication networks [2]. For example, interactive applications like augmented reality generally require response latencies under 200ms [3], while safety-critical applications like autonomous vehicles might require highly reliable delivery of high-priority packets [4].