Network Digital Twin for Route Optimization in 5G/B5G Transport Slicing with What-If Analysis

TL;DR

This paper presents a Network Digital Twin platform using Graph Neural Networks for dynamic route optimization and what-if analysis in 5G/B5G transport networks, improving QoS management and decision-making accuracy.

Contribution

It introduces a novel NDT platform with GNNs for virtual testing and optimization of transport routes in 5G/B5G networks, enabling accurate performance prediction and scenario analysis.

Findings

Achieved low MAPE in latency prediction across different topologies.

Demonstrated high accuracy of the GNN-based NDT in simulating network performance.

Enabled effective decision-making for route optimization in complex network scenarios.

Abstract

The advent of fifth-generation (5G) and Beyond 5G (B5G) networks introduces diverse service requirements, from ultra-low latency to high bandwidth, demanding dynamic monitoring and advanced solutions to ensure Quality of Service (QoS). The transport network - responsible for interconnecting the radio access network and core networks - will increasingly face challenges in efficiently managing complex traffic patterns. The Network Digital Twin (NDT) concept emerges as a promising solution for testing configurations and algorithms in a virtual network before real-world deployment. In this context, this work designs an experimental platform with NDT in a transport network domain, synchronizing with the virtual counterpart and a recommendation system for what-if analysis, enabling intelligent decision-making for dynamic route optimization problems in 5G/B5G scenarios. Our NDT, composed of a Graph Neural Network (GNN), was evaluated across three different network topologies consisting of 8, 16, and 30 nodes. It achieved lower MAPE values for URLLC and eMBB slices, comparing latency predictions with actual latency after the solution implementation. These values indicate high accuracy, demonstrating the solution's effectiveness in generating precise insights into network performance if a particular solution were implemented.