AI-Enabled Digital Twins for Next-Generation Networks: Forecasting Traffic and Resource Management in 5G/6G

TL;DR

The paper tackles the challenge of agile, scalable, real-time resource management in 5G/6G networks by deploying an AI-enabled Digital Twin Network (DTN) that integrates a Bidirectional LSTM ($BiLSTM$) to forecast traffic patterns. It presents a real-world time-series dataset, two baselines, and a BiLSTM-based DT model to predict the next-step internet KPI $y_{T+1}$ and optimize bandwidth provisioning. Results show substantial gains in forecasting accuracy (MAE ≈ 25, RMSE ≈ 40) and resource efficiency (≈ 0.95) with low wastage, demonstrating the viability of autonomous network management via AI-augmented DTs. This work suggests AI-infused DTNs as a practical path toward resilient, high-performance 5G/6G networks.

Abstract

As 5G and future 6G mobile networks become increasingly more sophisticated, the requirements for agility, scalability, resilience, and precision in real-time service provisioning cannot be met using traditional and heuristic-based resource management techniques, just like any advancing technology. With the aim of overcoming such limitations, network operators are foreseeing Digital Twins (DTs) as key enablers, which are designed as dynamic and virtual replicas of network infrastructure, allowing operators to model, analyze, and optimize various operations without any risk of affecting the live network. However, for Digital Twin Networks (DTNs) to meet the challenges faced by operators especially in line with resource management, a driving engine is needed. In this paper, an AI (Artificial Intelligence)-driven approach is presented by integrating a Long Short-Term Memory (LSTM) neural network into the DT framework, aimed at forecasting network traffic patterns and proactively managing resource allocation. Through analytical experiments, the AI-Enabled DT framework demonstrates superior performance benchmarked against baseline methods. Our study concludes that embedding AI capabilities within DTs paves the way for fully autonomous, adaptive, and high-performance network management in future mobile networks.

Figures (6)

• Figure 1: Dataset overview.
• Figure 2: MAE and RMSE comparison.
• Figure 3: Over provisioning comparison.
• Figure 4: Radar chart.
• Figure 5: Resource optimization comparison.