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

2025-10-23

AI Summary

To address the limitations of conventional heuristic resource management in 5G/6G networks—particularly in agility, scalability, and dynamic adaptability—this paper proposes an AI-driven Digital Twin Network (DTN) framework. The framework deeply integrates Long Short-Term Memory (LSTM) neural networks into the DTN architecture to construct a high-fidelity virtual network twin, enabling end-to-end temporal traffic prediction and proactive resource orchestration. Its key innovation lies in the native integration of LSTM within the DTN’s closed-loop control flow, facilitating autonomous, adaptive, real-time resource optimization. Simulation results demonstrate that, compared to traditional baseline methods, the proposed framework reduces traffic prediction error by 32.7% and improves resource utilization by 28.4%, significantly enhancing network stability and operational efficiency under highly dynamic conditions.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Deep Neural Architectures and Foundation Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Forecasting network traffic patterns in 5G/6G networks
• Proactively managing resource allocation dynamically
• Overcoming limitations of traditional resource management techniques

Innovation

• AI-driven Digital Twins for network management
• LSTM neural network forecasts traffic patterns
• Proactive resource allocation in 5G/6G networks