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.

翻译：随着5G及未来6G移动网络日益复杂化，实时业务供给对敏捷性、可扩展性、鲁棒性和精确性的要求已无法通过传统启发式资源管理技术满足，这与任何技术进步面临的挑战类似。为突破这些限制，网络运营商正将数字孪生视为关键赋能技术——其作为网络基础设施的动态虚拟副本，使运营商能够在不影响现网的前提下对各类操作进行建模、分析与优化。然而，要使数字孪生网络应对运营商在资源管理等方面面临的挑战，需要核心驱动引擎。本文提出一种人工智能驱动方案，通过将长短期记忆神经网络集成至数字孪生框架，实现网络流量模式预测与资源分配的主动管理。分析实验表明，该AI赋能数字孪生框架相较于基准方法展现出更优性能。研究得出结论：在数字孪生中嵌入AI能力为未来移动网络实现全自主、自适应的高性能网络管理开辟了道路。