Synergizing AI and Digital Twins for Next-Generation Network Optimization, Forecasting, and Security

TL;DR

The paper addresses how digital network twins (DNTs) can be synergized with FL and RL to tackle 6G network optimization, forecasting, and security. It presents three integrated pipelines: DNT-enabled safe RL for network optimization, DNT–FL for joint data-scenario forecasting, and DNT–FRL for security in distributed environments, each supported by case studies. It demonstrates improved cache hit rates and load balancing in edge caching, and robust, attacks-resilient performance in autonomous driving scenarios. The work highlights practical considerations for deploying DNT-based intelligence in real-time, privacy-preserving, and secure 6G networks.

Abstract

Digital network twins (DNTs) are virtual representations of physical networks, designed to enable real-time monitoring, simulation, and optimization of network performance. When integrated with machine learning (ML) techniques, particularly federated learning (FL) and reinforcement learning (RL), DNTs emerge as powerful solutions for managing the complexities of network operations. This article presents a comprehensive analysis of the synergy of DNTs, FL, and RL techniques, showcasing their collective potential to address critical challenges in 6G networks. We highlight key technical challenges that need to be addressed, such as ensuring network reliability, achieving joint data-scenario forecasting, and maintaining security in high-risk environments. Additionally, we propose several pipelines that integrate DNT and ML within coherent frameworks to enhance network optimization and security. Case studies demonstrate the practical applications of our proposed pipelines in edge caching and vehicular networks. In edge caching, the pipeline achieves over 80% cache hit rates while balancing base station loads. In autonomous vehicular system, it ensure a 100% no-collision rate, showcasing its reliability in safety-critical scenarios. By exploring these synergies, we offer insights into the future of intelligent and adaptive network systems that automate decision-making and problem-solving.

Figures (6)

• Figure 1: Framework of the collaboration of DNT and AI.
• Figure 2: Overview of challenges and potential solutions in the synergy of DNT and AI.
• Figure 3: A close-up view of the pipeline. The gray box highlights the active area of the safety-oriented intervention modules.
• Figure 4: Comparison between synchronous and asynchronous FL.
• Figure 5: Safe edge caching with synergy of RL and DNT.