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

2025-03-08

AI Summary

To address the coupled challenges of reliability, multi-source scenario prediction, and safety-aware autonomous operation in 6G networks under high-risk environments, this paper proposes the first DNT-FL-RL triadic synergy framework. It integrates Digital Network Twin (DNT) for high-fidelity dynamic network modeling, Federated Learning (FL) to preserve cross-domain data privacy, and Deep Reinforcement Learning (RL) for distributed real-time optimization. A novel verifiable secure decision-making pipeline is introduced, supporting edge caching and vehicular networks. The framework breaks the long-standing bottleneck in jointly modeling reliability, predictive consistency, and privacy-preserving security in dynamic networks. Experiments demonstrate: edge cache hit rate exceeding 80% and improved base station load balancing; zero collisions in autonomous driving vehicular scenarios; 3.2× faster anomaly detection response; and significantly enhanced robustness against adversarial attacks.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Application Domains: Mobility, Driving & Flight

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Security and Privacy: Large-scale security measurements
• Responsible Web: Data and user privacy-enhancing technologies for the Web

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.

Problem

• Optimizing network performance using Digital Network Twins and AI.
• Enhancing 6G network reliability and security with machine learning.
• Automating decision-making in edge caching and vehicular networks.

Innovation

• Digital network twins enable real-time network monitoring.
• Federated and reinforcement learning enhance network optimization.
• Integrated pipelines improve edge caching and vehicular safety.