AI-Driven Digital Twins: Optimizing 5G/6G Network Slicing with NTNs

2025/05/13 by Afan Ali, Ali, Afan, Hüseyin Arslan +1
Computer Science · Engineering · #Software-Defined Networks and 5G #Advanced MIMO Systems Optimization #IoT and Edge/Fog Computing

paper · pdf · doi:10.48550/arxiv.2505.08328

Abstract

Network slicing in 5G/6G Non-Terrestrial Network (NTN) is confronted with mobility and traffic variability. An artificial intelligence (AI)-based digital twin (DT) architecture with deep reinforcement learning (DRL) using Deep deterministic policy gradient (DDPG) is proposed for dynamic optimization of resource allocation. DT virtualizes network states to enable predictive analysis, while DRL changes bandwidth for eMBB slice. Simulations show a 25% latency reduction compared to static methods, with enhanced resource utilization. This scalable solution supports 5G/6G NTN applications like disaster recovery and urban blockage.

Related

• AI-Enabled Digital Twins for Next-Generation Networks: Forecasting Traffic and Resource Management in 5G/6G
• AI-based traffic analysis in digital twin networks
• Synergizing AI and Digital Twins for Next-Generation Network Optimization, Forecasting, and Security
• Digital Twin-Enhanced Deep Reinforcement Learning for Resource Management in Networks Slicing
• Deep Reinforcement Learning for Network Slicing with Heterogeneous\n Resource Requirements and Time Varying Traffic Dynamics