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

TL;DR

This paper presents an AI-driven digital twin framework utilizing deep reinforcement learning to optimize resource allocation in 5G/6G non-terrestrial networks, significantly reducing latency and improving resource utilization.

Contribution

It introduces a novel AI-based digital twin architecture with DRL for dynamic network slicing in NTNs, addressing mobility and traffic variability challenges.

Findings

Achieved 25% latency reduction over static methods.

Enhanced resource utilization in simulated environments.

Supports critical NTN applications like disaster recovery.

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.