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

Fuente: arXiv
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Main Authors: Ali, Afan, Arslan, Huseyin
Format: Preprint
Published: 2025
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author Ali, Afan
Arslan, Huseyin
author_facet Ali, Afan
Arslan, Huseyin
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2505_08328
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle AI-Driven Digital Twins: Optimizing 5G/6G Network Slicing with NTNs
Ali, Afan
Arslan, Huseyin
Networking and Internet Architecture
Signal Processing
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.
title AI-Driven Digital Twins: Optimizing 5G/6G Network Slicing with NTNs
topic Networking and Internet Architecture
Signal Processing
url https://arxiv.org/abs/2505.08328