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AI-Driven Dynamic Network Slicing Optimization Leveraging Temporal Graph Networks
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DOI：10.1109/LNET.2025.3617577.png)
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Abstract
En 中文
As 5th Generation (5G) and Beyond 5G (B5G) networks evolve, dynamic resource allocation and management is crucial for supporting the diversity of devices and the mixed data traffic types. Network slicing enables the logical segmentation of an infrastructure to meet specific Quality of Service (QoS) requirements posed by applications, but factors such as fluctuating traffic, user mobility, and cross-slice interference, pose challenges towards proactive resource allocation. Traditional methods struggle with these factors, leading to inefficiencies. Therefore, this letter explores the concept of an AI-driven network performance prediction and resource allocation framework using Temporal Graph Networks (TGNs). By integrating TGN with the NS-3 simulator, the work in this letter demonstrates an efficient approach to predict network throughput. The proposed solution advances spatiotemporal Artificial Intelligence (AI) techniques enabling more accurate prediction of network performance and adaptive resource optimization, supporting dynamic network slicing.
Keywords:
Resource management
5G mobile communication
Throughput
Dynamic scheduling
Artificial intelligence
Quality of service
Network slicing
Autonomous aerial vehicles
Adaptation models
Real-time systems
AI
slicing
NS-3
NS3-AI
TGN
throughput prediction
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