AI-Driven Dynamic Network Slicing Optimization leveraging Temporal Graph Networks

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
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 a proactive resource allocation. Traditional
methods struggle with these factors, leading to inefficiencies.
Therefore, this paper explores the concept of 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 the paper demonstrates an
efficient approach to predict throughput in a network. The
proposed solution advances spatiotemporal Artificial Intelligence
(AI) techniques enabling more accurate network prediction and
adaptive resource optimization, towards dynamic network slicing.