Exploring the Potential of AI in Network Slicing for 5G Networks: An Optimisation Framework

Abstract

ABSTRACT

The integration of artificial intelligence (AI) into 5G network slicing is essential for overcoming limitations in autonomous management and precise resource allocation in complex network environments. Traditional methods struggle with dynamic adaptability, often requiring manual intervention and lacking scalability. This research leverages AI models, specifically logistic regression and long short‐term memory (LSTM) to automate and optimise real time slice allocation. During testing across various values of the regularisation parameter (alpha), the models achieved classification accuracy up to 95% at alpha = 0.1 and maintained over 65% at higher values, demonstrating robustness. We also implement dynamic programming of segment routing over IPv6 (SRv6) Identifiers, enabling accurate differentiation of up to 40,000 enhanced mobile broadband (eMBB) slices, as well as ultra‐reliable low‐latency communication (URLLC) and massive machine type communications (mMTC) types. An adaptive application programming interface (API) based framework further adjusts SRv6 traffic engineering (SRv6 TE) policies in real time, ensuring uninterrupted service. High receiver operating characteristic‐area under the curve (ROC AUC) scores, reaching 0.99, validate the model's strong classification performance. This approach advances automated 5G slicing by enhancing responsiveness, scalability and service quality.