Intelligent RAN Slicing Orchestration Framework For Healthcare Application in 5G

Abstract

With the increase in the number of internet-connected devices, there is a need to improve reliability, lower latency, higher capacity, more security, and high-speed connectivity. Every application has its performance metrics in terms of QoS parameters. Network slicing enables slicing an extensive broadband network into multiple virtual networks to serve applications more cost-efficiently. With the advancements in Artificial Intelligence (AI), the performance of network decision-making accelerates. In this paper, we propose a dynamic RAN slicing framework for healthcare applications and develop a static Radio Access network slice simulation model by implementing KNN to predict the class. The deep slice data set from the public domain was leveraged to train the model and predict appropriate slice service types for healthcare applications.

Keywords: Intelligent Ran Slicing, K-Nearest Neighbors, Network Slicing

DOI: 10.54941/ahfe1001005