Network Slicing for Beyond 5G Networks using Machine Learning

This paper explores the application of machine learning for practical applications in the context of Beyond 5G (B5G) communications. A variety of machine learning techniques, including neural networks, was applied on a labeled dataset about network slicing. Neural network models demonstrate superior performance in optimizing virtual network slices, crucial for enhancing Internet of Things (IoT) connectivity and efficiency. The findings can assist telecommunications professionals and policymakers, offering practical perspectives on AI technologies that can be applied in B5G scenarios for large communications networks.