Autonomous Network Slicing Prototype Using Machine-Learning-Based Forecasting for Radio Resources

With the emergence of virtualization and software automation for mobile networks, network slicing is enabling operators to dynamically provision network resources tuned to suit heterogeneous service requirements. This article investigates the architectures of the fifth generation (5G) of mobile networks experimental prototypes with a focus on network slicing. We present some existing 5G prototypes and identify their gaps. We then propose an architecture and a design of a 5G micro-service-based prototype. This prototype has the ability to autoconfigure radio resources for network slices using machine-learning-powered decisions based on real-time acquired performance metrics. Finally, we discuss some use cases on top of this prototype and their related results before concluding.