Intent-based radio resource scheduling in ran slicing scenarios using reinforcement learning

Abstract

Network slicing at the radio access network (RAN) domain, called RAN slicing, requires elasticity, efficient resource sharing, and customization to deal with scarce and limited frequency spectrum resources while fulfilling the slice intents in an intent-based system. In this scenario, radio resource scheduling is an essential function to provide the resource management needed to prevent intent violations, hence providing sufficient radio resources for RAN slices to accomplish their intents. The wide variety of scenarios supported in 5G and beyond 5G (B5G) networks makes the radio resource scheduling (RRS) problem in RAN slicing scenarios a significant challenge. This thesis proposes an intent-based RRS for RAN slicing using reinforcement learning (RL) to fulfill the slice intent. The proposed method aims to prevent intent violations by making the management of resource block groups (RBGs) available between slices and users’ equipment (UEs) using inter-slice and intra-slice schedulers, respectively. This thesis also proposes investigating a slice prioritization structure to ensure the intent of more important slices when the available radio resources are insufficient to guarantee all slice’s intents. This thesis proposal presents results obtained using an intent-based RRS with RL for a fixed number of slices and also for multiple network scenarios, aiming to demonstrate the importance of intentbased RRS design for scenarios with RAN slicing. The proposed method outperformed the baselines in fixed and multiple network scenarios, protecting high-priority slices and minimizing the total number of violations.