Resource Allocation for Open Radio Access Networks Using Reinforcement Learning

This paper presents a novel approach to the resource allocation problem in Open Radio Access Networks (Open RAN), focusing on the coordination of independent xApps for efficient network slicing and physical resource block (PRB) allocation. The proposed framework targets enhanced mobile broadband (eMBB) services and aims to maximize the weighted throughput across user equipments (UEs) while ensuring resource constraints are met. Q-learning is employed as the decision-making mechanism for PRB assignment. Performance is evaluated through comparative analysis against an exact optimization method and a baseline greedy algorithm. Results show that the proposed approach achieves superior efficiency across varying scenarios, highlighting its effectiveness and potential for practical deployment in Open RAN environments.