Formal Verification- and AI/ML-Assisted Radio Resource Allocation for Open RAN Compliant 5G/6G Networks

This paper introduces a quantitative analytical framework for developing radio resource management (RRM) strategies tailored to 5G services of enhanced Mobile Broadband (eMBB) and ultra-Reliable Low Latency Communications (uRLLC). By leveraging the Open Radio Access Network (RAN) architecture, the framework enables flexible and efficient management of radio resources to meet the competing demands of services offered by the fifth/sixth generation (5G/6G) of wireless networks. The proposed RRM methodology incorporates formal verification capabilities to generate vast Pareto optimality datasets for specific RAN design parameters, establishing a foundation for rigorous RRM strategy selection. Additionally, the proposed approach enhances data-driven RRM decision-making through the application of unsupervised machine-learning techniques. Our proposed RRM methodology outperforms other baseline (i.e., stochastic and resource-proportional) RRM schemes, achieving up to 30% improvement in the 5G service reward.