Optimizing 5G Resource Allocation in PSO with Machine Learning Approach to Open RAN Architectures

Abstract

This paper proposes a novel machine learning-based approach to solve the resource allocation problem in 5G Open Radio Access Networks (O-RAN). While traditional methods rely on meta-heuristic optimization techniques such as Whale Optimization Algorithm (WOA), we present an ensemble learning framework that combines multiple advanced algorithms to achieve efficient and practical resource allocation. Our approach decomposes the complex mixed-integer non-linear programming (MINLP) problem into two complementary tasks: Remote Radio Head (RRH) assignment through classification and Physical Resource Block (PRB) allocation through regression. Through extensive experimentation, we demonstrate that our ensemble method achieves 75-78\% accuracy in RRH assignment with mean squared error of 0.3922 in PRB allocation, while providing near-instantaneous decision-making capabilities after training. The proposed solution offers significant advantages in computational efficiency and scalability compared to traditional optimization approaches, particularly in scenarios requiring real-time resource allocation decisions. Furthermore, we present a comprehensive comparative analysis between our machine learning approach and existing optimization-based methods, highlighting the trade-offs and complementary strengths of each approach. Our findings suggest that machine learning-based resource allocation can serve as a viable alternative or complement to traditional optimization methods in 5G networks.