Optimizing 5G Resource Allocation in PSO with Machine Learning Approach to Open RAN Architectures - Comparative Analysis and Ensemble Framework

Aug 3, 2026

This paper presents a machine learning-based solution for 5G resource allocation in Open Radio Access Networks (O-RAN). An ensemble learning framework combines advanced methods to address a mixed-integer non-linear programming (MINLP) formulation by splitting it into RRH assignment via classification and PRB allocation via regression. Experiments show 75–78% accuracy for RRH assignment and mean squared error of 0.3922 for PRB allocation, enabling near-instant decisions after training. Results also compare learning-based and traditional optimization approaches, highlighting efficiency and scalability for real-time scenarios.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages23

Reading Time58 min

AccessFree to download

1. Introduction

Evolution of mobile networks and resource management

Background and motivation

Technical challenges in 5G resource allocation

Optimizing 5G Resource Allocation in PSO with Machine Learning Approach to Open RAN Architectures Osama Akram Amin Metwally Hussien 1 Hamid Jahankhani2 1,2Northumbria University, Computer Science Departement, United Kingdom 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. Keywords: 5G Networks, Resource Allocation, Machine Learning, Ensemble Methods, Open Radio Access Networks, Network Optimization 1. Introduction The evolution of mobile communications has reached a pivotal moment with the advent of fifth-generation (5G) networks. This transformation represents not merely an incremental improvement over previous generations but a fundamental reimagining of wireless network architecture and capabilities. The journey from first-generation analog systems to today’s sophisticated 5G networks reflects the exponential growth in both technological capabilities and user demands, necessitating increasingly complex approaches to network resource management and optimization. Evolution of mobile networks and resource management The telecommunications landscape has undergone remarkable transformation since the introduction of firstgeneration mobile networks. While 1G networks provided basic voice services through analog transmission, each subsequent generation has introduced revolutionary capabilities. The transition to 2G brought digital voice transmission and basic data services, while 3G enabled mobile broadband and multimedia applications. The fourth generation marked a significant leap forward with all-IP networks and high-speed data services. However, 5G represents an unprecedented advancement in network architecture and service delivery capabilities. Unlike its predecessors, 5G networks are designed with a service-based architecture that supports three distinct categories of services: enhanced Mobile Broadband (eMBB), massive Machine-Type Communications (mMTC), and Ultra-Reliable Low-Latency Communications (URLLC) . This architectural approach fundamentally changes how network resources must be managed and allocated. The introduction of network slicing, virtualization, and software-defined networking creates a more flexible but inherently more complex system for resource allocation. The advent of Open Radio Access Networks (O-RAN) has further revolutionized network architecture by disaggregating traditional network components. This disaggregation enables unprecedented flexibility in network deployment and management but introduces new challenges in resource coordination and

FAQs

How does the proposed method decompose the 5G O-RAN resource allocation problem?

It decomposes the MINLP into RRH assignment using classification and Physical Resource Block (PRB) allocation using regression.

What performance results does the ensemble machine learning approach achieve?

It reports 75–78% accuracy for RRH assignment and a mean squared error of 0.3922 for PRB allocation.

Why can the trained model support near-instantaneous decisions?

After training, the approach can generate resource allocation decisions quickly without running heavy iterative optimization during operation.

Aug 3, 2026

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