Authors

Prof. Bourdillon O. Omijeh

NCC Professorial Chair, University of Port Harcourt, Rivers State, Nigeria

Ogochukwu P. Etu

Centre for Information and Telecommunication Engineering, University of Port Harcourt, Rivers State, Nigeria

Abstract

This paper presents a Multilayer Perceptron (MLP)-based model for dynamic bandwidth allocation in 5G network slicing. Traditional approaches to bandwidth allocation often suffer from rigidity and poor adaptability, particularly in handling diverse Quality of Service (QoS) requirements of heterogeneous services such as eMBB, URLLC, and mMTC. These limitations result in inefficient resource utilization and suboptimal user experience. To address this, an MLP model that dynamically classifies and allocates bandwidth with improved accuracy and responsiveness was developed. The model achieved 99.82% accuracy, 99.56% precision, 99.78% recall, and 0.03238 validation loss, demonstrating its ability to classify service types with exceptional precision. Compared to existing methods, the MLP model significantly outperforms prior works in classification performance and adaptability, making it a robust candidate for intelligent network slicing in next-generation networks.

Keywords

5G Network Slicing Multilayer Perceptron Bandwidth Allocation Machine Learning

Citation of this Article

Prof. Bourdillon O. Omijeh, & Ogochukwu P. Etu. (2025). Dynamic Bandwidth Allocation in 5G Networks Using Multilayer Perceptron (MLP). International Current Journal of Engineering and Science (ICJES), 4(8), 6-12. Article DOI: https://doi.org/10.47001/ICJES/2025.408002

Licence Copyright (c) 2026 International Current Journal of Engineering and Science. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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