Machine Learning-Based GPR with LBFGS Kernel Parameters Selection for Optimal Throughput Mining in 5G Wireless Networks

Joseph Isabona, Agbotiname Lucky Imoize (), Stephen Ojo, Dinh-Thuan Do and Cheng-Chi Lee ()
Additional contact information
Joseph Isabona: Department of Physics, Federal University Lokokja, Lokokja 260101, Nigeria
Agbotiname Lucky Imoize: Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Lagos 100213, Nigeria
Stephen Ojo: Department of Electrical and Computer Engineering, College of Engineering, Anderson University, Anderson, SC 29621, USA
Dinh-Thuan Do: School of Engineering, University of Mount Union, Alliance, OH 44601, USA
Cheng-Chi Lee: Research and Development Center for Physical Education, Health, and Information Technology, Department of Library and Information Science, Fu Jen Catholic University, New Taipei City 24205, Taiwan

Sustainability, 2023, vol. 15, issue 2, 1-22

Abstract: Considering the ever-growing demand for an efficient method of deductive mining and extrapolative analysis of large-scale dimensional datasets, it is very critical to explore advanced machine learning models and algorithms that can reliably meet the demands of modern cellular networks, satisfying computational efficiency and high precision requirements. One non-parametric supervised machine learning model that finds useful applications in cellular networks is the Gaussian process regression (GPR). The GPR model holds a key controlling kernel function whose hyperparameters can be tuned to enhance its supervised predictive learning and adaptive modeling capabilities. In this paper, the limited-memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) with kernel parameters selection (KPS) algorithm is employed to tune the GPR model kernel hyperparameters rather than using the standard Bayesian optimization (BOP), which is computationally expensive and does not guarantee substantive precision accuracy in the extrapolative analysis of a large-scale dimensional dataset. In particular, the hybrid GPR–LBFGS is exploited for adaptive optimal extrapolative learning and estimation of throughput data obtained from an operational 5G new radio network. The extrapolative learning accuracy of the proposed GPR–LBFGS with the KPS algorithm was analyzed and compared using standard performance metrics such as the mean absolute error, mean percentage error, root mean square error and correlation coefficient. Generally, results revealed that the GPR model combined with the LBFGS kernel hyperparameter selection is superior to the Bayesian hyperparameter selection method. Specifically, at a 25 m distance, the proposed GPR–LBFGS with the KPS method attained 0.16 MAE accuracy in throughput data prediction. In contrast, the other methods attained 46.06 and 53.68 MAE accuracies. Similarly, at 50 m, 75 m, 100 m, and 160 m measurement distances, the proposed method attained 0.24, 0.18, 0.25, and 0.11 MAE accuracies, respectively, in throughput data prediction, while the two standard methods attained 47.46, 49.93, 29.80, 53.92 and 47.61, 52.54, 53.43, 54.97, respectively. Overall, the GPR–LBFGS with the KPS method would find valuable applications in 5G and beyond 5 G wireless communication systems.

Keywords: 5G wireless networks; throughput data; kernel hyperparameters selection; Gaussian process regression; machine learning; Bayesian optimization; LBFGS–KPS algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/2/1678/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/2/1678/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:2:p:1678-:d:1036724

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().