Development of a Machine Learning Based Fault Detection Model for Received Signal Level in Telecommunication Enterprise Infrastructure

Aug 5, 2026

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A machine-learning fault detection model is developed for received signal levels (RSL) in telecommunication enterprise infrastructure to support timely maintenance and reduce service degradation. The study models an enterprise point-to-multipoint wireless network using pathloss 5.0, generating training data from channel and system parameters such as free space pathloss, transmit power, antenna gain, transmitter and miscellaneous losses, and receiver loss. Three regression approaches—GBR, RFR, and KNN—classify new RSL observations against a threshold into “Fault” or “No-fault” states. Evaluation metrics (MAE, MSE, R-squared, RMSE) identify RFR as optimal with R-squared of 0.999992, enabling improved uptime and customer satisfaction.

LanguageEnglish

CategoryResearch & Report

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Pages13

Reading Time33 min

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Abstract

Keywords

Introduction

See discussions, stats, and author profiles for this publication at: [https://www. researchgate. net/publication/381694354](https://www. researchgate. net/publication/381694354) Development of a Machine Learning Based Fault Detection Model for Received Signal Level in Telecommunication Enterprise Infrastructure Article in International Journal of Safety and Security Engineering · June 2024 DOI: 10.18280/ijsse.140302 CITATION 1 READS 219 4 authors, including: Kennedy Okokpujie Covenant University Ota Ogun State, Nigeria 139 PUBLICATIONS 1,268 CITATIONS Adenugba Akingunsoye University of the People 11 PUBLICATIONS 19 CITATIONS Morayo E Awomoyi American University Washington D.C. 16 PUBLICATIONS 34 CITATIONS SEE PROFILE All content following this page was uploaded by Kennedy Okokpujie on 27 June 2024. The user has requested enhancement of the downloaded file. International Journal of Safety and Security Engineering Vol. 14 , No. 3, June, 2024, pp. 679-690 Journal homepage: [http://iieta.org/journals/ijsse](http://iieta.org/journals/ijsse) | Development of a Machine Learning Based Fault Detection Model for Received Signal Level in Telecommunication Enterprise Infrastructure | | | --- | --- | | Kennedy Okokpujie 1,2,3* , Innocent O. Nwokolo 1 , Akingunsoye V. Adenugba4 , Morayo E. Awomoyi5
1 Department of Electrical and Information Engineering, Covenant University, Ota 112101, Nigeria
2 Africa Centre of Excellence for Innovative & Transformative STEM Education, Lagos State University, Ojo 102003, Nigeria
3 Informatics & Communication African Centre of Excellence (CApIC-ACE), Covenant University, Ota 112101, Nigeria
4 Directorate, OVA Foundation, Millington 21651, USA
5 US School of International Service, American University, Tenleytown 20016, USA
[Corresponding Author Email: kennedy.okokpujie@covenantuniversity.edu.ng](Corresponding Author Email: kennedy.okokpujie@covenantuniversity.edu.ng) | | | Copyright: ©2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license
([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)). | | [https://doi.org/10.18280/ijsse.140302](https://doi.org/10.18280/ijsse.140302) ABSTRACT | Received: 26 June 2023
Revised: 18 January 2024
Accepted: 29 January 2024
Available online: 24 June 2024 | This research develops a machine-learning fault detection model for received signal levels in telecommunication infrastructure. The methodology involves modeling an enterprise point-to-multipoint wireless network using pathloss 5.0 software. Data from the simulated network, including free space pathloss, transmit power output, transmit antenna gain, transmitter loss, miscellaneous loss, and receiver loss, is used to train three regression models: gradient boosting regression (GBR), random forest regression (RFR), and KNearest Neighbor (KNN) . The algorithm compares the received signal levels (RSL) of new data with a threshold value, triggering a "Fault" or "No-fault" condition. A "Fault"indicates a deviation in the RSL, prompting maintenance by the field support team. A"No-fault" means the RSL is within the accepted range, requiring no maintenance. Performance evaluation metrics such as mean absolute error (MAE), mean square error (MSE), R-squared, and root mean square error (RMSE) were compared to select the optimal model. Experimental results show that the RFR model outperforms GBR and KNN with MAE: 0.007101, MSE: 0.000610, R-squared: 0.999992, and RMSE: 0.024697. Leveraging these machine learning-based fault detection models enables telecom service providers to optimize network performance, reduce downtime, and increase customer satisfaction. | | --- | --- | | Keywords:
Machine learning, enterprise wireless, telecommunication, received signal levels (RSL) | | 1. INTRODUCTION Wireless telecommunication infrastructure can fail without notice for maintenance actio

FAQs

What does the proposed model detect in telecommunication infrastructure?

It detects deviations in received signal levels (RSL) by comparing new observations to a threshold and deciding between “Fault” and “No-fault” states.

How is the training data generated for the fault detection model?

The study models an enterprise point-to-multipoint wireless network in pathloss 5.0 and uses simulated parameters (e.g., pathloss, transmit power, antenna gain, losses) to train regression models.

Which regression model performs best and how is performance measured?

Random forest regression (RFR) outperforms gradient boosting regression (GBR) and KNN, measured using MAE, MSE, R-squared, and RMSE, with R-squared reported as 0.999992 for the best model.

Aug 5, 2026

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