ItemConference Paper

Predictive Modelling of Microwave Link Failures Using Machine Learning and Deep Learning Techniques

Title
Predictive Modelling of Microwave Link Failures Using Machine Learning and Deep Learning Techniques
Creator
Okewumi, Peter Olufeyi; Ojo, Olayinka Anthony; Iwendi, Celestine; Sharma, Vandana; Uwah, Salome Enoshi; Aboutorabi, Negin
Description
Microwave radio links play a vital role in keeping mobile networks running, especially when it comes to backhaul-the part of the network that connects base stations to the core. Gradual failure in these links could disrupt services and cost providers a lot in both revenue and customer trust. In this study, we explore how machine learning can help predict such failures before they happen. Network performance data from a mobile network operator in Nigeria was collected, cleaned and used to achieve the purpose of the study. Four algorithms belonging to machine learning (ML) and deep learning (DL) were adopted and used for training the dataset and predicting link failures. Results show that the Long ShortTerm Memory (LSTM - a deep learning model effective for handling time-series data) performed best with prediction accuracy of 92%, distantly followed by others. These findings indicate that the LSTM is better in modelling temporal patterns in network behaviours. This study provides a practical framework for automating microwave link monitoring and maintenance, thereby reducing manual diagnostics, preventing outages, and improving service reliability. The proposed solution supports the integration of predictive intelligence into network operations, enhancing the quality of service and operational efficiency for telecom providers. 2025 IEEE.
Source
2025 12th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Deep Learning; Long Short-Term Memory (LSTM); Machine Learning; Microwave Link Failure; Microwave radio; Quality of Service
Coverage
Okewumi P.O., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Ojo O.A., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Iwendi C., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Sharma V., Christ University, Computer Science Department, Bengaluru, India; Uwah S.E., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Aboutorabi N., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833155421-7;
Format
online
Language
English
Type
Conference paper

Citation

Okewumi, Peter Olufeyi; Ojo, Olayinka Anthony; Iwendi, Celestine; Sharma, Vandana; Uwah, Salome Enoshi; Aboutorabi, Negin, “Predictive Modelling of Microwave Link Failures Using Machine Learning and Deep Learning Techniques,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26102.

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