5G Network Optimization using Machine Learning

Abstract

In the era of 5G technology, predicting coverage areas is crucial for optimizing network performance and ensuring reliable connectivity. This study presents a comprehensive analysis of various machine learning algorithms for predicting 5G coverage based on the RF Signal Data. The target column, Band Width, is used to gauge prediction accuracy across different models. Traditional methods such as Logistic Regression, K-Nearest Neighbors (KNN), Naive Bayes, Random Forest, Support Vector Machine (SVM), XG Boost, Light GBM, AdaBoost, Bayesian Network Classifier models are compared with advanced techniques such as Stacking and Voting Classifiers, as well as Convolutional Neural Networks (CNN). The objective is to identify dominant feature parameters that significantly influence 5G coverage prediction. By implementing a diverse array of models, this research aims to benchmark the performance and accuracy of these algorithms. The comparative analysis highlights the strengths and limitations of each approach, providing valuable insights for network engineers and researchers. The findings suggest that ensemble methods, particularly Stacking and Voting Classifiers, along with CNN, offer superior prediction accuracy and robustness, thereby serving as promising tools for enhancing 5G network planning and deployment.

Introduction

This study focuses on enhancing 5G network coverage prediction to optimize deployment and improve connectivity. Using a comprehensive dataset with 27 parameters and over 164,000 observations, it applies and compares various machine learning models, including traditional algorithms (Logistic Regression, KNN, Naive Bayes, Random Forest, SVM, XGBoost, LightGBM, AdaBoost) and advanced techniques such as Stacking and Voting Classifiers and Convolutional Neural Networks (CNN).

The traditional models provide varying accuracy, with Random Forest performing best among them. The proposed system leverages ensemble methods (Stacking and Voting Classifiers) and CNNs to improve prediction accuracy and robustness. These advanced approaches combine strengths of multiple models and capture spatial patterns in data, leading to better identification of key factors affecting 5G coverage like frequency, signal strength, modulation, and bandwidth.

Experimental results show that Voting Classifier achieves perfect accuracy in the test setup, while CNN also demonstrates strong performance. The study highlights the importance of advanced machine learning in accurately forecasting 5G coverage, aiding network engineers in strategic planning and efficient resource allocation to ensure reliable, optimized 5G connectivity.

Conclusion

In conclusion, this study has demonstrated the effectiveness of ensemble methods such as Stacking and Voting Classifiers, alongside Convolutional Neural Networks, in predicting 5G coverage. Through analysis of 27 parameters across diverse locations, including Frequency, Signal Strength, Modulation, and Bandwidth, we identified critical features influencing coverage efficacy.The findings highlight the importance of integrating multiple data modalities to enhance prediction accuracy, crucial for optimizing 5G deployment strategies. By refining predictive models, this research contributes to more efficient network planning and management, offering valuable insights for future advancements in telecommunications infrastructure.

References

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Copyright

Copyright © 2025 Md. A. Khudhus, Panuganti Naveen, Dr. M. Humera Khanam, Dr. D. Vivekanandha Reddy. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.