User-Level Handover Decision Making Based on Machine Learning Approaches

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João Lima
Alvaro Medeiros
Eduardo Aguiar
Tarciana Guerra


This letter covers a broad comparison of methods for classification and regression applications for a user-level handover decision making in scenarios with adverse propagation conditions involving buildings, coverage holes, and shadowing effects. The simulation campaigns are based on network simulator ns-3. The comparison encompasses classical machine learning approaches, such as KNN, SVM, and neural networks, but also state-of-the-art fuzzy logic systems and latter boosting machines. The results indicate that SVM and MLP are the most suitable for the classification of the best handover target, although fuzzy system SOFL can perform similarly with lower processing time. Additionally, for the download time estimation, LightGBM provides the smallest error with short processing time, even in hard propagation scenarios.

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How to Cite
Lima, J., Medeiros, A., Aguiar, E., JUNIOR, V. A. D. S., & Guerra, T. (2022). User-Level Handover Decision Making Based on Machine Learning Approaches. Journal of Communication and Information Systems, 37(1), 104–108.

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