User-Level Handover Decision Making Based on Machine Learning Approaches

The paper compares various machine‑learning techniques for user‑level handover decisions in challenging propagation environments, using ns‑3 simulations. Classical methods such as KNN, SVM, and neural networks, along with fuzzy logic systems and boosting machines, are evaluated for classifying the optimal handover target and estimating download time. Results show that SVM and MLP perform best for target classification, while LightGBM yields the lowest error for download time estimation, with fuzzy SOFL offering comparable accuracy at lower processing time.

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