User-Level Handover Decision Making Based on Machine Learning Approaches

Abstract: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.

Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)

arXiv:2609.22593 [cs.LG]

(or arXiv:2609.22593v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.22593 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.14209/jcis.2022.11 Focus to learn more DOI(s) linking to related resources

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