发表机构
CPQD; Federal University of Juiz de Fora; Munster Technological University; Federal University of Rio Grande do Norte（CPQD; 茹伊斯迪福拉联邦大学; 芒斯特理工大学; 北里奥格兰德联邦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文基于ns-3仿真，比较多种机器学习方法在不利传播条件下用户级切换决策中的性能，发现SVM和MLP最适合分类，LightGBM在下载时间估计上误差最小且处理时间短。
AI 中文摘要
本文对在涉及建筑物、覆盖空洞和阴影效应的不利传播条件下，用户级切换决策的分类和回归应用方法进行了广泛比较。仿真活动基于网络模拟器ns-3。比较涵盖了经典机器学习方法，如KNN、SVM和神经网络，也包括最先进的模糊逻辑系统和后起的提升机器。结果表明，SVM和MLP最适合最佳切换目标的分类，尽管模糊系统SOFL也能以较低的处理时间实现类似性能。此外，对于下载时间估计，LightGBM即使在困难的传播场景下也能以较短的处理时间提供最小的误差。
英文摘要
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.
Journal refJournal of Communication and Information Systems (2022), 37(1), 104-108
