Fifth-generation (5G) mobile communication networks have recently emerged in various fields, including highspeed trains. However, the dense deployment of 5G millimeter wave (mmWave) base stations (BSs) and the high speed of moving trains lead to frequent handovers (HOs), which can adversely affect the Quality-of-Service (QoS) of mobile users. As a result, HO optimization and resource allocation are essential considerations for managing mobility in high-speed train systems. In this paper, we model system performance of a high-speed train system with a novel machine learning (ML) approach that is nested cross validation scheme that prevents information leakage from model evaluation into the model parameter tuning, thereby avoiding overfitting and resulting in better generalization error. To this end, we employ ML methods for the high-speed train system scenario. Handover Margin (HOM) and Time-to-Trigger (TTT) values are used as features, and several KPIs are used as outputs, and several ML methods including Gradient Boosting Regression (GBR), Adaptive Boosting (AdaBoost), CatBoost Regression (CBR), Artificial Neural Network (ANN), Kernel Ridge Regression (KRR), Support Vector Regression (SVR), and k-Nearest Neighbor Regression (KNNR) are employed for the problem. Finally, performance comparisons of the cross validation schemes with the methods are made in terms of mean absolute error (MAE) and mean square error (MSE) metrics are made. As per obtained results, boosting methods, ABR, CBR, GBR, with nested cross validation scheme superiorly outperforms conventional cross validation scheme results with the same methods. On the other hand, SVR, KNRR, KRR, ANN with the nested scheme produce promising results for prediction of some KPIs with respect to their conventional scheme employment.

翻译：第五代（5G）移动通信网络近期已在包括高速列车在内的多个领域涌现。然而，5G毫米波（mmWave）基站（BS）的密集部署以及列车的高速行驶导致频繁切换（HO），这会对移动用户的服务质量（QoS）产生不利影响。因此，切换优化与资源分配成为管理高速列车系统移动性的关键考量。本文提出一种新颖的机器学习（ML）方法对高速列车系统性能进行建模，该方法采用嵌套交叉验证方案，可防止模型评估过程中的信息泄露影响模型参数调整，从而避免过拟合，获得更优的泛化误差。为此，我们针对高速列车系统场景应用了多种机器学习方法。以切换余量（HOM）和时间触发（TTT）值作为特征，若干关键绩效指标（KPI）作为输出，采用多种ML方法（包括梯度提升回归（GBR）、自适应提升回归（AdaBoost）、CatBoost回归（CBR）、人工神经网络（ANN）、核岭回归（KRR）、支持向量回归（SVR）和k近邻回归（KNNR））处理该问题。最后，以平均绝对误差（MAE）和均方误差（MSE）为指标，对各交叉验证方案与方法的性能进行了比较。根据结果，采用嵌套交叉验证方案的提升方法（ABR、CBR、GBR）在性能上显著优于采用传统交叉验证方案的同一方法。另一方面，采用嵌套方案的SVR、KNRR、KRR、ANN在预测某些KPI时，相比其传统方案应用取得了令人期待的结果。