In 5G wireless communication, Intelligent Transportation Systems (ITS) and automobile applications, such as autonomous driving, are widely examined. These applications have strict requirements and often require high Quality of Service (QoS). In an urban setting, Ultra-Dense Networks (UDNs) have the potential to not only provide optimal QoS but also increase system capacity and frequency reuse. However, the current architecture of 5G UDN of dense Small Cell Nodes (SCNs) deployment prompts increased delay, handover times, and handover failures. In this paper, we propose a Machine Learning (ML) supported Mobility Prediction (MP) strategy to predict future Vehicle User Equipment (VUE) mobility and handover locations. The primary aim of the proposed methodology is to minimize Unnecessary Handover (UHO) while ensuring VUEs take full advantage of the deployed UDN. We evaluate and validate our approach on a downlink system-level simulator. We predict mobility using Support Vector Machine (SVM), Decision Tree Classifier (DTC), and Random Forest Classifier (RFC). The simulation results show an average reduction of 30% in handover times by utilizing ML-based MP, with RFC showing the most reduction up to 70% in some cases.

翻译：在5G无线通信中，智能交通系统（ITS）和汽车应用（如自动驾驶）得到了广泛研究。这些应用具有严格的要求，通常需要高服务质量（QoS）。在城市环境中，超密集网络（UDN）不仅有可能提供最优的QoS，还能提高系统容量和频率复用效率。然而，当前5G UDN中密集部署小蜂窝节点（SCN）的架构会导致时延增加、切换次数增多以及切换失败率上升。本文提出了一种基于机器学习（ML）支持的移动性预测（MP）策略，用于预测未来车辆用户设备（VUE）的移动性和切换位置。该方法的主要目标是最小化不必要切换（UHO），同时确保VUE充分利用部署的UDN。我们在下行链路系统级模拟器上评估并验证了所提出的方法。我们使用支持向量机（SVM）、决策树分类器（DTC）和随机森林分类器（RFC）来预测移动性。仿真结果显示，利用基于ML的MP平均可减少30%的切换次数，其中RFC在某些情况下甚至能减少高达70%。