The adoption of machine learning techniques in next-generation networks has increasingly attracted the attention of the research community. This is to provide adaptive learning and decision-making approaches to meet the requirements of different verticals, and to guarantee the appropriate performance requirements in complex mobility scenarios. In this perspective, the characterization of mobile service usage represents a funda-mental step. In this vein, this paper highlights the new features and capabilities offered by the "Network Slice Planner"(NSP) in its second version [12]. It also proposes a method combining both supervised and unsupervised learning techniques to analyze the behavior of a mass of mobile users in terms of service consumption. We exploit the data provided by the NSP v2 to conduct our analysis. Furthermore, we provide an evaluation of both the accuracy of the predictor and the performance of the underlying MEC infrastructure.
翻译:在下一代网络中采用机器学习技术已日益引起研究界的注意,目的是提供适应性学习和决策方法,以满足不同纵向的需求,并保证在复杂的流动情况下有适当的性能要求。从这个角度讲,移动服务使用的特点描述是一个基础性步骤。从这个角度讲,本文件强调了第二个版本“网络切片计划”(NSP)提供的新特点和能力[12]。它还提出了一种方法,将监督性和不受监督的学习技术结合起来,分析大批流动用户在服务消费方面的行为。我们利用NSP v2提供的数据进行分析。此外,我们评估了预测器的准确性以及基本的MEC基础设施的性能。