The increased programmability of communication networks makes them more autonomous, and with the ability to actuate fast in response to users and networks’ events. However, it is usually a difficult task to understand the root cause of the network problems, so that autonomous actuation can be provided in advance. This paper analyzes the probable root causes of reduced accessibility in 4G networks, taking into account the information of important key performance indicators (KPIs), and considering their evolution in previous time-frames. This approach resorts to interpretable machine learning models to measure the importance of each KPI in the decrease of the network accessibility in a posterior time-frame. The results show that the main root causes of reduced accessibility in the network are related with the number of failure handovers, the number of phone calls and text messages in the network, the overall download volume, and the availability of the cells. However, the main causes of reduced accessibility in each cell are more related to the number of users in each cell and its download volume produced. The results also show the number of principal component analysis (PCA) components required for a good prediction, as well as the best machine learning approach for this specific use case. In addition, we finished our considerations with a discussion about 5G network requirements where proactivity is mandatory.

通信网络可编程性的提高使它们更具自治性，并具有响应用户和网络事件而快速启动的能力。但是，了解网络问题的根本原因通常是一项艰巨的任务，因此可以提前提供自主驱动。本文分析了4G网络中可访问性降低的可能根本原因，同时考虑了重要的关键性能指标（KPI）的信息，并考虑了它们在先前时间范围内的发展。此方法采用可解释的机器学习模型来衡量每个KPI在后时间范围内降低网络可访问性的重要性。结果表明，网络可访问性降低的根本原因与故障切换次数有关，网络中电话和短信的数量，整体下载量以及单元的可用性。但是，每个单元中可访问性降低的主要原因与每个单元中的用户数量及其产生的下载量有关。结果还显示了良好预测所需的主成分分析（PCA）组件的数量，以及针对此特定用例的最佳机器学习方法。此外，我们在讨论强制性主动性的5G网络要求时结束了我们的考虑。结果还显示了良好预测所需的主成分分析（PCA）组件的数量，以及针对此特定用例的最佳机器学习方法。此外，我们在讨论强制性主动性的5G网络要求时结束了我们的考虑。结果还显示了良好预测所需的主成分分析（PCA）组件的数量，以及针对此特定用例的最佳机器学习方法。此外，我们在讨论强制性主动性的5G网络要求时结束了我们的考虑。