下一代网络中的无监督学习：实时的性能自诊断
Unsupervised Learning in Next-Generation Networks: Real-Time Performance Self-Diagnosis
摘要：
这封信演示了使用无监督机器学习来实现下一代蜂窝网络的性能自诊断。我们提出了两个简化的无监督学习的应用程序，可以使边缘节点，如无线接入网络智能控制器（RIC）的实时的【形】性能自我诊断。第一个应用程序检测异常性能，并找到故障、配置或网络过程故障的根本原因。第二个应用程序使用聚类来学习两个性能度量之间的关系。我们提出的应用程序运行在接近恒定的时间复杂度，使他们，结合主题的专业知识验证，适合实时的【形】RIC应用网络诊断。
英文摘要：
This letter demonstrates the use of unsupervised machine learning to enable performance self-diagnosis of next-generation cellular networks. We propose two simplified applications of unsupervised learning that can enable real-time performance self-diagnosis on edge nodes such as the radio access network intelligent controller (RIC). The first application detects anomalous performance and finds its root cause of faults, configuration, or network procedure failures. The second application uses clustering to learn the relationship between two performance measures. Our proposed applications run in near-constant time complexity, making them, combined with subject-matter expertise validation, suitable real-time RIC applications for network diagnosis.
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