  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.
