Abstract: An device edge may receive a machine learning model, and may receive data from an edge device associated with a RAN. The device edge may process the data, with the machine learning model, to generate edge analytic data associated with the edge device, and may process the edge analytic data, with the machine learning model, to detect a problem or to predict an issue associated with the edge device or a RAN device associated with the edge device. The device edge may provide particular edge analytic data associated with the problem or the issue to a cloud-computing system, and may perform one or more actions based on the problem or the issue associated with the edge device or the RAN device.

Abstract: A method of capturing data across interfaces of multiple radio network controllers, distributing the captured data for correlation on a call basis and then correlating and displaying all the captured data corresponding to each of the multiple radio network controllers. Data is captured across various interfaces of radio network controllers in a cellular network via a plurality of distributed network analyzers. Radio network controller events are identified in the captured data according to cell calls for each radio network controller according to analysis by signaling analyzers. The identified events from each signaling analyzer corresponding to captured data for each radio network controller is remotely analyzed and correlated together across the multiple radio network controllers. The correlated events are then displayed on a user interface to permit easy and quick monitoring of the radio network controllers simultaneously.