Wireless communication systems for identifying faults

A system for automatically identifying a fault condition in a wireless network, the system comprising:

• multiple subsystems configured to monitor, measure, and analyze a performance of the wireless network;
• memory;
• and
• at least one processor, coupled to the memory and using a trained machine learning model, the at least one processor configured to:
• receive, at the machine learning model from the multiple subsystems of the wireless network, information associated with multiple alerts triggered across the multiple subsystems, each of the multiple alerts being indicative of a corresponding potential fault condition in one of the multiple subsystems;
• receive, from a network platform, information regarding resources of the wireless network, including a topology defining relationships, connectivity, and dependencies among network elements of the multiple subsystems;
• identify, by the machine learning model, a subset of alerts of the multiple alerts by correlating the multiple alerts with the topology to identify a root cause of a cascade of alerts across the multiple subsystems based on the information regarding resources, wherein the subset of alerts represents a set of one or more root fault conditions associated with the multiple alerts triggered across the multiple subsystems and includes fewer alerts than are included in the multiple alerts;
• and
• identify, by the machine learning model, one or more remediating actions configured to address the one or more root fault conditions in at least one corresponding subsystem of the multiple subsystems,
• wherein the machine learning model is trained using the information regarding the resources including the topology and physical and virtual assets of the wireless network, and training data that identifies correlations between alerts generated in the multiple subsystems according to the dependencies defined by the topology.

A non-transitory computer-readable medium encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising:

• receiving, at a trained machine learning model from multiple subsystems of a wireless network, information associated with multiple alerts triggered across the multiple subsystems, each of the multiple alerts being indicative of a corresponding potential fault condition in one of the multiple subsystems;
• receiving, from a network platform, information regarding resources of the wireless network, including a topology defining relationship, connectivity, and dependencies among network elements of the multiple subsystems;
• identifying, by the machine learning model, a subset of alerts of the multiple alerts by correlating the multiple alerts with the topology to identify a root cause of a cascade of alerts across the multiple subsystems based on the information regarding resources, wherein the subset of alerts represents a set of one or more root fault conditions associated with the multiple alerts triggered across the multiple subsystems and include fewer alerts than are included in the multiple alerts;
• and
• identifying, by the machine learning model, one or more remediating actions configured to address the one or more root fault conditions in at least one corresponding subsystem of the multiple subsystems,
• wherein the machine learning model is trained using the information regarding the resources including the topology and physical and virtual assets of the wireless network, and training data that identifies correlations between alerts generated in the multiple subsystems according to the dependencies defined by the topology.