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 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;
• identify, by the machine-learning model, a subset of alerts of the multiple alerts, 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, a number of alerts in the subset of alerts being less than the number of 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 training data that identifies correlation between alerts generated in the multiple subsystems.