Wireless communication systems for predicting faults

A system for predicting 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 trained 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;
• receive, at the machine-learning model from a plurality of external devices, observation information regarding the wireless network;
• receive, at the machine-learning model from a network platform, information regarding resources of the wireless network;
• train the machine-learning model using the multiple alerts, the observation information, and the information regarding the resources;
• 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 predicted 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
• train the machine-learning model using the subset of alerts.