Wireless communication systems for identifying faults

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

• one or more 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 one or more subsystems of the wireless network, information associated with multiple alerts triggered across the one or more subsystems, each of the multiple alerts being indicative of a corresponding potential fault condition in one of the one or more subsystems;
• receive information regarding a topology defining dependencies between network elements of the one or more subsystems;
• identify, by the machine learning model, a subset of alerts of the multiple alerts based on the dependencies between network elements, wherein the subset of alerts represents a set of one or more root fault conditions associated with the multiple alerts triggered across the one or more 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 one or more subsystems,
• wherein the machine learning model is trained using training data that identifies one or more correlations between alerts generated in the one or more subsystems according to the dependencies.

A method for automatically remediating a fault condition in a wireless network, the method comprising:

• receiving, at a 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;
• identifying, 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;
• identifying, by the machine learning model, one or more remediating actions configured to address the one or more root fault conditions;
• and
• automatically initiating the one or more remediating actions, wherein the one or more remediating actions include at least one of:
• a microservice restart, a container orchestration service pod restart, an application restart, a routing unit restart, traffic redirection, cell lock after call draining, cell unlock after call draining, or graceful shutdown of a container network function or virtual network function.