Abstract: Systems and methods for analyzing and prioritizing alarms in a communications network are provided. A method, according to one implementation, includes the step of obtaining network information regarding the condition of a network. Using the network information, the method further includes performing a hybrid Machine Learning (ML) technique that includes training and inference of a plurality of ML models to calculate metrics of the network. Also, the method includes the step of selecting one of the plurality of ML models based on a combination of the metrics.

Abstract: Systems and methods for analyzing and prioritizing alarms in a communications network are provided. A method, according to one implementation, includes the step of obtaining network information regarding the condition of a network. Using the network information, the method further includes performing a hybrid Machine Learning (ML) technique that includes training and inference of a plurality of ML models to calculate metrics of the network. Also, the method includes the step of selecting one of the plurality of ML models based on a combination of the metrics.

Abstract: Systems and methods for analyzing root-causes of Wi-Fi issues in a Wi-Fi system associated with a Local Area Network (LAN) are described in the present disclosure. A method, according to one embodiment, includes a step of monitoring a Wi-Fi system associated with a LAN to detect authentication failures in the Wi-Fi system. In response to detecting an authentication failure in the Wi-Fi system, the method also includes the step of analyzing the authentication failure to determine one or more root-causes of the authentication failure. The method also includes pushing changes to the Wi-Fi system to automatically remediate the one or more root-causes in the Wi-Fi system.

Abstract: Systems and methods include receiving alarms from a network; utilizing a machine learning model to classify the alarms as one of important and non-important; and displaying the important alarms and suppressing display of the non-important alarms. The systems and methods can further include training the machine learning model with historical alarm data that includes features related to an associated device and comments related to how a Network Operations Center (NOC) handles an associated alarm or group of alarms. The training can be via supervised machine learning with the features used as labels or via reinforcement learning with the features used as a reward.

Abstract: Systems and methods for predicting the location of unknown network devices within a particular site of a multi-site network are provided. A method, according to one implementation, includes obtaining transmission characteristics related to any of packets and frames transmitted between an unknown network device and a plurality of beacon devices. Each beacon device is a network device associated with a fixed site location within a multi-site network. The method also includes utilizing the transmission characteristics to predict a site within the multi-site network where the unknown network device is located.

Abstract: Systems and methods for analyzing root-causes of Wi-Fi issues in a Wi-Fi system associated with a Local Area Network (LAN) are described in the present disclosure. A method, according to one embodiment, includes a step of monitoring a Wi-Fi system associated with a LAN to detect authentication failures in the Wi-Fi system. In response to detecting an authentication failure in the Wi-Fi system, the method also includes the step of analyzing the authentication failure to determine one or more root-causes of the authentication failure. The method also includes pushing changes to the Wi-Fi system to automatically remediate the one or more root-causes in the Wi-Fi system.