Abstract: Provided are systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to identify, diagnose, and mitigate occurrences of network faults or incidents within a network. Historical network incidents may be used to generate a model that may be used to evaluate real-time occurring network incidents, such as to identify a cause of the network incident. Clustering algorithms may be used to identify portions of the model that share similarities with a network incident and then actions taken to resolve similar network incidents in the past may be identified and proposed as candidate actions that may be executed to resolve the cause of the network incident. Execution of the candidate actions may be performed under control of a user or automatically based on execution criteria and the configuration of the fault mitigation system.

Abstract: Embodiments of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to identify, diagnose, and mitigate occurrences of network faults or incidents within a network. Historical network incidents may be used to generate a model that may be used to evaluate real-time occurring network incidents, such as to identify a cause of the network incident. Clustering algorithms may be used to identify portions of the model that share similarities with a network incident and then actions taken to resolve similar network incidents in the past may be identified and proposed as candidate actions that may be executed to resolve the cause of the network incident. Execution of the candidate actions may be performed under control of a user or automatically based on execution criteria and the configuration of the fault mitigation system.

Abstract: Embodiments of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to identify, diagnose, and mitigate occurrences of network faults or incidents within a network. Historical network incidents may be used to generate a model that may be used to evaluate real-time occurring network incidents, such as to identify a cause of the network incident. Clustering algorithms may be used to identify portions of the model that share similarities with a network incident and then actions taken to resolve similar network incidents in the past may be identified and proposed as candidate actions that may be executed to resolve the cause of the network incident. Execution of the candidate actions may be performed under control of a user or automatically based on execution criteria and the configuration of the fault mitigation system.

Abstract: Methods, systems, and apparatus for root cause analysis in a communication network. In one aspect, a method includes providing a quantum computer with data representing a topology of the communication network, the topology comprising a graph of vertices representing network devices and edges representing connections between network devices; receiving, from the quantum computer, data representing a first subset of network devices, wherein the first subset comprises a dominating set of vertices or a vertex cover for the graph; monitoring network devices in the first subset to generate alarm data representing triggered network device alarms; providing the alarm data to a quantum computer; receiving, from the quantum computer, data representing a second subset of network devices, wherein the second subset comprises a set cover for the alarm data and the network devices in the second subset comprise diagnosed sources of failures in the communication network.

Abstract: A device determines, from a network slice template associated with a network slice, a quality of service (QoS) profile for the network slice that includes performance metrics for corresponding QoS parameters associated with providing a service. The device monitors performance of the network slice in association with the QoS profile, and determines, based on the performance, that a performance indicator for a QoS parameter of the network slice is outside a threshold range of a performance metric. The device determines, based on the performance information and the QoS profile, a slice modification to the network slice template for the network slice, where the slice modification is configured to cause the performance indicator to be within the threshold range of the performance metric. The device causes a network slice orchestrator to update an instantiation of the network slice according to the slice modification and the network slice template.

Abstract: A device may receive site data identifying a site associated with a telecommunications network. The device may receive RF drive test data identifying signal strengths of the site at different location points. The device may receive network KPI data and throughput data associated with the site. The device may assign unique identifiers to the different location points. The device may determine, based on the site data, the RF drive test data, the network KPI data, and the unique identifiers, at least one of reference signal strength data for each of the different location points or downlink channel data for each of the different location points. The device may determine degraded signal strength data identifying a degraded signal strength of each of the different location points, if the site is decommissioned and based on the at least one of the reference signal strength data or the downlink channel data.

Abstract: Methods, systems, and apparatus for root cause analysis in a communication network. In one aspect, a method includes providing a quantum computer with data representing a topology of the communication network, the topology comprising a graph of vertices representing network devices and edges representing connections between network devices; receiving, from the quantum computer, data representing a first subset of network devices, wherein the first subset comprises a dominating set of vertices or a vertex cover for the graph; monitoring network devices in the first subset to generate alarm data representing triggered network device alarms; providing the alarm data to a quantum computer; receiving, from the quantum computer, data representing a second subset of network devices, wherein the second subset comprises a set cover for the alarm data and the network devices in the second subset comprise diagnosed sources of failures in the communication network.

Abstract: Embodiments of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to identify, diagnose, and mitigate occurrences of network faults or incidents within a network. Historical network incidents may be used to generate a model that may be used to evaluate real-time occurring network incidents, such as to identify a cause of the network incident. Clustering algorithms may be used to identify portions of the model that share similarities with a network incident and then actions taken to resolve similar network incidents in the past may be identified and proposed as candidate actions that may be executed to resolve the cause of the network incident. Execution of the candidate actions may be performed under control of a user or automatically based on execution criteria and the configuration of the fault mitigation system.

Abstract: Embodiments of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to identify, diagnose, and mitigate occurrences of network faults or incidents within a network. Historical network incidents may be used to generate a model that may be used to evaluate real-time occurring network incidents, such as to identify a cause of the network incident. Clustering algorithms may be used to identify portions of the model that share similarities with a network incident and then actions taken to resolve similar network incidents in the past may be identified and proposed as candidate actions that may be executed to resolve the cause of the network incident. Execution of the candidate actions may be performed under control of a user or automatically based on execution criteria and the configuration of the fault mitigation system.

Abstract: Embodiments of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to identify, diagnose, and mitigate occurrences of network faults or incidents within a network. Historical network incidents may be used to generate a model that may be used to evaluate real-time occurring network incidents, such as to identify a cause of the network incident. Clustering algorithms may be used to identify portions of the model that share similarities with a network incident and then actions taken to resolve similar network incidents in the past may be identified and proposed as candidate actions that may be executed to resolve the cause of the network incident. Execution of the candidate actions may be performed under control of a user or automatically based on execution criteria and the configuration of the fault mitigation system.

Abstract: A device determines, from a network slice template associated with a network slice, a quality of service (QoS) profile for the network slice that includes performance metrics for corresponding QoS parameters associated with providing a service. The device monitors performance of the network slice in association with the QoS profile, and determines, based on the performance, that a performance indicator for a QoS parameter of the network slice is outside a threshold range of a performance metric. The device determines, based on the performance information and the QoS profile, a slice modification to the network slice template for the network slice, where the slice modification is configured to cause the performance indicator to be within the threshold range of the performance metric. The device causes a network slice orchestrator to update an instantiation of the network slice according to the slice modification and the network slice template.

Abstract: A device may receive site data identifying a site associated with a telecommunications network. The device may receive RF drive test data identifying signal strengths of the site at different location points. The device may receive network KPI data and throughput data associated with the site. The device may assign unique identifiers to the different location points. The device may determine, based on the site data, the RF drive test data, the network KPI data, and the unique identifiers, at least one of reference signal strength data for each of the different location points or downlink channel data for each of the different location points. The device may determine degraded signal strength data identifying a degraded signal strength of each of the different location points, if the site is decommissioned and based on the at least one of the reference signal strength data or the downlink channel data.

Abstract: The present disclosure relates to a system, computer readable medium, and method for training workers in occupational skills. The disclosure provides highly automated, artificial intelligence driven, ways of improving human capital through the acquisition, development, and verification of multiple career related proficiencies. Generally, the disclosure provides an integrated digital platform that allows workers to (1) receive training on their existing job skills, (2) verify mastery of the existing job skills through various assessments, and (3) receive recommendations regarding which job skills may be useful to acquire.