Abstract: Aspects of the subject disclosure may include, for example, detecting an interruption of a supply of operating power to a cell site of a cellular communication network, estimating an estimated time to restoration (ETR) of the supply of operating power to the cell site, wherein the estimating is based on information of an operator of the cellular communication network, determining, based in part on the ETR, to dispatch a portable generator to the cell site to provide a new supply of operating power to the cell site, and initiating a communication to dispatch the portable generator. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, detecting an interruption of a supply of operating power to a cell site of a cellular communication network, estimating an estimated time to restoration (ETR) of the supply of operating power to the cell site, wherein the estimating is based on information of an operator of the cellular communication network, determining, based in part on the ETR, to dispatch a portable generator to the cell site to provide a new supply of operating power to the cell site, and initiating a communication to dispatch the portable generator. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, training a first machine learning model based on a combination of labeled training data and unlabeled training data, the first machine learning model producing augmented training data, training a second machine learning model based on a combination of the labeled training data and the augmented training data, receiving, at a client device, customer information about a service degradation at a user equipment (UE) device of a customer in a cellular network, providing the customer information to the second machine learning model, receiving, at the client device, from the second machine learning model, information identifying a root cause of the service degradation, and modifying a network component of the cellular network or the UE device, based on the information identifying a root cause of the service degradation. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, categorizing users of a cellular network according to a plurality of user categories, identifying, by a machine learning model, a service degradation in the cellular network, identifying at least one affected user, the at least one affected user being affected by the service degradation, identifying one or more affected user categories including the at least one affected user, identifying potentially affected users, the potentially affected users being categorized according to the one or more affected user categories, and taking action to isolate the potentially affected users from the service degradation. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, training a cell-level machine learning model to predict a likelihood of a cell site in a cellular network having service issues that impact customers of the cellular network, training a user equipment (UE) level machine learning model using output information from the cell-level machine learning model and historical information about UE-level performance metrics, receiving, from a customer associated with a UE device operating on the cellular network, information about a service degradation experienced by the customer on the UE device, providing the information about the service degradation to the UE-level machine learning model; and receiving, from the UE-level machine learning model, information identifying a source of the service degradation. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, receiving, from a machine learning model, information about an event causing a service degradation in a cellular network, wherein the event is external to the cellular network, determining one or more event categories associated with the event causing the service degradation, determining, based on the one or more event categories, likely affected customers, the likely affected customers being likely to experience the service degradation, determining, by the machine learning model, proper resources for resolution of the service degradation, wherein the determining proper resources is based on the one or more event categories, and dispatching the proper resources for resolution of the service degradation. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, detecting an interruption of a supply of operating power to a cell site of a cellular communication network, estimating an estimated time to restoration (ETR) of the supply of operating power to the cell site, wherein the estimating is based on information of an operator of the cellular communication network, determining, based in part on the ETR, to dispatch a portable generator to the cell site to provide a new supply of operating power to the cell site, and initiating a communication to dispatch the portable generator. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, automatically detecting a service issue of a telecommunication system that may impact a customer of a telecommunication service provider of the telecommunication system, predicting, by the processing system, a future customer care interaction by the customer as a result of the service issue, initiating a resolution action of the telecommunication system for the service issue before the customer contacts a customer care agent of the telecommunication service provider, wherein the initiating is responsive to the predicting the future customer care interaction and modifying a component of the telecommunication system to improve efficiency of operation of the telecommunication system by reducing a number of customer care contacts by customers of the telecommunication service provider. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, detecting an interruption of a supply of operating power to a cell site of a cellular communication network, estimating an estimated time to restoration (ETR) of the supply of operating power to the cell site, wherein the estimating is based on information of an operator of the cellular communication network, determining, based in part on the ETR, to dispatch a portable generator to the cell site to provide a new supply of operating power to the cell site, and initiating a communication to dispatch the portable generator. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, automatically detecting a service issue of a telecommunication system that may impact a customer of a telecommunication service provider of the telecommunication system, predicting, by the processing system, a future customer care interaction by the customer as a result of the service issue, initiating a resolution action of the telecommunication system for the service issue before the customer contacts a customer care agent of the telecommunication service provider, wherein the initiating is responsive to the predicting the future customer care interaction and modifying a component of the telecommunication system to improve efficiency of operation of the telecommunication system by reducing a number of customer care contacts by customers of the telecommunication service provider. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, automatically detecting a service issue of a telecommunication system that may impact a customer of a telecommunication service provider of the telecommunication system, predicting, by the processing system, a future customer care interaction by the customer as a result of the service issue, initiating a resolution action of the telecommunication system for the service issue before the customer contacts a customer care agent of the telecommunication service provider, wherein the initiating is responsive to the predicting the future customer care interaction and modifying a component of the telecommunication system to improve efficiency of operation of the telecommunication system by reducing a number of customer care contacts by customers of the telecommunication service provider. Other embodiments are disclosed.

Abstract: A method includes receiving fault management data and service quality management data from an integrated feedback control loop, wherein a first set of faults in the fault management data is correlated with service quality management data if a root cause of the first set of faults is known and wherein a second set of faults in the fault management data are categorized as silent faults if no root cause of the second faults is known, The silent faults in the fault management are correlated with the service quality management data. The disclosure includes prioritizing analysis of the silent faults that affect the service quality management data, and prioritizing repair of faults in the fault management data that affect the service quality management data.

Abstract: A method includes collecting operational data from a system, segregating the data into a first component comprising one or more service quality anomalies and a second component comprising one or more network-based events, correlating the first component and the second component to determine whether the one or more network-based events have an impact on service quality, and prioritizing a resolution of the network-based events that have an impact on service quality.

Abstract: A method includes collecting operational data from a system, segregating the data into a first component comprising one or more service quality anomalies and a second component comprising one or more network-based events, correlating the first component and the second component to determine whether the one or more network-based events have an impact on service quality, and prioritizing a resolution of the network-based events that have an impact on service quality.

Abstract: Systems and methods for providing accurate data about the distribution of UEs after disaster include a movable eNodeB that is moved to at least three locations. The coordinates for the at least three locations are determined and the distance between the eNodeB and a plurality of UEs is measured. The location of each UE relative to the eNodeB is calculated based on the distance between the eNodeB and the plurality of UEs using trilateration. The coordinates for the UEs is then calculated based on the coordinates for the at least three locations and the determined location of the UEs relative to the eNodeB.

Abstract: A method, computer-readable storage device and apparatus for quantifying a total number of customers impacted by a cellular tower outage are disclosed. For example, the method monitors a plurality of cellular towers, detects a failure of one of the plurality of cellular towers, identifies a subset of the plurality of cellular towers that are included in an affected region, calculates a number of additional customers served by each one of the subset of the plurality of cellular towers that are included in the affected region due to the failure of the one cellular tower, and quantifies the total number of customers impacted by the failure of the one cellular tower by adding the number of additional customers served by the each one of the subset of the plurality of cellular towers that are included in the affected region.

Abstract: A method, computer-readable storage device and apparatus for quantifying a total number of customers impacted by a cellular tower outage are disclosed. For example, the method monitors a plurality of cellular towers, detects a failure of one of the plurality of cellular towers, identifies a subset of the plurality of cellular towers that are included in an affected region, calculates a number of additional customers served by each one of the subset of the plurality of cellular towers that are included in the affected region due to the failure of the one cellular tower, and quantifies the total number of customers impacted by the failure of the one cellular tower by adding the number of additional customers served by the each one of the subset of the plurality of cellular towers that are included in the affected region.