Abstract: Aspects of the subject disclosure may include, for example, receiving a handover request from a user equipment (UE) device in a mobility network, wherein the handover request identifies a target cell for handing over radio communication with the UE device from a source cell, wherein the identifying is based on the handover request, determining a usage level of the UE device, wherein the usage level comprises one of performance-sensitive traffic and performance-tolerant traffic, selecting an alternative target cell for the handover request, wherein the selecting is responsive to determining a performance-sensitive traffic usage level of the UE device, and initiating a handover operation between the source cell and the alternative target cell. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, receiving source information and destination information from a user device, receiving information defining an interactive application from the user device. determining, using network information of a mobility network, a navigation path from a source to a destination, the navigation path selected to enable continuous use of the interactive application with the user device on the mobility network during a journey from the source to the destination, and communicating navigation path information to the user device. 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, 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: 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: Examples provided herein describe a method for managing virtualized network service bundles. For example, a physical processor may implement machine readable instructions to receive a first data packet in a virtual network, determine a first type of the first data packet, determine a first active virtualized network service bundle instance associated with a first virtualized network service bundle that handles the first type of data packet, and direct the first data packet to the first active virtualized network service bundle instance. In some examples, the first virtualized network service bundle may comprise a first set of virtual network functions, and the first virtualized network service bundle may be associated with a set of active virtualized network service bundles instances, where the set of active virtualized network service bundle instances may comprise the first active virtualized network service bundle instance.

Abstract: Examples provided herein describe a method for managing virtualized network service bundles. For example, a physical processor may implement machine readable instructions to receive a first data packet in a virtual network, determine a first type of the first data packet, determine a first active virtualized network service bundle instance associated with a first virtualized network service bundle that handles the first type of data packet, and direct the first data packet to the first active virtualized network service bundle instance. In some examples, the first virtualized network service bundle may comprise a first set of virtual network functions, and the first virtualized network service bundle may be associated with a set of active virtualized network service bundles instances, where the set of active virtualized network service bundle instances may comprise the first active virtualized network service bundle instance.