Abstract: Aspects of the subject disclosure may include, for example, receiving artificial intelligence (AI) analytics from a generative AI system, and based on the AI analytics, causing a software defined network (SDN) controller to identify a signaling path over a quantum signaling network, thereby enabling selection of a determined suitable path for quantum entanglement distribution. Other embodiments are disclosed.

Abstract: An example method includes identifying, using a first NLP agent, a database of an enterprise containing desired data, selecting, using a second NLP agent, a schema analysis tool that is believed to be suited to a type of the database, establishing, using a third NLP agent, a connection to the database, extracting, using a fourth NLP agent, schema information from the database, comparing, using a fifth NLP agent and the schema analysis tool and based on the schema information, a structure of a schema of the database to a structure of a schema of a similar database, determining, based on the comparing, a change to make to the schema of the database, sending, using a sixth NLP agent, an instruction to a network element of the enterprise to make the change to the schema of the database, and validating, using a seventh NLP agent, the change to the schema.

Abstract: Aspects of the subject disclosure may include, for example, a system, including: a plurality of drones; a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of: deploying one or more drones of the plurality of drones; and distributing entangled qubits via the plurality of drones to one or more ground stations. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a device having a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, including: selecting a first building block pattern function (bbDNA) having a first weight from a catalog, wherein the first bbDNA is based on a first mature pattern reaching a first bbDNA frequency threshold, wherein the first bbDNA is associated with at least one machine learning (ML) model of a plurality of ML models; selecting a second bbDNA having a second weight from the catalog, wherein the second bbDNA is associated with at least one ML model in the plurality of ML models; and creating a new ML model based on a combination of the first bbDNA and the second bbDNA. Other embodiments are disclosed.

Abstract: A processing system of a cellular network may receive at least a first measurement of at least one network performance indicator associated with the cellular network from at least one of: at least a first channel sounding measurement record from at least a first autonomous vehicle (AV), or a communication from at least one mobile endpoint device or at least one component of the cellular network. The processing system may next identify at a condition of a location based upon the first measurement and may direct at least one of: the first AV or a second AV to the location to capture at least a second channel sounding measurement record. The processing system may then obtain from the first AV or the second AV the at least the second channel sounding measurement record, and may adjust at least one aspect of the cellular network in response.

Abstract: Aspects of the subject disclosure may include, for example, a device with a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitates performance of operations, including: automatically detecting an intrusion of a secure communication channel that provides communication services to a node in a network; implementing a generative artificial intelligence that finds an alternative secure communication channel; and migrating the communication services to the alternative secure communication channel responsive to finding the alternative secure communication channel. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, obtaining a request pertaining to a communication service, processing a file associated with the request to generate asset details, obtaining data pertaining to demand for communication services within a communication network, processing at least the data to generate a forecast of demand for the communication service, and processing the asset details and the forecast of demand to allocate capacity, acquire resources, or a combination thereof, for facilitating the communication service. Other embodiments are disclosed.

Abstract: An automated system provides end-to-end (E2E) orchestration for machine learning (ML)-enabled and software-defined network (SDN)-enabled reactive and predictive (e.g., 5G) cell-on-drone (COD) dispatch and deployment. The system may be configured to detect or predict traffic surges (e.g., relative to typical or baseline levels) in a given cell site and orchestrate provisioning of resources, including COD deployment, to address the surge. COD deployment may involve one or more clusters or colonies of drones equipped with low-powered cellular radio access (or small cell) nodes, where each cluster includes (or is led by) one or more SDN equipped drones that provide intelligent SDN-on-drone (SOD) functionality.

Abstract: Aspects of the subject disclosure may include, for example, a device having a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, including: selecting a first building block pattern function (bbDNA) having a first weight from a catalog, wherein the first bbDNA is based on a first mature pattern reaching a first bbDNA frequency threshold, wherein the first bbDNA is associated with at least one machine learning (ML) model of a plurality of ML models; selecting a second bbDNA having a second weight from the catalog, wherein the second bbDNA is associated with at least one ML model in the plurality of ML models; and creating a new ML model based on a combination of the first bbDNA and the second bbDNA. Other embodiments are disclosed.

Abstract: Facilitating radio access network on-demand dynamic bandwidth allocation in advanced networks is provided herein. Operations of a system include determining that a group of network slice resources associated with network equipment fail to satisfy a specification applicable to a user equipment. The operations can also include extracting a first group of resource blocks from a first network slice resource of the group of network slice resources. Further, the operations can include reallocating the first group of resource blocks to a second network slice resource of the group of network slice resources. A combination of the first group of resource blocks and a second group of resource blocks of the second network slice resource is determined to satisfy the specification applicable to the user equipment.

Abstract: Aspects of the subject disclosure may include, for example, a system, including: a plurality of drones; a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of: deploying one or more drones of the plurality of drones; and distributing entangled qubits via the plurality of drones to one or more ground stations. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a system including a cross-segment slice controller (CSSC) configured to interface with a software-defined network (SDN) controller and a software-defined radio (SDR) controller. The SDN controller may be associated with a core network and the SDR controller may be associated with a radio access network (RAN). The system further includes a machine learning (ML) component configured to obtain and analyze data regarding the core network and the RAN, and an intelligent end-to-end (E2E) orchestration platform (IEOP) configured to coordinate with the SDN controller and the SDR controller via the CSSC based on outputs of the ML component to provide dynamic cross-segment network slice management. Other embodiments are disclosed.

Abstract: An automated system provides end-to-end (E2E) orchestration for machine learning (ML)-enabled and software-defined network (SDN)-enabled reactive and predictive (e.g., 5G) cell-on-drone (COD) dispatch and deployment. The system may be configured to detect or predict traffic surges (e.g., relative to typical or baseline levels) in a given cell site and orchestrate provisioning of resources, including COD deployment, to address the surge. COD deployment may involve one or more clusters or colonies of drones equipped with low-powered cellular radio access (or small cell) nodes, where each cluster includes (or is led by) one or more SDN-equipped drones that provide intelligent SDN-on-drone (SOD) functionality.

Abstract: Aspects of the subject disclosure may include, for example, a device with a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitates performance of operations, including: automatically detecting an intrusion of a secure communication channel that provides communication services to a node in a network; implementing a generative artificial intelligence that finds an alternative secure communication channel; and migrating the communication services to the alternative secure communication channel responsive to finding the alternative secure communication channel. Other embodiments are disclosed.

Abstract: In one example, the present disclosure describes a device, computer-readable medium, and method for proactively adjusting the infrastructure of a communications network in response to reporting of real-time network performance. For instance, in one example, a method includes obtaining real-time network performance metrics directly from a user endpoint device operated by a customer of a telecommunication service provider network, correlating the real-time network performance metrics with data from another data source, wherein the data includes data other than network performance metrics, and adjusting an infrastructure of the telecommunication service provider network in response to an insight gleaned through the correlating.

Abstract: Aspects of the subject disclosure may include, for example, monitoring status of a network configured to provide end-to-end delivery of services to priority user equipment authorized according to a prioritized class of service and other user equipment not authorized for the prioritized class of service. The prioritized class of service extends a reserved network capacity for end-to-end delivery of a service via first domain including first network slices and a second domain including second network slices. A network interruption compromising the reserved capacity is determined based on the monitored status and integration of an alternate group of slices into one of the first network domain, the second network domain, or both to replace the compromised group of slices, is orchestrated. Integration of the alternate group of slices restores reserved network capacity according to the prioritized class of service. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a system including a cross-segment slice controller (CSSC) configured to interface with a software-defined network (SDN) controller and a software-defined radio (SDR) controller. The SDN controller may be associated with a core network and the SDR controller may be associated with a radio access network (RAN). The system further includes a machine learning (ML) component configured to obtain and analyze data regarding the core network and the RAN, and an intelligent end-to-end (E2E) orchestration platform (IEOP) configured to coordinate with the SDN controller and the SDR controller via the CSSC based on outputs of the ML component to provide dynamic cross-segment network slice management. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, receiving artificial intelligence (AI) analytics from a generative AI system, and based on the AI analytics, causing a software defined network (SDN) controller to identify a signaling path over a quantum signaling network, thereby enabling selection of a determined suitable path for quantum entanglement distribution. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, obtaining first user-generated input, the first user-generated input indicates a request for generating an (API, identifying a first group of entities from the request, and determining whether there is a match between the first group of entities and a second group of entities stored in an API database according to API metadata. Further embodiments include identifying an API from the API database resulting in an identified API, presenting the identified API to the user, obtaining second user-generated input, and adjusting the request according to the second user-generated. Additional embodiments can include identifying a third group of entities from the adjusted request, and determining whether there is a match between the third group of entities and a fourth group of entities stored in the API database according to the API metadata. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, monitoring status of a network configured to provide end-to-end delivery of services to priority user equipment authorized according to a prioritized class of service and other user equipment not authorized for the prioritized class of service. The prioritized class of service extends a reserved network capacity for end-to-end delivery of a service via first domain including first network slices and a second domain including second network slices. A network interruption compromising the reserved capacity is determined based on the monitored status and integration of an alternate group of slices into one of the first network domain, the second network domain, or both to replace the compromised group of slices, is orchestrated. Integration of the alternate group of slices restores reserved network capacity according to the prioritized class of service. Other embodiments are disclosed.