Protocol and signaling framework enabling machine learning models in wireless communication networks
AssigneeAT&T Intellectual Property I, L.P.
InventorThomas Novlan
Life of this patent
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Abstract
Aspects of the subject disclosure may be directed to, for example, a method including determining a set of data collection parameters that are configurable to collect data indicative of network events occurring in real time or near real time in the wireless communication networks, receiving the collected data based on the set of data collection parameters from a group of network entities operating in the wireless communication networks, based on the received collected data, and generating training data for a machine learning model deployed in the wireless communication networks. Other embodiments are disclosed.
Claims20
A method,
- comprising:
- receiving, by a processing system including a processor, first data collected based on a set of data collection parameters from a group of network entities including a first network entity operating in a wireless communication networks, wherein the set of data collection parameters are configurable to collect data corresponding to one or more network events, and wherein the one or more network events occur in real time or near real time in wireless communication networks;
- receiving, by the processing system, second data collected based on the set of data collection parameters from the group of network entities including a second network entity operating in the wireless communication networks;
- generating, by the processing system, a training data based on the first collected data, the second collected data or both;
- training, by the processing system, a machine learning model based on the generated training data to learn the one or more network events;
- and
- broadcasting a message signaling life cycle management procedures of the machine learning model to at least one of the group of network entities, wherein the machine learning model operates in the at least one of the group of network entities, wherein the life cycle management procedure comprises at least one of activation, deactivation, switching, fallback, retraining, replacement, or upgrade of the machine learning model.
A device, comprising:
- a processing system including a processor;
- and
- a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising:
- collecting data based on a set of data collection parameters from a group of network entities operating in wireless communication networks, wherein the set of data collection parameters are configurable to collect data corresponding to one or more network events, and wherein the one or more network events occur in real time or near real time in wireless communication networks;
- generating a training data based on the collected data;
- training a machine learning model based on the generated training data to learn the network events;
- and
- broadcasting a message signaling life cycle management procedures of the machine learning model to at least one of the group of network entities, wherein the machine learning model operates in the at least one of the group of network entities, wherein the life cycle management procedure of the machine learning model comprises at least one of activation, deactivation, switching, fallback, retraining, replacement, or upgrade of the machine learning model.
A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising:
- receiving collected data based on a set of data collection parameters from a group of network entities operating in wireless communication networks, wherein the set of data collection parameters are configurable to collect data corresponding to one or more network events, and wherein the one or more network events occur in real time or near real time in the wireless communication networks;
- based on the received collected data, generating training data for a machine learning model deployed in the wireless communication networks;
- and
- broadcasting a message signaling life cycle management procedures of the machine learning model to at least one of the group of network entities, wherein the machine learning model is deployed in the at least one of the group of network entities, wherein the life cycle management procedure comprises at least one of activation, deactivation, switching, fallback, retraining, replacement, or upgrade of the machine learning model.
Dependencies are inferred from the claim text.