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US 12,604,278 B2Granted
5G inter- and intra-network node coordination for power consumption optimization and reduction
AssigneeCisco Technology, Inc.
InventorSukrit Dasgupta
Life of this patent
Estimating the term…
Abstract
Association and mobility patterns corresponding to client devices within a 5G network are tracked in real-time. A machine learning model is trained to identify, based on these patterns, periods of time for powering down one or more 5G nodes within the 5G network. The machine learning model, based on these periods of time, generates a set of power saving profiles that are used to automatically define power saving modes for the one or more 5G nodes. The machine learning model is updated according to changes to the association and mobility patterns resulting from the power saving modes.
Claims20
A computer-implemented method comprising:
- continuously monitoring association patterns corresponding to a set of client devices within a 5G network, wherein the 5G network includes a set of 5G nodes, and wherein the association patterns denote changes to associations between the set of client devices and the set of 5G nodes;
- tracking mobility patterns in the 5G network across the set of 5G nodes and the set of client devices, wherein the mobility patterns are tracked based on the association patterns;
- dynamically training a machine learning model to identify periods of time for powering down one or more 5G nodes of the set of 5G nodes, wherein the machine learning model is dynamically trained based on historical telemetry data associated with the 5G network;
- generating a set of power saving profiles for the set of 5G nodes, wherein the set of power saving profiles are generated using the association patterns and the mobility patterns as input to the machine learning model;
- automatically defining power saving modes for the set of 5G nodes, wherein the power saving modes are implemented according to the set of power saving profiles;
- and
- updating the machine learning model according to changes to the association patterns and the mobility patterns resulting from the power saving modes.
4 more elements
A system, comprising:
- one or more processors;
- and
- memory storing thereon instructions that, as a result of being executed by the one or more processors, cause the system to:
- continuously monitor association patterns corresponding to a set of client devices within a 5G network, wherein the 5G network includes a set of 5G nodes, and wherein the association patterns denote changes to associations between the set of client devices and the set of 5G nodes;
- track mobility patterns in the 5G network across the set of 5G nodes and the set of client devices, wherein the mobility patterns are tracked based on the association patterns;
- dynamically train a machine learning model to identify periods of time for powering down one or more 5G nodes of the set of 5G nodes, wherein the machine learning model is dynamically trained based on historical telemetry data associated with the 5G network;
- generate a set of power saving profiles for the set of 5G nodes, wherein the set of power saving profiles are generated using the association patterns and the mobility patterns as input to the machine learning model;
- automatically define power saving modes for the set of 5G nodes, wherein the power saving modes are implemented according to the set of power saving profiles;
- and
- update the machine learning model according to changes to the association patterns and the mobility patterns resulting from the power saving modes.
7 more elements
A non-transitory, computer-readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to:
- continuously monitor association patterns corresponding to a set of client devices within a 5G network, wherein the 5G network includes a set of 5G nodes, and wherein the association patterns denote changes to associations between the set of client devices and the set of 5G nodes;
- track mobility patterns in the 5G network across the set of 5G nodes and the set of client devices, wherein the mobility patterns are tracked based on the association patterns;
- dynamically train a machine learning model to identify periods of time for powering down one or more 5G nodes of the set of 5G nodes, wherein the machine learning model is dynamically trained based on historical telemetry data associated with the 5G network;
- generate a set of power saving profiles for the set of 5G nodes, wherein the set of power saving profiles are generated using the association patterns and the mobility patterns as input to the machine learning model;
- automatically define power saving modes for the set of 5G nodes, wherein the power saving modes are implemented according to the set of power saving profiles;
- and
- update the machine learning model according to changes to the association patterns and the mobility patterns resulting from the power saving modes.
4 more elements
Dependencies are inferred from the claim text.