US 11,109,283 B1Granted

Handover success rate prediction and management using machine learning for 5G networks

AssigneeAccenture Global Solutions Limited

InventorsApeksha Jayateerth Kulkarni, Shaik Nayeem, Shailesh Shashi, Ramesh Veera Venkata Durga Potta, Vikas Manohar Sharma

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Abstract

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to predict future handover success rates of cells of a wireless network and to determine updated network parameter values that may be used to improve the future handover success rates. Clustering algorithms may be applied to handover success rates associated with the cells to group the cells into different clusters. Machine learning (ML) models may be trained based on historical handover data associated with cells of the clusters to predict future handover success rates based on current handover success rates. The output of the ML models may be used to determine one or more updated network parameter values to be implemented by the wireless network to improve future handover success rates at one or more cells.

Claims20

Dependent claims

A method for predicting handover success rates and updating network parameter values using machine learning, the method comprising:

receiving, by one or more processors from one or more network entities of a wireless network, handover data associated with a plurality of cells of the wireless network,
the handover data indicating handover success rates associated with the plurality of cells;
providing, by the one or more processors, the handover data to a first set of machine learning (ML) models to group the plurality of cells into multiple clusters of cells,
the first set of ML models configured to cluster cells based at least in part on the handover success rates associated with the cells;
for each of one or more clusters of the multiple clusters:
providing, by the one or more processors, a portion of the handover data associated with cells of the cluster to a second set of ML models to predict future handover success rates for the cells of the cluster,
the second set of ML models configured to predict cluster-specific future handover success rates based on handover data associated with the cells of the cluster;
and
determining, by the one or more processors, one or more updated network parameter values for the cells of the cluster based on the predicted cluster-specific future handover success rates;
and
initiating transmission, by the one or more processors, of the one or more updated network parameter values to one or more network entities of the wireless network.

8 more elements

A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations for predicting handover success rates and updating network parameter values using machine learning, the operations comprising:

receiving, from one or more network entities of a wireless network, handover data associated with a plurality of cells of the wireless network,
the handover data indicating handover success rates associated with the plurality of cells;
providing the handover data to a first set of machine learning (ML) models to group the plurality of cells into multiple clusters of cells,
the first set of ML models configured to cluster cells based at least in part on the handover success rates associated with the cells;
for each of one or more clusters of the multiple clusters:
providing a portion of the handover data associated with cells of the cluster to a second set of ML models to predict future handover success rates for the cells of the cluster,
the second set of ML models configured to predict cluster-specific future handover success rates based on handover data associated with the cells of the cluster;
and
determining one or more updated network parameter values for the cells of the cluster based on the predicted cluster-specific future handover success rates;
and
initiating transmission of the one or more updated network parameter values to one or more network entities of the wireless network.

8 more elements

A system for predicting handover success rates and updating network parameter values using machine learning, the system comprising:

a memory;
and
one or more processors communicatively coupled to the memory, the one or more processors configured to:
receive, from one or more network entities of a wireless network, handover data associated with a plurality of cells of the wireless network,
the handover data indicating handover success rates associated with the plurality of cells;
provide the handover data to a first set of machine learning (ML) models to group the plurality of cells into multiple clusters of cells,
the first set of ML models configured to cluster cells based at least in part on the handover success rates associated with the cells;
for each of one or more clusters of the multiple clusters:
provide a portion of the handover data associated with cells of the cluster to a second set of ML models to predict future handover success rates for the cells of the cluster,
the second set of ML models configured to predict cluster-specific future handover success rates based on handover data associated with the cells of the cluster;
and
determine one or more updated network parameter values for the cells of the cluster based on the predicted cluster-specific future handover success rates;
and
initiate transmission of the one or more updated network parameter values to one or more network entities of the wireless network.

11 more elements

Dependencies are inferred from the claim text.