Optimization of cellular network architecture based on device type-specific traffic dynamics

A non-transitory computer readable medium storing instructions which, when executed by a processor, cause the processor to perform operations for adjusting a parameter of a cellular network, the operations comprising:

• obtaining network traffic data for a plurality of different endpoint device types, wherein the network traffic data is derived from voice calls of the cellular network, wherein the network traffic data comprises current network traffic data and a training data set comprising past network traffic data, wherein the network traffic data comprises network traffic data for each of the plurality of different endpoint device types, wherein each of the plurality of different endpoint device types comprises a different physical endpoint device type, wherein the network traffic data is stored in a plurality of records, wherein each record of the plurality of records contains a device type identifier that identifies an endpoint device type of the plurality of different endpoint device types;
• segregating, by the processor, at least one record in the plurality of records based on a corresponding device type identifier that identifies one of the plurality of different endpoint device types;
• predicting a future traffic pattern for the one of the plurality of different endpoint device types based on the at least one record in the plurality of records that contain the corresponding device type identifier that identifies the one of the plurality of different endpoint device types, wherein the predicting comprises:
• creating a model for the network traffic data that is specific to the one of the plurality of different endpoint device types, wherein the model indicates a degree of correlation between a present network traffic state represented by the current network traffic data and a number of past network traffic states represented by the training data set;
• and
• determining, using the model, that a geographic concentration of endpoint devices of the one of the plurality of different endpoint device types is expected at a first base station of the cellular network;
• and
• adjusting the parameter of the cellular network in response to the future traffic pattern predicted for the one of the plurality of different endpoint device types.

An apparatus for adjusting a parameter of a cellular network, the apparatus comprising:

• a processor;
• and
• a non-transitory computer-readable medium storing instructions which, when executed by the processor, cause the processor to perform operations, the operations comprising:
• obtaining network traffic data for a plurality of different endpoint device types, wherein the network traffic data is derived from voice calls of the cellular network, wherein the network traffic data comprises current network traffic data and a training data set comprising past network traffic data, wherein the network traffic data comprises network traffic data for each of the plurality of different endpoint device types, wherein each of the plurality of different endpoint device types comprises a different physical endpoint device type, wherein the network traffic data is stored in a plurality of records, wherein each record of the plurality of records contains a device type identifier that identifies an endpoint device type of the plurality of different endpoint device types;
• segregating at least one record in the plurality of records based on a corresponding device type identifier that identifies one of the plurality of different endpoint device types;
• predicting a future traffic pattern for the one of the plurality of different endpoint device types based on the at least one record in the plurality of records that contain the corresponding device type identifier that identifies the one of the plurality of different endpoint device types, wherein the predicting comprises:
• creating a model for the network traffic data that is specific to the one of the plurality of different endpoint device types, wherein the model indicates a degree of correlation between a present network traffic state represented by the current network traffic data and a number of past network traffic states represented by the training data set;
• and
• determining, using the model, that a geographic concentration of endpoint devices of the one of the plurality of different endpoint device types is expected at a first base station of the cellular network;
• and
• adjusting the parameter of the cellular network in response to the future traffic pattern predicted for the one of the plurality of different endpoint device types.