Predicting an attribute of a wireless telecommunication network, such as a 5G network

A method comprising:

• obtaining multiple key performance indicators and multiple configuration parameters directly from a wireless telecommunication network,
• wherein the multiple key performance indicators indicate an observed performance associated with the wireless telecommunication network,
• wherein the multiple configuration parameters indicate a configuration of the wireless telecommunication network,
• wherein the multiple key performance indicators and the multiple configuration parameters together comprise at least six of:
• a quality of service class identifier, a number of users, a modulation coding scheme, a multiple-input multiple-output, a carrier aggregation, a band, a bandwidth, and a generation of wireless technology associated with the wireless telecommunication network;
• predicting a value of a difficult to predict attribute of the wireless telecommunication network,
• wherein the difficult to predict attribute depends on multiple other attributes associated with the wireless telecommunication network,
• the predicting including:
• providing the multiple key performance indicators and the multiple configuration parameters to a machine learning model;
• and
• predicting, by the machine learning model, the value of the difficult to predict attribute associated with the wireless telecommunication network based on multiple key performance indicators and the multiple configuration parameters.

A system comprising:

• at least one hardware processor;
• and
• at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to:
• obtain multiple key performance indicators directly from a wireless telecommunication network,
• wherein the multiple key performance indicators indicate an observed performance associated with the wireless telecommunication network,
• wherein the multiple key performance indicators include a number of users associated with the wireless telecommunication network;
• obtain multiple configuration parameters directly from the wireless telecommunication network,
• wherein the multiple configuration parameters indicate a configuration of the wireless telecommunication network,
• wherein the multiple configuration parameters include a generation of wireless technology associated with the wireless telecommunication network;
• identify a difficult to predict attribute by identifying an attribute that depends on multiple other attributes associated with the wireless telecommunication network;
• predict a value of the difficult to predict attribute associated with the wireless telecommunication network by:
• providing the multiple key performance indicators and the multiple configuration parameters to a machine learning model trained on previous observations of attributes during previous operation of the wireless telecommunication network, the attributes comprising:
• a quality of service class identifier, the number of users, modulation coding scheme, multiple-input multiple-output, carrier aggregation, band, bandwidth, and the generation of wireless technology associated with the wireless telecommunication network;
• and
• predicting, by the machine learning model, the value of the difficult to predict attribute associated with the wireless telecommunication network based on the multiple key performance indicators and the multiple configuration parameters.