US 11,800,398 B2
Predicting an attribute of an immature wireless telecommunication network, such as a 5G network
Mohamed Abdullah Amer, Naperville, IL (US)
Assigned to T-Mobile USA, Inc., Bellevue, WA (US)
Filed by T-Mobile USA, Inc., Bellevue, WA (US)
Filed on Oct. 27, 2021, as Appl. No. 17/512,565.
Prior Publication US 2023/0128007 A1, Apr. 27, 2023
Int. Cl. H04W 28/02 (2009.01); H04W 16/18 (2009.01); H04W 24/02 (2009.01)
CPC H04W 28/0268 (2013.01) [H04W 16/18 (2013.01); H04W 24/02 (2013.01)] 20 Claims
OG exemplary drawing
 
1. At least one computer-readable storage medium, excluding transitory signals and carrying instructions to predict a throughput of an immature wireless telecommunication network, which, when executed by at least one data processor of a system, causes the system to:
obtain a first set of multiple key performance indicators associated with a mature wireless telecommunication network and a first set of multiple configuration parameters associated with the mature wireless telecommunication network,
wherein the first set of multiple key performance indicators indicates an observed performance associated with the mature wireless telecommunication network,
wherein the first set of multiple configuration parameters indicates a configuration of the mature wireless telecommunication network;
obtain a second set of multiple key performance indicators associated with the immature wireless telecommunication network and a second set of multiple configuration parameters associated with the immature wireless telecommunication network,
wherein a physical layer of the mature wireless telecommunication network corresponds to a physical layer of the immature wireless telecommunication network;
combine the first set of multiple key performance indicators and the second set of multiple key performance indicators to obtain multiple key performance indicators; and
predict the throughput of the immature wireless telecommunication network by:
providing the multiple key performance indicators and the second set of multiple configuration parameters to a machine learning model trained on data associated with the mature wireless telecommunication network; and
predicting, by the machine learning model, the throughput of the immature wireless telecommunication network based on the multiple key performance indicators and the second set of multiple configuration parameters.