US 11,696,153 B2
Transfer learning of network traffic prediction model among cellular base stations
Xi Chen, Montreal (CA); Ju Wang, Montreal (CA); Hang Li, Montreal (CA); Yi Tian Xu, Montreal (CA); Di Wu, Montreal (CA); Xue Liu, Montreal (CA); Gregory Lewis Dudek, Westmount (CA); Taeseop Lee, Seoul (KR); and Intaik Park, Seoul (KR)
Assigned to SAMSUNG ELECTRONICS CO., LTD., Suwon-si (KR)
Filed by SAMSUNG ELECTRONICS CO., LTD., Suwon-si (KR)
Filed on Aug. 2, 2021, as Appl. No. 17/391,708.
Claims priority of provisional application 63/065,090, filed on Aug. 13, 2020.
Prior Publication US 2022/0053341 A1, Feb. 17, 2022
Int. Cl. H04W 16/18 (2009.01); H04W 16/22 (2009.01)
CPC H04W 16/22 (2013.01) [H04W 16/18 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A server configured to manage traffic prediction model transfer learning among 5G base stations, the server comprising:
one or more processors; and
one or more memories, the one or more memories storing a program, wherein execution of the program by the one or more processors is configured to cause the server to at least:
receive a first plurality of base station statistics, wherein the first plurality of base station statistics includes a first data set of a first size from a first base station;
receive a second plurality of base station statistics, wherein the second plurality of base station statistics includes a second data set of a second size corresponding to a second base station;
select the first base station as a source base station;
train a similarity network;
receive a source prediction model from the first base station and an importance score matrix;
receive a prediction model request from a target base station, wherein the target base station is the second base station;
compute a first similarity using the similarity network;
obtain a first scaled importance score matrix based on the importance score matrix and based on the first similarity; and
send the source prediction model and the first scaled importance score matrix to the second base station,
whereby the second base station is configured to use the source prediction model, and the first scaled importance score matrix to generate a target prediction model and predict radio system parameters relevant to the second base station, wherein the radio system parameters include a future value of user data traffic passing through the second base station.