US 12,452,733 B2
Multi-batch reinforcement learning via multi-imitation learning
Di Wu, Saint-Laurent (CA); Tianyu Li, Montreal (CA); David Meger, Montreal (CA); Michael Jenkin, Toronto (CA); Xue Liu, Montreal (CA); and Gregory Lewis Dudek, Westmount (CA)
Assigned to SAMSUNG ELECTRONICS CO., LTD., Suwon-si (KR)
Filed by SAMSUNG ELECTRONICS CO., LTD., Suwon-si (KR)
Filed on Sep. 30, 2022, as Appl. No. 17/957,960.
Claims priority of provisional application 63/253,823, filed on Oct. 8, 2021.
Claims priority of provisional application 63/253,023, filed on Oct. 6, 2021.
Prior Publication US 2023/0107539 A1, Apr. 6, 2023
Int. Cl. H04W 28/086 (2023.01); G06N 3/08 (2023.01); H04L 41/16 (2022.01); H04W 24/02 (2009.01); H04W 24/10 (2009.01); G06N 20/00 (2019.01)
CPC H04W 28/0862 (2023.05) [H04L 41/16 (2013.01); H04W 24/02 (2013.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); H04W 24/10 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for performing traffic load balancing in a communication system, the method comprising:
receiving a first traffic data from a first base station;
receiving a second traffic data from a second base station;
obtaining a first augmented traffic data for the first base station, based on the first traffic data and a subset data of the second traffic data that is selected based on similarity between the first traffic data and the second traffic data;
obtaining a second augmented traffic data for the second base station, based on the second traffic data and a subset data of the first traffic data that is selected based on the similarity between the first traffic data and the second traffic data;
obtaining a first artificial intelligence (AI) model based on the first augmented traffic data;
obtaining a second AI model based on the second augmented traffic data;
obtaining a generalized AI model via knowledge distillation from the first AI model and the second AI model; and
predicting a traffic load of each of the first base station and the second base station based on the generalized AI model.