US 12,236,347 B2
Machine-learning architectures for broadcast and multicast communications
Jibing Wang, San Jose, CA (US); and Erik Stauffer, Sunnyvale, CA (US)
Assigned to Google LLC, Mountain View, CA (US)
Filed by Google LLC, Mountain View, CA (US)
Filed on Dec. 29, 2023, as Appl. No. 18/401,096.
Application 18/401,096 is a division of application No. 16/698,804, filed on Nov. 27, 2019, granted, now 11,886,991.
Prior Publication US 2024/0135175 A1, Apr. 25, 2024
Int. Cl. G06N 3/08 (2023.01); G06N 3/04 (2023.01); H04L 12/18 (2006.01); H04L 25/02 (2006.01)
CPC G06N 3/08 (2013.01) [G06N 3/04 (2013.01); H04L 12/189 (2013.01); H04L 25/0202 (2013.01)] 6 Claims
OG exemplary drawing
 
1. A method performed by a network entity associated with a wireless communication system, the method comprising:
processing broadcast or multicast communications using a deep neural network (DNN) to direct the one or more broadcast or multicast communications to a targeted group of user equipments (UEs) using the wireless communication system;
receiving feedback from at least one user equipment (UE) of the targeted group of UEs;
determining a modification to the DNN based on an architecture configuration change in response to at least one UE of the targeted group of UEs failing to meet a respective cost function threshold value based on the feedback;
transmitting an indication of the modification to the targeted group of UEs;
updating the DNN with the modification to form a modified DNN; and
processing the broadcast or multicast communications using the modified DNN to direct the broadcast or multicast communications to the targeted group of UEs using the wireless communication system.