MACHINE-LEARNING ARCHITECTURES FOR BROADCAST AND MULTICAST COMMUNICATIONS

Abstract: techniques and apparatuses are described for machine-learning architectures for broadcast and multicast communications. a network entity processes 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. the network entity receives feedback from at least one user equipment (ue) of the targeted group of ues. the network entity determines a modification to the dnn based on the feedback. the network entity transmits an indication of the modification to the targeted group of ues. the network entity updates the dnn with the modification to form a modified dnn. the network entity processes 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.

Inventor(s): Jibing Wang, Erik Stauffer

CPC Classification: G06N3/08 (Learning methods)

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