US 11,916,754 B2
Configuring a user equipment for machine learning
Xipeng Zhu, San Diego, CA (US); Gavin Bernard Horn, La Jolla, CA (US); Vanitha Aravamudhan Kumar, San Diego, CA (US); Vishal Dalmiya, San Diego, CA (US); Shankar Krishnan, San Diego, CA (US); Rajeev Kumar, San Diego, CA (US); Taesang Yoo, San Diego, CA (US); Eren Balevi, San Diego, CA (US); Aziz Gholmieh, Del Mar, CA (US); and Rajat Prakash, San Diego, CA (US)
Assigned to QUALCOMM Incorporated, San Diego, CA (US)
Filed by QUALCOMM Incorporated, San Diego, CA (US)
Filed on Sep. 3, 2021, as Appl. No. 17/467,156.
Prior Publication US 2023/0075276 A1, Mar. 9, 2023
Int. Cl. H04L 41/16 (2022.01); G06N 20/00 (2019.01); H04L 41/0803 (2022.01); H04W 88/08 (2009.01)
CPC H04L 41/16 (2013.01) [H04L 41/0803 (2013.01); H04W 88/08 (2013.01); G06N 20/00 (2019.01)] 32 Claims
OG exemplary drawing
 
1. An apparatus for wireless communication at a user equipment (UE), comprising:
at least one processor;
memory coupled with the at least one processor; and
instructions stored in the memory and executable by the at least one processor to cause the apparatus to:
transmit capability information that comprises a list of neural network functions supported by the UE, a list of machine learning models supported by the UE, or any combination thereof;
receive a machine learning model of one or more machine learning models, a set of parameters corresponding to the machine learning model, or a configuration corresponding to a neural network function of one or more neural network functions based at least in part on an address and the capability information, the address being for the machine learning model, the set of parameters, or the configuration, wherein:
the one or more machine learning models, the one or more neural network functions, or any combination thereof are associated with a machine learning model repository that is included in or coupled with a network entity; and
the address is based at least in part on a rule and an identifier, the identifier associated with the machine learning model, the set of parameters, or the configuration; and
receive, from the network entity, an activation message for the machine learning model, the neural network function, or both.