US 12,335,858 B2
Determining a machine-learning architecture for network slicing
Jibing Wang, San Jose, CA (US); and Erik Richard Stauffer, Sunnyvale, CA (US)
Assigned to Google LLC, Mountain View, CA (US)
Filed by Google LLC, Mountain View, CA (US)
Filed on Jul. 16, 2024, as Appl. No. 18/774,080.
Application 18/774,080 is a continuation of application No. 17/753,816, granted, now 12,075,346, previously published as PCT/US2019/059094, filed on Oct. 31, 2019.
Prior Publication US 2024/0373336 A1, Nov. 7, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. H04W 48/18 (2009.01); G06F 18/20 (2023.01); H04L 41/16 (2022.01); H04W 8/08 (2009.01); H04W 24/10 (2009.01); H04W 64/00 (2009.01)
CPC H04W 48/18 (2013.01) [G06F 18/285 (2023.01); H04L 41/16 (2013.01); H04W 8/08 (2013.01); H04W 24/10 (2013.01); H04W 64/003 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method performed by a user equipment, the method comprising:
transmitting, to a network-slice manager of a wireless network, a first machine-learning architecture request message to request permission to use a first machine-learning architecture;
receiving, from the network-slice manager, a first machine-learning architecture response message that grants permission to use the first machine-learning architecture based on a first network slice, the first machine-learning architecture forming a portion of at least one first end-to-end machine-learning architecture associated with the first network slice, the at least one first end-to-end machine-learning architecture being a distributed machine-learning architecture that is configured to process wireless communication signals and is formed by the first machine-learning architecture implemented by the user equipment, a machine-learning architecture implemented by a base station, and a machine-learning architecture implemented by an entity of a core network; and
wirelessly communicating data using the first machine-learning architecture.