This document describes techniques and devices for determining a machine-learning architecture for network slicing. A user equipment (UE) and a network-slice manager communicate with each other to determine a machine-learning (ML) architecture, which the UE then employs to wirelessly communicate data for an application. In particular, the UE selects a machine-learning architecture that provides a quality-of-service level requested by an application. The network-slice manager accepts or rejects the request based on one or more available end-to-end machine-learning architectures associated with a network slice that supports the quality-of-service level requested by the application. By working together, the UE and the network-slice manager can determine an appropriate machine-learning architecture that satisfies a quality-of-service level associated with the application and forms a portion of an end-to-end machine-learning architecture that meets the quality-of-service requested by the application.

This document describes techniques and devices for determining a machine-learning architecture. A user equipment (UE) transmits, 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. The UE receives 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. The UE wirelessly communicates data using the first machine-learning architecture.

This document describes techniques and devices for determining a machine-learning architecture for network slicing. A user equipment (UE) executes a first application associated with a first requested quality-of-service level. The UE selects a first machine-learning architecture based on the first requested quality-of-service level. The UE transmits, to a network-slice manager of a wireless network, a first machine-learning architecture request message to request permission to use the first machine-learning architecture. The UE receives, 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 UE wirelessly communicates data for the first application using the first machine-learning architecture, the first machine-learning architecture being configured to compute an output based on an input using coefficients determined by the user equipment.