End-to-end deep neural network adaptation for edge computing

A method performed by a wireless transmit/receive unit (WTRU) for adapting an end-to-end (E2E) machine-learning (ML) configuration, by determining adjustments to one or more E2E ML configurations, for processing communications transferred through an E2E communication in a wireless network, the method comprising:

• forming, based on a first E2E ML configuration identified by a network entity, at least a first portion of an E2E deep neural network (DNN) that implements an E2E communication;
• receiving an indication to update the E2E DNN using at least a second portion of a second E2E ML configuration based on a change in a participation mode of an edge compute server (ECS) in the E2E communication;
• updating the E2E DNN using the at least a second portion of the second E2E ML configuration;
• and
• implementing at least a portion of the E2E communication using the updated E2E DNN.

A network entity for adapting an end-to-end (E2E) machine-learning (ML) configuration that forms an E2E deep neural network (DNN), by determining adjustments to one or more E2E ML configurations, for processing communications transferred through an E2E communication between at least two endpoints, the E2E communication using a wireless network, the network entity comprising:

• a processor;
• and
• computer-readable storage media comprising instructions, executable by the processor to implement an end-to-end machine-learning controller to:
• direct a user equipment (UE) participating in an end-to-end (E2E) communication to implement the E2E communication by forming at least a first portion of an E2E deep neural network (DNN) based on a first E2E machine-learning (ML) configuration;
• direct a base station participating in the E2E communication to implement the E2E communication by forming at least a second portion of the E2E DNN based on the first E2E ML configuration;
• determine to update the first E2E ML configuration based on a change in a participation mode of an edge compute server (ECS) in the E2E communication;
• identify a second E2E ML configuration based on the change in participation mode of the ECS in the E2E communication;
• and
• direct at least the UE or the base station to update at least a third portion of the E2E DNN using the second E2E ML configuration for implementing the E2E communication.

A user equipment for adapting an end-to-end (E2E) machine-learning (ML) configuration, by determining adjustments to one or more E2E ML configurations, for processing communications transferred through an E2E communication in a wireless network, the user equipment comprising:

• a processor;
• and
• computer-readable storage media comprising instructions, executable by the processor, to configure the user equipment to:
• form, based on a first end-to-end (E2E) ML configuration identified by a network entity, at least a first portion of an E2E deep neural network (DNN) that implements an E2E communication;
• receive an indication to update the E2E DNN using at least a second portion of a second E2E machine-learning (ML) configuration based on a change in a participation mode of an edge compute server (ECS) in the E2E communication;
• update the E2E DNN using the at least a second portion of the second E2E ML configuration;
• and
• implement at least a portion of the E2E communication using the updated E2E DNN.

A base station for adapting an end-to-end (E2E) machine-learning (ML) configuration, by determining adjustments to one or more E2E ML configurations, for processing communications transferred through an E2E communication in a wireless network, the base station comprising:

• a processor;
• and
• computer-readable storage media comprising instructions, executable by the processor, to configure the base station to:
• form, based on a first end-to-end (E2E) ML configuration identified by a network entity, at least a first portion of an E2E DNN that implements an E2E communication;
• receive an indication to update the DNN using at least a second portion of a second E2E machine-learning (ML) configuration based on a change in a participation mode of an edge compute server (ECS) in the E2E communication;
• update the E2E DNN using the at least a second portion of the second E2E ML configuration;
• and
• implement at least a portion of the E2E communication using the updated E2E DNN.