Personalized tailored air interface

1. A method in a wireless communication network, the method comprising:

transmitting, by a first device, information regarding an artificial intelligence or machine learning (AI/ML) capability of the first device to a second device over an air interface between the first device and the second device, the information regarding an AI/ML capability of the first device identifying whether the first device supports AI/ML for optimization of at least one air interface component over the air interface, the at least one air interface component comprising one or more baseband signal processing components of the air interface;

receiving, by the first device, an AI/ML training request from the second device, the training request being based at least in part on the information regarding the AI/ML capability of the first device;

after receiving the AI/ML training request, communicating with the second device over the air interface as part of an AI/ML training process to train an AI/ML component of the first device to optimize at least one of the baseband signal processing components of the air interface, wherein training the AI/ML component comprises training the AI/ML component to optimize one or more baseband signal processing functions in a receive chain or a transmit chain of the first device; and

after the AI/ML component of the first device is trained, using the trained AI/ML component to optimize one or more of the baseband signal processing functions in the receive chain or the transmit chain of the first device for communication with the second device over the air interface.

2. The method of claim 1 , wherein the information regarding an AI/ML capability of the first device comprises information indicating at least one of the following:

the first device is capable of supporting a type and/or level of complexity of AI/ML;

whether the first device assists with the AI/ML training process for optimization of the at least one air interface component;

at least one component of the at least one air interface component for which the first device supports AI/ML optimization.

3. The method of claim 2 , wherein the information indicating at least one component of the at least one air interface component for which the first device supports AI/ML optimization further comprises information indicating whether the first device supports joint optimization of two or more air interface components.

4. The method of claim 1 , wherein the one or more baseband signal processing components of the air interface includes at least one of a coding component, a modulation component and a waveform component.

5. The method of claim 1 , wherein transmitting the information regarding an AI/ML capability of the first device comprises at least one of:

transmitting the information in response to receiving an enquiry; and

transmitting the information as part of an initial network access procedure.

6. A method in a wireless communication network, the method comprising:

receiving, by a second device, information regarding an artificial intelligence or machine learning (AI/ML) capability of a first device over an air interface between the first device and the second device, the information regarding an AI/ML capability of the first device identifying whether the first device supports AI/ML for optimization of at least one air interface component over the air interface, the at least one air interface component comprising one or more baseband signal processing components of the air interface;

transmitting an AI/ML training request to the first device based at least in part on the information regarding the AI/ML capability of the first device;

after transmitting the AI/ML training request, communicating with the first device over the air interface as part of an AI/ML training process to train an AI/ML component of the second device to optimize at least one of the baseband signal processing components of the air interface, wherein training the AI/ML component comprises training the AI/ML component to optimize one or more baseband signal processing functions in a receive chain or a transmit chain of the second device; and

after the AI/ML component of the second device is trained, using the trained AI/ML component to optimize one or more of the baseband signal processing functions in the receive chain or the transmit chain of the second device for communication with the first device over the air interface.

7. The method of claim 6 , wherein the information regarding an AI/ML capability of the first device comprises information indicating at least one of the following:

the first device is capable of supporting a type and/or level of complexity of AI/ML;

whether the first device assists with the AI/ML training process for optimization of the at least one air interface component;

at least one component of the at least one air interface component for which the first device supports AI/ML optimization.

8. The method of claim 7 , wherein the information indicating at least one component of the at least one air interface component for which the first device supports AI/ML optimization further comprises information indicating whether the first device supports joint optimization of two or more components of the at least one air interface component.

9. The method of claim 6 , wherein the one or more baseband signal processing components of the air interface includes at least one of a coding component, a modulation component and a waveform component.

10. The method of claim 6 , wherein receiving the information regarding an AI/ML capability of the first device comprises receiving the information as part of an initial network access procedure for the first device.

11. The method of claim 6 , wherein transmitting the AI/ML training request comprises transmitting the AI/ML training request through downlink control information (DCI) on a downlink control channel or RRC signaling or the combination of the DCI and RRC signaling.

12. The method of claim 11 , further comprising, receiving a training request response from the device confirming that the device has transitioned to an AI/ML training mode.

13. The method of claim 6 , further comprising:

transmitting a training termination signal to the first device to indicate that a training phase has finished.

14. An apparatus comprising:

at least one processor; and

a computer readable storage medium operatively coupled to the at least one processor, the computer readable storage medium storing programming for execution by the at least one processor, the programming comprising instructions to:

transmit, from the apparatus, information regarding an artificial intelligence or machine learning (AI/ML) capability of the apparatus to a network device over an air interface between the apparatus and the network device, the information regarding an AI/ML capability of the apparatus identifying whether the apparatus supports AI/ML for optimization of at least one air interface component over the air interface, the at least one air interface component comprising one or more baseband signal processing components of the air interface;

receive an AI/ML training request from the network device, the training request being based at least in part on the information regarding the AI/ML capability of the apparatus;

after receiving the AI/ML training request, communicate with the network device over the air interface as part of an AI/ML training process to train an AI/ML component of the apparatus to optimize at least one of the baseband signal processing components of the air interface, wherein training the AI/ML component comprises training the AI/ML component to optimize one or more baseband signal processing functions in a receive chain or a transmit chain of the apparatus; and

after the AI/ML component of the apparatus is trained, use the trained AI/ML component to optimize one or more of the baseband signal processing functions in the receive chain or the transmit chain of the apparatus for communication with the network device over the air interface.

15. The apparatus of claim 14 , wherein the information regarding an AI/ML capability of the apparatus comprises information indicating at least one of the following:

the apparatus is capable of supporting a type and/or level of complexity of AI/ML;

whether the apparatus assists with the AI/ML training process for optimization of the at least one air interface component; and

at least one component of the at least one air interface component for which the apparatus supports AI/ML optimization.

16. The apparatus of claim 15 , wherein the information indicating at least one component of the at least one air interface component for which the apparatus supports AI/ML optimization further comprises information indicating whether the apparatus supports joint optimization of two or more components of the at least one air interface component.

17. The apparatus of claim 14 , wherein the one or more baseband signal processing components of the air interface includes at least one of a coding component, a modulation component and a waveform component.

18. A network apparatus comprising:

at least one processor; and

a computer readable storage medium operatively coupled to the at least processor, the computer readable storage medium storing programming for execution by the at least processor, the programming comprising instructions to:

receive, by the network apparatus information regarding an artificial intelligence or machine learning (AI/ML) capability of a first device over an air interface between the first device and the network apparatus, the information regarding an AI/ML capability of the first device identifying whether the first device supports AI/ML for optimization of at least one air interface component over the air interface, the at least one air interface component comprising one or more baseband signal processing components of the air interface;

transmit an AI/ML training request to the first device based at least in part on the information regarding the AI/ML capability of the first device;

after transmitting the AI/ML training request, communicate with the first device over the air interface as part of an AI/ML training process to train an AI/ML component of the network apparatus to optimize at least one of the baseband signal processing components of the air interface, wherein training the AI/ML component comprises training the AI/ML component to optimize one or more baseband signal processing functions in a receive chain or a transmit chain of the network apparatus; and

after the AI/ML component of the network apparatus is trained, use the trained AI/ML component to optimize one or more of the baseband signal processing functions in the receive chain or the transmit chain of the network apparatus for communication with the first device over the air interface.

19. The network apparatus of claim 18 , wherein the information regarding an AI/ML capability of the first device comprises information indicating at least one of the following:

the first device is capable of supporting a type and/or level of complexity of AI/ML;

whether the first device assists with the AI/ML training process for optimization of the at least one air interface component; and

at least one component of the at least one air interface component for which the first device supports AI/ML optimization.

20. The network apparatus of claim 19 , wherein the information indicating at least one component of the at least one air interface component for which the first device supports AI/ML optimization further comprises information indicating whether the first device supports joint optimization of two or more components of the at least one air interface component.

21. The network apparatus of claim 18 , wherein the one or more baseband signal processing components of the air interface includes at least one of a coding component, a modulation component and a waveform component.