Personalized tailored air interface

Patent metadata

Field	Value
Publication number	US-12199836-B2
Application number	US-202318506618-A
Country	US
Kind code	B2
Filing date	Nov 10, 2023
Priority date	Nov 22, 2019
Publication date	Jan 14, 2025
Grant date	Jan 14, 2025

Abstract

Methods and devices utilizing artificial intelligence (AI) or machine learning (ML) for customization of a device specific air interface configuration in a wireless communication network are provided. An over the air information exchange to facilitate the training of one or more AI/ML modules involves the exchange of AI/ML capability information identifying whether a device supports AI/ML for optimization of the air interface.

First claim

The invention claimed is:

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, 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 an air interface between the first device and the second device;
receiving, by the first device, an AI/ML training request from the second device; after receiving the AI/ML training request;
communicating with the second device to train an AI/ML component of the first device; and
optimizing, by using the trained AI/ML component, one or more of baseband signal processing functions.

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 an 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 at least one air interface component comprising one or more baseband signal processing components of the air interface.

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

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, 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 an air interface between the first device and the second device;
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 to train an AI/ML component of the second device; and
optimizing, by using the trained AI/ML component, one or more of the baseband signal processing functions.

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 an 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 at least one air interface component comprising one or more baseband signal processing components of the air interface.

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

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, 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 an air interface between the apparatus and the network device;
receive an AI/ML training request from the network device;
after receiving the AI/ML training request, communicate with the network device over the air interface to train an AI/ML component of the apparatus; and
optimize, by using the trained AI/ML component, one or more of the baseband signal processing functions.

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 an 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. 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, 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 an air interface between the first device and the network apparatus;
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 to train an AI/ML component of the network; and
optimize, by using the trained AI/ML component, one or more of the baseband

Assignees

• Ma Jianglei
• Zhu Peiying
• Tong Wen
• Jia Ming
• Huawei Tech Co Ltd

Inventors

• Ma Jianglei
• Zhu Peiying
• Tong Wen
• Jia Ming

Classifications

• H04L1/0009

  by adapting the channel coding (H04L1/1812 takes precedence) · CPC title

• H04L1/0045

  Arrangements at the receiver end · CPC title

• H04L1/0041

  Arrangements at the transmitter end · CPC title

• G06N3/04

  Architecture, e.g. interconnection topology · CPC title

• G06N3/08

  Learning methods · CPC title