JP-7857407-B2 - Communication method and user device

JP7857407B2JP 7857407 B2JP7857407 B2JP 7857407B2JP-7857407-B2

Inventors

藤代真人

Assignees

京セラ株式会社

Dates

Publication Date: 20260512
Application Date: 20230721
Priority Date: 20220722

Claims (6)

A communication method that applies machine learning technology to wireless communication between a user device and a network node in a mobile communication system, The communication device of one of the user device and the network node transmits a notification to the other communication device of the user device and the network node indicating that it has at least one of the following: that it has an untrained model, that it has a model being trained, and that it has a trained model whose testing has been completed. The other communication device transmits control information to the first communication device for controlling the operation of the machine learning model in the first communication device based on the notification. The aforementioned communication device has the ability to receive the control information from the other communication device, A communication method wherein the control information includes at least one of the following: a dataset used for model training, information for initiating the use of the machine learning model, information for changing the settings of the machine learning model, and information for deleting the machine learning model.
The aforementioned transmission is the transmission of the notification indicating that the model has not been trained. The communication method according to claim 1, wherein the receiving includes receiving at least one of the dataset and setting parameters used for model training as control information .
The transmission described above means transmitting the notification indicating that the model being trained is being transmitted. The communication method according to claim 1, wherein the receiving includes receiving a dataset for continuing model learning as control information .
The transmission described above is the transmission of the notification indicating that the trained model has been inspected and the inspection has been completed. The communication method according to claim 1, wherein the receiving includes receiving information as control information that enables the use of the trained model after the inspection has been completed.
The communication method according to any one of claims 1 to 4, wherein the notification includes an index of the model and/or identification information for identifying the type or use of the model.
A user device that performs wireless communication with a network node to which machine learning technology is applied, A transmitting unit that sends a notification to the network node indicating that the user device has at least one of the following: that it has an untrained model, that it has a model being trained, and that it has a trained model for which testing has been completed. The device includes a receiving unit that receives control information from the network node for controlling the operation of a machine learning model in the user device, The control information includes at least one of the following: a dataset used for model training, information for initiating the use of the machine learning model, information for changing the settings of the machine learning model, and information for deleting the machine learning model.

Description

This disclosure relates to a communication method and user equipment used in mobile communication systems. In recent years, the 3GPP (Third Generation Partnership Project) (registered trademark; hereinafter the same), a standardization project for mobile communication systems, has been considering applying artificial intelligence (AI) technology, particularly machine learning (ML) technology, to wireless communication (air interface) in mobile communication systems. 3GPP contribution: RP-213599, “New SI: Study on Artificial Intelligence (AI)/Machine Learning (ML) for NR Air Interface” The first aspect of the communication method is a method for applying machine learning technology to wireless communication between a user device and a base station in a mobile communication system. The communication method includes the steps of: one of the communication devices, the user device and the base station, transmitting a notification to the other communication device, indicating that it has at least one of the following: that it has an untrained model, that it has a model being trained, and that it has a trained model for which testing has been completed; and the one communication device receiving a response from the other communication device corresponding to the notification. The second aspect of the communication method is a method for applying machine learning technology to wireless communication between a user device and a base station in a mobile communication system. The communication method includes the steps of: one of the communication devices, the user device and the base station, performing inference processing using a trained model obtained by training the model; one of the communication devices, the user device and the base station, monitoring the performance of the trained model to determine the need to retrain the model; and one of the communication devices, in response to determining that retraining is necessary, transmitting a notification indicating the need for retraining to the other of the user device and the base station. A third aspect of the communication method is a method for applying machine learning technology to wireless communication between a user device and a base station in a mobile communication system. The communication method includes the steps of: one of the user device and the base station receiving configuration information from the other of the user device and the base station, which includes information indicating the time at which a dataset for monitoring the performance of a trained model is provided; and the one of the communication devices receiving the dataset from the other communication device at the time and performing monitoring processing to monitor the performance of the trained model using the dataset. This diagram shows the configuration of a mobile communication system according to an embodiment.This diagram shows the configuration of the UE (User Equipment) according to the embodiment.This diagram shows the configuration of a gNB (base station) according to the embodiment.This diagram shows the protocol stack configuration of the user plane wireless interface that handles data.This diagram shows the protocol stack configuration of the wireless interface of the control plane that handles signaling (control signals).This diagram shows the functional block configuration of AI/ML technology (machine learning technology) in a mobile communication system according to the embodiment.This diagram shows an overview of the operation for each operation scenario according to the embodiment.This is a diagram showing a first operation scenario according to the embodiment.This figure shows a first example of reducing CSI-RS according to the embodiment.This figure shows a second example of reducing CSI-RS according to the embodiment.This is an operation flow diagram showing a first operation pattern related to a first operation scenario according to the embodiment.This is an operation flow diagram showing a second operation pattern related to the first operation scenario according to the embodiment.This is an operation flowchart showing a third operation pattern related to the first operation scenario according to the embodiment.This figure shows a second operation scenario according to the embodiment.This is an operation flowchart showing an example of operation related to the second operation scenario according to the embodiment.This is a diagram showing a third operation scenario according to this embodiment.This is an operation flowchart showing an example of operation related to the third operation scenario according to the embodiment.This figure shows a first operation pattern relating to model transfer according to the embodiment.This figure shows an example of a configuration message including a model and additional information according to the embodiment.This figure shows a second operation pattern relating to model transfer according to the embodiment.This figure sh