Network node such as base station for handling modulation and coding scheme (MCS) related information and method therefor

Patent metadata

Field	Value
Publication number	US-12476729-B2
Application number	US-202318335781-A
Country	US
Kind code	B2
Filing date	Jun 15, 2023
Priority date	May 9, 2022
Publication date	Nov 18, 2025
Grant date	Nov 18, 2025

Abstract

An example embodiment may provide a method to be performed by a base station and/or a network node, and relevant devices. The method may include: acquiring channel related information of a target user equipment (UE); acquiring a modulation and coding scheme (MCS) determined based on MCS related information, the MCS related information being acquired by an artificial intelligence (AI) network based on the channel related information; and, transmitting the determined MCS to the target UE, the target UE comprising an edge UE, the number of MCS change value of the edge UE being greater than the number of MCS change value of a center UE. The steps in this scheme can be implemented by a trained artificial intelligence method. The corresponding MCS related information may be acquired by an AI network based on the channel related information of the target UE, and the MCS of the target UE may then be determined by using the acquired MCS related information. Since the MCS related information acquired by the AI network can accurately reflect the channel state of the target UE, the determined MCS is more accurate, and the user throughput is thus improved.

First claim

What is claimed is:

1 . A method performed by a base station, the method comprising:
acquiring channel related information of a target user equipment (UE); identifying whether the target UE corresponds to a first type UE or a second type UE based on at least one of the channel related information or position information of the target UE; in case that the target UE corresponds to the first type UE, acquiring a modulation and coding scheme (MCS) based on MCS related information, wherein the MCS related information is obtained based on a first artificial intelligence (AI) network using the channel related information; and transmitting the MCS to the target UE, wherein the number of available MCS change values of the first type UE is greater than the number of available MCS change values of the second type UE.

2 . The method of claim 1 , wherein the acquiring of the MCS comprises:
transmitting, to a radio access network (RAN) intelligence controller (RIC), the channel related information, and receiving, from the RIC, the MCS related information, and wherein the first AI network is deployed on the RIC.

3 . The method of claim 1 , further comprising;
in case that the target UE is the second type UE, acquiring the MCS based on a fixed adjustment step for link adaptation and the channel related information, and wherein the first type UE comprises an edge UE and the second type UE comprises a center UE.

4 . The method of claim 1 , wherein the acquiring of the MCS based on the MCS related information comprises:
predicting, by the first AI network, a short-term signal to interference plus noise ratio (SINR) offset corresponding to the target UE based on the channel related information; acquiring the MCS related information corresponding to the target UE based on the short-term SINR offset; and determining the MCS corresponding to the target UE based on the MCS related information.

5 . The method of claim 4 , wherein the predicting, by the first AI network, the short-term SINR offset corresponding to the target UE based on the channel related information comprises:
acquiring first block error rate (BLER) information corresponding to the target UE based on a decoding feedback value fed back by the target UE, the first BLER information containing BLERs of different preset time windows under different preset SINR offsets; and predicting, by the first AI network, the short-term SINR offset corresponding to the target UE based on the first BLER information.

6 . The method of claim 5 , wherein the predicting, by the first AI network, the short-term SINR offset corresponding to the target UE based on the first BLER information comprises:
acquiring a second BLER information based on a movement feature and/or position feature of the target UE and the first BLER information; and predicting, by the first AI network, the short-term SINR offset corresponding to the target UE based on the second BLER information.

7 . The method of claim 6 , wherein the acquiring of the second BLER information based on the movement feature and/or position feature of the target UE and the first BLER information comprises:
selecting, based on the movement feature of the target UE, an effective preset time window from the preset time window contained in the first BLER information, and/or selecting, based on the position feature of the target UE, an effective preset SINR offset from the preset SINR offset of the first BLER information; and determining the second BLER information based on the effective preset time window and/or the effective preset SINR offset.

8 . The method of claim 4 , further comprising:
predicting, by a second AI network, a long-term SINR corresponding to the target UE based on the channel related information, and wherein the acquiring of the MCS related information corresponding to the target UE based on the short-term SINR offset comprises: acquiring the MCS related information corresponding to the target UE based on the short-term SINR offset and the long-term SINR.

9 . A device of a base station comprising:
a memory storing instructions; at least one processor for executing the instructions; and at least one transceiver coupled to the at least one processor, wherein the at least one processor is configured to: acquire channel related information of a target user equipment (UE); identify whether the target UE corresponds to a first type UE or a second type UE based on at least one of the channel related information or position information of the target UE; in case that the target UE corresponds to the first type UE, acquire a modulation and coding scheme (MCS) based on MCS related information, wherein the MCS related information is obtained based on a first artificial intelligence (AI) network using the channel related information; and transmit the MCS to the target UE, wherein the number of available MCS change values of the first type UE is greater than the number of available MCS change values of the second type UE.

10 . The device of claim 9 , wherein the at least one processor is, to acquire the MCS, configured to:
transmit, to a radio access network (RAN) intelligence controller (RIC), the channel related information, and receive, from the RIC, the MCS related information, and wherein the first AI network is deployed on the RIC.

11 . The device of claim 9 ,
wherein the at least one processor is, to acquire the MCS, configured to: in case that the target UE is the second type UE, acquire the MCS based on a fixed adjustment step and the channel related information, and wherein the first type UE comprises an edge UE and the second type UE comprises a center UE.

12 . The device of claim 9 , wherein the at least one processor is, to acquire the MCS, configured to:
predict, by the first AI network, a short-term signal to interference plus noise ratio (SINR) offset corresponding to the target UE based on the channel related information; acquire the MCS related information corresponding to the target UE based on the short-term SINR offset; and determine the MCS corresponding to the target UE based on the MCS related information.

13 . The device of claim 12 , wherein the at least one processor is, to predict, by the first AI network, the short-term SINR offset corresponding to the target UE based on the channel related information, configured to:
acquire first block error rate (BLER) information corresponding to the target UE based on a decoding feedback value fed back by the target UE, the first BLER information containing BLERs of different preset time windows under different preset SINR offsets; and predict, by the first AI network, the short-term SINR offset corresponding to the target UE based on the first BLER information.

14 . The device of claim 13 , wherein the at least one processor is, to predict, by the first AI network, the short-term SINR offset corresponding to the target UE based on the first BLER information, configured to:
acquire a second BLER information based on a movement feature and/or position feature of the target UE and the first BLER information; and predict, by the first AI network, the short-term SINR offset corresponding to the target UE based on the second BLER information.

15 . The device of claim 14 , wherein the at least one processor is, to acquire the second BLER information, configured to:
select, based on the movement feature of the target UE, an effective preset time window from the preset time window contained in the first BLER information, and/or selecting, based on the position feature of the target UE

Assignees

• Samsung Electronics Co Ltd

Inventors

• Li Yan
• Wang Huiyang
• He Huan
• Yan Guangcan
• Wang Jiajia
• Zhao Yi
• Jing Meifang

Classifications

• H04W24/02 Primary

  Arrangements for optimising operational condition · CPC title

• H04L1/0009

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

• H04L1/0019

  in which mode-switching is based on a statistical approach · CPC title

• H04L41/16

  using machine learning or artificial intelligence · CPC title

• H04L41/147

  for predicting network behaviour · CPC title