EP-4740665-A1 - METHOD PERFORMED BY NETWORK NODE IN COMMUNICATION SYSTEM AND NETWORK NODE

EP4740665A1EP 4740665 A1EP4740665 A1EP 4740665A1EP-4740665-A1

Abstract

The disclosure relates to a method performed by a network node in a communication system and the network node, which relates to a field of artificial intelligence. The method comprises: determining a first resource allocation pattern corresponding to at least one cell; obtaining a second resource allocation pattern corresponding to the at least one cell, by adjusting the first resource allocation pattern of the at least one cell based on information related to interference between cells. The method may be performed by an electronic apparatus and may be performed using an artificial intelligence model.

Inventors

YUAN, Yue
WANG, MINGLI
CAO, Nan
XIE, FANG
LIU, MEI
WANG, JIANKANG

Assignees

Samsung Electronics Co., Ltd.

Dates

Publication Date: 20260513
Application Date: 20240827

Claims (15)

A method performed by a network controller in a communication system, comprising: determining a first resource allocation pattern corresponding to at least one cell, the first resource allocation pattern comprising a movability level corresponding to a resource unit; obtaining a second resource allocation pattern corresponding to the at least one cell, by adjusting the first resource allocation pattern of the at least one cell based on information related to interference between cells; and transmitting the second resource allocation pattern to a network node, wherein the movability level indicates an adjustable range for the resource unit in a time domain.
The method according to claim 1, wherein the network controller comprises a radio access network intelligent controller (RIC) and the network node comprises a distributed unit (DU), wherein the movability level is related to at least one of a traffic load, a traffic priority, or a delay-related characteristic of a traffic, and wherein the delay-related characteristic comprises at least one of a resource type, a traffic priority, or a maximum acceptable delay.
The method according to claim 1, wherein determining the first resource allocation pattern corresponding to the at least one cell comprises: acquiring attribute-related information of the at least one cell; and determining the first resource allocation pattern corresponding to the at least one cell based on the attribute-related information.
The method according to claim 3, wherein the attribute-related information comprises historical traffic information and/or energy-saving time granularity information.
The method according to claim 4, wherein the determining the first resource allocation pattern corresponding to the at least one cell based on the attribute-related information comprises: allocating available resources for the at least one cell; and determining, via a first neural network, a predicted traffic load of the at least one cell and movability levels corresponding to resource units to which resources have been allocated, based on the historical traffic information; and determining the first resource allocation pattern corresponding to the at least one cell based on the energy-saving time granularity information, the allocated available resources, the predicted traffic load, and the movability levels corresponding to the resource units to which the resources have been allocated.
The method according to claim 5, wherein the first neural network comprises a first sub-neural network, a second sub-neural network and a third sub-neural network, wherein the determining, via the first neural network, the predicted traffic load of the at least one cell and the movability levels corresponding to the resource units to which the resources have been allocated, based on the historical traffic information comprises: determining, through the first sub-neural network, the predicted traffic load of the at least one cell based on the historical traffic information; determining, through the second sub-neural network, delay-related characteristics of the resource units to which the resources have been allocated, based on the predicted traffic load and traffic types corresponding to the resource units to which the resources have been allocated; and determining, through the third sub-neural network, the movability levels corresponding to the resource units to which the resources have been allocated, based on the delay-related characteristics of the resource units to which the resources have been allocated.
The method according to claim 6, wherein the historical traffic information comprises at least one historical traffic log, and wherein the determining, through the first sub-neural network, the predicted traffic load of the at least one cell based on the historical traffic information comprises: determining, through a temporal convolutional network in the first sub-neural network, the predicted traffic load of each historical traffic log corresponding to each resource unit based on the at least one historical traffic log of the at least one cell, and obtaining the predicted traffic load of the at least one cell, by fusing, through a fusion network in the first sub-neural network, the predicted traffic load of each historical traffic log corresponding to each resource unit, for the at least one cell.
The method according to claim 1, wherein the adjusting the first resource allocation pattern of the at least one cell based on the information related to interference between cells comprises: obtaining at least one cell cluster by clustering the at least one cell; and obtaining a corresponding second resource allocation pattern by adjusting a first resource allocation pattern of at least one cell within a cell cluster, based on the information related to interference between cells within each cell cluster.
The method according to claim 8, wherein the clustering the at least one cell comprises: clustering the at least one cell based on the information related to interference of the at least one cell, wherein an interference intensity between cells within one cell cluster is not less than a set threshold, and wherein an interference intensity between cells in different cell clusters is less than the set threshold.
The method according to claim 1, wherein the adjusting the first resource allocation pattern of the at least one cell based on the information related to interference between cells comprises: adjusting the first resource allocation pattern of the at least one cell based on the movability levels corresponding to the resource units and an interference relationship between cells.
The method according to claim 10, wherein the adjusting the first resource allocation pattern of the at least one cell based on the movability levels corresponding to the resource units and the interference relationship between cells comprises: adjusting, through a second neural network, locations of the resource units in the time domain in the first resource allocation pattern of the at least one cell based on the movability levels corresponding to the resource units and the interference relationship between cells.
The method according to claim 11, wherein the adjusting, through the second neural network, the locations of the resource units in the time domain in the first resource allocation pattern of the at least one cell based on the movability levels corresponding to the resource units and the interference relationship between cells comprises: obtaining at least one group of resource allocation pattern segment, by segmenting the first resource allocation pattern of the at least one cell according to a first length; adjusting, through the second neural network, the locations of the resource units in the time domain in the at least one group of resource allocation pattern segment based on the movability levels corresponding to the resource units and the interference relationship between cells; obtaining a corresponding second resource allocation pattern by splicing the adjusted at least one group of resource allocation pattern segment.
The method according to claim 1, further comprising: obtaining a third resource allocation pattern of the at least one cell, by compressing continuous resource units having the same movability level in the second resource allocation pattern of the at least one cell.
A network controller, comprising: a transceiver configured to transmit and/or receive signals; at least on processor, comprising processing circuitry, coupled to the transceiver; and memory storing instructions that, when executed by the at least one processor individually and/or collectively, cause the network node to perform one of methods 1 to 13.
A non-transitory computer-readable storage medium storing instructions, wherein the instructions, when executed by at least one processor, individually and/or collectively, cause an electronic device to perform one of methods 1 to 13

Description

METHOD PERFORMED BY NETWORK NODE IN COMMUNICATION SYSTEM AND NETWORK NODE The disclosure relates to a field of a communication technology, and for example, the disclosure relates to a method and a network node performed by a network node in a communication system. In a 5G system, a Distributed Unit (DU) of a base station periodically transmits a communication prediction request to a Centralized Unit (CU). After receiving the communication prediction request, the CU predicts which time domain resources will have data traffic in a future period (e.g., predicts a resource allocation pattern (which may also referred to as energy-saving pattern)) using information such as Reference Signal (RS) information, Physical Resource Block (PRB) utilization rate, traffic load, etc. collected from the DU, and feedbacks a prediction result to the DU. Thereafter, the DU performs an energy-saving operation based on the received prediction result (e.g., the resource allocation pattern). Specifically, time domain resource allocation of data traffic is centralized, e.g., the data traffic dispersed on a time dimension is centralized as much as possible in the same time domain for transmission, so as to increasing more idle time domains. Then, cell resources are switched off on the idle time domains, e.g., a Radio Unit (RF) apparatus performs a turnoff of a power amplifier. How to generate the resource allocation pattern more rationally and thus better resource allocation to meet a communication requirement is a technical problem that those skilled in the art have been working hard to study. The above and other aspects, features and advantages of certain embodiments of the present disclosure will be more apparent from the following detailed description, taken in conjunction with the accompanying drawings, in which: FIG. 1 is a flowchart illustrating an example method performed by a first network node in a communication system according to various embodiments; FIG. 2A is a flowchart illustrating an example process for determining a first resource allocation pattern corresponding to at least one cell according to various embodiments; FIG. 2B is a flowchart illustrating an example process for determining a first resource allocation pattern corresponding to at least one cell according to various embodiments; FIG. 3 is a diagram illustrating an example structure of a first neural network according to various embodiments; FIG. 4 is a flowchart illustrating an example process of determining, by a first neural network, a predicted traffic load of at least one cell and movability levels corresponding to resource units to which resources have been allocated, according to various embodiments; FIG. 5 is a diagram illustrating an example process for determining, by a first neural network, a predicted traffic load of at least one cell according to various embodiments; FIG. 6 is a diagram illustrating determined predicted traffic load according to various embodiments; FIG. 7 is a diagram illustrating an example process for determining, by a second sub-neural network, a delay-related characteristic of resource units to which resources have been allocated, for a cell according to various embodiments; FIG. 8 is a diagram illustrating an example process for determining, by a third sub-neural network, movability levels of resource units to which resources have been allocated, according to various embodiments; FIG. 9A is a diagram illustrating a first resource allocation pattern corresponding to a cell generated by a first network node according to various embodiments; FIG. 9B is a diagram illustrating an example process for determining a first resource allocation pattern corresponding to at least one cell according to various embodiments; FIG. 10 is a flowchart illustrating an example process for adjusting a first resource allocation pattern of the at least one cell based on information related to interference between cells according to various embodiments; FIG. 11A is a diagram illustrating an example process for segmenting a first resource allocation pattern according to various embodiments; FIG. 11B is a diagram illustrating an example process for adjusting, by a second neural network, each group of resource allocation pattern segment according to various embodiments; FIG. 11C is a diagram illustrating a comparison between a first resource allocation pattern before the adjustment of the at least one cell and a corresponding second resource allocation pattern obtained by the adjustment to the first resource allocation pattern according to various embodiments; FIG. 12 is a flowchart illustrating an example process for adjusting a first resource allocation pattern of the at least one cell based on information related to interference between cells according to various embodiments; FIG. 13 is a diagram illustrating receiving, by a first network node, information related to interference of a cell from a base station according to various embodiments; FIG