EP-4736491-A1 - NETWORK NODE AND METHOD PERFORMED BY THE SAME AND STORAGE MEDIUM

Abstract

Embodiments of the present disclosure provide a network node and a method performed by the network node and a storage medium, relating to a field of artificial intelligence. A method performed by a network node may comprise: detecting an overshooting cell; determining an adjustment step corresponding to the overshooting cell based on a coverage situation of the overshooting cell; and adjusting a coverage range of the overshooting cell based on the determined adjustment step. The method performed by the network node may be performed using an artificial intelligence model.

Inventors

• HE, HUAN
• YIN, Zhining
• ZHAO, YINGHUAN
• LI, YAN
• WANG, Huiyang
• WANG, Jiajia

Assignees

• Samsung Electronics Co., Ltd.

Dates

Publication Date

20260506

Application Date

20240802

Claims (15)

1. A method performed by a network node comprising: detecting an overshooting cell which has an actual coverage range larger than a planned coverage range; determining an adjustment step corresponding to the overshooting cell based on a coverage situation of the overshooting cell; and adjusting the actual coverage range of the overshooting cell based on the determined adjustment step.
2. The method of claim 1, wherein the detecting of the overshooting cell comprises: acquiring real-time transmission environment information of a cell; and determining whether the cell is the overshooting cell based on the real-time transmission environment information of the cell.
3. The method of claim 2, wherein the acquiring of the real-time transmission environment information of the cell comprises: acquiring cell-related data and/or user-related data; and acquiring the real-time transmission environment information of the cell based on the cell-related data and/or the user-related data.
4. The method of claim 3, wherein the acquiring of the real-time transmission environmental information of the cell based on the cell-related data and/or the user-related data comprises: acquiring a current user location distribution and a current timing advance (TA) distribution of the cell based on the cell-related data and/or the user-related data; and acquiring the real-time transmission environment information of the cell according to the current user location distribution and the current TA distribution.
5. The method of claim 3, wherein the cell-related data comprises at least one of cell configuration information and cell historical TA data; and/or the user-related data comprises measurement information reported by a served user in the cell.
6. The method of claim 5, wherein the acquiring of the current user location distribution and the current timing advance (TA) distribution based on the cell-related data and the user-related data comprises: predicting, using a first artificial intelligence network, a historical user location distribution of the cell based on the user-related data and the cell configuration information, and predicting, using a second artificial intelligence network, the current user location distribution based on the historical user location distribution; and predicting, using a third artificial intelligence network, the current TA distribution based on the cell historical TA data.
7. The method of claim 2, wherein the real-time transmission environment information includes a ratio of line-of-sight (LOS) transmissions and non-line-of-sight (NLOS) transmissions in the cell.
8. The method of claim 2, wherein the determining of whether the cell is the overshooting cell based on the real-time transmission environment information of the cell comprises: obtaining the actual coverage range of the cell based on a current user location distribution of the cell; obtaining the planned coverage range of the cell based on the real-time transmission environment information; and determining whether the cell is the overshooting cell according to the actual coverage range of the cell and the planned coverage range of the cell.
9. The method of claim 8, wherein the obtaining of the planned coverage range of the cell based on the real-time transmission environmental information comprises: determining a theoretical coverage range of the cell based on cell related data; and predicting, using a fourth artificial intelligence network, the planned coverage range based on the real-time transmission environmental information and the theoretical coverage range of the cell.
10. The method of claim 1, wherein the determining of the adjustment step corresponding to the overshooting cell based on the coverage situation of the overshooting cell comprises: determining the adjustment step corresponding to the overshooting cell according to the actual coverage range and the planned coverage range of the overshooting cell.
11. The method of claim 1, wherein the determining of the adjustment step corresponding to the overshooting cell based on the coverage situation of the overshooting cell comprises: constructing a cell group according to related information between cells, wherein the cell group comprises the overshooting cell; and determining the adjustment step corresponding to the overshooting cell according to overshooting related information between the overshooting cell and other cells in the cell group.
12. The method of claim 11, wherein the related information between the cells comprises at least one of neighbor cell information, handover information, and interference information.
13. The method of claim 11, wherein the constructing of the cell group according to the related information between the cells comprises: determining relationship intimacy degree between a plurality of cells comprising the overshooting cell according to the related information between the cells, wherein the relationship intimacy degree represents an influence degree between the cells; and constructing the cell group based on the acquired relationship intimacy degree.
14. A network node comprising: a transceiver; at least one processor comprising processing circuitry; and memory comprising one or more storage medium, storing instructions, wherein the instructions, when being executed by at least one processor individually and/or collectively, cause the network node to: detect an overshooting cell which has an actual coverage range larger than a planned coverage range; determine an adjustment step corresponding to the overshooting cell based on a coverage situation of the overshooting cell; and adjust the actual coverage range of the overshooting cell based on the determined adjustment step.
15. A non-transitory computer-readable storage medium storing instructions, the instructions, when executed by at least one processor, individually and/or collectively, cause a network node to: detect an overshooting cell which has an actual coverage range larger than a planned coverage range; determine an adjustment step corresponding to the overshooting cell based on a coverage situation of the overshooting cell; and adjust the actual coverage range of the overshooting cell based on the determined adjustment step.

Description

NETWORK NODE AND METHOD PERFORMED BY THE SAME AND STORAGE MEDIUM The present disclosure relates to the field of communication and for example, to a network node, a method performed by the network node, and a computer-readable storage medium. In the field of communication, it may occur that the signal of a cell appears outside the coverage range of this cell, for example, if the signal of a cell appears outside the coverage range of this cell due to the antenna height of the base station being too high or the down-tilt angle being too small and the cell is able to become a primary service cell, this cell may be referred to as an overshooting cell. Overshooting tends to occur in hilly terrain or along regions on two sides of roads or harbors. Overshooting results in too many users in the current cell and a drop in the rate per user, which may result in call blocking if the cell is fully loaded with users. Further, if the signal of an overshooting cell appears in a region apart from its neighbor cells and the cell becomes the primary service cell, then the "island effect" will occur, and the island effect will lead to call drops during the user's movement. In the prior art, when an overshooting cell is detected, the coverage range of each overshooting cell is adjusted based on a pre-set uniform fixed step for all overshooting cells, which, however, fails to meet the differentiated needs of different scenarios. The above information is presented as background information only to assist with an understanding of the disclosure. No determination has been made, and no assertion is made, as to whether any of the above might be applicable as prior art with regard to the disclosure. The accompanying drawings herein are incorporated into and form part of the disclosure, illustrate various example embodiments consistent with the disclosure, which are used in conjunction with the disclosure to explain the principles of the disclosure and do not limit of the disclosure. Further, 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: FIGS. 1A and 1B are diagrams illustrating and example of overshooting; FIGS. 2A and 2B are diagrams illustrating a problem existing in adjusting a coverage range by adopting a fixed adjustment step. FIGS. 3A and 3B are diagrams illustrating a problem existing in detecting an overshooting cell by adopting fixed transmission environment. FIG. 4 is a diagram illustrating a problem existing in adopting a distributed adjustment scheme for overshooting management. FIG. 5 is a flowchart illustrating an example method performed by a network node according to various embodiments; FIG. 6 is a diagram illustrating acquisition of multimodal data according to various embodiments; FIG. 7 is a diagram illustrating an example of detecting an overshooting cell and determining an overshooting level using an artificial intelligence network according to various embodiments; FIG. 8 is a diagram illustrating a user location distribution of a cell according to various embodiments; FIG. 9 is a diagram illustrating example construction of an adjacency relationship table according to various embodiments; FIG. 10 is a diagram illustrating an example of a feature relationship table according to various embodiments; FIG. 11 is a diagram illustrating example construction of a cell relationship intimacy degree table according to various embodiments; FIG. 12 is a diagram illustrating an example of constructing a cell group according to various embodiments; FIG. 13 is a diagram illustrating an example of calculating RSRP differences between cells according to various embodiments; FIG. 14 is a table illustrating an example of overlap coverage ratios between cells according to various embodiments; FIG. 15 is a table illustrating an example of the number of handover failures between cells according to various embodiments; FIG. 16 is a diagram illustrating an example of a mapping table between a coverage shrinkage factor and an adjustment step according to various embodiments; FIG. 17 is a diagram illustrating an example of a cell belonging to two cell groups at the same time according to various embodiments; FIG. 18 is a diagram illustrating an example of determining a credibility profile of a cell group according to various embodiments; FIG. 19 is a signal flow diagram illustrating an example method performed by a network node according to various embodiments; FIG. 20 is a diagram illustrating an example illustrating a deployment scenario according to various embodiments; FIG. 21 is a flowchart illustrating an example method performed by a network node according to various embodiments; FIG. 22 is a flowchart illustrating an example method performed by a network node according to various embodiments; FIG. 23 is a block diagram illustrating an example configu