Network node, execution method thereof and storage medium

Abstract

The embodiment of the invention provides a network node, an execution method thereof and a storage medium, and relates to the field of artificial intelligence. A method performed by a network node, comprising: detecting a cross-cell coverage cell; determining an adjustment step length corresponding to the cross-area coverage cell based on the coverage condition of the cross-area coverage cell; and adjusting the coverage range of the cross-cell coverage cell based on the determined adjustment step length. Optionally, the method performed by the network node may be performed using an artificial intelligence model.

Description

Technical Field

[0001] The present disclosure relates to the field of communications, and in particular to a network node, a method executed by the network node, and a computer-readable storage medium. Background Art

[0002] In the field of communications, the signal of a cell may appear outside the coverage of the cell. For example, due to the high height of the base station antenna or the small downtilt angle, the signal of a cell may appear outside the coverage of the cell. If it can become the main service cell, this cell is an out-of-area coverage cell. Out-of-area coverage is prone to occur in hilly terrain or along roads and harbors. Out-of-area coverage will cause too many users in the current cell and the rate of each user will decrease. If the user is fully loaded, the call may not be connected. What's more, if the signal of the out-of-area coverage cell appears in an area outside its neighboring area and becomes the main service cell, an "island effect" will occur, which will cause dropped calls during user movement. In the prior art, when an out-of-area coverage cell is detected, the coverage of each out-of-area coverage cell is always adjusted based on a pre-set unified fixed step size for all out-of-area coverage cells. However, this cannot meet the differentiated needs of different scenarios. Summary of the invention

[0003] According to a first aspect of an embodiment of the present disclosure, a method performed by a network node is provided, comprising: detecting a cell that has inter-zone coverage; determining an adjustment step corresponding to the cell that has inter-zone coverage based on the coverage condition of the cell that has inter-zone coverage; and adjusting the coverage range of the cell that has inter-zone coverage based on the determined adjustment step.

[0004] Optionally, the detecting of an out-of-area coverage cell includes: acquiring real-time transmission environment information of the cell; and determining whether the cell is an out-of-area coverage cell based on the real-time transmission environment information of the cell.

[0005] Optionally, the acquiring the real-time transmission environment information of the cell includes: 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.

[0006] Optionally, the obtaining of real-time transmission environment information of the cell based on the cell-related data and / or the user-related data includes: obtaining the current user location distribution and the current timing advance TA distribution of the cell based on the cell-related data and / or the user-related data; and obtaining the real-time transmission environment information of the cell according to the current user location distribution and the current TA distribution.

[0007] Optionally, the cell-related data includes at least one of cell configuration information and cell historical TA data; and / or, the user-related data includes measurement information reported by service users in the cell.

[0008] Optionally, the acquiring the current user location distribution and the current timing advance TA distribution based on the cell-related data and the user-related data includes: predicting the historical user location distribution of the cell using a first artificial intelligence network based on the user-related data and the cell configuration information, and predicting the current user location distribution using a second artificial intelligence network based on the historical user location distribution; and predicting the current TA distribution based on the cell historical TA data using a third artificial intelligence network.

[0009] Optionally, the real-time transmission environment information represents the ratio of line-of-sight LOS transmission and non-line-of-sight NLOS transmission in the cell.

[0010] Optionally, the determining whether the cell is an inter-zone coverage cell based on the real-time transmission environment information of the cell includes: obtaining an actual coverage range of the cell based on a current user location distribution of the cell; obtaining a planned coverage range of the cell based on the real-time transmission environment information; and determining whether the cell is an inter-zone coverage cell according to the actual coverage range of the cell and the planned coverage range of the cell.

[0011] Optionally, obtaining the planned coverage of the cell based on the real-time transmission environment information includes: determining a theoretical coverage of the cell based on cell-related data; and predicting the planned coverage using a fourth artificial intelligence network based on the real-time transmission environment information and the theoretical coverage of the cell.

[0012] Optionally, determining the adjustment step corresponding to the inter-zone coverage cell based on the coverage condition of the inter-zone coverage cell includes: determining the adjustment step corresponding to the inter-zone coverage cell according to an actual coverage range and a planned coverage range of the inter-zone coverage cell.

[0013] Optionally, determining the adjustment step corresponding to the inter-zone coverage cell based on the coverage condition of the inter-zone coverage cell includes: constructing a cell group according to relevant information between cells, wherein the cell group includes the inter-zone coverage cell; and determining the adjustment step corresponding to the inter-zone coverage cell according to inter-zone coverage related information between the inter-zone coverage cell and other cells in the cell group.

[0014] Optionally, the relevant information between the cells includes at least one of the following: neighboring cell information, switching information, and interference information.

[0015] Optionally, constructing the cell group according to the relevant information between the cells includes: determining the relationship intimacy between the multiple cells including the cross-area coverage cell according to the relevant information between the cells, wherein the relationship intimacy represents the influence between the cells; and constructing the cell group based on the acquired relationship intimacy.

[0016] Optionally, determining the relationship intimacy between multiple cells including the inter-zone coverage cell based on the relevant information between the cells includes: using a fifth artificial intelligence network to determine the relationship intimacy between multiple cells including the inter-zone coverage cell based on the relevant information between the cells.

[0017] Optionally, constructing the cell group based on the acquired relationship intimacy includes: determining the cross-cell coverage level of each cell according to the actual coverage and planned coverage of each cell in the multiple cells; and constructing the cell group based on the determined cross-cell coverage level and the acquired relationship intimacy.

[0018] Optionally, determining the cross-cell coverage level of each cell according to the actual coverage and planned coverage of each cell in the multiple cells includes: predicting the cross-cell coverage level of each cell using a sixth artificial intelligence network according to the actual coverage and planned coverage of each cell.

[0019] Optionally, constructing the cell group based on the determined inter-zone coverage level and the obtained relationship intimacy includes: performing the following operations for each inter-zone coverage cell in turn according to the inter-zone coverage level: selecting at least one cell from other cells to form a cell group with the inter-zone coverage cell based on the relationship intimacy between the inter-zone coverage cell and other cells.

[0020] Optionally, the cross-zone coverage related information includes: relationship closeness and / or overlapping coverage ratio.

[0021] Optionally, the method further includes: acquiring measurement information of the inter-zone coverage cell and at least one other cell in the cell group; and determining the overlapping coverage ratio between the inter-zone coverage cell and at least one other cell in the cell group based on the measurement information.

[0022] Optionally, the overlapping coverage ratio between the inter-regional coverage cell and at least one other cell in the cell group is determined based on the measurement information, including: determining a measurement information difference value between the inter-regional coverage cell and at least one other cell in the cell group for serving users based on the measurement information; determining the number of overlapping areas between the inter-regional coverage cell and at least one other cell in the cell group based on a comparison between the measurement information difference value and a set threshold; determining the overlapping coverage ratio between the inter-regional coverage cell and at least one other cell in the cell group based on the number of overlapping areas between the inter-regional coverage cell and at least one other cell in the cell group and the amount of measurement information.

[0023] Optionally, the measurement information includes reference signal received power RSRP.

[0024] Optionally, determining the adjustment step corresponding to the inter-zone coverage cell includes: determining, from the cell group, a first cell that is most affected by the inter-zone coverage of the inter-zone coverage cell; and determining the adjustment step corresponding to the inter-zone coverage cell based on the cell relationship intimacy and overlapping coverage rate between the inter-zone coverage cell and the first cell.

[0025] Optionally, determining the first cell most affected by the inter-zone coverage of the inter-zone coverage cell from the cell group includes: determining the first cell most affected by the inter-zone coverage of the inter-zone coverage cell from the other cells based on switching information of the inter-zone coverage cell and other cells in the cell group.

[0026] Optionally, the adjustment step corresponding to the inter-regional coverage cell is determined according to the cell relationship intimacy and the overlapping coverage ratio between the inter-regional coverage cell and the first cell, including: determining the coverage contraction factor corresponding to the inter-regional coverage cell according to the cell relationship intimacy and the overlapping coverage ratio between the inter-regional coverage cell and the first cell; determining the adjustment step corresponding to the inter-regional coverage cell according to the coverage contraction factor.

[0027] Optionally, the method further includes: determining an adjustment step corresponding to other cells in the cell group except the inter-zone coverage cell; and adjusting the coverage of the other cells based on the determined adjustment step corresponding to the other cells.

[0028] Optionally, determining the adjustment step corresponding to other cells in the cell group except the off-zone coverage cell includes: determining the adjustment step corresponding to the first cell in the cell group that is most affected by the off-zone coverage of the off-zone coverage cell as a predetermined value; determining the adjustment step corresponding to a second cell among the other cells based on the relationship closeness and / or overlapping coverage rate between the second cell and cells in the cell group except the second cell, wherein the second cell is a cell in the cell group except the off-zone coverage cell and the first cell.

[0029] Optionally, the determining the adjustment step sizes corresponding to other cells in the cell group except the cross-area coverage cell includes:

[0030] When the other cells belong to multiple cell groups including the cell group at the same time:

[0031] If the other cell is a first cell in at least one of the multiple cell groups, determining the adjustment step size of the other cell to be a predetermined value, wherein the first cell is the cell in the at least one cell group that is most affected by the coverage of the cross-coverage cell;

[0032] If the other cell is not the first cell in any of the multiple cell groups, the adjustment step corresponding to the other cell is determined according to the credibility portrait of each cell group in the multiple cell groups.

[0033] Optionally, the credibility portrait of each cell group is determined based on relevant network parameters of each cell group.

[0034] Optionally, the relevant network parameters of each cell group include at least one of the following items: the average cell relationship intimacy between all cells in each cell group, the total number of service users in each cell group, the total number of cells in each cell group, the average system throughput of each cell group, and the average edge user throughput of each cell group.

[0035] Optionally, determining the adjustment step corresponding to the other cells based on the credibility portrait of each cell group in the multiple cell groups includes: obtaining the adjustment step corresponding to the other cells determined based on each cell group respectively; determining the credibility of each cell group based on the credibility portrait of each cell group; and determining the adjustment step determined based on the cell group with the greatest credibility as the final adjustment step corresponding to the other cells.

[0036] Optionally, determining the credibility of each cell group based on the credibility portrait of each cell group includes: calculating the area in a closed loop of the credibility portrait of each cell group; and determining the credibility of each cell group based on the calculated area.

[0037] According to a second aspect of an embodiment of the present disclosure, a method executed by a network node is provided, including: acquiring real-time transmission environment information of a cell; and determining whether the cell is an inter-zone coverage cell based on the real-time transmission environment information of the cell.

[0038] Optionally, the acquiring the real-time transmission environment information of the cell includes: 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.

[0039] Optionally, the obtaining of real-time transmission environment information of the cell based on the cell-related data and / or the user-related data includes: obtaining the current user location distribution and the current timing advance TA distribution of the cell based on the cell-related data and / or the user-related data; and obtaining the real-time transmission environment information of the cell according to the current user location distribution and the current TA distribution.

[0040] Optionally, the cell-related data includes at least one of cell configuration information and cell historical TA data; and / or, the user-related data includes measurement information reported by service users in the cell.

[0041] Optionally, the acquiring the current user location distribution and the current timing advance TA distribution based on the cell-related data and the user-related data includes: predicting the historical user location distribution of the cell using a first artificial intelligence network based on the user-related data and the cell configuration information, and predicting the current user location distribution using a second artificial intelligence network based on the historical user location distribution; and predicting the current TA distribution based on the cell historical TA data using a third artificial intelligence network.

[0042] Optionally, the real-time transmission environment information represents the ratio of line-of-sight LOS transmission and non-line-of-sight NLOS transmission in the cell.

[0043] Optionally, the determining whether the cell is an inter-zone coverage cell based on the real-time transmission environment information of the cell includes: obtaining an actual coverage range of the cell based on a current user location distribution of the cell; obtaining a planned coverage range of the cell based on the real-time transmission environment information; and determining whether the cell is an inter-zone coverage cell according to the actual coverage range of the cell and the planned coverage range of the cell.

[0044] Optionally, obtaining the planned coverage of the cell based on the real-time transmission environment information includes: determining a theoretical coverage of the cell based on cell-related data; and predicting the planned coverage using a fourth artificial intelligence network based on the real-time transmission environment information and the theoretical coverage of the cell.

[0045] According to a third aspect of an embodiment of the present disclosure, a method performed by a network node is provided, comprising: constructing a cell group, wherein the cell group includes an inter-zone coverage cell and other cells related to the inter-zone coverage cell; and adjusting the respective coverage ranges of the inter-zone coverage cell and the other cells.

[0046] Optionally, the constructing of the cell group includes: constructing the cell group according to relevant information between cells, wherein the relevant information between the cells includes at least one of the following: neighboring cell information, switching information, and interference information.

[0047] Optionally, constructing the cell group according to the relevant information between the cells includes: determining the relationship intimacy between the multiple cells including the cross-area coverage cell according to the relevant information between the cells, wherein the relationship intimacy represents the influence between the cells; and constructing the cell group based on the acquired relationship intimacy.

[0048] Optionally, determining the relationship intimacy between multiple cells including the inter-zone coverage cell based on the relevant information between the cells includes: using a fifth artificial intelligence network to determine the relationship intimacy between multiple cells including the inter-zone coverage cell based on the relevant information between the cells.

[0049] Optionally, constructing the cell group based on the acquired relationship intimacy comprises: determining the cross-cell coverage level of each cell according to the actual coverage range and the planned coverage range of each cell in the plurality of cells;

[0050] The cell group is constructed based on the determined cross-cell coverage level and the obtained relationship intimacy.

[0051] Optionally, determining the cross-cell coverage level of each cell according to the actual coverage and planned coverage of each cell in the multiple cells includes: predicting the cross-cell coverage level of each cell using a sixth artificial intelligence network according to the actual coverage and planned coverage of each cell.

[0052] Optionally, constructing the cell group based on the determined inter-zone coverage level and the obtained relationship intimacy includes: performing the following operations for each inter-zone coverage cell in turn according to the inter-zone coverage level: selecting at least one cell from other cells to form a cell group with the inter-zone coverage cell based on the relationship intimacy between the inter-zone coverage cell and other cells.

[0053] Optionally, the method also includes: determining an adjustment step corresponding to the inter-zone coverage cell, and determining an adjustment step corresponding to the other cells, wherein adjusting the coverage of the inter-zone coverage cell and the other cells respectively includes: adjusting the coverage of the inter-zone coverage cell based on the adjustment step corresponding to the inter-zone coverage cell, and adjusting the coverage of the other cells based on the adjustment step corresponding to the other cells.

[0054] Optionally, the determining the adjustment step size corresponding to the inter-zone coverage cell includes: determining the adjustment step size corresponding to the inter-zone coverage cell according to inter-zone coverage related information between the inter-zone coverage cell and other cells in the cell group.

[0055] Optionally, determining the adjustment step corresponding to the other cells includes: determining the adjustment step corresponding to the first cell in the cell group that is most affected by the inter-zone coverage of the inter-zone coverage cell as a predetermined value; determining the adjustment step corresponding to the second cell based on the relationship closeness and / or overlapping coverage rate between the second cell in the other cells and the cells in the cell group other than the second cell, wherein the second cell is a cell in the cell group other than the inter-zone coverage cell and the first cell.

[0056] Optionally, determining the adjustment step size corresponding to the other cells includes:

[0057] When the other cells belong to multiple cell groups including the cell group at the same time:

[0058] If the other cell is a first cell in at least one of the multiple cell groups, determining the adjustment step size of the other cell to be a predetermined value, wherein the first cell is the cell in the at least one cell group that is most affected by the coverage of the cross-coverage cell;

[0059] If the other cell is not the first cell in any of the multiple cell groups, the adjustment step corresponding to the other cell is determined according to the credibility portrait of each cell group in the multiple cell groups.

[0060] Optionally, the credibility portrait of each cell group is determined based on relevant network parameters of each cell group.

[0061] Optionally, the relevant network parameters of each cell group include at least one of the following items: the average cell relationship intimacy between all cells in each cell group, the total number of service users in each cell group, the total number of cells in each cell group, the average system throughput of each cell group, and the average edge user throughput of each cell group.

[0062] Optionally, determining the adjustment step corresponding to the other cells based on the credibility portrait of each cell group in the multiple cell groups includes: obtaining the adjustment step corresponding to the other cells determined based on each cell group respectively; determining the credibility of each cell group based on the credibility portrait of each cell group; and determining the adjustment step determined based on the cell group with the greatest credibility as the final adjustment step corresponding to the other cells.

[0063] Optionally, determining the credibility of each cell group based on the credibility portrait of each cell group includes: calculating the area in a closed loop of the credibility portrait of each cell group; and determining the credibility of each cell group based on the calculated area.

[0064] According to a fourth aspect of an embodiment of the present disclosure, a network node is provided, comprising: a transceiver; and a processor coupled to the transceiver and configured to execute the method performed by the network node as described above.

[0065] According to a fifth aspect of an embodiment of the present disclosure, a computer-readable storage medium storing instructions is provided, which, when executed by at least one processor, prompts the at least one processor to execute any one of the methods described above.

[0066] According to the technical solution provided by the embodiments of the present disclosure, after detecting a cell with off-zone coverage, the network node determines the adjustment step corresponding to the cell with off-zone coverage based on the coverage situation of the cell with off-zone coverage, and then adjusts the coverage range of the cell with off-zone coverage based on the determined adjustment step. Therefore, compared with adjusting the coverage range using a pre-set adjustment step that is always fixed for all cells, it can better meet the differentiated needs of different scenarios.

[0067] According to the technical solution provided by the embodiment of the present disclosure, since whether a cell is an out-of-area coverage cell is determined based on the real-time transmission environment information of the cell, the accuracy of out-of-area coverage cell detection can be effectively improved.

[0068] According to the technical solution provided by the embodiments of the present disclosure, after constructing a cell group including an off-zone coverage cell and other cells related to the off-zone coverage cell, in addition to adjusting the coverage range of the off-zone coverage cell, the coverage range of the other cells is also adjusted. Therefore, not only the off-zone coverage problem of the off-zone coverage cell is solved, but also coverage holes between the off-zone coverage cell and other cells are avoided, thereby preventing user call drops, improving the overall stability of the network, and facilitating the realization of the global optimality of the overall network.

[0069] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The accompanying drawings herein are incorporated in and constitute a part of the specification, illustrate exemplary embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.

[0071] Figure 1 is a schematic diagram showing cross-area coverage.

[0072] Figure 2 It is a schematic diagram showing the problems existing in adjusting the coverage range by using a fixed adjustment step size.

[0073] Figure 3 It is a schematic diagram showing the problems existing in detecting cross-coverage cells using a fixed transmission environment.

[0074] Figure 4 It is a schematic diagram showing the problems existing in adopting a distributed adjustment scheme for cross-zone coverage management.

[0075] Figure 5 is a flowchart of a method performed by a network node according to an embodiment of the present disclosure.

[0076] Figure 6 is a schematic diagram showing acquisition of multimodal data.

[0077] Figure 7 is a schematic diagram showing an example of using an artificial intelligence network to detect an out-of-coverage cell and determine an out-of-coverage level.

[0078] Figure 8 is a schematic diagram showing the user location distribution in a cell.

[0079] Fig. 9 It is a schematic diagram showing the construction of an adjacency relationship table according to an embodiment of the present disclosure.

[0080] Fig.10 is an example of a feature relationship table according to an embodiment of the present disclosure.

[0081] Fig.11 It is a schematic diagram of constructing a community relationship intimacy table according to an embodiment of the present disclosure.

[0082] Fig.12 It is a schematic diagram of an example of constructing a cell group according to an embodiment of the present disclosure.

[0083] Fig.13 This is an example of calculating the RSRP difference between cells according to an embodiment of the present disclosure.

[0084] Fig.14 is an example of overlapping coverage between cells according to an embodiment of the present disclosure.

[0085] Fig.15 is an example of the number of inter-cell handover failures according to an embodiment of the present disclosure.

[0086] Fig.16 is an example of a mapping table between a coverage shrinkage factor and an adjustment step size according to an embodiment of the present disclosure.

[0087] Fig.17 is a schematic diagram of an example in which a cell belongs to two cell groups at the same time according to an embodiment of the present disclosure.

[0088] Fig.18 It is a schematic diagram of an example of determining the credibility portrait of a cell group according to an embodiment of the present disclosure.

[0089] Fig.19 is a schematic diagram of an example of a method performed by a network node according to an embodiment of the present disclosure.

[0090] Fig. 20 is an example showing a deployment scenario according to an embodiment of the present disclosure.

[0091] Fig.21 is a flowchart illustrating a method performed by a network node according to another embodiment of the present disclosure.

[0092] Fig. 22 is a flowchart illustrating a method performed by a network node according to yet another embodiment of the present disclosure.

[0093] Fig.23 is a block diagram illustrating a network node according to an embodiment of the present disclosure.

[0094] Fig.24 is a schematic structural diagram showing an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0095] The following description with reference to the accompanying drawings is provided to facilitate a comprehensive understanding of the various embodiments of the present disclosure as defined by the claims and their equivalents. This description includes various specific details to facilitate understanding but should be considered as exemplary only. Therefore, one of ordinary skill in the art will recognize that various changes and modifications can be made to the various embodiments described herein without departing from the scope and spirit of the present disclosure. In addition, for the sake of clarity and conciseness, descriptions of well-known functions and structures may be omitted.

[0096] The terms and expressions used in the following specification and claims are not limited to their dictionary meanings, but are merely used by the inventor to enable a clear and consistent understanding of the present disclosure. Therefore, it should be apparent to those skilled in the art that the following description of various embodiments of the present disclosure is provided for illustration purposes only and not for the purpose of limiting the present disclosure as defined in the appended claims and their equivalents.

[0097] It should be understood that the singular forms "a", "an", and "the" may also include plural references unless the context clearly indicates otherwise. Thus, for example, reference to "a component surface" includes reference to one or more such surfaces. When we refer to an element as being "connected" or "coupled" to another element, the one element may be directly connected or coupled to the other element, or the one element and the other element may establish a connection relationship through an intermediate element. In addition, "connected" or "coupled" as used herein may include wireless connection or wireless coupling.

[0098] The term "include" or "may include" refers to the presence of the corresponding disclosed functions, operations or components that can be used in various embodiments of the present disclosure, rather than limiting the presence of one or more additional functions, operations or features. In addition, the term "include" or "have" may be interpreted as indicating certain characteristics, numbers, steps, operations, constituent elements, components or combinations thereof, but should not be interpreted as excluding the possibility of the presence of one or more other characteristics, numbers, steps, operations, constituent elements, components or combinations thereof.

[0099] The term "or" used in various embodiments of the present disclosure includes any of the listed terms and all combinations thereof. For example, "A or B" may include A, may include B, or may include both A and B. When describing multiple (two or more) items, if the relationship between the multiple items is not clearly defined, the multiple items may refer to one, multiple, or all of the multiple items. For example, the description of "parameter A includes A1, A2, A3" may be implemented as parameter A including A1 or A2 or A3, or may be implemented as parameter A including at least two of the three items A1, A2, and A3.

[0100] Unless defined differently, all terms (including technical terms or scientific terms) used in the present disclosure have the same meanings as understood by those skilled in the art described in the present disclosure. Common terms as defined in dictionaries are interpreted as having meanings consistent with the context in the relevant technical field, and should not be interpreted ideally or overly formally unless clearly defined in the present disclosure.

[0101] At least some functions of the device or electronic device provided in the embodiments of the present disclosure can be implemented by an AI model, such as at least one module among multiple modules of the device or electronic device can be implemented by an AI model. Functions associated with AI can be performed by non-volatile memory, volatile memory and processor.

[0102] The processor may include one or more processors. In this case, the one or more processors may be general-purpose processors, such as a central processing unit (CPU), an application processor (AP), etc., or pure graphics processing units, such as a graphics processing unit (GPU), a visual processing unit (VPU), and / or an AI-specific processor, such as a neural processing unit (NPU).

[0103] The one or more processors control the processing of input data according to predefined operating rules or artificial intelligence (AI) models stored in non-volatile memory and volatile memory. The predefined operating rules or artificial intelligence models are provided by training or learning.

[0104] Here, providing by learning means obtaining a predefined operating rule or an AI model with desired characteristics by applying a learning algorithm to a plurality of learning data. The learning can be performed in the device or electronic device itself in which the AI ​​according to the embodiment is executed, and / or can be implemented by a separate server / system.

[0105] The AI ​​model may include multiple neural network layers. Each layer has multiple weight values, and each layer performs neural network calculations by calculating between the input data of the layer (such as the calculation results of the previous layer and / or the input data of the AI ​​model) and the multiple weight values ​​of the current layer. Examples of neural networks include, but are not limited to, convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), restricted Boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), generative adversarial networks (GANs), and deep Q networks.

[0106] A learning algorithm is a method of using a plurality of learning data to train a predetermined target device (e.g., a robot) to enable, allow, or control the target device to make a determination or prediction. Examples of the learning algorithm include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

[0107] According to the present disclosure, at least one step in the method performed by a network node or a user equipment, such as detecting an out-of-coverage cell, determining an adjustment step size specific to an out-of-coverage cell, etc., can be implemented using an artificial intelligence model. The processor of the electronic device can perform a preprocessing operation on the data to convert it into a form suitable for use as an input to the artificial intelligence model. The artificial intelligence model can be obtained through training. Here, "obtained through training" means obtaining a predefined operating rule or artificial intelligence model configured to perform a desired feature (or purpose) by training a basic artificial intelligence model with multiple training data through a training algorithm.

[0108] The following describes several optional embodiments to illustrate the technical solutions of the embodiments of the present disclosure and the technical effects produced by the technical solutions of the present disclosure. It should be noted that the following embodiments can refer to, draw on or combine with each other, and the same terms, similar features and similar implementation steps in different embodiments will not be described repeatedly.

[0109] The cross-coverage phenomenon has become a common phenomenon in wireless communication networks, for example, it is prevalent in B5G / 5G networks. Cross-coverage phenomenon is a long-standing phenomenon in B5G / 5G networks due to changes in the building environment and topology caused by the increase in the number of mobile stations, femtocells, and changes in antenna configuration caused by new functions. Cross-coverage management is a major challenge to improve communication performance, which has attracted global attention and deserves research. For user equipment, dropped calls or call blocking caused by severe cross-coverage are unacceptable and should be eliminated, and data rates need to be increased by solving the cross-coverage problem to obtain a better service experience.

[0110] The phenomenon of cross-zone coverage usually refers to the coverage of a cell exceeding the planned coverage, forming cross-zone coverage areas or isolated areas in other cells, resulting in dropped calls, call blocking, reduced user equipment (UE) throughput, etc. An isolated area is also a type of cross-zone coverage area, which is a special type of cross-zone coverage area that can be formed in an adjacent cell or in a cell other than an adjacent cell. Figure 1 is a schematic diagram showing cross-area coverage. Figure 1 As shown in (a), the coverage of cell 1 exceeds its planned coverage, forming not only a general cross-area coverage area in its neighboring cell 3, but also an isolated area in cell 2 outside its neighboring cell. Figure 1 As shown in (b), cross-zone coverage is usually caused by inappropriate electronic downtilt angle of the base station, inappropriate antenna height of the base station, etc. Therefore, the coverage range of the cross-zone coverage cell can be adjusted by adjusting the electronic downtilt angle and antenna height of the base station antenna, for example.

[0111] When solving the cross-region coverage problem, it is usually divided into two steps: first, the base station detects whether the current cell is a cross-region coverage cell based on a fixed transmission environment; second, if it is a cross-region coverage cell, the electronic downtilt angle of the antenna is adjusted downward based on a fixed step size to adjust its coverage range, and in one embodiment, a distributed cross-region coverage management solution is adopted, that is, only the coverage range of the cross-region coverage cell is adjusted, without considering other cells. Among them, the fixed transmission environment means that regardless of the transmission environment of the cell, the same and fixed planned coverage is used for all cells, and the same method is used to calculate the actual coverage. The planned coverage is a configuration value, and the actual coverage is calculated based on the TA (Timing Advance) distribution. TA is the signal transmission time calculated by the base station based on the random access code sent by the user. According to the TA value sent by the base station to each user, the distance of the signal transmission from the base station to the user can be calculated. If this distance is outside the planned coverage of this cell, this cell is a cross-region coverage cell. Fixed step size means that the adjustment step size of all cells is fixed and does not change with time.

[0112] However, the above-mentioned method for solving the cross-area coverage problem has at least the following three problems:

[0113] Problem 1: The adjustment solution based on a fixed step size cannot meet the differentiated requirements of different scenarios, resulting in long adjustment time or dropped calls.

[0114] - For cells with serious cross-area coverage, large step size adjustment is required. If a fixed step size is used, the adjustment will be slow and the performance improvement will be slow. For example, Figure 2 As shown in (a), if the fixed adjustment step size is very small, more adjustments are required for cells with more cross-coverage areas. If the adjustment period is at least 1 day, a longer adjustment time is required to solve the cross-coverage problem.

[0115] - For cells with slight cross-coverage, small step size adjustments are required. If a fixed step size is used, it will lead to over-adjustment and miss the optimal setting. For example, Figure 2 As shown in (b), if the fixed adjustment step size is large, a smaller adjustment is required for cells with less cross-zone coverage and smaller areas. After the large step size adjustment, the actual coverage area becomes too small, coverage holes may occur, and call drops may occur.

[0116] Problem 2: The detection scheme for cross-regional coverage of cells based on a fixed transmission environment has missed detections and false detections, resulting in low detection accuracy. The first reason is that the transmission environment of each cell is different, and different coverage plans should be used. The second reason is that TA is not equivalent to straight-line distance. In an NLOS (Non-Line of Sight) environment, the signal will be refracted many times, and the calculated distance is not a straight-line distance, which cannot represent the distance from the user to the base station. For example, the actual coverage is calculated based on the TA distribution in the LOS (Line of Sight) environment. However, if Figure 3 As shown in (a), for NLOS environment, a large TA will make the actual coverage too large, resulting in false detection. Figure 3 As shown in (b), for cells with more LOS, the signal is transmitted farther, so the planned coverage should be larger, while for cells with more NLOS, the planned coverage should be smaller. However, if the planned coverage is always calculated based on a fixed transmission environment (for example, LOS:NLOS=2:8), it may cause false detection and missed detection. False detection will increase the call drop rate and call blocking rate, and reduce the system throughput. Missed detection will result in the problem of cross-area coverage not being solved, the cell throughput not being improved, and dropped calls still existing.

[0117] Problem 3: Based on the distributed cross-area coverage management solution, only the performance of the local cell is considered, which may cause coverage holes in the network after adjustment, causing frequent call drops for users, destroying network stability, and failing to achieve the global optimality of the overall network. Figure 4 As shown in FIG. 1 , if cell A is an inter-zone coverage cell, the inter-zone coverage problem of cell A is solved after adjusting the electronic downtilt angle of the antenna of cell A. However, a coverage hole area appears between cell A and cell C, and the UE in the area experiences call drops (handover failure) or call blocking (cannot initiate a call).

[0118] In view of the above problems, the present disclosure proposes a method executed by a network node. According to the method of the present disclosure, cross-zone coverage management can be better implemented.

[0119] Figure 5 is a flowchart of a method performed by a network node according to an embodiment of the present disclosure.

[0120] Reference Figure 5 In step S510, an over-coverage cell is detected. In step S520, an adjustment step corresponding to the over-coverage cell is determined based on the coverage of the over-coverage cell. Finally, in step S530, the coverage of the over-coverage cell is adjusted based on the determined adjustment step.

[0121] according to Figure 5In the method shown, after detecting a cell with off-zone coverage, the network node determines the adjustment step corresponding to the cell with off-zone coverage based on the coverage situation of the cell with off-zone coverage, and then adjusts the coverage range of the cell with off-zone coverage based on the determined adjustment step. Therefore, compared with adjusting the coverage range using a pre-set and always fixed adjustment step, it can better meet the differentiated needs of different scenarios, thereby effectively solving the above-mentioned problem 1.

[0122] According to an embodiment, the network node mentioned in the present disclosure may be a control node on the network side, for example, a SON (Self Organization Network) manager, an ORAN (Open Radio Access Network) entity, a RIC (RAN Intelligent Controller) entity, but is not limited thereto. In addition, the different functions involved in the above method of the present disclosure may be distributed and completed by multiple sub-entities, and the connection between the sub-entities may be a wired connection or a wireless connection.

[0123] In the following, the network node is taken as an example to describe the SON manager. Figure 5 Each step in the method shown is described in detail.

[0124] Optionally, according to an embodiment, step S510 may include: acquiring real-time transmission environment information of a cell; and determining whether the cell is an inter-zone coverage cell based on the real-time transmission environment information of the cell.

[0125] According to the above-mentioned method of detecting cross-coverage cells disclosed in the present invention, since the real-time transmission environment information of the cell is first obtained, and then whether the cell is a cross-coverage cell is determined based on the real-time transmission environment information of the cell, compared with the detection scheme of cross-coverage cells that is always based on a fixed transmission environment, missed detection and false detection can be better avoided, and the accuracy of cross-coverage detection can be improved, thereby effectively solving the above-mentioned problem 2. According to an embodiment, the acquisition of the real-time transmission environment information of the cell may include: acquiring cell-related data and / or user-related data; acquiring the real-time transmission environment information of the cell based on the cell-related data and / or the user-related data. For example, Figure 6As shown, the SON manager can obtain cell-related data from the base station (gNB) and obtain user-related data from the UE measurement report. According to an embodiment, the cell-related data may include at least one of the cell configuration information and the cell historical TA data. Optionally, the cell-related data may also include cell performance data. For example, the cell configuration information may include the antenna configuration data and the neighboring cell relationship table of the cell, but is not limited to this. For example, the antenna configuration data may include at least one of the antenna height, antenna azimuth, antenna mechanical downtilt, antenna electronic downtilt, antenna transmit power and antenna position, but is not limited to this. For example, the cell historical TA data may include the TA value statistics under each TA index, but is not limited to this. For example, the cell performance information may include the number of users served by the cell, the throughput of the cell, the switching data of the cell, and so on. For example, the user-related data reported by the UE measurement may include at least one of the channel quality indicator (Channel Quality Indicator, CQI) and the reference signal receiving power (ReferenceSignal Receiving Power, RSRP), but is not limited to this.

[0126] According to an embodiment, optionally, obtaining the real-time transmission environment information of the cell based on the cell-related data and / or the user-related data includes: obtaining the current user location distribution and the current timing advance TA distribution of the cell based on the cell-related data and / or the user-related data; obtaining the real-time transmission environment information of the cell according to the current user location distribution and the current TA distribution.

[0127] According to an embodiment, the obtaining of the current user location distribution and the current timing advance TA distribution based on the cell-related data and the user-related data may include: predicting the historical user location distribution of the cell using a first artificial intelligence (AI) network based on the user-related data and the cell configuration information, and predicting the current user location distribution using a second artificial intelligence network based on the historical user location distribution; predicting the current TA distribution using a third artificial intelligence network based on the cell's historical TA data. For example, the cell configuration information may include the base station location, the mechanical downtilt angle, the electronic downtilt angle, the transmit power, and the height of the cell antenna, but is not limited thereto. For example, the cell's historical TA data may include TA value statistics under multiple TA indexes.

[0128] For example, Figure 7As shown in FIG, the historical user location distribution of each cell can be predicted using the Mixer network based on user-related data (e.g., historical RSRPs between different cells measured and reported by UE) and cell configuration information (e.g., including base station location, mechanical downtilt angle, electronic downtilt angle, transmit power, and height of cell antenna). The historical user location distribution represents the historical locations of all UEs in a cell at different historical moments, such as Figure 8 As shown. As an example, a 7-layer Mixer network can be selected because of its low complexity and strong data fitting ability. However, the number of layers of the Mixer network is not limited to 7 layers, but can be set to other layers according to actual needs. This disclosure has no restrictions on this. It should be noted that although Figure 7 In the example shown in FIG. 1 , the first artificial intelligence network is shown to be a Mixer network. However, the first artificial intelligence network is not limited to being a Mixer network, but can be any type of artificial intelligence network, and the present disclosure is not limited to this.

[0129] Next, if Figure 7 As shown in FIG. 1 , after predicting the historical user location distribution of the cell, a bidirectional long short-term memory network (Bidirectional Long Short-Term Memory Network, Bi-LSTM) network (in Figure 7 is marked as "Bi-LSTM 1 ”) predicts the current user location distribution. For example, a 4-layer Bi-LSTM network can be selected because of its high prediction accuracy, but the number of layers of the Bi-LSTM network is not limited to 4 layers, but can be set to other layers according to actual needs, and the present disclosure has no limitation on this. It should be noted that although Figure 7 In the example of FIG. 1 , the second artificial intelligence network is shown to be a Bi-LSTM network. However, the second artificial intelligence network is not limited to a Bi-LSTM network, but can be any type of artificial intelligence network, and the present disclosure has no limitation on this. The actual coverage range of the cell can be obtained based on the predicted user location distribution, for example, the actual coverage radius of the cell. In the above example of the present disclosure, the actual coverage range of the cell is obtained based on the predicted user location distribution, and the actual coverage range obtained is more accurate than the actual coverage range calculated based on the TA distribution.

[0130] like Figure 7 As shown, the Bi-LSTM network (in Figure 7 is marked as "Bi-LSTM 2 ”) predicts the current TA distribution of the cell. It should be noted that although Figure 7In the example shown in FIG. 1 , the third artificial intelligence network is shown to be a Bi-LSTM network. However, the third artificial intelligence network is not limited to being a Bi-LSTM network, but can be any type of artificial intelligence network, and the present disclosure is not limited to this.

[0131] After the current user location distribution and the current TA distribution are predicted using the artificial intelligence network, the real-time environmental information of the cell can be obtained based on the predicted current user location distribution and the current TA distribution. According to an embodiment, the real-time transmission environment can represent the ratio of LOS transmission and NLOS transmission in the cell. The more LOS, the larger the planned coverage. The ratio of LOS and NLOS for each cell can be calculated by TA distribution and user distribution. For each user, if the distance from the user to the base station calculated according to the TA distribution is the same as the distance from the user to the base station calculated according to the user location distribution, the user is in a LOS environment in the cell; otherwise, the user is in a NLOS environment in the cell. By counting the transmission environment of all users, the ratio of LOS and NLOS of the cell can be obtained, and then the real-time transmission environment information of the cell can be obtained.

[0132] After the real-time transmission environment information of the cell is acquired, it can be determined whether the cell is an inter-zone coverage cell based on the real-time transmission environment information of the cell.

[0133] According to an embodiment, optionally, determining whether the cell is an inter-zone coverage cell based on the real-time transmission environment information of the cell may include: obtaining an actual coverage range of the cell based on the current user location distribution of the cell; obtaining a planned coverage range of the cell based on the real-time transmission environment information; and determining whether the cell is an inter-zone coverage cell based on the actual coverage range of the cell and the planned coverage range of the cell.

[0134] As an example, obtaining the planned coverage of the cell based on the real-time transmission environment information may include: determining the theoretical coverage of the cell based on cell-related data; and predicting the planned coverage using a fourth artificial intelligence network based on the real-time transmission environment information and the theoretical coverage of the cell. Figure 7 As shown in , the theoretical coverage range can be determined based on the base station location, the mechanical downtilt angle, the electronic downtilt angle, the transmission power and the height of the cell antenna. Subsequently, the planned coverage range can be predicted using a convolutional neural network (CNN) based on the real-time transmission environment information and the theoretical coverage range of the cell. The planned coverage range may also be referred to as the "planned coverage range". For example, the planned coverage range can be indicated by a planned coverage radius. For example, the antenna configuration data in the cell modal data can indicate the upper boundary of the cell coverage range, which can be used as a reference for determining the dynamic planned coverage. In Figure 7 In the example of , the fourth artificial intelligence model can be CNN, and the CNN network is selected because of its strong feature extraction capability. For example, a 5-layer CNN network can be selected. However, the fourth artificial intelligence network is not limited to CNN, but can be any type of artificial intelligence network, and the number of layers of the artificial intelligence network is not limited to 5 layers.

[0135] After obtaining the actual coverage of the cell and the planned coverage of the cell, it can be determined whether the cell is an out-of-area coverage cell according to the actual coverage of the cell and the planned coverage of the cell. As mentioned above, the actual coverage of the cell can be obtained based on the predicted current user location distribution of the cell. For example, the actual coverage radius of the cell can be obtained based on the predicted current user location distribution of the cell, and whether the cell is an out-of-area coverage cell is determined by comparing the actual coverage radius of the cell with the predicted planned coverage radius. For example, if the actual coverage radius of the cell exceeds the planned coverage radius, it is determined that the cell is an out-of-area coverage cell, otherwise, it is not an out-of-area coverage cell.

[0136] According to an embodiment of the present disclosure, since the planned coverage of a cell is obtained based on the real-time transmission environment of the cell, the obtained planned coverage of the cell is dynamic, and since the actual coverage of the cell is obtained based on the current user location distribution, the obtained actual coverage is more accurate, and further, since whether the cell is an out-of-zone coverage cell is determined based on the more accurate actual coverage and the dynamic planned coverage, the out-of-zone coverage cell can be detected more accurately.

[0137] Return to reference Figure 5 After detecting the out-of-area coverage cell, in step S520, the adjustment step size corresponding to the out-of-area coverage cell may be determined based on the coverage condition of the out-of-area coverage cell.

[0138] According to an embodiment, optionally, step S520 may include: determining the adjustment step corresponding to the cross-coverage cell according to the actual coverage and planned coverage of the cross-coverage cell. For example, the actual coverage of the cross-coverage cell may be obtained based on the current user location distribution of the cross-coverage cell, and the planned coverage of the cross-coverage cell may be obtained based on the real-time transmission environment of the cross-coverage cell. Since the adjustment step specific to the cross-coverage cell is determined based on the more accurate real-time coverage and the dynamic planned coverage, the adjustment step specific to the cross-coverage cell may be determined dynamically and more accurately, so that the differentiated needs of different scenarios can be met.

[0139] According to another embodiment, optionally, step S520 may include: constructing a cell group according to relevant information between cells, wherein the cell group includes the cross-coverage cell; determining the adjustment step corresponding to the cross-coverage cell according to the cross-coverage related information between the cross-coverage cell and other cells in the cell group. As an example, the relevant information between the cells may include at least one of the following: neighboring cell information, switching information, and interference information. Since the adjustment step corresponding to the cross-coverage cell is determined according to the cross-coverage related information between the cross-coverage cell and other cells in the cell group, and the cross-coverage related information is dynamic, the adjustment step specific to the cross-coverage cell can be dynamically determined, so that the differentiated needs of different scenarios can be met. In addition, by constructing a cell group to determine the adjustment step corresponding to the cross-coverage cell according to the cross-coverage related information between the cross-coverage cell and other cells in the cell group, it is also more conducive to the joint adjustment of the cell group, thereby avoiding the generation of new coverage holes after the cross-coverage problem is solved.

[0140] According to an embodiment, constructing a cell group based on relevant information between cells may include: determining the relationship intimacy between multiple cells including the cross-cell coverage cell based on the relevant information between cells, wherein the relationship intimacy represents the impact of a change in one cell on another cell, and may represent the strength of the impact between cells; constructing the cell group based on the acquired relationship intimacy.

[0141] According to an embodiment, optionally, the relationship intimacy between cells can be obtained based on the neighboring information, switching information and interference information between cells. As a non-limiting example, an adjacency relationship table between cells can be first constructed based on the neighboring relationship table of each cell as the neighboring information, and secondly, a characteristic relationship table between cells can be constructed based on the switching information and interference information between cells, and finally, a cell relationship intimacy table between cells can be constructed based on the adjacency relationship table and the characteristic relationship table.

[0142] Specifically, for example, if the network topology between cells is Fig. 9 As shown in , based on this network topology, we can construct Fig. 9 The adjacency relationship table shown in FIG. 1 shows that the two areas are neighbors of each other, and 0 shows that the two areas are not neighbors of each other.

[0143] According to an embodiment, the inter-cell feature relationship table may include an inter-cell switching relationship table and an interference relationship table. For example, the switching relationship table may be as follows: Fig.10 As shown in Table 1, the interference relationship table can be Fig.10The numbers shown in Table 2 can be the number of inter-cell handover attempts, the number of successful handovers, or the number of failed handovers. The numbers shown in Table 2 represent the magnitude of the inter-cell interference value. It should be noted that the characteristic relationship between cells is not limited to only including the handover relationship and interference relationship between cells, and the handover relationship table and the interference relationship table are not limited to Fig.10 Example shown.

[0144] According to an embodiment, the fifth artificial intelligence network may be used to determine the degree of relationship intimacy between a plurality of cells including the inter-coverage cell based on the relevant information between the cells.

[0145] For example, after constructing the adjacency relationship table and the feature relationship table, Fig.11 As shown, for example, a cell relationship intimacy table between cells can be constructed using a graph convolutional network (GCN) based on the adjacency table and the feature relationship table. The larger the value of the cell relationship intimacy (CRID), the greater the mutual coverage influence between cells. The greater the switching data relationship, the greater the cell relationship intimacy; the greater the interference, the greater the cell relationship intimacy. If cell A is not in the neighboring cell relationship table of cell B, then the relationship intimacy between cells A and B is 0. It should be said that although in Fig.11 The example in FIG. 4 shows that the fifth artificial intelligence network is a GCN, but the fifth artificial intelligence network is not limited to the GCN, but may be any type of artificial intelligence network.

[0146] After obtaining the relationship intimacy between the cells, the cell group can be constructed based on the obtained relationship intimacy. As an example, the relationship intimacy between the cross-coverage cell and other cells in the cell group meets the predetermined relationship intimacy requirement. For example, the relationship intimacy between the cross-coverage cell and other cells in the cell group exceeds a predetermined threshold, such as 0.

[0147] Optionally, according to an embodiment of the present disclosure, the above-mentioned construction of the cell group based on the acquired relationship intimacy may include: determining the cross-zone coverage level of each cell according to the actual coverage and planned coverage of each cell in the multiple cells; and constructing the cell group based on the determined cross-zone coverage level and the acquired relationship intimacy. The above has introduced the method of obtaining the actual coverage and planned coverage of the cell, so how to obtain the actual coverage of each cell and the planned coverage of the cross-zone coverage cell will not be repeated here. For related examples, please refer to the description above.

[0148] As an example, the sixth artificial intelligence network can be used to predict the cross-area coverage level of each cell based on the actual coverage and planned coverage of each cell. Figure 7 , for example, Figure 7 As shown, a deep neural network (DNN) can be used to predict the cross-zone coverage level of each cell based on the actual coverage and planned coverage of each cell. For example, the cross-zone coverage level can be divided into multiple levels according to the size of the cross-zone coverage area, and the value range is 0 to 1. The larger the area of ​​the cross-zone coverage area, the higher the cross-zone coverage level, and vice versa, the smaller the area of ​​the cross-zone coverage area, the lower the cross-zone coverage level. The size of the cross-zone coverage area can be represented by the number of cross-zone users and the number of island users; the more cross-zone users and island users, the larger the cross-zone coverage area. When calculating the area size of the cross-zone coverage area, the weight of the number of island users can be higher than the number of cross-zone users. The number of cross-zone users and the number of island users can both be determined based on the predicted actual coverage and planned coverage of the cell. Figure 7 In the example, DNN is selected to predict the cross-zone coverage level because it can obtain better results with lower complexity and lower input data dimension. However, the sixth artificial intelligence network used to predict the cross-zone coverage level is not limited to DNN, but can be any other type of artificial intelligence network, and the present disclosure is not limited to this.

[0149] After the relationship intimacy between multiple cells has been obtained and the cross-region coverage level has been determined, a cell group can be constructed based on the determined cross-region coverage level and the obtained relationship intimacy. According to an embodiment, the following operations can be performed for each cross-region coverage cell in turn according to the cross-region coverage level: based on the relationship intimacy between the cross-region coverage cell and other cells, at least one cell is selected from other cells to form a cell group with the cross-region coverage cell. For example, a cell group can be constructed by the following steps: Step 1, sorting multiple cells in descending order of cross-region coverage level; Step 2, constructing a cell group for each cross-region coverage cell, starting from the first cell in the sorted queue, to construct a cell group. For example, the cross-region coverage cell and all cells with a relationship intimacy greater than 0 with it can be grouped together; Step 3, if an cross-region coverage cell has been assigned to another cell group with a higher cross-region coverage level, then skip this cell and start constructing a cell group for the next cross-region coverage cell. According to the above steps, a cell group can be constructed for each cross-area coverage cell, in which the relationship intimacy between the cross-area coverage cell and other cells in the cell group meets the predetermined relationship intimacy requirement, for example, the relationship intimacy is greater than 0. Fig.12 FIG. 1 is a schematic diagram of an example of constructing a cell group according to an embodiment of the present disclosure. Fig.12As shown, after obtaining the relationship intimacy and cross-zone coverage level from cell i to cell n, according to the above steps 1 to 3, the cell groups of the cross-zone coverage cells i and m can be constructed respectively, wherein the cell group of the cross-zone coverage cell i includes the cross-zone coverage cell i and the cells k and n whose relationship intimacy is greater than 0, and the cell group of the cross-zone coverage cell m includes the cross-zone coverage cell m and the cells j and n whose relationship intimacy is greater than 0.

[0150] After the cell group is constructed, the adjustment step size corresponding to the over coverage cell may be determined according to the over coverage related information between the over coverage cell and other cells in the cell group.

[0151] According to an embodiment, the cross-area coverage related information includes: relationship intimacy and / or overlapping coverage ratio. Optionally, Figure 5 The method shown may also include: obtaining measurement information of the cross-coverage cell and at least one other cell in the cell group; determining the overlapping coverage ratio between the cross-coverage cell and at least one other cell in the cell group according to the measurement information;

[0152] According to an embodiment, optionally, determining the overlapping coverage ratio between the inter-zone coverage cell and at least one other cell in the cell group based on the measurement information may include: determining a measurement information difference between the inter-zone coverage cell and at least one other cell in the cell group for serving users based on the measurement information; determining the number of overlapping areas between the inter-zone coverage cell and at least one other cell in the cell group based on a comparison between the measurement information difference and a set threshold; determining the overlapping coverage ratio between the inter-zone coverage cell and at least one other cell in the cell group based on the number of overlapping areas between the inter-zone coverage cell and at least one other cell in the cell group and the amount of measurement information. For example, the measurement information includes RSRP, but is not limited thereto.

[0153] For example, the overlapping coverage between the cross-area coverage cell and other cells can be determined by the following steps:

[0154] (I) Collect RSRP measurement results of served users in two cells in the cell group. Fig.13 As shown, RSRPs of users served in cell 1 (overcoverage cell) and cell 2 in cell group 1 can be collected. If there are two or more overcoverage cells in a cell group, the cell with the highest overcoverage level is selected as the only overcoverage cell in the cell group.

[0155] (II) Calculate the RSRP difference between two cells in the same cell group. Fig.13As shown, for the service users in cell 1 (cross-coverage cell) and cell 2, the RSRP difference between the service users in cell 1 (cross-coverage cell) and cell 2 can be calculated respectively. Fig.13 In the table, the same user may correspond to one index or multiple indexes. For example, the RSRP corresponding to each index may be the RSRP of the same user at different times.

[0156] (III) Calculate the overlap coverage ratio based on the RSRP difference. For example, if an RSRP difference is less than or equal to 3 dB, then the overlap area is increased by one.

[0157] (IV) Calculate the overlapping coverage ratio based on the overlapping area and the overall area. Here, the overall area can be represented by the total number of RSRPs of all service users in the two cells. For example, the overlapping coverage ratio between the cross-region coverage cell of cell group 1 and other cells is as follows: Fig.14 As shown in the table.

[0158] An exemplary method for determining relationship intimacy and overlapping coverage has been introduced above. After determining the relationship intimacy and / or overlapping coverage between the cross-region coverage cell and at least one other cell in the cell group, the adjustment step corresponding to the cross-region coverage cell can be determined according to the relationship intimacy and / or overlapping coverage between the cross-region coverage cell and at least one other cell. According to an embodiment, optionally, determining the adjustment step corresponding to the cross-region coverage cell may include: determining, from the cell group, a first cell that is most affected by the cross-region coverage of the cross-region coverage cell; determining the adjustment step corresponding to the cross-region coverage cell according to the cell relationship intimacy and overlapping coverage between the cross-region coverage cell and the first cell.

[0159] As an example, the first cell that is most affected by the inter-area coverage of the inter-area coverage cell can be determined among the other cells based on the switching information of the inter-area coverage cell and other cells in the cell group. For example, the more the number of switching failures between the inter-area coverage cell and other cells is, the greater the impact of the inter-area coverage of the inter-area coverage cell is on other cells. Therefore, the first cell can be determined based on the switching information between the inter-area coverage cell and other cells, wherein the first cell can also be referred to as the most affected cell of the inter-area coverage cell in the cell group. For example, the switching failure counts between the inter-area coverage cell and other cells in the cell group can be collected, and the first cell can be determined based on the switching failure counts. For example, in cell group 1, the switching failure count is as follows. Fig.15 The cell with the largest handover failure count value between the cell with cross-coverage coverage is the first cell. For example, in cell group 1, the first cell is cell 4.

[0160] After the first cell is determined, the adjustment step length corresponding to the inter-cell coverage cell may be determined according to the relationship intimacy and overlapping coverage ratio between the inter-cell coverage cell and the first cell.

[0161] For example, first, the coverage contraction factor corresponding to the inter-cell coverage cell can be determined based on the cell intimacy and overlapping coverage between the inter-cell coverage cell and the first cell. For example, coverage contraction factor = (α×cell intimacy)×(β×overlapping coverage), where α and β represent weight factors of cell intimacy and overlapping coverage, respectively, and their initial values ​​are both 1. Subsequently, the adjustment step size corresponding to the inter-cell coverage cell can be determined based on the coverage contraction factor. For example, a mapping relationship between the coverage contraction factor and the adjustment step size can be established in advance, and based on the mapping relationship, the corresponding adjustment step size can be determined after the coverage contraction factor is calculated. For example, the mapping table between the coverage contraction factor and the adjustment step size can be as follows Fig.16 shown.

[0162] The above has introduced how to determine the adjustment step size corresponding to the cross-coverage cell. As an example, the adjustment step size can be the adjustment step size of the electronic downtilt angle of the antenna in the cross-coverage cell, or the adjustment step size of the antenna height, but is not limited to this. In order to solve the cross-coverage problem, the adjustment step size corresponding to the cross-coverage cell is a positive number, which means that the electronic downtilt angle of the antenna needs to be adjusted upward. Return to reference Figure 5 After determining the adjustment step corresponding to the cross-region coverage cell, the coverage of the cross-region coverage cell may be adjusted based on the determined adjustment step at step S530. For example, the network node may send the determined adjustment step to the base station to control the base station to adjust the coverage of the cross-region coverage cell based on the received adjustment step. For example, the electronic downtilt angle of the antenna or the height of the antenna in the cross-region coverage cell may be adjusted according to the determined adjustment step to achieve the adjustment of the coverage of the cross-region coverage cell.

[0163] According to the method executed by the network node described above, the problem of cross-coverage of the cross-coverage cell can be solved. However, as mentioned in question 3 above, if only the coverage of the cross-coverage cell is adjusted, although the cross-coverage problem of the cross-coverage cell is solved, coverage holes may appear between the cross-coverage cell and other cells, resulting in frequent call drops for users, destroying the network stability, and failing to achieve the global optimum of the overall network. In view of this, the present disclosure further proposes a concept of joint adjustment of cross-coverage to solve the above-mentioned problem 3. According to this concept, optionally, after constructing a cell group including the cross-coverage cell, in addition to adjusting the coverage of the cross-coverage cell, the method executed by the network node may also include: determining the adjustment step corresponding to other cells in the cell group except the cross-coverage cell; adjusting the coverage of the other cells based on the determined adjustment step corresponding to the other cells.

[0164] According to an embodiment, determining the adjustment step corresponding to other cells in the cell group except the inter-zone coverage cell includes: determining the adjustment step of the first cell in the cell group that is most affected by the inter-zone coverage of the inter-zone coverage cell as a predetermined value; determining the adjustment step corresponding to the second cell according to the relationship intimacy and / or overlapping coverage between a second cell in the other cells and cells in the cell group except the second cell, wherein the second cell is a cell in the cell group except the inter-zone coverage cell and the first cell. According to an embodiment, the relationship intimacy and / or overlapping coverage between a second cell in the other cells and cells in the cell group except the second cell may include: the relationship intimacy and / or overlapping coverage between the second cell and the inter-zone coverage cell; and the relationship intimacy and / or overlapping coverage between the second cell and cells in the cell group except the second cell and the inter-zone coverage cell.

[0165] For example, the adjustment step size of the first cell may be determined to be 0. Determining the adjustment step size of the first cell to be 0 indicates that the coverage range of the first cell is not adjusted to avoid generating a coverage hole.

[0166] In order to avoid coverage holes and new cross-area coverage problems, the adjustment step of other cells is negative, which means that the antenna electronic downtilt angle needs to be adjusted downward. For any cell in the cell group other than the cross-area coverage cell and the first cell (hereinafter referred to as the second cell), the normalized cell relationship intimacy (NormalizedCRID) and the normalized overlap coverage ratio (NormalizedOverlapCoverageRatio) can be calculated according to the following formula:

[0167]

[0168] Among them, CRID overshootingCell is the degree of intimacy between the second cell and the cross-area coverage cell, is the sum of the cell relationship intimacy between the second cell and the cells in the cell group except the second cell and the cross-cell coverage cell, CellNumber cellparty is the number of cells included in the cell group, and γ is any value between 0 and 1.

[0169]

[0170] Among them, OverlapCoverageRatio overshootingCell is the overlapping coverage ratio between the second cell and the cross-cell coverage cell, is the sum of the overlapping coverage ratios between the second cell and the cells in the cell group except the second cell and the cross-cell coverage cell, CellNumber cellparty is the number of cells included in the cell group, and γ is any value between 0 and 1.

[0171] After determining the normalized cell relationship intimacy and the normalized overlap coverage of the second cell, the coverage contraction factor corresponding to the second cell can be calculated in the following manner: Coverage contraction factor = (α×normalized cell relationship intimacy)×(β×normalized overlap coverage), where α and β represent weight factors of cell relationship intimacy and overlap coverage, respectively. Subsequently, the adjustment step size corresponding to the second cell can be determined based on the calculated coverage contraction factor corresponding to the second cell. For example, based on Fig.16 The mapping table between the coverage contraction factor and the adjustment step size shown determines the adjustment step size corresponding to the second cell. Since the relationship intimacy and / or overlapping coverage ratio between the second cell and the cells in the cell group other than the second cell are taken into account in the calculation of the above-mentioned NormalizedCRID and NormalizedOverlapCoverageRatio, the determined adjustment step size corresponding to the second cell takes into account the possible impact on cells other than the second cell, thereby avoiding the adjustment of the coverage range of the second cell from causing new impacts on other cells, which is beneficial to improving the overall stability of the network.

[0172] For cells other than the cross-region coverage cell, it is possible that the other cells belong to two cell groups at the same time. Fig.17As shown, cell 6 belongs to both cell group 1 and cell group 2. If a cell belongs to both cell groups, each cell group will determine an adjustment step for it. In order to solve the problem of conflicting adjustment steps determined by the cell groups, a final adjustment step can be jointly negotiated based on the credibility of the cell groups. Cells with greater credibility have higher decision-making power. In this way, the stability of the overall network can be guaranteed as much as possible while solving the problem of cross-zone coverage.

[0173] To this end, according to an embodiment, determining the adjustment step corresponding to other cells in the cell group except the cross-coverage cell may include: when the other cells simultaneously belong to multiple cell groups including the cell group:

[0174] If the other cell is the first cell in at least one of the multiple cell groups, determining the adjustment step size of the other cell to be a predetermined value, wherein the first cell is the cell in the at least one cell group that is most affected by the coverage of the cross-coverage cell. For example, if the cell is the first cell in at least one cell group, determining the adjustment step size of its joint negotiation to be 0;

[0175] If the other cell is not the first cell in any of the multiple cell groups, the adjustment step corresponding to the other cell is determined according to the credibility portrait of each cell group in the multiple cell groups.

[0176] According to an embodiment, the credibility portrait of each cell group is determined based on relevant network parameters of each cell group.

[0177] As an example, the relevant network parameters of each cell group may include at least one of the following items: average cell relationship intimacy between all cells in each cell group, total number of service users in each cell group, total number of cells in each cell group, average system throughput of each cell group, and average edge user throughput of each cell group. Fig.17 To determine the adjustment step of the joint negotiation for cell 6 in the example, it is necessary to first determine the credibility portraits for cell group 1 and cell group 2. For example, the credibility portraits of cell group 1 and cell group 2 can be determined as follows: Fig.18 shown.

[0178] After determining the credibility portrait of each cell group of the multiple cell groups to which the other cells belong at the same time, the adjustment step corresponding to the other cells can be determined according to the credibility portrait of each cell group in the multiple cell groups. For example, the adjustment step corresponding to the other cells determined based on each cell group can be obtained, the credibility of each cell group can be determined based on the credibility portrait of each cell group, and the adjustment step determined based on the cell group with the largest credibility can be determined as the final adjustment step corresponding to the other cells. In the above, the method of determining the adjustment step corresponding to the other cells based on the constructed cell group after constructing the cell group has been described, so it will not be repeated here. For each cell group, the adjustment step corresponding to the other cells in each cell group can be determined in the manner mentioned above. As an example, determining the credibility of each cell group based on the credibility portrait of each cell group can include: calculating the area in the closed loop of the credibility portrait of each cell group, and determining the credibility of each cell group based on the calculated area. For example, the larger the area, the greater the credibility of the representative cell group.

[0179] The method performed by the network node according to the embodiment of the present disclosure has been described in detail above. Fig.19 An example of a method performed by a network node according to an embodiment of the present disclosure is briefly summarized.

[0180] like Fig.19As shown, the service user of the base station can report measurement information to the base station, such as CQI and RSRP. The base station can send CQI and RSRP from the service user, as well as configuration data (e.g., antenna configuration data), TA data, neighbor relationship table, cell throughput, number of users served by the cell, etc. to a network node, such as a SON manager or RIC. After the SON manager or RIC collects these data, it can detect the cross-coverage cell based on the collected data and determine the cross-coverage level. For example, the AI ​​network can be used to detect the cross-coverage cell and determine the cross-coverage level. Subsequently, a dynamic cell group can be constructed. For example, the AI ​​network can also be used to construct a cell group. After the cell group is constructed, the dynamic adjustment step corresponding to each cell in the cell group can be determined. For example, for the cross-coverage cell, the adjustment step of the cross-coverage cell can be determined based on the cell relationship intimacy and overlapping coverage between the cross-coverage cell and the first cell (i.e., the cell most affected by the cross-coverage of the cross-coverage cell). For example, for the first cell, its corresponding adjustment step can be determined to be 0, that is, its coverage range is not adjusted. For example, for other cells, the adjustment step size corresponding to the cell can be determined based on the normalized cell relationship intimacy and overlapping coverage. When other cells belong to multiple cell groups at the same time, the adjustment step size for joint negotiation between cell groups can be determined based on the credibility portrait of each cell group. The specific details of the above-mentioned operations have been described above, so they will not be repeated here. Finally, after determining the adjustment step size corresponding to each cell, the SON manager or RIC can send the determined adjustment step size to the base station to control the base station to adjust the electronic downtilt angle of the antenna based on the determined adjustment step size to achieve coverage adjustment.

[0181] Fig. 20 is an example showing a deployment scenario according to an embodiment of the present disclosure.

[0182] like Fig. 20 As shown, the training of the various artificial intelligence networks mentioned above can be implemented in the server in the SON manager or RIC. For example, the distributed unit (DU) of the base station collects data and regularly reports it to the SON manager or RIC for model training. The operations mentioned above using the artificial intelligence network to perform predictions (also referred to as "model reasoning") can be performed in the server of the SON manager or RIC. After determining the adjustment step size for each cell using the artificial intelligence network, the SON or RIC can send the adjustment step size to the active antenna unit (AAU) of the base station, and then the AAU can adjust the electronic downtilt angle of the antenna to achieve the adjustment of the coverage range of the cell.

[0183] It should be noted that the deployment scenario of the embodiment of the present disclosure is not limited to Fig. 20 The deployment method shown in the figure can be deployed in other ways as needed. For example, if the base station has sufficient computing power, both model training and model inference can be performed at the base station.

[0184] According to the embodiment of the present disclosure, since an artificial intelligence network is used to predict a dynamic planned coverage range based on the real-time transmission environment of a cell, and then the cross-coverage cell is detected based on the actual coverage range and the planned coverage range, the accuracy of the cross-coverage cell detection can be improved. Experiments show that compared with always detecting cross-coverage cells based on a fixed cell transmission environment, the accuracy of detecting cross-coverage cells according to the embodiment of the present disclosure is significantly improved. In addition, according to the embodiment of the present disclosure, the actual coverage can be calculated based on the user location distribution instead of the TA distribution. Therefore, the actual coverage range calculation is more accurate, and the accuracy of detecting cross-coverage cells can also be improved. Moreover, the cross-coverage level of the cell can be determined based on the actual coverage range and the planned coverage range, thereby realizing multi-level cross-coverage detection.

[0185] In addition, according to the embodiment of the present disclosure, since the adjustment step size corresponding to each cell is determined to adjust the cross-region coverage of the cell, and the credibility portrait is used to handle the adjustment step size conflict, the system throughput is improved. Since the adjustment method of the embodiment of the present disclosure not only solves the cross-region coverage problem, but also avoids the generation of new coverage holes in the entire network, it is possible to improve the throughput of edge users and reduce the call drop rate and call blocking rate. Experiments show that compared with the prior art solution, the solution of the present invention has significantly improved system throughput and edge user throughput, and significantly reduced call drop rate and call blocking rate.

[0186] Fig.21 is a flow chart showing a method performed by a network node according to another embodiment of the present disclosure. Fig.21 In step S2410, real-time transmission environment information of the cell is obtained. In step S2420, whether the cell is an inter-area coverage cell is determined based on the real-time transmission environment information of the cell. In the description of other embodiments above, the contents involved in the above steps have been described. For the sake of brevity, they will not be repeated here. For relevant contents, please refer to the corresponding contents above.

[0187] according to Fig.21 The method shown and executed by the network node can effectively improve the accuracy of detecting cells that are out of coverage, because it determines whether the cell is an out of coverage cell based on the real-time transmission environment information of the cell.

[0188] Fig. 22 is a flow chart showing a method performed by a network node according to another embodiment of the present disclosure. Fig. 22In step S2510, a cell group is constructed, wherein the cell group includes an inter-regional coverage cell and other cells related to the inter-regional coverage cell. For example, the construction of the cell group includes: constructing a cell group based on relevant information between cells. The relevant information between the cells may include at least one of the following: neighboring cell information, switching information, and interference information. Examples of how to construct a cell group have been introduced in the description of other embodiments above and will not be repeated here. In step S2520, the coverage range of the inter-regional coverage cell and the other cells is adjusted. Optionally, Fig. 22 The method shown also includes: determining the adjustment step corresponding to the inter-zone coverage cell, and determining the adjustment step corresponding to the other cells. In this case, step S2520 may include: adjusting the coverage of the inter-zone coverage cell based on the adjustment step corresponding to the inter-zone coverage cell, and adjusting the coverage of the other cells based on the adjustment step corresponding to the other cells. How to determine the adjustment step corresponding to the inter-zone coverage cell and how to determine the adjustment step corresponding to other cells in the cell group except the inter-zone coverage cell have been described in the description of other embodiments above. Relevant examples can be found in the description above, which will not be repeated here. For example, the respective antenna electronic downtilt angles of the inter-zone coverage cell and the other cells can be adjusted based on the determined adjustment step of the respective antenna electronic downtilt angles to achieve adjustment of their respective coverage ranges.

[0189] according to Fig. 22 The method shown, after constructing a cell group including an out-of-area coverage cell and other cells related to the out-of-area coverage cell, in addition to adjusting the coverage of the out-of-area coverage cell, also adjusts the coverage of the other cells. Therefore, not only the out-of-area coverage problem of the out-of-area coverage cell is solved, but also it is helpful to avoid the occurrence of coverage holes between the out-of-area coverage cell and other cells, thereby preventing user call drops, improving the overall stability of the network, and facilitating the realization of the global optimality of the overall network. In the above, the method performed by the network node according to the embodiment of the present disclosure has been described. Below, the network node according to the embodiment of the present disclosure is briefly described.

[0190] Fig.23 is a block diagram showing a network node according to an embodiment of the present disclosure. Fig.23 The network node 2600 may include a transceiver 2601 and a processor 2602, wherein the processor 2602 is coupled to the transceiver 2601 and configured to execute the method performed by the network node described above. According to an embodiment, the network node may include a base station, a SON manager, or a RIC, but is not limited thereto.

[0191] An electronic device is also provided in an embodiment of the present disclosure, which includes at least one processor and, optionally, may also include at least one transceiver and / or at least one memory coupled to the at least one processor, and the at least one processor is configured to execute the steps of the method provided in any optional embodiment of the present disclosure.

[0192] Fig.24 A schematic diagram of the structure of an electronic device applicable to an embodiment of the present invention is shown in FIG. Fig.24 As shown, Fig.24 The electronic device 4000 shown includes: a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, such as through a bus 4002. Optionally, the electronic device 4000 may also include a transceiver 4004, which may be used for data interaction between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, each of the processor 4001, the memory 4003 and the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present disclosure. Optionally, the electronic device may be a first network node, a second network node or a third network node.

[0193] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0194] The bus 4002 may include a path to transmit information between the above components. The bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.24Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0195] The memory 4003 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), magnetic disk storage media, other magnetic storage devices, or any other medium that can be used to carry or store computer programs and can be read by a computer, without limitation herein.

[0196] The memory 4003 is used to store computer programs or executable instructions for executing the embodiments of the present disclosure, and the execution is controlled by the processor 4001. The processor 4001 is used to execute the computer programs or executable instructions stored in the memory 4003 to implement the steps shown in the above method embodiments.

[0197] An embodiment of the present disclosure provides a computer-readable storage medium having a computer program or instructions stored thereon. When the computer program or instructions are executed by at least one processor, the steps and corresponding contents of the aforementioned method embodiment can be executed or implemented.

[0198] The embodiments of the present disclosure also provide a computer program product, including a computer program, which can implement the steps and corresponding contents of the aforementioned method embodiments when executed by a processor.

[0199] The terms "first", "second", "third", "fourth", "1", "2", etc. (if any) in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than that shown or described in the text.

[0200] It should be understood that, although the flowchart of the embodiment of the present disclosure indicates each operation step by arrows, the implementation order of these steps is not limited to the order indicated by the arrows. Unless clearly stated herein, in some implementation scenarios of the embodiment of the present disclosure, the implementation steps in each flowchart can be executed in other orders according to demand. In addition, some or all of the steps in each flowchart may include multiple sub-steps or multiple stages based on the actual implementation scenario. Some or all of these sub-steps or stages can be executed at the same time, and each sub-step or stage in these sub-steps or stages can also be executed at different times. In scenarios with different execution times, the execution order of these sub-steps or stages can be flexibly configured according to demand, and the embodiment of the present disclosure does not limit this.

[0201] The above text and drawings are provided only as examples to help readers understand the present disclosure. They are not intended and should not be interpreted as limiting the scope of the present disclosure in any way. Although certain embodiments and examples have been provided, based on the contents disclosed herein, it is obvious to those skilled in the art that the embodiments and examples shown can be changed without departing from the scope of the present disclosure, and other similar implementation means based on the technical ideas of the present disclosure are adopted, which also fall within the protection scope of the embodiments of the present disclosure.

Claims

1. A method performed by a network node, comprising: Detection of out-of-area coverage cells; Determining an adjustment step size corresponding to the cross-coverage cell based on the coverage condition of the cross-coverage cell; The coverage of the inter-zone coverage cell is adjusted based on the determined adjustment step size.

2. The method according to claim 1, wherein: The detecting of the cross-area coverage cell comprises: Obtain real-time transmission environment information of the cell; Whether the cell is an out-of-area coverage cell is determined based on the real-time transmission environment information of the cell.

3. The method according to claim 2, wherein: The obtaining of the real-time transmission environment information of the cell includes: Acquiring cell-related data and / or user-related data; Real-time transmission environment information of the cell is acquired based on the cell-related data and / or the user-related data.

4. The method according to claim 3, wherein: The acquiring the real-time transmission environment information of the cell based on the cell-related data and / or the user-related data includes: Acquire 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; According to the current user location distribution and the current TA distribution, real-time transmission environment information of the cell is acquired.

5. The method according to claim 3 or 4, wherein: The cell-related data includes at least one of cell configuration information and cell historical TA data; and / or The user-related data includes measurement information reported by service users in the cell.

6. The method according to claim 5, wherein: The acquiring the current user location distribution and the current timing advance TA distribution based on the cell-related data and the user-related data includes: Based on the user-related data and the cell configuration information, using a first artificial intelligence network to predict the historical user location distribution of the cell, and based on the historical user location distribution, using a second artificial intelligence network to predict the current user location distribution; The current TA distribution is predicted using a third artificial intelligence network based on the historical TA data of the cell.

7. The method according to claim 2, wherein: The real-time transmission environment information indicates the ratio of line-of-sight (LOS) transmission and non-line-of-sight (NLOS) transmission in the cell.

8. The method according to claim 2, wherein: The determining whether the cell is an inter-area coverage cell based on the real-time transmission environment information of the cell comprises: Obtaining an actual coverage of the cell based on current user location distribution of the cell; Obtaining a planned coverage range of the cell based on the real-time transmission environment information; It is determined whether the cell is an out-of-area coverage cell according to the actual coverage of the cell and the planned coverage of the cell.

9. The method according to claim 8, wherein: The obtaining the planned coverage of the cell based on the real-time transmission environment information includes: Determining a theoretical coverage of the cell based on the cell-related data; Based on the real-time transmission environment information and the theoretical coverage of the cell, the planned coverage is predicted using a fourth artificial intelligence network.

10. The method according to any one of claims 1 to 9, wherein: The determining, based on the coverage condition of the cross-coverage cell, an adjustment step size corresponding to the cross-coverage cell comprises: An adjustment step length corresponding to the inter-zone coverage cell is determined according to the actual coverage range and the planned coverage range of the inter-zone coverage cell.

11. The method according to any one of claims 1 to 9, wherein: The determining, based on the coverage condition of the cross-coverage cell, an adjustment step size corresponding to the cross-coverage cell comprises: Constructing a cell group according to the related information between cells, wherein the cell group includes the cross-area coverage cell; An adjustment step length corresponding to the inter-coverage cell is determined according to inter-coverage related information between the inter-coverage cell and other cells in the cell group.

12. The method according to claim 11, wherein: The related information between cells includes at least one of the following: neighboring cell information, switching information, and interference information.

13. The method according to claim 11 or 12, wherein: The constructing of a cell group according to the related information between cells includes: Determine, based on the relevant information between the cells, the relationship intimacy between the multiple cells including the cross-area coverage cell, wherein the relationship intimacy represents the influence strength between the cells; The cell group is constructed based on the acquired relationship intimacy.

14. The method according to claim 13, wherein: The determining, based on the relevant information between the cells, the degree of intimacy between the multiple cells including the cross-area coverage cell comprises: According to the relevant information between the cells, the fifth artificial intelligence network is used to determine the relationship intimacy between multiple cells including the inter-area coverage cell.

15. The method according to claim 13, wherein: The step of constructing the cell group based on the acquired relationship intimacy comprises: Determine the cross-area coverage level of each cell according to the actual coverage and the planned coverage of each cell in the plurality of cells; The cell group is constructed based on the determined cross-cell coverage level and the obtained relationship intimacy.

16. The method according to claim 15, wherein: The determining the cross-cell coverage level of each cell according to the actual coverage range and the planned coverage range of each cell in the plurality of cells comprises: Based on the actual coverage and planned coverage of each cell, the sixth artificial intelligence network is used to predict the cross-cell coverage level of each cell.

17. A method performed by a network node, comprising: Obtain real-time transmission environment information of the cell; Whether the cell is an out-of-area coverage cell is determined based on the real-time transmission environment information of the cell.

18. A method performed by a network node, comprising: Constructing a cell group, wherein the cell group includes an inter-area coverage cell and other cells related to the inter-area coverage cell; The coverage areas of the inter-zone coverage cell and the other cells are adjusted.

19. A network node, comprising: Transceiver; A processor is coupled to the transceiver and configured to execute the method according to any one of claims 1 to 16.

20. A computer-readable storage medium storing instructions, which, when executed by at least one processor, cause the at least one processor to perform the method of any one of claims 1 to 16.

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