WO2022017012A1

WO2022017012A1 - Network configuration method and apparatus

Info

Publication number: WO2022017012A1
Application number: PCT/CN2021/098022
Authority: WO
Inventors: 常瑞娜; 康怡彬; 吴珏莹; 杨达
Original assignee: 华为技术有限公司
Priority date: 2020-07-21
Filing date: 2021-06-02
Publication date: 2022-01-27
Also published as: CN113965942A

Abstract

Disclosed in the embodiments of the present application are a network configuration method and apparatus. The network configuration method can be executed by an offline tool that is deployed offline on a stand-alone computer or cloud, or an online tool that is deployed online to a network management system OMC or an online tool platform connected to the OMC. Said method can comprise constructing a three-dimensional grid according to the average RSRP of each cell in MR data as well as horizontal beam information and vertical beam information corresponding to the main beam identification of each cell, thus achieving the goal of constructing a three-dimensional grid without relying on MDT data. Moreover, a path loss matrix can be constructed according to the three-dimensional grid to achieve three-dimensional beam optimization. The described method can also comprise determining a network configuration parameter of a target cell according to the path loss matrix so as to facilitate optimizing the coverage and/or capacity of a network.

Description

A network configuration method and device

This application claims the priority of the Chinese patent application with the application number 202010706451.2 and the application title "A method and device for network configuration" filed with the State Intellectual Property Office of China on July 21, 2020, the entire contents of which are incorporated herein by reference Applying.

technical field

The present application relates to the field of communication technologies, and in particular, to a network configuration method and apparatus.

Background technique

In a wireless communication network, sites are usually deployed in a cellular shape, and each site is determined by radio frequency parameters to its coverage. Among them, coverage is an important indicator to measure wireless network performance. In order to quantify the coverage performance index of the wireless network, the coverage area of one or more cells can be modeled by means of two-dimensional plane rasterization, so as to optimize the coverage performance index. However, the 2D plane rasterization modeling needs to project the vertical plane features in the stereo coverage to the 2D plane, and the vertical plane coverage features are lost, which will lead to poor vertical plane coverage signals, resulting in weak coverage, overlapping coverage and other problems.

SUMMARY OF THE INVENTION

The embodiment of the present application provides a network configuration method, which can construct a three-dimensional stereo coverage feature according to the MR data of a measurement report, which is beneficial to optimize the three-dimensional stereo coverage.

In the first aspect, an embodiment of the present application provides a network configuration method, which can be executed by a network management device, and the network management device can be deployed offline on a stand-alone computer or cloud (ie, an offline tool), or can be deployed online to a network On the management system OMC or the online tool platform connected to OMC (ie online tool). The network management device may perform aggregation processing on the multiple pieces of MR data according to the cell information of each MR in the multiple measurement report MR data to obtain N groups of MR data, and obtain the average of each cell of each group of MRs in the N groups of MRs. Reference Signal Received Power RSRP. For each group of MRs in the N groups of MRs, the network management apparatus creates a three-dimensional grid of the group of MRs according to the average RSRP of each cell in the group of MRs and the vertical beam range and horizontal beam range corresponding to the main beam identifier of each cell . For the stereo grids of each group of MRs, the path loss between the main beam of each cell in the group of MRs and the stereo grid is calculated to obtain a path loss matrix of the stereo grid. Finally, the network management apparatus may determine the network configuration parameters of the target cell according to the path loss matrix of the N three-dimensional grids, and the target cell is a cell whose coverage index does not meet the preset coverage index and/or the capacity index does not meet the preset capacity index.

It can be seen that the network management device can construct a three-dimensional three-dimensional grid according to the average RSRP of each cell in the MR data and the horizontal beam information and vertical beam information corresponding to the main beam identifier of each cell. A target for constructing a 3D solid grid. In addition, a three-dimensional path loss matrix can be constructed according to the three-dimensional three-dimensional grid to realize stereo beam optimization. The network management apparatus may also determine the network configuration parameters of the target cell according to the three-dimensional path loss matrix, which is beneficial to optimizing the coverage and/or capacity of the network.

In a possible design, the cell information of each MR includes the cell information of the serving cell and/or the cell information of the neighboring cells, wherein the RSRP of one or more cells includes the RSRP of the serving cell and/or the RSRP of the neighboring cells . Before performing aggregation processing on the multiple pieces of MR data according to the cell information of each MR in the multiple measurement report MR data, the network management apparatus may, for each MR, perform the aggregation process according to the cell identifier of the serving cell and the RSRP of the serving cell in the MR. , the main beam identifier of the serving cell, and the main beam prediction model of the serving cell of the MR is determined. Then, according to the main beam prediction model of the serving cell of the MR, the cell identity of the adjacent cell, and the RSRP of the adjacent cell, the main beam identity of the adjacent cell of the MR is determined.

It can be seen that the network management device can predict the main beam identifier of the MR in its serving cell and each neighboring cell, so as to obtain the full main beam attributes of the MR, which is beneficial for the network management device to take into account the overall network performance when performing coverage and/or capacity optimization. .

In a possible design, when performing aggregation processing on the MR data, the network management apparatus may perform aggregation processing on the MR data with the same cell identifier, the same main beam identifier, and the level difference value satisfying a preset level difference value condition, Determine the pooled set of MRs. Wherein, the level difference value is the difference value of RSRP between each pair of cells with the same cell identifier and the same main beam identifier in the MR data.

It can be seen that the MR data collected by the network management device is MR data with similarities in wireless spatial propagation, which is beneficial to modeling a three-dimensional grid of a specified location. In addition, according to the cell attributes, RSRP, beam attributes, etc. of the MR, the similar feature MRs are aggregated and processed, which is conducive to more precise simulation of actual network propagation and greatly reduces the amount of calculation.

In a possible design, for the three-dimensional grid of each group of MRs, the network management apparatus may obtain the beam gain corresponding to the main beam identifier of cell i according to the main beam identifier of any cell i in the group of MRs; the The beam gain of cell i is determined according to the horizontal beam range, vertical beam range, antenna gain, signal attenuation value corresponding to the horizontal beam range, and signal attenuation value corresponding to the vertical beam range corresponding to the main beam identifier of cell i. The network management device then calculates the path loss from the main beam of cell i to the stereo grid of the group of MRs according to the transmit power of cell i, the beam gain of cell i and the average RSRP of cell i.

It can be seen that when the network management device calculates the path loss of the three-dimensional grid of each group of MRs, the horizontal beam, the vertical beam and the average RSRP of the cell are fully considered. That is to say, the calculated path loss reflects the path loss of the three-dimensional three-dimensional grid. , avoiding the loss of coverage and capacity features caused by averaging processing of 2D geographic rasters.

In a possible design, for each cubic grid, the network management apparatus determines the target cell of the cubic grid according to the coverage index and/or the capacity index of each cell of the cubic grid. The network configuration parameters of the target cell are adjusted according to the preset coverage index and the preset capacity index of the target cell. According to the adjusted network configuration parameters of the target cell, the adjusted antenna gain of the target cell is obtained; according to the path loss matrix of the three-dimensional grid, the adjusted antenna gain of the target cell and the adjusted network configuration parameters of the target cell, Determine the coverage index and capacity index of the target cell. If the coverage index of the target cell reaches the preset coverage index, and/or the capacity index of the target cell reaches the preset capacity index, it is determined that the adjusted network configuration parameters of the target cell are the network configuration parameters of the target cell.

It can be seen that the network management apparatus can screen out target cells with coverage problems and/or capacity problems, and optimize the coverage and/or capacity of the target cells by adjusting network configuration parameters of the target cells.

In one possible design, the network configuration parameters include one or more of horizontal beamwidth, physical azimuth, digital azimuth, vertical beamwidth, physical downtilt, or digital downtilt;

Among them, the horizontal beam width represents the horizontal envelope width covered by the horizontal plane controlled by the beam weight; the vertical beam width represents the vertical envelope width covered by the horizontal plane controlled by the beam weight; the physical azimuth represents the facing direction and the true north of the physical antenna panel. The digital azimuth angle represents the angle between the strongest direction of the horizontal beam energy controlled by the beam weight and the true north; the physical downtilt angle represents the angle between the plane perpendicular to the physical antenna panel and the horizontal plane; the digital downtilt angle represents the beam weight value The steered vertical beam energy is most strongly directed at the angle to the horizontal.

In a second aspect, an embodiment of the present application provides a network configuration method, and the network configuration method can be executed by a network management apparatus. Wherein, the network management device can aggregate and process multiple pieces of MDT data according to the cell information of each MDT in the multiple minimization road test MDT data to obtain N groups of MDT data, each group of MDTs includes MDTs with the same vertical beam identification of cells. For each group of MDTs in the N groups of MDTs, a three-dimensional grid of the group of MDTs is created according to the longitude and latitude of each cell in the group of MDTs and the vertical beam identifier of each cell. For each group of three-dimensional grids of MDT, the path loss between the main beam of each cell in the group of MDTs and the three-dimensional grid is calculated to obtain the path loss matrix of the three-dimensional grid. Determine the network configuration parameters of the target cell according to the path loss matrix of the N three-dimensional grids, wherein the target cell is a cell whose coverage index does not meet the preset coverage index and/or the capacity index does not meet the preset capacity index.

It can be seen that the network management device can construct a three-dimensional three-dimensional grid according to the longitude and latitude information in the MDT data and the vertical beam identification determined according to the main beam identification of the cell, so as to avoid the average processing of the data by the two-dimensional geographic grid from blurring the stereo information. Conducive to achieve more accurate stereo optimization. In addition, the network management device can generate a three-dimensional path loss matrix according to the three-dimensional three-dimensional grid, and can determine the network configuration parameters of the target cell according to the three-dimensional path loss matrix, which is conducive to optimizing network coverage and/or capacity.

In a possible design, for each MDT, the network management apparatus determines the main beam of the serving cell of the MDT according to the cell identifier of the serving cell, the RSRP of the serving cell, and the main beam identifier of the serving cell in the MDT. prediction model. According to the main beam prediction model of the serving cell of the MDT, the cell identifier of the adjacent cell and the RSRP of the adjacent cell, the main beam identifier of the adjacent cell of the MDT is determined. According to the main beam identifiers of the cells in the MDT, the vertical beam identifiers of the cells in the MDT are determined.

It can be seen that the network management apparatus can determine the vertical beam identifier of each cell in the MDT data, so as to mark each cell in the MDT data on a three-dimensional level, and determine the three-dimensional information of each cell.

In a possible design, the network management device determines the grid longitude and grid latitude of the plane grid formed by the group of MDTs according to the longitude and latitude of each cell in the group of MDTs; and then according to the vertical direction of the group of MDTs Beam identification, to determine the vertical layer where the group of MDTs constitutes the plane grid, so as to obtain the three-dimensional grid of the group of MDTs.

It can be seen that the network management apparatus can determine the two-dimensional plane grid according to the longitude and latitude information in the MDT data, and then determine the three-dimensional position of the plane grid according to the vertical beam identifier, thereby constructing the three-dimensional grid.

In a possible design, the network management apparatus acquires the vertical beam range corresponding to the vertical beam identifier, the antenna gain and the signal attenuation value corresponding to the vertical beam range according to the vertical beam identifier in the group of MDTs. According to the grid longitude and grid latitude of the group of MDTs, obtain the horizontal beam range, antenna gain and signal attenuation value corresponding to the horizontal beam range corresponding to the geographic grid determined by the grid longitude and grid latitude in the group of MDTs. The vertical beam range, antenna gain, signal attenuation value corresponding to the vertical beam range corresponding to the vertical beam identifier in the group of MDTs, and the horizontal beam range corresponding to the geographic grid determined by the grid longitude and grid latitude in the group of MDTs , the antenna gain, and the signal attenuation value corresponding to the horizontal beam range, and calculate the antenna gain from any cell i in the group of MDTs to the three-dimensional grid of the group of MDTs. According to the transmit power of cell i, the antenna gain of the cubic grid from cell i to the group of MDTs, and the average RSRP of cell i, the path loss from cell i to the cubic grid of the group of MDTs is calculated.

It can be seen that the network management device fully considers the average RSRP of the horizontal beam, the vertical beam and the cell when calculating the path loss of the three-dimensional grid of each group of MDTs. That is to say, the calculated path loss reflects the path loss of the three-dimensional three-dimensional grid. , avoiding the loss of coverage and capacity features caused by averaging processing of 2D geographic rasters.

In a possible design, for each cubic grid, the network management apparatus determines the target cell of the cubic grid according to the coverage index and/or the capacity index of each cell of the cubic grid. Then, the network configuration parameters of the target cell are adjusted according to the preset coverage index and the preset capacity index of the target cell. According to the adjusted network configuration parameters of the target cell, the adjusted antenna gain of the target cell is obtained. According to the path loss matrix of the three-dimensional grid, the adjusted antenna gain of the target cell, and the adjusted network configuration parameters of the target cell, the coverage index and the capacity index of the target cell are determined. If the coverage index of the target cell reaches the preset coverage index, and/or the capacity index of the target cell reaches the preset capacity index, it is determined that the adjusted network configuration parameters of the target cell are the network configuration parameters of the target cell .

In a third aspect, an embodiment of the present application provides a network management apparatus, the apparatus includes a convergence unit, an acquisition unit, a creation unit, a calculation unit, and a determination unit. The aggregation unit is configured to perform aggregation processing on the multiple pieces of MR data according to the cell information of each MR in the multiple measurement report MR data to obtain N groups of MR data, where each group of MRs includes MRs with wireless spatial propagation similarity. The obtaining unit is configured to obtain the average reference signal received power RSRP of each cell of each group of MRs in the N groups of MRs. The creating unit is configured to, for each group of MRs in the N groups of MRs, create a stereo grid of the group of MRs according to the average RSRP of each cell in the group of MRs and the vertical beam range and horizontal beam range corresponding to the main beam identifier of each cell grid. The calculation unit is configured to calculate, for the stereo grids of each group of MRs, the path loss between the main beam of each cell in the group of MRs and the stereo grid, so as to obtain a path loss matrix of the stereo grid. The determining unit is configured to determine the network configuration parameters of the target cell according to the path loss matrix of the N three-dimensional grids, wherein the target cell is a cell whose coverage index does not meet the preset coverage index and/or the capacity index does not meet the preset capacity index.

In a possible design, the cell information of each MR includes the cell information of the serving cell and/or the cell information of the neighboring cells, and the RSRP of one or more cells includes the RSRP of the serving cell and/or the RSRP of the neighboring cells. The determining unit is further configured to, for each MR, determine the main beam prediction model of the serving cell of the MR according to the cell identifier of the serving cell, the RSRP of the serving cell, and the main beam identifier of the serving cell in the MR. According to the main beam prediction model of the serving cell of the MR, the cell identifier of the adjacent cell, and the RSRP of the adjacent cell, the main beam identifier of the adjacent cell of the MR is determined.

In a possible design, the aggregation unit is configured to perform aggregation processing on MR data with the same cell identifiers, the same main beam identifiers, and the level difference satisfying the preset level difference condition, to determine a group of MRs after aggregation; wherein , and the level difference value is the RSRP difference between the cells in the MR data with the same cell ID and the same main beam ID.

In a possible design, the calculation unit is configured to obtain, according to the main beam identifier of any cell i in the group MR, the beam gain corresponding to the main beam identifier of the cell i; the beam gain of the cell i is based on the The horizontal beam range, vertical beam range, antenna gain, signal attenuation value corresponding to the horizontal beam range, and signal attenuation value corresponding to the vertical beam range corresponding to the main beam identifier of cell i are determined; according to the transmit power of cell i, the beam of cell i The gain and the average RSRP of cell i, calculate the path loss from the main beam of cell i to the stereo grid of the group MR.

In a possible design, for each three-dimensional grid, the determining unit is configured to determine the target cell of the three-dimensional grid according to the coverage index and/or the capacity index of each cell of the three-dimensional grid. According to the preset coverage index and preset capacity index of the target cell, the network configuration parameters of the target cell are adjusted; according to the adjusted network configuration parameters of the target cell, the adjusted antenna gain of the target cell is obtained; according to the three-dimensional grid The path loss matrix, the adjusted antenna gain of the target cell and the adjusted network configuration parameters of the target cell are used to determine the coverage index and capacity index of the target cell. If the coverage index of the target cell reaches the preset coverage index, and/or the capacity index of the target cell reaches the preset capacity index, it is determined that the adjusted network configuration parameters of the target cell are the network configuration parameters of the target cell.

In a fourth aspect, an embodiment of the present application provides a network management device, where the network management device includes a convergence unit, a creation unit, a calculation unit, and a determination unit. The aggregation unit is configured to perform aggregation processing on the multiple pieces of MDT data according to the cell information of each MDT in the multiple minimized road test MDT data to obtain N groups of MDT data, each group of MDTs includes MDTs with the same vertical beam identifiers of cells. The creating unit is configured to, for each group of MDTs in the N groups of MDTs, create a three-dimensional grid of the group of MDTs according to the longitude and latitude of each cell in the group of MDTs and the vertical beam identifier of each cell. The calculation unit is configured to, for each group of MDTs, calculate the path loss between the main beam of each cell in the group of MDTs and the three-dimensional grid, so as to obtain a path loss matrix of the three-dimensional grid. The determining unit is configured to determine the network configuration parameters of the target cell according to the path loss matrix of the N three-dimensional grids, wherein the target cell is a cell whose coverage index does not meet the preset coverage index and/or the capacity index does not meet the preset capacity index.

In a possible design, the cell information of each MDT includes the cell information of the serving cell and/or the cell information of the neighboring cells, and the RSRP of one or more cells includes the RSRP of the serving cell and/or the neighboring cells RSRP. For each MDT, the determining unit is further configured to determine the main beam prediction model of the serving cell of the MDT according to the cell identifier of the serving cell, the RSRP of the serving cell, and the main beam identifier of the serving cell in the MDT. According to the main beam prediction model of the serving cell of the MDT, the cell identifier of the adjacent cell and the RSRP of the adjacent cell, determine the main beam identifier of the adjacent cell of the MDT; according to the main beam identifier of each cell in the MDT , and determine the vertical beam identifier of each cell in the MDT.

In a possible design, a unit is created for determining the grid longitude and grid latitude of the plane grid formed by the group of MDTs according to the longitude and latitude of each cell in the group of MDTs; according to the vertical direction of the group of MDTs Beam identification, to determine the vertical layer where the group of MDTs constitutes the plane grid, so as to obtain the three-dimensional grid of the group of MDTs.

In a possible design, the computing unit is configured to obtain, according to the vertical beam identifiers in the group of MDTs, the vertical beam range corresponding to the vertical beam identifier, the antenna gain, and the signal attenuation value corresponding to the vertical beam range. According to the grid longitude and grid latitude of the group of MDTs, obtain the horizontal beam range, antenna gain and signal attenuation value corresponding to the horizontal beam range corresponding to the geographic grid determined by the grid longitude and grid latitude in the group of MDTs. The vertical beam range, antenna gain, signal attenuation value corresponding to the vertical beam range corresponding to the vertical beam identifier in the group of MDTs, and the horizontal beam range corresponding to the geographic grid determined by the grid longitude and grid latitude in the group of MDTs , the antenna gain, and the signal attenuation value corresponding to the horizontal beam range, and calculate the antenna gain from any cell i in the group of MDTs to the three-dimensional grid of the group of MDTs. According to the transmit power of the cell i, the antenna gain of the cubic grid from the cell i to the group of MDTs, and the average RSRP of the cell i, the path loss from the cell i to the cubic grid of the group of MDTs is calculated.

In a possible design, for each three-dimensional grid, the determining unit is configured to determine the target cell of the three-dimensional grid according to the coverage index and/or the capacity index of each cell of the three-dimensional grid. The network configuration parameters of the target cell are adjusted according to the preset coverage index and the preset capacity index of the target cell; the adjusted antenna gain of the target cell is obtained according to the adjusted network configuration parameters of the target cell. According to the path loss matrix of the three-dimensional grid, the adjusted antenna gain of the target cell, and the adjusted network configuration parameters of the target cell, the coverage index and the capacity index of the target cell are determined. If the coverage index of the target cell reaches the preset coverage index, and/or the capacity index of the target cell reaches the preset capacity index, it is determined that the adjusted network configuration parameter of the target cell is the target cell network configuration parameters.

In a fifth aspect, an embodiment of the present application provides a network device, and the device has a function of implementing the network configuration method provided in the first aspect. This function can be implemented by hardware or by executing corresponding software by hardware. The hardware or software includes one or more modules corresponding to the above functions.

In a sixth aspect, an embodiment of the present application provides a network device, and the device has a function of implementing the network configuration method provided in the second aspect. This function can be implemented by hardware or by executing corresponding software by hardware. The hardware or software includes one or more modules corresponding to the above functions.

In a seventh aspect, embodiments of the present application provide a computer-readable storage medium, where the readable storage medium includes a program or an instruction, and when the program or instruction is run on a computer, the computer executes the first aspect or the first aspect. method in any of the possible implementations.

In an eighth aspect, embodiments of the present application provide a computer-readable storage medium, where the readable storage medium includes a program or an instruction, and when the program or instruction is run on a computer, the computer executes the second aspect or the second aspect. method in any of the possible implementations.

In a ninth aspect, an embodiment of the present application provides a chip or a chip system, the chip or chip system includes at least one processor and an interface, the interface and the at least one processor are interconnected through a line, and the at least one processor is used to run a computer program or instruction, to perform the method described in any one of the first aspect or any of the possible implementations of the first aspect.

In a tenth aspect, an embodiment of the present application provides a chip or a chip system, the chip or chip system includes at least one processor and an interface, the interface and the at least one processor are interconnected through a line, and the at least one processor is used for running a computer program or instruction, to perform the method described in any one of the second aspect or any of the possible implementations of the second aspect.

Wherein, the interface in the chip may be an input/output interface, a pin or a circuit, or the like.

The chip system in the above aspects may be a system on chip (system on chip, SOC), or a baseband chip, etc., where the baseband chip may include a processor, a channel encoder, a digital signal processor, a modem, an interface module, and the like.

In a possible implementation, the chip or chip system described above in this application further includes at least one memory, where instructions are stored in the at least one memory. The memory may be a storage unit inside the chip, such as a register, a cache, etc., or a storage unit of the chip (eg, a read-only memory, a random access memory, etc.).

In an eleventh aspect, the embodiments of the present application provide a computer program or computer program product, including codes or instructions, when the codes or instructions are run on a computer, the computer executes the first aspect or any one of the first aspects may be implemented method in method.

In a twelfth aspect, embodiments of the present application provide a computer program or computer program product, including codes or instructions, when the codes or instructions are run on a computer, the computer executes the second aspect or any one of the second aspects may be implemented method in method.

Description of drawings

FIG. 1 is a schematic diagram of a wireless network according to an embodiment of the present application;

FIG. 2 is a schematic diagram of network coverage and capacity provided by an embodiment of the present application;

3 is a schematic diagram of a common antenna and a Massive MIMO antenna;

4 is a schematic diagram of a two-dimensional plane grid path loss matrix;

FIG. 5 is a schematic diagram of a communication system provided by an embodiment of the present application;

FIG. 6 is a schematic diagram of beam coverage of a Massive MIMO antenna according to an embodiment of the present application;

7 is a schematic flowchart of a network configuration method provided by an embodiment of the present application;

8 is a schematic diagram of multiple beams transmitted by a Massive MIMO antenna and a main beam received by a terminal device according to an embodiment of the present application;

9a is a schematic diagram of a three-dimensional grid provided by an embodiment of the present application;

FIG. 9b is a schematic diagram of a path loss matrix provided by an embodiment of the present application;

10 is a schematic diagram of a network optimization target optimization process provided by an embodiment of the present application;

11 is a schematic flowchart of another network configuration method provided by an embodiment of the present application;

12 is a schematic diagram of a region formed by projecting a vertical layer onto the ground according to an embodiment of the present application;

13a is a schematic diagram of another three-dimensional grid provided by an embodiment of the present application;

13b is a schematic diagram of another path loss matrix provided by an embodiment of the present application;

FIG. 14 is a schematic diagram of a network management apparatus according to an embodiment of the present application;

15 is a schematic diagram of a network management device according to an embodiment of the present application;

FIG. 16 is a schematic diagram of another network management apparatus provided by an embodiment of the present application;

FIG. 17 is a schematic diagram of another network management device provided by an embodiment of the present application.

detailed description

The technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application.

The wireless communication network deploys sites according to the shape of the cell. Each site is determined by its radio frequency parameters, and the coverage determines the strength of the wireless signal received by the user, which affects the number of users (capacity) that the site can access. Please refer to FIG. 1. FIG. 1 is a schematic diagram of a wireless network according to an embodiment of the present application. The wireless network includes multiple sites, and each site is deployed according to a certain spatial distribution, as shown in FIG. 1 . The coverage area (also referred to as a cell) of one site is a dotted line area shown in FIG. 1 , and the coverage area formed by multiple sites is shown in FIG. 1 .

Coverage and capacity are important indicators to measure the network performance of wireless networks. However, due to differences between network planning and actual physical environment, changes in urban construction, and user development, wireless networks may have weak coverage, overlapping coverage, etc. Coverage problems, as well as capacity problems such as unbalanced load and hot traffic suppression, are shown in Figure 2.

Wherein, the weak coverage means that the average reference signal received power (reference signal received power, RSRP) of the current area is lower than a certain threshold value. For example, the weak coverage area in FIG. 2 is located between cell 1 and cell 2, and is located at the edge area of cell 1 and cell 2. Users in this weak coverage area may not be able to receive signals normally. Overlapping coverage refers to the overlapping area between adjacent areas, eg, the shaded area between cell 2 and cell 3 in FIG. 2 . Traffic suppression in hotspots means that the signal quality in the current area is poor, and more energy is required to send data. For example, the number of access users in the hotspot coverage area of cell 1 in FIG. 2 is relatively large, and there is a problem of traffic suppression in the hotspot.

In order to ensure a better user experience (for example, a higher network speed), a wireless network should have better reception levels, less interference, and more balanced user distribution. That is to say, the wireless network needs to consider the two important performance indicators of coverage and capacity at the same time. In order to achieve the coverage index and capacity index of the wireless network, the coverage and capacity of the site can be controlled by adjusting the radio frequency parameters of the antenna.

Common antennas may include single input single output (SISO), multiple input multiple output (MIMO), and the like. For example, a common antenna may include 2TRx, 4TRx, or 8TRx, etc., as shown in FIG. 3 . With the evolution of communication technology, Massive MIMO (Massive MIMO) emerges as the times require. Massive MIMO is a fifth-generation mobile communication technology and the basic key technology networks ^{(the 5 th generation, 5G)} . Massive MIMO antennas can achieve three-dimensional precise beamforming and multi-user beam multiplexing by integrating more antennas, thereby achieving better coverage and greater capacity. Common Massive MIMO antennas can include 32TRx and 64TRx, as shown in Figure 3.

After the introduction of Massive MIMO technology, broadcast beam weights for different coverage scenarios are provided, so that the RF parameters change from three types of ordinary antennas (physical azimuth, physical downtilt, and digital downtilt) to six types of Massive MIMO antennas (physical azimuth, physical downtilt, and digital downtilt). Azimuth, Physical Downtilt, Digital Azimuth, Digital Downtilt, Horizontal Beamwidth, Vertical Beamwidth). Moreover, in the 5G system, in addition to the overall adjustment of the shape of the horizontal envelope and the vertical envelope of the beam, the beam of the broadcast beam can also be adjusted independently to form coverage of any shape. That is to say, by adjusting the radio frequency parameters of the MIMO antenna, the coverage of the cell can be improved.

The existing method for adjusting the radio frequency parameters of the MIMO antenna is usually to perform geographic grid modeling on the coverage optimization area according to the minimization of drive tests (MDT) data reported by the terminal equipment to obtain a two-dimensional plane grid. The path loss matrix is shown in Figure 4. Among them, the set optimization target is the coverage index. Based on the two-dimensional grid-level path loss matrix and the optimization target, the radio frequency parameters are derived, and the radio frequency parameters are adjusted according to the derivatives of the radio frequency parameters to improve the cell coverage.

However, compared with ordinary antennas, Massive MIMO antennas have enhanced stereo coverage. Two-dimensional plane rasterization directly projects vertical plane features to a two-dimensional plane, and averages the data in the same grid, which loses vertical plane coverage characteristics. It will lead to poor signal coverage on vertical surfaces such as buildings. Moreover, in a scenario with low MDT terminal penetration (such as the shaded area shown in Figure 4), due to the lack of MDT data in this scenario, the above adjustment method cannot be used to model the coverage area by geographic rasterization, and thus coverage adjustment cannot be performed. excellent.

In order to solve the above problem, an embodiment of the present application provides a network configuration method, which can construct a three-dimensional stereo coverage feature according to MR data of a measurement report, and model a path loss matrix, thereby facilitating optimization of the three-dimensional stereo coverage.

The network configuration method can be deployed offline on a stand-alone personal computer (personal computer, PC) or on the cloud (ie, an offline tool), or can be deployed online to a network management system (operation maintenance center, OMC) or an online tool platform connected to the OMC ( i.e. online tools). Please refer to FIG. 5. FIG. 5 provides a communication system according to an embodiment of the present application, where the communication system includes an OMC, a network device, a terminal device, and the like. Wherein, the embodiment of the present application is described by taking the online tool deployed to connect to the OMC as an example, and the OMC can communicate with each network device, and can also communicate with the online tool Online Tool, as shown in Figure 5.

Among them, the OMC can obtain the data reported by the terminal device and the network device.

To facilitate understanding of the embodiments of the present application, technical terms involved in the embodiments of the present application are described below.

Mass multiple input multiple output (massive multiple input multiple output, Massive MIMO) is a fifth-generation mobile communication ^(the 5 ^th generation, 5G) technology and the basic key technology networks. Massive MIMO antennas can achieve three-dimensional precise beamforming and multi-user beam multiplexing by integrating more antennas, thereby achieving better coverage and greater capacity.

Referring to FIG. 6, FIG. 6 is a schematic diagram of beam coverage of a Massive MIMO antenna according to an embodiment of the present application. The left side of FIG. 6 is a schematic diagram of a horizontal beam of the Massive MIMO antenna. From a horizontal cross section, the horizontal beam includes 8 beams. The horizontal beam width of the horizontal beam is shown in FIG. 6 , that is, the horizontal beam width is the width of the horizontal beam envelope.

The middle of FIG. 6 is a schematic diagram of a vertical beam of the Massive MIMO antenna. From a vertical cross-section, the vertical beam includes 4 layers. The vertical beam width of the vertical beam is shown in FIG. 6 , that is, the vertical beam width is the width of the vertical beam envelope. That is to say, the Massive MIMO antenna is divided into 4 layers in the vertical direction, and the horizontal beam corresponding to each layer includes 8 beams, that is, the Massive MIMO antenna has a total of 32 narrow beams, which form different coverage areas in the horizontal and vertical directions.

The right side of FIG. 6 is a schematic diagram of an arbitrary beam of a Massive MIMO antenna. Among them, for 5G, in addition to the overall adjustment of the shape of the horizontal and vertical envelopes, each narrow beam can be adjusted independently to form coverage of any shape, as shown in Figure 6.

The beam weight (also referred to as the antenna weight) refers to the quantitative expression form after applying a specific excitation signal to each port of the antenna, the purpose of which is to obtain specific coverage or achieve the effect of beam deformation. Among them, various beam shapes can be generated through different beam weights, for example, cell-level broadcast beams (ie, SSB beams) and user-level static beams (including SRS beams and CSI-RS beams).

Among them, the SSB (SS/PBCH blocks) beam is used to stagger the interference between adjacent areas, and more beams are used to achieve spatial coverage and achieve the optimal coverage of the 5G network. A sounding reference signal (SRS) is an uplink pilot signal sent by a terminal device to a network device, and is used to judge the channel quality of each channel in each frequency band. According to the SRS, the network device selects an appropriate channel to dynamically schedule and allocate resources to the terminal device to obtain the best transmission efficiency and quality. A channel state information reference signal (CSI-RS) is a downlink pilot signal sent by a network device to a terminal device for CSI-RS channel measurement, time-frequency offset tracking, beam management, and mobility management. The CSI-RS in this embodiment refers to the CSI-RS used for mobility management. According to the protocol, the mobility management CSI-RS can measure the beam-level RSRP of the serving cell and its neighboring cells.

Beam gain (also called antenna gain) refers to the ratio of the power density of the signal generated by the actual antenna and the ideal radiating element at the same point in space under the condition of equal input power. Among them, the beam gain is closely related to the antenna pattern. The narrower the main lobe of the pattern, the smaller the side lobe, and the higher the gain. Antenna gain is used to measure the ability of the antenna to send and receive signals in a specific direction, and it is one of the important parameters for selecting a base station antenna.

A measurement report (measurement report, MR) refers to a measurement report reported by a terminal device, and the MR includes information such as the cell ID of the serving cell, the RSRP of the serving cell, the cell ID of the neighboring cell, and the RSRP of the neighboring cell. But MR does not contain latitude and longitude information.

MDT is the minimum road test data defined by 3GPP, including the latitude and longitude information reported by the terminal equipment, the cell ID of the serving cell, the RSRP of the serving cell, the cell ID of the neighboring cell, and the RSRP of the neighboring cell and other information. That is, MDT can be considered as MR with latitude and longitude.

A station (also referred to as a network device) can be any device with a wireless transceiver function, which provides wireless communication services for terminal devices within its coverage. The stations may include but are not limited to: an evolved base station (NodeB or eNB or e-NodeB, evolutionalNodeB) in a long term evolution (longtermevolution, LTE) system, a base station (gNodeB or gNB) in a new generation radio access technology (new radio access technology, NR) ) or transmission receiving point/transmission reception point (TRP), base station for subsequent evolution of 3GPP, access node in WiFi system, wireless relay node, wireless backhaul node, Internet of Vehicles, D2D communication, and equipment that undertakes base station functions in machine communication , satellite, etc.

The terminal device may be a device with a wireless transceiver function, or the terminal device may also be a chip. The terminal device may be a user equipment (userequipment, UE), a mobile phone (mobilephone), a tablet computer (Pad), a computer with a wireless transceiver function, a virtual reality (virtual reality, VR) terminal device, an augmented reality (augmented reality, AR) terminal Equipment, in-vehicle terminal equipment, wireless terminals in telemedical, wireless terminals in smart grid, wearable terminal equipment, Internet of Vehicles, D2D communication, sensors in machine communication, etc.

The following will be described in conjunction with specific embodiments.

Please refer to FIG. 7, which is a schematic flowchart of a network configuration method provided by an embodiment of the present application. The network configuration method can be performed by an offline tool deployed offline on a stand-alone computer or cloud, or by an online tool deployed online to the network management system OMC or an online tool platform connected to the OMC. The network configuration method is applied to scenarios with no MDT data or insufficient MDT data, and includes the following steps:

S701, the network management apparatus performs aggregation processing on the multiple pieces of MR data according to the cell information of each MR in the multiple pieces of measurement report MR data to obtain N groups of MR data;

S702, the network management apparatus acquires the average reference signal received power RSRP of each cell of each group of MRs in the N groups of MRs;

S703, for each group of MRs in the N groups of MRs, the network management apparatus creates a stereoscopic image of the group of MRs according to the average RSRP of each cell in the group of MRs and the vertical beam range and horizontal beam range corresponding to the main beam identifier of each cell grid;

S704, for the three-dimensional grid of each group of MRs, the network management device calculates the path loss between the main beam of each cell in the group of MRs to the three-dimensional grid, to obtain a path loss matrix of the three-dimensional grid;

S705, the network management apparatus determines the network configuration parameters of the target cell according to the path loss matrix of the N three-dimensional grids.

In the embodiment of the present application, it is assumed that the measurement report reported by the terminal device does not include longitude and latitude information. That is to say, the network management apparatus receives the MR reported by the terminal device, but does not include the MDT or the MDT is insufficient. In the above scenario, the network management apparatus may perform aggregation processing on the MRs reported by the terminal equipment to obtain N groups of MR data. Specifically, the network management apparatus may first perform data preprocessing on the MR reported by the terminal device, and the data preprocessing includes MR main beam identification and similar feature MR data aggregation.

The process of identifying the MR main beam by the network management apparatus will be described in detail below.

In addition to receiving the MR reported by the terminal device, the network management apparatus may also obtain a call history record (CHR) of the terminal device. The CHR can record each call of the terminal device according to the method of feature extraction. The CHR includes the cell identity of the serving cell (service cell identity, S_Cell_ID), the uplink throughput of the serving cell (service cell uplink throughput, ULThroughput), the downlink throughput of the serving cell (service cell downlink throughput, DLThroughput), the main beam identifier of the serving cell ( service main beam identity, S_MainBeam_ID), may also include access related information of the terminal device, handover related information, etc., which is not limited in this embodiment.

Optionally, the network management device may also receive an operating parameter/configuration file and an antenna file. The working parameter/configuration file includes information such as radio frequency parameters of each cell, and the antenna file includes broadcast beam, horizontal pattern of service beam, vertical pattern of service beam, beam gain and attenuation information, etc., which are not limited in this embodiment.

Since MR and CHR are two different types of data reported by a terminal device, in order to facilitate subsequent processing, MR and CHR may be associated with each other. For example, the network management apparatus may perform association processing on the MR and the CHR according to "call time+Cell_ID+call_ID". Among them, since the number of call_IDs is limited, it will be repeated after a period of time. In this embodiment, the call_IDs are distinguished by limiting the call time.

For the convenience of description, in this embodiment, the MR and CHR after correlation processing are referred to as MR data, which includes one or more MRs. Wherein, an MR represents a measurement report reported by a terminal device within a period of time, and the cell information of the MR includes the cell identifiers of one or more cells, the main beam identifiers of one or more cells, and the RSRP of one or more cells. . That is, the cell information of each MR includes cell identifiers of one or more cells, main beam identifiers of one or more cells, and RSRPs of one or more cells.

The serving cell refers to a cell that provides a channel for the terminal device when the terminal device communicates. Neighboring cells represent cells adjacent to the serving cell. For example, if cell 2 in FIG. 2 is a serving cell of a terminal device, cell 3 is a neighboring cell of the terminal device.

The main beam of the serving cell is the beam with the strongest signal among the multiple beams transmitted by the Massive MIMO antenna detected by the terminal device. That is to say, the main beam identifier of the serving cell indicates the beam identifier of the beam with the strongest signal detected by the terminal device.

For example, after the terminal device accesses the serving cell, it can detect multiple beams transmitted by the Massive MIMO antenna at the current location. Assume that the Massive MIMO antenna is 64TRx, 64TRx has 64 beams, horizontal 8*vertical 4*polarization 2, as shown in Figure 8. It should be noted that the 64 beams (including dual polarizations) of the Massive MIMO antenna correspond to 32 physical beam positions, of which the two beams of positive 45-degree polarization and negative 45-degree polarization can be considered as one physical beam. The 32 physical beams respectively have their corresponding beam identifiers. Assuming that the beam identifiers of the 32 physical beams start from 0 to 31, according to the vertical layers, the beam identifiers of the bottom first layer are 0 to 7 from south to north, and the beam identifiers of the second layer are from south to north. 8 to 15, the beam identifiers of the third layer are 16 to 23 from south to north, and the beam identifiers of the fourth layer are 24 to 31 from south to north. The beam identifier of the beam with the strongest signal detected by the terminal device is 11, as shown in FIG. 8 .

In an implementation manner, the cell information of each MR includes cell information of a serving cell and/or cell information of a neighboring cell. That is to say, the related information of the neighboring cells is introduced in the embodiment of the present application, so that the network management apparatus also fully considers the influence of the neighboring cells of the cell when adjusting the network configuration parameters of a cell. Specifically, the cell information of each MR includes the cell identity of the serving cell, the RSRP (S_RSRP) of the serving cell, the cell identity (neighbor cell identity, N_Cell_ID) of the neighboring cell, the RSRP (N_RSRP) of the neighboring cell, and the like.

Limited by the measurement capability of the terminal equipment, the terminal equipment cannot obtain the main beam identifier of the adjacent cell. In order to facilitate subsequent MR main beam identification, in this embodiment, a main beam identity (neighbor main beam identity, N_MainBeam_ID) field of a neighboring cell is added to the MR data to record the main beam identification of a neighboring cell.

The MR data may be stored in the network management apparatus in the form of a table, or may be stored in other forms, which is not limited in this embodiment. Please refer to Table 1. Table 1 is an MR data information table provided in the embodiment of the present application. The information table includes fields such as S_Cell_ID, S_RSRP, S_MainBeam_ID, N_Cell_ID, N_RSRP, N_MainBeam_ID, ULThroughput, DLThroughput, etc. Table 1 shows the physical meaning and source of each field.

Table 1: An MR Data Information Sheet

It should be noted that the value of the N_MainBeam_ID field can be set to -1 before the MR main beam identification is performed. When the associated MR data is stored, it is arranged in the order of serving cells and neighboring cells. Among them, different serving cells are sorted and stored in order according to the size of S_RSRP; similarly, adjacent cells are sorted and stored in order according to the size of N_RSRP.

Please refer to Table 2. Table 2 provides an MR data storage table provided in this embodiment of the present application. The storage table includes one or more MRs. The cell information of each MR includes cell identifiers of one or more cells, primary beam identifiers of one or more cells, RSRP of one or more cells, ULThroughput of one or more cells, and DLThroughput of one or more cells.

Table 2: An MR data storage table

Among them, the MR whose MainBeam_ID value is -1 in Table 2 refers to the neighboring cell, and the values of ULThroughput and DLThroughput in this row are both 0, that is, the throughput information of the neighboring cell cannot be directly measured by the terminal device.

In an implementation manner, in order to facilitate the subsequent aggregation of MR data with similar characteristics, the network management apparatus can predict the adjacent main beam in the MR data, including the following steps:

s11, for each MR in the MR data, according to the cell identifier of the serving cell in the MR, the RSRP of the serving cell, and the main beam identifier of the serving cell, determine the main beam prediction model of the serving cell of the MR;

s12: Determine the main beam identifier of the adjacent cell of the MR according to the main beam prediction model of the serving cell of the MR, the cell identifier of the adjacent cell, and the RSRP of the adjacent cell.

For example, for all cells in Table 2, cell information when each cell is used as a serving cell is obtained. Taking cell i as an example, the network management apparatus can obtain from Table 2 the Cell_ID, RSRP and MainBeam_ID of all cell i including cell i, and the value of MainBeam_ID of cell i is not -1.

After traversing all MRs in Table 2, the Cell_ID, RSRP and corresponding MainBeam_ID of each cell as a serving cell are used as training data, and the training data may be stored in the form of a table. Among them, Cell_ID and RSRP are used as feature vectors, denoted as X_Train, MainBeam_ID is used as a label, denoted as Y_Train, and the training data of the main beam prediction model of the cell is shown in Table 3.

Table 3: Training data table of a main beam prediction model for a cell

Among them, the serving cell in MR 1 is cell 1, then MainBeam_ID is the MainBeam_ID of cell 1, according to Table 2, it can be known that the value of MainBeam_ID of cell 1 is 31. It should be noted that if cell n is not included in MR 1, the RSRP value of cell n in MR 1 is 0. The definition of the data of the remaining rows in Table 3 is similar to the definition of the row of MR 1, which is not repeated here.

The network management apparatus may use the data in Table 3 as training data, and use a machine learning algorithm to train the main beam prediction model. Taking cell i as an example, the main beam prediction model of cell i is recorded as CellBeamModel_cell i. The training process of the cell main beam prediction model can be simply understood as: for the function y=f(x), x and y are known, and f is obtained by summarizing and summarizing. That is to say, the function y is Y_Train in Table 3, and x is X_Train in Table 3. According to the multiple X_Train and Y_Train stored in Table 3, the main beam prediction model of each cell can be obtained by training. For example, according to the X_Train and Y_Train corresponding to cell i, the main beam prediction model of cell i can be obtained by training.

Similarly, for all the cells in Table 2, cell information when each cell is used as a neighbor cell is obtained. Taking cell i as an example, the network management apparatus can obtain from Table 2 the Cell_ID and RSRP of all cell i including cell i, and the value of the MainBeam_ID of cell i is -1. After traversing all MRs in Table 2, the Cell_ID and RSRP when each cell is used as a neighbor cell are used as prediction input values, denoted as X_Pre, and the prediction input value can also be stored in the form of a table, as shown in Table 4.

Table 4: Prediction input table for a neighbor main beam prediction

Among them, for cell i, according to the X_Pre of cell i and the main beam prediction model of cell i, Y_Pre of cell i can be obtained, that is, the main beam identifier of cell i can be predicted. Traverse the MR data whose MainBeam_ID value of all cells in Table 2 is -1, so that all cells in each piece of MR data are marked with the main beam identifier.

Please refer to Table 5. Table 5 is a main beam marked MR data storage table provided by an embodiment of the present application. Compared with Table 2, the value of MainBeam_ID of each cell in Table 5 is a value greater than -1, that is, the main beam identifier of each cell can indicate one of the beams generated by the Massive MIMO antenna.

Table 5: An MR data storage table after main beam marking

Optionally, since the amount of MR data marked by the main beam is large, which may occupy a large amount of computing resources and reduce the operation efficiency of the OMC, the network management apparatus may aggregate the MR data marked by the main beam according to certain rules. That is to say, the network management apparatus may perform aggregation of MR data with similar characteristics on the MR data marked by the main beam.

The following will describe in detail the process for the network management apparatus to aggregate MR data with similar characteristics.

The network management device can firstly analyze the similarity of wireless spatial propagation on the MR data marked by the main beam, and then aggregate the MR data with the similarity of wireless spatial propagation to obtain N groups of MR data.

Among them, the similarity of wireless spatial propagation means that there are different propagation paths such as refraction, reflection, diffraction, etc., for wireless signals to propagate in space. Signals transmitted from the same cell, propagate through wireless space, and reach terminal devices with similar distances, and their wireless space propagation is similar. For example, terminal equipment 1 and terminal equipment 2 that are close in distance measure that the main beam identifiers of the surrounding cells are the same, and the difference in RSRP is small. Then it can be said that the wireless spatial propagation of terminal device 1 and terminal device 2 are similar.

In an implementation manner, the network management apparatus may aggregate MR data with wireless spatial propagation similarity according to each MR in Table 5 and the RSRP of each cell in each MR to obtain N groups of MR data. The following steps can be included:

Combine the MR data with the same cell identifier, the same main beam identifier, and the level difference value satisfying the preset level difference value condition to determine a group of MRs after aggregation; wherein, the level difference value is the cell identifier in the MR data The difference in RSRP between pairs of the same cells.

Among them, in this embodiment, multiple MRs with the same cell ID, the same main beam ID, and the level difference value satisfying the preset level difference value condition (for example, the RSRP difference value is less than 3dB) are regarded as MRs with wireless spatial propagation similarity .

For example, the network management apparatus may first perform data preprocessing on the MR data marked by the main beam. Among them, Cell_ID, RSRP and MainBeam_ID are used as feature vectors, and the MR data marked by the main beam is subjected to data preprocessing to obtain a similar feature MR data aggregation input table, as shown in Table 6.

Table 6: A similar feature MR data pooling input table

The network management device may perform aggregation processing on the data in Table 6 according to a certain clustering rule (eg, a clustering algorithm) according to the data in Table 6 above. For example, the network management apparatus aggregates MR data with the same Cell_ID, the same MainBeam_ID, and the RSRP difference less than 3 dB, to obtain a group of MRs.

For example, the MainBeam_ID of cell1 in MR1 in Table 6 is 31, the RSRP is 80, the MainBeam_ID of cell1 in MR2 is 31, and the RSRP is 78. The MainBeam_ID of cell 2 in MR 1 in Table 6 is 29, the RSRP is 70, the MainBeam_ID of cell 2 in MR 2 is 29, and the RSRP is 70. It can be seen that the cell identifiers of the two MRs are the same, the main beam identifiers of the cells are the same, the difference between the RSRP of cell 1 in MR 1 and the RSRP of cell 1 in MR 2 is less than 3dB, and the RSRP of cell 2 in MR 1 is less than 3dB. The value is the same as the value of RSRP of cell 1 in MR 2. That is, MR 1 and MR 2 can be combined into a set of MRs.

Optionally, when the network management apparatus performs aggregation processing on MR data with similar characteristics, the following situations may exist: the cell identifiers in the two MRs are the same, the main beam identifiers of the cells are the same, and the level difference of the cells also satisfies the preset voltage. Adjustment value condition; however, one of the MRs also includes other cell information. For example, for MR1 and MR3 in Table 6, both MR1 and MR3 include cell information of cell 1 and cell information of cell 2. However, MR 3 also includes cell information of cell 3.

In order to analyze whether the above-mentioned MRs can be aggregated into a group of MRs, the network management apparatus may process the MR data of similar characteristics. For example, for each MR in Table 6, calculate the difference between the maximum value of RSRP in each MR and the RSRP of each cell, and use the difference as a new eigenvector, and expand Table 6 as shown in Table 7 Show.

Table 7: An input table of similar feature MR data aggregation and expanded feature vector

For example, for MR 1 and MR 3 in Table 7, MR 3 also includes cell information of cell 3. However, the difference in RSRP of cell 3 in MR 3 is large. That is to say, compared with the serving cell cell 1, the RSRP of the neighboring cell 3 is smaller, so the distance between the cell 3 and the cell 1 may be farther, that is to say, the cell 3 in the neighboring cell may be invalid and can be ignored. cell 3. That is, for MR 1 and MR 3, they can be aggregated into a group of MRs with wireless spatial propagation similarity.

It should be noted that the data in Tables 6 and 7 above are only a part of the aggregation of MR data with similar characteristics, and the aggregated MR data also includes throughput information of a cell, etc., which is not limited in this embodiment.

Optionally, the network configuration apparatus may determine how many groups of MRs the MR data is aggregated into according to the number of cells, the distribution of users in the cells, the number of users, and the overlapping coverage between cells.

For example, it is assumed that there are 3 cells cell1, cell2 and cell3, wherein there are no users in cell2 and cell3, and there are 2 users UE1 and UE2 in cell1. Among them, UE1 is stationary, and its main beam is beam 31; UE2 is a mobile user, and its main beams are beams 8 and 9. Two users reported four MRs respectively, and the processed MR information is shown in Table 8.

Table 8: Similar feature MR data input table for UE 1 and UE 2

The network management device can aggregate and process the 4 pieces of MR data reported by UE 1 and UE 2, and can be divided into 3 groups through the aggregation operation. Among them, the 4 pieces of MR data of UE1 are 1 group of MRs, and the 4 pieces of MR data of UE2 are divided into 2 groups. Group MR. It should be noted that the above example is only an example. The total number of groups of MRs aggregated by MR data is analyzed and obtained according to the specific network conditions. The analysis is based on the number of cells, the distribution of users in the cell, the number of users, the Parameters such as the average RSRP of the cell are not limited in this embodiment.

The network management apparatus performs data processing on the aggregated groups of MRs, including performing averaging processing on the RSRPs of each group, and/or performing summation processing on the throughputs of each group. For example, for the nth group of MRs, the average RSRP of the same cell in the group of MRs is averaged to obtain the average RSRP of the same cell in the group of MRs. The uplink throughputs of the same cell in the group MR are summed to obtain the sum of the uplink throughputs of the same cell in the group MR. The downlink throughputs of the same cell in the group MR are summed to obtain the total downlink throughput of the same cell in the group MR.

Optionally, the network management apparatus may also record the number of occurrences of each cell in each group of MRs. For example, for the nth group of MRs, the number of times each cell in the group MR appears as a serving cell is recorded, and the number of times each cell in the group MR appears as a neighbor cell is recorded.

Please refer to Table 9. Table 9 provides a format table of the converged MR data provided in the embodiment of the present application. Wherein, taking the nth group of MRs as an example, the table 9 records the cell identifiers of one or more cells in the nth group of MR data, the main beam identifiers of one or more cells, and the average RSRP of one or more cells, etc. data.

Table 9: A format table of MR data after pooling

Among them, a three-dimensional grid (grid) can be regarded as a group of MRs in the aggregated MR data. That is to say, the three-dimensional grid n (Grid n) corresponds to the nth group of MRs in the aggregated MR data.

Optionally, the three-dimensional grid can also be regarded as a three-dimensional space position in a three-dimensional coordinate system. Wherein, the three dimensions in the three-dimensional coordinate system are different from the conventional three-dimensional coordinate system (such as a three-dimensional coordinate system composed of longitude, latitude and height), and the three dimensions in the three-dimensional coordinate system are respectively corresponding to the main beam identification of the cell. Horizontal beam range, vertical beam range, and path loss (also called path loss) from the main beam of the cell to the cubic grid.

Please refer to FIG. 9a, which is a schematic diagram of a three-dimensional grid provided by an embodiment of the present application. Wherein, it is assumed that cell 1 in Fig. 9a includes 32 narrow beams, wherein the path loss from beam 1 of cell 1 to the three-dimensional grid n is shown in Fig. 9a. It should be noted that the plurality of three-dimensional grids in FIG. 9a respectively represent three-dimensional spatial positions. The reference coordinate system is shown in Figure 9a, that is, the three-dimensional space of the three-dimensional grid shown in Figure 9a is determined by the antenna plane of the cell and the distance determined by the RSRP of the cell to the three-dimensional grid.

The network management device may generate a path loss matrix according to a plurality of three-dimensional grids, engineering parameters/configurations, antenna files, and the like. That is to say, for the stereo grid of each group of MRs, the path loss between the main beam of each cell in the group of MRs and the stereo grid can be calculated to obtain the path loss matrix of the stereo grid, including the following steps:

s21, according to the main beam identifier of any cell i in the group MR, obtain the beam gain corresponding to the main beam identifier of the cell i; the beam gain of the cell i is the horizontal beam corresponding to the main beam identifier of the cell i range, vertical beam range, antenna gain, signal attenuation value corresponding to the horizontal beam range and signal attenuation value corresponding to the vertical beam range;

s22, according to the transmit power of the cell i, the beam gain of the cell i, and the average RSRP of the cell i, calculate the path loss from the main beam of the cell i to the stereo grid of the group of MRs.

Among them, taking the three-dimensional grid n as an example, since the three-dimensional grid n may include multiple cells, when calculating the path loss of the three-dimensional grid n, it is necessary to calculate the distance from each cell in the three-dimensional grid n to the three-dimensional grid n. road damage. The following takes the celli in the three-dimensional grid n as an example to describe in detail. It should be noted that each cell in all three-dimensional grids can be calculated by referring to the following steps, and each cell in each three-dimensional grid can be calculated by referring to the following steps. The path loss of the cubic grid.

First, the network management apparatus can acquire the radio frequency parameters of the celli from the work parameters/configuration. The radio frequency parameters may include, but are not limited to: horizontal beam width, physical azimuth, digital azimuth, vertical beam width, physical downtilt angle or digital downtilt angle.

The horizontal beam width represents the horizontal envelope width covered by the horizontal plane controlled by the beam weight, for example, the horizontal beam width shown in FIG. 6 . The vertical beamwidth represents the vertical envelope width covered by the horizontal plane controlled by the beam weight, for example, the vertical beamwidth shown in FIG. 6 . The physical azimuth represents the angle between the facing direction of the physical antenna panel and the true north, and its value ranges from 0 degrees to 359 degrees. The digital azimuth represents the angle between the strongest direction of the horizontal beam energy controlled by the beam weight and true north, and its value ranges from 0 degrees to 359 degrees. The physical downtilt angle represents the angle between the plane perpendicular to the physical antenna panel and the horizontal plane, and its value ranges from -90 degrees to 90 degrees. The digital downtilt angle represents the angle between the strongest vertical beam energy controlled by the beam weight and the horizontal plane, and its value ranges from -90 degrees to 90 degrees.

Then, the network management device obtains the horizontal beam range, vertical beam range, antenna gain, signal attenuation value corresponding to the horizontal beam range and vertical beam range corresponding to the MainBeam_ID from the antenna file from the antenna file according to the MainBeam_ID of the celli.

For example, the horizontal beam range corresponding to the MainBeam_ID of cell i is [0 degrees, 19 degrees], and the vertical beam range is [3 degrees, 9 degrees]. Antenna gain is 50dB. According to the horizontal beam range and the vertical beam range, the network management apparatus can determine the signal attenuation value corresponding to each degree of the horizontal beam range and the signal attenuation value corresponding to each degree of the vertical beam range. According to the calculation formula of beam gain, the beam gain of MainBeam_ID of cell i can be calculated. Among them, the calculation formula of the beam gain is as follows: the beam gain of the MainBeam_ID of cell i = the antenna gain + the average value of the signal attenuation value. The average value of signal attenuation includes the average value of signal attenuation values corresponding to the horizontal beam and the average value of signal attenuation values corresponding to the vertical beam.

Finally, the network management device can use the classical propagation model formula to calculate the path loss from the main beam of cell i to the three-dimensional grid n. The calculation formula of the path loss is as follows: path loss from the main beam of cell i to the three-dimensional grid n = transmit power of cell i + beam gain of MainBeam_ID of cell i - average RSRP of cell i. Wherein, the transmit power of cell i can also be obtained from the antenna file, and the average RSRP of cell i can be obtained from the aggregated MR data shown in Table 9.

Referring to FIG. 9b, FIG. 9b is a schematic diagram of a path loss matrix provided by an embodiment of the present application. Wherein, Figure 9b includes three cells, namely cell 1, cell 2 and cell 3. Taking the cubic grid n as an example, the cubic grid n includes multiple cells and main beam identifiers of the multiple cells, and the path losses from different cells and/or different main beams to the cubic grid n are different. For example, for cell 2, the path loss from beam 0 of cell 2 to cubic grid n is shown in Figure 9b. But for cell 1, the path loss from beam 1 of cell 1 to stereo grid n is shown in Figure 9b, which is different from the path loss from beam 0 of cell 2 to stereo grid n.

The path loss matrix may be stored in the network management device in the form of a table. Please refer to Table 10. Table 10 is a data format table of a path loss matrix provided by an embodiment of the present application, including a three-dimensional grid identifier, a cell identifier, a main beam identifier of a cell, and a path loss.

Table 10: A data format table of a path loss matrix

名称name	物理意义physical meaning
GridnGridn	立体栅格标识three-dimensional grid logo
Cell_IDCell_ID	小区标识，一个立体栅格中有多个小区Cell ID, there are multiple cells in a three-dimensional grid
MainBeam_IDMainBeam_ID	小区的主波束标识Main beam identifier of the cell
路损road loss	小区到立体栅格的路径损耗值Path loss value from cell to stereo grid

Among them, Gridn represents the nth three-dimensional grid, and each three-dimensional grid has a unique three-dimensional grid identifier. There are multiple cells in a three-dimensional grid, and the cell ID and main beam ID of each cell, as well as the path loss from the cell to the three-dimensional grid, are recorded in the path loss matrix.

The process of network optimization is described in detail below.

According to the descriptions in the above embodiments, the network management apparatus can obtain the three-dimensional grids of N groups of MRs, and the path loss matrix from cells to the three-dimensional grids. If there is a coverage problem and/or a capacity problem in the network, the network management apparatus may determine the network configuration parameters of the target cell according to the path loss matrix. That is to say, the network management apparatus can adjust the network configuration parameters of the cells with coverage problems and/or capacity problems according to the path loss matrix, so as to solve the coverage problems and/or capacity problems in the network.

The network configuration parameters may be the radio frequency parameters described in the foregoing embodiments, or a combination of radio frequency parameters. For example, the network configuration parameter (referred to as Conf) that can be adjusted by the network management apparatus may be any one of horizontal beam width, physical azimuth, digital azimuth, vertical beam width, physical downtilt or digital downtilt. For another example, the network management apparatus may determine a combination of radio frequency parameters to be adjusted, such as adjusting a combination of radio frequency parameters related to horizontal beams, according to information such as beam range, traffic flow, and RSRP of cells in the network.

Optionally, the network configuration parameters may also be configuration parameters in other radio resource management (radio resource management, RRM) scenarios. For example, the network configuration parameter may be a cell individual offset (CIO) parameter for handover.

In order to measure whether there is a coverage problem and/or a capacity problem in the network, this embodiment uses a coverage index and/or a capacity index to quantify the goal of network optimization. Coverage indicators include RSRP, signal to interference plus noise ratio (SINR), overlap coverage ratio, etc., which are recorded as coverage. Capacity indicators include traffic balance, spectral efficiency, etc., which are recorded as capacity.

The RSRP refers to the reception level of the cell, which can be measured by the cell and reported to the network management apparatus. The formula for calculating SINR is as follows:

Determined by the RSRP of the serving cell, the sum of the RSRPs of neighboring cells, and the noise power. The formula for calculating the overlap coverage ratio is as follows:

Wherein, if the difference between the RSRP of the serving cell and the RSRP of the neighboring cell is less than a threshold (for example, 3dB), it is considered that the serving cell has overlapping coverage, and it is included in the number of serving cells with overlapping coverage.

The traffic equalization degree means that the sum of the uplink and downlink throughputs between adjacent cells is as equal as possible. That is to say, the sum of the uplink and downlink throughputs of the serving cell and the sum of the uplink and downlink throughputs of the neighboring cells should be the same as possible. Spectral efficiency refers to the amount of traffic on a unit resource block (RB) per unit time.

In one implementation, the two metrics of coverage and capacity may not be optimal at the same time. That is to say, when the coverage index of the network is adjusted to the optimal state, for example, it can cover a larger area and have less overlapping coverage areas; however, the capacity index of the network may not be optimal, for example, the spectral efficiency of some areas is low . In order to optimize the overall performance of the network, the embodiments of the present application propose a multi-objective and multi-parameter joint optimization. That is to say, according to the above-mentioned coverage index, capacity index, and network configuration parameters, the embodiment of the present application models the goal (fitness) of network optimization as the following formula:

fitness=Func((k ₁ *coverage,k ₂ *capacity),Conf)

Among them, fitness represents the joint optimization objective of capacity and coverage, Conf represents an adjustable network configuration parameter, k ₁ represents the coverage weight, and k ₂ represents the capacity weight. By setting different coverage weights and capacity weights, the degree of influence of coverage and capacity on the joint optimization objective is controlled.

For example, in the current network scenario, a cell covers a hotspot area, and users in the hotspot area are mostly concentrated in high places (for example, in a 5-story building, users are mostly concentrated on the 4th and 5th floors). In this scenario, the capacity weight can be given priority, that is, _{the weight value of k 2} is greater than the weight value of k ₁ . And when adjusting the network configuration parameters, priority is given to the adjustment of the vertical beam, that is, the network management device can adjust the parameter combination of vertical beam width, physical downtilt angle and digital downtilt angle to optimize capacity and coverage.

In an implementation manner, the network management apparatus determines the network configuration parameters of the target cell according to the path loss matrices of the N three-dimensional grids, which may include the following steps:

s31, for each three-dimensional grid, determine the target cell of the three-dimensional grid according to the coverage index and/or the capacity index of each cell of the three-dimensional grid;

s32, adjusting the network configuration parameters of the target cell according to the preset coverage index and the preset capacity index of the target cell;

s33, obtaining the adjusted antenna gain of the target cell according to the adjusted network configuration parameters of the target cell;

S34, according to the path loss matrix of the three-dimensional grid, the adjusted antenna gain of the target cell, and the adjusted network configuration parameter of the target cell, determine the coverage index and the capacity index of the target cell;

s35, if the coverage index of the target cell reaches the preset coverage index, and/or the capacity index of the target cell reaches the preset capacity index, determine that the adjusted network configuration parameters of the target cell are the network configuration parameters of the target cell.

Among them, the above s31 can be divided into several small steps, including:

s311, according to the fitness formula and based on the measured data, determine the three-dimensional grid with coverage problems and/or capacity problems before the network configuration parameter adjustment;

s312, using a clustering algorithm to aggregate and process the three-dimensional grids with coverage problems and/or capacity problems;

s313: Determine that the cells included in the converged problem three-dimensional grid are the target cells.

Wherein, the target cell is a cell whose coverage index does not meet the preset coverage index and/or the capacity index does not meet the preset capacity index. That is to say, the target cell may be determined according to the coverage index of the cell, or may be determined according to the capacity index of the cell. It can be understood that the coverage index of the cell refers to the current coverage of the cell, and the capacity index of the cell refers to the current capacity of the cell.

The preset coverage index may be determined according to parameters such as RSRP, SINR, and overlapping coverage ratio; for example, the preset coverage index of a cell is that the overlapping coverage ratio of the cell is less than 10%. The preset capacity indicator may be determined according to parameters such as traffic balance, spectral efficiency, etc. For example, the preset capacity indicator of a cell is that the traffic balance of the cell is 1, that is, the uplink throughput and downlink throughput of the cell The sum is the same as the sum of the uplink throughput and downlink throughput of adjacent cells.

For example, the network management apparatus may determine the target cell based on the measured data. The measured data can be determined according to the aggregated MR data shown in Table 9. The measured data may be the average RSRP of each cell in the three-dimensional grid. If the average RSRP of the cell is lower than the preset RSRP threshold (for example, 100 dB), it is determined that the cell has a coverage problem. The measured data may also be the sum of the uplink and downlink throughputs of each cell in the three-dimensional grid. If the sum of the uplink and downlink throughputs of the cell is less than the preset throughput threshold, it is determined that the cell has a capacity problem.

Optionally, a clustering algorithm (density-based spatial clustering of applications with noise, DBSCAN), etc. may be used when performing aggregation processing on the three-dimensional grids with coverage problems and/or capacity problems. For example, if there are a total of 50 cells with coverage problems and/or capacity problems, the three-dimensional grids with higher wireless spatial propagation similarity can be aggregated according to the clustering algorithm. It is assumed that the three-dimensional grids aggregated by the clustering algorithm include There are 30 cells in total, so the network management apparatus can determine the 30 cells as target cells.

After the target cell is determined, according to the formula of the target (fitness) of network optimization, the maximum value of the target of network optimization is obtained, that is, the target formula of network optimization is converted into the following formula:

max{fi _t ness=Func((k ₁ *coverage,k ₂ *capacity),Conf)}

Among them, max represents the maximum value of fitness that can be obtained by adjusting Conf.

In an implementation manner, the network management apparatus may obtain adjustable network configuration parameters from the antenna file, and obtain current network configuration parameters through configuration and work parameters. The network management device can adopt an optimization algorithm (for example, an operation research optimization algorithm based on gradient descent), take max{fitness} as the optimization goal, take the current network configuration parameter as the initial value, and take the adjustable network configuration parameter as the variable, through continuous Adjust Conf to obtain the Conf that maximizes fitness; the above optimization process is shown in Figure 10. Among them, the stopping condition of optimization needs to comprehensively consider the efficiency and convergence of the algorithm. That is to say, the stopping condition of optimization includes: the overall gain reaches the standard (for example, the fitness reaches the maximum value), or the gain for consecutive rounds is less than the threshold, or the number of iterations reaches the maximum. Optimization times threshold.

The embodiment of the present application provides a network configuration method, which can construct a three-dimensional three-dimensional grid according to the average RSRP of each cell and the horizontal beam information and vertical beam information corresponding to the main beam identifier of each cell in the MR data, so as to realize In order not to rely on MDT data can also build three-dimensional grid objects. In addition, a three-dimensional path loss matrix can be constructed according to the three-dimensional three-dimensional grid to realize stereo beam optimization. The method can also determine the network configuration parameters of the target cell according to the three-dimensional path loss matrix, and by adjusting the network configuration parameters of the target cell, the coverage index and/or the capacity index of the target cell can be optimized, thereby helping to optimize the coverage and/or capacity of the network .

Referring to FIG. 11 , FIG. 11 is a schematic flowchart of another network configuration method provided by an embodiment of the present application. The network configuration method can be performed by an offline tool deployed offline on a stand-alone computer or cloud, or by an online tool deployed online to the network management system OMC or an online tool platform connected to the OMC. This network configuration method is applied in scenarios where MDT data is sufficient or road testing, and includes the following steps:

S1101, the network management apparatus performs aggregation processing on the multiple pieces of MDT data according to the cell information of each MDT in the multiple minimization road test MDT data to obtain N groups of MDT data, each group of MDTs includes MDTs with the same vertical beam identifiers of the cells ;

S1102, for each group of MDTs in the N groups of MDTs, the network management device creates a three-dimensional grid of the group of MDTs according to the longitude and latitude of each cell in the group of MDTs and the vertical beam identifier of each cell;

S1103, for the three-dimensional grid of each group of MDTs, the network management device calculates the path loss between the main beam of each cell in the group of MDTs to the three-dimensional grid, to obtain the path loss matrix of the three-dimensional grid;

S1104, the network management apparatus determines the network configuration parameters of the target cell according to the path loss matrix of the N three-dimensional grids.

In this embodiment of the present application, it is assumed that the measurement report reported by the terminal device is a measurement report including longitude and latitude information, that is, the terminal device reports MDT data. In the above scenario, the network management apparatus may perform aggregation processing on the MDT reported by the terminal device to obtain N groups of MDT data. Similar to the aggregation processing of the MR data by the network management apparatus, the network management apparatus may first perform data preprocessing on the MDT reported by the terminal equipment, and the data preprocessing includes MDT main beam identification and MDT geographic grid aggregation.

The process of identifying the MDT main beam by the network management apparatus will be described in detail below.

In addition to receiving the MDT reported by the terminal device, the network management apparatus may also acquire the CHR of the terminal device. For the relevant description of the CHR, reference may be made to the description of the CHR in the embodiment of FIG. 7 , and details are not repeated here.

Optionally, the network management device may also acquire work parameters/configuration files, antenna files, and electronic maps. Wherein, for the related description of the work parameter/configuration file and the antenna file, reference may be made to the description of the work parameter/configuration file and the antenna file in the embodiment of FIG. 7 , which will not be repeated here. The electronic map includes the location information of the geographic space, and can show the location relationship between the site and the terminal device.

Since MDT and CHR are two different types of data reported by terminal equipment, in order to facilitate subsequent processing, MDT and CHR may be associated with each other. For the description of the association processing performed on the MDT and the CHR, reference may be made to the description of the associated processing performed on the MDT and the CHR in the embodiment of FIG. 7 , which will not be repeated here.

For convenience of description, in this embodiment, the MDT and CHR after the associated processing are referred to as MDT data, which includes one or more MDTs. One MDT represents a measurement report including longitude and latitude information reported by a terminal device within a period of time, and the cell information of this MDT includes cell identifiers of one or more cells, main beam identifiers of one or more cells, one or more cell identifiers RSRP of each cell, longitude and latitude of one or more cells. That is, the cell information of each MDT includes cell identifiers of one or more cells, main beam identifiers of one or more cells, RSRP of one or more cells, and longitude and latitude of one or more cells.

In an implementation manner, the cell information of each MDT includes cell information of a serving cell and/or cell information of a neighboring cell. That is to say, the related information of the neighboring cells is introduced in the embodiment of the present application, so that the network management apparatus also fully considers the influence of the neighboring cells of the cell when adjusting the network configuration parameters of a cell. Specifically, the cell information of each MR includes the cell identity of the serving cell, the RSRP (S_RSRP) of the serving cell, the cell identity (neighbor cell identity, N_Cell_ID) of the neighboring cell, the RSRP (N_RSRP) of the neighboring cell, and the like.

Limited by the measurement capability of the terminal equipment, the terminal equipment cannot obtain the main beam identifier of the adjacent cell. In order to facilitate subsequent MDT main beam identification, in this embodiment, a main beam identity (neighbor main beam identity, N_MainBeam_ID) field of a neighboring cell is added to the MDT data to record the main beam identification of a neighboring cell.

Similar to the MR data, the MDT data may be stored in the network management apparatus in the form of a table, or may be stored in other forms, which is not limited in this embodiment. Please refer to Table 11, which is an MR data information table provided by the embodiment of the present application. The information table includes fields such as S_Cell_ID, S_RSRP, S_MainBeam_ID, N_Cell_ID, N_RSRP, N_MainBeam_ID, ULThroughput, DLThroughput, etc. The physical meaning of each field and the source of the field are shown in Table 11.

Table 11: A MDT Data Information Sheet

Similar to the step of predicting the adjacent main beam in the MR data by the network management apparatus in the embodiment of FIG. 7 , the network management apparatus in this embodiment can also predict the adjacent main beam in the MDT data according to similar steps, so that All cells in each piece of MDT data are marked with the main beam identifier.

Please refer to Table 12. Table 12 is a MDT data storage table after the main beam is marked provided by this embodiment of the application. The main beam identifier of each cell can indicate one of the beams generated by the Massive MIMO antenna.

Table 12: A MDT data storage table after main beam marking

Since the MDT data marked by the main beam only represents the information of the two-dimensional geographic grid, in order to model the three-dimensional three-dimensional grid, the network management apparatus can perform the aggregation of the geographic grid from the vertical plane. The following describes in detail the process of the network management device performing geographic grid aggregation, which may include the following steps:

s41, the network management device determines the grid longitude and grid latitude of the plane grid formed by the group of MDTs according to the longitude and latitude of each cell in the group of MDTs;

s42, the network management apparatus determines, according to the vertical beam identifiers of the group of MDTs, the vertical layer where the plane grid of the group of MDTs is located, so as to obtain the three-dimensional grid of the group of MDTs.

For example, the network management apparatus may calculate the vertical beam identifier V_Beam_ID corresponding to each cell according to the MainBeam_ID corresponding to each cell based on the MDT data marked by the main beam. The formula for calculating V_Beam_ID in the embodiment of the present application is as follows: V_Beam_ID=int(MainBeam_ID÷8). For example, if the MainBeam_ID of the cell is 31, according to the above formula of V_Beam_ID, the V_Beam_ID of the cell is calculated to be 3.

After the network management device processes the MDT data, the MDT data is shown in Table 13.

Table 13: MDT data storage table after vertical beam marking

After acquiring the MDT data marked by the vertical beam, the network management apparatus may aggregate the data of the same vertical layer according to the V_Beam_ID to obtain the MDT data of each vertical layer. That is, the MDT data of each vertical layer is a set of MDTs. For example, according to the V_Beam_ID of each cell in Table 13, the MDT data with the same V_Beam_ID are aggregated to obtain multiple sets of MDT data.

The network management apparatus may perform geographic grid processing on the MDT data of each vertical layer to obtain the geographically gridded MDT data. Among them, the geographic grid processing refers to determining the area formed by projecting each vertical layer onto a two-dimensional plane (such as the ground) according to the longitude and latitude information of each vertical layer; A division of a geographic grid. For example, the area formed by projecting the vertical layer of V_Beam0 to the ground determined by the longitude and latitude information of the vertical layer of V_Beam0 as V_Beam_ID is shown in FIG. 12 . Assuming that the geographic grid is divided according to the scale of 20 meters (m)*20m, the area formed by the vertical layer of the V_Beam0 after the geographic grid processing is projected to the ground is shown in Figure 12. For another example, compared to the vertical layer of V_Beam0, the vertical layer of V_Beam1 is moved up a distance in the vertical direction on the basis of being perpendicular to the ground, as shown in Figure 12. That is to say, the area formed by the projection of the vertical layer of V_Beam1 to the ground is the same as the area formed by the projection of the vertical layer of V_Beam0 to the ground, but the two are located at different heights perpendicular to the ground, that is, the stereo of the vertical layers of different V_Beam_IDs The grids are not the same. It should be noted that the three-dimensional grid here refers to a geographic grid with V_Beam_ID, that is, the three-dimensional grid can be a position in a three-dimensional space.

In an implementation manner, after the MDT data with the same V_Beam_ID is aggregated, the network management apparatus may further perform data processing on a group of MDTs with the same V_Beam_ID, including performing average processing on the RSRP of each group, and calculating the throughput of each group. and processing. For a specific implementation manner, reference may be made to the description of performing averaging processing on the RSRP of each group and performing summation processing on the throughputs of each group in the embodiment of FIG. 7 , which is not repeated here.

Optionally, the network management apparatus may also record the occurrence times of each cell in each group of MDTs. For example, for the nth group of MDTs, the number of times each cell in the group of MDTs appears as a serving cell is recorded, and the number of times each cell in the group of MDTs appears as a neighbor cell is recorded.

Please refer to Table 14. Table 14 is a format table of the aggregated MDT data provided by this embodiment of the present application. Wherein, taking the nth group of MDTs as an example, the table 14 records the cell identifiers of one or more cells in the nth group of MDT data, the vertical beam identifiers of one or more cells, the average RSRP of one or more cells, Raster longitude, raster latitude, etc.

Table 14: An aggregated MDT data format table

Among them, a three-dimensional grid (grid) can be regarded as a group of MRs in the aggregated MDT data. That is to say, the three-dimensional grid n (Grid n) corresponds to the nth group of MDTs in the aggregated MDT data.

Please refer to FIG. 13a, which is a schematic diagram of another three-dimensional grid provided by an embodiment of the present application. Among them, the path loss of stereo grid n in beams from cell 1 to V_Beam3, the path loss of stereo grid n in beams from cell 2 to V_Beam3, and the path loss of stereo grid n in beams from cell 3 to V_Beam3 are shown in Figure 13a. It should be noted that a plurality of three-dimensional grids in Fig. 13a respectively represent three-dimensional spatial positions. Wherein, what is determined according to the longitude and latitude is a two-dimensional geographic plane, and the three-dimensional grid in this embodiment includes a vertical beam identifier, that is to say, the three-dimensional grid n includes data of different Beam layers, that is, the three-dimensional grid n is a three-dimensional grid position in three-dimensional space.

The network management device may generate a path loss matrix according to a plurality of three-dimensional grids, engineering parameters/configurations, antenna files, and the like. That is to say, for each group of three-dimensional grids of MDT, the path loss between the main beam of each cell in the group of MDTs and the three-dimensional grid can be calculated to obtain the path loss matrix of the three-dimensional grid, including the following steps:

s51, according to the vertical beam identifier in the group of MDTs, obtain the vertical beam range corresponding to the vertical beam identifier, the antenna gain and the signal attenuation value corresponding to the vertical beam range;

s52, according to the grid longitude and grid latitude of the group of MDTs, obtain the horizontal beam range, the antenna gain and the signal attenuation value corresponding to the horizontal beam range corresponding to the geographic grid determined by the grid longitude and grid latitude in the group of MDTs ;

s53, according to the vertical beam range corresponding to the vertical beam identifier in the group of MDTs, the antenna gain, the signal attenuation value corresponding to the vertical beam range, and the level corresponding to the geographic grid determined by the grid longitude and grid latitude in the group of MDTs Beam range, antenna gain, signal attenuation value corresponding to horizontal beam range, calculate the antenna gain from any cell i in the group of MDTs to the three-dimensional grid of the group of MDTs;

s54: Calculate the path loss from cell i to the three-dimensional grid of the group of MDTs according to the transmit power of cell i, the antenna gain from cell i to the three-dimensional grid of the group of MDTs, and the average RSRP of cell i.

Among them, taking the three-dimensional grid n as an example, since the three-dimensional grid n can include multiple cells, when calculating the path loss of the three-dimensional grid n, it is necessary to calculate the distance from each cell in the three-dimensional grid n to the three-dimensional grid. path loss of n. The following takes the cell i in the three-dimensional grid n as an example for detailed description. It should be noted that each cell in all three-dimensional grids can be calculated by referring to the following steps, and each cell in each three-dimensional grid can be calculated to The path loss of the cube grid.

First, the network management device can obtain the radio frequency parameters of cell i from the work parameters/configuration. The radio frequency parameters may include, but are not limited to: horizontal beam width, physical azimuth, digital azimuth, vertical beam width, physical downtilt angle or digital downtilt angle. For a detailed description of the above radio frequency parameters, please refer to the description in the embodiment of FIG. 7 , which will not be repeated here.

Then, according to the V_Beam_ID of the cell i, the network management apparatus obtains the vertical beam range, the antenna gain, and the signal attenuation value corresponding to the vertical beam range corresponding to the V_Beam_ID from the antenna file. For example, the vertical beam range corresponding to the V_Beam_ID of cell i is [3 degrees, 9 degrees]. Antenna gain is 50dB. According to the vertical beam range, the network management apparatus can determine the signal attenuation value corresponding to each degree of the vertical beam range.

The network management device also calculates the horizontal beam range corresponding to the three-dimensional grid n where the cell i is located according to the grid longitude, the grid latitude, and the working parameters of the cell i. For example, the horizontal beam range of cell i is [0 degrees, 19 degrees]. Then, according to the horizontal beam range, the signal attenuation value corresponding to each degree of the horizontal beam range is obtained from the antenna file. Based on the above data, the network management apparatus can calculate the antenna gain from cell i to the three-dimensional grid n = the average value of the antenna gain + the signal attenuation value.

Finally, the network management device can use the classical propagation model formula and combine with the electronic map to calculate the path loss from cell i to the three-dimensional grid n. Among them, the calculation formula of the path loss is as follows: path loss from cell i to three-dimensional grid n = transmit power of cell i + antenna gain from cell i to three-dimensional grid n - average RSRP of cell i. Among them, the transmit power of cell i can also be obtained from the antenna file, and the average RSRP of cell i can be obtained from the aggregated MDT data shown in Table 14.

Referring to FIG. 13b, FIG. 13b is a schematic diagram of another path loss matrix provided by an embodiment of the present application. Among them, Figure 13b includes three cells, namely cell 1, cell 2 and cell 3. Wherein, taking the three-dimensional grid n as an example, the three-dimensional grid n includes multiple cells and vertical beam identifiers of the multiple cells, and the path losses of the three-dimensional grid n in different cells to different V_Beam_ID beams are different. For example, for cell 2, the path loss of the cubic grid n in the beam from cell 2 to V_Beam0 is shown in Figure 13b, and the cubic grid n in the V_Beam0 beam is located at the bottom of the multi-layer in the vertical direction, as shown in Figure 13b 13b.

The path loss matrix may be stored in the network management device in the form of a table. Please refer to Table 15. Table 15 is a data format table of another path loss matrix provided by this embodiment of the application, including a three-dimensional grid identifier, a cell identifier, a vertical beam identifier of a cell, grid longitude, grid latitude, and road damage.

Table 15: A data format table of a path loss matrix

名称name	物理意义physical meaning
GridnGridn	立体栅格标识three-dimensional grid logo
Cell_IDCell_ID	小区标识，一个立体栅格中有多个小区Cell ID, there are multiple cells in a three-dimensional grid
V_Beam_IDV_Beam_ID	小区的主波束标识Main beam identifier of the cell
LongitudeLongitude	栅格经度grid longitude
LatitudeLatitude	栅格纬度grid latitude
路损road loss	小区到垂直波束的立体栅格的路径损耗值Pathloss value of the stereo grid from the cell to the vertical beam

Among them, Gridn represents the nth three-dimensional grid, and each three-dimensional grid has a unique three-dimensional grid identifier. There are multiple cells in a three-dimensional grid. The cell ID and vertical beam ID of each cell, the grid longitude and grid latitude, and the path loss from the cell to the vertical beam are recorded in the path loss matrix.

Similar to the process of network optimization in the embodiment of FIG. 7 , the network management apparatus in this embodiment can also perform network optimization according to the three-dimensional grid of N groups of MRs and the path loss matrix of the three-dimensional grid from cells to vertical beams. If there is a coverage problem and/or a capacity problem in the network, the network management apparatus may determine the network configuration parameters of the target cell according to the path loss matrix. That is to say, the network management apparatus can adjust the network configuration parameters of the cells with coverage problems and/or capacity problems according to the path loss matrix, so as to solve the coverage problems and/or capacity problems in the network. For a specific implementation manner, reference may be made to the description in the embodiment of FIG. 7 , which will not be repeated here.

The embodiment of the present application provides a network configuration method, which can construct a three-dimensional three-dimensional grid according to the longitude and latitude information in the MDT data and the vertical beam identification determined according to the main beam identification of the cell, so as to avoid the two-dimensional geographic grid from affecting the data. The average processing blurs the stereo information, which is beneficial to achieve more accurate stereo optimization. In addition, the method can also generate a three-dimensional path loss matrix according to the three-dimensional three-dimensional grid, and according to the three-dimensional path loss matrix, the network configuration parameters of the target cell can be determined, which is conducive to optimizing the coverage and/or capacity of the network.

The apparatus and equipment of the embodiments of the present application will be described in detail below with reference to FIG. 14 to FIG. 17 .

An embodiment of the present application provides a network management apparatus. As shown in FIG. 14 , the network management apparatus 1400 may be used to implement the network configuration method in the embodiment of the present application. The network management apparatus 1400 may include:

an aggregation unit 1401, configured to perform aggregation processing on multiple pieces of MR data according to the cell information of each MR in the multiple pieces of measurement report MR data to obtain N groups of MR data, where each group of MRs includes MRs with wireless spatial propagation similarity;

an obtaining unit 1402, configured to obtain the average reference signal received power RSRP of each cell of each group of MRs in the N groups of MRs;

The creating unit 1403 is configured to, for each group of MRs in the N groups of MRs, create the MRs of the group according to the average RSRP of each cell in the group of MRs and the vertical beam range and horizontal beam range corresponding to the main beam identifier of each cell. three-dimensional grid;

The calculation unit 1404 is used to calculate the path loss between the main beam of each cell in the group of MRs and the three-dimensional grid for the three-dimensional grid of each group of MRs, so as to obtain the path loss matrix of the three-dimensional grid;

The determining unit 1405 is configured to determine the network configuration parameters of the target cell according to the path loss matrix of the N three-dimensional grids, wherein the target cell is the one whose coverage index does not meet the preset coverage threshold and/or the capacity index does not meet the preset capacity threshold community.

For a specific implementation manner, please refer to the detailed descriptions in S701 to S705 in the embodiment of FIG. 7 , which will not be repeated here.

In an implementation manner, the cell information of each MR includes the cell information of the serving cell and/or the cell information of the neighboring cells, and the RSRP of one or more cells includes the RSRP of the serving cell and/or the RSRP of the neighboring cells. The determining unit 1405 is specifically configured to, for each MR, determine the main beam prediction model of the serving cell of the MR according to the cell identifier of the serving cell, the RSRP of the serving cell, and the main beam identifier of the serving cell in the MR; The main beam prediction model of the serving cell of the MR, the cell identifier of the adjacent cell and the RSRP of the adjacent cell are used to determine the main beam identifier of the adjacent cell of the MR.

For a specific implementation, please refer to the detailed description of the process of identifying the MR main beam by the network management apparatus in the embodiment of FIG. 7 , which is not repeated here.

In an implementation manner, the aggregation unit 1401 is specifically configured to perform aggregation processing on the MR data with the same cell identifier, the same main beam identifier, and the level difference value satisfying a preset level difference value condition, and determine a group of MRs after aggregation; Wherein, the level difference value is the difference value of RSRP between each pair of cells with the same cell identifier and the same main beam identifier in the MR data.

For a specific implementation, please refer to the detailed description of the process of the network management apparatus performing MR data aggregation with similar characteristics in the embodiment of FIG. 7 , and details are not repeated here.

In an implementation manner, the computing unit 1404 is specifically used for:

According to the main beam identifier of any cell i in the group MR, the beam gain corresponding to the main beam identifier of the cell i is obtained; the beam gain of the cell i is the horizontal beam range and vertical beam corresponding to the main beam identifier of the cell i. range, antenna gain, signal attenuation value corresponding to the horizontal beam range and signal attenuation value corresponding to the vertical beam range;

According to the transmit power of cell i, the beam gain of cell i and the average RSRP of cell i, the path loss from the main beam of cell i to the stereo grid of the group MR is calculated.

For a specific implementation manner, please refer to the detailed description of the process of calculating the path loss by the network management apparatus in the embodiment of FIG. 7 , which will not be repeated here.

In an implementation manner, the determining unit 1405 is specifically configured to:

For each three-dimensional grid, determine the target cell of the three-dimensional grid according to the coverage index and/or the capacity index of each cell of the three-dimensional grid;

Adjust the network configuration parameters of the target cell according to the preset coverage index and the preset capacity index of the target cell; obtain the adjusted antenna gain of the target cell according to the adjusted network configuration parameters of the target cell;

Determine the coverage index and capacity index of the target cell according to the path loss matrix of the three-dimensional grid, the adjusted antenna gain of the target cell, and the adjusted network configuration parameters of the target cell;

If the coverage index of the target cell reaches the preset coverage index, and/or the capacity index of the target cell reaches the preset capacity index, it is determined that the adjusted network configuration parameters of the target cell are the network configuration parameters of the target cell.

For a specific implementation manner, please refer to the detailed description of the network optimization process in the embodiment of FIG. 7 , which will not be repeated here.

In one implementation, the network configuration parameters include one or more of horizontal beamwidth, physical azimuth, digital azimuth, vertical beamwidth, physical downtilt, or digital downtilt;

In an implementation manner, the relevant functions implemented by each unit in FIG. 14 may be implemented by a processor. Referring to FIG. 15 , FIG. 15 is a schematic structural diagram of a network management device provided by an embodiment of the present application. The network management device may be a device (eg, a chip) having the function of performing the network configuration described in the embodiment of the present application. The network device 1500 may include a transceiver 1501 , at least one processor 1502 and a memory 1503 . The transceiver 1501, the processor 1502 and the memory 1503 may be connected to each other through one or more communication buses, or may be connected to each other in other ways. In this embodiment, a bus connection is used as an example, as shown in FIG. 15 .

The transceiver 1501 may be used to transmit or receive data. For example, the transceiver 1501 may receive MR data reported by terminal equipment and network equipment. It can be understood that the transceiver 1501 is a general term and may include a receiver and a transmitter.

The processor 1502 may be used to process data. The processor 1502 may include one or more processors, for example, the processor 1502 may be one or more central processing units (CPUs), network processors (NPs), hardware chips, or any combination thereof . In the case where the processor 1502 is a CPU, the CPU may be a single-core CPU or a multi-core CPU.

Among them, the memory 1503 is used for storing program codes and the like. The memory 1503 may include volatile memory, such as random access memory (RAM). The memory 1503 may also include non-volatile memory (non-volatile memory), such as read-only memory (ROM), flash memory (flash memory), hard disk drive (HDD) or solid state hard disk ( solid-state drive, SSD). Memory 1503 may also include a combination of the above-described types of memory.

The foregoing processor 1502 may be used to implement the network configuration method in this embodiment of the present application, where the specific implementation is as follows:

Perform aggregation processing on multiple pieces of MR data according to the cell information of each MR in the multiple pieces of measurement report MR data to obtain N groups of MR data, where each group of MRs includes MRs with wireless spatial propagation similarity;

Obtain the average reference signal received power RSRP of each cell of each group of MRs in the N groups of MRs;

For each group of MRs in the N groups of MRs, create a three-dimensional grid of the group of MRs according to the average RSRP of each cell in the group of MRs and the vertical beam range and horizontal beam range corresponding to the main beam identifier of each cell;

For the three-dimensional grid of each group of MRs, calculate the path loss between the main beam of each cell in the group of MRs to the three-dimensional grid to obtain the path loss matrix of the three-dimensional grid;

Determine the network configuration parameters of the target cell according to the path loss matrix of the N three-dimensional grids, where the target cell is a cell whose coverage index does not meet the preset coverage threshold and/or the capacity index does not meet the preset capacity threshold.

In an implementation manner, the cell information of each MR includes the cell information of the serving cell and/or the cell information of the neighboring cells, and the RSRP of one or more cells includes the RSRP of the serving cell and/or the RSRP of the neighboring cells. The processor 1502 is further configured to, for each MR, determine the main beam prediction model of the serving cell of the MR according to the cell identifier of the serving cell in the MR, the RSRP of the serving cell, and the main beam identifier of the serving cell; The main beam prediction model of the serving cell of the MR, the cell identifier of the adjacent cell and the RSRP of the adjacent cell are used to determine the main beam identifier of the adjacent cell of the MR.

In an implementation manner, the processor 1502 is further configured to perform aggregation processing on the MR data with the same cell identifier, the same main beam identifier, and the level difference value satisfying a preset level difference value condition, to determine a group of MRs after aggregation; Wherein, the level difference value is the difference value of RSRP between each pair of cells with the same cell identifier and the same main beam identifier in the MR data.

In one implementation, the processor 1502 is specifically configured to:

An embodiment of the present application provides a network management apparatus. As shown in FIG. 16 , the network management apparatus 1600 may be used to implement the network configuration method in the embodiment of the present application. The network management apparatus 1600 may include:

Convergence unit 1601, is used for according to the cell information of each MDT in the multiple minimization road test MDT data to carry out aggregation processing to multiple MDT data to obtain N groups of MDT data, and each group of MDT comprises the same MDT of the vertical beam identification of cell;

A creating unit 1602 is configured to, for each group of MDTs in the N groups of MDTs, create a three-dimensional grid of the group of MDTs according to the longitude and latitude of each cell in the group of MDTs and the vertical beam identifier of each cell;

The calculation unit 1603 is used to calculate the path loss between the main beam of each cell in the group of MDTs to the three-dimensional grid for the three-dimensional grid of each group of MDTs, to obtain the path loss matrix of the three-dimensional grid;

The determining unit 1604 is configured to determine the network configuration parameters of the target cell according to the path loss matrix of the N three-dimensional grids, wherein the target cell is a cell whose coverage index does not meet the preset coverage threshold and/or the capacity index does not meet the preset capacity threshold community.

For a specific implementation manner, please refer to the detailed descriptions in S1101 to S1104 in the embodiment of FIG. 11 , which will not be repeated here.

In an implementation manner, the cell information of each MDT includes the cell information of the serving cell and/or the cell information of the neighboring cells, and the RSRP of one or more cells includes the RSRP of the serving cell and/or the RSRP of the neighboring cells. For each MDT, the determining unit 1604 is also used to:

Determine the main beam prediction model of the serving cell of the MDT according to the cell identifier of the serving cell, the RSRP of the serving cell, and the main beam identifier of the serving cell in the MDT;

According to the main beam prediction model of the serving cell of the MDT, the cell identity of the adjacent cell and the RSRP of the adjacent cell, determine the main beam identity of the adjacent cell of the MDT;

According to the main beam identifiers of the cells in the MDT, the vertical beam identifiers of the cells in the MDT are determined.

For a specific implementation, please refer to the detailed description of the process of identifying the MDT main beam by the network management apparatus in the embodiment of FIG. 11 , which is not repeated here.

In an implementation manner, the creating unit 1602 is specifically used for:

According to the longitude and latitude of each cell in the group of MDTs, determine the grid longitude and grid latitude of the plane grid formed by the group of MDTs;

According to the vertical beam identifier of the group of MDTs, determine the vertical layer where the plane grid of the group of MDTs is located, so as to obtain the three-dimensional grid of the group of MDTs.

For a specific implementation manner, please refer to the detailed description of the process of performing geographic grid aggregation by the network management apparatus in the embodiment of FIG. 11 , which will not be repeated here.

In an implementation manner, the computing unit 1603 is specifically used for:

According to the vertical beam identifier in the group of MDTs, obtain the vertical beam range corresponding to the vertical beam identifier, the antenna gain and the signal attenuation value corresponding to the vertical beam range;

According to the grid longitude and grid latitude of the group of MDTs, obtain the horizontal beam range, the antenna gain and the signal attenuation value corresponding to the horizontal beam range corresponding to the geographic grid determined by the grid longitude and grid latitude in the group of MDTs;

The vertical beam range, antenna gain, signal attenuation value corresponding to the vertical beam range corresponding to the vertical beam identifier in the group of MDTs, and the horizontal beam range corresponding to the geographic grid determined by the grid longitude and grid latitude in the group of MDTs , the antenna gain, the signal attenuation value corresponding to the horizontal beam range, and calculate the antenna gain from any cell i in the group of MDTs to the three-dimensional grid of the group of MDTs;

According to the transmit power of cell i, the antenna gain of the cubic grid from cell i to the group of MDTs, and the average RSRP of cell i, the path loss from cell i to the cubic grid of the group of MDTs is calculated.

For a specific implementation, please refer to the detailed description of the process of calculating the path loss matrix of the three-dimensional grid by the network management apparatus in the embodiment of FIG. 11 , which is not repeated here.

In an implementation manner, the determining unit 1604 is specifically configured to:

Adjust the network configuration parameters of the target cell according to the preset coverage index and the preset capacity index of the target cell;

Obtain the adjusted antenna gain of the target cell according to the network configuration parameters of the adjusted target cell;

In an implementation manner, the relevant functions implemented by each unit in FIG. 16 may be implemented by a processor. Referring to FIG. 17 , FIG. 17 is a schematic structural diagram of a network management device provided by an embodiment of the present application. The network management device may be a device (eg, a chip) having the function of performing the network configuration described in the embodiment of the present application. The network management device 1700 may include a transceiver 1701 , at least one processor 1702 and a memory 1703 . Wherein, the transceiver 1701, the processor 1702 and the memory 1703 may be connected to each other through one or more communication buses, and may also be connected to each other in other ways. In this embodiment, a bus connection is used as an example, as shown in FIG. 17 .

The transceiver 1701 may be used to transmit or receive data. For example, the transceiver 1501 may receive MDT data reported by terminal equipment and network equipment. It can be understood that the transceiver 1501 is a general term and may include a receiver and a transmitter.

Among them, the processor 1702 can be used to process the data. The processor 1702 may include one or more processors, for example, the processor 1702 may be one or more central processing units (CPUs), network processors (NPs), hardware chips, or any combination thereof . In the case where the processor 1702 is a CPU, the CPU may be a single-core CPU or a multi-core CPU.

Among them, the memory 1703 is used for storing program codes and the like. The memory 1703 may include volatile memory, such as random access memory (RAM). The memory 1703 may also include non-volatile memory (non-volatile memory), such as read-only memory (ROM), flash memory (flash memory), hard disk drive (HDD) or solid state hard disk ( solid-state drive, SSD). The memory 1703 may also include a combination of the above-described types of memory.

The above-mentioned processor 1702 may be used to implement the network configuration method in the embodiment of the present application, wherein the specific implementation is as follows:

Perform aggregation processing on multiple pieces of MDT data according to the cell information of each MDT in the multiple minimization road test MDT data to obtain N groups of MDT data, each group of MDTs includes MDTs with the same vertical beam identifiers of the cells;

For each group of MDTs in the N groups of MDTs, create a three-dimensional grid of the group of MDTs according to the longitude and latitude of each cell in the group of MDTs and the vertical beam identifier of each cell;

For the three-dimensional grid of each group of MDTs, calculate the path loss between the main beam of each cell in the group of MDTs to the three-dimensional grid to obtain the path loss matrix of the three-dimensional grid;

In an implementation manner, the cell information of each MDT includes the cell information of the serving cell and/or the cell information of the neighboring cells, and the RSRP of one or more cells includes the RSRP of the serving cell and/or the RSRP of the neighboring cells. For each MDT, the processor 1702 is also used to:

In one implementation, the processor 1702 is specifically configured to:

The embodiments of the present application provide a computer-readable storage medium, where a program or an instruction is stored in the computer-readable storage medium, and when the program or the instruction runs on a computer, the computer executes the network configuration method in the embodiment of the present application.

An embodiment of the present application provides a chip or a chip system, the chip or chip system includes at least one processor and an interface, the interface and the at least one processor are interconnected by a line, and the at least one processor is used to run a computer program or instruction to perform the present application The network configuration method in the embodiment.

In an implementation manner, the chip or chip system described above in this application further includes at least one memory, where instructions are stored in the at least one memory. The memory may be a storage unit inside the chip, such as a register, a cache, etc., or a storage unit of the chip (eg, a read-only memory, a random access memory, etc.).

An embodiment of the present application provides a communication system, including a network management device, a network device, and a terminal device according to the embodiment of the present application.

In the above-mentioned embodiments, it may be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented in software, it can be implemented in whole or in part in the form of a computer program product. A computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer may be a general purpose computer, special purpose computer, computer network, or other programmable device. Computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server, or data center over a wire (e.g. Coaxial cable, optical fiber, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (such as infrared, wireless, microwave, etc.) means to transmit to another website site, computer, server or data center. A computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, a data center, or the like that includes an integration of one or more available media. The available media may be magnetic media (eg, floppy disks, hard disks, magnetic tapes), optical media (eg, high-density digital video discs (DVDs)), or semiconductor media (eg, solid state disks, SSD)) etc.

Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. Interchangeability, the above description has generally described the components and steps of each example in terms of function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of this application.

The above are only specific embodiments of the present application, but the protection scope of the present application is not limited to this. should be covered within the scope of protection of this application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

A network configuration method, comprising:

The multiple pieces of MR data are aggregated and processed according to the cell information of each MR in the multiple measurement report MR data to obtain N groups of MR data, each group of MRs includes MRs with wireless spatial propagation similarity, and the cell information of each MR Including the cell identity of one or more cells, the main beam identity of one or more cells, and the reference signal received power RSRP of one or more cells;

Obtain the average reference signal received power RSRP of each cell of each group of MRs in the N groups of MRs;

For each group of MRs in the N groups of MRs, create a three-dimensional grid of the group of MRs according to the average RSRP of each cell in the group of MRs and the vertical beam range and horizontal beam range corresponding to the main beam identifier of each cell;

For the three-dimensional grid of each group of MRs, calculate the path loss between the main beam of each cell in the group of MRs to the three-dimensional grid to obtain the path loss matrix of the three-dimensional grid;

According to the path loss matrix of the N three-dimensional grids, network configuration parameters of the target cell are determined, where the target cell is a cell whose coverage index does not meet the preset coverage index and/or the capacity index does not meet the preset capacity index.
The method according to claim 1, wherein the cell information of each MR includes cell information of a serving cell and/or cell information of a neighboring cell, and the RSRP of the one or more cells includes the serving cell The RSRP and/or the RSRP of the neighboring cell; before performing aggregation processing on the multiple pieces of MR data according to the cell information of each MR in the multiple measurement report MR data, the method further includes:

For each MR, determine the main beam prediction model of the serving cell of the MR according to the cell identifier of the serving cell in the MR, the RSRP of the serving cell, and the main beam identifier of the serving cell;

According to the main beam prediction model of the serving cell of the MR, the cell identity of the adjacent cell, and the RSRP of the adjacent cell, the main beam identity of the adjacent cell of the MR is determined.
The method according to claim 1 or 2, wherein the performing aggregation processing on the multiple pieces of MR data according to the cell information of each MR in the multiple pieces of measurement report MR data to obtain N groups of MR data, comprising:

The MR data with the same cell identifier, the same main beam identifier, and the level difference value satisfying the preset level difference value condition are aggregated and processed to determine a group of MRs after aggregation; wherein, the level difference value is the cell in the MR data. The difference between the RSRPs between the cells with the same identifier and the same main beam identifier.
The method according to claim 1 or 3, wherein, for each group of MR stereo grids, calculating the path loss from the main beam of each cell in the group of MRs to the stereo grid, comprising:

According to the main beam identifier of any cell i in the group MR, the beam gain corresponding to the main beam identifier of the cell i is obtained; the beam gain of the cell i is the horizontal beam range corresponding to the main beam identifier of the cell i, The vertical beam range, antenna gain, the signal attenuation value corresponding to the horizontal beam range and the signal attenuation value corresponding to the vertical beam range are determined;

According to the transmit power of the cell i, the beam gain of the cell i and the average RSRP of the cell i, the path loss from the main beam of the cell i to the stereo grid of the group of MRs is calculated.
The method according to claim 1, wherein the determining the network configuration parameters of the target cell according to the path loss matrix of the N three-dimensional grids comprises:

For each three-dimensional grid, determine the target cell of the three-dimensional grid according to the coverage index and/or the capacity index of each cell of the three-dimensional grid;

adjusting the network configuration parameters of the target cell according to the preset coverage index and the preset capacity index of the target cell;

Obtain the adjusted antenna gain of the target cell according to the network configuration parameters of the adjusted target cell;

Determine the coverage index and capacity index of the target cell according to the path loss matrix of the three-dimensional grid, the adjusted antenna gain of the target cell, and the adjusted network configuration parameters of the target cell;

If the coverage index of the target cell reaches the preset coverage index, and/or the capacity index of the target cell reaches the preset capacity index, it is determined that the adjusted network configuration parameter of the target cell is the target cell network configuration parameters.
A network configuration method, comprising:

Perform aggregation processing on the multiple pieces of MDT data according to the cell information of each MDT in the multiple minimization road test MDT data to obtain N groups of MDT data. The cell information includes cell identifiers of one or more cells, main beam identifiers of one or more cells, and longitude and latitude of one or more cells; the vertical beam identifiers of the cells are determined according to the main beam identifiers of the cells ;

For each group of MDTs in the N groups of MDTs, create a three-dimensional grid of the group of MDTs according to the longitude and latitude of each cell in the group of MDTs and the vertical beam identifier of each cell;

For the three-dimensional grid of each group of MDTs, calculate the path loss between the main beam of each cell in the group of MDTs to the three-dimensional grid to obtain the path loss matrix of the three-dimensional grid;

According to the path loss matrix of the N three-dimensional grids, network configuration parameters of the target cell are determined, where the target cell is a cell whose coverage index does not meet the preset coverage index and/or the capacity index does not meet the preset capacity index.
The method according to claim 6, wherein the cell information of each MDT includes cell information of a serving cell and/or cell information of a neighboring cell, and the RSRP of the one or more cells includes the serving cell The RSRP and/or the RSRP of the adjacent cell; before the cell information of each MDT in the multiple minimized road test MDT data is aggregated and processed on the multiple MDT data, the method further includes:

For each MDT, determine the main beam prediction model of the serving cell of the MDT according to the cell identifier of the serving cell in the MDT, the RSRP of the serving cell, and the main beam identifier of the serving cell;

According to the main beam prediction model of the serving cell of the MDT, the cell identity of the adjacent cell and the RSRP of the adjacent cell, determine the main beam identity of the adjacent cell of the MDT;

According to the main beam identifiers of the cells in the MDT, the vertical beam identifiers of the cells in the MDT are determined.
The method according to claim 7, wherein, for each group of MDTs in the N groups of MDTs, according to the longitude, latitude and vertical beam identifier of each cell in the group of MDTs, the stereoscopic view of the group of MDTs is created Grid, including:

According to the longitude and latitude of each cell in the group of MDTs, determine the grid longitude and grid latitude of the plane grid formed by the group of MDTs;

According to the vertical beam identifier of the group of MDTs, determine the vertical layer where the plane grid of the group of MDTs is located, so as to obtain the three-dimensional grid of the group of MDTs.
The method according to any one of claims 6 to 8, wherein, for each group of three-dimensional grids of MDT, the path loss between the main beam of each cell in the group of MDTs and the three-dimensional grid is calculated, include:

According to the vertical beam identifier in the group of MDTs, obtain the vertical beam range corresponding to the vertical beam identifier, the antenna gain and the signal attenuation value corresponding to the vertical beam range;

According to the grid longitude and grid latitude of the group of MDTs, obtain the horizontal beam range, the antenna gain and the signal attenuation value corresponding to the horizontal beam range corresponding to the geographic grid determined by the grid longitude and grid latitude in the group of MDTs;

The vertical beam range, antenna gain, signal attenuation value corresponding to the vertical beam range corresponding to the vertical beam identifier in the group of MDTs, and the horizontal beam range corresponding to the geographic grid determined by the grid longitude and grid latitude in the group of MDTs , the antenna gain, the signal attenuation value corresponding to the horizontal beam range, and calculate the antenna gain from any cell i in the group of MDTs to the three-dimensional grid of the group of MDTs;

According to the transmit power of the cell i, the antenna gain of the cubic grid from the cell i to the group of MDTs, and the average RSRP of the cell i, the path loss from the cell i to the cubic grid of the group of MDTs is calculated.
The method according to claim 6, wherein the determining the network configuration parameters of the target cell according to the path loss matrix of the N three-dimensional grids comprises:

For each three-dimensional grid, determine the target cell of the three-dimensional grid according to the coverage index and/or the capacity index of each cell of the three-dimensional grid;

adjusting the network configuration parameters of the target cell according to the preset coverage index and the preset capacity index of the target cell;

Obtain the adjusted antenna gain of the target cell according to the network configuration parameters of the adjusted target cell;

Determine the coverage index and capacity index of the target cell according to the path loss matrix of the three-dimensional grid, the adjusted antenna gain of the target cell, and the adjusted network configuration parameters of the target cell;

If the coverage index of the target cell reaches the preset coverage index, and/or the capacity index of the target cell reaches the preset capacity index, it is determined that the adjusted network configuration parameter of the target cell is the target cell network configuration parameters.
A network management device, comprising:

The aggregation unit is configured to perform aggregation processing on the multiple pieces of MR data according to the cell information of each MR in the multiple measurement report MR data to obtain N groups of MR data, each group of MRs includes MRs with wireless spatial propagation similarity, and each The cell information of the MR includes cell identifiers of one or more cells, main beam identifiers of one or more cells, and reference signal received power RSRP of one or more cells;

an obtaining unit, configured to obtain the average reference signal received power RSRP of each cell of each group of MRs in the N groups of MRs;

A creating unit is configured to, for each group of MRs in the N groups of MRs, create the group of MRs according to the average RSRP of each cell in the group of MRs and the vertical beam range and horizontal beam range corresponding to the main beam identifier of each cell the three-dimensional grid;

A calculation unit, for calculating the path loss between the main beam of each cell in the group of MRs to the three-dimensional grid for the three-dimensional grid of each group of MRs, to obtain the path loss matrix of the three-dimensional grid;

A determination unit, configured to determine network configuration parameters of a target cell according to the path loss matrix of the N three-dimensional grids, where the target cell is a cell whose coverage index does not meet the preset coverage index and/or the capacity index does not meet the preset capacity index .
The apparatus according to claim 11, wherein the cell information of each MR includes cell information of a serving cell and/or cell information of a neighboring cell, and the RSRP of the one or more cells includes the serving cell The RSRP and/or the RSRP of the adjacent cell; the determining unit is also used for:

For each MR, determine the main beam prediction model of the serving cell of the MR according to the cell identifier of the serving cell in the MR, the RSRP of the serving cell, and the main beam identifier of the serving cell;

According to the main beam prediction model of the serving cell of the MR, the cell identity of the adjacent cell, and the RSRP of the adjacent cell, the main beam identity of the adjacent cell of the MR is determined.
The apparatus according to claim 11 or 12, wherein the aggregation unit is configured to perform aggregation processing on the multiple pieces of MR data according to cell information of each MR in the multiple pieces of measurement report MR data, and is specifically used for:

The MR data with the same cell identifier, the same main beam identifier, and the level difference value satisfying the preset level difference value condition are aggregated and processed to determine a group of MRs after aggregation; wherein, the level difference value is the cell in the MR data. The difference between the RSRPs between the cells with the same identifier and the same main beam identifier.
The apparatus according to claim 11 or 13, wherein the calculation unit is configured to calculate, for each stereo grid of MRs, a path loss between the main beam of each cell in the group of MRs and the stereo grid , specifically for:

According to the main beam identifier of any cell i in the group MR, the beam gain corresponding to the main beam identifier of the cell i is obtained; the beam gain of the cell i is the horizontal beam range corresponding to the main beam identifier of the cell i, The vertical beam range, antenna gain, the signal attenuation value corresponding to the horizontal beam range and the signal attenuation value corresponding to the vertical beam range are determined;

According to the transmit power of the cell i, the beam gain of the cell i and the average RSRP of the cell i, the path loss from the main beam of the cell i to the stereo grid of the group of MRs is calculated.
The device according to claim 11, wherein the determining unit is configured to determine the network configuration parameters of the target cell according to the path loss matrix of the N three-dimensional grids, and is specifically configured to:

For each three-dimensional grid, determine the target cell of the three-dimensional grid according to the coverage index and/or the capacity index of each cell of the three-dimensional grid;

adjusting the network configuration parameters of the target cell according to the preset coverage index and the preset capacity index of the target cell;

Obtain the adjusted antenna gain of the target cell according to the network configuration parameters of the adjusted target cell;

Determine the coverage index and capacity index of the target cell according to the path loss matrix of the three-dimensional grid, the adjusted antenna gain of the target cell, and the adjusted network configuration parameters of the target cell;

If the coverage index of the target cell reaches the preset coverage index, and/or the capacity index of the target cell reaches the preset capacity index, it is determined that the adjusted network configuration parameter of the target cell is the target cell network configuration parameters.
A network management device, comprising:

an aggregation unit, configured to perform aggregation processing on the multiple pieces of MDT data according to the cell information of each MDT in the multiple minimization road test MDT data to obtain N groups of MDT data, each group of MDTs includes MDTs with the same vertical beam identifiers of the cells , the cell information of each MDT includes the cell ID of one or more cells, the main beam ID of one or more cells, and the longitude and latitude of one or more cells; the vertical beam ID of the cell is based on the cell ID The main beam identification is determined;

A creation unit is used for, for each group of MDTs in the N groups of MDTs, according to the longitude and latitude of each cell in the group of MDTs and the vertical beam identifier of each cell, to create a three-dimensional grid of the group of MDTs;

A calculation unit, for calculating the path loss between the main beam of each cell in the group of MDTs to the three-dimensional grid for the three-dimensional grid of each group of MDTs, to obtain the path loss matrix of the three-dimensional grid;

A determination unit, configured to determine network configuration parameters of a target cell according to the path loss matrix of the N three-dimensional grids, where the target cell is a cell whose coverage index does not meet the preset coverage index and/or the capacity index does not meet the preset capacity index .
The apparatus according to claim 16, wherein the cell information of each MDT includes cell information of a serving cell and/or cell information of a neighboring cell, and the RSRP of the one or more cells includes the serving cell The RSRP and/or the RSRP of the adjacent cell; the determining unit is also used for:

For each MDT, determine the main beam prediction model of the serving cell of the MDT according to the cell identifier of the serving cell in the MDT, the RSRP of the serving cell, and the main beam identifier of the serving cell;

According to the main beam prediction model of the serving cell of the MDT, the cell identity of the adjacent cell and the RSRP of the adjacent cell, determine the main beam identity of the adjacent cell of the MDT;

According to the main beam identifiers of the cells in the MDT, the vertical beam identifiers of the cells in the MDT are determined.
The apparatus according to claim 17, wherein the creating unit is configured to, for each group of MDTs in the N groups of MDTs, create the MDT according to the longitude, latitude and vertical beam identifier of each cell in the group of MDTs A volumetric grid of group MDTs, specifically for:

According to the longitude and latitude of each cell in the group of MDTs, determine the grid longitude and grid latitude of the plane grid formed by the group of MDTs;

According to the vertical beam identifier of the group of MDTs, determine the vertical layer where the plane grid of the group of MDTs is located, so as to obtain the three-dimensional grid of the group of MDTs.
The apparatus according to any one of claims 16 to 18, wherein the calculation unit is configured to, for each group of three-dimensional grids of MDT, calculate between the main beam of each cell in the group of MDTs and the distance between the three-dimensional grid The path loss of , specifically for:

According to the vertical beam identifier in the group of MDTs, obtain the vertical beam range corresponding to the vertical beam identifier, the antenna gain and the signal attenuation value corresponding to the vertical beam range;

According to the grid longitude and grid latitude of the group of MDTs, obtain the horizontal beam range, the antenna gain and the signal attenuation value corresponding to the horizontal beam range corresponding to the geographic grid determined by the grid longitude and grid latitude in the group of MDTs;

The vertical beam range, antenna gain, signal attenuation value corresponding to the vertical beam range corresponding to the vertical beam identifier in the group of MDTs, and the horizontal beam range corresponding to the geographic grid determined by the grid longitude and grid latitude in the group of MDTs , the antenna gain, the signal attenuation value corresponding to the horizontal beam range, and calculate the antenna gain from any cell i in the group of MDTs to the three-dimensional grid of the group of MDTs;

According to the transmit power of the cell i, the antenna gain of the cubic grid from the cell i to the group of MDTs, and the average RSRP of the cell i, the path loss from the cell i to the cubic grid of the group of MDTs is calculated.
The device according to claim 16, wherein the determining unit is configured to determine the network configuration parameters of the target cell according to the path loss matrix of the N three-dimensional grids, and is specifically configured to:

For each three-dimensional grid, determine the target cell of the three-dimensional grid according to the coverage index and/or the capacity index of each cell of the three-dimensional grid;

adjusting the network configuration parameters of the target cell according to the preset coverage index and the preset capacity index of the target cell;

Obtain the adjusted antenna gain of the target cell according to the network configuration parameters of the adjusted target cell;

Determine the coverage index and capacity index of the target cell according to the path loss matrix of the three-dimensional grid, the adjusted antenna gain of the target cell, and the adjusted network configuration parameters of the target cell;

If the coverage index of the target cell reaches the preset coverage index, and/or the capacity index of the target cell reaches the preset capacity index, it is determined that the adjusted network configuration parameter of the target cell is the target cell network configuration parameters.
A network management device, comprising: a memory and a processor;

the memory for storing computer instructions;

The processor for executing the computer instructions such that the method of any one of claims 1 to 5 is performed.
A network management device, comprising: a memory and a processor;

the memory for storing computer instructions;

The processor for executing the computer instructions such that the method of any one of claims 6 to 10 is performed.
A computer-readable storage medium, characterized by comprising programs or computer instructions, when the program or computer instructions are executed on a computer, the method according to any one of claims 1 to 10 is performed.