CN113965942A

CN113965942A - Network configuration method and device

Info

Publication number: CN113965942A
Application number: CN202010706451.2A
Authority: CN
Inventors: 常瑞娜; 康怡彬; 吴珏莹; 杨达
Original assignee: Huawei Technical Service Co Ltd
Current assignee: Huawei Technical Service Co Ltd
Priority date: 2020-07-21
Filing date: 2020-07-21
Publication date: 2022-01-21
Also published as: WO2022017012A1

Abstract

The embodiment of the application discloses a network configuration method and a device, wherein the network configuration method can be executed by an offline tool deployed on a stand-alone computer or a cloud offline, or an online tool deployed on a network management system OMC online or an online tool platform connected with the OMC online. The method can construct the three-dimensional grid according to the average RSRP of each cell in the MR data and the horizontal beam information and the vertical beam information corresponding to the main beam identification of each cell, and achieves the aim of constructing the three-dimensional grid without depending on MDT data. And a path loss matrix can be constructed according to the three-dimensional grid, and the three-dimensional beam optimization is realized. The method can also determine the network configuration parameters of the target cell according to the path loss matrix, and is favorable for optimizing the coverage and/or capacity of the network.

Description

Network configuration method and device

Technical Field

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

Background

In a wireless communication network, stations are typically deployed in a cellular shape, and each station has its coverage (coverage) determined by radio frequency parameters. Among them, coverage is an important indicator for measuring the performance of a wireless network. In order to quantify the coverage performance index of the wireless network, a coverage area of one or more cells may be modeled in a two-dimensional planar rasterization manner, so as to optimize the coverage performance index. However, the two-dimensional plane rasterization modeling needs to project the vertical plane features in the stereo coverage to the two-dimensional plane, so that the vertical plane coverage features are lost, the vertical plane coverage signal is poor, and the problems of weak coverage, overlapping coverage and the like are caused.

Disclosure of Invention

The embodiment of the application provides a network configuration method, which can construct three-dimensional coverage characteristics according to measurement report MR data and is beneficial to optimizing three-dimensional coverage.

In a first aspect, an embodiment of the present application provides a network configuration method, which may be performed by a network management apparatus, where the network management apparatus may be deployed offline on a stand-alone computer or a cloud (i.e., an offline tool), or may be deployed online on a network management system OMC or an online tool platform connected to the OMC (i.e., an online tool). The network management device may aggregate 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 sets of MR data, and obtain an average reference signal received power RSRP of each cell of each set of MR in the N sets of MRs. For each of the N sets of MRs, the network management device creates a stereoscopic grid of the set of MRs according to the average RSRP of each cell in the set of MRs and the vertical beam range and the horizontal beam range corresponding to the main beam identification of each cell. And calculating the path loss from the main beam of each cell in each group of MR to the three-dimensional grid aiming at the three-dimensional grid of each group of MR so as to obtain a path loss matrix of the three-dimensional grid. Finally, the network management device can determine the network configuration parameters of the target cell according to the path loss matrixes of the N three-dimensional grids, wherein the target cell is a cell of which the coverage index does not meet the preset coverage index and/or the capacity index does not meet the preset capacity index.

Therefore, the network management device can construct a three-dimensional grid according to the average RSRP of each cell in the MR data and the horizontal beam information and the vertical beam information corresponding to the main beam identifier of each cell, thereby achieving the purpose of constructing the three-dimensional grid without depending on MDT data. And a three-dimensional path loss matrix can be constructed according to the three-dimensional grid, so that three-dimensional beam optimization is realized. The network management device can also determine the network configuration parameters of the target cell according to the three-dimensional path loss matrix, and is favorable for optimizing the coverage and/or capacity of the network.

In one possible design, the cell information of each MR includes cell information of a serving cell and/or cell information of a neighbor cell, wherein the RSRP of one or more cells includes the RSRP of the serving cell and/or the RSRP of the neighbor cell. Before aggregating multiple pieces of MR data according to the cell information of each MR in multiple pieces of measurement report MR data, the network management apparatus may determine, for each MR, a main beam prediction model of the serving cell of the MR according to the cell identifier of the serving cell, RSRP of the serving cell, and the main beam identifier of the serving cell in the MR. And determining the main beam identifier of the neighbor cell of the MR according to the main beam prediction model of the service cell of the MR, the cell identifier of the neighbor cell and the RSRP of the neighbor cell.

Therefore, the network management device can predict the primary beam identifier of the MR in the serving cell and each neighboring cell thereof, so as to obtain the total primary beam attribute of the MR, which is beneficial for the network management device to consider the overall network performance when performing coverage and/or capacity optimization.

In a possible design, when the network management apparatus performs aggregation processing on MR data, the network management apparatus may perform aggregation processing on MR data with the same cell identifier, the same main beam identifier, and the level difference satisfying the preset level difference condition, and determine a group of aggregated MRs. The level difference is the difference of RSRP between every two cells with the same cell identity and the same main beam identity in the MR data.

Therefore, the MR data gathered by the network management device is the MR data with wireless space propagation similarity, so that the three-dimensional grid of the specified position can be modeled. And moreover, similar characteristic MRs are summarized according to the cell attribute, RSRP, beam attribute and the like of the MR, so that the method is beneficial to more finely simulating the actual network propagation, and the calculation amount is greatly reduced.

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

Therefore, when calculating the path loss of the three-dimensional grid of each group of MRs, the network management device fully considers the average RSRP of the horizontal beams, the vertical beams and the cells, that is, the calculated path loss represents the path loss of the three-dimensional grid, and avoids the coverage and capacity characteristic loss caused by the average processing of the two-dimensional geographic grid.

In one possible design, the network management device determines, for each stereoscopic grid, a target cell of the stereoscopic grid according to the coverage index and/or capacity index of the respective cells of the stereoscopic grid. And adjusting the network configuration parameters of the target cell according to the preset coverage index and the preset capacity index of the target cell. Obtaining the antenna gain of the adjusted target cell according to the adjusted network configuration parameters of the target cell; and determining the coverage index and the 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. And 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, determining the adjusted network configuration parameter of the target cell as the network configuration parameter of the target cell.

Therefore, the network management device can screen out the target cell with coverage problem and/or capacity problem, and realize the optimization of the coverage and/or capacity of the target cell by adjusting the network configuration parameters of the target cell.

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;

wherein, 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 angle represents an included angle between the positive direction of the physical antenna panel and the positive north; the digital azimuth angle represents an included angle between the strongest energy direction of the horizontal wave beam controlled by the wave beam weight and the north direction; the physical downtilt angle represents an angle between a plane perpendicular to the physical antenna panel and a horizontal plane; the digital downward inclination angle represents the included angle between the strongest energy direction of the vertical wave beam controlled by the wave beam weight and the horizontal plane.

In a second aspect, an embodiment of the present application provides a network configuration method, which may be performed by a network management device. The network management device may perform aggregation processing on the multiple pieces of MDT data according to cell information of each piece of MDT in the multiple pieces of minimization road test MDT data to obtain N sets of MDT data, where each set of MDT includes MDTs with the same vertical beam identifier of a cell. For each of the N sets of MDTs, a stereoscopic grid of the set of MDTs is created according to the longitude and latitude of each cell in the set of MDTs and the vertical beam identification of each cell. And aiming at the three-dimensional grid of each group of MDT, calculating the path loss from the main beam of each cell in the group of MDT to the three-dimensional grid so as to obtain a path loss matrix of the three-dimensional grid. And determining network configuration parameters of a target cell according to the path loss matrixes of the N three-dimensional grids, wherein the target cell is a cell of which the coverage index does not meet the preset coverage index and/or the capacity index does not meet the preset capacity index.

Therefore, the network management device can construct a 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, avoid the three-dimensional information from being blurred by the average processing of the two-dimensional geographic grid on the data, and be beneficial to realizing more accurate three-dimensional optimization. And the network management device can generate a three-dimensional path loss matrix according to the three-dimensional grid, and can determine the network configuration parameters of the target cell according to the three-dimensional path loss matrix, thereby being beneficial to optimizing the coverage and/or capacity of the network.

In one possible design, for each MDT, the network management device determines a primary beam prediction model of the serving cell of the MDT according to a cell identifier of the serving cell, an RSRP of the serving cell, and a primary beam identifier of the serving cell in the MDT. And determining the main beam identifier of the neighboring cell of the MDT according to the main beam prediction model of the serving cell of the MDT, the cell identifier of the neighboring cell and the RSRP of the neighboring cell. And determining the vertical beam identification of each cell in the MDT according to the main beam identification of each cell in the MDT.

Therefore, the network management device may determine the vertical beam identifier of each cell in the MDT data, so as to mark each cell in the MDT data on a stereo level and determine stereo information of each cell.

In one possible design, the network management device determines a grid longitude and a grid latitude of a planar grid formed by the set of MDTs according to the longitude and latitude of each cell in the set of MDTs; and determining a vertical layer where the planar grid formed by the MDT is located according to the vertical beam identification of the MDT so as to obtain a three-dimensional grid of the MDT.

Therefore, the network management device can determine the two-dimensional plane grid according to the latitude and longitude information in the MDT data, and determine the three-dimensional position of the plane grid according to the vertical beam identifier, so as to construct the three-dimensional grid.

In one possible design, the network management apparatus obtains, according to the vertical beam identifier in the set of MDTs, a vertical beam range corresponding to the vertical beam identifier, an antenna gain, and a signal attenuation value corresponding to the vertical beam range. And acquiring a horizontal beam range, an antenna gain and a signal attenuation value corresponding to the horizontal beam range, which are determined by the grid longitude and the grid latitude in the set of MDT and correspond to the geographic grid according to the grid longitude and the grid latitude of the set of MDT. And calculating the antenna gain from any cell i in the group of MDT to the three-dimensional grid of the group of MDT according to the vertical beam range, the antenna gain and the signal attenuation value corresponding to the vertical beam range and the vertical beam range corresponding to the vertical beam identification in the group of MDT, and the signal attenuation value corresponding to the horizontal beam range, the antenna gain and the horizontal beam range corresponding to the geographical grid determined by the grid longitude and the grid latitude in the group of MDT. And calculating the path loss from the cell i to the stereo grid of the group of MDT according to the transmitting power of the cell i, the antenna gain from the cell i to the stereo grid of the group of MDT and the average RSRP of the cell i.

Therefore, when the network management device calculates the path loss of the three-dimensional grid of each group of MDTs, the average RSRP of the horizontal beams, the vertical beams and the cells is fully considered, that is, the calculated path loss represents the path loss of the three-dimensional grid, and the coverage and capacity characteristic loss caused by the average processing of the two-dimensional geographic grid is avoided.

In one possible design, the network management device determines, for each stereoscopic grid, a target cell of the stereoscopic grid according to the coverage index and/or capacity index of the respective cells of the stereoscopic grid. And adjusting the network configuration parameters of the target cell according to the preset coverage index and the preset capacity index of the target cell. And obtaining the antenna gain of the adjusted target cell according to the adjusted network configuration parameters of the target cell. And determining the coverage index and the 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. And 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, determining the adjusted network configuration parameter of the target cell as the network configuration parameter of the target cell.

In a third aspect, an embodiment of the present application provides a network management apparatus, which includes a convergence unit, an acquisition unit, a creation unit, a calculation unit, and a determination unit. The convergence unit is used for converging 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, wherein each group of MR comprises MRs with wireless space propagation similarity. The acquiring unit is used for acquiring the average Reference Signal Received Power (RSRP) of each cell of each group of the N groups of MRs. The creating unit is configured to create, for each MR in the N sets of MRs, a stereoscopic grid of the set of MRs according to the average RSRP of each cell in the set of MRs and a vertical beam range and a horizontal beam range corresponding to a main beam identifier of each cell. The calculation unit is used for calculating, for the stereoscopic grid of each group of MRs, a path loss from a main beam of each cell in the group of MRs to the stereoscopic grid, so as to obtain a path loss matrix of the stereoscopic grid. The determining unit is used for determining network configuration parameters of a target cell according to the path loss matrixes of the N three-dimensional grids, wherein the target cell is a cell of which the coverage index does not meet the preset coverage index and/or the capacity index does not meet the preset capacity index.

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

In one possible design, the convergence unit is configured to perform convergence processing on MR data with the same cell identifier, the same main beam identifier, and the level difference meeting a preset level difference condition, and determine a group of converged MRs; the level difference is the difference of RSRP between every two cells with the same cell identity and the same main beam identity in the MR data.

In a possible design, the calculating unit is configured to obtain, according to a main beam identifier of any one cell i in the MR group, a beam gain corresponding to the main beam identifier of the cell i; the beam gain of the cell i is determined according to a horizontal beam range, a vertical beam range, an antenna gain, a signal attenuation value corresponding to the horizontal beam range and a signal attenuation value corresponding to the vertical beam range corresponding to the main beam identification of the cell i; and calculating the path loss from the main beam of the cell i to the stereo grid of the group of MRs according to the transmitting power of the cell i, the beam gain of the cell i and the average RSRP of the cell i.

In one possible design, for each stereoscopic grid, the determining unit is configured to determine the target cell of the stereoscopic grid according to the coverage index and/or the capacity index of the respective cell of the stereoscopic grid. Adjusting network configuration parameters of a target cell according to a preset coverage index and a preset capacity index of the target cell; obtaining the antenna gain of the adjusted target cell according to the adjusted network configuration parameters of the target cell; and determining the coverage index and the 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. And 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, determining the adjusted network configuration parameter of the target cell as the network configuration parameter of the target cell.

In a fourth aspect, an embodiment of the present application provides a network management apparatus, which includes a convergence unit, a creation unit, a calculation unit, and a determination unit. The aggregation unit is used for aggregating the multiple pieces of MDT data according to the cell information of each MDT in the multiple pieces of MDT data for the minimization of road tests to obtain N groups of MDT data, wherein each group of MDT comprises MDTs with the same vertical beam identification of the cell. The creating unit is configured to create, for each of the N sets of MDTs, a solid grid of the set of MDTs according to the longitude and latitude of each cell in the set of MDTs and the vertical beam identifier of each cell. The calculation unit is configured to calculate, for each group of the three-dimensional grids of the MDT, a path loss from a main beam of each cell in the group of the MDT to the three-dimensional grid, so as to obtain a path loss matrix of the three-dimensional grid. The determining unit is used for determining network configuration parameters of a target cell according to the path loss matrixes of the N three-dimensional grids, wherein the target cell is a cell of which the coverage index does not meet the preset coverage index and/or the capacity index does not meet the preset capacity index.

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

In one possible design, the creating unit is configured to determine a grid longitude and a grid latitude of a planar grid formed by the set of MDTs according to the longitude and latitude of each cell in the set of MDTs; and determining a vertical layer where the planar grid is formed by the MDT according to the vertical beam identification of the MDT so as to obtain a three-dimensional grid of the MDT.

In a possible design, the calculation unit is configured to obtain, according to the vertical beam identifier in the set of MDTs, a vertical beam range corresponding to the vertical beam identifier, an antenna gain, and a signal attenuation value corresponding to the vertical beam range. And acquiring a horizontal beam range, an antenna gain and a signal attenuation value corresponding to the horizontal beam range, which are determined by the grid longitude and the grid latitude in the set of MDT and correspond to the geographic grid according to the grid longitude and the grid latitude of the set of MDT. And calculating the antenna gain from any cell i in the group of MDT to the three-dimensional grid of the group of MDT according to the vertical beam range, the antenna gain and the signal attenuation value corresponding to the vertical beam range and the vertical beam range corresponding to the vertical beam identification in the group of MDT, and the signal attenuation value corresponding to the horizontal beam range, the antenna gain and the horizontal beam range corresponding to the geographical grid determined by the grid longitude and the grid latitude in the group of MDT. And calculating the path loss from the cell i to the stereo grid of the set of MDT according to the transmitting power of the cell i, the antenna gain from the cell i to the stereo grid of the set of MDT and the average RSRP of the cell i.

In one possible design, for each stereoscopic grid, the determining unit is configured to determine the target cell of the stereoscopic grid according to the coverage index and/or the capacity index of the respective cell of the stereoscopic grid. Adjusting network configuration parameters of the target cell according to a preset coverage index and a preset capacity index of the target cell; and obtaining the antenna gain of the adjusted target cell according to the adjusted network configuration parameters of the target cell. And determining the coverage index and the 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. And 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, determining the adjusted network configuration parameter of the target cell as the network configuration parameter of the target cell.

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

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

In a seventh aspect, an embodiment of the present application provides a computer-readable storage medium, which includes a program or instructions, and when the program or instructions are executed on a computer, the computer is caused to execute the first aspect or the method in any possible implementation manner of the first aspect.

In an eighth aspect, embodiments of the present application provide a computer-readable storage medium, which includes a program or instructions, which when executed on a computer, causes the computer to perform the method of the second aspect or any of the possible implementations of the second aspect.

In a ninth aspect, an embodiment of the present application provides a chip or a chip system, where the chip or the 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 configured to execute a computer program or instructions to perform the method described in any one of the first aspect or any one of the possible implementation manners of the first aspect.

In a tenth aspect, an embodiment of the present application provides a chip or a chip system, where the chip or the 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 configured to execute a computer program or instructions to perform the method described in any one of the second aspect or any one of the possible implementation manners of the second aspect.

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

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

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

In an eleventh aspect, embodiments of the present application provide a computer program or a computer program product, which includes code or instructions, when the code or instructions are run on a computer, cause the computer to execute the method of the first aspect or any possible implementation manner of the first aspect.

In a twelfth aspect, embodiments of the present application provide a computer program or a computer program product, which includes code or instructions, when the code or instructions are executed on a computer, cause the computer to execute the method of the second aspect or any possible implementation manner of the second aspect.

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;

FIG. 3 is a diagram of a conventional antenna and a Massive MIMO antenna;

FIG. 4 is a schematic diagram of a two-dimensional land grid path loss matrix;

fig. 5 is a schematic diagram of a communication system according to an embodiment of the present application;

fig. 6 is a schematic beam coverage diagram of a MassiveMIMO antenna according to an embodiment of the present application;

fig. 7 is a flowchart illustrating a network configuration method according to an embodiment of the present application;

fig. 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;

fig. 9a is a schematic view of a three-dimensional grid according to an embodiment of the present application;

fig. 9b is a schematic diagram of a path loss matrix according to an embodiment of the present application;

fig. 10 is a schematic diagram of a network optimization target optimizing process provided in an embodiment of the present application;

fig. 11 is a schematic flowchart of another network configuration method according to an embodiment of the present application;

FIG. 12 is a schematic view of an area formed by projecting a vertical layer onto the ground according to an embodiment of the present disclosure;

fig. 13a is a schematic view of another three-dimensional grid provided in the embodiments of the present application;

fig. 13b is a schematic diagram of another path loss matrix provided in the embodiment of the present application;

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

fig. 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 according to an embodiment of the present application;

fig. 17 is a schematic diagram of another network management device according to 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 drawings in the embodiments of the present application.

The wireless communication network deploys sites in a cellular shape, each site determines its coverage area 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. Referring to fig. 1, fig. 1 is a schematic diagram of a wireless network according to an embodiment of the present disclosure. The wireless network includes a plurality of stations, and each station is deployed according to a certain spatial distribution, as shown in fig. 1. The coverage area (also referred to as a cell) of one station is a dashed area as shown in fig. 1, and the coverage area formed by a plurality of stations is shown in fig. 1.

Coverage (coverage) and capacity (capacity) are important indexes for measuring network performance of a wireless network, but due to changes of network planning and actual physical environment, city construction, user development and the like, coverage problems such as weak coverage and overlapping coverage and capacity problems such as load imbalance and hot spot traffic depression may exist in the wireless network, as shown in fig. 2.

The weak coverage means that the average Reference Signal Received Power (RSRP) of the current region is lower than a certain threshold. For example, the weak coverage area in fig. 2 is located between cell1 and cell2, and is located at the edge area of cell1 and cell 2. Users in the weak coverage area may not be able to receive signals properly. Overlapping coverage refers to the area of overlap between adjacent regions, e.g., the shaded area between cell2 and cell3 in fig. 2. Hot spot traffic depression means that the signal quality of the current area is poor, and more energy is needed for sending data. For example, the number of access users in the hot spot coverage area of cell1 in fig. 2 is large, and there is a problem of hot spot traffic suppression.

To ensure a better user experience (e.g., higher network speed), the wireless network should have a better reception level, less interference, and a more balanced user distribution. That is, the wireless network needs to consider both coverage and capacity, which are important performance indicators. In order to achieve the coverage index and the capacity index of the wireless network, the coverage and the capacity of the station can be controlled by adjusting the radio frequency parameters of the antenna.

The common antenna may include a Single Input Single Output (SISO), a Multiple Input Multiple Output (MIMO), and the like. For example, a common antenna may include 2TRx, 4TRx, 8TRx, or the like, as shown in fig. 3. With the evolution of communication technology, Massive multiple input multiple output (Massive MIMO) has come into play. Massive MIMO is a fifth generation mobile communication (the 5)^thgeneration, 5G) network. The Massive MIMO antenna can realize three-dimensional accurate beam forming and multi-user beam multiplexing by integrating more antennas, thereby achieving better coverage and larger capacity. Common Massive MIMO antennas may include 32TRx and 64TRx, as shown in fig. 3.

After the introduction of the Massive MIMO technology, broadcast beam weights of different coverage scenes are provided, so that radio frequency parameters are changed from 3 types (physical azimuth angle, physical downtilt angle and digital downtilt angle) of a common antenna to 6 types (physical azimuth angle, physical downtilt angle, digital azimuth angle, digital downtilt angle, horizontal beam width and vertical beam width) of the Massive MIMO antenna. In the 5G system, the beam of the broadcast beam can be independently adjusted to form coverage of an arbitrary shape, in addition to the shapes of the horizontal envelope and the vertical envelope of the beam being integrally adjustable. That is, the coverage of the cell can be improved by adjusting the radio frequency parameters of the MIMO antennas.

The existing method for adjusting radio frequency parameters of the MIMO antenna generally performs geographic rasterization modeling on a coverage optimization area according to Minimization of Drive Tests (MDT) data reported by a terminal device, so as to obtain a path loss matrix of a two-dimensional planar grid, as shown in fig. 4. The set optimization target is a coverage index, the derivation is carried out on the radio frequency parameters based on the two-dimensional grid-level path loss matrix and the optimization target, the radio frequency parameters are adjusted according to the derivative of the radio frequency parameters, and the coverage rate of the cell is improved.

However, the Massive MIMO antenna enhances the stereoscopic coverage compared to the general antenna, and the two-dimensional plane rasterization directly projects the vertical plane feature to the two-dimensional plane, and averages the data of the same grid, so that the vertical plane coverage feature is lost, which may cause the difference of the vertical plane coverage signal of the building and the like. In addition, in a scene with low MDT terminal permeability (for example, a shaded area shown in fig. 4), because the scene lacks MDT data, the coverage area cannot be geographically rasterized and modeled by using the above adjustment method, and thus coverage tuning cannot be performed.

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

The network configuration method can be deployed on a stand-alone Personal Computer (PC) or a cloud offline (i.e., an offline tool) or on a network management system (OMC) or an online tool platform connected to the OMC online (i.e., an online tool). Referring to fig. 5, fig. 5 is a communication system provided in an embodiment of the present application, where the communication system includes an OMC, a network device, a terminal device, and the like. In the embodiment of the present application, an Online Tool deployed to be connected to an OMC is taken as an example for explanation, and the OMC may communicate with each network device or with an Online Tool, as shown in fig. 5.

The OMC can acquire data reported by the terminal equipment and the network equipment.

For the purpose of facilitating understanding of the embodiments of the present application, technical terms related to the embodiments of the present application will be described below.

Massive multiple input multiple output (Massive MIMO) is a fifth generation mobile communication (the 5)^thgeneration, 5G) network. The Massive MIMO antenna can realize three-dimensional accurate beam forming and multi-user beam multiplexing by integrating more antennas, thereby achieving better coverage and larger capacity.

Referring to fig. 6, fig. 6 is a schematic beam coverage diagram of a MassiveMIMO 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 MassiveMIMO antenna, and the horizontal beam includes 8 beams as viewed from a horizontal cross section. The horizontal beamwidth of the horizontal beam is shown in fig. 6, i.e., the horizontal beamwidth is the width of the horizontal beam envelope.

In the middle of fig. 6 is a schematic diagram of a vertical beam of the MassiveMIMO antenna, which includes 4 layers as viewed in a vertical cross section. The vertical beamwidth of the vertical beam is shown in fig. 6, i.e., the vertical beamwidth is the width of the vertical beam envelope. That is, the masivemimo antenna is divided into 4 layers in the vertical direction, and the horizontal beam corresponding to each layer includes 8 beams, that is, the masivemimo antenna has 32 narrow beams in total, and forms different coverage areas in the horizontal direction and the vertical direction.

The right side of fig. 6 is a schematic diagram of an arbitrary beam of a MassiveMIMO antenna. Wherein, for 5G, in addition to the shape of the overall adjustable horizontal and vertical envelopes, each narrow beam can be adjusted independently to form an arbitrarily shaped coverage, as shown in fig. 6.

The beam weights (also referred to as antenna weights) refer to a quantized representation of each port of the antenna after a specific excitation signal is applied thereto, and are intended to obtain a specific coverage or achieve a beam deformation effect. Wherein a variety of beam shapes may be generated by different beam weights, e.g., cell-level broadcast beams (i.e., SSB beams) and user-level static beams (including SRS beams and CSI-RS beams).

The SSB (SS/PBCH blocks) beams are used for staggering the interference between adjacent zones, and more beams are adopted to realize the space coverage, so that the optimal coverage of the 5G network is achieved. A Sounding Reference Signal (SRS) is an uplink pilot signal sent by a terminal device to a network device, and is used to judge channel quality of each channel in each frequency band. And the network equipment selects a proper channel to dynamically schedule and allocate resources to the terminal equipment according to the SRS so as to obtain the optimal 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, and is used for CSI-RS channel measurement, time-frequency offset tracking, beam management, mobility management, and the like. The CSI-RS described in this embodiment refers to a CSI-RS for mobility management, and according to the protocol, the CSI-RS for mobility management may measure a beam level RSRP of a serving cell and a neighboring cell thereof.

The beam gain (also referred to as antenna gain) is the ratio of the power density of signals generated by an actual antenna and an ideal radiating element at the same point in space under the condition that the input power is equal. The beam gain and the antenna directional pattern have a close relationship, and the narrower the main lobe of the directional pattern, the smaller the side lobe and the higher the gain. Antenna gain is a measure of the ability of an antenna to transmit and receive signals in a particular direction, and is one of the important parameters for selecting a base station antenna.

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

The MDT is minimum road test data defined by 3GPP, and includes longitude and latitude information reported by the terminal device, and information such as a cell identifier of a serving cell, an RSRP of the serving cell, a cell identifier of a neighboring cell, and an RSRP of the neighboring cell. That is, MDT can be considered as MR with latitude and longitude.

A station (also referred to as a network device) may be any device having a wireless transceiving function, and provides a wireless communication service for a terminal device within a coverage area. Sites may include, but are not limited to: an evolved node b (NodeB or eNB or e-NodeB, evolutionalcob) in a Long Term Evolution (LTE) system, a base station (gnnodeb or gNB) or a transmitting/receiving point (TRP) in a new radio access technology (NR), a base station for 3GPP subsequent evolution, an access node in a WiFi system, a wireless relay node, a wireless backhaul node, a device for taking over a function of a base station in car networking, D2D communication, machine communication, a satellite, and the like.

The terminal device may be a device with wireless transceiving function, or the terminal device may also be a chip. The terminal device may be a User Equipment (UE), a mobile phone (mobile phone), a tablet computer (Pad), a computer with a wireless transceiving function, a Virtual Reality (VR) terminal device, an Augmented Reality (AR) terminal device, a vehicle-mounted terminal device, a wireless terminal in telemedicine (remote), a wireless terminal in a smart grid (smart grid), a wearable terminal device, a vehicle networking, a sensor in D2D communication, machine communication, or the like.

The following description will be made in conjunction with specific embodiments.

Referring to fig. 7, fig. 7 is a flowchart illustrating a network configuration method according to an embodiment of the present disclosure. The network configuration method can be executed by an offline tool deployed offline on a stand-alone computer or cloud, or by an online tool deployed online on a network management system OMC or an online tool platform connected with the OMC. The network configuration method is applied to scenes without MDT data or with insufficient MDT data, and comprises the following steps:

s701, the network management device carries out 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 device acquires the average Reference Signal Received Power (RSRP) of each cell of each group of MRs in the N groups of MRs;

s703, aiming at each group MR in the N groups of MRs, the network management device creates a three-dimensional grid of the group MR according to the average RSRP of each cell in the group MR and the vertical beam range and the horizontal beam range corresponding to the main beam identification of each cell;

s704, aiming at the three-dimensional grid of each group of MRs, the network management device calculates the path loss from the main beam of each cell in the group of MRs to the three-dimensional grid so as 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 matrices of the N stereoscopic grids.

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

The following describes the procedure of MR main beam identification by the network management apparatus in detail.

The network management apparatus may also obtain a Call History Record (CHR) of the terminal device, in addition to receiving the MR reported by the terminal device. Wherein, the CHR can record each call of the terminal device according to the feature extraction mode. The CHR includes a Cell identifier (S _ Cell _ ID) of a serving Cell, an uplink throughput (ULThroughput) of the serving Cell, a downlink throughput (DLThroughput) of the serving Cell, and a main beam identifier (S _ MainBeam _ ID) of the serving Cell, and may further include access related information and handover related information of the terminal device, which is not limited in this embodiment.

Optionally, the network management apparatus may further receive an operation parameter/configuration file and an antenna file. The operating parameter/configuration file includes information such as radio frequency parameters of each cell, and the antenna file includes a broadcast beam, a horizontal pattern of a service beam, a vertical pattern of the service beam, beam gain, attenuation information, and the like.

Because the MR and the CHR are two different types of data reported by the terminal device, the MR and the CHR can be associated for subsequent processing. For example, the network management apparatus may perform association processing on the MR and the CHR according to "call time + Cell _ ID + call _ ID". Since the number of call _ IDs is limited and will be repeated after a period of time, in this embodiment, the call _ IDs are distinguished by limiting the call time.

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

The serving cell represents a cell that provides a channel for the terminal device when the terminal device performs communication. The neighboring cell means a cell adjacent to the serving cell, for example, if the cell2 in fig. 2 is the serving cell of the terminal device, the cell3 is the neighboring cell of the terminal device.

The main beam of the serving cell is a beam with the strongest signal among a plurality of beams transmitted by Massive MIMO antennas detected by the terminal equipment. That is, the primary beam id of the serving cell indicates the beam id of the strongest signal beam detected by the terminal device.

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

In one implementation, 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, in the embodiment of the present application, relevant information of a neighboring cell is introduced, so that when a network management device adjusts a network configuration parameter for a cell, the influence of the neighboring cell of the cell on the cell is also fully considered. Specifically, the Cell information of each MR includes a Cell identifier of a serving Cell, an RSRP (S _ RSRP) of the serving Cell, a Cell identifier (N _ Cell _ ID) of a neighboring Cell, an RSRP (N _ RSRP) of the neighboring Cell, and the like.

The terminal device cannot acquire the main beam identifier of the neighboring cell due to the measurement capability of the terminal device. In order to facilitate the subsequent MR main beam identification, in this embodiment, a main beam identification (N _ main beam _ ID) field of the neighboring region is added to the MR data, and is used to record the main beam identification of the neighboring region.

The MR data may be stored in the network management apparatus in the form of a table, or may be stored in other forms, and this embodiment is not limited thereto. Please refer to table 1, where table 1 is a table of MR data information provided in an embodiment of the present application. The information table includes fields of S _ Cell _ ID, S _ RSRP, S _ MainBeam _ ID, N _ Cell _ ID, N _ RSRP, N _ MainBeam _ ID, ulthreughput, dlthreughput, etc., and the physical meanings and field sources of the respective fields are as shown in table 1.

Table 1: MR data information table

It is noted that the value of the N _ MainBeam _ ID field prior to MR main beam identification may be set to-1. And when the associated MR data is stored, arranging the MR data according to the sequence of the service cell and the adjacent cell. The different service cells are sequentially sorted and stored according to the size of S _ RSRP; similarly, the adjacent cells are sequentially sorted and stored according to the size of the N _ RSRP.

Referring to table 2, table 2 is a MR data storage table provided in the embodiments of the present application. The memory table includes one or more MRs. The cell information of each MR includes cell identification of one or more cells, main beam identification of one or more cells, RSRP of one or more cells, ulthrough of one or more cells, and dlThroughugh of one or more cells.

Table 2: MR data storage table

In table 2, the MR with the MainBeam _ ID value of-1 refers to the neighboring cell, and the values of ultthreadhput and dlthreadhput in the row are both 0, that is, the terminal device cannot directly measure the throughput information of the neighboring cell.

In one implementation, in order to facilitate subsequent similar-feature MR data aggregation, the network management apparatus may predict a main beam of a neighboring cell in the MR data, including the following steps:

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

s12, determining the main beam identifier of the neighbor cell of the MR according to the main beam prediction model of the serving cell of the MR, the cell identifier of the neighbor cell, and the RSRP of the neighbor cell.

For example, for all cells in table 2, cell information is obtained when each cell is used as a serving cell. Taking Cell i as an example, the network management device may obtain Cell _ IDs, RSRP, and MainBeam _ IDs of all cells i that include Cell i and whose MainBeam _ ID value of Cell i is not-1 from table 2.

After all MRs in table 2 are traversed, Cell _ ID and RSRP and their corresponding MainBeam _ ID when each Cell is a serving Cell are used as training data, and the training data may be stored in the form of a table. The Cell _ ID and RSRP are used as feature vectors and are marked as X _ Train, the MainBeam _ ID is used as a label and is marked as Y _ Train, and the training data of the main beam prediction model of the Cell are shown in table 3.

Table 3: training data table of main beam prediction model of cell

Wherein, if the serving cell in MR 1 is cell1, the MainBeam _ ID is the MainBeam _ ID of cell1, and it can be known from table 2 that the value of the MainBeam _ ID of cell1 is 31. It should be noted that if cell n is not included in MR 1, the RSRP of cell n in MR 1 has a value of 0. The definition of the data of the remaining rows in table 3 is similar to that of MR 1, and is not described herein again.

The network management apparatus may perform the training of the main beam prediction model by using the data in table 3 as training data and using a machine learning algorithm. Also taking cell i as an example, the main beam prediction model of cell i is denoted as cellbeam model _ cell i. The cell main beam prediction model training process can be simply understood as follows: for the function y ═ f (x), x and y are known, and f is obtained in summary and summary. That is, the function Y is Y _ Train in table 3, X is X _ Train in table 3, and the main beam prediction model of each cell can be trained according to a plurality of X _ Train and Y _ Train stored in table 3. For example, according to X _ Train and Y _ Train corresponding to the cell i, a main beam prediction model of the cell i can be trained.

Similarly, for all cells in table 2, cell information when each cell is a neighbor cell is acquired. Taking Cell i as an example, the network management device may obtain Cell _ IDs and RSRPs of all cells i including Cell i whose MainBeam _ ID value is-1 from table 2. After all MRs in table 2 are traversed, Cell _ ID and RSRP when each Cell is a neighbor Cell are denoted as X _ Pre as prediction input values, which may also be stored in the form of a table, as shown in table 4.

Table 4: prediction input table for adjacent cell main beam prediction

For a cell i, according to X _ Pre of the cell i and a main beam prediction model of the cell i, Y _ Pre of the cell i can be obtained, that is, a main beam identifier of the cell i is obtained through prediction. MR data with a MainBeam _ ID value of-1 for all cells in table 2 is traversed so that all cells in each MR data mark the primary beam identification.

Please refer to table 5, table 5 is a table for storing MR data after main beam marking according to the embodiment of the present application. Compared to table 2, the MainBeam _ ID of each cell in table 5 has a value greater than-1, that is, the primary beam ID of each cell may indicate one of the beams generated by the Massive MIMO antenna.

Table 5: MR data storage table after main beam marking

Optionally, because the amount of MR data after the main beam is marked is large, a large amount of computing resources may be occupied, and the operation efficiency of the OMC is reduced, the network management device may converge the MR data after the main beam is marked according to a certain rule. That is, the network management apparatus may perform similar-feature MR data aggregation on the MR data after the main beam marker.

The following describes in detail a process of performing similar feature MR data aggregation by the network management apparatus.

The network management device can firstly perform wireless space propagation similarity analysis on the MR data marked by the main beam by combining data characteristics, and then converge the MR data with the wireless space propagation similarity to obtain N groups of MR data.

The similarity of wireless spatial propagation refers to that different propagation paths such as refraction, reflection, diffraction and the like exist in the wireless signals during spatial propagation. The same cell transmits signals, the signals are transmitted through a wireless space and reach terminal equipment with close distances, and the wireless space transmission of the signals has similarity. For example, the terminal device 1 and the terminal device 2 with close distances measure that the main beam identifications of the peripheral cells are the same, and the difference between the RSRPs is small. It can be said that the wireless spatial propagation of terminal device 1 and terminal device 2 have similarities.

In an implementation manner, the network management apparatus may aggregate MR data with wireless space propagation similarity according to each MR in table 5 and RSRP of each cell in each MR, to obtain N sets of MR data, which specifically includes the following steps:

combining the MR data with the same cell identification, the same main beam identification and the level difference value meeting the preset level difference value condition, and determining a group of converged MRs; and the level difference is the difference of RSRP between every two cells with the same cell identification in the MR data.

In this embodiment, a plurality of MRs having the same cell identifier, the same main beam identifier, and a level difference satisfying a preset level difference condition (for example, an RSRP difference is smaller than 3dB) are regarded as MRs having wireless spatial propagation similarity.

For example, the network management apparatus may first perform data preprocessing on the MR data after the main beam marking. The 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 convergence input table, as shown in table 6.

Table 6: similar characteristic MR data aggregation input table

The network management device may perform aggregation processing on the data in table 6 according to a certain clustering rule (e.g., a clustering algorithm) based on the data in table 6. For example, the network management device aggregates MR data with the same Cell _ ID, the same MainBeam _ ID, and an RSRP difference smaller than 3dB to obtain a set of MRs.

For example, cell1 in MR 1 in Table 6 has a MainBeam _ ID of 31 and an RSRP of 80, and cell1 in MR 2 has a MainBeam _ ID of 31 and an RSRP of 78. The MainBeam _ ID of cell2 in MR 1 in Table 6 is 29, the RSRP is 70, the MainBeam _ ID of cell2 in MR 2 is 29, and the RSRP is 70. It can be seen that the cell identities of the two MRs are the same, the main beam identity of the cells is the same, the difference between the RSRP of cell1 in MR 1 and the RSRP of cell1 in MR 2 is less than 3dB, and the value of the RSRP of cell2 in MR 1 is the same as the value of the RSRP of cell1 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 value of the cells also meets the preset level difference value condition; however, other cell information is also included in one of the MRs. For example, for MR 1 and MR 3 in table 6, MR 1 and MR 3 each include cell information of cell1 and cell information of cell 2. However, MR 3 also includes cell information of cell 3.

In order to analyze whether the MRs can be aggregated into a group of MRs, the network management apparatus may process MR data with similar characteristics. For example, for each MR in table 6, the difference between the maximum RSRP value in each MR and the RSRP of each cell is calculated, and the difference is used as a new feature vector, and table 6 is expanded as shown in table 7.

Table 7: input table with similar characteristic MR data converged and expanded with characteristic vectors

For example, for MR 1 and MR 3 in table 7, MR 3 also includes cell information for cell 3. However, the difference in RSRP of cell3 in MR 3 is large. That is, the RSRP of the neighboring cell3 is smaller than that of the serving cell1, and then the distance between the cell3 and the cell1 may be farther, that is, the cell3 in the neighboring cell may be invalid, and the cell3 may be ignored. That is, it is possible for MR 1 and MR 3 to converge into a set of MRs that has wireless spatial propagation similarity.

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

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

For example, assume there are 3 cells 1, 2, and 3, where there are no users in cell2 and cell3, and 2 users in cell1, UE1 and UE 2. Wherein, the UE1 is stationary, and its main beam is number 31 beam; UE2 is a mobile user whose main beams are beams number 8 and 9. The two users report 4 MRs, respectively, and the MR information after processing is shown in table 8.

Table 8: similar feature MR data entry tables for UE1 and UE2

The network management device may perform aggregation processing on 4 pieces of MR data reported by the UE1 and the UE2, and may divide the data into 3 groups through aggregation operation, where 4 pieces of MR data of the UE1 are 1 group of MR, and 4 pieces of MR data of the UE2 are 2 groups of MR. It should be noted that the above example is only an example, how many groups of MRs the MR data are aggregated together are analyzed and obtained according to specific network conditions, the analysis depends on parameters including the number of cells, the user distribution in the cells, the number of users, and the average RSRP of each cell, and the embodiment is not limited in this embodiment.

And the network management device respectively performs data processing on the aggregated MR groups, including averaging the RSRP of each group and/or summing the throughput of each group. For example, for the nth set of MRs, RSRPs of the same cell in the set of MRs are averaged to obtain an average RSRP of the same cell in the set of MRs. And summing the uplink throughputs of the same cell in the group of MRs to obtain the sum of the uplink throughputs of the same cell in the group of MRs. And summing the downlink throughputs of the same cell in the group of MRs to obtain the downlink throughput sum of the same cell in the group of MRs.

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

Please refer to table 9, where table 9 is a table of aggregated MR data formats provided in the embodiments of the present application. Taking the nth set of MR as an example, the table 9 records data such as cell identifiers of one or more cells, main beam identifiers of one or more cells, and average RSRP of one or more cells in the nth set of MR data.

Table 9: converged MR data format table

Wherein, a stereo grid (grid) can be regarded as a group of MRs in the converged MR data. That is, the stereoscopic grid n (grid n) corresponds to the nth set of MRs in the converged MR data.

Alternatively, the three-dimensional grid can also be regarded as a three-dimensional spatial position in a three-dimensional coordinate system. The three dimensions in the three-dimensional coordinate system are different from a conventional three-dimensional coordinate system (e.g., a three-dimensional coordinate system composed of longitude, latitude and altitude), and respectively identify a corresponding horizontal beam range, a corresponding vertical beam range for a main beam of a cell, and a path loss (also referred to as a path loss) from the main beam of the cell to a stereo grid.

Referring to fig. 9a, fig. 9a is a schematic view of a three-dimensional grid according to an embodiment of the present disclosure. Therein, it is assumed that cell1 in fig. 9a comprises 32 narrow beams, wherein the path loss of beam1 of cell1 to the stereoscopic grid n is as shown in fig. 9 a. It should be noted that the plurality of stereoscopic grids in fig. 9a respectively represent three-dimensional spatial positions. Wherein the reference coordinate system is as shown in fig. 9a, that is, the three-dimensional space of the stereoscopic grid shown in fig. 9a is determined by the antenna planes of the cells and the distances determined by the RSRPs of the cells to the stereoscopic grid.

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

s21, obtaining the beam gain corresponding to the main beam identifier of any cell i in the group of MRs according to the main beam identifier of the cell i; the beam gain of the cell i is determined according to a horizontal beam range, a vertical beam range, an antenna gain, a signal attenuation value corresponding to the horizontal beam range and a signal attenuation value corresponding to the vertical beam range corresponding to the main beam identification of the cell i;

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

Taking the stereoscopic grid n as an example, since the stereoscopic grid n may include a plurality of cells, when calculating the path loss of the stereoscopic grid n, the path loss from each cell in the stereoscopic grid n to the stereoscopic grid n needs to be calculated. In the following, a detailed description is given by taking celli in the stereoscopic grid n as an example, and it should be noted that each cell in all the stereoscopic grids can be calculated by referring to the following steps, so as to obtain a path loss from each cell in each stereoscopic grid to the stereoscopic grid.

First, the network management device may obtain the rf parameters of the celli from the working parameters/configuration. The radio frequency parameters may include, but are not limited to: horizontal beamwidth, physical azimuth, digital azimuth, vertical beamwidth, physical downtilt, or digital downtilt.

Wherein, the horizontal beam width represents the horizontal envelope width covered by the horizontal plane of the beam weight control, for example, the horizontal beam width shown in fig. 6. The vertical beamwidth represents the vertical envelope width covered by the horizontal plane of the beam weight control, e.g., the vertical beamwidth shown in fig. 6. The physical azimuth angle represents an included angle between the positive direction of the physical antenna panel and the positive north, and the value range of the included angle is 0-359 degrees. The digital azimuth angle represents an included angle between the strongest energy direction of the horizontal beam controlled by the beam weight and the north, and the value range of the included angle is 0-359 degrees. The physical downtilt angle represents an included angle between a plane perpendicular to the physical antenna panel and a horizontal plane, and the value range of the included angle is from-90 degrees to 90 degrees. The digital down dip represents the included angle between the strongest energy direction of the vertical wave beam controlled by the wave beam weight and the horizontal plane, and the value range is from-90 degrees to 90 degrees.

Then, the network management device acquires a horizontal beam range, a vertical beam range, an antenna gain, a signal attenuation value corresponding to the horizontal beam range and a signal attenuation value corresponding to the vertical beam range corresponding to the MainBeam _ ID from the antenna file according to the MainBeam _ ID of the celli.

For example, the MainBeam _ ID for cell i corresponds to a horizontal beam range of [0 degrees, 19 degrees ], and a vertical beam range of [3 degrees, 9 degrees ]. The antenna gain is 50 dB. From the horizontal beam range and the vertical beam range, the network management apparatus can determine a signal attenuation value for each degree of the horizontal beam range and a signal attenuation value for each degree of the vertical beam range. According to the calculation formula of the beam gain, the beam gain of the MainBeam _ ID of the cell i can be calculated. Wherein, the calculation formula of the beam gain is as follows: the beam gain of MainBeam _ ID of cell i is the average of the antenna gain + the signal attenuation value. The average value of the signal attenuation includes an average value of signal attenuation values corresponding to the horizontal beam and an average value of signal attenuation values corresponding to the vertical beam.

Finally, the network management device can calculate the path loss from the main beam of the cell i to the three-dimensional grid n by using a classical propagation model formula. The calculation formula of the path loss is as follows: the path loss from the main beam of cell i to the stereo grid n is the transmit power of cell i + the beam gain of MainBeam _ ID of cell i-the average RSRP of cell i. The transmission power of the cell i may also be obtained from the antenna file, and the average RSRP of the cell i may 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 according to an embodiment of the present disclosure. Fig. 9b includes three cells, which are cell1, cell2, and cell 3. Taking a stereo grid n as an example, the stereo grid n includes a plurality of cells and main beam identifiers of the plurality of cells, and path losses from different cells and/or different main beams to the stereo grid n are different. For example, for cell2, the path loss of beam0 of cell2 to the stereo grid n is shown in fig. 9 b. However, for cell1, the path loss of beam1 of cell1 to the stereoscopic grid n is as shown in fig. 9b, which is different from the path loss of beam0 of cell2 to the stereoscopic grid n.

Wherein, the path loss matrix can be stored in the network management device in a form of table. Referring to table 10, table 10 is a data format table of a path loss matrix provided in this embodiment, and includes an identifier of a stereo grid, a cell identifier, a main beam identifier of a cell, and a path loss.

Table 10: data format table of path loss matrix

Name (R)	Physical significance
		Gridn	Three-dimensional grid mark
Cell_ID	Cell identity, a three-dimensional grid with a plurality of cells
		MainBeam_ID	Primary beam identification of a cell
Loss of way	Cell to three-dimensional grid path loss value

Wherein Gridn represents the nth stereo grid, and each stereo grid has a unique stereo grid identifier. A stereo grid is provided with a plurality of cells, and the cell identification and the main beam identification of each cell and the path loss from the cell to the stereo grid are recorded in a path loss matrix.

The following describes the process of network optimization in detail.

According to the description in the above embodiments, the network management apparatus may obtain a stereoscopic grid of N sets of MRs, and a cell-to-stereoscopic grid path loss matrix. If the network has coverage problem and/or capacity problem, the network management device may determine the network configuration parameter of the target cell according to the path loss matrix. That is, the network management apparatus may adjust the network configuration parameters of the cells with coverage and/or capacity problems according to the path loss matrix to solve the coverage and/or capacity problems in the network.

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

Optionally, the network configuration parameter may also be a configuration parameter in other 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 the network has coverage problems and/or capacity problems, the embodiment uses the coverage index and/or capacity index to quantify the target of network optimization. The coverage indicator includes RSRP, signal to interference plus noise ratio (SINR), and overlay coverage ratio, and is denoted as coverage. The capacity index includes the telephone traffic balance, the spectrum efficiency, etc., and is recorded as capacity.

Wherein RSRP refers to cell handoverThe received power level can be measured by the cell and reported to the network management device. The SINR calculation formula is as follows:

the RSRP of the serving cell, the sum of the RSRPs of the neighbor cells and the noise power are determined. The calculation formula of the overlapping coverage ratio is as follows:

if the difference between the RSRP of the serving cell and the RSRP of the neighboring cell is smaller than a threshold (for example, 3dB), it is determined that the serving cell has overlapping coverage, and the number of the overlapping coverage serving cells is counted.

The traffic balance means that the sum of uplink and downlink throughputs between adjacent cells is as same as possible. That is, the sum of the uplink and downlink throughputs of the serving cell is as equal as possible to the sum of the uplink and downlink throughputs of the neighboring cell. Spectral efficiency refers to the amount of traffic per Resource Block (RB) per unit time.

In one implementation, both the coverage and capacity indicators may not be optimal at the same time. That is, when the coverage index of the network is adjusted to an optimal state, for example, a larger range can be covered and the overlapping coverage area is less; but the capacity index of the network may not be optimal, e.g. the spectral efficiency of the part area is low. In order to optimize the overall performance of the network, the embodiment of the application provides multi-objective and multi-parameter combined optimization. That is, according to the coverage index, the capacity index and the network configuration parameter, the embodiment of the present application models the target (fixness) of network optimization as the following formula:

fitness＝Func((k₁*coverage,k₂*capacity),Conf)

wherein, the fitness represents the joint optimization target of capacity and coverage, the Conf represents the adjustable network configuration parameter, k₁Represents the coverage weight, k₂Representing the capacity weight. And controlling the influence degree of coverage and capacity on the joint optimization target by setting different coverage weights and capacity weights.

For example, the current network farmThe scene cell covers a hot spot area, and users in the hot spot area are mostly concentrated at high places (for example, in a building with 5 floors high, users are mostly concentrated on 4 th and 5 th). Then the capacity weight can be prioritized in this scenario, i.e., k₂Has a weight value of more than k₁The weight value of (2). And when adjusting the network configuration parameters, the adjustment of the vertical beam is preferentially considered, that is, the network management apparatus may adjust the parameter combination of the vertical beam width, the physical downtilt, and the digital downtilt, so as to optimize the capacity and the coverage.

In one implementation, the network management apparatus determines the network configuration parameter of the target cell according to the path loss matrices of the N stereoscopic grids, and may include the following steps:

s31, determining the target cell of each three-dimensional grid according to the coverage index and/or 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 antenna gain of the adjusted target cell according to the network configuration parameters of the adjusted target cell;

s34, determining 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;

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, determining the adjusted network configuration parameter of the target cell as the network configuration parameter of the target cell.

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

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

s312, adopting a clustering algorithm to perform convergence processing on the three-dimensional grids with coverage problems and/or capacity problems;

and s313, determining the cell included in the problem stereo grid after convergence as a target cell.

The target cell is a cell of which the coverage index does not meet the preset coverage index and/or the capacity index does not meet the preset capacity index. That is, the target cell may be determined based on the coverage index of the cell, or may be determined based on the capacity index of the cell. It can be understood that the coverage index of the cell refers to the current coverage condition of the cell, and the capacity index of the cell refers to the current capacity condition of the cell.

The preset coverage index can be determined according to parameters such as RSRP, SINR, overlapping coverage proportion and the like; for example, the preset coverage index of the cell is that the overlapping coverage ratio of the cell is less than 10%. The preset capacity index can be determined according to parameters such as telephone traffic balance degree, spectrum efficiency and the like; for example, the preset capacity index of the cell is that the traffic balance of the cell is 1, that is, the sum of the uplink throughput and the downlink throughput of the cell is the same as the sum of the uplink throughput and the downlink throughput of the adjacent cell.

For example, the network management device may determine the target cell based on the measured data. Wherein the measured data may be determined from the pooled MR data as shown in table 9. The measured data may be an average RSRP for each cell in the stereoscopic grid. If the average RSRP of a cell is lower than a preset RSRP threshold (e.g., 100dB), 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 the cells in the stereoscopic grid. And if the sum of the uplink throughput and the downlink throughput of the cell is less than a preset throughput threshold value, determining that the cell has the capacity problem.

Optionally, when performing convergence processing on a stereoscopic grid with coverage and/or capacity problems, a clustering algorithm (DBSCAN) and the like may be used. For example, if there are 50 cells with coverage and/or capacity problems, the three-dimensional grids with higher similarity of wireless spatial propagation may be aggregated according to the clustering algorithm, and if there are 30 cells included in the three-dimensional grids aggregated by the clustering algorithm, the network management apparatus may determine that the 30 cells are target cells.

After the target cell is determined, according to a formula of a target (fixness) of network optimization, the target of the network optimization is maximized, that is, the target formula of the network optimization is converted into the following formula:

max{fitness＝Func((k₁*coverage,k₂*capacity),Conf)}

where max represents the maximum value that a fitness can be obtained by adjusting Conf.

In one implementation, the network management device may obtain adjustable network configuration parameters from the antenna file and obtain current network configuration parameters through configuration and working parameters. The network management device may adopt an optimization algorithm (for example, an operation optimization algorithm based on gradient descent), take max { fixness } as an optimization target, take a current network configuration parameter as an initial value, take an adjustable network configuration parameter as a variable, and continuously adjust the Conf so as to obtain the Conf that maximizes the fixness; the above-described optimization process is shown in fig. 10. The optimization stopping condition needs to comprehensively consider the efficiency and convergence of the algorithm, that is, the optimization stopping condition includes: the overall gain reaches the standard (e.g. the fitness reaches the maximum), or the gain is smaller than the threshold in a plurality of continuous rounds, or the iteration number reaches the maximum optimization threshold.

The embodiment of the application provides a network configuration method, which can construct a three-dimensional grid according to the average RSRP of each cell in MR data and horizontal beam information and vertical beam information corresponding to the main beam identifier of each cell, thereby achieving the aim of constructing the three-dimensional grid without depending on MDT data. And a three-dimensional path loss matrix can be constructed according to the three-dimensional grid, so that three-dimensional beam optimization is realized. The method can also determine the network configuration parameters of the target cell according to the three-dimensional path loss matrix, and can optimize the coverage index and/or the capacity index of the target cell by adjusting the network configuration parameters of the target cell, thereby being beneficial to optimizing the coverage and/or the capacity of the network.

Referring to fig. 11, fig. 11 is a schematic flowchart of another network configuration method according to an embodiment of the present application. The network configuration method can be executed by an offline tool deployed offline on a stand-alone computer or cloud, or by an online tool deployed online on a network management system OMC or an online tool platform connected with the OMC. The network configuration method is applied to a scene with sufficient MDT data or road test, and comprises the following steps:

s1101, the network management device carries out aggregation processing on the multiple pieces of MDT data according to cell information of each MDT in the multiple pieces of MDT data for the minimization of road test to obtain N groups of MDT data, wherein each group of MDT comprises MDTs with the same cell vertical beam identification;

s1102, for each of the N sets of MDTs, the network management apparatus creates a three-dimensional grid of the set of MDTs according to the longitude and latitude of each cell in the set of MDTs and the vertical beam identifier of each cell;

s1103, for each group of the three-dimensional grids of the MDT, the network management apparatus calculates a path loss from a main beam of each cell in the group of the MDTs to the three-dimensional grid, so as to obtain a path loss matrix of the three-dimensional grid;

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

In this embodiment, it is assumed that the measurement report reported by the terminal device is a measurement report including latitude and longitude 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 sets of MDT data. Similar to the aggregation processing of the MR data by the network management device, the network management device may first perform data preprocessing on the MDT reported by the terminal device, where the data preprocessing includes MDT main beam identification and MDT geographical grid aggregation.

The following describes in detail a procedure of the network management apparatus for MDT primary beam identification.

The network management device can also obtain the CHR of the terminal equipment besides receiving the MDT reported by the terminal equipment. For the description of the CHR, reference may be made to the description of the CHR in the embodiment of fig. 7, which is not repeated herein.

Optionally, the network management apparatus may further obtain an engineering parameter/configuration file, an antenna file, and an electronic map. For the description of the working parameters/configuration files and the antenna files, reference may be made to the description of the working parameters/configuration files and the antenna files in the embodiment in fig. 7, which is not repeated herein. The electronic map comprises position information of a geographic space and can show the position relation between the site and the terminal equipment.

Because the MDT and the CHR are two different types of data reported by the terminal device, the MDT and the CHR can be associated for subsequent processing. For the description of the association processing performed on the MDT and the CHR, reference may be made to the description of the association processing performed on the MDT and the CHR in the embodiment in fig. 7, which is not described herein again.

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

In one implementation, 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, in the embodiment of the present application, relevant information of a neighboring cell is introduced, so that when a network management device adjusts a network configuration parameter for a cell, the influence of the neighboring cell of the cell on the cell is also fully considered. Specifically, the Cell information of each MR includes a Cell identifier of a serving Cell, an RSRP (S _ RSRP) of the serving Cell, a Cell identifier (N _ Cell _ ID) of a neighboring Cell, an RSRP (N _ RSRP) of the neighboring Cell, and the like.

The terminal device cannot acquire the main beam identifier of the neighboring cell due to the measurement capability of the terminal device. In order to facilitate the identification of the main beam of the subsequent MDT, in this embodiment, a main beam identifier (N _ main beam _ ID) field of the neighboring cell is added to the MDT data, and is used to record the main beam identifier of the neighboring cell.

Similarly to the MR data, the MDT data may be stored in the network management apparatus in a table form, or may be stored in other forms, and this embodiment is not limited thereto. Please refer to table 11, table 11 is a table of MR data information provided in the embodiments of the present application. The information table includes fields of S _ Cell _ ID, S _ RSRP, S _ MainBeam _ ID, N _ Cell _ ID, N _ RSRP, N _ MainBeam _ ID, ulthreughput, dlthreughput, etc., and the physical meanings and field sources of the respective fields are shown in table 11.

Table 11: MDT data information table

Similar to the step of predicting the main beam of the neighboring cell in the MR data by the network management apparatus in the embodiment of fig. 7, the network management apparatus in this embodiment may also predict the main beam of the neighboring cell in the MDT data according to the similar step, so that all cells in each piece of MDT data mark the main beam identifier.

Please refer to table 12, table 12 is a table for storing MDT data after main beam marking according to the embodiment of the present application. The primary beam identity of each cell may indicate one of the beams generated by the Massive MIMO antenna.

Table 12: MDT data storage table marked by main beam

Because the MDT data after the main beam marking only represents information of a two-dimensional geographic grid, the network management device may perform geographic grid convergence from a vertical plane layer by layer in order to model a three-dimensional grid. The following detailed description of the process of geographic grid aggregation performed by the network management device 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 MDT according to the longitude and latitude of each cell in the group of MDT;

s42, the network management device determines the vertical layer where the planar grid is formed by the set of MDTs according to the vertical beam identifiers of the set of MDTs, so as to obtain the three-dimensional grid of the set of MDTs.

For example, the network management device may calculate, based on the MDT data after the main Beam mark, a vertical Beam identifier V _ Beam _ ID corresponding to each cell according to the MainBeam _ ID corresponding to each cell. The formula for calculating the V _ Beam _ ID in the embodiment of the application is as follows: v _ Beam _ ID int (MainBeam _ ID ÷ 8). For example, if the MainBeam _ ID of a cell is 31, the V _ Beam _ ID of the cell is 3 calculated according to the formula of the V _ Beam _ ID.

After the network management apparatus processes the MDT data, the MDT data is shown in table 13.

Table 13: MDT data storage table after vertical beam marking

After the network management device obtains the MDT data marked by the vertical Beam, the network management device may converge 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 device may perform geography rasterization processing on the MDT data of each vertical layer to obtain the geographically rasterized MDT data. The geography grid processing means that an area formed by projecting each vertical layer to a two-dimensional plane (such as the ground) is determined according to longitude and latitude information of each vertical layer; and dividing the geographical grid of the two-dimensional plane position area according to a certain proportion. For example, an area formed by projecting a vertical layer of V _ Beam0, in which V _ Beam _ ID is determined by latitude and longitude information of the vertical layer of V _ Beam0, onto the ground is shown in fig. 12. Assuming that the geographic grid is divided at a ratio of 20 meters (m) × 20m, the vertical layer of V _ Beam0 after geographic rasterization is projected onto the ground to form an area as shown in fig. 12. As another example, the vertical level of V _ Beam1 is shifted upward a vertical distance on a level perpendicular to the ground as compared to the vertical level of V _ Beam0, as shown in FIG. 12. That is, the area of the vertical layer of V _ Beam1 projected onto the ground is the same as the area of V _ Beam0 projected onto the ground, but the two are at different heights perpendicular to the ground, i.e., the stereoscopic grids of the vertical layers of different V _ Beam _ IDs are not the same. It should be noted that the stereo grid herein refers to a geographical grid with a V _ Beam _ ID, that is, the stereo grid may be a position in a three-dimensional stereo space.

In an implementation manner, after the MDT data with the same V _ Beam _ ID are aggregated, the network management device may further perform data processing on a group of MDTs with the same V _ Beam _ ID, including performing average processing on RSRP of each group and performing summation processing on throughput of each group. For a specific implementation, reference may be made to the description of performing averaging processing on RSRPs of each group and performing summation processing on throughput of each group in the embodiment in fig. 7, which is not described herein again.

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

Please refer to table 14, where table 14 is a table of aggregated MDT data formats provided in the embodiments of the present application. Taking the nth set of MDT as an example, the table 14 records data such as cell identifiers of one or more cells, vertical beam identifiers of one or more cells, average RSRP of one or more cells, grid longitude, grid latitude, and the like in the nth set of MDT data.

Table 14: aggregated MDT data format table

Here, a stereo grid (grid) can be regarded as a set of MRs in the aggregated MDT data. That is, the stereoscopic grid n (grid n) corresponds to the nth set of MDT in the aggregated MDT data.

Referring to fig. 13a, fig. 13a is a schematic view of another stereoscopic grid according to an embodiment of the present application. Wherein the path loss of the stereo grid n in the cell1 to V _ Beam3 beams, the path loss of the stereo grid n in the cell2 to V _ Beam3 beams, and the path loss of the stereo grid n in the cell3 to V _ Beam3 beams are shown in fig. 13 a. It is noted that the plurality of stereoscopic grids in fig. 13a respectively represent three-dimensional spatial positions. The three-dimensional grid in this embodiment includes a vertical Beam identifier, that is, the three-dimensional grid n includes different Beam layer data, that is, the three-dimensional grid n is a position in a three-dimensional space.

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

s51, obtaining a vertical beam range, an antenna gain and a signal attenuation value corresponding to the vertical beam range corresponding to the vertical beam identifier according to the vertical beam identifier in the MDT group;

s52, obtaining 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 the grid latitude in the group of MDTs according to the grid longitude and the grid latitude of the group of MDTs;

s53, calculating the antenna gain from any cell i in the group of MDTs to the stereo grid of the group of MDTs according to the vertical beam range, the antenna gain and the signal attenuation value corresponding to the vertical beam range corresponding to the vertical beam identification in the group of MDTs, and the signal attenuation value corresponding to the horizontal beam range, the antenna gain and the horizontal beam range corresponding to the geographical grid determined by the grid longitude and the grid latitude in the group of MDTs;

s54, calculating the path loss of cell i to the stereo grid of the set of MDTs according to the transmission power of cell i, the antenna gain of cell i to the stereo grid of the set of MDTs, and the average RSRP of cell i.

Taking the stereoscopic grid n as an example, since the stereoscopic grid n may include a plurality of cells, when calculating the path loss of the stereoscopic grid n, the path loss from each cell in the stereoscopic grid n to the stereoscopic grid n needs to be calculated. In the following, a detailed description is given by taking the cell i in the stereoscopic grid n as an example, and it should be noted that each cell in all the stereoscopic grids can be calculated by referring to the following steps, so as to obtain the path loss from each cell in each stereoscopic grid to the stereoscopic grid.

First, the network management device may obtain the rf parameters of the cell i from the working parameters/configuration. The radio frequency parameters may include, but are not limited to: horizontal beamwidth, physical azimuth, digital azimuth, vertical beamwidth, physical downtilt, or digital downtilt. For a detailed description of the rf parameters, please refer to the description in the embodiment of fig. 7, which is not repeated herein.

Then, the network management device acquires a vertical Beam range, an antenna gain and a signal attenuation value corresponding to the vertical Beam range corresponding to the V _ Beam _ ID from the antenna file according to the V _ Beam _ ID of the cell i. For example, the vertical Beam range corresponding to V _ Beam _ ID of cell i is [3 degrees, 9 degrees ]. The antenna gain is 50 dB. From the vertical beam range, the network management apparatus can determine a signal attenuation value for 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 work parameter of the cell i. For example, the horizontal beam range of cell i is [0 degrees, 19 degrees ]. And then according to the horizontal beam range, acquiring a signal attenuation value corresponding to each degree of the horizontal beam range from an antenna file. Based on the above data, the network management apparatus may calculate the average of the antenna gain from the cell i to the stereo grid n, which is the antenna gain plus the signal attenuation value.

Finally, the network management device can calculate the path loss from the cell i to the three-dimensional grid n by using a classical propagation model formula and combining an electronic map. The calculation formula of the path loss is as follows: the path loss from cell i to the stereo grid n is the transmission power of cell i + the antenna gain from cell i to the stereo grid n-the average RSRP of cell i. The transmission power of the cell i may also be obtained from the antenna file, and the average RSRP of the cell i may 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 according to an embodiment of the present disclosure. Fig. 13b includes three cells, which are cell1, cell2, and cell 3. Taking a three-dimensional grid n as an example, the three-dimensional grid n includes a plurality of cells and vertical Beam identifiers of the plurality of cells, and path loss from different cells to the three-dimensional grid n in different V _ Beam _ ID beams is different. For example, for cell2, the path loss of the solid grid n in the cell2 to V _ Beam0 beams is shown in fig. 13b, and the solid grid n in the V _ Beam0 Beam is located at the lowest layer of the multiple layers in the vertical direction, as shown in fig. 13 b.

Wherein, the path loss matrix can be stored in the network management device in a form of table. Referring to table 15, table 15 is a data format table of another path loss matrix provided in this embodiment, which includes an identifier of a stereo grid, a cell identifier, a vertical beam identifier of a cell, a grid longitude, a grid latitude, and a path loss.

Table 15: data format table of path loss matrix

Name (R)	Physical significance
		Gridn	Three-dimensional grid mark
Cell_ID	Cell identity, a plurality of cells in a three-dimensional gridZone(s)
		V_Beam_ID	Primary beam identification of a cell
Longitude	Grid longitude
		Latitude	Grid latitude
Loss of way	Cell to vertical beam three-dimensional grid path loss value

Wherein Gridn represents the nth stereo grid, and each stereo grid has a unique stereo grid identifier. A three-dimensional grid is provided with a plurality of cells, and the cell identification and the vertical beam identification of each cell, the grid longitude and grid latitude and the path loss of the three-dimensional grid from the cell to the vertical beam are recorded in a path loss matrix.

Similar to the process of network optimization in the embodiment of fig. 7, the network management apparatus in this embodiment may also perform network optimization according to the path loss matrix of the stereoscopic grid of N sets of MRs and the stereoscopic grid of cell-to-vertical beams. If the network has coverage problem and/or capacity problem, the network management device may determine the network configuration parameter of the target cell according to the path loss matrix. That is, the network management apparatus may adjust the network configuration parameters of the cells with coverage and/or capacity problems according to the path loss matrix to solve the coverage and/or capacity problems in the network. The specific implementation manner may refer to the description in the embodiment of fig. 7, and is not described herein again.

The embodiment of the application provides a network configuration method, which can construct a three-dimensional grid according to latitude and longitude information in MDT data and a vertical beam identifier determined according to a main beam identifier of a cell, avoid the three-dimensional information from being blurred by average processing of data by a two-dimensional geographic grid, and is beneficial to realizing more accurate three-dimensional optimization. In addition, the method can also generate a three-dimensional path loss matrix according to the three-dimensional stereoscopic grid, and the network configuration parameters of the target cell can be determined according to the three-dimensional path loss matrix, so that the method is favorable for optimizing the coverage and/or capacity of the network.

The following describes the apparatus and device according to the embodiments of the present application in detail with reference to fig. 14 to 17.

As shown in fig. 14, the network management apparatus 1400 may be configured to implement the network configuration method in this embodiment. The network management apparatus 1400 may include:

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

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

a creating unit 1403, configured to create, for each MR in the N sets of MRs, a stereoscopic grid of the set of MRs according to an average RSRP of each cell in the set of MRs and a vertical beam range and a horizontal beam range corresponding to a main beam identifier of each cell;

a calculating unit 1404, configured to calculate, for the stereo grid of each group of MRs, a path loss from a main beam of each cell in the group of MRs to the stereo grid, so as to obtain a path loss matrix of the stereo grid;

a determining unit 1405, configured to determine, according to the path loss matrices of the N stereoscopic grids, a network configuration parameter of a target cell, where the target cell is a cell whose coverage index does not satisfy the preset coverage threshold and/or whose capacity index does not satisfy the preset capacity threshold.

For a specific implementation manner, please refer to detailed descriptions in S701 to S705 in the embodiment of fig. 7, which are not repeated herein.

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

For a specific implementation manner, please refer to a detailed description of a process of performing MR main beam identification on the network management device in the embodiment of fig. 7, which is not described herein again.

In an implementation manner, the aggregation unit 1401 is specifically configured to perform aggregation processing on MR data with the same cell identifier, the same main beam identifier, and the level difference meeting a preset level difference condition, and determine a set of aggregated MRs; the level difference is the difference of RSRP between every two cells with the same cell identity and the same main beam identity in the MR data.

For a specific implementation manner, please refer to a detailed description of a process of performing similar feature MR data aggregation on the network management device in the embodiment of fig. 7, which is not described herein again.

In one implementation, the computing unit 1404 is specifically configured to:

acquiring beam gain corresponding to the main beam identifier of any cell i in the group of MRs according to the main beam identifier of the cell i; the beam gain of the cell i is determined according to a horizontal beam range, a vertical beam range, an antenna gain, a signal attenuation value corresponding to the horizontal beam range and a signal attenuation value corresponding to the vertical beam range corresponding to the main beam identification of the cell i;

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

For a specific implementation manner, please refer to the detailed description of the process of calculating the path loss by the network management device in the embodiment of fig. 7, which is not described herein again.

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

aiming at each three-dimensional grid, determining a 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 network configuration parameters of the target cell according to the preset coverage index and the preset capacity index of the target cell; obtaining the antenna gain of the adjusted target cell according to the adjusted network configuration parameters of the target cell;

determining a coverage index and a 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;

and 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, determining the adjusted network configuration parameter of the target cell as the network configuration parameter of the target cell.

For a specific implementation manner, please refer to a detailed description of a network optimization process in the embodiment of fig. 7, which is not described herein again.

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 one implementation, the relevant functions implemented by the various units 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 according to an embodiment of the present disclosure, where the network management device may be a device (e.g., a chip) having a network configuration function described in the embodiment of the present disclosure. The network device 1500 may include a transceiver 1501, at least one processor 1502, and memory 1503. The transceiver 1501, the processor 1502, and the memory 1503 may be connected to each other via one or more communication buses, or may be connected in other manners. The bus connection is used in the present embodiment as an example, as shown in fig. 15.

The transceiver 1501 may be used to transmit or receive data, among other things. For example, the transceiver 1501 may receive MR data reported by a terminal device and a network device. It is to be appreciated that the transceiver 1501 is a generic term and may include both receivers and transmitters.

The processor 1502 may be used to process data, among other things. 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 single CPU, the CPU may be a single-core CPU or a multi-core CPU.

The memory 1503 is used to store program codes and the like. The memory 1503 may include a volatile memory (RAM), such as a Random Access Memory (RAM). The memory 1503 may also include a non-volatile memory (non-volatile memory), such as a read-only memory (ROM), a flash memory (flash memory), a Hard Disk Drive (HDD), or a solid-state drive (SSD). Memory 1503 may also include combinations of the above types of memory.

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

aggregating 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, wherein each group of MR comprises MRs with wireless space propagation similarity;

acquiring the average Reference Signal Received Power (RSRP) of each cell of each group of MRs in the N groups of MRs;

aiming at each group MR in the N groups of MRs, according to the average RSRP of each cell in the group of MRs and the vertical beam range and the horizontal beam range corresponding to the main beam identification of each cell, creating a stereo grid of the group of MRs;

aiming at the three-dimensional grid of each group of MRs, calculating the path loss from the main beam of each cell in the group of MRs to the three-dimensional grid so as to obtain a path loss matrix of the three-dimensional grid;

and determining network configuration parameters of a target cell according to the path loss matrixes of the N three-dimensional grids, wherein the target cell is a cell of which the coverage index does not meet a preset coverage threshold and/or the capacity index does not meet a preset capacity threshold.

In one implementation, the cell information of each MR includes cell information of a serving cell and/or cell information of a neighboring cell, and the RSRPs of one or more cells include RSRPs of the serving cell and/or RSRPs of the neighboring cell. The processor 1502 is further configured to determine, for each MR, a primary beam prediction model of a serving cell of the MR according to a cell identifier of the serving cell, an RSRP of the serving cell, and a primary beam identifier of the serving cell in the MR; and determining the main beam identifier of the neighbor cell of the MR according to the main beam prediction model of the service cell of the MR, the cell identifier of the neighbor cell and the RSRP of the neighbor cell.

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 meeting the preset level difference condition, and determine a group of aggregated MRs; the level difference is the difference of RSRP between every two cells with the same cell identity and the same main beam identity in the MR data.

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

As shown in fig. 16, the network management apparatus 1600 may be used to implement the network configuration method in this embodiment. The network management apparatus 1600 may include:

the aggregation unit 1601 is configured to perform aggregation processing on the multiple pieces of MDT data according to cell information of each piece of MDT in the multiple pieces of minimization road test MDT data to obtain N groups of MDT data, where each group of MDT includes MDTs with the same vertical beam identifier of a cell;

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

a calculating unit 1603, configured to calculate, for each group of the three-dimensional grids of the MDT, a path loss from a main beam of each cell in the group of the MDT to the three-dimensional grid, so as to obtain a path loss matrix of the three-dimensional grid;

a determining unit 1604, configured to determine a network configuration parameter of a target cell according to the path loss matrices of the N stereoscopic grids, where the target cell is a cell whose coverage index does not satisfy the preset coverage threshold and/or whose capacity index does not satisfy the preset capacity threshold.

For a specific implementation manner, please refer to detailed descriptions in S1101 to S1104 in the embodiment of fig. 11, which are not repeated herein.

In one implementation, the cell information of each MDT includes cell information of a serving cell and/or cell information of a neighboring cell, and the RSRPs of one or more cells include RSRPs of the serving cell and/or RSRPs of the neighboring cell. For each MDT, determining unit 1604 is further to:

determining a 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;

determining a main beam identifier of a neighboring cell of the MDT according to a main beam prediction model of a serving cell of the MDT, a cell identifier of the neighboring cell and RSRP of the neighboring cell;

and determining the vertical beam identification of each cell in the MDT according to the main beam identification of each cell in the MDT.

For a specific implementation manner, please refer to a detailed description of a process of performing MDT primary beam identification on a network management device in the embodiment of fig. 11, which is not described herein again.

In one implementation, the creating unit 1602 is specifically configured to:

determining grid longitude and grid latitude of a planar grid formed by the MDT according to the longitude and latitude of each cell in the MDT;

and determining a vertical layer where the planar grid is formed by the MDT according to the vertical beam identification of the MDT so as to obtain a three-dimensional grid of the MDT.

For a specific implementation manner, please refer to a detailed description of a process of performing geographic grid convergence on a network management device in the embodiment of fig. 11, which is not described herein again.

In one implementation, the calculating unit 1603 is specifically configured to:

acquiring a vertical beam range, antenna gain and a signal attenuation value corresponding to the vertical beam range corresponding to the vertical beam identifier according to the vertical beam identifier in the MDT group;

acquiring a horizontal beam range, antenna gain and a signal attenuation value corresponding to the horizontal beam range, which are determined by the grid longitude and the grid latitude in the MDT according to the grid longitude and the grid latitude of the MDT;

calculating the antenna gain from any cell i in the group of MDT to the three-dimensional grid of the group of MDT according to the vertical beam range, the antenna gain and the signal attenuation value corresponding to the vertical beam range and the vertical beam range which correspond to the vertical beam identification in the group of MDT, and the signal attenuation value corresponding to the horizontal beam range, the antenna gain and the horizontal beam range which correspond to the geographical grid and are determined by the grid longitude and the grid latitude in the group of MDT;

and calculating the path loss from the cell i to the stereo grid of the group of MDT according to the transmitting power of the cell i, the antenna gain from the cell i to the stereo grid of the group of MDT and the average RSRP of the cell i.

For a specific implementation manner, please refer to the detailed description of the process of calculating the path loss matrix of the stereoscopic grid by the network management device in the embodiment of fig. 11, which is not described herein again.

In one implementation, determining unit 1604 is specifically configured to:

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

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

and if the coverage index of the target cell reaches a preset coverage index and/or the capacity index of the target cell reaches a preset capacity index, determining the adjusted network configuration parameter of the target cell as the network configuration parameter of the target cell.

In one implementation, the relevant functions implemented by the various units 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 according to an embodiment of the present disclosure, where the network management device may be a device (e.g., a chip) having a network configuration function described in the embodiment of the present disclosure. The network management apparatus 1700 may include a transceiver 1701, at least one processor 1702, and a memory 1703. The transceiver 1701, the processor 1702 and the memory 1703 may be interconnected via one or more communication buses, or may be otherwise connected. The bus connection is used in the present embodiment as an example, as shown in fig. 17.

The transceiver 1701 may be used to transmit or receive data, among other things. For example, the transceiver 1501 may receive MDT data reported by a terminal device and a network device. It is to be appreciated that the transceiver 1501 is a generic term and may include both receivers and transmitters.

The processor 1702 may be used to process data, among other things. 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.

The memory 1703 stores a program code and the like. The memory 1703 may include a volatile memory (volatile memory), such as a Random Access Memory (RAM). The memory 1703 may also include a non-volatile memory (non-volatile memory), such as a read-only memory (ROM), a flash memory (flash memory), a Hard Disk Drive (HDD), or a solid-state drive (SSD). The memory 1703 may also include a combination of the above types of memories.

The processor 1702 may be configured to implement the network configuration method in the embodiment of the present application, where a specific implementation manner is as follows:

aggregating the multiple pieces of MDT data according to the cell information of each MDT in the multiple pieces of MDT data to obtain N groups of MDT data, wherein each group of MDT comprises MDTs with the same cell vertical beam identification;

aiming at each MDT in the N groups of MDTs, creating a three-dimensional grid of the MDT according to the longitude and the latitude of each cell in the MDT and the vertical beam identification of each cell;

aiming at the three-dimensional grid of each group of MDT, calculating the path loss from the main beam of each cell in the group of MDT to the three-dimensional grid so as to obtain a path loss matrix of the three-dimensional grid;

In one implementation, the cell information of each MDT includes cell information of a serving cell and/or cell information of a neighboring cell, and the RSRPs of one or more cells include RSRPs of the serving cell and/or RSRPs of the neighboring cell. For each MDT, the processor 1702 is further configured to:

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

Embodiments of the present application provide a computer-readable storage medium, which stores a program or instructions, and when the program or instructions are run on a computer, the program or instructions cause the computer to execute a network configuration method in an embodiment of the present application.

The embodiment of the present application provides a chip or a chip system, where the chip or the 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 an instruction to perform the network configuration method in the embodiment of the present application.

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

The embodiment of the application provides a communication system, which comprises network management equipment, network equipment and terminal equipment.

In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website, computer, server, or data center to another website, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The 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, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., Digital Video Disk (DVD)), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.

Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method of network configuration, comprising:

aggregating the multiple pieces of MR data according to cell information of each MR in the multiple pieces of measurement report MR data to obtain N sets of MR data, wherein each set of MR comprises MRs with wireless space propagation similarity, and the cell information of each MR comprises cell identification of one or more cells, main beam identification of one or more cells and Reference Signal Received Power (RSRP) of one or more cells;

for each MR in the N groups of MRs, creating a stereo grid of the MR according to the average RSRP of each cell in the MR and a vertical beam range and a horizontal beam range corresponding to the main beam identification of each cell;

and determining network configuration parameters of a target cell according to the path loss matrixes of the N three-dimensional grids, wherein the target cell is a cell of which the coverage index does not meet the preset coverage index and/or the capacity index does not meet the preset capacity index.

2. The method of claim 1, wherein the cell information of each MR comprises cell information of a serving cell and/or cell information of a neighbor cell, and wherein the RSRP of the one or more cells comprises the RSRP of the serving cell and/or the RSRP of the neighbor cell; before the aggregating the plurality of pieces of MR data according to the cell information of each MR in the plurality of pieces of measurement report MR data, the method further includes:

for each MR, determining a 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;

and determining the main beam identifier of the neighbor cell of the MR according to the main beam prediction model of the service cell of the MR, the cell identifier of the neighbor cell and the RSRP of the neighbor cell.

3. The method according to claim 1 or 2, wherein the aggregating the plurality of pieces of measurement report MR data according to the cell information of each MR in the plurality of pieces of MR data to obtain N sets of MR data comprises:

converging MR data with the same cell identification, the same main beam identification and the level difference value meeting the preset level difference value condition, and determining a group of converged MRs; the level difference is the difference of RSRP between every two cells with the same cell identity and the same main beam identity in the MR data.

4. The method according to claim 1 or 3, wherein the calculating, for the stereo grid of each group of MRs, a path loss from a main beam of each cell in the group of MRs to the stereo grid comprises:

and calculating the path loss from the main beam of the cell i to the stereo grid of the MR group according to the transmitting power of the cell i, the beam gain of the cell i and the average RSRP of the cell i.

5. The method of claim 1, wherein determining the network configuration parameters of the target cell according to the path loss matrices of the N stereoscopic grids comprises:

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

6. A method of network configuration, comprising:

performing aggregation processing on the multiple pieces of MDT data according to cell information of each MDT in the multiple pieces of MDT data to obtain N groups of MDT data, wherein each group of MDT comprises MDTs with the same vertical beam identification of the cells, and the cell information of each MDT comprises cell identifications of one or more cells, main beam identifications of one or more cells and longitudes and latitudes of one or more cells; the vertical beam identifier of the cell is determined according to the main beam identifier of the cell;

for each of the N sets of MDTs, creating a three-dimensional grid of the set of MDTs according to the longitude and latitude of each cell in the set of MDTs and the vertical beam identification of each cell;

7. The method of claim 6, wherein the cell information of each MDT comprises cell information of a serving cell and/or cell information of a neighbor cell, and wherein the RSRP of the one or more cells comprises the RSRP of the serving cell and/or the RSRP of the neighbor cell; before the aggregating the plurality of pieces of MDT data according to the cell information of each MDT of the plurality of pieces of minimization of road test MDT data, the method further includes:

for each MDT, determining a 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;

determining a main beam identification of the neighboring cell of the MDT according to a main beam prediction model of the serving cell of the MDT, the cell identification of the neighboring cell and the RSRP of the neighboring cell;

8. The method of claim 7, wherein the creating a solid grid of the set of MDTs for each of the N sets of MDTs according to a longitude, a latitude, and a vertical beam identification of each cell in the set of MDTs comprises:

9. The method according to any one of claims 6 to 8, wherein the calculating, for the stereo grid of each group of MDTs, a path loss from a main beam of each cell in the group of MDTs to the stereo grid comprises:

according to the vertical beam identifier in the MDT group, acquiring a vertical beam range corresponding to the vertical beam identifier, antenna gain and a signal attenuation value corresponding to the vertical beam range;

and calculating the path loss from the cell i to the stereo grid of the set of MDT according to the transmitting power of the cell i, the antenna gain from the cell i to the stereo grid of the set of MDT and the average RSRP of the cell i.

10. The method of claim 6, wherein determining the network configuration parameters of the target cell according to the path loss matrices of the N stereoscopic grids comprises:

11. A network management apparatus, comprising:

the aggregation unit is used for aggregating 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 sets of MR data, each set of MR comprises MRs with wireless space propagation similarity, and the cell information of each MR comprises cell identification of one or more cells, main beam identification of one or more cells and Reference Signal Received Power (RSRP) of one or more cells;

the acquisition unit is used for acquiring the average Reference Signal Received Power (RSRP) of each cell of each group of MR in the N groups of MRs;

a creating unit, configured to create, for each MR in the N sets of MRs, a stereoscopic grid of the MR according to an average RSRP of each cell in the MR set and a vertical beam range and a horizontal beam range corresponding to a main beam identifier of each cell;

the calculation unit is used for calculating the path loss from the main beam of each cell in each group of MR to the three-dimensional grid aiming at the three-dimensional grid of each group of MR so as to obtain a path loss matrix of the three-dimensional grid;

and the determining unit is used for determining the network configuration parameters of the target cell according to the path loss matrixes of the N three-dimensional grids, wherein the target cell is a cell of which the coverage index does not meet the preset coverage index and/or the capacity index does not meet the preset capacity index.

12. The apparatus of claim 11, wherein the cell information of each MR comprises cell information of a serving cell and/or cell information of a neighbor cell, and wherein the RSRPs of the one or more cells comprise the RSRP of the serving cell and/or the RSRP of the neighbor cell; the determination unit is further configured to:

13. The apparatus according to claim 11 or 12, wherein the aggregating unit is configured to aggregate a plurality of pieces of measurement report MR data according to the cell information of each MR of the plurality of pieces of MR data, and is specifically configured to:

14. The apparatus according to claim 11 or 13, wherein the computing unit is configured to compute, for the stereo grid of each group of MRs, a path loss between a main beam of each cell in the group of MRs to the stereo grid, and specifically is configured to:

15. The apparatus according to claim 11, wherein the determining unit is configured to determine the network configuration parameter of the target cell according to the path loss matrices of the N stereoscopic grids, and specifically is configured to:

16. A network management apparatus, comprising:

the device comprises a gathering unit, a processing unit and a processing unit, wherein the gathering unit is used for gathering a plurality of MDT data according to cell information of each MDT in a plurality of MDT data for testing the minimized road to obtain N groups of MDT data, each group of MDT comprises MDTs with the same vertical beam identification of the cells, and the cell information of each MDT comprises cell identifications of one or more cells, main beam identifications of one or more cells and longitudes and latitudes of one or more cells; the vertical beam identifier of the cell is determined according to the main beam identifier of the cell;

a creating unit, configured to create, for each of the N sets of MDTs, a three-dimensional grid of the set of MDTs according to a longitude and a latitude of each cell in the set of MDTs and a vertical beam identifier of each cell;

a calculating unit, configured to calculate, for each group of the three-dimensional grids of the MDT, a path loss from a main beam of each cell in the group of the MDTs to the three-dimensional grid, so as to obtain a path loss matrix of the three-dimensional grid;

17. The apparatus of claim 16, wherein the cell information of each MDT comprises cell information of a serving cell and/or cell information of a neighbor cell, and wherein the RSRP of the one or more cells comprises the RSRP of the serving cell and/or the RSRP of the neighbor cell; the determination unit is further configured to:

18. The apparatus of claim 17, wherein the creating unit is configured to create, for each of the N sets of MDTs, a stereo grid of the set of MDTs according to a longitude, a latitude, and a vertical beam identification of each cell in the set of MDTs, and is specifically configured to:

19. The apparatus according to any of claims 16 to 18, wherein the calculating unit is configured to calculate, for each group of the three-dimensional grids of the MDT, a path loss between a main beam of each cell in the group of the MDT to the three-dimensional grid, and in particular:

20. The apparatus according to claim 16, wherein the determining unit is configured to determine the network configuration parameter of the target cell according to the path loss matrices of the N stereoscopic grids, and specifically is configured to:

21. A network management apparatus, comprising: a memory and a processor;

the memory to store computer instructions;

the processor to execute the computer instructions such that the method of any of claims 1 to 5 is performed.

22. A network management apparatus, comprising: a memory and a processor;

the memory to store computer instructions;

the processor to execute the computer instructions such that the method of any of claims 6 to 10 is performed.

23. A computer-readable storage medium, comprising a program or computer instructions, which when run on a computer, performs the method of any one of claims 1 to 10.