WO2022017012A1 - 一种网络配置方法及装置 - Google Patents

一种网络配置方法及装置 Download PDF

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Publication number
WO2022017012A1
WO2022017012A1 PCT/CN2021/098022 CN2021098022W WO2022017012A1 WO 2022017012 A1 WO2022017012 A1 WO 2022017012A1 CN 2021098022 W CN2021098022 W CN 2021098022W WO 2022017012 A1 WO2022017012 A1 WO 2022017012A1
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WIPO (PCT)
Prior art keywords
cell
group
grid
mdts
main beam
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PCT/CN2021/098022
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English (en)
French (fr)
Inventor
常瑞娜
康怡彬
吴珏莹
杨达
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华为技术有限公司
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Publication of WO2022017012A1 publication Critical patent/WO2022017012A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports

Definitions

  • the present application relates to the field of communication technologies, and in particular, to a network configuration method and apparatus.
  • sites are usually deployed in a cellular shape, and each site is determined by radio frequency parameters to its coverage.
  • coverage is an important indicator to measure wireless network performance.
  • the coverage area of one or more cells can be modeled by means of two-dimensional plane rasterization, so as to optimize the coverage performance index.
  • the 2D plane rasterization modeling needs to project the vertical plane features in the stereo coverage to the 2D plane, and the vertical plane coverage features are lost, which will lead to poor vertical plane coverage signals, resulting in weak coverage, overlapping coverage and other problems.
  • the embodiment of the present application provides a network configuration method, which can construct a three-dimensional stereo coverage feature according to the MR data of a measurement report, which is beneficial to optimize the three-dimensional stereo coverage.
  • an embodiment of the present application provides a network configuration method, which can be executed by a network management device, and the network management device can be deployed offline on a stand-alone computer or cloud (ie, an offline tool), or can be deployed online to a network On the management system OMC or the online tool platform connected to OMC (ie online tool).
  • the network management device may perform aggregation processing on the multiple pieces of MR data according to the cell information of each MR in the multiple measurement report MR data to obtain N groups of MR data, and obtain the average of each cell of each group of MRs in the N groups of MRs.
  • Reference Signal Received Power RSRP Reference Signal Received Power
  • the network management apparatus For each group of MRs in the N groups of MRs, the network management apparatus creates a three-dimensional grid of the group of MRs according to the average RSRP of each cell in the group of MRs and the vertical beam range and horizontal beam range corresponding to the main beam identifier of each cell .
  • the path loss between the main beam of each cell in the group of MRs and the stereo grid is calculated to obtain a path loss matrix of the stereo grid.
  • the network management apparatus may determine the network configuration parameters of the target cell according to the path loss matrix of the N three-dimensional grids, and the target cell is a cell whose coverage index does not meet the preset coverage index and/or the capacity index does not meet the preset capacity index.
  • the network management device can construct a three-dimensional three-dimensional grid according to the average RSRP of each cell in the MR data and the horizontal beam information and vertical beam information corresponding to the main beam identifier of each cell.
  • a target for constructing a 3D solid grid A target for constructing a 3D solid grid.
  • a three-dimensional path loss matrix can be constructed according to the three-dimensional three-dimensional grid to realize stereo beam optimization.
  • the network management apparatus may also determine the network configuration parameters of the target cell according to the three-dimensional path loss matrix, which is beneficial to optimizing the coverage and/or capacity of the network.
  • the cell information of each MR includes the cell information of the serving cell and/or the cell information of the neighboring cells, wherein the RSRP of one or more cells includes the RSRP of the serving cell and/or the RSRP of the neighboring cells .
  • the network management apparatus may, for each MR, perform the aggregation process according to the cell identifier of the serving cell and the RSRP of the serving cell in the MR. , the main beam identifier of the serving cell, and the main beam prediction model of the serving cell of the MR is determined. Then, according to the main beam prediction model of the serving cell of the MR, the cell identity of the adjacent cell, and the RSRP of the adjacent cell, the main beam identity of the adjacent cell of the MR is determined.
  • the network management device can predict the main beam identifier of the MR in its serving cell and each neighboring cell, so as to obtain the full main beam attributes of the MR, which is beneficial for the network management device to take into account the overall network performance when performing coverage and/or capacity optimization. .
  • the network management apparatus may perform aggregation processing on the MR data with the same cell identifier, the same main beam identifier, and the level difference value satisfying a preset level difference value condition, Determine the pooled set of MRs.
  • the level difference value is the difference value of RSRP between each pair of cells with the same cell identifier and the same main beam identifier in the MR data.
  • the MR data collected by the network management device is MR data with similarities in wireless spatial propagation, which is beneficial to modeling a three-dimensional grid of a specified location.
  • the similar feature MRs are aggregated and processed, which is conducive to more precise simulation of actual network propagation and greatly reduces the amount of calculation.
  • the network management apparatus may obtain the beam gain corresponding to the main beam identifier of cell i according to the main beam identifier of any cell i in the group of MRs; the The beam gain of cell i is determined according to the horizontal beam range, vertical beam range, antenna gain, signal attenuation value corresponding to the horizontal beam range, and signal attenuation value corresponding to the vertical beam range corresponding to the main beam identifier of cell i.
  • the network management device then calculates the path loss from the main beam of cell i to the stereo grid of the group of MRs according to the transmit power of cell i, the beam gain of cell i and the average RSRP of cell i.
  • the network management device calculates the path loss of the three-dimensional grid of each group of MRs, the horizontal beam, the vertical beam and the average RSRP of the cell are fully considered. That is to say, the calculated path loss reflects the path loss of the three-dimensional three-dimensional grid. , avoiding the loss of coverage and capacity features caused by averaging processing of 2D geographic rasters.
  • the network management apparatus determines the target cell of the cubic grid according to the coverage index and/or the capacity index of each cell of the cubic grid.
  • the network configuration parameters of the target cell are adjusted according to the preset coverage index and the preset capacity index of the target cell.
  • the adjusted antenna gain of the target cell is obtained; according to the path loss matrix of the three-dimensional grid, the adjusted antenna gain of the target cell and the adjusted network configuration parameters of the target cell, Determine the coverage index and capacity index of the target cell. If the coverage index of the target cell reaches the preset coverage index, and/or the capacity index of the target cell reaches the preset capacity index, it is determined that the adjusted network configuration parameters of the target cell are the network configuration parameters of the target cell.
  • the network management apparatus can screen out target cells with coverage problems and/or capacity problems, and optimize the coverage and/or capacity of the target cells by adjusting network configuration parameters of the target cells.
  • the network configuration parameters include one or more of horizontal beamwidth, physical azimuth, digital azimuth, vertical beamwidth, physical downtilt, or digital downtilt;
  • the horizontal beam width represents the horizontal envelope width covered by the horizontal plane controlled by the beam weight
  • the vertical beam width represents the vertical envelope width covered by the horizontal plane controlled by the beam weight
  • the physical azimuth represents the facing direction and the true north of the physical antenna panel.
  • the digital azimuth angle represents the angle between the strongest direction of the horizontal beam energy controlled by the beam weight and the true north
  • the physical downtilt angle represents the angle between the plane perpendicular to the physical antenna panel and the horizontal plane
  • the digital downtilt angle represents the beam weight value
  • the steered vertical beam energy is most strongly directed at the angle to the horizontal.
  • an embodiment of the present application provides a network configuration method, and the network configuration method can be executed by a network management apparatus.
  • the network management device can aggregate and process multiple pieces of MDT data according to the cell information of each MDT in the multiple minimization road test MDT data to obtain N groups of MDT data, each group of MDTs includes MDTs with the same vertical beam identification of cells.
  • a three-dimensional grid of the group of MDTs is created according to the longitude and latitude of each cell in the group of MDTs and the vertical beam identifier of each cell.
  • the path loss between the main beam of each cell in the group of MDTs and the three-dimensional grid is calculated to obtain the path loss matrix of the three-dimensional grid.
  • the network management device can construct a three-dimensional three-dimensional grid according to the longitude and latitude information in the MDT data and the vertical beam identification determined according to the main beam identification of the cell, so as to avoid the average processing of the data by the two-dimensional geographic grid from blurring the stereo information. Conducive to achieve more accurate stereo optimization.
  • the network management device can generate a three-dimensional path loss matrix according to the three-dimensional three-dimensional grid, and can determine the network configuration parameters of the target cell according to the three-dimensional path loss matrix, which is conducive to optimizing network coverage and/or capacity.
  • the network management apparatus determines the main beam of the serving cell of the MDT according to the cell identifier of the serving cell, the RSRP of the serving cell, and the main beam identifier of the serving cell in the MDT. prediction model.
  • the main beam prediction model of the serving cell of the MDT the cell identifier of the adjacent cell and the RSRP of the adjacent cell, the main beam identifier of the adjacent cell of the MDT is determined.
  • the vertical beam identifiers of the cells in the MDT are determined.
  • the network management apparatus can determine the vertical beam identifier of each cell in the MDT data, so as to mark each cell in the MDT data on a three-dimensional level, and determine the three-dimensional information of each cell.
  • the network management device determines the grid longitude and grid latitude of the plane grid formed by the group of MDTs according to the longitude and latitude of each cell in the group of MDTs; and then according to the vertical direction of the group of MDTs Beam identification, to determine the vertical layer where the group of MDTs constitutes the plane grid, so as to obtain the three-dimensional grid of the group of MDTs.
  • the network management apparatus can determine the two-dimensional plane grid according to the longitude and latitude information in the MDT data, and then determine the three-dimensional position of the plane grid according to the vertical beam identifier, thereby constructing the three-dimensional grid.
  • the network management apparatus acquires the vertical beam range corresponding to the vertical beam identifier, the antenna gain and the signal attenuation value corresponding to the vertical beam range according to the vertical beam identifier in the group of MDTs. According to the grid longitude and grid latitude of the group of MDTs, obtain the horizontal beam range, antenna gain and signal attenuation value corresponding to the horizontal beam range corresponding to the geographic grid determined by the grid longitude and grid latitude in the group of MDTs.
  • the network management device fully considers the average RSRP of the horizontal beam, the vertical beam and the cell when calculating the path loss of the three-dimensional grid of each group of MDTs. That is to say, the calculated path loss reflects the path loss of the three-dimensional three-dimensional grid. , avoiding the loss of coverage and capacity features caused by averaging processing of 2D geographic rasters.
  • the network management apparatus determines the target cell of the cubic grid according to the coverage index and/or the capacity index of each cell of the cubic grid. Then, the network configuration parameters of the target cell are adjusted according to the preset coverage index and the preset capacity index of the target cell. According to the adjusted network configuration parameters of the target cell, the adjusted antenna gain of the target cell is obtained. According to the path loss matrix of the three-dimensional grid, the adjusted antenna gain of the target cell, and the adjusted network configuration parameters of the target cell, the coverage index and the capacity index of the target cell are determined. If the coverage index of the target cell reaches the preset coverage index, and/or the capacity index of the target cell reaches the preset capacity index, it is determined that the adjusted network configuration parameters of the target cell are the network configuration parameters of the target cell .
  • the network management apparatus can screen out target cells with coverage problems and/or capacity problems, and optimize the coverage and/or capacity of the target cells by adjusting network configuration parameters of the target cells.
  • the network configuration parameters include one or more of horizontal beamwidth, physical azimuth, digital azimuth, vertical beamwidth, physical downtilt, or digital downtilt;
  • the horizontal beam width represents the horizontal envelope width covered by the horizontal plane controlled by the beam weight
  • the vertical beam width represents the vertical envelope width covered by the horizontal plane controlled by the beam weight
  • the physical azimuth represents the facing direction and the true north of the physical antenna panel.
  • the digital azimuth angle represents the angle between the strongest direction of the horizontal beam energy controlled by the beam weight and the true north
  • the physical downtilt angle represents the angle between the plane perpendicular to the physical antenna panel and the horizontal plane
  • the digital downtilt angle represents the beam weight value
  • the steered vertical beam energy is most strongly directed at the angle to the horizontal.
  • an embodiment of the present application provides a network management apparatus, the apparatus includes a convergence unit, an acquisition unit, a creation unit, a calculation unit, and a determination unit.
  • the aggregation unit is configured to perform aggregation processing on the multiple pieces of MR data according to the cell information of each MR in the multiple measurement report MR data to obtain N groups of MR data, where each group of MRs includes MRs with wireless spatial propagation similarity.
  • the obtaining unit is configured to obtain the average reference signal received power RSRP of each cell of each group of MRs in the N groups of MRs.
  • the creating unit is configured to, for each group of MRs in the N groups of MRs, create a stereo grid of the group of MRs according to the average RSRP of each cell in the group of MRs and the vertical beam range and horizontal beam range corresponding to the main beam identifier of each cell grid.
  • the calculation unit is configured to calculate, for the stereo grids of each group of MRs, the path loss between the main beam of each cell in the group of MRs and the stereo grid, so as to obtain a path loss matrix of the stereo grid.
  • the determining unit is configured to determine the network configuration parameters of the target cell according to the path loss matrix of the N three-dimensional grids, wherein the target cell is a cell whose coverage index does not meet the preset coverage index and/or the capacity index does not meet the preset capacity index.
  • the cell information of each MR includes the cell information of the serving cell and/or the cell information of the neighboring cells
  • the RSRP of one or more cells includes the RSRP of the serving cell and/or the RSRP of the neighboring cells.
  • the determining unit is further configured to, for each MR, determine the main beam prediction model of the serving cell of the MR according to the cell identifier of the serving cell, the RSRP of the serving cell, and the main beam identifier of the serving cell in the MR. According to the main beam prediction model of the serving cell of the MR, the cell identifier of the adjacent cell, and the RSRP of the adjacent cell, the main beam identifier of the adjacent cell of the MR is determined.
  • the aggregation unit is configured to perform aggregation processing on MR data with the same cell identifiers, the same main beam identifiers, and the level difference satisfying the preset level difference condition, to determine a group of MRs after aggregation; wherein , and the level difference value is the RSRP difference between the cells in the MR data with the same cell ID and the same main beam ID.
  • the calculation unit is configured to obtain, according to the main beam identifier of any cell i in the group MR, the beam gain corresponding to the main beam identifier of the cell i; the beam gain of the cell i is based on the The horizontal beam range, vertical beam range, antenna gain, signal attenuation value corresponding to the horizontal beam range, and signal attenuation value corresponding to the vertical beam range corresponding to the main beam identifier of cell i are determined; according to the transmit power of cell i, the beam of cell i The gain and the average RSRP of cell i, calculate the path loss from the main beam of cell i to the stereo grid of the group MR.
  • the determining unit is configured to determine the target cell of the three-dimensional grid according to the coverage index and/or the capacity index of each cell of the three-dimensional grid.
  • the network configuration parameters of the target cell are adjusted; according to the adjusted network configuration parameters of the target cell, the adjusted antenna gain of the target cell is obtained; according to the three-dimensional grid
  • the path loss matrix, the adjusted antenna gain of the target cell and the adjusted network configuration parameters of the target cell are used to determine the coverage index and capacity index of the target cell. If the coverage index of the target cell reaches the preset coverage index, and/or the capacity index of the target cell reaches the preset capacity index, it is determined that the adjusted network configuration parameters of the target cell are the network configuration parameters of the target cell.
  • the network configuration parameters include one or more of horizontal beamwidth, physical azimuth, digital azimuth, vertical beamwidth, physical downtilt, or digital downtilt;
  • the horizontal beam width represents the horizontal envelope width covered by the horizontal plane controlled by the beam weight
  • the vertical beam width represents the vertical envelope width covered by the horizontal plane controlled by the beam weight
  • the physical azimuth represents the facing direction and the true north of the physical antenna panel.
  • the digital azimuth angle represents the angle between the strongest direction of the horizontal beam energy controlled by the beam weight and the true north
  • the physical downtilt angle represents the angle between the plane perpendicular to the physical antenna panel and the horizontal plane
  • the digital downtilt angle represents the beam weight value
  • the steered vertical beam energy is most strongly directed at the angle to the horizontal.
  • an embodiment of the present application provides a network management device, where the network management device includes a convergence unit, a creation unit, a calculation unit, and a determination unit.
  • the aggregation unit is configured to perform aggregation processing on the multiple pieces of MDT data according to the cell information of each MDT in the multiple minimized road test MDT data to obtain N groups of MDT data, each group of MDTs includes MDTs with the same vertical beam identifiers of cells.
  • the creating unit is configured to, for each group of MDTs in the N groups of MDTs, create a three-dimensional grid of the group of MDTs according to the longitude and latitude of each cell in the group of MDTs and the vertical beam identifier of each cell.
  • the calculation unit is configured to, for each group of MDTs, calculate the path loss between the main beam of each cell in the group of MDTs and the three-dimensional grid, so as to obtain a path loss matrix of the three-dimensional grid.
  • the determining unit is configured to determine the network configuration parameters of the target cell according to the path loss matrix of the N three-dimensional grids, wherein the target cell is a cell whose coverage index does not meet the preset coverage index and/or the capacity index does not meet the preset capacity index.
  • the cell information of each MDT includes the cell information of the serving cell and/or the cell information of the neighboring cells
  • the RSRP of one or more cells includes the RSRP of the serving cell and/or the neighboring cells RSRP.
  • the determining unit is further configured to determine the main beam prediction model of the serving cell of the MDT according to the cell identifier of the serving cell, the RSRP of the serving cell, and the main beam identifier of the serving cell in the MDT.
  • the cell identifier of the adjacent cell and the RSRP of the adjacent cell determine the main beam identifier of the adjacent cell of the MDT; according to the main beam identifier of each cell in the MDT , and determine the vertical beam identifier of each cell in the MDT.
  • a unit is created for determining the grid longitude and grid latitude of the plane grid formed by the group of MDTs according to the longitude and latitude of each cell in the group of MDTs; according to the vertical direction of the group of MDTs Beam identification, to determine the vertical layer where the group of MDTs constitutes the plane grid, so as to obtain the three-dimensional grid of the group of MDTs.
  • the computing unit is configured to obtain, according to the vertical beam identifiers in the group of MDTs, the vertical beam range corresponding to the vertical beam identifier, the antenna gain, and the signal attenuation value corresponding to the vertical beam range.
  • the grid longitude and grid latitude of the group of MDTs obtain the horizontal beam range, antenna gain and signal attenuation value corresponding to the horizontal beam range corresponding to the geographic grid determined by the grid longitude and grid latitude in the group of MDTs.
  • the determining unit is configured to determine the target cell of the three-dimensional grid according to the coverage index and/or the capacity index of each cell of the three-dimensional grid.
  • the network configuration parameters of the target cell are adjusted according to the preset coverage index and the preset capacity index of the target cell; the adjusted antenna gain of the target cell is obtained according to the adjusted network configuration parameters of the target cell.
  • the adjusted antenna gain of the target cell, and the adjusted network configuration parameters of the target cell, the coverage index and the capacity index of the target cell are determined. If the coverage index of the target cell reaches the preset coverage index, and/or the capacity index of the target cell reaches the preset capacity index, it is determined that the adjusted network configuration parameter of the target cell is the target cell network configuration parameters.
  • an embodiment of the present application provides a network device, and the device has a function of implementing the network configuration method provided in the first aspect.
  • This function can be implemented by hardware or by executing corresponding software by hardware.
  • the hardware or software includes one or more modules corresponding to the above functions.
  • an embodiment of the present application provides a network device, and the device has a function of implementing the network configuration method provided in the second aspect.
  • This function can be implemented by hardware or by executing corresponding software by hardware.
  • the hardware or software includes one or more modules corresponding to the above functions.
  • embodiments of the present application provide a computer-readable storage medium, where the readable storage medium includes a program or an instruction, and when the program or instruction is run on a computer, the computer executes the first aspect or the first aspect. method in any of the possible implementations.
  • embodiments of the present application provide a computer-readable storage medium, where the readable storage medium includes a program or an instruction, and when the program or instruction is run on a computer, the computer executes the second aspect or the second aspect. method in any of the possible implementations.
  • an embodiment of the present application provides a chip or a chip system, the chip or chip system includes at least one processor and an interface, the interface and the at least one processor are interconnected through a line, and the at least one processor is used to run a computer program or instruction, to perform the method described in any one of the first aspect or any of the possible implementations of the first aspect.
  • an embodiment of the present application provides a chip or a chip system, the chip or chip system includes at least one processor and an interface, the interface and the at least one processor are interconnected through a line, and the at least one processor is used for running a computer program or instruction, to perform the method described in any one of the second aspect or any of the possible implementations of the second aspect.
  • the interface in the chip may be an input/output interface, a pin or a circuit, or the like.
  • the chip system in the above aspects may be a system on chip (system on chip, SOC), or a baseband chip, etc.
  • the baseband chip may include a processor, a channel encoder, a digital signal processor, a modem, an interface module, and the like.
  • the chip or chip system described above in this application further includes at least one memory, where instructions are stored in the at least one memory.
  • the memory may be a storage unit inside the chip, such as a register, a cache, etc., or a storage unit of the chip (eg, a read-only memory, a random access memory, etc.).
  • the embodiments of the present application provide a computer program or computer program product, including codes or instructions, when the codes or instructions are run on a computer, the computer executes the first aspect or any one of the first aspects may be implemented method in method.
  • embodiments of the present application provide a computer program or computer program product, including codes or instructions, when the codes or instructions are run on a computer, the computer executes the second aspect or any one of the second aspects may be implemented method in method.
  • 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 schematic diagram of a common antenna and a Massive MIMO antenna
  • FIG. 4 is a schematic diagram of a two-dimensional plane grid path loss matrix
  • FIG. 5 is a schematic diagram of a communication system provided by an embodiment of the present application.
  • FIG. 6 is a schematic diagram of beam coverage of a Massive MIMO antenna according to an embodiment of the present application.
  • FIG. 7 is a schematic flowchart of a network configuration method provided by 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;
  • 9a is a schematic diagram of a three-dimensional grid provided by an embodiment of the present application.
  • FIG. 9b is a schematic diagram of a path loss matrix provided by an embodiment of the present application.
  • FIG. 10 is a schematic diagram of a network optimization target optimization process provided by an embodiment of the present application.
  • FIG. 11 is a schematic flowchart of another network configuration method provided by an embodiment of the present application.
  • FIG. 12 is a schematic diagram of a region formed by projecting a vertical layer onto the ground according to an embodiment of the present application
  • FIG. 13a is a schematic diagram of another three-dimensional grid provided by an embodiment of the present application.
  • 13b is a schematic diagram of another path loss matrix provided by an embodiment of the present application.
  • FIG. 14 is a schematic diagram of a network management apparatus according to an embodiment of the present application.
  • 15 is a schematic diagram of a network management device according to an embodiment of the present application.
  • FIG. 16 is a schematic diagram of another network management apparatus provided by an embodiment of the present application.
  • FIG. 17 is a schematic diagram of another network management device provided by an embodiment of the present application.
  • the wireless communication network deploys sites according to the shape of the cell. Each site is determined by its radio frequency parameters, and the coverage determines the strength of the wireless signal received by the user, which affects the number of users (capacity) that the site can access. Please refer to FIG. 1.
  • FIG. 1 is a schematic diagram of a wireless network according to an embodiment of the present application.
  • the wireless network includes multiple sites, and each site is deployed according to a certain spatial distribution, as shown in FIG. 1 .
  • the coverage area (also referred to as a cell) of one site is a dotted line area shown in FIG. 1 , and the coverage area formed by multiple sites is shown in FIG. 1 .
  • Coverage and capacity are important indicators to measure the network performance of wireless networks. However, due to differences between network planning and actual physical environment, changes in urban construction, and user development, wireless networks may have weak coverage, overlapping coverage, etc. Coverage problems, as well as capacity problems such as unbalanced load and hot traffic suppression, are shown in Figure 2.
  • the weak coverage means that the average reference signal received power (reference signal received power, RSRP) of the current area is lower than a certain threshold value.
  • the weak coverage area in FIG. 2 is located between cell 1 and cell 2, and is located at the edge area of cell 1 and cell 2. Users in this weak coverage area may not be able to receive signals normally.
  • Overlapping coverage refers to the overlapping area between adjacent areas, eg, the shaded area between cell 2 and cell 3 in FIG. 2 .
  • Traffic suppression in hotspots means that the signal quality in the current area is poor, and more energy is required to send data.
  • the number of access users in the hotspot coverage area of cell 1 in FIG. 2 is relatively large, and there is a problem of traffic suppression in the hotspot.
  • a wireless network In order to ensure a better user experience (for example, a higher network speed), a wireless network should have better reception levels, less interference, and more balanced user distribution. That is to say, the wireless network needs to consider the two important performance indicators of coverage and capacity at the same time.
  • the coverage and capacity of the site can be controlled by adjusting the radio frequency parameters of the antenna.
  • Common antennas may include single input single output (SISO), multiple input multiple output (MIMO), and the like.
  • a common antenna may include 2TRx, 4TRx, or 8TRx, etc., as shown in FIG. 3 .
  • Massive MIMO Massive MIMO is a fifth-generation mobile communication technology and the basic key technology networks (the 5 th generation, 5G) .
  • Massive MIMO antennas can achieve three-dimensional precise beamforming and multi-user beam multiplexing by integrating more antennas, thereby achieving better coverage and greater capacity.
  • Common Massive MIMO antennas can include 32TRx and 64TRx, as shown in Figure 3.
  • the RF parameters change from three types of ordinary antennas (physical azimuth, physical downtilt, and digital downtilt) to six types of Massive MIMO antennas (physical azimuth, physical downtilt, and digital downtilt).
  • the beam of the broadcast beam can also be adjusted independently to form coverage of any shape. That is to say, by adjusting the radio frequency parameters of the MIMO antenna, the coverage of the cell can be improved.
  • the existing method for adjusting the radio frequency parameters of the MIMO antenna is usually to perform geographic grid modeling on the coverage optimization area according to the minimization of drive tests (MDT) data reported by the terminal equipment to obtain a two-dimensional plane grid.
  • the path loss matrix is shown in Figure 4.
  • the set optimization target is the coverage index.
  • the radio frequency parameters are derived, and the radio frequency parameters are adjusted according to the derivatives of the radio frequency parameters to improve the cell coverage.
  • Massive MIMO antennas have enhanced stereo coverage.
  • Two-dimensional plane rasterization directly projects vertical plane features to a two-dimensional plane, and averages the data in the same grid, which loses vertical plane coverage characteristics. It will lead to poor signal coverage on vertical surfaces such as buildings.
  • the above adjustment method cannot be used to model the coverage area by geographic rasterization, and thus coverage adjustment cannot be performed. excellent.
  • an embodiment of the present application provides a network configuration method, which can construct a three-dimensional stereo coverage feature according to MR data of a measurement report, and model a path loss matrix, thereby facilitating optimization of the three-dimensional stereo coverage.
  • the network configuration method can be deployed offline on a stand-alone personal computer (personal computer, PC) or on the cloud (ie, an offline tool), or can be deployed online to a network management system (operation maintenance center, OMC) or an online tool platform connected to the OMC ( i.e. online tools).
  • OMC operation maintenance center
  • FIG. 5 provides a communication system according to an embodiment of the present application, where the communication system includes an OMC, a network device, a terminal device, and the like.
  • the embodiment of the present application is described by taking the online tool deployed to connect to the OMC as an example, and the OMC can communicate with each network device, and can also communicate with the online tool Online Tool, as shown in Figure 5.
  • the OMC can obtain the data reported by the terminal device and the network device.
  • Mass multiple input multiple output is a fifth-generation mobile communication (the 5 th generation, 5G) technology and the basic key technology networks.
  • Massive MIMO antennas can achieve three-dimensional precise beamforming and multi-user beam multiplexing by integrating more antennas, thereby achieving better coverage and greater capacity.
  • FIG. 6 is a schematic diagram of beam coverage of a Massive MIMO antenna according to an embodiment of the present application.
  • the left side of FIG. 6 is a schematic diagram of a horizontal beam of the Massive MIMO antenna. From a horizontal cross section, the horizontal beam includes 8 beams.
  • the horizontal beam width of the horizontal beam is shown in FIG. 6 , that is, the horizontal beam width is the width of the horizontal beam envelope.
  • the middle of FIG. 6 is a schematic diagram of a vertical beam of the Massive MIMO antenna. From a vertical cross-section, the vertical beam includes 4 layers.
  • the vertical beam width of the vertical beam is shown in FIG. 6 , that is, the vertical beam width is the width of the vertical beam envelope. That is to say, the Massive MIMO antenna is divided into 4 layers in the vertical direction, and the horizontal beam corresponding to each layer includes 8 beams, that is, the Massive MIMO antenna has a total of 32 narrow beams, which form different coverage areas in the horizontal and vertical directions.
  • FIG. 6 is a schematic diagram of an arbitrary beam of a Massive MIMO antenna.
  • each narrow beam can be adjusted independently to form coverage of any shape, as shown in Figure 6.
  • the beam weight (also referred to as the antenna weight) refers to the quantitative expression form after applying a specific excitation signal to each port of the antenna, the purpose of which is to obtain specific coverage or achieve the effect of beam deformation.
  • various beam shapes can be generated through different beam weights, for example, cell-level broadcast beams (ie, SSB beams) and user-level static beams (including SRS beams and CSI-RS beams).
  • the SSB (SS/PBCH blocks) beam is used to stagger the interference between adjacent areas, and more beams are used to achieve spatial coverage and achieve the optimal coverage of the 5G network.
  • a sounding reference signal (SRS) is an uplink pilot signal sent by a terminal device to a network device, and is used to judge the channel quality of each channel in each frequency band. According to the SRS, the network device selects an appropriate channel to dynamically schedule and allocate resources to the terminal device to obtain the best transmission efficiency and quality.
  • a channel state information reference signal (CSI-RS) is a downlink pilot signal sent by a network device to a terminal device for CSI-RS channel measurement, time-frequency offset tracking, beam management, and mobility management.
  • the CSI-RS in this embodiment refers to the CSI-RS used for mobility management.
  • the mobility management CSI-RS can measure the beam-level RSRP of the serving cell and its neighboring cells.
  • Beam gain also called antenna gain
  • antenna gain refers to the ratio of the power density of the signal generated by the actual antenna and the ideal radiating element at the same point in space under the condition of equal input power.
  • the beam gain is closely related to the antenna pattern. The narrower the main lobe of the pattern, the smaller the side lobe, and the higher the gain.
  • Antenna gain is used to measure the ability of the antenna to send and receive signals in a specific direction, and it is one of the important parameters for selecting a base station antenna.
  • a measurement report refers to a measurement report reported by a terminal device, and the MR includes information such as the cell ID of the serving cell, the RSRP of the serving cell, the cell ID of the neighboring cell, and the RSRP of the neighboring cell. But MR does not contain latitude and longitude information.
  • MDT is the minimum road test data defined by 3GPP, including the latitude and longitude information reported by the terminal equipment, the cell ID of the serving cell, the RSRP of the serving cell, the cell ID of the neighboring cell, and the RSRP of the neighboring cell and other information. That is, MDT can be considered as MR with latitude and longitude.
  • a station can be any device with a wireless transceiver function, which provides wireless communication services for terminal devices within its coverage.
  • the stations may include but are not limited to: an evolved base station (NodeB or eNB or e-NodeB, evolutionalNodeB) in a long term evolution (longtermevolution, LTE) system, a base station (gNodeB or gNB) in a new generation radio access technology (new radio access technology, NR) ) or transmission receiving point/transmission reception point (TRP), base station for subsequent evolution of 3GPP, access node in WiFi system, wireless relay node, wireless backhaul node, Internet of Vehicles, D2D communication, and equipment that undertakes base station functions in machine communication , satellite, etc.
  • NodeB or eNB or e-NodeB, evolutionalNodeB in a long term evolution (longtermevolution, LTE) system
  • gNodeB or gNB in a new generation radio access technology (new radio access technology, NR) ) or
  • the terminal device may be a device with a wireless transceiver function, or the terminal device may also be a chip.
  • the terminal device may be a user equipment (userequipment, UE), a mobile phone (mobilephone), a tablet computer (Pad), a computer with a wireless transceiver function, a virtual reality (virtual reality, VR) terminal device, an augmented reality (augmented reality, AR) terminal Equipment, in-vehicle terminal equipment, wireless terminals in telemedical, wireless terminals in smart grid, wearable terminal equipment, Internet of Vehicles, D2D communication, sensors in machine communication, etc.
  • VR virtual reality
  • AR augmented reality
  • FIG. 7 is a schematic flowchart of a network configuration method provided by an embodiment of the present application.
  • the network configuration method can be performed by an offline tool deployed offline on a stand-alone computer or cloud, or by an online tool deployed online to the network management system OMC or an online tool platform connected to the OMC.
  • the network configuration method is applied to scenarios with no MDT data or insufficient MDT data, and includes the following steps:
  • the network management apparatus performs aggregation processing on the multiple pieces of MR data according to the cell information of each MR in the multiple pieces of measurement report MR data to obtain N groups of MR data;
  • the network management apparatus acquires the average reference signal received power RSRP of each cell of each group of MRs in the N groups of MRs;
  • the network management apparatus creates a stereoscopic image of the group of MRs according to the average RSRP of each cell in the group of MRs and the vertical beam range and horizontal beam range corresponding to the main beam identifier of each cell grid;
  • the network management device calculates the path loss between the main beam of each cell in the group of MRs to the three-dimensional grid, to obtain a path loss matrix of the three-dimensional grid;
  • the network management apparatus determines the network configuration parameters of the target cell according to the path loss matrix of the N three-dimensional grids.
  • the network management apparatus may perform aggregation processing on the MRs reported by the terminal equipment to obtain N groups of MR data. Specifically, the network management apparatus may first perform data preprocessing on the MR reported by the terminal device, and the data preprocessing includes MR main beam identification and similar feature MR data aggregation.
  • the network management apparatus may also obtain a call history record (CHR) of the terminal device.
  • the CHR can record each call of the terminal device according to the method of feature extraction.
  • the CHR includes the cell identity of the serving cell (service cell identity, S_Cell_ID), the uplink throughput of the serving cell (service cell uplink throughput, ULThroughput), the downlink throughput of the serving cell (service cell downlink throughput, DLThroughput), the main beam identifier of the serving cell ( service main beam identity, S_MainBeam_ID), may also include access related information of the terminal device, handover related information, etc., which is not limited in this embodiment.
  • the network management device may also receive an operating parameter/configuration file and an antenna file.
  • the working parameter/configuration file includes information such as radio frequency parameters of each cell, and the antenna file includes broadcast beam, horizontal pattern of service beam, vertical pattern of service beam, beam gain and attenuation information, etc., which are not limited in this embodiment.
  • MR and CHR are two different types of data reported by a terminal device, in order to facilitate subsequent processing, MR and CHR may be associated with each other.
  • the network management apparatus may perform association processing on the MR and the CHR according to "call time+Cell_ID+call_ID". Among them, since the number of call_IDs is limited, it will be repeated after a period of time. In this embodiment, the call_IDs are distinguished by limiting the call time.
  • the MR and CHR after correlation processing are referred to as MR data, which includes one or more MRs.
  • an MR represents a measurement report reported by a terminal device within a period of time
  • the cell information of the MR includes the cell identifiers of one or more cells, the main beam identifiers of one or more cells, and the RSRP of one or more cells.
  • the cell information of each MR includes cell identifiers of one or more cells, main beam identifiers of one or more cells, and RSRPs of one or more cells.
  • the serving cell refers to a cell that provides a channel for the terminal device when the terminal device communicates.
  • Neighboring cells represent cells adjacent to the serving cell. For example, if cell 2 in FIG. 2 is a serving cell of a terminal device, cell 3 is a neighboring cell of the terminal device.
  • the main beam of the serving cell is the beam with the strongest signal among the multiple beams transmitted by the Massive MIMO antenna detected by the terminal device. That is to say, the main beam identifier of the serving cell indicates the beam identifier of the beam with the strongest signal detected by the terminal device.
  • the terminal device can detect multiple beams transmitted by the Massive MIMO antenna at the current location.
  • the Massive MIMO antenna is 64TRx
  • 64TRx has 64 beams, horizontal 8*vertical 4*polarization 2, as shown in Figure 8.
  • the 64 beams (including dual polarizations) of the Massive MIMO antenna correspond to 32 physical beam positions, of which the two beams of positive 45-degree polarization and negative 45-degree polarization can be considered as one physical beam.
  • the 32 physical beams respectively have their corresponding beam identifiers.
  • the beam identifiers of the 32 physical beams start from 0 to 31, according to the vertical layers, the beam identifiers of the bottom first layer are 0 to 7 from south to north, and the beam identifiers of the second layer are from south to north. 8 to 15, the beam identifiers of the third layer are 16 to 23 from south to north, and the beam identifiers of the fourth layer are 24 to 31 from south to north.
  • the beam identifier of the beam with the strongest signal detected by the terminal device is 11, as shown in FIG. 8 .
  • the cell information of each MR includes cell information of a serving cell and/or cell information of a neighboring cell. That is to say, the related information of the neighboring cells is introduced in the embodiment of the present application, so that the network management apparatus also fully considers the influence of the neighboring cells of the cell when adjusting the network configuration parameters of a cell.
  • the cell information of each MR includes the cell identity of the serving cell, the RSRP (S_RSRP) of the serving cell, the cell identity (neighbor cell identity, N_Cell_ID) of the neighboring cell, the RSRP (N_RSRP) of the neighboring cell, and the like.
  • a main beam identity (neighbor main beam identity, N_MainBeam_ID) field of a neighboring cell is added to the MR data to record the main beam identification of a neighboring cell.
  • the MR data may be stored in the network management apparatus in the form of a table, or may be stored in other forms, which is not limited in this embodiment. Please refer to Table 1.
  • Table 1 is an MR data information table provided in the embodiment of the present application.
  • the information table includes fields such as S_Cell_ID, S_RSRP, S_MainBeam_ID, N_Cell_ID, N_RSRP, N_MainBeam_ID, ULThroughput, DLThroughput, etc.
  • Table 1 shows the physical meaning and source of each field.
  • the value of the N_MainBeam_ID field can be set to -1 before the MR main beam identification is performed.
  • the associated MR data is stored, it is arranged in the order of serving cells and neighboring cells. Among them, different serving cells are sorted and stored in order according to the size of S_RSRP; similarly, adjacent cells are sorted and stored in order according to the size of N_RSRP.
  • Table 2 provides an MR data storage table provided in this embodiment of the present application.
  • the storage table includes one or more MRs.
  • the cell information of each MR includes cell identifiers of one or more cells, primary beam identifiers of one or more cells, RSRP of one or more cells, ULThroughput of one or more cells, and DLThroughput of one or more cells.
  • Table 2 An MR data storage table
  • the MR whose MainBeam_ID value is -1 in Table 2 refers to the neighboring cell, and the values of ULThroughput and DLThroughput in this row are both 0, that is, the throughput information of the neighboring cell cannot be directly measured by the terminal device.
  • the network management apparatus can predict the adjacent main beam in the MR data, including the following steps:
  • the s11 for each MR in the MR data, according to the cell identifier of the serving cell in the MR, the RSRP of the serving cell, and the main beam identifier of the serving cell, determine the main beam prediction model of the serving cell of the MR;
  • s12 Determine the main beam identifier of the adjacent cell of the MR according to the main beam prediction model of the serving cell of the MR, the cell identifier of the adjacent cell, and the RSRP of the adjacent cell.
  • cell information when each cell is used as a serving cell is obtained.
  • the network management apparatus can obtain from Table 2 the Cell_ID, RSRP and MainBeam_ID of all cell i including cell i, and the value of MainBeam_ID of cell i is not -1.
  • the Cell_ID, RSRP and corresponding MainBeam_ID of each cell as a serving cell are used as training data, and the training data may be stored in the form of a table.
  • Cell_ID and RSRP are used as feature vectors, denoted as X_Train
  • MainBeam_ID is used as a label, denoted as Y_Train
  • the training data of the main beam prediction model of the cell is shown in Table 3.
  • Table 3 Training data table of a main beam prediction model for a cell
  • the serving cell in MR 1 is cell 1
  • MainBeam_ID is the MainBeam_ID of cell 1, according to Table 2, it can be known that the value of MainBeam_ID of cell 1 is 31. It should be noted that if cell n is not included in MR 1, the RSRP value of cell n in MR 1 is 0. The definition of the data of the remaining rows in Table 3 is similar to the definition of the row of MR 1, which is not repeated here.
  • the network management apparatus may use the data in Table 3 as training data, and use a machine learning algorithm to train the main beam prediction model.
  • the main beam prediction model of cell i is recorded as CellBeamModel_cell i.
  • cell information when each cell is used as a neighbor cell is obtained.
  • the network management apparatus can obtain from Table 2 the Cell_ID and RSRP of all cell i including cell i, and the value of the MainBeam_ID of cell i is -1.
  • the Cell_ID and RSRP when each cell is used as a neighbor cell are used as prediction input values, denoted as X_Pre, and the prediction input value can also be stored in the form of a table, as shown in Table 4.
  • Table 4 Prediction input table for a neighbor main beam prediction
  • Y_Pre of cell i can be obtained, that is, the main beam identifier of cell i can be predicted. Traverse the MR data whose MainBeam_ID value of all cells in Table 2 is -1, so that all cells in each piece of MR data are marked with the main beam identifier.
  • Table 5 is a main beam marked MR data storage table provided by an embodiment of the present application. Compared with Table 2, the value of MainBeam_ID of each cell in Table 5 is a value greater than -1, that is, the main beam identifier of each cell can indicate one of the beams generated by the Massive MIMO antenna.
  • Table 5 An MR data storage table after main beam marking
  • the network management apparatus may aggregate the MR data marked by the main beam according to certain rules. That is to say, the network management apparatus may perform aggregation of MR data with similar characteristics on the MR data marked by the main beam.
  • the network management device can firstly analyze the similarity of wireless spatial propagation on the MR data marked by the main beam, and then aggregate the MR data with the similarity of wireless spatial propagation to obtain N groups of MR data.
  • the similarity of wireless spatial propagation means that there are different propagation paths such as refraction, reflection, diffraction, etc., for wireless signals to propagate in space. Signals transmitted from the same cell, propagate through wireless space, and reach terminal devices with similar distances, and their wireless space propagation is similar.
  • terminal equipment 1 and terminal equipment 2 that are close in distance measure that the main beam identifiers of the surrounding cells are the same, and the difference in RSRP is small. Then it can be said that the wireless spatial propagation of terminal device 1 and terminal device 2 are similar.
  • the network management apparatus may aggregate MR data with wireless spatial propagation similarity according to each MR in Table 5 and the RSRP of each cell in each MR to obtain N groups of MR data. The following steps can be included:
  • the level difference value is the cell identifier in the MR data
  • multiple MRs with the same cell ID, the same main beam ID, and the level difference value satisfying the preset level difference value condition are regarded as MRs with wireless spatial propagation similarity .
  • the network management apparatus may first perform data preprocessing on the MR data marked by the main beam.
  • Cell_ID, RSRP and MainBeam_ID are used as feature vectors, and the MR data marked by the main beam is subjected to data preprocessing to obtain a similar feature MR data aggregation input table, as shown in Table 6.
  • Table 6 A similar feature MR data pooling input table
  • the network management device may perform aggregation processing on the data in Table 6 according to a certain clustering rule (eg, a clustering algorithm) according to the data in Table 6 above. For example, the network management apparatus aggregates MR data with the same Cell_ID, the same MainBeam_ID, and the RSRP difference less than 3 dB, to obtain a group of MRs.
  • a certain clustering rule eg, a clustering algorithm
  • the MainBeam_ID of cell1 in MR1 in Table 6 is 31, the RSRP is 80, the MainBeam_ID of cell1 in MR2 is 31, and the RSRP is 78.
  • the MainBeam_ID of cell 2 in MR 1 in Table 6 is 29, the RSRP is 70, the MainBeam_ID of cell 2 in MR 2 is 29, and the RSRP is 70.
  • the cell identifiers of the two MRs are the same, the main beam identifiers of the cells are the same, the difference between the RSRP of cell 1 in MR 1 and the RSRP of cell 1 in MR 2 is less than 3dB, and the RSRP of cell 2 in MR 1 is less than 3dB.
  • the value is the same as the value of RSRP of cell 1 in MR 2. That is, MR 1 and MR 2 can be combined into a set of MRs.
  • the network management apparatus when the network management apparatus performs aggregation processing on MR data with similar characteristics, the following situations may exist: the cell identifiers in the two MRs are the same, the main beam identifiers of the cells are the same, and the level difference of the cells also satisfies the preset voltage. Adjustment value condition; however, one of the MRs also includes other cell information. For example, for MR1 and MR3 in Table 6, both MR1 and MR3 include cell information of cell 1 and cell information of cell 2. However, MR 3 also includes cell information of cell 3.
  • the network management apparatus may process the MR data of similar characteristics. For example, for each MR in Table 6, calculate the difference between the maximum value of RSRP in each MR and the RSRP of each cell, and use the difference as a new eigenvector, and expand Table 6 as shown in Table 7 Show.
  • Table 7 An input table of similar feature MR data aggregation and expanded feature vector
  • MR 3 also includes cell information of cell 3.
  • the difference in RSRP of cell 3 in MR 3 is large. That is to say, compared with the serving cell cell 1, the RSRP of the neighboring cell 3 is smaller, so the distance between the cell 3 and the cell 1 may be farther, that is to say, the cell 3 in the neighboring cell may be invalid and can be ignored.
  • cell 3. That is, for MR 1 and MR 3, they can be aggregated into a group of MRs with wireless spatial propagation similarity.
  • the network configuration apparatus may determine how many groups of MRs the MR data is aggregated into according to the number of cells, the distribution of users in the cells, the number of users, and the overlapping coverage between cells.
  • UE1 is stationary, and its main beam is beam 31;
  • UE2 is a mobile user, and its main beams are beams 8 and 9.
  • Two users reported four MRs respectively, and the processed MR information is shown in Table 8.
  • Table 8 Similar feature MR data input table for UE 1 and UE 2
  • the network management device can aggregate and process the 4 pieces of MR data reported by UE 1 and UE 2, and can be divided into 3 groups through the aggregation operation. Among them, the 4 pieces of MR data of UE1 are 1 group of MRs, and the 4 pieces of MR data of UE2 are divided into 2 groups. Group MR. It should be noted that the above example is only an example. The total number of groups of MRs aggregated by MR data is analyzed and obtained according to the specific network conditions. The analysis is based on the number of cells, the distribution of users in the cell, the number of users, the Parameters such as the average RSRP of the cell are not limited in this embodiment.
  • the network management apparatus performs data processing on the aggregated groups of MRs, including performing averaging processing on the RSRPs of each group, and/or performing summation processing on the throughputs of each group. For example, for the nth group of MRs, the average RSRP of the same cell in the group of MRs is averaged to obtain the average RSRP of the same cell in the group of MRs.
  • the uplink throughputs of the same cell in the group MR are summed to obtain the sum of the uplink throughputs of the same cell in the group MR.
  • the downlink throughputs of the same cell in the group MR are summed to obtain the total downlink throughput of the same cell in the group MR.
  • the network management apparatus may also record the number of occurrences of each cell in each group of MRs. For example, for the nth group of MRs, the number of times each cell in the group MR appears as a serving cell is recorded, and the number of times each cell in the group MR appears as a neighbor cell is recorded.
  • Table 9 provides a format table of the converged MR data provided in the embodiment of the present application. Wherein, taking the nth group of MRs as an example, the table 9 records the cell identifiers of one or more cells in the nth group of MR data, the main beam identifiers of one or more cells, and the average RSRP of one or more cells, etc. data.
  • Table 9 A format table of MR data after pooling
  • a three-dimensional grid can be regarded as a group of MRs in the aggregated MR data. That is to say, the three-dimensional grid n (Grid n) corresponds to the nth group of MRs in the aggregated MR data.
  • the three-dimensional grid can also be regarded as a three-dimensional space position in a three-dimensional coordinate system.
  • the three dimensions in the three-dimensional coordinate system are different from the conventional three-dimensional coordinate system (such as a three-dimensional coordinate system composed of longitude, latitude and height), and the three dimensions in the three-dimensional coordinate system are respectively corresponding to the main beam identification of the cell.
  • FIG. 9a is a schematic diagram of a three-dimensional grid provided by an embodiment of the present application.
  • cell 1 in Fig. 9a includes 32 narrow beams, wherein the path loss from beam 1 of cell 1 to the three-dimensional grid n is shown in Fig. 9a.
  • the plurality of three-dimensional grids in FIG. 9a respectively represent three-dimensional spatial positions.
  • the reference coordinate system is shown in Figure 9a, that is, the three-dimensional space of the three-dimensional grid shown in Figure 9a is determined by the antenna plane of the cell and the distance determined by the RSRP of the cell to the three-dimensional grid.
  • the network management device may generate a path loss matrix according to a plurality of three-dimensional grids, engineering parameters/configurations, antenna files, and the like. That is to say, for the stereo grid of each group of MRs, the path loss between the main beam of each cell in the group of MRs and the stereo grid can be calculated to obtain the path loss matrix of the stereo grid, including the following steps:
  • the beam gain of the cell i is the horizontal beam corresponding to the main beam identifier of the cell i range, vertical beam range, antenna gain, signal attenuation value corresponding to the horizontal beam range and signal attenuation value corresponding to the vertical beam range;
  • the three-dimensional grid n since the three-dimensional grid n may include multiple cells, when calculating the path loss of the three-dimensional grid n, it is necessary to calculate the distance from each cell in the three-dimensional grid n to the three-dimensional grid n. road damage.
  • the following takes the celli in the three-dimensional grid n as an example to describe in detail. It should be noted that each cell in all three-dimensional grids can be calculated by referring to the following steps, and each cell in each three-dimensional grid can be calculated by referring to the following steps. The path loss of the cubic grid.
  • the network management apparatus can acquire the radio frequency parameters of the celli from the work parameters/configuration.
  • the radio frequency parameters may include, but are not limited to: horizontal beam width, physical azimuth, digital azimuth, vertical beam width, physical downtilt angle or digital downtilt angle.
  • the horizontal beam width represents the horizontal envelope width covered by the horizontal plane controlled by the beam weight, for example, the horizontal beam width shown in FIG. 6 .
  • the vertical beamwidth represents the vertical envelope width covered by the horizontal plane controlled by the beam weight, for example, the vertical beamwidth shown in FIG. 6 .
  • the physical azimuth represents the angle between the facing direction of the physical antenna panel and the true north, and its value ranges from 0 degrees to 359 degrees.
  • the digital azimuth represents the angle between the strongest direction of the horizontal beam energy controlled by the beam weight and true north, and its value ranges from 0 degrees to 359 degrees.
  • the physical downtilt angle represents the angle between the plane perpendicular to the physical antenna panel and the horizontal plane, and its value ranges from -90 degrees to 90 degrees.
  • the digital downtilt angle represents the angle between the strongest vertical beam energy controlled by the beam weight and the horizontal plane, and its value ranges from -90 degrees to 90 degrees.
  • the network management device obtains the horizontal beam range, vertical beam range, antenna gain, signal attenuation value corresponding to the horizontal beam range and vertical beam range corresponding to the MainBeam_ID from the antenna file from the antenna file according to the MainBeam_ID of the celli.
  • the horizontal beam range corresponding to the MainBeam_ID of cell i is [0 degrees, 19 degrees], and the vertical beam range is [3 degrees, 9 degrees].
  • Antenna gain is 50dB.
  • the network management apparatus can determine the signal attenuation value corresponding to each degree of the horizontal beam range and the signal attenuation value corresponding to each degree of the vertical beam range.
  • the beam gain of MainBeam_ID of cell i can be calculated.
  • the average value of signal attenuation includes the average value of signal attenuation values corresponding to the horizontal beam and the average value of signal attenuation values corresponding to the vertical beam.
  • the network management device can use the classical propagation model formula to calculate the path loss from the main beam of cell i to the three-dimensional grid n.
  • the transmit power of cell i can also be obtained from the antenna file, and the average RSRP of cell i can be obtained from the aggregated MR data shown in Table 9.
  • FIG. 9b is a schematic diagram of a path loss matrix provided by an embodiment of the present application.
  • Figure 9b includes three cells, namely cell 1, cell 2 and cell 3.
  • the cubic grid n includes multiple cells and main beam identifiers of the multiple cells, and the path losses from different cells and/or different main beams to the cubic grid n are different.
  • the path loss from beam 0 of cell 2 to cubic grid n is shown in Figure 9b.
  • Figure 9b the path loss from beam 1 of cell 1 to stereo grid n is shown in Figure 9b, which is different from the path loss from beam 0 of cell 2 to stereo grid n.
  • the path loss matrix may be stored in the network management device in the form of a table. Please refer to Table 10.
  • Table 10 is a data format table of a path loss matrix provided by an embodiment of the present application, including a three-dimensional grid identifier, a cell identifier, a main beam identifier of a cell, and a path loss.
  • Table 10 A data format table of a path loss matrix
  • Gridn represents the nth three-dimensional grid, and each three-dimensional grid has a unique three-dimensional grid identifier.
  • the network management apparatus can obtain the three-dimensional grids of N groups of MRs, and the path loss matrix from cells to the three-dimensional grids. If there is a coverage problem and/or a capacity problem in the network, the network management apparatus may determine the network configuration parameters of the target cell according to the path loss matrix. That is to say, the network management apparatus can adjust the network configuration parameters of the cells with coverage problems and/or capacity problems according to the path loss matrix, so as to solve the coverage problems and/or capacity problems in the network.
  • the network configuration parameters may be the radio frequency parameters described in the foregoing embodiments, or a combination of radio frequency parameters.
  • the network configuration parameter (referred to as Conf) that can be adjusted by the network management apparatus may be any one of horizontal beam width, physical azimuth, digital azimuth, vertical beam width, physical downtilt or digital downtilt.
  • the network management apparatus may determine a combination of radio frequency parameters to be adjusted, such as adjusting a combination of radio frequency parameters related to horizontal beams, according to information such as beam range, traffic flow, and RSRP of cells in the network.
  • the network configuration parameters may also be configuration parameters in other radio resource management (radio resource management, RRM) scenarios.
  • the network configuration parameter may be a cell individual offset (CIO) parameter for handover.
  • CIO cell individual offset
  • this embodiment uses a coverage index and/or a capacity index to quantify the goal of network optimization.
  • Coverage indicators include RSRP, signal to interference plus noise ratio (SINR), overlap coverage ratio, etc., which are recorded as coverage.
  • Capacity indicators include traffic balance, spectral efficiency, etc., which are recorded as capacity.
  • the RSRP refers to the reception level of the cell, which can be measured by the cell and reported to the network management apparatus.
  • the formula for calculating SINR is as follows: Determined by the RSRP of the serving cell, the sum of the RSRPs of neighboring cells, and the noise power.
  • the formula for calculating the overlap coverage ratio is as follows: Wherein, if the difference between the RSRP of the serving cell and the RSRP of the neighboring cell is less than a threshold (for example, 3dB), it is considered that the serving cell has overlapping coverage, and it is included in the number of serving cells with overlapping coverage.
  • a threshold for example, 3dB
  • the traffic equalization degree means that the sum of the uplink and downlink throughputs between adjacent cells is as equal as possible. That is to say, the sum of the uplink and downlink throughputs of the serving cell and the sum of the uplink and downlink throughputs of the neighboring cells should be the same as possible.
  • Spectral efficiency refers to the amount of traffic on a unit resource block (RB) per unit time.
  • the two metrics of coverage and capacity may not be optimal at the same time. That is to say, when the coverage index of the network is adjusted to the optimal state, for example, it can cover a larger area and have less overlapping coverage areas; however, the capacity index of the network may not be optimal, for example, the spectral efficiency of some areas is low .
  • the embodiments of the present application propose a multi-objective and multi-parameter joint optimization. That is to say, according to the above-mentioned coverage index, capacity index, and network configuration parameters, the embodiment of the present application models the goal (fitness) of network optimization as the following formula:
  • fitness represents the joint optimization objective of capacity and coverage
  • Conf represents an adjustable network configuration parameter
  • k 1 represents the coverage weight
  • k 2 represents the capacity weight.
  • the capacity weight can be given priority, that is, the weight value of k 2 is greater than the weight value of k 1 .
  • priority is given to the adjustment of the vertical beam, that is, the network management device can adjust the parameter combination of vertical beam width, physical downtilt angle and digital downtilt angle to optimize capacity and coverage.
  • the network management apparatus determines the network configuration parameters of the target cell according to the path loss matrices of the N three-dimensional grids, which may include the following steps:
  • s31 for each three-dimensional grid, determine the target cell of the three-dimensional grid according to the coverage index and/or the capacity index of each cell of the three-dimensional grid;
  • the coverage index of the target cell reaches the preset coverage index, and/or the capacity index of the target cell reaches the preset capacity index, determine that the adjusted network configuration parameters of the target cell are the network configuration parameters of the target cell.
  • the above s31 can be divided into several small steps, including:
  • s311 according to the fitness formula and based on the measured data, determine the three-dimensional grid with coverage problems and/or capacity problems before the network configuration parameter adjustment;
  • s313 Determine that the cells included in the converged problem three-dimensional grid are the target cells.
  • the target cell is a cell whose coverage index does not meet the preset coverage index and/or the capacity index does not meet the preset capacity index. That is to say, the target cell may be determined according to the coverage index of the cell, or may be determined according to the capacity index of the cell. It can be understood that the coverage index of the cell refers to the current coverage of the cell, and the capacity index of the cell refers to the current capacity of the cell.
  • the preset coverage index may be determined according to parameters such as RSRP, SINR, and overlapping coverage ratio; for example, the preset coverage index of a cell is that the overlapping coverage ratio of the cell is less than 10%.
  • the preset capacity indicator may be determined according to parameters such as traffic balance, spectral efficiency, etc. For example, the preset capacity indicator of a cell is that the traffic balance of the cell is 1, that is, the uplink throughput and downlink throughput of the cell The sum is the same as the sum of the uplink throughput and downlink throughput of adjacent cells.
  • the network management apparatus may determine the target cell based on the measured data.
  • the measured data can be determined according to the aggregated MR data shown in Table 9.
  • the measured data may be the average RSRP of each cell in the three-dimensional grid. If the average RSRP of the cell is lower than the preset RSRP threshold (for example, 100 dB), it is determined that the cell has a coverage problem.
  • the measured data may also be the sum of the uplink and downlink throughputs of each cell in the three-dimensional grid. If the sum of the uplink and downlink throughputs of the cell is less than the preset throughput threshold, it is determined that the cell has a capacity problem.
  • a clustering algorithm density-based spatial clustering of applications with noise, DBSCAN, etc. may be used when performing aggregation processing on the three-dimensional grids with coverage problems and/or capacity problems. For example, if there are a total of 50 cells with coverage problems and/or capacity problems, the three-dimensional grids with higher wireless spatial propagation similarity can be aggregated according to the clustering algorithm. It is assumed that the three-dimensional grids aggregated by the clustering algorithm include There are 30 cells in total, so the network management apparatus can determine the 30 cells as target cells.
  • the target cell is determined, according to the formula of the target (fitness) of network optimization, the maximum value of the target of network optimization is obtained, that is, the target formula of network optimization is converted into the following formula:
  • max represents the maximum value of fitness that can be obtained by adjusting Conf.
  • the network management apparatus may obtain adjustable network configuration parameters from the antenna file, and obtain current network configuration parameters through configuration and work parameters.
  • the network management device can adopt an optimization algorithm (for example, an operation research optimization algorithm based on gradient descent), take max ⁇ fitness ⁇ as the optimization goal, take the current network configuration parameter as the initial value, and take the adjustable network configuration parameter as the variable, through continuous Adjust Conf to obtain the Conf that maximizes fitness; the above optimization process is shown in Figure 10.
  • the stopping condition of optimization needs to comprehensively consider the efficiency and convergence of the algorithm. That is to say, the stopping condition of optimization includes: the overall gain reaches the standard (for example, the fitness reaches the maximum value), or the gain for consecutive rounds is less than the threshold, or the number of iterations reaches the maximum. Optimization times threshold.
  • the embodiment of the present application provides a network configuration method, which can construct a three-dimensional three-dimensional grid according to the average RSRP of each cell and the horizontal beam information and vertical beam information corresponding to the main beam identifier of each cell in the MR data, so as to realize In order not to rely on MDT data can also build three-dimensional grid objects.
  • a three-dimensional path loss matrix can be constructed according to the three-dimensional three-dimensional grid to realize stereo beam optimization.
  • the method can also determine the network configuration parameters of the target cell according to the three-dimensional path loss matrix, and by adjusting the network configuration parameters of the target cell, the coverage index and/or the capacity index of the target cell can be optimized, thereby helping to optimize the coverage and/or capacity of the network .
  • FIG. 11 is a schematic flowchart of another network configuration method provided by an embodiment of the present application.
  • the network configuration method can be performed by an offline tool deployed offline on a stand-alone computer or cloud, or by an online tool deployed online to the network management system OMC or an online tool platform connected to the OMC.
  • This network configuration method is applied in scenarios where MDT data is sufficient or road testing, and includes the following steps:
  • the network management apparatus performs aggregation processing on the multiple pieces of MDT data according to the cell information of each MDT in the multiple minimization road test MDT data to obtain N groups of MDT data, each group of MDTs includes MDTs with the same vertical beam identifiers of the cells ;
  • the network management device creates a three-dimensional grid of the group of MDTs according to the longitude and latitude of each cell in the group of MDTs and the vertical beam identifier of each cell;
  • the network management device calculates the path loss between the main beam of each cell in the group of MDTs to the three-dimensional grid, to obtain the path loss matrix of the three-dimensional grid;
  • the network management apparatus determines the network configuration parameters of the target cell according to the path loss matrix of the N three-dimensional grids.
  • the measurement report reported by the terminal device is a measurement report including longitude and latitude information, that is, the terminal device reports MDT data.
  • the network management apparatus may perform aggregation processing on the MDT reported by the terminal device to obtain N groups of MDT data. Similar to the aggregation processing of the MR data by the network management apparatus, the network management apparatus may first perform data preprocessing on the MDT reported by the terminal equipment, and the data preprocessing includes MDT main beam identification and MDT geographic grid aggregation.
  • the network management apparatus may also acquire the CHR of the terminal device.
  • the CHR For the relevant description of the CHR, reference may be made to the description of the CHR in the embodiment of FIG. 7 , and details are not repeated here.
  • the network management device may also acquire work parameters/configuration files, antenna files, and electronic maps.
  • work parameters/configuration files for the related description of the work parameter/configuration file and the antenna file, reference may be made to the description of the work parameter/configuration file and the antenna file in the embodiment of FIG. 7 , which will not be repeated here.
  • the electronic map includes the location information of the geographic space, and can show the location relationship between the site and the terminal device.
  • MDT and CHR are two different types of data reported by terminal equipment, in order to facilitate subsequent processing, MDT and CHR may be associated with each other.
  • association processing performed on the MDT and the CHR reference may be made to the description of the associated processing performed on the MDT and the CHR in the embodiment of FIG. 7 , which will not be repeated here.
  • the MDT and CHR after the associated processing are referred to as MDT data, which includes one or more MDTs.
  • One MDT represents a measurement report including longitude and latitude information reported by a terminal device within a period of time, and the cell information of this MDT includes cell identifiers of one or more cells, main beam identifiers of one or more cells, one or more cell identifiers RSRP of each cell, longitude and latitude of one or more cells. That is, the cell information of each MDT includes cell identifiers of one or more cells, main beam identifiers of one or more cells, RSRP of one or more cells, and longitude and latitude of one or more cells.
  • the cell information of each MDT includes cell information of a serving cell and/or cell information of a neighboring cell. That is to say, the related information of the neighboring cells is introduced in the embodiment of the present application, so that the network management apparatus also fully considers the influence of the neighboring cells of the cell when adjusting the network configuration parameters of a cell.
  • the cell information of each MR includes the cell identity of the serving cell, the RSRP (S_RSRP) of the serving cell, the cell identity (neighbor cell identity, N_Cell_ID) of the neighboring cell, the RSRP (N_RSRP) of the neighboring cell, and the like.
  • a main beam identity (neighbor main beam identity, N_MainBeam_ID) field of a neighboring cell is added to the MDT data to record the main beam identification of a neighboring cell.
  • the MDT data may be stored in the network management apparatus in the form of a table, or may be stored in other forms, which is not limited in this embodiment.
  • Table 11 is an MR data information table provided by the embodiment of the present application.
  • the information table includes fields such as S_Cell_ID, S_RSRP, S_MainBeam_ID, N_Cell_ID, N_RSRP, N_MainBeam_ID, ULThroughput, DLThroughput, etc.
  • the physical meaning of each field and the source of the field are shown in Table 11.
  • the network management apparatus in this embodiment can also predict the adjacent main beam in the MDT data according to similar steps, so that All cells in each piece of MDT data are marked with the main beam identifier.
  • Table 12 is a MDT data storage table after the main beam is marked provided by this embodiment of the application.
  • the main beam identifier of each cell can indicate one of the beams generated by the Massive MIMO antenna.
  • Table 12 A MDT data storage table after main beam marking
  • the network management apparatus can perform the aggregation of the geographic grid from the vertical plane.
  • the following describes in detail the process of the network management device performing geographic grid aggregation, which may include the following steps:
  • the network management device determines the grid longitude and grid latitude of the plane grid formed by the group of MDTs according to the longitude and latitude of each cell in the group of MDTs;
  • the network management apparatus determines, according to the vertical beam identifiers of the group of MDTs, the vertical layer where the plane grid of the group of MDTs is located, so as to obtain the three-dimensional grid of the group of MDTs.
  • the network management apparatus may calculate the vertical beam identifier V_Beam_ID corresponding to each cell according to the MainBeam_ID corresponding to each cell based on the MDT data marked by the main beam.
  • the MDT data is shown in Table 13.
  • Table 13 MDT data storage table after vertical beam marking
  • the network management apparatus may aggregate the data of the same vertical layer according to the V_Beam_ID to obtain the MDT data of each vertical layer. That is, the MDT data of each vertical layer is a set of MDTs. For example, according to the V_Beam_ID of each cell in Table 13, the MDT data with the same V_Beam_ID are aggregated to obtain multiple sets of MDT data.
  • the network management apparatus may perform geographic grid processing on the MDT data of each vertical layer to obtain the geographically gridded MDT data.
  • the geographic grid processing refers to determining the area formed by projecting each vertical layer onto a two-dimensional plane (such as the ground) according to the longitude and latitude information of each vertical layer; A division of a geographic grid. For example, the area formed by projecting the vertical layer of V_Beam0 to the ground determined by the longitude and latitude information of the vertical layer of V_Beam0 as V_Beam_ID is shown in FIG. 12 .
  • the area formed by the vertical layer of the V_Beam0 after the geographic grid processing is projected to the ground is shown in Figure 12.
  • the vertical layer of V_Beam1 is moved up a distance in the vertical direction on the basis of being perpendicular to the ground, as shown in Figure 12.
  • the area formed by the projection of the vertical layer of V_Beam1 to the ground is the same as the area formed by the projection of the vertical layer of V_Beam0 to the ground, but the two are located at different heights perpendicular to the ground, that is, the stereo of the vertical layers of different V_Beam_IDs
  • the grids are not the same.
  • the three-dimensional grid here refers to a geographic grid with V_Beam_ID, that is, the three-dimensional grid can be a position in a three-dimensional space.
  • the network management apparatus may further perform data processing on a group of MDTs with the same V_Beam_ID, including performing average processing on the RSRP of each group, and calculating the throughput of each group. and processing.
  • data processing including performing average processing on the RSRP of each group, and calculating the throughput of each group. and processing.
  • FIG. 7 For a specific implementation manner, reference may be made to the description of performing averaging processing on the RSRP of each group and performing summation processing on the throughputs of each group in the embodiment of FIG. 7 , which is not repeated here.
  • the network management apparatus may also record the occurrence times of each cell in each group of MDTs. For example, for the nth group of MDTs, the number of times each cell in the group of MDTs appears as a serving cell is recorded, and the number of times each cell in the group of MDTs appears as a neighbor cell is recorded.
  • Table 14 is a format table of the aggregated MDT data provided by this embodiment of the present application. Wherein, taking the nth group of MDTs as an example, the table 14 records the cell identifiers of one or more cells in the nth group of MDT data, the vertical beam identifiers of one or more cells, the average RSRP of one or more cells, Raster longitude, raster latitude, etc.
  • Table 14 An aggregated MDT data format table
  • a three-dimensional grid can be regarded as a group of MRs in the aggregated MDT data. That is to say, the three-dimensional grid n (Grid n) corresponds to the nth group of MDTs in the aggregated MDT data.
  • FIG. 13a is a schematic diagram of another three-dimensional grid provided by an embodiment of the present application.
  • the path loss of stereo grid n in beams from cell 1 to V_Beam3, the path loss of stereo grid n in beams from cell 2 to V_Beam3, and the path loss of stereo grid n in beams from cell 3 to V_Beam3 are shown in Figure 13a.
  • a plurality of three-dimensional grids in Fig. 13a respectively represent three-dimensional spatial positions.
  • the three-dimensional grid in this embodiment includes a vertical beam identifier, that is to say, the three-dimensional grid n includes data of different Beam layers, that is, the three-dimensional grid n is a three-dimensional grid position in three-dimensional space.
  • the network management device may generate a path loss matrix according to a plurality of three-dimensional grids, engineering parameters/configurations, antenna files, and the like. That is to say, for each group of three-dimensional grids of MDT, the path loss between the main beam of each cell in the group of MDTs and the three-dimensional grid can be calculated to obtain the path loss matrix of the three-dimensional grid, including the following steps:
  • the s51 according to the vertical beam identifier in the group of MDTs, obtain the vertical beam range corresponding to the vertical beam identifier, the antenna gain and the signal attenuation value corresponding to the vertical beam range;
  • the s52 according to the grid longitude and grid latitude of the group of MDTs, obtain the horizontal beam range, the antenna gain and the signal attenuation value corresponding to the horizontal beam range corresponding to the geographic grid determined by the grid longitude and grid latitude in the group of MDTs ;
  • s54 Calculate the path loss from cell i to the three-dimensional grid of the group of MDTs according to the transmit power of cell i, the antenna gain from cell i to the three-dimensional grid of the group of MDTs, and the average RSRP of cell i.
  • the three-dimensional grid n since the three-dimensional grid n can include multiple cells, when calculating the path loss of the three-dimensional grid n, it is necessary to calculate the distance from each cell in the three-dimensional grid n to the three-dimensional grid. path loss of n.
  • the following takes the cell i in the three-dimensional grid n as an example for detailed description. It should be noted that each cell in all three-dimensional grids can be calculated by referring to the following steps, and each cell in each three-dimensional grid can be calculated to The path loss of the cube grid.
  • the network management device can obtain the radio frequency parameters of cell i from the work parameters/configuration.
  • the radio frequency parameters may include, but are not limited to: horizontal beam width, physical azimuth, digital azimuth, vertical beam width, physical downtilt angle or digital downtilt angle.
  • horizontal beam width physical azimuth
  • digital azimuth digital azimuth
  • vertical beam width physical downtilt angle
  • digital downtilt angle digital downtilt angle
  • the network management apparatus obtains the vertical beam range, the antenna gain, and the signal attenuation value corresponding to the vertical beam range corresponding to the V_Beam_ID from the antenna file.
  • the vertical beam range corresponding to the V_Beam_ID of cell i is [3 degrees, 9 degrees].
  • Antenna gain is 50dB.
  • the network management apparatus can determine the signal attenuation value corresponding to each degree of the vertical beam range.
  • the network management device can use the classical propagation model formula and combine with the electronic map to calculate the path loss from cell i to the three-dimensional grid n.
  • the transmit power of cell i can also be obtained from the antenna file, and the average RSRP of cell i can be obtained from the aggregated MDT data shown in Table 14.
  • FIG. 13b is a schematic diagram of another path loss matrix provided by an embodiment of the present application.
  • Figure 13b includes three cells, namely cell 1, cell 2 and cell 3.
  • the three-dimensional grid n includes multiple cells and vertical beam identifiers of the multiple cells, and the path losses of the three-dimensional grid n in different cells to different V_Beam_ID beams are different.
  • the path loss of the cubic grid n in the beam from cell 2 to V_Beam0 is shown in Figure 13b
  • the cubic grid n in the V_Beam0 beam is located at the bottom of the multi-layer in the vertical direction, as shown in Figure 13b 13b.
  • the path loss matrix may be stored in the network management device in the form of a table. Please refer to Table 15.
  • Table 15 is a data format table of another path loss matrix provided by this embodiment of the application, including a three-dimensional grid identifier, a cell identifier, a vertical beam identifier of a cell, grid longitude, grid latitude, and road damage.
  • Table 15 A data format table of a path loss matrix
  • Gridn represents the nth three-dimensional grid, and each three-dimensional grid has a unique three-dimensional grid identifier.
  • the cell ID and vertical beam ID of each cell, the grid longitude and grid latitude, and the path loss from the cell to the vertical beam are recorded in the path loss matrix.
  • the network management apparatus in this embodiment can also perform network optimization according to the three-dimensional grid of N groups of MRs and the path loss matrix of the three-dimensional grid from cells to vertical beams. If there is a coverage problem and/or a capacity problem in the network, the network management apparatus may determine the network configuration parameters of the target cell according to the path loss matrix. That is to say, the network management apparatus can adjust the network configuration parameters of the cells with coverage problems and/or capacity problems according to the path loss matrix, so as to solve the coverage problems and/or capacity problems in the network. For a specific implementation manner, reference may be made to the description in the embodiment of FIG. 7 , which will not be repeated here.
  • the embodiment of the present application provides a network configuration method, which can construct a three-dimensional three-dimensional grid according to the longitude and latitude information in the MDT data and the vertical beam identification determined according to the main beam identification of the cell, so as to avoid the two-dimensional geographic grid from affecting the data.
  • the average processing blurs the stereo information, which is beneficial to achieve more accurate stereo optimization.
  • the method can also generate a three-dimensional path loss matrix according to the three-dimensional three-dimensional grid, and according to the three-dimensional path loss matrix, the network configuration parameters of the target cell can be determined, which is conducive to optimizing the coverage and/or capacity of the network.
  • An embodiment of the present application provides a network management apparatus. As shown in FIG. 14 , the network management apparatus 1400 may be used to implement the network configuration method in the embodiment of the present application.
  • the network management apparatus 1400 may include:
  • an aggregation unit 1401 configured to perform aggregation processing on multiple pieces of MR data according to the cell information of each MR in the multiple pieces of measurement report MR data to obtain N groups of MR data, where each group of MRs includes MRs with wireless spatial propagation similarity;
  • an obtaining unit 1402 configured to obtain the average reference signal received power RSRP of each cell of each group of MRs in the N groups of MRs;
  • the creating unit 1403 is configured to, for each group of MRs in the N groups of MRs, create the MRs of the group according to the average RSRP of each cell in the group of MRs and the vertical beam range and horizontal beam range corresponding to the main beam identifier of each cell. three-dimensional grid;
  • the calculation unit 1404 is used to calculate the path loss between the main beam of each cell in the group of MRs and the three-dimensional grid for the three-dimensional grid of each group of MRs, so as to obtain the path loss matrix of the three-dimensional grid;
  • the determining unit 1405 is configured to determine the network configuration parameters of the target cell according to the path loss matrix of the N three-dimensional grids, wherein the target cell is the one whose coverage index does not meet the preset coverage threshold and/or the capacity index does not meet the preset capacity threshold community.
  • the cell information of each MR includes the cell information of the serving cell and/or the cell information of the neighboring cells
  • the RSRP of one or more cells includes the RSRP of the serving cell and/or the RSRP of the neighboring cells.
  • the determining unit 1405 is specifically configured to, for each MR, determine the main beam prediction model of the serving cell of the MR according to the cell identifier of the serving cell, the RSRP of the serving cell, and the main beam identifier of the serving cell in the MR;
  • the main beam prediction model of the serving cell of the MR, the cell identifier of the adjacent cell and the RSRP of the adjacent cell are used to determine the main beam identifier of the adjacent cell of the MR.
  • the aggregation unit 1401 is specifically configured to perform aggregation processing on the MR data with the same cell identifier, the same main beam identifier, and the level difference value satisfying a preset level difference value condition, and determine a group of MRs after aggregation;
  • the level difference value is the difference value of RSRP between each pair of cells with the same cell identifier and the same main beam identifier in the MR data.
  • the computing unit 1404 is specifically used for:
  • the beam gain corresponding to the main beam identifier of the cell i is obtained;
  • the beam gain of the cell i is the horizontal beam range and vertical beam corresponding to the main beam identifier of the cell i. range, antenna gain, signal attenuation value corresponding to the horizontal beam range and signal attenuation value corresponding to the vertical beam range;
  • the transmit power of cell i the beam gain of cell i and the average RSRP of cell i, the path loss from the main beam of cell i to the stereo grid of the group MR is calculated.
  • the determining unit 1405 is specifically configured to:
  • For each three-dimensional grid determine the target cell of the three-dimensional grid according to the coverage index and/or the capacity index of each cell of the three-dimensional grid;
  • the coverage index of the target cell reaches the preset coverage index, and/or the capacity index of the target cell reaches the preset capacity index, it is determined that the adjusted network configuration parameters of the target cell are the network configuration parameters of the target cell.
  • the network configuration parameters include one or more of horizontal beamwidth, physical azimuth, digital azimuth, vertical beamwidth, physical downtilt, or digital downtilt;
  • the horizontal beam width represents the horizontal envelope width covered by the horizontal plane controlled by the beam weight
  • the vertical beam width represents the vertical envelope width covered by the horizontal plane controlled by the beam weight
  • the physical azimuth represents the facing direction and the true north of the physical antenna panel.
  • the digital azimuth angle represents the angle between the strongest direction of the horizontal beam energy controlled by the beam weight and the true north
  • the physical downtilt angle represents the angle between the plane perpendicular to the physical antenna panel and the horizontal plane
  • the digital downtilt angle represents the beam weight value
  • the steered vertical beam energy is most strongly directed at the angle to the horizontal.
  • FIG. 15 is a schematic structural diagram of a network management device provided by an embodiment of the present application.
  • the network management device may be a device (eg, a chip) having the function of performing the network configuration described in the embodiment of the present application.
  • the network device 1500 may include a transceiver 1501 , at least one processor 1502 and a memory 1503 .
  • the transceiver 1501, the processor 1502 and the memory 1503 may be connected to each other through one or more communication buses, or may be connected to each other in other ways. In this embodiment, a bus connection is used as an example, as shown in FIG. 15 .
  • the transceiver 1501 may be used to transmit or receive data.
  • the transceiver 1501 may receive MR data reported by terminal equipment and network equipment. It can be understood that the transceiver 1501 is a general term and may include a receiver and a transmitter.
  • the processor 1502 may be used to process data.
  • the processor 1502 may include one or more processors, for example, the processor 1502 may be one or more central processing units (CPUs), network processors (NPs), hardware chips, or any combination thereof .
  • the processor 1502 is a CPU, the CPU may be a single-core CPU or a multi-core CPU.
  • the memory 1503 is used for storing program codes and the like.
  • the memory 1503 may include volatile memory, such as random access memory (RAM).
  • the memory 1503 may also include non-volatile memory (non-volatile memory), such as read-only memory (ROM), flash memory (flash memory), hard disk drive (HDD) or solid state hard disk ( solid-state drive, SSD).
  • ROM read-only memory
  • flash memory flash memory
  • HDD hard disk drive
  • SSD solid state hard disk
  • Memory 1503 may also include a combination of the above-described types of memory.
  • the foregoing processor 1502 may be used to implement the network configuration method in this embodiment of the present application, where the specific implementation is as follows:
  • each group of MRs includes MRs with wireless spatial propagation similarity
  • each group of MRs in the N groups of MRs create a three-dimensional grid of the group of MRs according to the average RSRP of each cell in the group of MRs and the vertical beam range and horizontal beam range corresponding to the main beam identifier of each cell;
  • the target cell is a cell whose coverage index does not meet the preset coverage threshold and/or the capacity index does not meet the preset capacity threshold.
  • the cell information of each MR includes the cell information of the serving cell and/or the cell information of the neighboring cells
  • the RSRP of one or more cells includes the RSRP of the serving cell and/or the RSRP of the neighboring cells.
  • the processor 1502 is further configured to, for each MR, determine the main beam prediction model of the serving cell of the MR according to the cell identifier of the serving cell in the MR, the RSRP of the serving cell, and the main beam identifier of the serving cell;
  • the main beam prediction model of the serving cell of the MR, the cell identifier of the adjacent cell and the RSRP of the adjacent cell are used to determine the main beam identifier of the adjacent cell of the MR.
  • the processor 1502 is further configured to perform aggregation processing on the MR data with the same cell identifier, the same main beam identifier, and the level difference value satisfying a preset level difference value condition, to determine a group of MRs after aggregation;
  • the level difference value is the difference value of RSRP between each pair of cells with the same cell identifier and the same main beam identifier in the MR data.
  • the processor 1502 is specifically configured to:
  • the beam gain corresponding to the main beam identifier of the cell i is obtained;
  • the beam gain of the cell i is the horizontal beam range and vertical beam corresponding to the main beam identifier of the cell i. range, antenna gain, signal attenuation value corresponding to the horizontal beam range and signal attenuation value corresponding to the vertical beam range;
  • the transmit power of cell i the beam gain of cell i and the average RSRP of cell i, the path loss from the main beam of cell i to the stereo grid of the group MR is calculated.
  • the processor 1502 is specifically configured to:
  • For each three-dimensional grid determine the target cell of the three-dimensional grid according to the coverage index and/or the capacity index of each cell of the three-dimensional grid;
  • the coverage index of the target cell reaches the preset coverage index, and/or the capacity index of the target cell reaches the preset capacity index, it is determined that the adjusted network configuration parameters of the target cell are the network configuration parameters of the target cell.
  • the network configuration parameters include one or more of horizontal beamwidth, physical azimuth, digital azimuth, vertical beamwidth, physical downtilt, or digital downtilt;
  • the horizontal beam width represents the horizontal envelope width covered by the horizontal plane controlled by the beam weight
  • the vertical beam width represents the vertical envelope width covered by the horizontal plane controlled by the beam weight
  • the physical azimuth represents the facing direction and the true north of the physical antenna panel.
  • the digital azimuth angle represents the angle between the strongest direction of the horizontal beam energy controlled by the beam weight and the true north
  • the physical downtilt angle represents the angle between the plane perpendicular to the physical antenna panel and the horizontal plane
  • the digital downtilt angle represents the beam weight value
  • the steered vertical beam energy is most strongly directed at the angle to the horizontal.
  • An embodiment of the present application provides a network management apparatus. As shown in FIG. 16 , the network management apparatus 1600 may be used to implement the network configuration method in the embodiment of the present application.
  • the network management apparatus 1600 may include:
  • Convergence unit 1601 is used for according to the cell information of each MDT in the multiple minimization road test MDT data to carry out aggregation processing to multiple MDT data to obtain N groups of MDT data, and each group of MDT comprises the same MDT of the vertical beam identification of cell;
  • a creating unit 1602 is configured to, for each group of MDTs in the N groups of MDTs, create a three-dimensional grid of the group of MDTs according to the longitude and latitude of each cell in the group of MDTs and the vertical beam identifier of each cell;
  • the calculation unit 1603 is used to calculate the path loss between the main beam of each cell in the group of MDTs to the three-dimensional grid for the three-dimensional grid of each group of MDTs, to obtain the path loss matrix of the three-dimensional grid;
  • the determining unit 1604 is configured to determine the network configuration parameters of the target cell according to the path loss matrix of the N three-dimensional grids, wherein the target cell is a cell whose coverage index does not meet the preset coverage threshold and/or the capacity index does not meet the preset capacity threshold community.
  • the cell information of each MDT includes the cell information of the serving cell and/or the cell information of the neighboring cells, and the RSRP of one or more cells includes the RSRP of the serving cell and/or the RSRP of the neighboring cells.
  • the determining unit 1604 is also used to:
  • the cell identity of the adjacent cell and the RSRP of the adjacent cell determine the main beam identity of the adjacent cell of the MDT;
  • the vertical beam identifiers of the cells in the MDT are determined.
  • the creating unit 1602 is specifically used for:
  • each cell in the group of MDTs determine the grid longitude and grid latitude of the plane grid formed by the group of MDTs;
  • the vertical beam identifier of the group of MDTs determine the vertical layer where the plane grid of the group of MDTs is located, so as to obtain the three-dimensional grid of the group of MDTs.
  • the computing unit 1603 is specifically used for:
  • the vertical beam identifier in the group of MDTs obtain the vertical beam range corresponding to the vertical beam identifier, the antenna gain and the signal attenuation value corresponding to the vertical beam range;
  • the grid longitude and grid latitude of the group of MDTs obtain the horizontal beam range, the antenna gain and the signal attenuation value corresponding to the horizontal beam range corresponding to the geographic grid determined by the grid longitude and grid latitude in the group of MDTs;
  • the transmit power of cell i the antenna gain of the cubic grid from cell i to the group of MDTs, and the average RSRP of cell i, the path loss from cell i to the cubic grid of the group of MDTs is calculated.
  • the determining unit 1604 is specifically configured to:
  • For each three-dimensional grid determine the target cell of the three-dimensional grid according to the coverage index and/or the capacity index of each cell of the three-dimensional grid;
  • the coverage index of the target cell reaches the preset coverage index, and/or the capacity index of the target cell reaches the preset capacity index, it is determined that the adjusted network configuration parameters of the target cell are the network configuration parameters of the target cell.
  • the network configuration parameters include one or more of horizontal beamwidth, physical azimuth, digital azimuth, vertical beamwidth, physical downtilt, or digital downtilt;
  • the horizontal beam width represents the horizontal envelope width covered by the horizontal plane controlled by the beam weight
  • the vertical beam width represents the vertical envelope width covered by the horizontal plane controlled by the beam weight
  • the physical azimuth represents the facing direction and the true north of the physical antenna panel.
  • the digital azimuth angle represents the angle between the strongest direction of the horizontal beam energy controlled by the beam weight and the true north
  • the physical downtilt angle represents the angle between the plane perpendicular to the physical antenna panel and the horizontal plane
  • the digital downtilt angle represents the beam weight value
  • the steered vertical beam energy is most strongly directed at the angle to the horizontal.
  • FIG. 17 is a schematic structural diagram of a network management device provided by an embodiment of the present application.
  • the network management device may be a device (eg, a chip) having the function of performing the network configuration described in the embodiment of the present application.
  • the network management device 1700 may include a transceiver 1701 , at least one processor 1702 and a memory 1703 . Wherein, the transceiver 1701, the processor 1702 and the memory 1703 may be connected to each other through one or more communication buses, and may also be connected to each other in other ways. In this embodiment, a bus connection is used as an example, as shown in FIG. 17 .
  • the transceiver 1701 may be used to transmit or receive data.
  • the transceiver 1501 may receive MDT data reported by terminal equipment and network equipment. It can be understood that the transceiver 1501 is a general term and may include a receiver and a transmitter.
  • the processor 1702 can be used to process the data.
  • the processor 1702 may include one or more processors, for example, the processor 1702 may be one or more central processing units (CPUs), network processors (NPs), hardware chips, or any combination thereof .
  • the processor 1702 is a CPU
  • the CPU may be a single-core CPU or a multi-core CPU.
  • the memory 1703 is used for storing program codes and the like.
  • the memory 1703 may include volatile memory, such as random access memory (RAM).
  • the memory 1703 may also include non-volatile memory (non-volatile memory), such as read-only memory (ROM), flash memory (flash memory), hard disk drive (HDD) or solid state hard disk ( solid-state drive, SSD).
  • non-volatile memory such as read-only memory (ROM), flash memory (flash memory), hard disk drive (HDD) or solid state hard disk ( solid-state drive, SSD).
  • ROM read-only memory
  • flash memory flash memory
  • HDD hard disk drive
  • SSD solid state hard disk
  • the memory 1703 may also include a combination of the above-described types of memory.
  • processor 1702 may be used to implement the network configuration method in the embodiment of the present application, wherein the specific implementation is as follows:
  • each group of MDTs includes MDTs with the same vertical beam identifiers of the cells;
  • each group of MDTs in the N groups of MDTs create a three-dimensional grid of the group of MDTs according to the longitude and latitude of each cell in the group of MDTs and the vertical beam identifier of each cell;
  • the target cell is a cell whose coverage index does not meet the preset coverage threshold and/or the capacity index does not meet the preset capacity threshold.
  • the cell information of each MDT includes the cell information of the serving cell and/or the cell information of the neighboring cells, and the RSRP of one or more cells includes the RSRP of the serving cell and/or the RSRP of the neighboring cells.
  • the processor 1702 is also used to:
  • the cell identity of the adjacent cell and the RSRP of the adjacent cell determine the main beam identity of the adjacent cell of the MDT;
  • the vertical beam identifiers of the cells in the MDT are determined.
  • the processor 1702 is specifically configured to:
  • each cell in the group of MDTs determine the grid longitude and grid latitude of the plane grid formed by the group of MDTs;
  • the vertical beam identifier of the group of MDTs determine the vertical layer where the plane grid of the group of MDTs is located, so as to obtain the three-dimensional grid of the group of MDTs.
  • the processor 1702 is specifically configured to:
  • the vertical beam identifier in the group of MDTs obtain the vertical beam range corresponding to the vertical beam identifier, the antenna gain and the signal attenuation value corresponding to the vertical beam range;
  • the grid longitude and grid latitude of the group of MDTs obtain the horizontal beam range, the antenna gain and the signal attenuation value corresponding to the horizontal beam range corresponding to the geographic grid determined by the grid longitude and grid latitude in the group of MDTs;
  • the transmit power of cell i the antenna gain of the cubic grid from cell i to the group of MDTs, and the average RSRP of cell i, the path loss from cell i to the cubic grid of the group of MDTs is calculated.
  • the processor 1702 is specifically configured to:
  • For each three-dimensional grid determine the target cell of the three-dimensional grid according to the coverage index and/or the capacity index of each cell of the three-dimensional grid;
  • the coverage index of the target cell reaches the preset coverage index, and/or the capacity index of the target cell reaches the preset capacity index, it is determined that the adjusted network configuration parameters of the target cell are the network configuration parameters of the target cell.
  • the network configuration parameters include one or more of horizontal beamwidth, physical azimuth, digital azimuth, vertical beamwidth, physical downtilt, or digital downtilt;
  • the horizontal beam width represents the horizontal envelope width covered by the horizontal plane controlled by the beam weight
  • the vertical beam width represents the vertical envelope width covered by the horizontal plane controlled by the beam weight
  • the physical azimuth represents the facing direction and the true north of the physical antenna panel.
  • the digital azimuth angle represents the angle between the strongest direction of the horizontal beam energy controlled by the beam weight and the true north
  • the physical downtilt angle represents the angle between the plane perpendicular to the physical antenna panel and the horizontal plane
  • the digital downtilt angle represents the beam weight value
  • the steered vertical beam energy is most strongly directed at the angle to the horizontal.
  • the embodiments of the present application provide a computer-readable storage medium, where a program or an instruction is stored in the computer-readable storage medium, and when the program or the instruction runs on a computer, the computer executes the network configuration method in the embodiment of the present application.
  • An embodiment of the present application provides a chip or a chip system, the chip or chip system includes at least one processor and an interface, the interface and the at least one processor are interconnected by a line, and the at least one processor is used to run a computer program or instruction to perform the present application
  • the network configuration method in the embodiment is used to run a computer program or instruction to perform the present application.
  • the interface in the chip may be an input/output interface, a pin or a circuit, or the like.
  • the chip system in the above aspects may be a system on chip (system on chip, SOC), or a baseband chip, etc.
  • the baseband chip may include a processor, a channel encoder, a digital signal processor, a modem, an interface module, and the like.
  • the chip or chip system described above in this application further includes at least one memory, where instructions are stored in the at least one memory.
  • the memory may be a storage unit inside the chip, such as a register, a cache, etc., or a storage unit of the chip (eg, a read-only memory, a random access memory, etc.).
  • An embodiment of the present application provides a communication system, including a network management device, a network device, and a terminal device according to the embodiment of the present application.
  • a computer program product includes one or more computer instructions.
  • the computer may be a general purpose computer, special purpose computer, computer network, or other programmable device.
  • Computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server, or data center over a wire (e.g.
  • Coaxial cable, optical fiber, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (such as infrared, wireless, microwave, etc.) means to transmit to another website site, computer, server or data center.
  • a computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, a data center, or the like that includes an integration of one or more available media.
  • the available media may be magnetic media (eg, floppy disks, hard disks, magnetic tapes), optical media (eg, high-density digital video discs (DVDs)), or semiconductor media (eg, solid state disks, SSD)) etc.

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Abstract

本申请实施例公开了一种网络配置方法及装置，该网络配置方法可以由离线部署在单机电脑或者云上的离线工具，或者由在线部署到网络管理***OMC或连接OMC的在线工具平台上的在线工具所执行。该方法可以根据MR数据中的每个小区的平均RSRP和每个小区的主波束标识对应的水平波束信息、垂直波束信息，构建三维立体栅格，实现了不依赖于MDT数据也可构建三维立体栅格的目标。并且，根据三维立体栅格可以构建路损矩阵，实现立体波束优化。该方法还可以根据路损矩阵确定目标小区的网络配置参数，有利于优化网络的覆盖和/或容量。

Description

一种网络配置方法及装置
本申请要求于2020年7月21日提交中国国家知识产权局、申请号为202010706451.2、申请名称为“一种网络配置方法及装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及通信技术领域，尤其涉及一种网络配置方法及装置。
背景技术
无线通信网络中通常按照蜂窝形状部署站点，每个站点由射频参数决定其覆盖范围(coverage)。其中，覆盖是衡量无线网络性能的重要指标。为了量化无线网络的覆盖性能指标，可以采用二维平面栅格化的方式对一个或多个小区的覆盖区域进行建模，从而对覆盖性能指标进行优化。但是，二维平面栅格化建模需要将立体覆盖中的垂直面特征投影到二维平面，损失了垂直面覆盖特征，会导致垂直面覆盖信号差，从而导致弱覆盖、重叠覆盖等问题。
发明内容
本申请实施例提供一种网络配置方法，该方法可以根据测量报告MR数据构建三维立体覆盖特征，有利于优化三维立体覆盖。
第一方面，本申请实施例提供一种网络配置方法，该方法可以由网络管理装置所执行，该网络管理装置可以离线部署在单机电脑或者云上(即离线工具)，也可以在线部署到网络管理***OMC或连接OMC的在线工具平台上(即在线工具)。其中，网络管理装置可以根据多条测量报告MR数据中每条MR的小区信息对多条MR数据进行汇聚处理以获得N组MR数据，并获取N组MR中每组MR的每个小区的平均参考信号接收功率RSRP。针对N组MR中的每组MR，网络管理装置根据该组MR中每个小区的平均RSRP以及每个小区的主波束标识对应的垂直波束范围、水平波束范围，创建该组MR的立体栅格。针对每组MR的立体栅格，计算该组MR中各小区的主波束到该立体栅格之间的路损，以获得该立体栅格的路损矩阵。最终网络管理装置可以根据N个立体栅格的路损矩阵，确定目标小区的网络配置参数，目标小区为覆盖指标不满足预设覆盖指标和/或容量指标不满足预设容量指标的小区。
可见，网络管理装置可以根据MR数据中的每个小区的平均RSRP和每个小区的主波束标识对应的水平波束信息、垂直波束信息，构建三维立体栅格，实现了不依赖于MDT数据也可构建三维立体栅格的目标。并且，根据三维立体栅格可以构建三维路损矩阵，实现立体波束优化。网络管理装置还可以根据三维路损矩阵确定目标小区的网络配置参数，有利于优化网络的覆盖和/或容量。
在一种可能的设计中，每条MR的小区信息包括服务小区的小区信息和/或邻区的小区信息，其中，一个或多个小区的RSRP包括服务小区的RSRP和/或邻区的RSRP。网络管理装置在根据多条测量报告MR数据中每条MR的小区信息对多条MR数据进行汇聚处理 之前，可以针对每条MR，根据该条MR中的服务小区的小区标识、服务小区的RSRP、服务小区的主波束标识，确定该条MR的服务小区的主波束预测模型。再根据该条MR的服务小区的主波束预测模型、邻区的小区标识和邻区的RSRP，确定该条MR的邻区的主波束标识。
可见，网络管理装置可以预测MR在其服务小区、各邻区中主波束标识，从而得到MR的全量主波束属性，有利于网络管理装置在进行覆盖和/或容量优化时可以考虑到整体网络性能。
在一种可能的设计中，网络管理装置在对MR数据进行汇聚处理时，可以将小区标识相同、主波束标识相同、电平差值满足预设电平差值条件的MR数据进行汇聚处理，确定汇聚后的一组MR。其中，电平差值为MR数据中小区标识相同、主波束标识相同的小区两两之间的RSRP的差值。
可见，网络管理装置汇聚的MR数据是具有无线空间传播相似性的MR数据，从而有利于建模指定位置的立体栅格。并且，依据MR的小区属性、RSRP、波束属性等，将相似特征MR进行汇总处理，有利于更精细模拟实际网络传播，大幅减少运算量。
在一种可能的设计中，针对每组MR的立体栅格，网络管理装置可以根据该组MR中的任意一个小区i的主波束标识，获取该小区i的主波束标识对应的波束增益；该小区i的波束增益是根据小区i的主波束标识对应的水平波束范围、垂直波束范围、天线增益、水平波束范围对应的信号衰减值和垂直波束范围对应的信号衰减值确定的。网络管理装置再根据小区i的发射功率、小区i的波束增益和小区i的平均RSRP，计算小区i的主波束到该组MR的立体栅格的路径损耗。
可见，网络管理装置在计算每组MR的立体栅格的路损时，充分考虑了水平波束、垂直波束以及小区的平均RSRP，也就是说，计算的路损体现了三维立体栅格的路损，避免了二维地理栅格的平均处理而导致的覆盖和容量特征损失。
在一种可能的设计中，网络管理装置针对每个立体栅格，根据该立体栅格的各个小区的覆盖指标和/或容量指标确定该立体栅格的目标小区。根据目标小区的预设覆盖指标和预设容量指标，调整目标小区的网络配置参数。根据调整后的目标小区的网络配置参数，得到调整后的目标小区的天线增益；根据该立体栅格的路损矩阵、调整后的目标小区的天线增益和调整后的目标小区的网络配置参数，确定目标小区的覆盖指标和容量指标。若目标小区的覆盖指标达到预设覆盖指标，和/或，目标小区的容量指标达到预设容量指标，确定调整后的目标小区的网络配置参数为目标小区的网络配置参数。
可见，网络管理装置可以筛选出存在覆盖问题和/或容量问题的目标小区，并通过调整目标小区的网络配置参数，实现目标小区的覆盖和/或容量的优化。
在一种可能的设计中，网络配置参数包括水平波束宽度、物理方位角、数字方位角、垂直波束宽度、物理下倾角或数字下倾角中的一种或多种；
其中，水平波束宽度表示波束权值控制的水平面覆盖的水平包络宽度；垂直波束宽度表示波束权值控制的水平面覆盖的垂直包络宽度；物理方位角表示物理天线面板的正对方向与正北方的夹角；数字方位角表示波束权值控制的水平波束能量最强指向与正北方的夹角；物理下倾角表示垂直于物理天线面板的平面与水平面的夹角；数字下倾角表示波束权 值控制的垂直波束能量最强指向与水平面的夹角。
第二方面，本申请实施例提供一种网络配置方法，该网络配置方法可以由网络管理装置所执行。其中，网络管理装置可以根据多条最小化道路测试MDT数据中每条MDT的小区信息对多条MDT数据进行汇聚处理以获得N组MDT数据，每组MDT包括小区的垂直波束标识相同的MDT。针对N组MDT中的每组MDT，根据该组MDT中每个小区的经度、纬度以及每个小区的垂直波束标识，创建该组MDT的立体栅格。针对每组MDT的立体栅格，计算该组MDT中各个小区的主波束到该立体栅格之间的路损，以获得该立体栅格的路损矩阵。根据N个立体栅格的路损矩阵，确定目标小区的网络配置参数，其中，目标小区为覆盖指标不满足预设覆盖指标和/或容量指标不满足预设容量指标的小区。
可见，网络管理装置可以根据MDT数据中的经纬度信息，以及根据小区的主波束标识确定的垂直波束标识，构建三维立体栅格，避免二维地理栅格对数据的平均处理模糊了立体信息，有利于实现更精准的立体优化。并且，网络管理装置可以根据三维立体栅格生成三维路损矩阵，根据该三维路损矩阵可以确定目标小区的网络配置参数，有利于优化网络的覆盖和/或容量。
在一种可能的设计中，针对每条MDT，网络管理装置根据该条MDT中的服务小区的小区标识、服务小区的RSRP、服务小区的主波束标识，确定该条MDT的服务小区的主波束预测模型。根据该条MDT的服务小区的主波束预测模型、邻区的小区标识和邻区的RSRP，确定该条MDT的邻区的主波束标识。根据该条MDT中的各个小区的主波束标识，确定该条MDT中的各个小区的垂直波束标识。
可见，网络管理装置可以确定MDT数据中的每个小区的垂直波束标识，从而在立体层面上对MDT数据中的每个小区进行标记，确定各个小区的立体信息。
在一种可能的设计中，网络管理装置根据该组MDT中的每个小区的经度和纬度，确定该组MDT构成的平面栅格的栅格经度和栅格纬度；再根据该组MDT的垂直波束标识，确定该组MDT构成平面栅格所在的垂直层，以获得该组MDT的立体栅格。
可见，网络管理装置可以根据MDT数据中的经纬度信息确定二维平面栅格，在根据垂直波束标识确定该平面栅格的立***置，从而构建立体栅格。
在一种可能的设计中，网络管理装置根据该组MDT中的垂直波束标识，获取垂直波束标识对应的垂直波束范围、天线增益和垂直波束范围对应的信号衰减值。根据该组MDT的栅格经度和栅格纬度，获取该组MDT中的栅格经度和栅格纬度确定的地理栅格对应的水平波束范围、天线增益和水平波束范围对应的信号衰减值。根据该组MDT中的垂直波束标识对应的垂直波束范围、天线增益、垂直波束范围对应的信号衰减值，以及该组MDT中的栅格经度和栅格纬度确定的地理栅格对应的水平波束范围、天线增益、水平波束范围对应的信号衰减值，计算该组MDT中的任意一个小区i到该组MDT的立体栅格的天线增益。根据小区i的发射功率、小区i到该组MDT的立体栅格的天线增益和小区i的平均RSRP，计算小区i到该组MDT的立体栅格的路径损耗。
可见，网络管理装置在计算每组MDT的立体栅格的路损时，充分考虑了水平波束、垂直波束以及小区的平均RSRP，也就是说，计算的路损体现了三维立体栅格的路损，避免了二维地理栅格的平均处理而导致的覆盖和容量特征损失。
在一种可能的设计中，网络管理装置针对每个立体栅格，根据该立体栅格的各个小区的覆盖指标和/或容量指标确定该立体栅格的目标小区。再根据目标小区的预设覆盖指标和预设容量指标，调整目标小区的网络配置参数。根据调整后的目标小区的网络配置参数，得到调整后的目标小区的天线增益。根据该立体栅格的路损矩阵、调整后的目标小区的天线增益和调整后的目标小区的网络配置参数，确定目标小区的覆盖指标和容量指标。若目标小区的覆盖指标达到所述预设覆盖指标，和/或，目标小区的容量指标达到所述预设容量指标，确定调整后的目标小区的网络配置参数为所述目标小区的网络配置参数。
可见，网络管理装置可以筛选出存在覆盖问题和/或容量问题的目标小区，并通过调整目标小区的网络配置参数，实现目标小区的覆盖和/或容量的优化。
在一种可能的设计中，网络配置参数包括水平波束宽度、物理方位角、数字方位角、垂直波束宽度、物理下倾角或数字下倾角中的一种或多种；
其中，水平波束宽度表示波束权值控制的水平面覆盖的水平包络宽度；垂直波束宽度表示波束权值控制的水平面覆盖的垂直包络宽度；物理方位角表示物理天线面板的正对方向与正北方的夹角；数字方位角表示波束权值控制的水平波束能量最强指向与正北方的夹角；物理下倾角表示垂直于物理天线面板的平面与水平面的夹角；数字下倾角表示波束权值控制的垂直波束能量最强指向与水平面的夹角。
第三方面，本申请实施例提供一种网络管理装置，该装置包括汇聚单元、获取单元、创建单元、计算单元和确定单元。其中，汇聚单元用于根据多条测量报告MR数据中每条MR的小区信息对所述多条MR数据进行汇聚处理以获得N组MR数据，每组MR包括具有无线空间传播相似性的MR。获取单元用于获取N组MR中每组MR的每个小区的平均参考信号接收功率RSRP。创建单元用于针对N组MR中的每组MR，根据该组MR中每个小区的平均RSRP以及每个小区的主波束标识对应的垂直波束范围、水平波束范围，创建该组MR的立体栅格。计算单元用于针对每组MR的立体栅格，计算该组MR中各小区的主波束到该立体栅格之间的路损，以获得该立体栅格的路损矩阵。确定单元用于根据N个立体栅格的路损矩阵，确定目标小区的网络配置参数，其中，目标小区为覆盖指标不满足预设覆盖指标和/或容量指标不满足预设容量指标的小区。
在一种可能的设计中，每条MR的小区信息包括服务小区的小区信息和/或邻区的小区信息，一个或多个小区的RSRP包括服务小区的RSRP和/或邻区的RSRP。确定单元还用于针对所述每条MR，根据该条MR中的服务小区的小区标识、服务小区的RSRP、服务小区的主波束标识，确定该条MR的服务小区的主波束预测模型。根据该条MR的服务小区的主波束预测模型、邻区的小区标识和所述邻区的RSRP，确定该条MR的邻区的主波束标识。
在一种可能的设计中，汇聚单元用于将小区标识相同，主波束标识相同、电平差值满足预设电平差值条件的MR数据进行汇聚处理，确定汇聚后的一组MR；其中，电平差值为MR数据中小区标识相同、主波束标识相同的小区两两之间的RSRP的差值。
在一种可能的设计中，计算单元用于根据该组MR中的任意一个小区i的主波束标识，获取该小区i的主波束标识对应的波束增益；该小区i的波束增益是根据所述小区i的主波束标识对应的水平波束范围、垂直波束范围、天线增益、水平波束范围对应的信号衰减值 和垂直波束范围对应的信号衰减值确定的；根据小区i的发射功率、小区i的波束增益和小区i的平均RSRP，计算小区i的主波束到该组MR的立体栅格的路径损耗。
在一种可能的设计中，针对每个立体栅格，确定单元用于根据该立体栅格的各个小区的覆盖指标和/或容量指标确定该立体栅格的目标小区。根据目标小区的预设覆盖指标和预设容量指标，调整所述目标小区的网络配置参数；根据调整后的目标小区的网络配置参数，得到调整后的目标小区的天线增益；根据该立体栅格的路损矩阵、调整后的目标小区的天线增益和调整后的目标小区的网络配置参数，确定所述目标小区的覆盖指标和容量指标。若目标小区的覆盖指标达到预设覆盖指标，和/或，目标小区的容量指标达到预设容量指标，确定调整后的目标小区的网络配置参数为目标小区的网络配置参数。
在一种可能的设计中，网络配置参数包括水平波束宽度、物理方位角、数字方位角、垂直波束宽度、物理下倾角或数字下倾角中的一种或多种；
其中，水平波束宽度表示波束权值控制的水平面覆盖的水平包络宽度；垂直波束宽度表示波束权值控制的水平面覆盖的垂直包络宽度；物理方位角表示物理天线面板的正对方向与正北方的夹角；数字方位角表示波束权值控制的水平波束能量最强指向与正北方的夹角；物理下倾角表示垂直于物理天线面板的平面与水平面的夹角；数字下倾角表示波束权值控制的垂直波束能量最强指向与水平面的夹角。
第四方面，本申请实施例提供一种网络管理装置，该网络管理装置包括汇聚单元、创建单元、计算单元和确定单元。其中，汇聚单元用于根据多条最小化道路测试MDT数据中每条MDT的小区信息对多条MDT数据进行汇聚处理以获得N组MDT数据，每组MDT包括小区的垂直波束标识相同的MDT。创建单元用于针对N组MDT中的每组MDT，根据该组MDT中每个小区的经度、纬度以及每个小区的垂直波束标识，创建该组MDT的立体栅格。计算单元用于针对每组MDT的立体栅格，计算该组MDT中各个小区的主波束到该立体栅格之间的路损，以获得该立体栅格的路损矩阵。确定单元用于根据N个立体栅格的路损矩阵，确定目标小区的网络配置参数，其中，目标小区为覆盖指标不满足预设覆盖指标和/或容量指标不满足预设容量指标的小区。
在一种可能的设计中，每条MDT的小区信息包括服务小区的小区信息和/或邻区的小区信息，一个或多个小区的RSRP包括所述服务小区的RSRP和/或所述邻区的RSRP。针对所述每条MDT，确定单元还用于根据该条MDT中的服务小区的小区标识、服务小区的RSRP、服务小区的主波束标识，确定该条MDT的服务小区的主波束预测模型。根据该条MDT的服务小区的主波束预测模型、邻区的小区标识和所述邻区的RSRP，确定该条MDT的邻区的主波束标识；根据该条MDT中的各个小区的主波束标识，确定该条MDT中的各个小区的垂直波束标识。
在一种可能的设计中，创建单元用于根据该组MDT中的每个小区的经度和纬度，确定该组MDT构成的平面栅格的栅格经度和栅格纬度；根据该组MDT的垂直波束标识，确定该组MDT构成平面栅格所在的垂直层，以获得该组MDT的立体栅格。
在一种可能的设计中，计算单元用于根据该组MDT中的垂直波束标识，获取所述垂直波束标识对应的垂直波束范围、天线增益和垂直波束范围对应的信号衰减值。根据该组MDT的栅格经度和栅格纬度，获取该组MDT中的栅格经度和栅格纬度确定的地理栅格对 应的水平波束范围、天线增益和水平波束范围对应的信号衰减值。根据该组MDT中的垂直波束标识对应的垂直波束范围、天线增益、垂直波束范围对应的信号衰减值，以及该组MDT中的栅格经度和栅格纬度确定的地理栅格对应的水平波束范围、天线增益、水平波束范围对应的信号衰减值，计算该组MDT中的任意一个小区i到该组MDT的立体栅格的天线增益。根据所述小区i的发射功率、所述小区i到该组MDT的立体栅格的天线增益和所述小区i的平均RSRP，计算所述小区i到该组MDT的立体栅格的路径损耗。
在一种可能的设计中，针对每个立体栅格，确定单元用于根据该立体栅格的各个小区的覆盖指标和/或容量指标确定该立体栅格的目标小区。根据所述目标小区的预设覆盖指标和预设容量指标，调整所述目标小区的网络配置参数；根据调整后的目标小区的网络配置参数，得到调整后的目标小区的天线增益。根据该立体栅格的路损矩阵、调整后的目标小区的天线增益和调整后的目标小区的网络配置参数，确定所述目标小区的覆盖指标和容量指标。若所述目标小区的覆盖指标达到所述预设覆盖指标，和/或，所述目标小区的容量指标达到所述预设容量指标，确定调整后的目标小区的网络配置参数为所述目标小区的网络配置参数。
第五方面，本申请实施例提供一种网络设备，该装置具有实现第一方面所提供的网络配置方法的功能。该功能可以通过硬件实现，也可以通过硬件执行相应的软件实现。该硬件或软件包括一个或多个与上述功能相对应的模块。
第六方面，本申请实施例提供一种网络设备，该设备具有实现第二方面所提供的网络配置方法的功能。该功能可以通过硬件实现，也可以通过硬件执行相应的软件实现。该硬件或软件包括一个或多个与上述功能相对应的模块。
第七方面，本申请实施例提供一种计算机可读存储介质，该可读存储介质包括程序或指令，当所述程序或指令在计算机上运行时，使得计算机执行第一方面或第一方面中任一种可能实现方式中的方法。
第八方面，本申请实施例提供一种计算机可读存储介质，该可读存储介质包括程序或指令，当所述程序或指令在计算机上运行时，使得计算机执行第二方面或第二方面中任一种可能实现方式中的方法。
第九方面，本申请实施例提供一种芯片或者芯片***，该芯片或者芯片***包括至少一个处理器和接口，接口和至少一个处理器通过线路互联，至少一个处理器用于运行计算机程序或指令，以进行第一方面或第一方面的任一种可能的实现方式中任一项所描述的方法。
第十方面，本申请实施例提供一种芯片或者芯片***，该芯片或者芯片***包括至少一个处理器和接口，接口和至少一个处理器通过线路互联，至少一个处理器用于运行计算机程序或指令，以进行第二方面或第二方面的任一种可能的实现方式中任一项所描述的方法。
其中，芯片中的接口可以为输入/输出接口、管脚或电路等。
上述方面中的芯片***可以是片上***(system on chip，SOC)，也可以是基带芯片等，其中基带芯片可以包括处理器、信道编码器、数字信号处理器、调制解调器和接口模块等。
在一种可能的实现中，本申请中上述描述的芯片或者芯片***还包括至少一个存储器， 该至少一个存储器中存储有指令。该存储器可以为芯片内部的存储单元，例如，寄存器、缓存等，也可以是该芯片的存储单元(例如，只读存储器、随机存取存储器等)。
第十一方面，本申请实施例提供一种计算机程序或计算机程序产品，包括代码或指令，当代码或指令在计算机上运行时，使得计算机执行第一方面或第一方面中任一种可能实现方式中的方法。
第十二方面，本申请实施例提供一种计算机程序或计算机程序产品，包括代码或指令，当代码或指令在计算机上运行时，使得计算机执行第二方面或第二方面中任一种可能实现方式中的方法。
附图说明
图1为本申请实施例提供的一种无线网络的示意图；
图2为本申请实施例提供的一种网络覆盖和容量的示意图；
图3为一种普通天线和Massive MIMO天线的示意图；
图4为一种二维平面栅格路损矩阵的示意图；
图5为本申请实施例提供的一种通信***的示意图；
图6为本申请实施例提供的一种MassiveMIMO天线的波束覆盖示意图；
图7为本申请实施例提供的一种网络配置方法的流程示意图；
图8为本申请实施例提供的一种Massive MIMO天线发射的多个波束以及终端设备接收到的主波束的示意图；
图9a为本申请实施例提供的一种立体栅格的示意图；
图9b为本申请实施例提供的一种路损矩阵的示意图；
图10为本申请实施例提供的一种网络优化目标寻优过程的示意图；
图11为本申请实施例提供的另一种网络配置方法的流程示意图；
图12为本申请实施例提供的一种垂直层投影到地面构成的区域的示意图；
图13a为本申请实施例提供的另一种立体栅格的示意图；
图13b为本申请实施例提供的另一种路损矩阵的示意图；
图14为本申请实施例提供的一种网络管理装置的示意图；
图15为本申请实施例提供的一种网络管理设备的示意图；
图16为本申请实施例提供的另一种网络管理装置的示意图；
图17为本申请实施例提供的另一种网络管理设备的示意图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行描述。
无线通信网络按照蜂窝形状部署站点，每个站点由其射频参数决定其覆盖范围，而覆盖决定了用户收到无线信号的强度，其影响了站点能够接入的用户量(容量)。请参见图1，图1为本申请实施例提供的一种无线网络的示意图。该无线网络包括多个站点，各个站点按照一定的空间分布进行部署，如图1所示。其中，一个站点的覆盖区域(也称为小区)为如图1所示的一个虚线区域，多个站点构成的覆盖区域如图1所示。
覆盖(coverage)和容量(capacity)是衡量无线网络的网络性能的重要指标，但由于 网络规划与实际物理环境存在差异、城市建设、用户发展等变化，导致无线网络可能存在弱覆盖、重叠覆盖等覆盖问题，以及负荷不均衡、热点话务压抑等容量问题，如图2所示。
其中，弱覆盖是指当前区域的平均参考信号接收功率(reference signal received power，RSRP)低于一定的门限值。例如，图2中的弱覆盖区域位于小区1和小区2之间，且位于小区1和小区2的边缘区域。该弱覆盖区域中的用户可能无法正常接收信号。重叠覆盖是指相邻区之间重叠的区域，例如，图2中的小区2和小区3之间的阴影区域。热点话务压抑是指当前区域的信号质量差，发送数据需要更多能量。例如，图2中的小区1的热点覆盖区域中的接入用户数量较大，存在热点话务压抑的问题。
为了保证用户能有更好体验(例如保证较高的网速)，无线网络要有更好的接收电平、较小的干扰、更均衡的用户分布。也就是说，无线网络需要同时考虑覆盖和容量这两个重要的性能指标。为了达到无线网络的覆盖指标和容量指标，可以通过调整天线的射频参数从而控制站点的覆盖和容量。
普通天线可以包括单输入单输出(single input single output，SISO)和多输入多输出(multiple input multiple output，MIMO)等。例如，普通天线可以包括2TRx、4TRx或8TRx等，如图3所示。随着通信技术的演进，大规模多输入多输出(Massive MIMO)应运而生。Massive MIMO是第五代移动通信(the 5 th generation，5G)网络的基本技术和关键技术。Massive MIMO天线通过集成更多的天线，可以实现三维精准波束赋形和多用户波束复用，从而达到更好的覆盖和更大的容量。常见的Massive MIMO天线可以包括32TRx和64TRx，如图3所示。
Massive MIMO技术引入后，提供了不同覆盖场景的广播波束权值，使得射频参数从普通天线的3种(物理方位角、物理下倾角、数字下倾角)，变为Massive MIMO天线的6种(物理方位角、物理下倾角、数字方位角、数字下倾角、水平波束宽度、垂直波束宽度)。并且，在5G***中，除了可整体调整波束的水平包络和垂直包络的形状外，还可以独立调整广播波束的波束，形成任意形状的覆盖。也就是说，通过调整MIMO天线的射频参数，可以提升小区的覆盖率。
现有的调整MIMO天线的射频参数的方法通常是根据终端设备上报的最小化道路测试(minimization of drive tests，MDT)数据，对覆盖优化区域进行地理栅格化建模，得到二维平面栅格的路损矩阵，如图4所示。其中，设定的优化目标是覆盖指标，基于二维栅格级路损矩阵和优化目标，对射频参数求导，根据射频参数的导数调整射频参数，提升小区覆盖率。
但是，Massive MIMO天线相比普通天线增强了立体覆盖，二维平面栅格化将垂直面特征直接投影到二维平面，并对同栅格的数据进行平均化处理，损失了垂直面覆盖特征，会导致楼宇等垂直面覆盖信号差。并且，在MDT终端渗透率低场景(比如图4所示的阴影区域)，由于该场景缺乏MDT数据，那么采用上述调整方法无法对覆盖区域进行地理栅格化建模，也就无法进行覆盖调优。
为了解决上述问题，本申请实施例提供一种网络配置方法，该方法可以根据测量报告MR数据构建三维立体覆盖特征，建模路损矩阵，从而有利于优化三维立体覆盖。
该网络配置方法可以离线部署在单机个人电脑(personal computer，PC)或者云上(即 离线工具)，也可以在线部署到网络管理***(operation maintenance center，OMC)或连接OMC的在线工具平台上(即在线工具)。请参见图5，图5为本申请实施例提供的一种通信***，该通信***包括OMC、网络设备和终端设备等。其中，本申请实施例以部署到连接OMC的在线工具为例子进行说明，OMC可以与各网络设备进行通讯，也可以和在线工具Online Tool通信，如图5所示。
其中，OMC可以获取终端设备和网络设备上报的数据。
为便于理解本申请实施例，下面对本申请实施例所涉及的技术术语进行描述。
大规模多输入多输出(massive multiple input multiple output，Massive MIMO)是第五代移动通信(the 5 th generation，5G)网络的基本技术和关键技术。Massive MIMO天线通过集成更多的天线，可以实现三维精准波束赋形和多用户波束复用，从而达到更好的覆盖和更大的容量。
请参见图6，图6为本申请实施例提供的一种MassiveMIMO天线的波束覆盖示意图。其中，图6的左侧为该MassiveMIMO天线的水平波束的示意图，从水平截面来看，该水平波束包括8个波束。该水平波束的水平波束宽度如图6所示，即水平波束宽度为水平波束包络的宽度。
图6的中间为该MassiveMIMO天线的垂直波束的示意图，从垂直截面来看，该垂直波束包括4层。该垂直波束的垂直波束宽度如图6所示，即垂直波束宽度为垂直波束包络的宽度。也就是说，该MassiveMIMO天线在垂直方向分为4层，每层对应的水平波束包括8个波束，即该MassiveMIMO天线共有32个窄波束，在水平方向和垂直方向形成不同的覆盖区域。
图6的右侧为一种MassiveMIMO天线的任意波束的示意图。其中，对于5G来说，除了可整体调整水平和垂直包络的形状外，还可以独立调整各个窄波束，从而形成任意形状的覆盖，如图6所示。
波束权值(也称为天线权值)是指天线各端口上施加特定激励信号后的量化表达形式，其目的是为了得到特定覆盖或者达到波束变形的效果。其中，通过不同的波束权值可生成多种波束形状，例如，小区级广播波束(即SSB波束)和用户级静态波束(包含SRS波束和CSI-RS波束)。
其中，SSB(SS/PBCH blocks)波束用于错开邻区间干扰，采用更多的波束实现空间覆盖，达到5G网络最优覆盖。探测参考信号(sounding reference signal，SRS)为终端设备发送给网络设备的上行导频信号，用于评判出各频带内各信道的信道质量。网络设备根据SRS，选择合适的信道对终端设备进行动态调度分配资源，以获得最佳传送效率和质量。信道状态信息参考信号(channel state information reference signal，CSI-RS)为网络设备发送给终端设备的下行导频信号，用于CSI-RS信道测量、时频偏跟踪、波束管理及移动性管理等。本实施例所述的CSI-RS是指用于移动性管理的CSI-RS，按照协议规定，该移动性管理CSI-RS可以测量服务小区及其邻区的波束级RSRP。
波束增益(也称为天线增益)是指在输入功率相等的条件下，实际天线与理想的辐射单元在空间同一点处所产生的信号的功率密度之比。其中，波束增益与天线方向图有密切的关系，方向图主瓣越窄，副瓣越小，增益越高。天线增益是用来衡量天线朝一个特定方 向收发信号的能力，它是选择基站天线的重要参数之一。
测量报告(measurement report，MR)是指终端设备上报的测量报告，MR中包含服务小区的小区标识、服务小区的RSRP、邻区的小区标识、邻区的RSRP等信息。但是MR中不包含经纬度信息。
MDT是3GPP定义的最小化道路测试数据，包括终端设备上报的经纬度信息以及服务小区的小区标识、服务小区的RSRP、邻区的小区标识、邻区的RSRP等信息。也就是说，MDT可认为是带有经纬度的MR。
站点(也称为网络设备)可以是任意一种具有无线收发功能的设备，为覆盖范围内的终端设备提供无线通信服务。站点可以包括但不限于：长期演进(longtermevolution，LTE)***中的演进型基站(NodeB或eNB或e-NodeB，evolutionalNodeB)，新一代无线接入技术(newradioaccesstechnology，NR)中的基站(gNodeB或gNB)或收发点(transmissionreceivingpoint/transmissionreceptionpoint，TRP)，3GPP后续演进的基站，WiFi***中的接入节点，无线中继节点，无线回传节点，车联网、D2D通信、机器通信中承担基站功能的设备，卫星等。
终端设备可以是一种具有无线收发功能的设备，或者终端设备也可以是一种芯片。所述终端设备可以是用户设备(userequipment，UE)、手机(mobilephone)、平板电脑(Pad)、带无线收发功能的电脑、虚拟现实(virtualreality，VR)终端设备、增强现实(augmentedreality，AR)终端设备、车载终端设备、远程医疗(remotemedical)中的无线终端、智能电网(smartgrid)中的无线终端、可穿戴终端设备、车联网、D2D通信、机器通信中的传感器等。
下面将结合具体的实施例进行描述。
请参见图7，图7为本申请实施例提供的一种网络配置方法的流程示意图。该网络配置方法可以由离线部署在单机电脑或者云上的离线工具，或者由在线部署到网络管理***OMC或连接OMC的在线工具平台上的在线工具所执行。该网络配置方法应用于无MDT数据或MDT数据不充足的场景中，包括以下步骤：
S701，网络管理装置根据多条测量报告MR数据中每条MR的小区信息对所述多条MR数据进行汇聚处理以获得N组MR数据；
S702，网络管理装置获取N组MR中每组MR的每个小区的平均参考信号接收功率RSRP；
S703，针对N组MR中的每组MR，网络管理装置根据该组MR中每个小区的平均RSRP以及每个小区的主波束标识对应的垂直波束范围、水平波束范围，创建该组MR的立体栅格；
S704，针对每组MR的立体栅格，网络管理装置计算该组MR中各小区的主波束到该立体栅格之间的路损，以获得该立体栅格的路损矩阵；
S705，网络管理装置根据N个立体栅格的路损矩阵，确定目标小区的网络配置参数。
在本申请实施例中，假设终端设备上报的测量报告不包括经纬度信息。也就是说，网络管理装置接收终端设备上报的MR，但是不包括MDT或MDT不充足。在上述场景下，网络管理装置可以对终端设备上报的MR进行汇聚处理以获得N组MR数据。具体的，网络管理装置可以首先对终端设备上报的MR进行数据预处理，该数据预处理包括MR主波 束识别和相似特征MR数据汇聚。
下面对网络管理装置进行MR主波束识别的过程进行详细的描述。
网络管理装置除了接收到终端设备上报的MR，还可以获取终端设备的呼叫历史记录(call history record，CHR)。其中，CHR可以把该终端设备的每次呼叫按照特征提取的方式进行记录。CHR包括服务小区的小区标识(service cell identity，S_Cell_ID)、服务小区上行吞吐量(service cell uplink throughput，ULThroughput)、服务小区下行吞吐量(service cell downlink throughput，DLThroughput)、服务小区的主波束标识(service main beam identity，S_MainBeam_ID)，还可以包括终端设备的接入相关信息、切换相关信息等，本实施例不作限定。
可选的，网络管理装置还可以接收工参/配置文件以及天线文件。其中，工参/配置文件包括各个小区的射频参数等信息，天线文件包括广播波束、业务波束的水平方向图、业务波束的垂直方向图、波束增益及衰减信息等，本实施例不作限定。
由于MR和CHR是终端设备上报的两类不同的数据，为了便于后续处理，可以将MR和CHR进行关联处理。例如，网络管理装置可以根据“call时间+Cell_ID+call_ID”对MR和CHR进行关联处理。其中，由于call_ID的数量是有限的，在一段时间后会重复，本实施例中通过限定call时间，来区分call_ID。
为了便于描述，本实施例中关联处理后的MR和CHR称为MR数据，该包括一条或多条MR。其中，一条MR表示一个终端设备在一段时间内上报的测量报告，该一条MR的小区信息包括一个或多个小区的小区标识、一个或多个小区的主波束标识和一个或多个小区的RSRP。也就是说，每条MR的小区信息包括一个或多个小区的小区标识、一个或多个小区的主波束标识和一个或多个小区的RSRP。
其中，服务小区表示终端设备进行通信时，为该终端设备提供信道的小区。邻区表示与服务小区相邻的小区，例如，若图2中的小区2为终端设备的服务小区，那么小区3为该终端设备的邻区。
服务小区的主波束为终端设备检测到的Massive MIMO天线发射的多个波束中的信号最强的波束。也就是说，服务小区的主波束标识指示的是该终端设备检测到的信号最强波束的波束标识。
例如，终端设备接入服务小区后，在当前位置可以检测到Massive MIMO天线发射的多个波束。假设该Massive MIMO天线为64TRx，64TRx有64个波束，水平8*垂直4*极化2，如图8所示。需要注意的是，该Massive MIMO天线的64个波束(含双极化)对应32个物理波束位置，其中，正45度极化和负45度极化两个波束可以认为是一个物理波束。该32个物理波束分别有其对应的波束标识。假设该32个物理波束的波束标识从0开始直至31，按照垂直分层，最下面的第一层的波束标识从南到北依次为0至7，第二层的波束标识从南到北依次为8至15，第三层的波束标识从南到北依次为16至23，第四层的波束标识从南到北依次为24至31。终端设备检测到的信号最强波束的波束标识为11，如图8所示。
在一种实现方式中，每条MR的小区信息包括服务小区的小区信息和/或邻区的小区信息。也就是说，本申请实施例中引入了邻区的相关信息，使得网络管理装置在对一个小区 进行网络配置参数的调整时，也充分考虑到该小区的邻区对其的影响。具体的，每条MR的小区信息包括服务小区的小区标识、服务小区的RSRP(S_RSRP)、邻区的小区标识(neighbor cell identity，N_Cell_ID)、邻区的RSRP(N_RSRP)等。
受限于终端设备的测量能力，终端设备无法获取邻区的主波束标识。为了便于后续MR主波束识别，本实施例在MR数据中增加邻区的主波束标识(neighbor main beam identity，N_MainBeam_ID)字段，用于记录邻区的主波束标识。
MR数据可以以表格的形式存储在网络管理装置中，也可以以其他形式存储，本实施例不作限定。请参见表1，表1为本申请实施例提供的一种MR数据信息表。该信息表包括S_Cell_ID、S_RSRP、S_MainBeam_ID、N_Cell_ID、N_RSRP、N_MainBeam_ID、ULThroughput、DLThroughput等字段，各个字段的物理意义以及字段来源如表1所示。
表1：一种MR数据信息表
Figure PCTCN2021098022-appb-000001
需要注意的是，N_MainBeam_ID字段在进行MR主波束识别前的值可以设置为-1。关联后MR数据存储时，按照服务小区、邻区的顺序进行排列。其中，不同服务小区之间按照S_RSRP的大小依次排序存放；类似的，邻区按照N_RSRP的大小依次排序存放。
请参见表2，表2为本申请实施例提供的一种MR数据存储表。该存储表包括一条或多条MR。每条MR的小区信息包括一个或多个小区的小区标识、一个或多个小区的主波束标识、一个或多个小区的RSRP、一个或多个小区的ULThroughput以及一个或多个小区的DLThroughput。
表2：一种MR数据存储表
Figure PCTCN2021098022-appb-000002
Figure PCTCN2021098022-appb-000003
其中，表2中MainBeam_ID的值为-1的MR是指邻区，并且该行中的ULThroughput和DLThroughput的值均为0，也就是说，邻区的吞吐量信息该终端设备无法直接测量得到。
在一种实现方式中，为了便于后续的相似特征MR数据汇聚，网络管理装置可以对MR数据中的邻区主波束进行预测，包括以下步骤：
s11，针对MR数据中的每条MR，根据该条MR中的服务小区的小区标识、服务小区的RSRP、服务小区的主波束标识，确定该条MR的服务小区的主波束预测模型；
s12，根据该条MR的服务小区的主波束预测模型、邻区的小区标识和邻区的RSRP，确定该条MR的邻区的主波束标识。
举例来说，针对表2中的所有小区，获取各个小区作为服务小区时的小区信息。以cell i为例，网络管理装置可以从表2中获取包含cell i，且cell i的MainBeam_ID的值不为-1的所有cell i的Cell_ID、RSRP以及MainBeam_ID。
在遍历表2中的所有MR后，将各个小区作为服务小区时的Cell_ID、RSRP及其对应的MainBeam_ID作为训练数据，可以以表格的形式存储该训练数据。其中，Cell_ID和RSRP作为特征向量，记为X_Train，MainBeam_ID作为标签，记为Y_Train，小区的主波束预测模型的训练数据如表3所示。
表3：一种小区的主波束预测模型的训练数据表
Figure PCTCN2021098022-appb-000004
其中，MR 1中的服务小区为cell 1，那么MainBeam_ID为cell 1的MainBeam_ID，根据表2可知cell 1的MainBeam_ID的值为31。需要注意的是，MR 1中不包括cell n，则MR 1中的cell n的RSRP的值为0。表3中的其余行的数据的定义类似于MR 1这一行的定义，在此不再赘述。
网络管理装置可以将表3中的数据作为训练数据，采用机器学习算法进行主波束预测模型训练。还是以cell i为例，cell i的主波束预测模型记为CellBeamModel_cell i。其中，小区主波束预测模型训练过程可简单理解为：对于函数y＝f(x)，已知x和y，总结归纳求得f。也就是说，函数y为表3中的Y_Train，x为表3中的X_Train，根据表3存储的多个X_Train和Y_Train，可以训练得到各个小区的主波束预测模型。例如，根据cell i对应的X_Train和Y_Train，可以训练得到cell i的主波束预测模型。
类似的，针对表2中的所有小区，获取各个小区作为邻区时的小区信息。以cell i为例，网络管理装置可以从表2中获取包含cell i，且cell i的MainBeam_ID的值为-1的所有cell i的Cell_ID和RSRP。在遍历表2中的所有MR后，将各个小区作为邻区时的Cell_ID和RSRP作为预测输入值，记为X_Pre，该预测输入值也可以以表格的形式存储，如表4所示。
表4：一种邻区主波束预测的预测输入表
Figure PCTCN2021098022-appb-000005
其中，对于cell i，根据cell i的X_Pre以及cell i的主波束预测模型，可以得到cell i的Y_Pre，即预测得到cell i的主波束标识。遍历表2中所有小区的MainBeam_ID的值为-1的MR数据，使得每条MR数据中的所有小区都标记主波束标识。
请参见表5，表5为本申请实施例提供的一种主波束标记后的MR数据存储表。相较于表2，表5中的每个小区的MainBeam_ID的值均为大于-1的值，也就是说，每个小区的主波束标识都可以指示Massive MIMO天线产生的波束中的一个波束。
表5：一种主波束标记后的MR数据存储表
Figure PCTCN2021098022-appb-000006
可选的，由于主波束标记后的MR数据量很大，可能会占用大量的计算资源，降低OMC的运行效率，网络管理装置可以将主波束标记后的MR数据按照一定的规则进行汇聚。也就是说，网络管理装置可以对主波束标记后的MR数据进行相似特征MR数据汇聚。
下面对网络管理装置进行相似特征MR数据汇聚的过程进行详细的描述。
网络管理装置可以结合数据特征，首先对主波束标记后的MR数据进行无线空间传播相似性分析，然后将具备无线空间传播相似性的MR数据进行汇聚，获得N组MR数据。
其中，无线空间传播相似性是指无线信号在空间传播会存在折射、反射、衍射等不同传播路径。同一个小区发射信号，经过无线空间传播，到达距离相近的终端设备，其无线空间传播具有相似性。例如，距离相近的终端设备1和终端设备2测量到周边各小区的主波束标识相同，RSRP相差较小。那么可以说终端设备1和终端设备2的无线空间传播具有相似性。
在一种实现方式中，网络管理装置可以根据表5中的每条MR以及每条MR中的各个小区的RSRP，对具备无线空间传播相似性的MR数据进行汇聚，获得N组MR数据，具 体可以包括以下步骤：
将小区标识相同，主波束标识相同、电平差值满足预设电平差值条件的MR数据进行合并，确定汇聚后的一组MR；其中，所述电平差值为MR数据中小区标识相同的小区两两之间的RSRP的差值。
其中，本实施例中将小区标识相同、主波束标识相同、电平差值满足预设电平差值条件(例如RSRP差值小于3dB)的多条MR视为具有无线空间传播相似性的MR。
举例来说，网络管理装置可以将主波束标记后的MR数据首先进行数据预处理。其中，将Cell_ID、RSRP和MainBeam_ID作为特征向量，将主波束标记后的MR数据进行数据预处理后得到相似特征MR数据汇聚输入表，如表6所示。
表6：一种相似特征MR数据汇聚输入表
Figure PCTCN2021098022-appb-000007
网络管理装置可以根据上述表6中的数据，按照一定的聚类规则(如聚类算法)将表6中的数据进行汇聚处理。例如，网络管理装置将Cell_ID相同、MainBeam_ID相同以及RSRP差值小于3dB的MR数据进行汇聚，得到一组MR。
例如，表6中的MR 1中的cell1的MainBeam_ID为31，RSRP为80，MR 2中的cell1的MainBeam_ID为31，RSRP为78。表6中的MR 1中的cell 2的MainBeam_ID为29，RSRP为70，MR 2中的cell 2的MainBeam_ID为29，RSRP为70。可见，该两条MR的小区标识相同、小区的主波束标识相同、MR 1中的cell 1的RSRP与MR 2中的cell 1的RSRP的差值小于3dB、MR 1中的cell 2的RSRP的值与MR 2中的cell 1的RSRP的值相同。也就是说，MR 1和MR 2可以合并为一组MR。
可选的，网络管理装置在对相似特征MR数据进行汇聚处理时，可能存在以下情况：两条MR中的小区标识相同，小区的主波束标识相同，小区的电平差值也满足预设电平差值条件；但是，其中一条MR中还包括其他的小区信息。例如，对于表6中的MR 1和MR3，MR 1和MR 3均包括cell 1的小区信息和cell 2的小区信息。但是，MR 3还包括cell 3的小区信息。
为了分析上述MR是否可以汇聚成一组MR，网络管理装置可以对相似特征MR数据进行处理。例如，针对表6中的每条MR，分别计算每条MR中RSRP的最大值与每个小区的RSRP的差值，并将该差值作为新的特征向量，将表6扩充如表7所示。
表7：一种相似特征MR数据汇聚扩充特征向量后的输入表
Figure PCTCN2021098022-appb-000008
例如，对于表7中的MR 1和MR 3，MR 3还包括cell 3的小区信息。但是，MR 3中的cell 3的RSRP的差值较大。也就是说，相较于服务小区cell 1，邻区cell 3的RSRP较小，那么cell 3与cell 1的距离可能较远，也就是说，邻区中的cell 3可能是无效的，可以忽略cell 3。也就是说，对于MR 1和MR 3可以汇聚成一组MR，该组MR具有无线空间传播相似性。
需要注意的是，上述表6和表7中的数据仅为相似特征MR数据汇聚的一部分，汇聚后的MR数据还包括小区的吞吐量信息等，本实施例不作限定。
可选的，网络配置装置可以根据小区数、小区中用户分布情况、用户数量、小区间的重叠覆盖情况来确定MR数据一共汇聚成了多少组MR。
举例来说，假设有3个小区cell1、cell2和cell3，其中cell2和cell3中无用户、cell1中有2个用户UE1和UE2。其中，UE1固定不动，其主波束为31号波束；UE2是移动用户，其主波束为8号和9号波束。两个用户分别上报了4条MR，处理后MR信息如表8所示。
表8：UE 1和UE 2的相似特征MR数据输入表
Figure PCTCN2021098022-appb-000009
Figure PCTCN2021098022-appb-000010
网络管理装置可以将UE 1和UE 2上报的4条MR数据进行汇聚处理，通过汇聚运算共计可分3组，其中UE1的4条MR数据为1组MR，UE2的4条MR数据分为2组MR。需要注意的是，上述举例仅为一种示例，MR数据一共汇聚成了多少组MR是根据具体的网络情况来分析获取的，分析的依据包括小区数、小区中用户分布情况、用户数量、各个小区的平均RSRP等参数，本实施例不作限定。
网络管理装置将汇聚后的各组MR分别进行数据处理，包括对各组的RSRP进行平均处理，和/或对各组的吞吐量进行求和处理。例如，对于第n组MR，将该组MR中的同一小区的RSRP求平均值，得到该组MR中的同一小区的平均RSRP。将该组MR中的同一小区的上行吞吐量进行求和，得到该组MR中的同一小区的上行吞吐量总和。将该组MR中的同一小区的下行吞吐量进行求和，得到该组MR中的同一小区的下行吞吐量总和。
可选的，网络管理装置还可以记录各组MR中的每个小区出现的次数。例如，对于第n组MR，记录该组MR中的每个小区作为服务小区出现的次数，记录该组MR中的每个小区作为邻区出现的次数。
请参见表9，表9为本申请实施例提供的一种汇聚后的MR数据格式表。其中，以第n组MR为例，该表9记录了第n组MR数据中的一个或多个小区的小区标识、一个或多个小区的主波束标识、一个或多个小区的平均RSRP等数据。
表9：一种汇聚后MR数据格式表
Figure PCTCN2021098022-appb-000011
其中，立体栅格(grid)可以视为汇聚后MR数据中的一组MR。也就是说，立体栅格n(Grid n)对应汇聚后MR数据中的第n组MR。
可选的，立体栅格也可以视为一种三维坐标系中的三维空间位置。其中，该三维坐标系中的三个维度不同于常规的三维坐标系(如经度、纬度和高度构成的三维坐标系)，该三维坐标系中的三个维度分别为小区的主波束标识对应的水平波束范围、垂直波束范围和小区的主波束到立体栅格的路径损耗(也称为路损)。
请参见图9a，图9a为本申请实施例提供的一种立体栅格的示意图。其中，假设图9a中的小区1包括32个窄波束，其中，小区1的波束1到立体栅格n的路损如图9a所示。需要注意的是，图9a中的多个立体栅格分别代表三维空间位置。其中，参考坐标系如图9a所示，也就是说，图9a所示的立体栅格的三维空间是由小区的天线平面以及小区到立体栅格的RSRP确定的距离来确定的。
网络管理装置可以根据多个立体栅格、工参/配置、天线文件等，生成路损矩阵。也就是说，针对每组MR的立体栅格，可以计算该组MR中各小区的主波束到该立体栅格之间的路损，以获得该立体栅格的路损矩阵，包括以下步骤：
s21，根据该组MR中的任意一个小区i的主波束标识，获取该小区i的主波束标识对应的波束增益；该小区i的波束增益是根据所述小区i的主波束标识对应的水平波束范围、垂直波束范围、天线增益、水平波束范围对应的信号衰减值和垂直波束范围对应的信号衰减值确定的；
s22，根据所述小区i的发射功率、所述小区i的波束增益和所述小区i的平均RSRP，计算所述小区i的主波束到该组MR的立体栅格的路径损耗。
其中，以立体栅格n为例，由于立体栅格n中可以包括多个小区，在计算立体栅格n的路损时，需要计算立体栅格n中的每个小区到该立体栅格n的路损。下面以立体栅格n中的celli为例进行详细的描述，需要注意的是，所有立体栅格中的各个小区都可以参考下文的步骤进行计算，得到各个立体栅格中的每个小区到该立体栅格的路损。
首先，网络管理装置可以从工参/配置中获取celli的射频参数。其中，射频参数可以包括但不限于：水平波束宽度、物理方位角、数字方位角、垂直波束宽度、物理下倾角或数字下倾角。
其中，水平波束宽度表示波束权值控制的水平面覆盖的水平包络宽度，例如，图6所示的水平波束宽度。垂直波束宽度表示波束权值控制的水平面覆盖的垂直包络宽度，例如，图6所示的垂直波束宽度。物理方位角表示物理天线面板的正对方向与正北方的夹角，其取值范围为0度至359度。数字方位角表示波束权值控制的水平波束能量最强指向与正北方的夹角，其取值范围为0度至359度。物理下倾角表示垂直于物理天线面板的平面与水平面的夹角，其取值范围为﹣90度至90度。数字下倾角表示波束权值控制的垂直波束能量最强指向与水平面的夹角，其取值范围为﹣90度至90度。
然后，网络管理装置根据celli的MainBeam_ID，从天线文件中获取MainBeam_ID对应的水平波束范围、垂直波束范围、天线增益、水平波束范围对应的信号衰减值和垂直波束范围对应的信号衰减值。
例如，cell i的MainBeam_ID对应的水平波束范围是[0度，19度]，垂直波束范围是[3度，9度]。天线增益是50dB。根据该水平波束范围和垂直波束范围，网络管理装置可以确定水平波束范围的每个度数对应的信号衰减值以及垂直波束范围的每个度数对应的信号衰 减值。根据波束增益的计算公式，可以计算cell i的MainBeam_ID的波束增益。其中，波束增益的计算公式如下：cell i的MainBeam_ID的波束增益＝天线增益+信号衰减值的平均值。信号衰减的平均值包括水平波束对应的信号衰减值的平均值和垂直波束对应的信号衰减值的平均值。
最后，网络管理装置可以利用经典传播模型公式计算cell i的主波束到立体栅格n的路损。其中，路损的计算公式如下：cell i的主波束到立体栅格n的路损＝cell i的发射功率+cell i的MainBeam_ID的波束增益－cell i的平均RSRP。其中，cell i的发射功率也可以从天线文件中获取，cell i的平均RSRP可以从表9所示的汇聚后MR数据中获取。
请参见图9b，图9b为本申请实施例提供的一种路损矩阵的示意图。其中，图9b中包括三个小区，分别为cell 1、cell 2和cell 3。以立体栅格n为例，该立体栅格n包括多个小区以及该多个小区的主波束标识，且不同小区和/或不同主波束到立体栅格n的路损是不同的。例如，对于cell 2来说，cell 2的波束0到立体栅格n的路损如图9b所示。但是对于cell 1来说，cell 1的波束1到立体栅格n的路损如图9b所示，不同于cell 2的波束0到立体栅格n的路损。
其中，该路损矩阵可以采用表格的形式存储在网络管理装置中。请参见表10，表10为本申请实施例提供的一种路损矩阵的数据格式表，包括立体栅格的标识、小区标识、小区的主波束标识以及路损。
表10：一种路损矩阵的数据格式表
名称 物理意义
Gridn 立体栅格标识
Cell_ID 小区标识，一个立体栅格中有多个小区
MainBeam_ID 小区的主波束标识
路损 小区到立体栅格的路径损耗值
其中，Gridn表示第n个立体栅格，每个立体栅格都有唯一的立体栅格标识。一个立体栅格中有多个小区，各个小区的小区标识和主波束标识，以及小区到该立体栅格的路损都记录在路损矩阵中。
下面对网络优化的过程进行详细的描述。
根据上文实施例中的描述，网络管理装置可以得到N组MR的立体栅格，以及小区到立体栅格的路损矩阵。若网络存在覆盖问题和/或容量问题，网络管理装置可以根据路损矩阵，确定目标小区的网络配置参数。也就是说，网络管理装置可以根据路损矩阵调整存在覆盖问题和/或容量问题的小区的网络配置参数，以解决网络中的覆盖问题和/或容量问题。
网络配置参数可以是前文实施例所述的射频参数，或者是射频参数组合。例如，网络管理装置可调整的网络配置参数(记为Conf)可以是水平波束宽度、物理方位角、数字方位角、垂直波束宽度、物理下倾角或数字下倾角中的任意一种。又例如，网络管理装置可以根据网络中的小区的波束范围、话务流量、RSRP等信息，确定待调整的射频参数组合，如调整水平波束相关的射频参数组合。
可选的，网络配置参数还可以是其他无线资源管理(radio resource management，RRM)场景下的配置参数。例如，网络配置参数可以是小区个性偏置(cell individual offset，CIO) 参数，用于切换。
为了衡量网络是否存在覆盖问题和/或容量问题，本实施例采用覆盖指标和/或容量指标来量化网络优化的目标。覆盖指标包括RSRP、信干噪比(signal to interference plus noise ratio，SINR)、重叠覆盖比例等，记为coverage。容量指标包括话务量均衡度、谱效率等，记为capacity。
其中，RSRP是指小区的接收电平，可以由小区测量并上报至网络管理装置。SINR的计算公式如下：
Figure PCTCN2021098022-appb-000012
由服务小区的RSRP、邻区的RSRP之和、噪声功率确定。重叠覆盖比例的计算公式如下：
Figure PCTCN2021098022-appb-000013
其中，若服务小区RSRP与邻区RSRP差值小于门限(比如3dB)，则认为该服务小区存在重叠覆盖，计入重叠覆盖的服务小区数量中。
其中，话务量均衡度是指相邻小区间的上行和下行吞吐量之和尽可能相同。也就是说，服务小区的上下行吞吐量之和与邻区的上下行吞吐量之和尽量相同。谱效率是指单位时间的单位资源块(resource block，RB)上的话务量。
在一种实现方式中，覆盖和容量两种指标可能无法同时达到最优。也就是说，当网络的覆盖指标调整到最优的状态时，例如可以覆盖较大的范围，并且重叠覆盖区域较少；但是网络的容量指标可能不是最优，例如部分区域的谱效率较低。为了优化网络整体性能，本申请实施例提出多目标、多参数的联合寻优。也就是说，根据上述覆盖指标、容量指标以及网络配置参数，本申请实施例将网络优化的目标(fitness)建模为如下公式：
fitness＝Func((k 1*coverage,k 2*capacity),Conf)
其中，fitness表示容量和覆盖的联合优化目标，Conf表示可调整的网络配置参数，k 1表示覆盖权重，k 2表示容量权重。通过设置不同的覆盖权重和容量权重，控制coverage和capacity对联合优化目标的影响程度。
举例来说，当前网络场景中小区覆盖一个热点区域，并且该热点区域中的用户多集中在高处(例如5层高的楼房中，用户多集中在4楼和5楼)。那么该场景下可以优先考虑容量权重，也就是说，k 2的权重值大于k 1的权重值。并且在调整网络配置参数时，优先考虑对垂直波束的调整，也就是说，网络管理装置可以对垂直波束宽度、物理下倾角和数字下倾角的参数组合进行调整，以优化容量和覆盖。
在一种实现方式中，网络管理装置根据N个立体栅格的路损矩阵，确定目标小区的网络配置参数，可以包括以下步骤：
s31，针对每个立体栅格，根据该立体栅格的各个小区的覆盖指标和/或容量指标确定该立体栅格的目标小区；
s32，根据目标小区的预设覆盖指标和预设容量指标，调整目标小区的网络配置参数；
s33，根据调整后的目标小区的网络配置参数，得到调整后的目标小区的天线增益；
s34，根据该立体栅格的路损矩阵、调整后的目标小区的天线增益和调整后的目标小区的网络配置参数，确定目标小区的覆盖指标和容量指标；
s35，若目标小区的覆盖指标达到预设覆盖指标，和/或，目标小区的容量指标达到预设容量指标，确定调整后的目标小区的网络配置参数为目标小区的网络配置参数。
其中，上述s31可以分为几个小步骤，包括：
s311，根据fitness公式，并基于实测数据，确定网络配置参数调整前的存在覆盖问题和/或容量问题的立体栅格；
s312，采用聚类算法将存在覆盖问题和/或容量问题的立体栅格进行汇聚处理；
s313，确定汇聚后的问题立体栅格包括的小区为目标小区。
其中，目标小区为覆盖指标不满足预设覆盖指标和/或容量指标不满足预设容量指标的小区。也就是说，目标小区可以根据小区的覆盖指标进行判定，也可以根据小区的容量指标进行判定。可以理解的是，小区的覆盖指标是指该小区当前的覆盖情况，小区的容量指标是指该小区当前的容量情况。
其中，预设覆盖指标可以是根据RSRP、SINR、重叠覆盖比例等参数确定的；例如，小区的预设覆盖指标为小区的重叠覆盖比例小于10％。预设容量指标可以是根据话务量均衡度、谱效率等参数确定的；例如，小区的预设容量指标为小区的话务量均衡度为1，即该小区的上行吞吐量和下行吞吐量之和，与相邻小区的上行吞吐量和下行吞吐量之和相同。
举例来说，网络管理装置可以基于实测数据确定目标小区。其中，实测数据可以根据表9所示的汇聚后MR数据来确定。实测数据可以是立体栅格中的各个小区的平均RSRP。若小区的平均RSRP低于预设的RSRP门限值(例如100dB)，则确定该小区存在覆盖问题。实测数据也可以是立体栅格中的各个小区的上下行吞吐量之和。若小区的上下行吞吐量之和小于预设的吞吐量门限值，则确定该小区存在容量问题。
可选的，在对存在覆盖问题和/或容量问题的立体栅格进行汇聚处理时，可以采用聚类算法(density-based spatial clustering of applications with noise，DBSCAN)等。例如，若存在覆盖问题和/或容量问题的小区一共有50个，根据聚类算法可以将无线空间传播相似性更高的立体栅格汇聚起来，假设通过聚类算法汇聚后的立体栅格包括的小区一共有30个，那么网络管理装置可以确定该30个小区为目标小区。
在确定目标小区后，根据网络优化的目标(fitness)的公式，对该网络优化的目标求最大值，即网络优化的目标公式转换为如下公式：
max{fi tness＝Func((k 1*coverage,k 2*capacity),Conf)}
其中，max表示通过调整Conf可以得到fitness的最大值。
在一种实现方式中，网络管理装置可以从天线文件中获取可调整的网络配置参数，并且通过配置和工参获取当前的网络配置参数。网络管理装置可以采用优化算法(例如采用基于梯度下降的运筹优化算法)，以max{fitness}为优化目标，以当前的网络配置参数为初始值，以可调整的网络配置参数为变量，通过不断调整Conf，以期获取使得fitness最大的Conf；上述寻优过程如图10所示。其中，寻优的停止条件需要综合考虑算法效率及收敛性，也就是说，停止寻优条件包括：整体增益达标(如fitness达到最大值)，或者连续多轮增益小于门限，或者迭代次数达到最大优化次数门限。
本申请实施例提供一种网络配置方法，该方法可以根据MR数据中的每个小区的平均 RSRP和每个小区的主波束标识对应的水平波束信息、垂直波束信息，构建三维立体栅格，实现了不依赖于MDT数据也可构建三维立体栅格的目标。并且，根据三维立体栅格可以构建三维路损矩阵，实现立体波束优化。该方法还可以根据三维路损矩阵确定目标小区的网络配置参数，通过调整目标小区的网络配置参数，可以优化目标小区的覆盖指标和/或容量指标，从而有利于优化网络的覆盖和/或容量。
请参见图11，图11为本申请实施例提供的另一种网络配置方法的流程示意图。该网络配置方法可以由离线部署在单机电脑或者云上的离线工具，或者由在线部署到网络管理***OMC或连接OMC的在线工具平台上的在线工具所执行。该网络配置方法应用于MDT数据充足或道路测试的场景中，包括以下步骤：
S1101，网络管理装置根据多条最小化道路测试MDT数据中每条MDT的小区信息对所述多条MDT数据进行汇聚处理以获得N组MDT数据，每组MDT包括小区的垂直波束标识相同的MDT；
S1102，针对N组MDT中的每组MDT，网络管理装置根据该组MDT中每个小区的经度、纬度以及每个小区的垂直波束标识，创建该组MDT的立体栅格；
S1103，针对每组MDT的立体栅格，网络管理装置计算该组MDT中各个小区的主波束到该立体栅格之间的路损，以获得该立体栅格的路损矩阵；
S1104，网络管理装置根据N个立体栅格的路损矩阵，确定目标小区的网络配置参数。
在本申请实施例中，假设终端设备上报的测量报告为包括经纬度信息的测量报告，也就是说，终端设备上报的是MDT数据。在上述场景下，网络管理装置可以对终端设备上报的MDT进行汇聚处理以获得N组MDT数据。类似于网络管理装置对MR数据的汇聚处理，网络管理装置可以首先对终端设备上报的MDT进行数据预处理，该数据预处理包括MDT主波束识别和MDT地理化栅格汇聚。
下面对网络管理装置进行MDT主波束识别的过程进行详细的描述。
网络管理装置除了接收到终端设备上报的MDT，还可以获取终端设备的CHR。其中，对CHR的相关描述可以参考图7实施例中对CHR的描述，在此不再赘述。
可选的，网络管理装置还可以获取工参/配置文件、天线文件、电子地图。其中，对工参/配置文件、天线文件的相关描述可以参考图7实施例中对工参/配置文件、天线文件的描述，在此不再赘述。电子地图包括地理空间的位置信息，可以展示站点和终端设备的位置关系。
由于MDT和CHR是终端设备上报的两类不同的数据，为了便于后续处理，可以将MDT和CHR进行关联处理。其中，对MDT和CHR进行关联处理的描述可以参考图7实施例中对MDT和CHR进行关联处理的描述，在此不再赘述。
为了便于描述，本实施例中关联处理后的MDT和CHR称为MDT数据，该包括一条或多条MDT。其中，一条MDT表示一个终端设备在一段时间内上报的包括经纬度信息的测量报告，该一条MDT的小区信息包括一个或多个小区的小区标识、一个或多个小区的主波束标识、一个或多个小区的RSRP、一个或多个小区的经度和纬度。也就是说，每条MDT的小区信息包括一个或多个小区的小区标识、一个或多个小区的主波束标识、一个或多个小区的RSRP、一个或多个小区的经度和纬度。
在一种实现方式中，每条MDT的小区信息包括服务小区的小区信息和/或邻区的小区信息。也就是说，本申请实施例中引入了邻区的相关信息，使得网络管理装置在对一个小区进行网络配置参数的调整时，也充分考虑到该小区的邻区对其的影响。具体的，每条MR的小区信息包括服务小区的小区标识、服务小区的RSRP(S_RSRP)、邻区的小区标识(neighbor cell identity，N_Cell_ID)、邻区的RSRP(N_RSRP)等。
受限于终端设备的测量能力，终端设备无法获取邻区的主波束标识。为了便于后续MDT主波束识别，本实施例在MDT数据中增加邻区的主波束标识(neighbor main beam identity，N_MainBeam_ID)字段，用于记录邻区的主波束标识。
类似于MR数据，MDT数据可以以表格的形式存储在网络管理装置中，也可以以其他形式存储，本实施例不作限定。请参见表11，表11为本申请实施例提供的一种MR数据信息表。该信息表包括S_Cell_ID、S_RSRP、S_MainBeam_ID、N_Cell_ID、N_RSRP、N_MainBeam_ID、ULThroughput、DLThroughput等字段，各个字段的物理意义以及字段来源如表11所示。
表11：一种MDT数据信息表
Figure PCTCN2021098022-appb-000014
类似于图7实施例中网络管理装置对MR数据中的邻区主波束进行预测的步骤，本实施例中网络管理装置也可以按照类似的步骤对MDT数据中的邻区主波束进行预测，使得每条MDT数据中的所有小区都标记主波束标识。
请参见表12，表12为本申请实施例提供的一种主波束标记后的MDT数据存储表。每个小区的主波束标识都可以指示Massive MIMO天线产生的波束中的一个波束。
表12：一种主波束标记后的MDT数据存储表
Figure PCTCN2021098022-appb-000015
Figure PCTCN2021098022-appb-000016
由于主波束标记后的MDT数据只代表二维地理栅格的信息，为了建模三维立体栅格，网络管理装置可以从垂直面分层进行地理化栅格汇聚。下面对网络管理装置进行地理化栅格汇聚的过程进行详细的描述，可以包括以下步骤：
s41，网络管理装置根据该组MDT中的每个小区的经度和纬度，确定该组MDT构成的平面栅格的栅格经度和栅格纬度；
s42，网络管理装置根据该组MDT的垂直波束标识，确定该组MDT构成平面栅格所在的垂直层，以获得该组MDT的立体栅格。
举例来说，网络管理装置可以基于主波束标记后的MDT数据，依据每个小区对应的MainBeam_ID，计算每个小区对应的垂直波束标识V_Beam_ID。本申请实施例中计算V_Beam_ID的公式如下：V_Beam_ID＝int(MainBeam_ID÷8)。例如，若小区的MainBeam_ID为31，根据上述V_Beam_ID的公式，计算得到该小区的V_Beam_ID为3。
网络管理装置对MDT数据进行处理后，MDT数据如表13所示。
表13：垂直波束标记后的MDT数据存储表
Figure PCTCN2021098022-appb-000017
网络管理装置获取垂直波束标记后的MDT数据后，可以根据V_Beam_ID将同一垂直层的数据进行汇聚，得到每个垂直层的MDT数据。也就是说，每个垂直层的MDT数据为一组MDT。例如，根据表13中的每个小区的V_Beam_ID，将V_Beam_ID相同的MDT数据进行汇聚，得到多组MDT数据。
网络管理装置可以对每个垂直层的MDT数据进行地理化栅格处理，得到地理栅格化后的MDT数据。其中，该地理化栅格处理是指根据每个垂直层的经纬度信息，确定每个垂直层投影到二维平面(如地面)构成的区域；再将该二维平面位置区域按照一定的比例进行地理栅格的划分。例如，V_Beam_ID为V_Beam0的垂直层的经纬度信息确定的V_Beam0的垂直层投影到地面构成的区域如图12所示。假设按照20米(m)*20m的比例进行地理栅格的划分，地理栅格化处理后的V_Beam0的垂直层投影到地面构成的区域如图12所示。又例如，V_Beam1的垂直层相较于V_Beam0的垂直层，垂直于地面的基础上向 上移动了一段垂直方向上的距离，如图12所示。也就是说，V_Beam1的垂直层投影到地面构成的区域与V_Beam0的垂直层投影到地面构成的区域是相同的，但是两者是位于不同的垂直于地面的高度，即不同V_Beam_ID的垂直层的立体栅格是不相同的。需要注意的是，这里的立体栅格是指带有V_Beam_ID的地理栅格，也就是说，该立体栅格可以是三维立体空间中的位置。
在一种实现方式中，V_Beam_ID相同的MDT数据进行汇聚后，网络管理装置还可以将V_Beam_ID相同的一组MDT进行数据处理，包括对各组的RSRP进行平均处理，对各组的吞吐量进行求和处理。具体实现方式可以参考图7实施例中对各组的RSRP进行平均处理，对各组的吞吐量进行求和处理的描述，在此不再赘述。
可选的，网络管理装置还可以记录各组MDT中的每个小区出现的次数。例如，对于第n组MDT，记录该组MDT中的每个小区作为服务小区出现的次数，记录该组MDT中的每个小区作为邻区出现的次数。
请参见表14，表14为本申请实施例提供的一种汇聚后的MDT数据格式表。其中，以第n组MDT为例，该表14记录了第n组MDT数据中的一个或多个小区的小区标识、一个或多个小区的垂直波束标识、一个或多个小区的平均RSRP、栅格经度、栅格纬度等数据。
表14：一种汇聚后MDT数据格式表
Figure PCTCN2021098022-appb-000018
其中，立体栅格(grid)可以视为汇聚后MDT数据中的一组MR。也就是说，立体栅格n(Grid n)对应汇聚后MDT数据中的第n组MDT。
请参见图13a，图13a为本申请实施例提供的另一种立体栅格的示意图。其中，小区1到V_Beam3波束中立体栅格n的路损，小区2到V_Beam3波束中立体栅格n的路损，小区3到V_Beam3波束中立体栅格n的路损如图13a所示。需要注意的是，图13a中的多个 立体栅格分别代表三维空间位置。其中，根据经度和纬度确定的为二维地理平面，本实施例中的立体栅格包括了垂直波束标识，也就是说，立体栅格n包括不同Beam层数据，即该立体栅格n为三维立体空间中的位置。
网络管理装置可以根据多个立体栅格、工参/配置、天线文件等，生成路损矩阵。也就是说，针对每组MDT的立体栅格，可以计算该组MDT中各小区的主波束到该立体栅格之间的路损，以获得该立体栅格的路损矩阵，包括以下步骤：
s51，根据该组MDT中的垂直波束标识，获取垂直波束标识对应的垂直波束范围、天线增益和垂直波束范围对应的信号衰减值；
s52，根据该组MDT的栅格经度和栅格纬度，获取该组MDT中的栅格经度和栅格纬度确定的地理栅格对应的水平波束范围、天线增益和水平波束范围对应的信号衰减值；
s53，根据该组MDT中的垂直波束标识对应的垂直波束范围、天线增益、垂直波束范围对应的信号衰减值，以及该组MDT中的栅格经度和栅格纬度确定的地理栅格对应的水平波束范围、天线增益、水平波束范围对应的信号衰减值，计算该组MDT中的任意一个小区i到该组MDT的立体栅格的天线增益；
s54，根据小区i的发射功率、小区i到该组MDT的立体栅格的天线增益和小区i的平均RSRP，计算小区i到该组MDT的立体栅格的路径损耗。
其中，以立体栅格n为例，由于立体栅格n中可以包括多个小区，那么在计算立体栅格n的路损时，需要计算立体栅格n中的每个小区到该立体栅格n的路损。下面以立体栅格n中的cell i为例进行详细的描述，需要注意的是，所有立体栅格中的各个小区都可以参考下文的步骤进行计算，得到各个立体栅格中的每个小区到该立体栅格的路损。
首先，网络管理装置可以从工参/配置中获取cell i的射频参数。其中，射频参数可以包括但不限于：水平波束宽度、物理方位角、数字方位角、垂直波束宽度、物理下倾角或数字下倾角。对上述射频参数的详细描述请参考图7实施例中的描述，在此不再赘述。
然后，网络管理装置根据cell i的V_Beam_ID，从天线文件中获取V_Beam_ID对应的垂直波束范围、天线增益、垂直波束范围对应的信号衰减值。例如，cell i的V_Beam_ID对应的垂直波束范围是[3度，9度]。天线增益是50dB。根据该垂直波束范围，网络管理装置可以确定垂直波束范围的每个度数对应的信号衰减值。
网络管理装置还依据栅格经度、栅格纬度、cell i的工参，计算cell i所在的立体栅格n对应的水平波束范围。例如，cell i的水平波束范围是[0度，19度]。再根据该水平波束范围，从天线文件中获取该水平波束范围的每个度数对应的信号衰减值。基于上述数据，网络管理装置可以计算cell i到立体栅格n的天线增益＝天线增益+信号衰减值的平均值。
最后，网络管理装置可以利用经典传播模型公式，并结合电子地图，计算cell i到立体栅格n的路损。其中，路损的计算公式如下：cell i到立体栅格n的路损＝cell i的发射功率+cell i到立体栅格n的天线增益－cell i的平均RSRP。其中，cell i的发射功率也可以从天线文件中获取，cell i的平均RSRP可以从表14所示的汇聚后MDT数据中获取。
请参见图13b，图13b为本申请实施例提供的另一种路损矩阵的示意图。其中，图13b中包括三个小区，分别为cell 1、cell 2和cell 3。其中，以立体栅格n为例，该立体栅格n包括多个小区以及该多个小区的垂直波束标识，且不同小区到不同V_Beam_ID波束中立体 栅格n的路损是不同的。例如，对于cell 2来说，cell 2到V_Beam0波束中立体栅格n的路损如图13b所示，V_Beam0波束中立体栅格n位于垂直方向上的多层中的最下面一层，如图13b所示。
其中，该路损矩阵可以采用表格的形式存储在网络管理装置中。请参见表15，表15为本申请实施例提供的另一种路损矩阵的数据格式表，包括立体栅格的标识、小区标识、小区的垂直波束标识、栅格经度、栅格纬度以及路损。
表15：一种路损矩阵的数据格式表
名称 物理意义
Gridn 立体栅格标识
Cell_ID 小区标识，一个立体栅格中有多个小区
V_Beam_ID 小区的主波束标识
Longitude 栅格经度
Latitude 栅格纬度
路损 小区到垂直波束的立体栅格的路径损耗值
其中，Gridn表示第n个立体栅格，每个立体栅格都有唯一的立体栅格标识。一个立体栅格中有多个小区，各个小区的小区标识和垂直波束标识，栅格经度和栅格纬度以及小区到垂直波束的立体栅格的路损都记录在路损矩阵中。
类似于图7实施例中网络优化的过程，本实施例中的网络管理装置也可以根据N组MR的立体栅格，以及小区到垂直波束的立体栅格的路损矩阵进行网络优化。若网络存在覆盖问题和/或容量问题，网络管理装置可以根据路损矩阵，确定目标小区的网络配置参数。也就是说，网络管理装置可以根据路损矩阵调整存在覆盖问题和/或容量问题的小区的网络配置参数，以解决网络中的覆盖问题和/或容量问题。具体的实现方式可以参考图7实施例中的描述，在此不再赘述。
本申请实施例提供一种网络配置方法，该方法可以根据MDT数据中的经纬度信息，以及根据小区的主波束标识确定的垂直波束标识，构建三维立体栅格，避免二维地理栅格对数据的平均处理模糊了立体信息，有利于实现更精准的立体优化。并且，该方法还可以根据三维立体栅格生成三维路损矩阵，根据该三维路损矩阵可以确定目标小区的网络配置参数，有利于优化网络的覆盖和/或容量。
以下结合图14至图17详细说明本申请实施例的装置及设备。
本申请实施例提供一种网络管理装置，如图14所示，该网络管理装置1400可用于实现本申请实施例中的网络配置方法。该网络管理装置1400可以包括：
汇聚单元1401，用于根据多条测量报告MR数据中每条MR的小区信息对多条MR数据进行汇聚处理以获得N组MR数据，每组MR包括具有无线空间传播相似性的MR；
获取单元1402，用于获取N组MR中每组MR的每个小区的平均参考信号接收功率RSRP；
创建单元1403，用于针对N组MR中的每组MR，根据该组MR中每个小区的平均RSRP以及每个小区的主波束标识对应的垂直波束范围、水平波束范围，创建该组MR的立体栅格；
计算单元1404，用于针对每组MR的立体栅格，计算该组MR中各小区的主波束到该立体栅格之间的路损，以获得该立体栅格的路损矩阵；
确定单元1405，用于根据N个立体栅格的路损矩阵，确定目标小区的网络配置参数，其中，目标小区为覆盖指标不满足预设覆盖门限和/或容量指标不满足预设容量门限的小区。
具体实现方式，请参考图7实施例中的S701至S705中的详细描述，在此不再赘述。
在一种实现方式中，每条MR的小区信息包括服务小区的小区信息和/或邻区的小区信息，一个或多个小区的RSRP包括服务小区的RSRP和/或邻区的RSRP。确定单元1405具体用于针对每条MR，根据该条MR中的服务小区的小区标识、服务小区的RSRP、服务小区的主波束标识，确定该条MR的服务小区的主波束预测模型；根据该条MR的服务小区的主波束预测模型、邻区的小区标识和邻区的RSRP，确定该条MR的邻区的主波束标识。
具体实现方式，请参考图7实施例中对网络管理装置进行MR主波束识别的过程的详细描述，在此不再赘述。
在一种实现方式中，汇聚单元1401具体用于将小区标识相同、主波束标识相同、电平差值满足预设电平差值条件的MR数据进行汇聚处理，确定汇聚后的一组MR；其中，电平差值为MR数据中小区标识相同、主波束标识相同的小区两两之间的RSRP的差值。
具体实现方式，请参考图7实施例中对网络管理装置进行相似特征MR数据汇聚的过程程的详细描述，在此不再赘述。
在一种实现方式中，计算单元1404具体用于：
根据该组MR中的任意一个小区i的主波束标识，获取该小区i的主波束标识对应的波束增益；该小区i的波束增益是根据小区i的主波束标识对应的水平波束范围、垂直波束范围、天线增益、水平波束范围对应的信号衰减值和垂直波束范围对应的信号衰减值确定的；
根据小区i的发射功率、小区i的波束增益和小区i的平均RSRP，计算小区i的主波束到该组MR的立体栅格的路径损耗。
具体实现方式，请参考图7实施例中对网络管理装置计算路损的过程的详细描述，在此不再赘述。
在一种实现方式中，确定单元1405具体用于：
针对每个立体栅格，根据该立体栅格的各个小区的覆盖指标和/或容量指标确定该立体栅格的目标小区；
根据目标小区的预设覆盖指标和预设容量指标，调整目标小区的网络配置参数；根据调整后的目标小区的网络配置参数，得到调整后的目标小区的天线增益；
根据该立体栅格的路损矩阵、调整后的目标小区的天线增益和调整后的目标小区的网络配置参数，确定目标小区的覆盖指标和容量指标；
若目标小区的覆盖指标达到预设覆盖指标，和/或，目标小区的容量指标达到预设容量指标，确定调整后的目标小区的网络配置参数为目标小区的网络配置参数。
具体实现方式，请参考图7实施例中对网络优化的过程的详细描述，在此不再赘述。
在一种实现方式中，网络配置参数包括水平波束宽度、物理方位角、数字方位角、垂直波束宽度、物理下倾角或数字下倾角中的一种或多种；
其中，水平波束宽度表示波束权值控制的水平面覆盖的水平包络宽度；垂直波束宽度表示波束权值控制的水平面覆盖的垂直包络宽度；物理方位角表示物理天线面板的正对方向与正北方的夹角；数字方位角表示波束权值控制的水平波束能量最强指向与正北方的夹角；物理下倾角表示垂直于物理天线面板的平面与水平面的夹角；数字下倾角表示波束权值控制的垂直波束能量最强指向与水平面的夹角。
在一种实现方式中，图14中的各个单元所实现的相关功能可以通过处理器来实现。请参见图15，图15是本申请实施例提供的一种网络管理设备的结构示意图，该网络管理设备可以为具有执行本申请实施例所述的网络配置功能的设备(例如芯片)。该网络设备1500可以包括收发器1501、至少一个处理器1502和存储器1503。其中，收发器1501、处理器1502和存储器1503可以通过一条或多条通信总线相互连接，也可以通过其它方式相连接。本实施例中采用总线连接的方式作为一种示例，如图15所示。
其中，收发器1501可以用于发送或者接收数据。例如，收发器1501可以接收终端设备和网络设备上报MR数据。可以理解的是，收发器1501是统称，可以包括接收器和发送器。
其中，处理器1502可以用于对数据进行处理。处理器1502可以包括一个或多个处理器，例如该处理器1502可以是一个或多个中央处理器(central processing unit，CPU)，网络处理器(network processor，NP)，硬件芯片或者其任意组合。在处理器1502是一个CPU的情况下，该CPU可以是单核CPU，也可以是多核CPU。
其中，存储器1503用于存储程序代码等。存储器1503可以包括易失性存储器(volatile memory)，例如随机存取存储器(random access memory，RAM)。存储器1503也可以包括非易失性存储器(non-volatile memory)，例如只读存储器(read-only memory，ROM)，快闪存储器(flash memory)，硬盘(hard disk drive，HDD)或固态硬盘(solid-state drive，SSD)。存储器1503还可以包括上述种类的存储器的组合。
上述处理器1502可以用于实现本申请实施例中的网络配置方法，其中，具体实现方式如下：
根据多条测量报告MR数据中每条MR的小区信息对多条MR数据进行汇聚处理以获得N组MR数据，每组MR包括具有无线空间传播相似性的MR；
获取N组MR中每组MR的每个小区的平均参考信号接收功率RSRP；
针对N组MR中的每组MR，根据该组MR中每个小区的平均RSRP以及每个小区的主波束标识对应的垂直波束范围、水平波束范围，创建该组MR的立体栅格；
针对每组MR的立体栅格，计算该组MR中各小区的主波束到该立体栅格之间的路损，以获得该立体栅格的路损矩阵；
根据N个立体栅格的路损矩阵，确定目标小区的网络配置参数，其中，目标小区为覆盖指标不满足预设覆盖门限和/或容量指标不满足预设容量门限的小区。
具体实现方式，请参考图7实施例中的S701至S705中的详细描述，在此不再赘述。
在一种实现方式中，每条MR的小区信息包括服务小区的小区信息和/或邻区的小区信息，一个或多个小区的RSRP包括服务小区的RSRP和/或邻区的RSRP。处理器1502还用于针对每条MR，根据该条MR中的服务小区的小区标识、服务小区的RSRP、服务小区的 主波束标识，确定该条MR的服务小区的主波束预测模型；根据该条MR的服务小区的主波束预测模型、邻区的小区标识和邻区的RSRP，确定该条MR的邻区的主波束标识。
具体实现方式，请参考图7实施例中对网络管理装置进行MR主波束识别的过程的详细描述，在此不再赘述。
在一种实现方式中，处理器1502还用于将小区标识相同、主波束标识相同、电平差值满足预设电平差值条件的MR数据进行汇聚处理，确定汇聚后的一组MR；其中，电平差值为MR数据中小区标识相同、主波束标识相同的小区两两之间的RSRP的差值。
具体实现方式，请参考图7实施例中对网络管理装置进行相似特征MR数据汇聚的过程程的详细描述，在此不再赘述。
在一种实现方式中，处理器1502具体用于：
根据该组MR中的任意一个小区i的主波束标识，获取该小区i的主波束标识对应的波束增益；该小区i的波束增益是根据小区i的主波束标识对应的水平波束范围、垂直波束范围、天线增益、水平波束范围对应的信号衰减值和垂直波束范围对应的信号衰减值确定的；
根据小区i的发射功率、小区i的波束增益和小区i的平均RSRP，计算小区i的主波束到该组MR的立体栅格的路径损耗。
具体实现方式，请参考图7实施例中对网络管理装置计算路损的过程的详细描述，在此不再赘述。
在一种实现方式中，处理器1502具体用于：
针对每个立体栅格，根据该立体栅格的各个小区的覆盖指标和/或容量指标确定该立体栅格的目标小区；
根据目标小区的预设覆盖指标和预设容量指标，调整目标小区的网络配置参数；根据调整后的目标小区的网络配置参数，得到调整后的目标小区的天线增益；
根据该立体栅格的路损矩阵、调整后的目标小区的天线增益和调整后的目标小区的网络配置参数，确定目标小区的覆盖指标和容量指标；
若目标小区的覆盖指标达到预设覆盖指标，和/或，目标小区的容量指标达到预设容量指标，确定调整后的目标小区的网络配置参数为目标小区的网络配置参数。
具体实现方式，请参考图7实施例中对网络优化的过程的详细描述，在此不再赘述。
在一种实现方式中，网络配置参数包括水平波束宽度、物理方位角、数字方位角、垂直波束宽度、物理下倾角或数字下倾角中的一种或多种；
其中，水平波束宽度表示波束权值控制的水平面覆盖的水平包络宽度；垂直波束宽度表示波束权值控制的水平面覆盖的垂直包络宽度；物理方位角表示物理天线面板的正对方向与正北方的夹角；数字方位角表示波束权值控制的水平波束能量最强指向与正北方的夹角；物理下倾角表示垂直于物理天线面板的平面与水平面的夹角；数字下倾角表示波束权值控制的垂直波束能量最强指向与水平面的夹角。
本申请实施例提供一种网络管理装置，如图16所示，该网络管理装置1600可用于实现本申请实施例中的网络配置方法。该网络管理装置1600可以包括：
汇聚单元1601，用于根据多条最小化道路测试MDT数据中每条MDT的小区信息对多条MDT数据进行汇聚处理以获得N组MDT数据，每组MDT包括小区的垂直波束标识相 同的MDT；
创建单元1602，用于针对N组MDT中的每组MDT，根据该组MDT中每个小区的经度、纬度以及每个小区的垂直波束标识，创建该组MDT的立体栅格；
计算单元1603，用于针对每组MDT的立体栅格，计算该组MDT中各个小区的主波束到该立体栅格之间的路损，以获得该立体栅格的路损矩阵；
确定单元1604，用于根据N个立体栅格的路损矩阵，确定目标小区的网络配置参数，其中，目标小区为覆盖指标不满足预设覆盖门限和/或容量指标不满足预设容量门限的小区。
具体实现方式，请参考图11实施例中的S1101至S1104中的详细描述，在此不再赘述。
在一种实现方式中，每条MDT的小区信息包括服务小区的小区信息和/或邻区的小区信息，一个或多个小区的RSRP包括服务小区的RSRP和/或邻区的RSRP。针对每条MDT，确定单元1604还用于：
根据该条MDT中的服务小区的小区标识、服务小区的RSRP、服务小区的主波束标识，确定该条MDT的服务小区的主波束预测模型；
根据该条MDT的服务小区的主波束预测模型、邻区的小区标识和邻区的RSRP，确定该条MDT的邻区的主波束标识；
根据该条MDT中的各个小区的主波束标识，确定该条MDT中的各个小区的垂直波束标识。
具体实现方式，请参考图11实施例中对网络管理装置进行MDT主波束识别的过程的详细描述，在此不再赘述。
在一种实现方式中，创建单元1602具体用于：
根据该组MDT中的每个小区的经度和纬度，确定该组MDT构成的平面栅格的栅格经度和栅格纬度；
根据该组MDT的垂直波束标识，确定该组MDT构成平面栅格所在的垂直层，以获得该组MDT的立体栅格。
具体实现方式，请参考图11实施例中对网络管理装置进行地理化栅格汇聚的过程的详细描述，在此不再赘述。
在一种实现方式中，计算单元1603具体用于：
根据该组MDT中的垂直波束标识，获取垂直波束标识对应的垂直波束范围、天线增益和垂直波束范围对应的信号衰减值；
根据该组MDT的栅格经度和栅格纬度，获取该组MDT中的栅格经度和栅格纬度确定的地理栅格对应的水平波束范围、天线增益和水平波束范围对应的信号衰减值；
根据该组MDT中的垂直波束标识对应的垂直波束范围、天线增益、垂直波束范围对应的信号衰减值，以及该组MDT中的栅格经度和栅格纬度确定的地理栅格对应的水平波束范围、天线增益、水平波束范围对应的信号衰减值，计算该组MDT中的任意一个小区i到该组MDT的立体栅格的天线增益；
根据小区i的发射功率、小区i到该组MDT的立体栅格的天线增益和小区i的平均RSRP，计算小区i到该组MDT的立体栅格的路径损耗。
具体实现方式，请参考图11实施例中对网络管理装置计算立体栅格的路损矩阵的过程的详细描述，在此不再赘述。
在一种实现方式中，确定单元1604具体用于：
针对每个立体栅格，根据该立体栅格的各个小区的覆盖指标和/或容量指标确定该立体栅格的目标小区；
根据目标小区的预设覆盖指标和预设容量指标，调整该目标小区的网络配置参数；
根据调整后的目标小区的网络配置参数，得到调整后的目标小区的天线增益；
根据该立体栅格的路损矩阵、调整后的目标小区的天线增益和调整后的目标小区的网络配置参数，确定该目标小区的覆盖指标和容量指标；
若该目标小区的覆盖指标达到预设覆盖指标，和/或，该目标小区的容量指标达到预设容量指标，确定调整后的目标小区的网络配置参数为目标小区的网络配置参数。
具体实现方式，请参考图7实施例中对网络优化的过程的详细描述，在此不再赘述。
在一种实现方式中，网络配置参数包括水平波束宽度、物理方位角、数字方位角、垂直波束宽度、物理下倾角或数字下倾角中的一种或多种；
其中，水平波束宽度表示波束权值控制的水平面覆盖的水平包络宽度；垂直波束宽度表示波束权值控制的水平面覆盖的垂直包络宽度；物理方位角表示物理天线面板的正对方向与正北方的夹角；数字方位角表示波束权值控制的水平波束能量最强指向与正北方的夹角；物理下倾角表示垂直于物理天线面板的平面与水平面的夹角；数字下倾角表示波束权值控制的垂直波束能量最强指向与水平面的夹角。
在一种实现方式中，图16中的各个单元所实现的相关功能可以通过处理器来实现。请参见图17，图17是本申请实施例提供的一种网络管理设备的结构示意图，该网络管理设备可以为具有执行本申请实施例所述的网络配置功能的设备(例如芯片)。该网络管理设备1700可以包括收发器1701、至少一个处理器1702和存储器1703。其中，收发器1701、处理器1702和存储器1703可以通过一条或多条通信总线相互连接，也可以通过其它方式相连接。本实施例中采用总线连接的方式作为一种示例，如图17所示。
其中，收发器1701可以用于发送或者接收数据。例如，收发器1501可以接收终端设备和网络设备上报MDT数据。可以理解的是，收发器1501是统称，可以包括接收器和发送器。
其中，处理器1702可以用于对数据进行处理。处理器1702可以包括一个或多个处理器，例如该处理器1702可以是一个或多个中央处理器(central processing unit，CPU)，网络处理器(network processor，NP)，硬件芯片或者其任意组合。在处理器1702是一个CPU的情况下，该CPU可以是单核CPU，也可以是多核CPU。
其中，存储器1703用于存储程序代码等。存储器1703可以包括易失性存储器(volatile memory)，例如随机存取存储器(random access memory，RAM)。存储器1703也可以包括非易失性存储器(non-volatile memory)，例如只读存储器(read-only memory，ROM)，快闪存储器(flash memory)，硬盘(hard disk drive，HDD)或固态硬盘(solid-state drive，SSD)。存储器1703还可以包括上述种类的存储器的组合。
上述处理器1702可以用于实现本申请实施例中的网络配置方法，其中，具体实现方式 如下：
根据多条最小化道路测试MDT数据中每条MDT的小区信息对多条MDT数据进行汇聚处理以获得N组MDT数据，每组MDT包括小区的垂直波束标识相同的MDT；
针对N组MDT中的每组MDT，根据该组MDT中每个小区的经度、纬度以及每个小区的垂直波束标识，创建该组MDT的立体栅格；
针对每组MDT的立体栅格，计算该组MDT中各个小区的主波束到该立体栅格之间的路损，以获得该立体栅格的路损矩阵；
根据N个立体栅格的路损矩阵，确定目标小区的网络配置参数，其中，目标小区为覆盖指标不满足预设覆盖门限和/或容量指标不满足预设容量门限的小区。
具体实现方式，请参考图11实施例中的S1101至S1104中的详细描述，在此不再赘述。
在一种实现方式中，每条MDT的小区信息包括服务小区的小区信息和/或邻区的小区信息，一个或多个小区的RSRP包括服务小区的RSRP和/或邻区的RSRP。针对每条MDT，处理器1702还用于：
根据该条MDT中的服务小区的小区标识、服务小区的RSRP、服务小区的主波束标识，确定该条MDT的服务小区的主波束预测模型；
根据该条MDT的服务小区的主波束预测模型、邻区的小区标识和邻区的RSRP，确定该条MDT的邻区的主波束标识；
根据该条MDT中的各个小区的主波束标识，确定该条MDT中的各个小区的垂直波束标识。
具体实现方式，请参考图11实施例中对网络管理装置进行MDT主波束识别的过程的详细描述，在此不再赘述。
在一种实现方式中，处理器1702具体用于：
根据该组MDT中的每个小区的经度和纬度，确定该组MDT构成的平面栅格的栅格经度和栅格纬度；
根据该组MDT的垂直波束标识，确定该组MDT构成平面栅格所在的垂直层，以获得该组MDT的立体栅格。
具体实现方式，请参考图11实施例中对网络管理装置进行地理化栅格汇聚的过程的详细描述，在此不再赘述。
在一种实现方式中，处理器1702具体用于：
根据该组MDT中的垂直波束标识，获取垂直波束标识对应的垂直波束范围、天线增益和垂直波束范围对应的信号衰减值；
根据该组MDT的栅格经度和栅格纬度，获取该组MDT中的栅格经度和栅格纬度确定的地理栅格对应的水平波束范围、天线增益和水平波束范围对应的信号衰减值；
根据该组MDT中的垂直波束标识对应的垂直波束范围、天线增益、垂直波束范围对应的信号衰减值，以及该组MDT中的栅格经度和栅格纬度确定的地理栅格对应的水平波束范围、天线增益、水平波束范围对应的信号衰减值，计算该组MDT中的任意一个小区i到该组MDT的立体栅格的天线增益；
根据小区i的发射功率、小区i到该组MDT的立体栅格的天线增益和小区i的平均 RSRP，计算小区i到该组MDT的立体栅格的路径损耗。
具体实现方式，请参考图11实施例中对网络管理装置计算立体栅格的路损矩阵的过程的详细描述，在此不再赘述。
在一种实现方式中，处理器1702具体用于：
针对每个立体栅格，根据该立体栅格的各个小区的覆盖指标和/或容量指标确定该立体栅格的目标小区；
根据目标小区的预设覆盖指标和预设容量指标，调整该目标小区的网络配置参数；
根据调整后的目标小区的网络配置参数，得到调整后的目标小区的天线增益；
根据该立体栅格的路损矩阵、调整后的目标小区的天线增益和调整后的目标小区的网络配置参数，确定该目标小区的覆盖指标和容量指标；
若该目标小区的覆盖指标达到预设覆盖指标，和/或，该目标小区的容量指标达到预设容量指标，确定调整后的目标小区的网络配置参数为目标小区的网络配置参数。
具体实现方式，请参考图7实施例中对网络优化的过程的详细描述，在此不再赘述。
在一种实现方式中，网络配置参数包括水平波束宽度、物理方位角、数字方位角、垂直波束宽度、物理下倾角或数字下倾角中的一种或多种；
其中，水平波束宽度表示波束权值控制的水平面覆盖的水平包络宽度；垂直波束宽度表示波束权值控制的水平面覆盖的垂直包络宽度；物理方位角表示物理天线面板的正对方向与正北方的夹角；数字方位角表示波束权值控制的水平波束能量最强指向与正北方的夹角；物理下倾角表示垂直于物理天线面板的平面与水平面的夹角；数字下倾角表示波束权值控制的垂直波束能量最强指向与水平面的夹角。
本申请实施例提供一种计算机可读存储介质，该计算机可读存储介质存储有程序或指令，当所述程序或指令在计算机上运行时，使得计算机执行本申请实施例中的网络配置方法。
本申请实施例提供一种芯片或者芯片***，该芯片或者芯片***包括至少一个处理器和接口，接口和至少一个处理器通过线路互联，至少一个处理器用于运行计算机程序或指令，以进行本申请实施例中的网络配置方法。
其中，芯片中的接口可以为输入/输出接口、管脚或电路等。
上述方面中的芯片***可以是片上***(system on chip，SOC)，也可以是基带芯片等，其中基带芯片可以包括处理器、信道编码器、数字信号处理器、调制解调器和接口模块等。
在一种实现方式中，本申请中上述描述的芯片或者芯片***还包括至少一个存储器，该至少一个存储器中存储有指令。该存储器可以为芯片内部的存储单元，例如，寄存器、缓存等，也可以是该芯片的存储单元(例如，只读存储器、随机存取存储器等)。
本申请实施例提供一种通信***，包括本申请实施例的网络管理设备、网络设备和终端设备。
在上述实施例中，可以全部或部分地通过软件、硬件、固件或者其任意组合来实现。当使用软件实现时，可以全部或部分地以计算机程序产品的形式实现。计算机程序产品包括一个或多个计算机指令。在计算机上加载和执行所述计算机指令时，全部或部分地产生按照本申请实施例所述的流程或功能。计算机可以是通用计算机、专用计算机、计算机网 络、或者其他可编程装置。计算机指令可以存储在计算机可读存储介质中，或者从一个计算机可读存储介质向另一个计算机可读存储介质传输，例如，计算机指令可以从一个网站站点、计算机、服务器或数据中心通过有线(例如同轴电缆、光纤、数字用户线(Digital Subscriber Line，DSL))或无线(例如红外、无线、微波等)方式向另一个网站站点、计算机、服务器或数据中心进行传输。计算机可读存储介质可以是计算机能够存取的任何可用介质或者是包含一个或多个可用介质集成的服务器、数据中心等数据存储设备。所述可用介质可以是磁性介质(例如，软盘、硬盘、磁带)、光介质(例如，高密度数字视频光盘(Digital Video Disc，DVD))、或者半导体介质(例如，固态硬盘(Solid State Disk，SSD))等。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、计算机软件或者二者的结合来实现，为了清楚地说明硬件和软件的可互换性，在上述说明中已经按照功能一般性地描述了各示例的组成及步骤。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
以上所述，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应以所述权利要求的保护范围为准。

Claims (23)

  1. 一种网络配置方法，其特征在于，包括：
    根据多条测量报告MR数据中每条MR的小区信息对所述多条MR数据进行汇聚处理以获得N组MR数据，每组MR包括具有无线空间传播相似性的MR，每条MR的小区信息包括一个或多个小区的小区标识、一个或多个小区的主波束标识和一个或多个小区的参考信号接收功率RSRP；
    获取N组MR中每组MR的每个小区的平均参考信号接收功率RSRP；
    针对所述N组MR中的每组MR，根据该组MR中每个小区的平均RSRP以及每个小区的主波束标识对应的垂直波束范围、水平波束范围，创建该组MR的立体栅格；
    针对每组MR的立体栅格，计算该组MR中各小区的主波束到该立体栅格之间的路损，以获得该立体栅格的路损矩阵；
    根据N个立体栅格的路损矩阵，确定目标小区的网络配置参数，所述目标小区为覆盖指标不满足预设覆盖指标和/或容量指标不满足预设容量指标的小区。
  2. 根据权利要求1所述的方法，其特征在于，所述每条MR的小区信息包括服务小区的小区信息和/或邻区的小区信息，所述一个或多个小区的RSRP包括所述服务小区的RSRP和/或所述邻区的RSRP；所述根据多条测量报告MR数据中每条MR的小区信息对所述多条MR数据进行汇聚处理之前，所述方法还包括：
    针对所述每条MR，根据该条MR中的服务小区的小区标识、所述服务小区的RSRP、所述服务小区的主波束标识，确定该条MR的服务小区的主波束预测模型；
    根据该条MR的服务小区的主波束预测模型、所述邻区的小区标识和所述邻区的RSRP，确定该条MR的邻区的主波束标识。
  3. 根据权利要求1或2所述的方法，其特征在于，所述根据多条测量报告MR数据中每条MR的小区信息对所述多条MR数据进行汇聚处理以获得N组MR数据，包括：
    将小区标识相同，主波束标识相同、电平差值满足预设电平差值条件的MR数据进行汇聚处理，确定汇聚后的一组MR；其中，所述电平差值为MR数据中小区标识相同、主波束标识相同的小区两两之间的RSRP的差值。
  4. 根据权利要求1或3所述的方法，其特征在于，所述针对每组MR的立体栅格，计算该组MR中各小区的主波束到该立体栅格之间的路损，包括：
    根据该组MR中的任意一个小区i的主波束标识，获取该小区i的主波束标识对应的波束增益；该小区i的波束增益是根据所述小区i的主波束标识对应的水平波束范围、垂直波束范围、天线增益、水平波束范围对应的信号衰减值和垂直波束范围对应的信号衰减值确定的；
    根据所述小区i的发射功率、所述小区i的波束增益和所述小区i的平均RSRP，计算所述小区i的主波束到该组MR的立体栅格的路径损耗。
  5. 根据权利要求1所述的方法，其特征在于，所述根据N个立体栅格的路损矩阵，确定目标小区的网络配置参数，包括：
    针对每个立体栅格，根据该立体栅格的各个小区的覆盖指标和/或容量指标确定该立体 栅格的目标小区；
    根据所述目标小区的预设覆盖指标和预设容量指标，调整所述目标小区的网络配置参数；
    根据调整后的目标小区的网络配置参数，得到调整后的目标小区的天线增益；
    根据该立体栅格的路损矩阵、调整后的目标小区的天线增益和调整后的目标小区的网络配置参数，确定所述目标小区的覆盖指标和容量指标；
    若所述目标小区的覆盖指标达到所述预设覆盖指标，和/或，所述目标小区的容量指标达到所述预设容量指标，确定调整后的目标小区的网络配置参数为所述目标小区的网络配置参数。
  6. 一种网络配置方法，其特征在于，包括：
    根据多条最小化道路测试MDT数据中每条MDT的小区信息对所述多条MDT数据进行汇聚处理以获得N组MDT数据，每组MDT包括小区的垂直波束标识相同的MDT，每条MDT的小区信息包括一个或多个小区的小区标识、一个或多个小区的主波束标识和一个或多个小区的经度和纬度；所述小区的垂直波束标识是根据所述小区的主波束标识确定的；
    针对所述N组MDT中的每组MDT，根据该组MDT中每个小区的经度、纬度以及每个小区的垂直波束标识，创建该组MDT的立体栅格；
    针对每组MDT的立体栅格，计算该组MDT中各个小区的主波束到该立体栅格之间的路损，以获得该立体栅格的路损矩阵；
    根据N个立体栅格的路损矩阵，确定目标小区的网络配置参数，所述目标小区为覆盖指标不满足预设覆盖指标和/或容量指标不满足预设容量指标的小区。
  7. 根据权利要求6所述的方法，其特征在于，所述每条MDT的小区信息包括服务小区的小区信息和/或邻区的小区信息，所述一个或多个小区的RSRP包括所述服务小区的RSRP和/或所述邻区的RSRP；所述根据多条最小化道路测试MDT数据中每条MDT的小区信息对所述多条MDT数据进行汇聚处理之前，所述方法还包括：
    针对所述每条MDT，根据该条MDT中的服务小区的小区标识、所述服务小区的RSRP、所述服务小区的主波束标识，确定该条MDT的服务小区的主波束预测模型；
    根据该条MDT的服务小区的主波束预测模型、所述邻区的小区标识和所述邻区的RSRP，确定该条MDT的邻区的主波束标识；
    根据该条MDT中的各个小区的主波束标识，确定该条MDT中的各个小区的垂直波束标识。
  8. 根据权利要求7所述的方法，其特征在于，所述针对所述N组MDT中的每组MDT，根据该组MDT中每个小区的经度、纬度以及垂直波束标识，创建该组MDT的立体栅格，包括：
    根据该组MDT中的每个小区的经度和纬度，确定该组MDT构成的平面栅格的栅格经度和栅格纬度；
    根据该组MDT的垂直波束标识，确定该组MDT构成平面栅格所在的垂直层，以获得该组MDT的立体栅格。
  9. 根据权利要求6至8任一项所述的方法，其特征在于，所述针对每组MDT的立体栅格，计算该组MDT中各个小区的主波束到该立体栅格之间的路损，包括：
    根据该组MDT中的垂直波束标识，获取所述垂直波束标识对应的垂直波束范围、天线增益和垂直波束范围对应的信号衰减值；
    根据该组MDT的栅格经度和栅格纬度，获取该组MDT中的栅格经度和栅格纬度确定的地理栅格对应的水平波束范围、天线增益和水平波束范围对应的信号衰减值；
    根据该组MDT中的垂直波束标识对应的垂直波束范围、天线增益、垂直波束范围对应的信号衰减值，以及该组MDT中的栅格经度和栅格纬度确定的地理栅格对应的水平波束范围、天线增益、水平波束范围对应的信号衰减值，计算该组MDT中的任意一个小区i到该组MDT的立体栅格的天线增益；
    根据所述小区i的发射功率、所述小区i到该组MDT的立体栅格的天线增益和所述小区i的平均RSRP，计算所述小区i到该组MDT的立体栅格的路径损耗。
  10. 根据权利要求6所述的方法，其特征在于，所述根据N个立体栅格的路损矩阵，确定目标小区的网络配置参数，包括：
    针对每个立体栅格，根据该立体栅格的各个小区的覆盖指标和/或容量指标确定该立体栅格的目标小区；
    根据所述目标小区的预设覆盖指标和预设容量指标，调整所述目标小区的网络配置参数；
    根据调整后的目标小区的网络配置参数，得到调整后的目标小区的天线增益；
    根据该立体栅格的路损矩阵、调整后的目标小区的天线增益和调整后的目标小区的网络配置参数，确定所述目标小区的覆盖指标和容量指标；
    若所述目标小区的覆盖指标达到所述预设覆盖指标，和/或，所述目标小区的容量指标达到所述预设容量指标，确定调整后的目标小区的网络配置参数为所述目标小区的网络配置参数。
  11. 一种网络管理装置，其特征在于，包括：
    汇聚单元，用于根据多条测量报告MR数据中每条MR的小区信息对所述多条MR数据进行汇聚处理以获得N组MR数据，每组MR包括具有无线空间传播相似性的MR，每条MR的小区信息包括一个或多个小区的小区标识、一个或多个小区的主波束标识和一个或多个小区的参考信号接收功率RSRP；
    获取单元，用于获取N组MR中每组MR的每个小区的平均参考信号接收功率RSRP；
    创建单元，用于针对所述N组MR中的每组MR，根据该组MR中每个小区的平均RSRP以及每个小区的主波束标识对应的垂直波束范围、水平波束范围，创建该组MR的立体栅格；
    计算单元，用于针对每组MR的立体栅格，计算该组MR中各小区的主波束到该立体栅格之间的路损，以获得该立体栅格的路损矩阵；
    确定单元，用于根据N个立体栅格的路损矩阵，确定目标小区的网络配置参数，所述目标小区为覆盖指标不满足预设覆盖指标和/或容量指标不满足预设容量指标的小区。
  12. 根据权利要求11所述的装置，其特征在于，所述每条MR的小区信息包括服务小 区的小区信息和/或邻区的小区信息，所述一个或多个小区的RSRP包括所述服务小区的RSRP和/或所述邻区的RSRP；所述确定单元还用于：
    针对所述每条MR，根据该条MR中的服务小区的小区标识、所述服务小区的RSRP、所述服务小区的主波束标识，确定该条MR的服务小区的主波束预测模型；
    根据该条MR的服务小区的主波束预测模型、所述邻区的小区标识和所述邻区的RSRP，确定该条MR的邻区的主波束标识。
  13. 根据权利要求11或12所述的装置，其特征在于，所述汇聚单元用于根据多条测量报告MR数据中每条MR的小区信息对所述多条MR数据进行汇聚处理，具体用于：
    将小区标识相同，主波束标识相同、电平差值满足预设电平差值条件的MR数据进行汇聚处理，确定汇聚后的一组MR；其中，所述电平差值为MR数据中小区标识相同、主波束标识相同的小区两两之间的RSRP的差值。
  14. 根据权利要求11或13所述的装置，其特征在于，所述计算单元用于针对每组MR的立体栅格，计算该组MR中各小区的主波束到该立体栅格之间的路损，具体用于：
    根据该组MR中的任意一个小区i的主波束标识，获取该小区i的主波束标识对应的波束增益；该小区i的波束增益是根据所述小区i的主波束标识对应的水平波束范围、垂直波束范围、天线增益、水平波束范围对应的信号衰减值和垂直波束范围对应的信号衰减值确定的；
    根据所述小区i的发射功率、所述小区i的波束增益和所述小区i的平均RSRP，计算所述小区i的主波束到该组MR的立体栅格的路径损耗。
  15. 根据权利要求11所述的装置，其特征在于，所述确定单元用于根据N个立体栅格的路损矩阵，确定目标小区的网络配置参数，具体用于：
    针对每个立体栅格，根据该立体栅格的各个小区的覆盖指标和/或容量指标确定该立体栅格的目标小区；
    根据所述目标小区的预设覆盖指标和预设容量指标，调整所述目标小区的网络配置参数；
    根据调整后的目标小区的网络配置参数，得到调整后的目标小区的天线增益；
    根据该立体栅格的路损矩阵、调整后的目标小区的天线增益和调整后的目标小区的网络配置参数，确定所述目标小区的覆盖指标和容量指标；
    若所述目标小区的覆盖指标达到所述预设覆盖指标，和/或，所述目标小区的容量指标达到所述预设容量指标，确定调整后的目标小区的网络配置参数为所述目标小区的网络配置参数。
  16. 一种网络管理装置，其特征在于，包括：
    汇聚单元，用于根据多条最小化道路测试MDT数据中每条MDT的小区信息对所述多条MDT数据进行汇聚处理以获得N组MDT数据，每组MDT包括小区的垂直波束标识相同的MDT，每条MDT的小区信息包括一个或多个小区的小区标识、一个或多个小区的主波束标识和一个或多个小区的经度和纬度；所述小区的垂直波束标识是根据所述小区的主波束标识确定的；
    创建单元，用于针对所述N组MDT中的每组MDT，根据该组MDT中每个小区的经 度、纬度以及每个小区的垂直波束标识，创建该组MDT的立体栅格；
    计算单元，用于针对每组MDT的立体栅格，计算该组MDT中各个小区的主波束到该立体栅格之间的路损，以获得该立体栅格的路损矩阵；
    确定单元，用于根据N个立体栅格的路损矩阵，确定目标小区的网络配置参数，所述目标小区为覆盖指标不满足预设覆盖指标和/或容量指标不满足预设容量指标的小区。
  17. 根据权利要求16所述的装置，其特征在于，所述每条MDT的小区信息包括服务小区的小区信息和/或邻区的小区信息，所述一个或多个小区的RSRP包括所述服务小区的RSRP和/或所述邻区的RSRP；所述确定单元还用于：
    针对所述每条MDT，根据该条MDT中的服务小区的小区标识、所述服务小区的RSRP、所述服务小区的主波束标识，确定该条MDT的服务小区的主波束预测模型；
    根据该条MDT的服务小区的主波束预测模型、所述邻区的小区标识和所述邻区的RSRP，确定该条MDT的邻区的主波束标识；
    根据该条MDT中的各个小区的主波束标识，确定该条MDT中的各个小区的垂直波束标识。
  18. 根据权利要求17所述的装置，其特征在于，所述创建单元用于针对所述N组MDT中的每组MDT，根据该组MDT中每个小区的经度、纬度以及垂直波束标识，创建该组MDT的立体栅格，具体用于：
    根据该组MDT中的每个小区的经度和纬度，确定该组MDT构成的平面栅格的栅格经度和栅格纬度；
    根据该组MDT的垂直波束标识，确定该组MDT构成平面栅格所在的垂直层，以获得该组MDT的立体栅格。
  19. 根据权利要求16至18任一项所述的装置，其特征在于，所述计算单元用于针对每组MDT的立体栅格，计算该组MDT中各个小区的主波束到该立体栅格之间的路损，具体用于：
    根据该组MDT中的垂直波束标识，获取所述垂直波束标识对应的垂直波束范围、天线增益和垂直波束范围对应的信号衰减值；
    根据该组MDT的栅格经度和栅格纬度，获取该组MDT中的栅格经度和栅格纬度确定的地理栅格对应的水平波束范围、天线增益和水平波束范围对应的信号衰减值；
    根据该组MDT中的垂直波束标识对应的垂直波束范围、天线增益、垂直波束范围对应的信号衰减值，以及该组MDT中的栅格经度和栅格纬度确定的地理栅格对应的水平波束范围、天线增益、水平波束范围对应的信号衰减值，计算该组MDT中的任意一个小区i到该组MDT的立体栅格的天线增益；
    根据所述小区i的发射功率、所述小区i到该组MDT的立体栅格的天线增益和所述小区i的平均RSRP，计算所述小区i到该组MDT的立体栅格的路径损耗。
  20. 根据权利要求16所述的装置，其特征在于，所述确定单元用于根据N个立体栅格的路损矩阵，确定目标小区的网络配置参数，具体用于：
    针对每个立体栅格，根据该立体栅格的各个小区的覆盖指标和/或容量指标确定该立体栅格的目标小区；
    根据所述目标小区的预设覆盖指标和预设容量指标，调整所述目标小区的网络配置参数；
    根据调整后的目标小区的网络配置参数，得到调整后的目标小区的天线增益；
    根据该立体栅格的路损矩阵、调整后的目标小区的天线增益和调整后的目标小区的网络配置参数，确定所述目标小区的覆盖指标和容量指标；
    若所述目标小区的覆盖指标达到所述预设覆盖指标，和/或，所述目标小区的容量指标达到所述预设容量指标，确定调整后的目标小区的网络配置参数为所述目标小区的网络配置参数。
  21. 一种网络管理装置，其特征在于，包括：存储器和处理器；
    所述存储器，用于存储计算机指令；
    所述处理器，用于执行所述计算机指令，使得如权利要求1至5中任一项所述的方法被执行。
  22. 一种网络管理装置，其特征在于，包括：存储器和处理器；
    所述存储器，用于存储计算机指令；
    所述处理器，用于执行所述计算机指令，使得如权利要求6至10中任一项所述的方法被执行。
  23. 一种计算机可读存储介质，其特征在于，包括程序或计算机指令，当所述程序或计算机指令在计算机上运行时，如权利要求1至10中任一项所述的方法被执行。
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