CN115002795A - Beam forming method and device, electronic equipment and readable storage medium - Google Patents
Beam forming method and device, electronic equipment and readable storage medium Download PDFInfo
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Abstract
The embodiment of the invention provides a beam forming method, a beam forming device, electronic equipment and a readable storage medium, wherein the method comprises the following steps: acquiring map data of an area to be optimized; the map data comprises building data of buildings in the area to be optimized and base station positions of base stations; dividing the area to be optimized according to the position of the base station to obtain the cell coverage area of each base station; counting the building data of the building within the cell coverage range to obtain characteristic data; the characteristic data is used for reflecting the distribution condition of buildings in the coverage area of the cell; selecting a target beam configuration from preset beam configurations according to the characteristic data; and carrying out beam forming on the beam of the base station according to the target beam configuration. The cell coverage range of the area to be optimized is accurately divided, a large amount of measurement report data does not need to be collected and analyzed, large-scale evaluation planning and optimization are suitable, and evaluation and optimization efficiency is improved.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a beamforming method, a beamforming apparatus, an electronic device, and a computer-readable storage medium.
Background
Massive MIMO (large-scale antenna Technology) is a key Technology for improving system capacity and spectrum utilization in 5G (5th Generation Mobile Communication, fifth Generation Mobile Communication), and has the remarkable characteristic that the horizontal wave width, vertical wave width, azimuth angle and downtilt of a broadcast beam can be adjusted by adjusting antenna weight and beam forming Technology, so as to obtain a more ideal coverage effect. However, in an actual existing network, various coverage scenes such as high buildings, shopping malls, squares and the like are various, and effective coverage of buildings and the like cannot be guaranteed only by performing beamforming with fixed beam configuration, so that beamforming needs to be optimized to meet requirements of different coverage scenes.
Currently, the optimization of beamforming mainly determines the coverage area of each cell (5G cell) by artificially presetting a sector graph with a fixed distance as a radius or analyzing based on a large amount of MR (measurement Report) data, and then separately calculates the beam configuration of each cell to perform beamforming; however, the above method is difficult to accurately cover the region to be optimized, the problem of overlapping or missing cell coverage exists, and a large amount of MR data needs to be collected and analyzed, which is not suitable for the whole-network large-scale evaluation planning and optimization.
Disclosure of Invention
The embodiment of the invention provides a beam forming method, a beam forming device, electronic equipment and a computer readable storage medium, and aims to solve the problems of inaccurate cell coverage, large data collection and calculation amount and low optimization efficiency in the prior art.
The embodiment of the invention discloses a beam forming method, which comprises the following steps:
obtaining map data of an area to be optimized; the map data comprises building data of buildings in the area to be optimized and base station positions of base stations;
dividing the area to be optimized according to the position of the base station to obtain the cell coverage area of each base station;
counting the building data of the building within the cell coverage range to obtain characteristic data; the characteristic data is used for reflecting the distribution condition of buildings in the coverage area of the cell;
selecting a target beam configuration from preset beam configurations according to the characteristic data;
and carrying out beam forming on the beam of the base station according to the target beam configuration.
Optionally, the dividing the area to be optimized according to the position of the base station to obtain the cell coverage of each base station includes:
and according to the position of the base station, dividing the area to be optimized by adopting a Thiessen polygon algorithm to obtain the cell coverage area of the base station.
Optionally, the dividing the area to be optimized by using a thiessen polygon algorithm according to the position of the base station to obtain a cell coverage area of the base station includes:
determining the adjacent base stations in the area to be optimized according to the positions of the base stations;
connecting the adjacent base stations in the area to be optimized into a triangular area;
making a vertical bisector on each side of the triangular area;
and taking a polygonal area surrounded by the vertical bisectors as a polygonal coverage area of the base station.
Optionally, the coverage area of the base station is one or more.
Optionally, the base station has corresponding cell data, and the target cell location of the cell coverage is determined by:
when the initial cell position of the cell coverage range exists in the cell data, taking the initial cell position as a target cell position of the cell coverage range;
when the initial cell position of the cell coverage does not exist in the cell data, offsetting the base station position to the normal direction of the cell azimuth of the cell coverage by a preset distance to obtain an offset position, and taking the offset position as the target cell position of the cell coverage; the cell azimuth angle is a horizontal included angle between the clockwise direction and the cell coverage range by taking the north direction of the base station as an initial direction.
Optionally, the building data comprises at least a number of buildings, a building area, a building height, and a building type of the building, the building type having a corresponding weight value; the counting building data of the building within the cell coverage area to obtain feature data includes:
counting the number of buildings, the area of the buildings, the height of the buildings and the weight value of all the buildings in the cell coverage area, and calculating to obtain the total number of the buildings, the weighted total area of the buildings and the weighted total height of the buildings in the cell coverage area;
calculating to obtain the weighted average area of the buildings in the cell coverage according to the total number of the buildings and the weighted total area of the buildings;
calculating the weighted average height of the buildings in the cell coverage area according to the total number of the buildings and the weighted total height of the buildings;
and taking the weighted average area and the weighted average height as characteristic data.
Optionally, the beam configuration includes a horizontal wave width angle and a vertical wave width angle, and the beam configuration has corresponding preset feature data; the building types are classified according to the use of the building.
The embodiment of the invention also discloses a beam forming device, which comprises:
the map data acquisition module is used for acquiring map data of an area to be optimized; the map data comprises building data of buildings in the area to be optimized and base station positions of base stations;
the area division module is used for dividing the area to be optimized according to the position of the base station to obtain the cell coverage area of each base station;
the characteristic data statistics module is used for carrying out statistics on the building data of the building within the cell coverage range to obtain characteristic data; the characteristic data is used for reflecting the distribution condition of buildings in the coverage area of the cell;
the target beam configuration selection module is used for selecting target beam configuration from preset beam configuration according to the characteristic data;
and the beam forming module is used for carrying out beam forming on the beam of the base station according to the target beam configuration.
Optionally, the area division module is configured to divide the area to be optimized by using a thiessen polygon algorithm according to the position of the base station, so as to obtain a cell coverage area of the base station.
Optionally, the area dividing module includes:
the adjacent base station determining submodule is used for determining the adjacent base stations in the area to be optimized according to the positions of the base stations;
the area connection submodule is used for connecting the adjacent base stations in the area to be optimized into a triangular area;
the area processing submodule is used for making a vertical bisector on each side of the triangular area;
and the polygonal coverage area determining module is used for taking a polygonal area surrounded by the vertical bisectors as a polygonal coverage area of the base station.
Optionally, the coverage area of the base station is one or more.
Optionally, the base station has corresponding cell data, and the cell position of the cell coverage is determined by using the following modules:
a first cell location determining submodule, configured to, when an initial cell location of the cell coverage exists in the cell data, use the initial cell location as a target cell location of the cell coverage;
a second cell location determining submodule, configured to, when the initial cell location of the cell coverage does not exist in the cell data, shift the base station location by a preset distance in a normal direction of a cell azimuth of the cell coverage to obtain a shift location, and use the shift location as a target cell location of the cell coverage; the cell azimuth angle is a horizontal included angle between the clockwise direction and the cell coverage range by taking the north direction of the base station as an initial direction.
Optionally, the building data comprises at least a number of buildings, a building area, a building height, and a building type of the building, the building type having a corresponding weight value; the characteristic data statistics module is configured to count the number of buildings, the building area, the building height, and the weight value of all the buildings in the cell coverage area, and calculate a total building number, a total building weighted area, and a total building weighted height of the buildings in the cell coverage area; calculating to obtain the weighted average area of the buildings in the cell coverage according to the total number of the buildings and the weighted total area of the buildings; calculating the weighted average height of the buildings in the cell coverage area according to the total number of the buildings and the weighted total height of the buildings; and taking the weighted average area and the weighted average height as characteristic data.
Optionally, the beam configuration includes a horizontal wave width angle and a vertical wave width angle, and the beam configuration has corresponding preset feature data; the building types are classified according to the use of the building.
The embodiment of the invention also discloses electronic equipment which comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory finish mutual communication through the communication bus;
the memory is used for storing a computer program;
the processor is configured to implement the method according to the embodiment of the present invention when executing the program stored in the memory.
Also disclosed is a computer-readable storage medium having instructions stored thereon, which, when executed by one or more processors, cause the processors to perform a method according to an embodiment of the invention.
The embodiment of the invention has the following advantages:
in the embodiment of the invention, the map data of the area to be optimized is obtained, wherein the map data comprises the building data of buildings in the area to be optimized and the base station position of the base station; then, dividing the area to be optimized according to the position of the base station to obtain the cell coverage area of each base station, and counting the building data of the buildings in the cell coverage area to obtain characteristic data, wherein the characteristic data is used for reflecting the distribution condition of the buildings in the cell coverage area; and then selecting target beam configuration from preset beam configuration according to the characteristic data, and finally carrying out beam forming on the beam of the base station according to the target beam configuration. The embodiment of the invention can accurately divide the cell coverage range of the base station in the area to be optimized through the map data, avoids overlapping or omission of the cell coverage range, selects proper target beam configuration for beam forming through counting the characteristic data of the cell coverage range, does not need to collect and analyze a large amount of measurement report data, is suitable for large-scale evaluation planning and optimization, and improves the evaluation and optimization efficiency.
Drawings
Fig. 1 is a flowchart illustrating steps of a beamforming method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a map data supplement building data provided in an embodiment of the present invention;
FIG. 3 is a diagram illustrating a cell coverage area plotted according to a preset sector pattern in the prior art;
fig. 4 is a schematic diagram of dividing an area to be optimized based on the thiessen polygon algorithm provided in the embodiment of the present invention;
fig. 5 is a block diagram of a beamforming apparatus provided in an embodiment of the present invention;
fig. 6 is a block diagram of a network device according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Massive MIMO (large-scale antenna technology) is a key technology for improving system capacity and spectrum utilization rate in fifth generation mobile communication (5G). Specifically, when the number of base station antennas in a cell tends to infinity, negative effects such as additive white gaussian noise and rayleigh fading can be ignored, and the data transmission rate can be greatly improved. Besides increasing the number of antennas, Massive MIMO also supports vertical-dimension spatial coverage in signal coverage dimension, and has the remarkable characteristic that the horizontal wave width, vertical wave width, azimuth angle and downtilt of broadcast beams can be adjusted by adjusting antenna weights and a beam forming technology, so that an ideal coverage effect is obtained. However, in an actual existing network, various coverage scenes such as high buildings, shopping malls, squares and the like are various, and beam forming is performed on the coverage scenes only by means of initial beam configuration, so that effective coverage of buildings and the like cannot be guaranteed, and therefore, beam configuration needs to be optimized to meet requirements of different coverage scenes.
Currently, the following methods are mainly used for optimizing the beam configuration:
1. based on sector graph with preset radius as cell coverage area, planning wave beam configuration method according to characteristic data of each cell coverage area;
2. specifically, according to pre-acquired MR data, rasterization processing is performed on 5G information reported by User Equipment (UE) according to a preset grid size to obtain a coverage grid of each 5G cell, morphological characteristic information of a building covered by each 5G cell is obtained by combining a 3D electronic map, then a coverage scene of the 5G cell is determined according to the morphological characteristic information, a target beam configuration mode corresponding to each 5G cell is determined based on the coverage scene and a mapping relation between the coverage scene and the beam configuration mode, and beam configuration is performed on the 5G cell according to the target beam configuration mode corresponding to each 5G cell;
3. the beam stereo optimization method based on the MR big data comprises the following steps: the relative position of the UE in a three-dimensional space is constructed through multi-cell-based beam-level MR measurement, the distribution of users with weak coverage and overlapping coverage is identified, the coverage change after the adjustment of Pattern parameters is predicted based on a beam-level path loss matrix and a coverage prediction algorithm, and then the Pattern parameters are subjected to iterative optimization.
However, the above method has the following problems:
1. the sector graph with the preset radius is used as a cell coverage area, a large amount of coverage overlap exists in a dense urban area, but the problem of incomplete coverage exists in a suburban area with a large inter-station distance, so that the collected characteristic data of the cell coverage area is distorted;
2. the coverage area of a cell is determined through the MR grid envelope, a large amount of MR data needs to be acquired, analyzed and positioned, the calculated amount is large, at present, 5G lacks MDT (Minimization of Drive Tests) data, the positioning accuracy is not high, and the obtained coverage area of the cell has large difference;
3. the beam stereo optimization method based on the MR big data needs a large amount of beam-level MR data, has extremely high input requirement, huge calculation workload, long optimization feedback period, higher requirements on analysis software and hardware, high investment cost and less cell number of single analysis, is suitable for regional fine optimization and is not suitable for large-scale and whole-network planning and optimization.
In order to solve the problems, one of the core inventive concepts of the present application is to accurately divide the cell coverage of the base station in the area to be optimized through map data, and select a proper target beam configuration for beam forming according to the feature data of the statistical cell coverage, so as to avoid overlapping or omission of the cell coverage, and to avoid collecting and analyzing a large amount of measurement report data, so that the present application is suitable for large-scale evaluation planning and optimization, and the evaluation and optimization efficiency is improved.
Referring to fig. 1, a flowchart illustrating steps of a beamforming method provided in the embodiment of the present invention is shown, which specifically may include the following steps:
the area to be optimized can be a preset area with any size and any position, and at least one building and one base station are arranged in the area to be optimized. The map data can be high-precision electronic map data, in specific application, the precision of the electronic map data can be 5 meters or more than 5 meters, and the electronic map data can comprise building data in an area to be optimized and the base station position of a base station; specifically, the building data may include the number, area, height, and type of buildings, wherein the types of buildings may be divided according to the purpose of the buildings; the base station location of a base station may be latitude and longitude data of the base station.
In a possible case, when the buildings in the area to be optimized are changed, including newly built or removed buildings, and the map data is not updated in time, the building data in the area to be optimized and the building data in the map data are not matched, and at this time, the building data of the changed buildings may be supplemented in the map data so that the building data in the area to be optimized and the building data in the map data are matched. As shown in fig. 2, the map data of the area to be optimized is shown, and the blocks of various filling shapes in the map data are the current buildings of the map data; if the building 1 is newly built in the area to be optimized in reality and the map data is not updated, the building 1 can be supplemented in the map data according to the position relationship between the building 1 and the map data, as shown in fig. 2, the dashed square is the supplemented building 1; and matching the supplemented building data in the area to be optimized with the building data in the map data.
102, dividing the area to be optimized according to the position of the base station to obtain the cell coverage area of each base station;
in a specific implementation, the area to be optimized may include a plurality of base stations, each base station is discretely distributed in the area to be optimized, and the area to be optimized may be divided according to a base station position corresponding to each base station to obtain a cell coverage area of each base station, where the cell coverage area is an area that a beam of a cell of the base station can cover.
specifically, the cell coverage area may be a polygonal coverage area, the polygonal coverage area has a plurality of buildings therein, and the building data in the polygonal coverage area may be obtained by counting building data of the buildings, such as counting the number, area, height, type, and the like of the buildings, where the feature data may be data of an average area, an average height, a main building type, and the like, and may be specifically used to reflect the building distribution in the cell coverage area.
104, selecting target beam configuration from preset beam configurations according to the characteristic data;
the beam configuration may be a broadcast beam configuration of Massive MIMO, where the Massive MIMO broadcast beam includes a horizontal beam and a vertical beam, a half-power angle of the horizontal beam is defined as a horizontal beam width α, and a half-power angle of the vertical beam is defined as a vertical beam width β.
In a specific implementation, the horizontal wave width may be set to 6 different angles, namely 15 °, 25 °, 45 °, 65 °, 90 ° and 110 °; the vertical wave width can be set at 3 different angles, namely 6 °, 12 ° and 25 °. The preset beam configurations in table 1 below can be obtained after combining with each other:
TABLE 1
In a specific implementation, a target beam configuration may be selected from preset beam configurations according to the feature data, for example, if the feature data of a cell coverage reflects that a building with a large area and a low height is mainly used in the cell coverage, a beam configuration with a large horizontal wave width and a small vertical wave width may be selected as the target beam configuration, such as SC _1(110 °,6 °) in the above table, and if the beam configuration of SC _1 in table 1 is selected, SC _1 may be used as the target beam configuration. It should be noted that the corresponding relationship between the feature data and the beam configuration may be set according to actual requirements, and specifically, multiple beam configurations as in the above table may be respectively adopted in a cell coverage area, beams are emitted in a polling manner, the UE measures the beams and obtains an optimal beam, and the corresponding relationship between the feature data and the beam configuration is determined according to the beam configuration corresponding to the optimal beam.
Of course, the angle setting and combination of the horizontal wave width and the vertical wave width are only examples, and may be set and selected according to actual situations, so as to meet the beam configuration requirement of the cell coverage under various different characteristic data, which is not limited in the embodiment of the present invention.
And 105, carrying out beam forming on the beam of the base station according to the target beam configuration.
In a specific implementation, the beamforming in the embodiment of the present invention may use a static beam, where the static beam uses a predefined beam configuration during beamforming, that is, a fixed beam is formed in a cell coverage, for example, the number, width, and direction of the beams are determined, and specifically, the static beam may be a broadcast beam in the static beam, where the broadcast beam is a beam transmitted by a base station in a polling manner. Specifically, after determining the target beam configuration, the base station may transmit a beam in a polling manner by using a broadcast beam in a static beam, and complete beamforming, where the beam configuration used by the static beam is the target beam configuration.
In specific implementation, white list setting can be performed according to actual requirements, the coverage area of the designated cell in the area to be optimized is set as a white list cell, and when the coverage area of the designated cell is set as a white list, optimization evaluation is not performed on the coverage area of the designated cell, so that evaluation efficiency can be improved. Specifically, the white list setting may be performed in the following case: 1. a cell which has completed beamforming optimization or a cell which definitely does not need to be adjusted; 2. a Radio Resource Control (RRU) cell partially using 2/4/8TR, or a cell not supporting beamforming; 3. the coverage area of the indoor distribution system cell is indoor, the coverage area is clear, complex wave beam configuration does not need to be matched, and only defaults are adopted; 4. cells that have been field tested and precisely optimized. Of course, the white list may be set according to actual requirements, and the above situation is only used as an example, and the embodiment of the present invention is not limited to this.
In the embodiment of the invention, the map data of the area to be optimized is obtained, wherein the map data comprises the building data of buildings in the area to be optimized and the base station position of the base station; then, dividing the area to be optimized according to the position of the base station to obtain the cell coverage area of each base station, and counting the building data of the buildings in the cell coverage area to obtain characteristic data, wherein the characteristic data is used for reflecting the distribution condition of the buildings in the cell coverage area; and then selecting target beam configuration from preset beam configuration according to the characteristic data, and finally carrying out beam forming on the beam of the base station according to the target beam configuration. According to the embodiment of the invention, the cell coverage range of the base station in the area to be optimized can be accurately divided through the map data, the cell coverage range overlapping or omission is avoided, the proper target beam configuration can be selected for beam forming through counting the characteristic data of the cell coverage range, a large amount of MR data does not need to be collected and analyzed, the method is suitable for large-scale evaluation planning and optimization, and the evaluation and optimization efficiency is improved.
In an exemplary embodiment, the step 102 of dividing the area to be optimized according to the base station location to obtain the cell coverage area of each base station includes:
and dividing the area to be optimized by adopting a Thiessen polygon algorithm according to the position of the base station to obtain the cell coverage area of the base station.
In the prior art, a sector graph according to a preset distance is usually taken as a cell coverage area, as shown in fig. 3, the graph is an area to be optimized, a plurality of sector graphs taking a three-sector base station as a center and a preset distance as a radius are taken as the cell coverage area, wherein the three-sector base station is a base station with three cell coverage areas; as shown in fig. 3, each base station has a cell coverage area of a three-sector pattern, but there are cases where a large amount of overlapping of partial areas exists in an area to be optimized, and a partial area is not covered.
In the exemplary embodiment, the area to be optimized may be divided by using a thiessen polygon algorithm according to the position of the base station, where a thiessen polygon is a group of continuous polygons formed by perpendicular bisectors connecting line segments of two adjacent points, and is a subdivision of a spatial plane, and is characterized in that any position in the polygon is closest to a sampling point of the polygon and is far from the sampling point in the adjacent polygon, and each polygon includes only one sampling point. Specifically, after the regions to be optimized are divided by adopting the Thiessen polygon algorithm, a plurality of polygon regions can be obtained, and each base station corresponds to one polygon region.
In a specific implementation, the dividing the area to be optimized by using a thiessen polygon algorithm according to the position of the base station to obtain a cell coverage area of the base station includes:
determining the adjacent base stations in the area to be optimized according to the positions of the base stations;
connecting the adjacent base stations in the area to be optimized into a triangular area;
making a vertical bisector on each side of the triangular area;
and taking a polygonal area surrounded by the vertical bisectors as a polygonal coverage area of the base station.
Specifically, referring to fig. 4, a schematic diagram of dividing a region to be optimized based on the thiessen polygon algorithm in an embodiment of the beam forming method of the present invention is shown, where the region shown in the diagram is the region to be optimized, and dots in the diagram are base stations. According to the base station positions of the base stations distributed discretely, all the adjacent base stations in the area to be optimized are connected into a triangle, then vertical bisectors are made on each side of the triangle, and then a plurality of vertical bisectors around each base station enclose a polygonal area which is the polygonal coverage area of the base stations. After division, each polygonal area is provided with only one base station, the distance from any position in the polygonal area to the corresponding base station is the shortest, and the distances from the positions on the sides of the polygonal area to the adjacent base stations are equal.
In the above exemplary embodiment, by dividing the area to be optimized by using the thiessen polygon algorithm, omission or overlapping of the cell coverage of the base station can be avoided, the cell coverage of the base station in the area to be optimized is accurately divided, and the full-range coverage of the area to be optimized is realized.
In an exemplary embodiment, the cell coverage area of the base station may be one or more, and since the coverage direction of each cell is different, the cell position of each cell coverage area needs to be collected, where the cell position may be the longitude and latitude of a cell antenna corresponding to the cell coverage area, and further, when the thiessen polygon division is performed, the cell coverage areas of different cells of one base station may be distinguished. Specifically, the regions to be optimized may be divided by using a thiessen polygon algorithm according to the cell position of the cell coverage area of the base station.
In a specific implementation, the base station has corresponding cell data, and the target cell location of the cell coverage is determined by:
when an initial cell position of the cell coverage range exists in the cell data, taking the initial cell position as a target cell position of the cell coverage range;
when the initial cell position of the cell coverage does not exist in the cell data, offsetting the base station position to the normal direction of the cell azimuth of the cell coverage by a preset distance to obtain an offset position, and taking the offset position as the target cell position of the cell coverage; the cell azimuth angle is a horizontal included angle between the clockwise direction and the cell coverage range by taking the north direction of the base station as an initial direction.
Specifically, the base station has corresponding cell data, and the cell data may include data such as a position of the base station, an altitude of an antenna, an azimuth angle of an antenna, a pitch angle, a longitude and latitude, and the like, where the cell data of some base stations has an initial cell position of a cell coverage area of the base station.
In a specific implementation, for a cell coverage range with an initial cell position, the initial cell position may be used as a target cell position of the cell coverage range, and for a cell coverage range without the initial cell position, a base station position of a base station corresponding to the cell coverage range may be obtained first, where the base station position may be a longitude and latitude of the base station, and then the base station position is shifted by a certain distance towards a normal direction of a cell azimuth angle of the cell coverage range to obtain a shifted position, where the shifted position is the target cell position of the cell coverage range. And the azimuth angle of the cell is a horizontal included angle formed by starting from the north direction of the position of the base station and the coverage area of the cell along the clockwise direction.
In the above exemplary embodiment, the cell position of the cell coverage area of the base station may be obtained or calculated, and based on the cell position, the thiessen polygon algorithm is adopted to divide the area to be optimized according to the cell coverage area, so that the division accuracy is higher.
In an exemplary embodiment, the building data includes at least a number of buildings, a building area, a building height, and a building type for the building, the building type having a corresponding weight value; the step 103 of performing statistics on the building data of the building within the cell coverage to obtain feature data includes:
counting the number of buildings, the area of the buildings, the height of the buildings and the weight value of all the buildings in the cell coverage area, and calculating to obtain the total number of the buildings, the weighted total area of the buildings and the weighted total height of the buildings in the cell coverage area;
calculating to obtain the weighted average area of the buildings in the cell coverage according to the total number of the buildings and the weighted total area of the buildings;
calculating the weighted average height of the buildings in the cell coverage area according to the total number of the buildings and the weighted total height of the buildings;
and taking the weighted average area and the weighted average height as characteristic data.
The building data includes the number of buildings, and the number of buildings refers to the number of buildings displayed on the electronic map. The building area refers to the floor area of a building displayed in an electronic map, and reflects the horizontal distribution condition of the building to a certain extent. The building height refers to the construction height from the ground to the highest point of the building, and reflects the vertical distribution condition of the building to a certain extent. The building types are divided according to the use of the building, and reflect the number of users in the building and the use degree of wireless communication to a certain extent, so that the building types can be quantized into the weight value of the building. Based on the building information in the electronic map, the building types can be classified into the following categories: CBD (Central Business District), large high-rise residential District, comprehensive Business District, school, transportation hub, hospital, hotel, office building, town-village residential building, etc. Generally, CBD, schools, transportation hubs, hospitals, large high-rise residential districts, etc. may be set to a high weight value, a general business district, a medium residential district, etc. may be set to a medium weight value, and a city village residential building, a square, etc. may be set to a low weight value. Of course, the weight value may be set according to actual requirements, and the embodiment of the present invention is not limited to this.
In a specific implementation, the total number of buildings, the total building weighted area, and the total building weighted height of the buildings in the cell coverage area can be obtained through statistics according to the number of buildings, the building area, the building height, and the building weight value of all the buildings in the cell coverage area. Specifically, the weighted average area of the buildings in the coverage area of the cell may be calculated according to the total number of buildings and the weighted total area of the buildings, and one way of calculation may be as follows:
wherein the weighted average area is represented by η; NCGI (NR cell global identifier) for uniquely identifying a cell, and ID is an identifier of a certain building and may represent a corresponding building; s Buiding T is the building area of a certain building, and N is the weight value of the building NCGI Is the total number of buildings under the cell coverage.
The weighted average height of the buildings in the cell coverage area can be calculated according to the total number of buildings in the cell coverage area and the weighted total height of the buildings, and one way of calculation can be as follows:
wherein the weighted average height is represented by θ; h Buiding Is the building height of a certain building; ID. T and N NCGI The meaning of the designation is the same as above.
In the above exemplary embodiment, the characteristic data may be obtained by calculating building data within the cell coverage, and the characteristic data may reflect the building distribution condition within the cell coverage, and the corresponding beam configuration may be accurately selected through the characteristic data, so as to improve the accuracy of beam forming.
In order to make those skilled in the art better understand the technical solutions of the embodiments of the present invention, the following describes the embodiments of the present invention by an example.
1. Firstly, map data of an area to be optimized is obtained, wherein the map data may include building data of buildings and base station positions of base stations in the area to be optimized, and may further include other data of the base stations in the area to be optimized, specifically, the other data may include base station identifiers, total antenna hanging heights, antenna azimuth angles, cell identifiers, and the like. Specifically, the building data in the map data may be as shown in table 2 below:
TABLE 2
Wherein Id is the identification of the building; the Type is the Type of the building, and the Type of the building is divided according to the use of the building; floor, Height and Structure are respectively the Floor number, Height and Structure of the building; shape _ len and Shape _ Area are respectively the length and the Area of the Shape of the building, and specifically, the length and the Area of the Shape nep respectively refer to the length and the Area corresponding to the Shape of the building in the electronic map.
Specifically, the base station data of the base station may be as shown in table 3 below:
TABLE 3
As in table 3 above, the three-sector base station is named as SA _ H _ hangzhou beamforming xBBU1, that is, the base station has 3 cell coverage areas, and each cell coverage area has a corresponding cell antenna; the total hanging height of the antenna is the vertical distance between the antenna of the cell and the ground in practice; the antenna digital inclination angle is an antenna inclination angle set in the control system; the mechanical tilt is the antenna tilt at which the cell antenna is fixed when installed.
2. The method includes the steps of dividing an area to be optimized according to the position of a base station to obtain a cell coverage area of each base station, specifically, dividing the area to be optimized by adopting a Thiessen polygon algorithm, and further, obtaining building data in the cell coverage area, which can be specifically shown in the following table 4:
TABLE 4
Wherein, the meaning of each field under the "key field" in the above table 4 is the same as that described above, and the Type (i.e. the building Type) can be specifically divided into school, hospital, residential area, business district, city village, etc.; the Thiessen polygon algorithm is adopted for division, so that the coverage area of each cell is a Thiessen polygon, and the Thiessen polygon identifier is the identifier of the Thiessen polygon corresponding to the coverage area of the cell.
3. Building data of buildings in the cell coverage range are counted to obtain characteristic data; specifically, the number, area, height and type of buildings in a cell coverage area can be counted to obtain the total number of buildings, the total area of buildings, the total height of buildings and a weight value of the buildings in the cell coverage area; and then calculating to obtain the weighted average area of the buildings in the cell coverage area according to the total number, the total area and the weight value, and calculating to obtain the weighted average height of the buildings in the cell coverage area according to the total height and the weight value.
4. And selecting a target beam configuration from preset beam configurations according to the characteristic data, and carrying out beam forming on the beam of the base station according to the target beam configuration.
In the optimization example of beamforming for the base station in the area to be optimized, the coverage rate and the residence ratio of the building are improved, and the resource utilization rate of the network equipment is improved. The residence ratio refers to the ratio of the residence time of the network above 5G and 5G to the residence time of the network below 5G and 5G.
Specifically, the comparison of the building coverage before and after optimization by the beamforming method of the present invention is shown in table 5 below:
TABLE 5
The residence ratio before and after optimization is shown in table 6 below:
TABLE 6
Wherein the inter-station distance is the distance between base stations.
In addition, the beamforming method of the present invention and the beam optimization method in the prior art have a significantly improved input-output ratio (Return On Investment), specifically, the input-output ratio of the beamforming method of the present invention and the beam optimization method in the prior art is shown in table 7 below with reference to a beam stereo optimization method based On MR big data as a reference value, that is, with reference to 100% of the input cost of the method:
TABLE 7
Wherein, the equal scale evaluation period is the period of effect evaluation before and after optimization under the condition that each scheme has the same application scale; the scene feature extraction accuracy is the extraction accuracy of the feature data in the cell coverage of the base station.
In the above example, the cell coverage of the base station in the area to be optimized is accurately divided through the map data, and appropriate target beam configuration is selected for beam forming according to the characteristic data of the statistical cell coverage, so that the cell coverage overlapping or omission is avoided, a large amount of MR data does not need to be collected and analyzed, the method is suitable for large-scale evaluation planning and optimization, and the evaluation and optimization efficiency is improved.
It should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the illustrated order of acts, as some steps may occur in other orders or concurrently in accordance with the embodiments of the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.
Referring to fig. 5, a block diagram of a structure of a beamforming apparatus provided in the embodiment of the present invention is shown, which specifically includes the following modules:
a map data obtaining module 501, configured to obtain map data of an area to be optimized; the map data comprises building data of buildings in the area to be optimized and base station positions of base stations;
a region dividing module 502, configured to divide the region to be optimized according to the location of the base station, so as to obtain a cell coverage area of each base station;
a feature data statistics module 503, configured to perform statistics on building data of the building within the cell coverage to obtain feature data; the characteristic data is used for reflecting the distribution condition of buildings in the coverage area of the cell;
a target beam configuration selection module 504, configured to select a target beam configuration from preset beam configurations according to the feature data;
and a beam forming module 505, configured to perform beam forming on the beam of the base station according to the target beam configuration.
In an exemplary embodiment, the area dividing module 502 is configured to divide the area to be optimized by using a thiessen polygon algorithm according to the location of the base station, so as to obtain a cell coverage area of the base station.
In an exemplary embodiment, the region dividing module 502 includes:
the adjacent base station determining submodule is used for determining the adjacent base stations in the area to be optimized according to the positions of the base stations;
the area connection submodule is used for connecting the adjacent base stations in the area to be optimized into a triangular area;
the area processing submodule is used for making a vertical bisector on each side of the triangular area;
and the polygonal coverage area determining module is used for taking a polygonal area surrounded by the vertical bisectors as a polygonal coverage area of the base station.
In an exemplary embodiment, the cell coverage of the base station is one or more.
In an exemplary embodiment, the base station has corresponding cell data, and the cell location of the cell coverage is determined by employing:
a first cell location determining submodule, configured to, when an initial cell location of the cell coverage exists in the cell data, take the initial cell location as a target cell location of the cell coverage;
a second cell location determining submodule, configured to, when the initial cell location of the cell coverage does not exist in the cell data, shift the base station location by a preset distance in a normal direction of a cell azimuth of the cell coverage to obtain a shift location, and use the shift location as a target cell location of the cell coverage; the cell azimuth angle is a horizontal included angle between the clockwise direction and the cell coverage range by taking the north direction of the base station as an initial direction.
In an exemplary embodiment, the building data includes at least a number of buildings, a building area, a building height, and a building type for the building, the building type having a corresponding weight value; the characteristic data statistics module 503 is configured to count the number of buildings, the building area, the building height, and the weight value of all the buildings in the cell coverage area, and calculate a total building number, a total building weighted area, and a total building weighted height of the buildings in the cell coverage area; calculating to obtain the weighted average area of the buildings in the cell coverage according to the total number of the buildings and the weighted total area of the buildings; calculating the weighted average height of the buildings in the cell coverage area according to the total number of the buildings and the weighted total height of the buildings; and taking the weighted average area and the weighted average height as characteristic data.
In an exemplary embodiment, the beam configuration includes a horizontal wave width angle and a vertical wave width angle, and the beam configuration has corresponding preset characteristic data; the building types are classified according to the use of the building.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
In addition, an embodiment of the present invention further provides an electronic device, including: the processor, the memory, and the computer program stored in the memory and capable of running on the processor, when being executed by the processor, implement each process of the above-mentioned beamforming method embodiment, and can achieve the same technical effect, and for avoiding repetition, details are not described here again.
The embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements each process of the beamforming method embodiment, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here. The computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk.
It should be noted that, the apparatus provided in the embodiment of the present invention can implement all the method steps implemented by the method embodiment and achieve the same technical effect, and detailed descriptions of the same parts and beneficial effects as the method embodiment in this embodiment are omitted here.
An embodiment of the present invention further provides a network device, as shown in fig. 6, the network device includes a memory 620, a transceiver 610, a processor 600;
a memory 620 for storing a computer program;
a transceiver 610 for receiving and transmitting data under the control of the processor 600;
a processor 600 for reading the computer program in the memory 620 and executing the beamforming method as described above.
Wherein in fig. 6, the bus architecture may include any number of interconnected buses and bridges, with one or more processors represented by the processor 600x10 and various circuits of memory represented by the memory 620x20 being linked together. The bus architecture may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. The bus interface provides an interface. The transceiver 610 may be a number of elements including a transmitter and a receiver that provide a means for communicating with various other apparatus over a transmission medium including wireless channels, wired channels, fiber optic cables, and the like. The processor 600 is responsible for managing the bus architecture and general processing, and the memory 620 may store data used by the processor 600 in performing operations.
The processor 600 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or a Complex Programmable Logic Device (CPLD), and the processor 600 may also adopt a multi-core architecture.
It should be noted that, the apparatus provided in the embodiment of the present invention can implement all the method steps implemented by the method embodiment and achieve the same technical effect, and detailed descriptions of the same parts and beneficial effects as the method embodiment in this embodiment are omitted here.
An embodiment of the present invention further provides a processor-readable storage medium, where the processor-readable storage medium stores a computer program, and the computer program is configured to enable the processor to execute the service switching method described above.
The processor-readable storage medium can be any available medium or data storage device that can be accessed by a processor, including, but not limited to, magnetic memory (e.g., floppy disks, hard disks, magnetic tape, magneto-optical disks (MOs), etc.), optical memory (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor memory (e.g., ROMs, EPROMs, EEPROMs, non-volatile memory (NAND FLASH), Solid State Disks (SSDs)), etc.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer-executable instructions. These computer-executable instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These processor-executable instructions may also be stored in a processor-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the processor-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These processor-executable instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. A method for beamforming, the method comprising:
obtaining map data of an area to be optimized; the map data comprises building data of buildings in the area to be optimized and base station positions of base stations;
dividing the area to be optimized according to the position of the base station to obtain the cell coverage area of each base station;
counting the building data of the building in the cell coverage area to obtain characteristic data; the characteristic data is used for reflecting the distribution condition of buildings in the coverage area of the cell;
selecting a target beam configuration from preset beam configurations according to the characteristic data;
and carrying out beam forming on the beam of the base station according to the target beam configuration.
2. The method of claim 1, wherein the dividing the area to be optimized according to the base station location to obtain the cell coverage of each base station comprises:
and dividing the area to be optimized by adopting a Thiessen polygon algorithm according to the position of the base station to obtain the cell coverage area of the base station.
3. The method according to claim 2, wherein the dividing the area to be optimized by using a Thiessen polygon algorithm according to the position of the base station to obtain the cell coverage of the base station comprises:
determining the adjacent base stations in the area to be optimized according to the positions of the base stations;
connecting the adjacent base stations in the area to be optimized into a triangular area;
making a vertical bisector on each side of the triangular area;
and taking a polygonal area surrounded by the vertical bisectors as a polygonal coverage area of the base station.
4. The method of claim 3, wherein the cell coverage of the base station is one or more.
5. The method of claim 1, wherein the base station has corresponding cell data, and wherein the target cell location of the cell coverage is determined by:
when the initial cell position of the cell coverage range exists in the cell data, taking the initial cell position as a target cell position of the cell coverage range;
when the initial cell position of the cell coverage does not exist in the cell data, offsetting the base station position to the normal direction of the cell azimuth of the cell coverage by a preset distance to obtain an offset position, and taking the offset position as the target cell position of the cell coverage; the cell azimuth angle is a horizontal included angle between the clockwise direction and the cell coverage range by taking the north direction of the base station as an initial direction.
6. The method of claim 1, wherein the building data includes at least a number of buildings, a building area, a building height, and a building type of building, the building type having a corresponding weight value; the counting building data of the building within the cell coverage area to obtain feature data includes:
counting the number of buildings, the area of the buildings, the height of the buildings and the weight value of all the buildings in the cell coverage area, and calculating to obtain the total number of the buildings, the weighted total area of the buildings and the weighted total height of the buildings in the cell coverage area;
calculating to obtain the weighted average area of the buildings in the cell coverage according to the total number of the buildings and the weighted total area of the buildings;
calculating to obtain the weighted average height of the buildings in the cell coverage according to the total number of the buildings and the weighted total height of the buildings;
and taking the weighted average area and the weighted average height as characteristic data.
7. The method of claim 6, wherein the beam configuration comprises a horizontal wave width angle and a vertical wave width angle, and the beam configuration has corresponding preset characteristic data; the building types are classified according to the use of the building.
8. A beamforming apparatus, comprising:
the map data acquisition module is used for acquiring map data of an area to be optimized; the map data comprises building data of buildings in the area to be optimized and base station positions of base stations;
the area division module is used for dividing the area to be optimized according to the position of the base station to obtain the cell coverage area of each base station;
the characteristic data statistics module is used for carrying out statistics on the building data of the building within the cell coverage range to obtain characteristic data; the characteristic data is used for reflecting the distribution condition of buildings in the coverage area of the cell;
the target beam configuration selection module is used for selecting target beam configuration from preset beam configuration according to the characteristic data;
and the beam forming module is used for carrying out beam forming on the beam of the base station according to the target beam configuration.
9. An electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;
the memory is used for storing a computer program;
the processor, when executing a program stored on the memory, implementing the method of any of claims 1-7.
10. A computer-readable storage medium having instructions stored thereon, which when executed by one or more processors, cause the processors to perform the method recited by any of claims 1-7.
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