CN117939484B - 5G base station address selection method and device considering signal loss based on linear programming - Google Patents
5G base station address selection method and device considering signal loss based on linear programming Download PDFInfo
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Abstract
The application provides a 5G base station site selection method and a device considering signal loss based on linear programming, and relates to the technical field of 5G, wherein the method comprises the following steps: constructing a raster data layer of the observed surface based on the spatial data in the planning range; selecting a preset number of candidate 5G base stations according to a specified station setting principle, and calculating signal path loss from each candidate 5G base station to an observed surface based on a raster data layer of the observed surface according to a 5G propagation model of a 3GPP protocol; and determining the target base station site selection information through a target coverage optimization model based on the signal path loss of the observation surface and the loss standard. The application saves the labor cost, the site selection simulation effect is more practical, and the site selection efficiency of the 5G base station is improved.
Description
Technical Field
The application relates to the technical field of 5G, in particular to a 5G base station address selection method and device considering signal loss based on linear programming.
Background
The fifth generation mobile communication technology (5 th Generation Mobile Communication Technology, abbreviated as 5G) is a new generation broadband mobile communication technology with high speed, low time delay and large connection characteristics, and the 5G facility is a network infrastructure for realizing everything interconnection, and is applied to more diverse scenes, such as the field of automatic driving.
In a multiple scenario, some businesses have higher requirements on coverage performance of a 5G network, for example, for the automatic driving field, if the network coverage is insufficient, data synchronization may be not timely, so that potential safety hazards are generated.
However, compared with the 3G/4G network, the working frequency band of the 5G network is higher, millimeter waves or centimeter waves are generally adopted for transmission, and the millimeter waves and centimeter waves are blocked by buildings and the like in the transmission process and have larger loss, so that the signal coverage capacity of the 5G base station is far less than that of the 3G/4G base station, and more 5G base stations are required to be built in the same coverage area to meet the signal use requirement, so that the more scientific and efficient site selection layout technical method can greatly save the investment cost.
The existing base station site selection mode mainly uses simulation software, and the base station site selection is performed manually point by means of communication professional technicians, so that the simulation software is complex to use, and single site selection consumes a long time. In actual work, there are many uncontrollable factors, such as unsuitable physical conditions of the building itself, unfavorable land rights and quotients, etc., so the site selection scheme also needs to be modified, and the final scheme usually needs to be selected for multiple times to be determined, which makes the determination of the final scheme take longer time.
Disclosure of Invention
The application aims to provide a 5G base station site selection method and device based on linear programming and considering signal loss, which saves labor cost, enables site selection simulation effect to be more practical and improves the site selection efficiency of the 5G base station.
In a first aspect, the present invention provides a 5G base station location method considering signal loss based on linear programming, including: constructing a raster data layer of the observed surface based on the spatial data in the planning range; selecting a preset number of candidate 5G base stations according to a specified station setting principle, and calculating signal path loss from each candidate 5G base station to an observed surface based on a raster data layer of the observed surface according to a 5G propagation model of a 3GPP protocol; and determining the target base station site selection information through a target coverage optimization model based on the signal path loss of the observation surface and the loss standard.
In an alternative embodiment, the spatial data includes at least terrain data and building data; constructing a raster data layer of the observed surface based on spatial data within the planning horizon, comprising: converting the topographic data in the planning range into topographic raster data, and representing a topographic height value through the first raster value; converting building data in the planning range into building raster data, and representing building height values through second raster values; and superposing the building raster data on the terrain raster data, superposing the terrain height value and the building height value to obtain target height data, and constructing a raster data layer of the observed surface.
In an alternative embodiment, calculating signal path loss of each candidate 5G base station to the observed surface based on the raster data layer of the observed surface according to the 5G propagation model of the 3GPP protocol comprises: converting each candidate 5G base station and the grid surface position corresponding to each candidate 5G base station into a 3D vector point with height information; calculating signal path loss from each candidate 5G base station to each point of the observed surface according to a 5G propagation model based on 3GPP 38.901 protocol; wherein the 5G propagation model comprises UMa propagation models.
In an alternative embodiment, the signal strength is converted into a binary matrix according to whether the loss standard is met or not according to the requirements of an operator or related standard specifications, wherein a first value in the binary is used for indicating that the loss standard is met, and a second value is used for indicating that the loss standard is not met; deleting the matrix row where the grid point of the observed surface meeting the loss standard in the coverage area of the current base station is located; and importing the remaining matrix data to be selected into a target coverage optimization model, and determining the site selection information of the target base station.
In an alternative embodiment, the target coverage optimization model includes a target cost full coverage optimization model and a specified cost target coverage optimization model; the method further comprises the steps of: firstly, constructing a target cost full-coverage optimization model with coverage rate meeting a first coverage standard and station building quantity meeting a first quantity standard; the first objective function of the objective cost full-coverage optimization model is as follows:
first constraint: wherein j is a grid number; i is the candidate 5G base station number; m is the total number of base stations; ；
Gradually reducing the number of base stations, and constructing a specified cost target coverage optimization model with coverage rate reaching a preset coverage standard and the number of stations being built being less than a preset number threshold;
Wherein the second objective function specifying the cost objective coverage optimization model is:
the second constraint: ， ; wherein L is the number of the appointed base stations.
In an alternative embodiment, after selecting a preset number of candidate 5G base stations according to a specified station setting rule, the method further includes: the observation parameters are set, and the visibility result of each candidate 5G base station to the observed surface grid is generated through a vision field analysis tool.
In an alternative embodiment, after determining the target base station site selection information by the target coverage optimization model, the method further includes: extracting signal path loss matrixes from all selected base stations comprising the current base station to the observed surface; calculating the lowest value of signal loss from each observed surface grid point to each selected base station point; and visualizing the minimum loss value result of each base station point to obtain a signal coverage performance graph.
In a second aspect, the present invention provides a 5G base station address selecting apparatus considering signal loss based on linear programming, including: the grid construction module is used for constructing a grid data layer of the observed surface based on the space data in the planning range; the path loss calculation module is used for selecting a preset number of candidate 5G base stations according to a specified station setting principle, and calculating the signal path loss from each candidate 5G base station to the observed surface based on the raster data layer of the observed surface according to a 5G propagation model of the 3GPP protocol; and the addressing module is used for determining the target base station addressing information through the target coverage optimization model based on the signal path loss of the observation surface and the loss standard.
In a third aspect, the invention provides an electronic device comprising a processor and a memory storing computer executable instructions executable by the processor to implement the 5G base station addressing method of any of the preceding embodiments based on linear programming taking into account signal loss.
In a fourth aspect, the present invention provides a computer readable storage medium storing computer executable instructions that, when invoked and executed by a processor, cause the processor to implement the 5G base station site selection method of any of the preceding embodiments based on linear programming with consideration of signal loss.
The 5G base station site selection method and device based on the linear programming and considering the signal loss fully considers the signal loss in the 5G signal transmission process, realizes the rapid decision of large-scale 5G base station site selection through the simple and easy-to-use linear programming model, saves the labor cost, has better site selection simulation effect in accordance with the actual situation, and improves the site selection efficiency of the 5G base station.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a 5G base station address selection method based on consideration of signal loss by linear programming according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a raster data layer of an observed surface according to an embodiment of the present application;
FIG. 3 is a view of a candidate 5G base station to an observed surface according to an embodiment of the present application;
FIG. 4 is a schematic diagram of selecting all possible candidate base stations according to an address rule according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a propagation model according to an embodiment of the present application;
FIG. 6 is an example of signal path loss from a candidate 5G base station to an observed surface provided by an embodiment of the present application;
FIG. 7 is a graph showing the result of a specified cost maximum coverage model addressing scheme according to an embodiment of the present application;
FIG. 8 is a signal coverage performance chart according to an embodiment of the present application;
Fig. 9 is a block diagram of a 5G base station address selecting device based on consideration of signal loss by linear programming according to an embodiment of the present application;
fig. 10 is a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
The inventors of the present application found by analysis that:
When 5G base station site selection is carried out, because simulation software is complex to use, single site selection is long in time by means of manually carrying out site selection point by means of communication professional technicians, and the overall site selection efficiency is low. It is difficult to meet the large-scale and rapid site selection requirements of 5G base stations. The simulation software herein may refer to signal simulation software, such as Forsk Atoll, and the like. The signal simulation software needs to carry out operations such as propagation model teaching, key parameter configuration and the like, and an operator needs to have more specialized knowledge storage, so that the threshold is higher and the universality is poor. In actual work, there are many uncontrollable factors, such as unsuitable physical conditions of the building itself, unfavorable land rights and quotients, etc., so the site selection scheme also needs to be modified, and the final scheme usually needs to be selected for multiple times to be determined, which makes the determination of the final scheme take longer time.
In addition, from the point of view of scientific research, in the existing research about 5G base station site selection, some schemes divide the requirement points into two categories of "can/can not receive signals" according to whether the requirement points can be seen by the coverage area of the candidate base station. However, in reality, even if the partial area is blocked by a building, signals meeting the intensity of the communication standard can be received through diffuse reflection, so that excessive layout of the base station may be caused, resource waste is generated, or the coverage prediction accuracy is poor, so that more times of scheme modification are required to obtain a final scheme.
Based on the method and the device, provided by the embodiment of the application, for 5G base station site selection based on linear programming and considering signal loss, the labor cost is saved, the site selection simulation effect is more practical, the site selection efficiency of the 5G base station is improved, and the site selection result can be rapidly determined, so that the method and the device can be used as a rapid and simple pre-selection scheme for prepositioning.
The embodiment of the application provides a 5G base station address selection method considering signal loss based on linear programming, which is shown in fig. 1 and mainly comprises the following steps:
Step S110, constructing a raster data layer of the observed surface based on the spatial data in the planning range.
The spatial data may include spatial location information, which may be location coordinates, and attribute information, which may be a type tag of an object, for example, the type of the object may include a building, a road, and the like.
For example, the spatial location information herein may include a planar map and a height Cheng Biaoqian, but may also be a three-dimensional map.
Wherein the above-mentioned spatial data includes at least terrain data and building data, such as may include terrain data, building layout, building height, etc.
Constructing a raster data layer of the observed surface based on spatial data within the planning horizon may comprise the following steps 1.1) to 1.3):
Step 1.1), converting the topographic data in the planning range into topographic raster data (the accuracy is determined according to the simulation requirement, for example, 3m×3m, 5m×5m, 10m×10m, preferably, 5m×5m can be selected), and characterizing the topographic elevation value by the first raster value; for example, the terrain data includes terrain distribution data for characterizing planar locations and terrain elevation data for characterizing elevations. The terrain elevation data in the terrain data may be converted to a first gray value in the terrain raster map according to a mapping of the terrain distribution data in the terrain data to the raster positions in the terrain raster map. Each grid in the terrain grid map corresponds to a gray value, and the gray value is used for representing the elevation average value of the terrain corresponding to the grid. The topographic data may be three-dimensional point cloud data or image data, and when the topographic data is image data, the topographic distribution is indicated by pixel coordinates, and the altitude is indicated by pixel values or altitude labels.
Step 1.2), converting the building data in the planning range into building raster data (the accuracy is determined according to the simulation requirement, for example, the accuracy can be 3m×3m, 5m×5m, 10m×10m, preferably, 5m×5m can be selected), and representing the building height value by the second raster value; for example, the building data includes a building layout for characterizing a planar location and a building height for characterizing a height. The building height may be converted to a second gray value in the terrain grid map according to a mapping of the building layout to grid locations in the building grid map. Wherein each grid in the building grid map corresponds to a gray value that is used to characterize the height average of the building to which the grid corresponds. The building data may be three-dimensional point cloud data or image data, and when the building data is image data, the building layout is indicated by pixel coordinates, and the height is indicated by pixel values or height tags.
Step 1.3), building raster data are overlapped on the topographic raster data, the topographic height value and the building height value are overlapped to obtain target height data, and a raster data layer of the observed surface is constructed. For example, the raster data layer of the observed surface herein may refer to an overlay raster map. The building grid map may be superimposed with the terrain grid map to obtain a superimposed grid map as shown in fig. 2. The superimposed grid map shown in fig. 2 is a general plan for indicating buildings and terrain, the abscissa of which is used to characterize position information, and the gray values represent heights. The conversion relation between the gray value and the height can be determined according to actual needs. In fig. 2, the height is 0 (black), that is, the relative elevation of the ground point, and if there is an area of the type such as an underground square, it may also be negative, and the embodiment of the present application defaults to the ground in the planned area being a plane, that is, the height is 0. For grids at the same position, a first gray value and a second gray value are corresponding to each other, and superposition of the maps can refer to adding the first gray value and the second gray value to obtain the gray value of the grid at the same position in the superposition grid maps.
Step S120, selecting a preset number of candidate 5G base stations according to a specified station setting principle, and calculating the signal path loss from each candidate 5G base station to the observed surface based on the raster data layer of the observed surface according to a 5G propagation model of the 3GPP protocol.
In an alternative embodiment, a preset number of candidate 5G base stations are selected according to a specified station setting principle, and all possible candidate base stations can be selected in an area conforming to the construction of the 5G base stations according to operator requirements or related standard specifications (such as meeting a proper building height, keeping away from a populated area and schools, etc.).
Further, after a preset number of candidate 5G base stations are selected according to a specified station setting principle, an observation parameter may be set, and a visibility result of each candidate 5G base station to an observed surface grid is generated by a vision analysis tool, fig. 3 shows a visibility of one candidate 5G base station to an observed surface, which may be binarized data, that is, only two visible and invisible values, may be indicated using two specific gray values, which are required to have a clear visual difference for better observation.
The above calculation of the signal path loss may further comprise the following steps 2.1) and 2.2):
Step 2.1), converting each candidate 5G base station and the grid surface position corresponding to each candidate 5G base station into a 3D vector point with height information;
Step 2.2), calculating signal path loss from each candidate 5G base station to each point of the observed surface according to a 5G propagation model based on 3GPP 38.901 protocol; the 5G propagation model can be UMa propagation models, and can also be applicable propagation models of other scenes according to actual scene requirements.
Step S130, determining target base station site selection information through a target coverage optimization model based on the signal path loss of the observation surface and loss standard.
In practice, the following steps 3.1) to 3.3) may be used to perform:
step 3.1), converting the signal strength into a binarization matrix according to whether the signal strength meets the loss standard or not according to the requirements of operators or related standard specifications, wherein a first value in the binarization is used for indicating that the signal strength meets the loss standard, and a second value is used for indicating that the signal strength does not meet the loss standard; for example, the binarization matrix here may be a "0/1" matrix, 0 being used to indicate that the loss criterion is not met, 1 being used to indicate that the loss criterion is met.
Step 3.2), deleting the matrix row where the grid point of the observed surface meeting the loss standard in the coverage area of the current base station is located; wherein the current base station is a necessary base station.
And 3.3) importing the residual matrix data to be selected into a target coverage optimization model, and determining the site selection information of the target base station. The target coverage optimization model comprises a target cost full coverage optimization model and a specified cost target coverage optimization model.
In a specific implementation, a target cost full-coverage optimization model with coverage rate meeting a first coverage standard and the number of stations built meeting a first number standard may be first constructed, and the target cost full-coverage optimization model may also be referred to as a lowest cost full-coverage optimization model. The coverage rate meets the first coverage standard and the number of stations built meets the first number standard, and in practical application, the standard with the highest coverage rate and the least number of stations built can be selected.
Referring to fig. 3, a first objective function of the objective cost full coverage optimization model is:
i.e. the number of the selected base stations is the smallest. First constraint: wherein j is a grid number; i is the candidate 5G base station number; m is the total number of base stations; ; i.e. each grid may be covered by at least one selected base station.
Then gradually reducing the number of base stations, and constructing a specified cost target coverage optimization model with coverage rate reaching a preset coverage standard and the number of stations being built being less than a preset number threshold;
referring to fig. 4, a second objective function specifying a cost objective coverage optimization model is:
I.e. the total number of grids covered by all selected base stations is the largest. Second constraint: I.e. the covered grid is covered by at least one base station; that is, the number of selected base stations is less than or equal to the total number of designated base stations; wherein L is the number of the appointed base stations.
Further, after determining the target base station site selection information through the target coverage optimization model, the signal path loss matrix from all selected base stations including the current base station to the observed surface can be extracted; calculating the lowest value of signal loss from each observed surface grid point to each selected base station point; and visualizing the minimum loss value result of each base station point to obtain a signal coverage performance graph.
The embodiment of the application also provides another 5G base station address selecting method considering signal loss based on linear programming, which comprises the following steps:
Step A: and integrating spatial data such as building layout, building height and topography in the planning area, and constructing the observed surface raster data layer. The specific operation is as follows: ① Converting the topographic data in the planning range into grids (the precision is determined according to the simulation requirement, 5m is suggested to be 5m is suggested), and the grid value is a topographic height value; ② Converting building data in a planning range into grids (the precision is determined according to simulation requirements, 5m is suggested to be 5 m), and the grid value is a building height value; ③ The building grid is inlaid onto the terrain grid, and the two height data are superimposed to construct the observed surface raster data layer, e.g., resulting in the results shown in fig. 2.
And (B) step (B): according to the appointed station setting principle, a plurality of candidate 5G base stations are selected, observation parameters are set, and a visual field analysis tool is used for generating a visibility result from each candidate point to an observed surface grid. The specific operation is as follows: ① Selecting all feasible candidate base stations in large scale in the area conforming to the construction of 5G base stations according to the requirements of operators or related standard specifications (such as meeting the requirements of proper building height, keeping away from living dense areas and schools, and the like); ② Setting basic observation parameters of candidate base stations, such as elevation values of base stations, vertical offsets (base station hanging heights, receiving end heights and the like), horizontal and vertical observation angles, observation radiuses and the like; ③ Grid surface locations visible to each candidate base station are generated using a GIS view analysis tool. For example, the results shown in fig. 4 were obtained.
Step C: signal path loss from each candidate point to the observed surface is calculated based on UMa or the like propagation model. The specific operation is as follows: ① Converting each candidate base station point and the visible grid surface position thereof into a 3D vector point with height information; ② The signal path loss from each candidate point to each point on the observed surface is calculated according to a propagation model such as UMa based on the 3gpp 38.901 protocol, and the specific propagation model is shown in fig. 5. Fig. 6 shows an example of signal path loss from a candidate 5G base station to an observed surface.
Step D: and converting the signal path loss from each candidate point to the observed surface into a '1/0' matrix according to whether the loss standard is met, and calculating through an integer linear programming model to obtain an optimized target solution. The specific operation is as follows: ① Converting the signal strength into a 1/0 matrix according to whether the signal strength meets the loss standard (110-120 dB at most) or not according to the requirements of operators or related standard specifications; ② Deleting the matrix row where the grid points of the observed surface meet the loss standard in the coverage area of the essential base station (namely the current base station); ③ The method comprises the steps of importing residual matrix data to be selected into a linear programming model to solve the optimal location scheme, wherein the solving algorithm is a branch-and-bound method, and two addressing optimization schemes of ' lowest cost full coverage ' and ' specified cost maximum coverage ' can be carried out by adopting a pulp open source Python packet, the former is the ' number of base stations required for full coverage and the ' base stations respectively ', the latter is the number of the base stations required to be respectively specified, and the coverage under different numbers can be calculated, for example, the ' number of base stations required to be set up to 95% coverage ' is the target (for example, the service coverage target of the current telecom operator is about 95%). The description of the specific model parameter set formula is referred to in the foregoing description, and will not be repeated here. ④ In practical application, the 'lowest cost full coverage' site selection model can be firstly solved to obtain the optimal scheme with highest coverage rate and least number of sites to be built, then the number of base stations is gradually reduced, and the 'specified cost maximum coverage' model is solved to obtain the optimal cost performance scheme with coverage rate still meeting the requirements and less number of sites to be built. Fig. 7 shows a specified cost maximum coverage model addressing scheme result.
Step E: and calculating the lowest value of the signal path loss from the selected base stations to the observed surface, and generating a signal coverage performance visualization result of the final site selection scheme. The specific operation is as follows: ① Extracting signal path loss matrixes from all selected base stations (including current essential points) to the observed surface; ② Solving the lowest value of signal loss from each observed surface grid point to each selected base station point; ③ And visualizing the minimum loss value result of each point to obtain a final signal coverage performance diagram, which is shown in fig. 8.
The embodiment of the application fully considers the signal loss in the 5G signal transmission process, realizes the rapid decision of the large-scale 5G base station site selection through a simple and easy-to-use linear programming model, saves the labor cost, and has the site selection simulation effect more in line with the reality and rapid decision process. And by providing two addressing optimization model solutions of 'lowest cost full coverage' and 'specified cost maximum coverage', the application flexibility and universality of 5G base station addressing are improved.
Based on the above method embodiment, the embodiment of the present application further provides a 5G base station site selection device considering signal loss based on linear programming, as shown in fig. 9, the device includes the following parts:
A grid construction module 902 for constructing a grid data layer of the observed surface based on the spatial data in the planning range;
a path loss calculation module 904, configured to select a preset number of candidate 5G base stations according to a specified station setting principle, and calculate signal path loss from each candidate 5G base station to the observed surface based on a raster data layer of the observed surface according to a 5G propagation model of the 3GPP protocol;
And an addressing module 906, configured to determine target base station addressing information through a target coverage optimization model based on the signal path loss of the observation surface and the loss criteria.
In a possible embodiment, the spatial data includes at least terrain data and building data; the grid construction module 902 is further configured to:
converting the topographic data in the planning range into topographic raster data, and representing a topographic height value through the first raster value;
Converting building data in the planning range into building raster data, and representing building height values through second raster values;
and superposing the building raster data on the terrain raster data, superposing the terrain height value and the building height value to obtain target height data, and constructing a raster data layer of the observed surface.
In a possible implementation, the path loss calculation module 904 is further configured to:
converting each candidate 5G base station and the grid surface position corresponding to each candidate 5G base station into a 3D vector point with height information;
calculating signal path loss from each candidate 5G base station to each point of the observed surface according to a 5G propagation model based on 3GPP 38.901 protocol; wherein the 5G propagation model comprises UMa propagation models.
In a possible embodiment, the addressing module 906 is further configured to:
According to the requirements of operators or related standard specifications, converting the signal strength into a binarization matrix according to whether the signal strength meets the loss standard, wherein a first value in binarization is used for indicating that the signal strength meets the loss standard, and a second value is used for indicating that the signal strength does not meet the loss standard;
deleting the matrix row where the grid point of the observed surface meeting the loss standard in the coverage area of the current base station is located;
And importing the remaining matrix data to be selected into a target coverage optimization model, and determining the site selection information of the target base station.
In a possible implementation, the target coverage optimization model includes a target cost full coverage optimization model and a specified cost target coverage optimization model;
the device further comprises a model determining module for:
Firstly, constructing a target cost full-coverage optimization model with coverage rate meeting a first coverage standard and station building quantity meeting a first quantity standard;
the first objective function of the objective cost full-coverage optimization model is as follows:
first constraint: wherein j is a grid number; i is the candidate 5G base station number; m is the total number of base stations; ；
Gradually reducing the number of base stations, and constructing a specified cost target coverage optimization model with coverage rate reaching a preset coverage standard and the number of stations being built being less than a preset number threshold;
Wherein the second objective function specifying the cost objective coverage optimization model is:
the second constraint: ， ; wherein L is the number of the appointed base stations.
In a possible implementation manner, after selecting a preset number of candidate 5G base stations according to a specified station setting rule, the apparatus further includes: a grid visualization module for:
the observation parameters are set, and the visibility result of each candidate 5G base station to the observed surface grid is generated through a vision field analysis tool.
In a possible implementation manner, after determining the target base station site selection information through the target coverage optimization model, the apparatus further includes: a signal coverage visualization module for:
extracting signal path loss matrixes from all selected base stations comprising the current base station to the observed surface;
calculating the lowest value of signal loss from each observed surface grid point to each selected base station point;
and visualizing the minimum loss value result of each base station point to obtain a signal coverage performance graph.
The implementation principle and the generated technical effects of the 5G base station address selecting device based on the linear programming consideration signal loss provided by the embodiment of the present application are the same as those of the foregoing method embodiment, and for a brief description, reference may be made to corresponding contents in the foregoing 5G base station address selecting method embodiment based on the linear programming consideration signal loss, where the embodiment portion of the 5G base station address selecting device based on the linear programming consideration signal loss is not mentioned.
The embodiment of the present application further provides an electronic device, as shown in fig. 10, which is a schematic structural diagram of the electronic device, where the electronic device 100 includes a processor 101 and a memory 100, where the memory 100 stores computer executable instructions that can be executed by the processor 101, and the processor 101 executes the computer executable instructions to implement any of the above 5G base station location methods considering signal loss based on linear programming.
In the embodiment shown in fig. 10, the electronic device further comprises a bus 1002 and a communication interface 1003, wherein the processor 1001, the communication interface 1003 and the memory 1000 are connected by the bus 1002.
The memory 1000 may include a high-speed random access memory (RAM, random Access Memory), and may further include a non-volatile memory (non-volatile memory), such as at least one disk memory. The communication connection between the system network element and at least one other network element is implemented via at least one communication interface 1003 (which may be wired or wireless), and may use the internet, a wide area network, a local network, a metropolitan area network, etc. Bus 1002 may be an ISA (Industry Standard Architecture ) bus, a PCI (PERIPHERAL COMPONENT INTERCONNECT, peripheral component interconnect standard) bus, or EISA (Extended Industry Standard Architecture ) bus, among others. The bus 1002 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one bi-directional arrow is shown in FIG. 10, but not only one bus or type of bus.
The processor 1001 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in the processor 1001 or by instructions in the form of software. The processor 1001 may be a general-purpose processor, including a central processing unit (Central Processing Unit, abbreviated as CPU), a network processor (Network Processor, abbreviated as NP), and the like; but may also be a digital signal Processor (DIGITAL SIGNAL Processor, DSP), application Specific Integrated Circuit (ASIC), field-Programmable gate array (FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor 101 reads the information in the memory, and in combination with its hardware, performs the steps of the 5G base station addressing method based on the linear programming considering signal loss of the foregoing embodiment.
The embodiment of the application also provides a computer readable storage medium, which stores computer executable instructions that, when being called and executed by a processor, cause the processor to implement the above-mentioned 5G base station location method considering signal loss based on linear programming, and the specific implementation of the method embodiment can be seen from the foregoing, and will not be described herein.
The computer program product of the 5G base station location method and apparatus based on the linear programming considering signal loss provided in the embodiments of the present application includes a computer readable storage medium storing program codes, where the instructions included in the program codes may be used to execute the method described in the foregoing method embodiment, and specific implementation may refer to the method embodiment and will not be described herein.
The relative steps, numerical expressions and numerical values of the components and steps set forth in these embodiments do not limit the scope of the present application unless it is specifically stated otherwise.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer readable storage medium executable by a processor. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In the description of the present application, it should be noted that, directions or positional relationships indicated by terms such as "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., are directions or positional relationships based on those shown in the drawings, or are directions or positional relationships conventionally put in use of the inventive product, are merely for convenience of describing the present application and simplifying the description, and are not indicative or implying that the apparatus or element to be referred to must have a specific direction, be constructed and operated in a specific direction, and thus should not be construed as limiting the present application. Furthermore, the terms "first," "second," and the like, are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application.
Claims (8)
1. A 5G base station location method based on linear programming considering signal loss, comprising:
Constructing a raster data layer of the observed surface based on the spatial data in the planning range;
selecting a preset number of candidate 5G base stations according to a specified station setting principle, and calculating signal path loss from each candidate 5G base station to an observed surface based on a raster data layer of the observed surface according to a 5G propagation model of a 3GPP protocol;
determining target base station site selection information through a target coverage optimization model based on signal path loss and loss standard of an observation surface;
Determining target base station site selection information by a target coverage optimization model based on signal path loss and loss criteria of the observation surface, comprising:
According to the requirements of operators or related standard specifications, converting the signal strength into a binarization matrix according to whether the signal strength meets the loss standard, wherein a first value in binarization is used for indicating that the signal strength meets the loss standard, and a second value is used for indicating that the signal strength does not meet the loss standard;
deleting the matrix row where the grid point of the observed surface meeting the loss standard in the coverage area of the current base station is located;
Importing the remaining matrix data to be selected into a target coverage optimization model, and determining the address selection information of the target base station; the target coverage optimization model comprises a target cost full coverage optimization model and a specified cost target coverage optimization model;
The method further comprises the steps of:
Firstly, constructing a target cost full-coverage optimization model with coverage rate meeting a first coverage standard and station building quantity meeting a first quantity standard;
the first objective function of the objective cost full-coverage optimization model is as follows:
first constraint: wherein j is a grid number; i is the candidate 5G base station number; m is the total number of base stations; ；
Gradually reducing the number of base stations, and constructing a specified cost target coverage optimization model with coverage rate reaching a preset coverage standard and the number of stations being built being less than a preset number threshold;
Wherein the second objective function specifying the cost objective coverage optimization model is:
the second constraint: ， ; wherein L is the number of the appointed base stations.
2. The 5G base station addressing method based on linear programming considering signal loss according to claim 1, wherein the spatial data includes at least terrain data and building data; constructing a raster data layer of the observed surface based on spatial data within the planning horizon, comprising:
converting the topographic data in the planning range into topographic raster data, and representing a topographic height value through the first raster value;
Converting building data in the planning range into building raster data, and representing building height values through second raster values;
And superposing the building raster data on the terrain raster data, superposing the terrain height value and the building height value to obtain target height data, and constructing a raster data layer of the observed surface.
3. The linear programming based 5G base station addressing method of considering signal loss according to claim 1, wherein calculating signal path loss of each candidate 5G base station to the observed surface based on the raster data layer of the observed surface according to the 5G propagation model of the 3GPP protocol comprises:
converting each candidate 5G base station and the grid surface position corresponding to each candidate 5G base station into a 3D vector point with height information;
Calculating signal path loss from each candidate 5G base station to each point of the observed surface according to a 5G propagation model based on 3GPP 38.901 protocol; wherein the 5G propagation model comprises UMa propagation models.
4. The method for 5G base station site selection based on linear programming considering signal loss according to claim 1, wherein after selecting a preset number of candidate 5G base stations according to a specified site setting rule, the method further comprises:
setting observation parameters, and generating a visibility result of each candidate 5G base station to an observed surface grid through a vision field analysis tool.
5. The 5G base station locating method based on linear programming considering signal loss according to claim 1, wherein after determining the target base station locating information by the target coverage optimization model, the method further comprises:
extracting signal path loss matrixes from all selected base stations comprising the current base station to the observed surface;
calculating the lowest value of signal loss from each observed surface grid point to each selected base station point;
and visualizing the minimum loss value result of each base station point to obtain a signal coverage performance graph.
6. A 5G base station site selection apparatus considering signal loss based on linear programming, comprising:
The grid construction module is used for constructing a grid data layer of the observed surface based on the space data in the planning range;
The path loss calculation module is used for selecting a preset number of candidate 5G base stations according to a specified station setting principle, and calculating the signal path loss from each candidate 5G base station to the observed surface based on the raster data layer of the observed surface according to a 5G propagation model of the 3GPP protocol;
The system comprises an addressing module, a target coverage optimization module and a target base station addressing module, wherein the addressing module is used for determining target base station addressing information through the target coverage optimization module based on signal path loss of an observation surface and loss standard;
The addressing module is further configured to: according to the requirements of operators or related standard specifications, converting the signal strength into a binarization matrix according to whether the signal strength meets the loss standard, wherein a first value in binarization is used for indicating that the signal strength meets the loss standard, and a second value is used for indicating that the signal strength does not meet the loss standard;
deleting the matrix row where the grid point of the observed surface meeting the loss standard in the coverage area of the current base station is located;
Importing the remaining matrix data to be selected into a target coverage optimization model, and determining the address selection information of the target base station; the target coverage optimization model comprises a target cost full coverage optimization model and a specified cost target coverage optimization model;
the apparatus further comprises a model determination module, further for:
Firstly, constructing a target cost full-coverage optimization model with coverage rate meeting a first coverage standard and station building quantity meeting a first quantity standard;
the first objective function of the objective cost full-coverage optimization model is as follows:
first constraint: wherein j is a grid number; i is the candidate 5G base station number; m is the total number of base stations; ；
Gradually reducing the number of base stations, and constructing a specified cost target coverage optimization model with coverage rate reaching a preset coverage standard and the number of stations being built being less than a preset number threshold;
Wherein the second objective function specifying the cost objective coverage optimization model is:
the second constraint: ， ; wherein L is the number of the appointed base stations.
7. An electronic device comprising a processor and a memory, the memory storing computer executable instructions executable by the processor to implement the linear programming-based signal loss-considering 5G base station site selection method of any one of claims 1 to 5.
8. A computer readable storage medium storing computer executable instructions which, when invoked and executed by a processor, cause the processor to implement the linear programming based signal loss considering 5G base station site selection method of any one of claims 1 to 5.
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| CN116390109A (en) * | 2023-04-23 | 2023-07-04 | 中国人民解放军火箭军工程大学 | Wireless broadband site deployment system based on geographic information |
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| CN116390109A (en) * | 2023-04-23 | 2023-07-04 | 中国人民解放军火箭军工程大学 | Wireless broadband site deployment system based on geographic information |
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