A user demand driven 5G base station site selection method

Abstract

The application discloses a user demand driven 5G base station site selection method, determines a rectangular planning area needing a newly-built 5G base station and carries out grid division, each grid center point is taken as a candidate base station site, and the number of user demand points of each grid is obtained, then the closeness between candidate base station sites is calculated according to the number of demand points covered by the newly-built 5G base station arranged at different candidate base station sites, then the optimal base station site set is obtained based on the greedy method, and the optimal base station site of each round is determined by the separation degree calculated based on the closeness in the solving process. The application can achieve the maximum coverage rate by using the least number of base stations, increase the number of base stations in the area with large user demand, and efficiently and accurately obtain the result of 5G base station site selection.

Description

Technical Field

[0001] This invention belongs to the field of 5G base station technology, and more specifically, relates to a user demand-driven 5G base station site selection method. Background Technology

[0002] 5G mobile communication technology plays a vital role in today's economic and social development and daily life. 5G networks, characterized by high bandwidth, high speed, and low latency, serve as the network infrastructure for realizing the interconnection and integration of humans, machines, and things. The construction of mobile communication base stations is a significant investment for telecommunications operators. However, the millimeter waves used in 5G technology have high frequencies and difficulty penetrating buildings, resulting in a smaller coverage area and higher base station density for 5G base stations compared to 4G base stations. Therefore, the number of base stations required in a specific area far exceeds that of 4G base stations. Base station site selection typically considers factors such as cost, coverage, and base station capacity. With the rapid advancement of 5G infrastructure, how to optimize the site planning of the vast number of 5G base stations based on user needs, computer technology, and optimization strategies has become an urgent problem to be solved. This has significant practical implications and can effectively overcome the shortcomings of manual site selection methods, such as high human and time costs and the potential for biased planning decisions.

[0003] Traditional base station site selection is carried out through manual planning. Although it is highly targeted and accurate, it requires a lot of human resources and time. It also relies heavily on the experience and knowledge of the planning and design personnel, which means that manual planning methods can only be applied to a small number of base station sites and cannot be applied to a large number of 5G base station sites.

[0004] To assist telecommunications operators in planning and constructing 5G base stations with reasonable layouts that meet user needs at the lowest cost, some known methods utilize heuristic algorithms and data mining techniques for 5G base station site selection. For example, Ma Xiaoya et al. (<Patent 202110844402.X>, 2021) transformed the 5G base station layout site selection problem into a maximum coverage area problem. They used a visible polygon algorithm to simulate the propagation range of millimeter waves, and integrated the visible polygon area of ​​the total base station coverage area as an evaluation index into an artificial immune optimization algorithm to solve the base station site selection problem. This method can improve the base station coverage and reduce the number of base stations, but it is easy to get trapped in local optima, and its accuracy and efficiency are also low. Liu Jiaxin (Liu Jiaxin. 5G Network Base Station Site Selection Optimization Based on Weighted Minimum Modulus Ideal Point Method [D]. Xi'an University of Electronic Science and Technology, 2019) proposed a 5G base station site selection method based on the weighted minimum modulus ideal point method. It combines genetic algorithm and particle swarm optimization algorithm to construct the minimum modulus ideal point objective function. Finally, through selection, crossover, mutation and other operations, the optimal solution that satisfies multiple objective functions is obtained. Due to the low search efficiency of genetic algorithm, this method cannot efficiently realize the selection of a large number of 5G base stations in practice. Liu Lu et al. (Liu Lu, Wang Peng, Pang Zefeng. Agile planning scheme for 5G base station sites based on continuous clustering algorithm [J]. Telecommunications Engineering Technology and Standardization, 2022, 35 (3): 68-71.) proposed a continuous clustering algorithm based on grid density. The weak coverage grid obtained from 5G simulation is used as the data source input. The DBSCAN clustering algorithm is used to obtain preliminary effective grid data. Then, the K-Means clustering algorithm is used to output the 5G base station sites. Finally, the K-Means clustering algorithm with a K value of 3 is used to complete the initial engineering parameter planning. Although the clustering method can efficiently obtain the base station site selection results, it cannot guarantee that the maximum coverage rate is achieved with the fewest base stations, cannot reasonably control the cost, and has low accuracy. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a user demand-driven 5G base station site selection method that achieves maximum coverage with the fewest number of base stations and increases the number of base stations in areas with high user demand, thereby efficiently and accurately obtaining the results of 5G base station site selection.

[0006] To achieve the above-mentioned objectives, the user-demand-driven 5G base station site selection method of the present invention includes the following steps:

[0007] S1: For the planned area where new 5G base stations need to be built, construct the smallest enclosing rectangle covering the entire planned area as the rectangular planning area. Obtain the rectangular planning area The latitude and longitude coordinates of the vertices are transformed to Cartesian coordinates, and the rectangular planning area is defined. Transform to a Euclidean Cartesian coordinate system;

[0008] In the Euclidean Cartesian coordinate system, the rectangular planning area is determined according to the preset grid side lengths. Divide the grid into multiple square grids of the same size, and denote the number of square grids obtained as . The center point of each square grid is used as a candidate base station site to obtain a set of candidate base station sites. ,in Indicates the first Candidate base station sites , record Candidate base station sites The coordinates are ;

[0009] Obtain a rectangular planning area at a specific historical moment using existing base stations. The latitude and longitude coordinates of all users are obtained, and then transformed to a Euclidean Cartesian coordinate system; the number of users covered by each square grid is taken as the number of demand points for that square grid. ;

[0010] S2: Determine the coverage radius based on the parameters of the newly built 5G base station. For rectangular planning areas Each candidate base station site According to the coverage radius Determine when a new 5G base station is deployed at this candidate base station site. The set of square grids that can be covered at any time The sum of the demand points for all square grids in the set of square grids is used to determine the deployment of the new 5G base station at the candidate base station site. Number of demand points covered in time ;

[0011] Then for any two candidate base station sites , , and Find the set of square grids. and intersection , will intersect The summation of the demand points in all square grids yields two candidate base station sites. , Number of demand points covered ;

[0012] The calculations show that the new 5G base stations will be deployed at the candidate base station sites. Capacity coefficient at time :

[0013] ,

[0014] Then, the candidate base station sites are calculated using the following formula. , Proximity :

[0015] ,

[0016] S3: Obtain the optimal set of base station sites based on the greedy algorithm. The specific method is as follows:

[0017] S3.1: Initialize the base station site set Cumulative separation Demand point coverage ;

[0018] S3.2: Set of candidate base station sites Each candidate base station site Calculate the cumulative separation increment :

[0019] ,

[0020] in, , representing the set of base station sites The cumulative separation of each base station site in the middle; Represents the set of base station sites Candidate base station sites The separation degree is calculated using the following formula:

[0021] ,

[0022] in, Indicates the set of base station addresses Candidate base station sites deleted The set after, This indicates the number of base station addresses in the set;

[0023] S3.3: Selecting a set of candidate base station sites The candidate base station site with the largest cumulative separation increment is added to the base station site set. The base station site set is obtained. And remove the candidate base station site from the candidate base station site set. Delete;

[0024] S3.4: Set the current base station addresses The set of base station sites is obtained by summing the number of demand points covered by all newly built 5G base stations across all base station sites. Number of demand points covered Calculate the coverage rate of demand points , Indicates the total number of demand points;

[0025] S3.5: Determine if and ,in This indicates the preset coverage threshold. This indicates a preset coverage increase threshold. If so, proceed to step S3.6; otherwise, proceed to step S3.7.

[0026] S3.6: Set up base station sites As the optimal set of base station sites ;

[0027] S3.7: Set up base station sites Return to step S3.2;

[0028] S4: Set the optimal base station sites The coordinates of each base station site in the Euclidean Cartesian coordinate system are converted into latitude and longitude coordinates to obtain the 5G base station site selection.

[0029] This invention discloses a user demand-driven 5G base station site selection method. It determines the rectangular planning area where new 5G base stations need to be built and divides it into grids. The center point of each grid is used as a candidate base station site, and the number of user demand points in each grid is obtained. Then, based on the number of demand points covered by the new 5G base station deployed at different candidate base station sites, the proximity between candidate base station sites is calculated. Finally, an optimal set of base station sites is obtained using a greedy algorithm. During the solution process, the optimal base station site for each round is determined by the separation degree calculated based on the proximity.

[0030] The present invention has the following beneficial effects:

[0031] (1) This invention proposes a method for calculating the proximity between candidate 5G base station sites, providing a new strategy for the quantitative calculation of the overlap of base station coverage demand points. By selecting a smaller proximity, a larger base station coverage rate can be achieved with fewer base stations, thereby reducing costs.

[0032] (2) Driven by user needs, this invention models the 5G base station site selection problem as an optimization problem with a submodular objective function that can be solved efficiently using a greedy method. The resulting 5G base station site can achieve the maximum 5G network signal coverage at the lowest cost while meeting the base station capacity requirements. Attached Figure Description

[0033] Figure 1This is a flowchart illustrating a specific implementation of the user-demand-driven 5G base station site selection method of the present invention.

[0034] Figure 2 This is a diagram showing the proximity of two candidate base station sites;

[0035] Figure 3 This invention uses a greedy algorithm to solve for the optimal set of base station sites. Flowchart;

[0036] Figure 4 This is a schematic diagram of the distribution of demand points in this embodiment;

[0037] Figure 5 This is a schematic diagram of 5G base station site selection in this embodiment. Detailed Implementation

[0038] The specific embodiments of the present invention will now be described with reference to the accompanying drawings to enable those skilled in the art to better understand the invention. It should be particularly noted that in the following description, detailed descriptions of known functions and designs that might obscure the main content of the invention will be omitted here.

[0039] Example

[0040] Figure 1 This is a flowchart illustrating a specific implementation of the user-demand-driven 5G base station site selection method of the present invention. (See attached flowchart.) Figure 1 As shown, the specific steps of the user demand-driven 5G base station site selection method of the present invention include:

[0041] S101: Obtain planning area data:

[0042] For the planned area where new 5G base stations need to be built, the smallest enclosing rectangle covering the entire planned area is constructed as the rectangular planning area. Obtain the rectangular planning area. The latitude and longitude coordinates of the vertices are transformed to Cartesian coordinates, and the rectangular planning area is defined. Transform to Euclidean Cartesian coordinate system.

[0043] In the Euclidean Cartesian coordinate system, the rectangular planning area is determined according to the preset grid side lengths. Divide the grid into multiple square grids of the same size, and denote the number of square grids obtained as . The center point of each square grid is used as a candidate base station site, resulting in a set of candidate base station sites. ,in Indicates the first Candidate base station sites , record Candidate base station sites The coordinates are .

[0044] Obtain a rectangular planning area at a specific historical moment using existing base stations (e.g., 2G, 3G, or 4G base stations). The latitude and longitude coordinates of all users are obtained and then transformed to a Euclidean Cartesian coordinate system. The number of users covered by each square grid is taken as the number of demand points for that square grid. .

[0045] S102: Calculate the proximity between candidate base station sites:

[0046] Determine the coverage radius based on the parameters of the newly built 5G base station. For rectangular planning areas Each candidate base station site According to the coverage radius Determine when a new 5G base station is deployed at this candidate base station site. The set of square grids that can be covered at any time The sum of the demand points for all square grids in the set of square grids is used to determine the deployment of the new 5G base station at the candidate base station site. Number of demand points covered in time .

[0047] Then for any two candidate base station sites , , and Find the set of square grids. and intersection , will intersect The summation of the demand points in all square grids yields two candidate base station sites. , Number of demand points covered .

[0048] Base station capacity refers to the number of channels that a base station or cell should be configured with, that is, the maximum number of users that a base station can serve. This invention introduces a capacity coefficient. This indicates that the number of users actually connected to the base station exceeds the capacity of the 5G base station. The extent to which the number of users covered by the base station does not exceed the capacity of the 5G base station. At that time, capacity factor When the number of users covered by a base station exceeds the base station's capacity. When the capacity is exceeded, the capacity factor increases. The smaller the value, the better. Therefore, the following formula is used to calculate the deployment location of the new 5G base station at the candidate base station site. Capacity coefficient at time :

[0049] ,

[0050] Then, the candidate base station sites are calculated using the following formula. , Proximity :

[0051] ,

[0052] Proximity This is used to characterize the overlap of coverage when a newly built 5G base station is deployed at two candidate base station sites. Figure 2 This is a diagram illustrating the proximity of two candidate base station sites. (As shown in the formula above...) Figure 2 As shown, the more demand points two candidate base station sites simultaneously cover, the greater the proximity. The capacity coefficient also affects the proximity value. When the number of users covered by a base station does not exceed its capacity, the capacity coefficient is 1 and does not affect the proximity. When the number exceeds the base station capacity, the greater the excess, the smaller the capacity coefficient, and thus the smaller the proximity value. This makes it easier for the base station to overlap with demand points covered by other base stations, providing more base station capacity for areas with high user demand.

[0053] S103: Select the optimal set of base station sites:

[0054] To select the optimal base station site, this invention introduces a separation degree to represent the set of base station sites. (Collection of candidate base station sites) The separation degree is the degree of distance between a base station deployed at a certain site and other base stations in a subset of the base station site set. Specifically, it represents the proportion of demand points not covered by other base stations out of the total demand point coverage. The greater the overlap in coverage of the same demand points between base stations, the smaller the separation degree. Therefore, the separation degree of candidate base station sites can be calculated based on their proximity. Candidate base station sites Resolution The calculation formula is as follows:

[0055] ,

[0056] in, Indicates the set of base station addresses delete The set after, This indicates the number of base station addresses in the set.

[0057] This invention has found that the 5G base station site selection problem can be transformed into selecting from a set of candidate base station sites. Select the subset with the highest overall separation. To find the optimal set of base station sites Construct the following objective function :

[0058] ,

[0059] in, Represents the set of base station sites The cumulative separation of each base station site in the middle.

[0060] Studies have shown that if the objective function of an optimization problem satisfies submodularity, the result obtained by solving the problem using a greedy algorithm can approach the optimal solution. In this invention, when a candidate base station site is added to the set of base station sites... At that time, if the base station site set The smaller the value, the less likely the base station coverage points will overlap, and the greater the increase in separation. Satisfying the submodularity property, a greedy algorithm can be used to efficiently solve for the optimal set of base station sites. . Figure 3 This invention uses a greedy algorithm to solve for the optimal set of base station sites. The flowchart. For example... Figure 3 As shown, this invention uses a greedy algorithm to solve for the optimal set of base station sites. The specific steps include:

[0061] S301: Initialization parameters:

[0062] Initialize the base station address set Cumulative separation Demand point coverage .

[0063] S302: Calculate the cumulative separation increment:

[0064] For the candidate base station site set Each candidate base station site Calculate the cumulative separation increment :

[0065] ,

[0066] in, , representing the set of base station sites The cumulative separation of each base station site in the middle, Represents the set of base station sites Candidate base station sites The separation degree is calculated using the following formula:

[0067] ,

[0068] in, Indicates the set of base station addresses Candidate base station sites deleted The set after, This indicates the number of base station addresses in the set.

[0069] S303: Selecting the optimal base station site for this round:

[0070] Selecting a set of candidate base station sites The candidate base station site with the largest cumulative separation increment is added to the base station site set. The base station site set is obtained. And remove the candidate base station site from the candidate base station site set. Delete it.

[0071] In order to obtain the base station site set Even better, in this step, if there are two or more candidate base station sites with the largest cumulative separation increment, the demand point coverage rate of the newly built 5G base station at each of these candidate base station sites is calculated. That is, the proportion of demand points covered by the newly built 5G base station at the candidate base station site to the total number of demand points. The candidate base station site with the largest demand point coverage rate is then selected to be added to the base station site set. .

[0072] S304: Calculate the coverage of demand points:

[0073] Set the current base station addresses The set of base station sites is obtained by summing the number of demand points covered by all newly built 5G base stations across all base station sites. Number of demand points covered Calculate the coverage rate of demand points , This indicates the total number of demand points.

[0074] S305: Determine if and ,in This indicates the preset coverage threshold. This indicates a preset coverage increase threshold. If so, proceed to step S306; otherwise, proceed to step S307.

[0075] S306: Obtain the optimal set of base station sites:

[0076] Set up base station sites As the optimal set of base station sites .

[0077] S307: Set up base station sites Return to step S302.

[0078] S104: Determining 5G base station locations:

[0079] Set of optimal base station sites The coordinates of each base station site in the Euclidean Cartesian coordinate system are converted into latitude and longitude coordinates to obtain the 5G base station site selection.

[0080] To better illustrate the technical effects of the present invention, a specific example is used to experimentally verify the invention. In this embodiment, the latitude and longitude coordinates of the southwest and northeast corners of the rectangular planning area are (102.838259, 24.847629) and (102.862501, 24.85672), respectively. The latitude and longitude coordinates of the southwest corner are taken as the origin of the rectangular coordinate system. Since the latitude of the planning area is around 24.8 degrees, it is taken as... Convert (102.838359, 24.847629) and (102.862501, 24.85672) to Cartesian coordinates to obtain (0, 0) and (2400, 1000).

[0081] A square grid with a side length of 100 meters is selected. The length and width can be divided into 24 segments and 10 segments respectively, resulting in a total of 240 grids. The coordinates of the center point of each grid are used as candidate base station sites, resulting in 240 candidate base station sites. The coordinate set can be represented as {(50, 50), (150, 50), (200, 50), (250, 50), (300, 50), (350, 50), (400, 50), (450, 50), (500, 50), …, (2350, 950)}.

[0082] Then, the latitude and longitude coordinates of all users in the rectangular planning area at a certain historical moment are obtained using existing base stations, and then transformed into the Euclidean Cartesian coordinate system to obtain the demand point. Figure 4 This is a schematic diagram showing the distribution of demand points in this embodiment. For example... Figure 4 As shown, the black rectangle represents the rectangular planning area, and the white dots represent the demand points. Table 1 is a coordinate transformation table for some of the demand points.

[0083]

[0084] Table 1

[0085] In this embodiment, the coverage radius of the newly built 5G base station Meters. The set of square grids that can be covered when a new 5G base station is deployed at each candidate base station site is determined, and the number of required points is calculated. Then, the number of required points jointly covered by any two candidate base station sites is obtained. Table 2 shows the number of required points covered when a new 5G base station is deployed at each candidate base station site in this embodiment.

[0086]

[0087] Table 2

[0088] Table 3 shows the number of required points that are jointly covered by the two candidate base station sites in this embodiment.

[0089]

[0090] Table 3

[0091] This embodiment sets the 5G base station capacity. Based on the number of required points covered by the candidate base station sites, the proximity of any two candidate base station sites is calculated. Table 4 is a proximity data table of candidate base station sites in this embodiment.

[0092]

[0093] Table 4

[0094] Next, the optimal set of base station sites is obtained using a greedy algorithm. In this embodiment, a threshold is set. , During the solution process, when the number of base station addresses in the base station address set... When the value is 12, the demand point coverage rate is 0.939. When the value is 13, the demand point coverage rate is 0.976. When the value is 14, the demand coverage ratio is 0.982. Since 0.976 > 0.95 and 0.982 - 0.976 < 0.01, therefore, [the value is selected]. The set of base station sites when =13, i.e. The 13 Cartesian coordinates obtained from the given coordinates {(650.0, 550.0), (1450.0, 350.0), (150.0, 850.0), (1750.0, 850.0), (350.0, 50.0), (2050.0, 250.0), (1150.0, 950.0), (2350.0, 750.0), (950.0, 50.0), (650.0, 950.0), (1550.0, 250.0), (50.0, 450.0), (1050.0, 550.0)} are used as 5G base station sites, with a required coverage rate of 0.976. The Cartesian coordinates of the optimal base station sites are then converted to latitude and longitude. Table 5 shows the latitude and longitude coordinates of the optimal base station sites in this embodiment.

[0095]

[0096] Table 5

[0097] Figure 5 This is a schematic diagram of 5G base station site selection in this embodiment. For example... Figure 5 As shown, the circles represent the coverage area of ​​the 5G base stations. It can be observed that the 5G base stations almost completely cover the planned area with minimal overlap, and more base stations are used in areas with high user demand. Therefore, the 5G base station site selection method obtained by this invention is reasonable and effective.

[0098] Although the illustrative specific embodiments of the present invention have been described above to enable those skilled in the art to understand the invention, it should be understood that the invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the invention as defined and determined by the appended claims, and all inventions utilizing the concept of the present invention are protected.

Claims

1. A user-demand-driven 5G base station site selection method, characterized in that, Includes the following steps: S1: For the planned area where new 5G base stations need to be built, construct the smallest enclosing rectangle covering the entire planned area as the rectangular planning area. Obtain the rectangular planning area The latitude and longitude coordinates of the vertices are transformed to Cartesian coordinates, and the rectangular planning area is defined. Transform to a Euclidean Cartesian coordinate system; In the Euclidean Cartesian coordinate system, the rectangular planning area is determined according to the preset grid side lengths. Divide the grid into multiple square grids of the same size, and denote the number of square grids obtained as . The center point of each square grid is used as a candidate base station site to obtain a set of candidate base station sites. ,in Indicates the first Candidate base station sites , record Candidate base station sites The coordinates are ; Obtain a rectangular planning area at a specific historical moment using existing base stations. The latitude and longitude coordinates of all users are obtained, and then transformed to a Euclidean Cartesian coordinate system; the number of users covered by each square grid is taken as the number of demand points for that square grid. ; S2: Determine the coverage radius based on the parameters of the newly built 5G base station. For rectangular planning areas Each candidate base station site According to the coverage radius Determine when a new 5G base station is deployed at this candidate base station site. The set of square grids that can be covered at any time The sum of the demand points for all square grids in the set of square grids is used to determine the deployment of the new 5G base station at the candidate base station site. Number of demand points covered in time ; Then for any two candidate base station sites , , and Find the set of square grids. and intersection , will intersect The summation of the demand points in all square grids yields two candidate base station sites. , Number of demand points covered ; The calculations show that the new 5G base stations will be deployed at the candidate base station sites. Capacity coefficient at time : ， in, Indicates base station capacity; Then, the candidate base station sites are calculated using the following formula. , Proximity : ， S3: Obtain the optimal set of base station sites based on the greedy algorithm. The specific method is as follows: S3.1: Initialize the base station site set Cumulative separation Demand point coverage ; S3.2: Set of candidate base station sites Each candidate base station site Calculate the cumulative separation increment : ， in, , representing the set of base station sites The cumulative separation of each base station site in the middle; Represents the set of base station sites Candidate base station sites The separation degree is calculated using the following formula: ， in, Indicates the set of base station addresses Candidate base station sites deleted The set after, This indicates the number of base station addresses in the set; S3.3: Selecting a set of candidate base station sites The candidate base station site with the largest cumulative separation increment is added to the base station site set. The base station site set is obtained. And remove the candidate base station site from the candidate base station site set. Delete; S3.4: Set the current base station addresses The set of base station sites is obtained by summing the number of demand points covered by all newly built 5G base stations across all base station sites. Number of demand points covered Calculate the coverage rate of demand points , Indicates the total number of demand points; S3.5: Determine if and ,in This indicates the preset coverage threshold. This indicates a preset coverage increase threshold. If so, proceed to step S3.6; otherwise, proceed to step S3.

7. S3.6: Set up base station sites As the optimal set of base station sites ; S3.7: Set up base station sites Return to step S3.2; S4: Set the optimal base station sites The coordinates of each base station site in the Euclidean Cartesian coordinate system are converted into latitude and longitude coordinates to obtain the 5G base station site selection.

2. The 5G base station site selection method according to claim 1, characterized in that, In step S3.3, if there are two or more candidate base station sites with the largest cumulative separation increment, the demand point coverage rate of the newly built 5G base station at each of these candidate base station sites is calculated, which is the proportion of the number of demand points covered by the newly built 5G base station at the candidate base station site to the total number of demand points. The candidate base station site with the largest demand coverage rate is then selected to be added to the base station site set. .

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