Base station site selection and layout method and system

Abstract

The invention relates to a base station site selection and layout method and system. The base station site selection and layout method comprises the following steps: acquiring acquisition data of a target area; preprocessing the collected data based on a geographic information system; carrying out electromagnetic environment survey on the target area; performing user demand analysis based on the user density data and the historical communication data; obtaining a base station candidate position based on the topographic data, the building distribution data, the electromagnetic environment survey result and the user demand analysis result; and selecting the optimal position of the base station from the candidate positions of the base station based on a genetic algorithm for layout. In the aspect of electromagnetic environment exploration, potential interference areas and signal blind areas can be accurately identified, and reliable electromagnetic environment data are provided for base station selection; in the aspect of user demand analysis, a high-density user area and a low-density user area can be clearly identified, and the distribution of the base stations can meet communication demands of users in different areas.

Description

Technical Field

[0001] This application relates to the field of base station site selection technology, and in particular to a base station site selection layout method and system. Background Technology

[0002] In the field of mobile communication networks, the site selection and layout of microcell base stations play a crucial role in ensuring network coverage and capacity. With accelerating urbanization, buildings in cities are becoming increasingly dense, while users' demand for high-quality mobile communication is constantly increasing. However, traditional base station deployment methods are no longer adequate to meet the evolving needs of modern networks.

[0003] In current technologies, base station site selection typically relies on simple coverage calculations and user density estimations. For example, the number and location of base stations are often planned based on the population of an area, neglecting the impact of complex terrain and building distribution on signal propagation. In mountainous areas, for instance, undulating terrain can cause signal obstruction and reflection, resulting in significant differences in signal strength across different locations. In urban areas with numerous high-rise buildings, structures can block signals, creating numerous signal blind spots. This can lead to base stations being overly concentrated in seemingly densely populated areas, such as commercial centers. In these areas, the close proximity of base stations can cause near-field interference, degrading network performance and resulting in unstable signals and slow internet speeds for users. Summary of the Invention

[0004] Therefore, it is necessary to provide a base station site selection and layout method and system to address the problems in related technologies.

[0005] To achieve the above objectives, firstly, this application provides a base station site selection and layout method, the base station site selection and layout method comprising:

[0006] Acquire data from the target area, including terrain data, building distribution data, and user density data.

[0007] The collected data is preprocessed based on a geographic information system;

[0008] Electromagnetic environment surveys are conducted on the target area to identify potential interference areas and signal blind spots within the target area;

[0009] User demand analysis is performed based on the user density data and historical communication data to identify high-demand and low-demand areas within the target region.

[0010] Candidate locations for base stations are obtained based on the terrain data, the building distribution data, the results of the electromagnetic environment survey, and the results of the user demand analysis.

[0011] The optimal location for a base station is selected from the candidate locations using a genetic algorithm for deployment.

[0012] In some embodiments, the terrain data includes: altitude, slope and landform type; the building distribution data includes: building height, building density and building material; and the user density data includes: number of users and user distribution.

[0013] In some embodiments, the preprocessing of the collected data based on the geographic information system includes:

[0014] The collected data is cleaned;

[0015] The cleaned and collected data is then converted to a different format.

[0016] The collected data, after format conversion, is subjected to spatial analysis to generate topographic maps, building distribution maps, and user density distribution maps.

[0017] In some embodiments, the electromagnetic environment survey of the target area to identify potential interference areas and signal blind spots within the target area includes:

[0018] Electromagnetic environment surveying equipment is deployed within the target area, and the signal strength, interference level, and propagation characteristics of the target area are collected based on the electromagnetic environment surveying equipment.

[0019] The electromagnetic environment distribution map is obtained by preprocessing the signal strength, interference level and propagation characteristics based on the geographic information system.

[0020] Based on the electromagnetic environment distribution map and the spatial analysis function of the geographic information system, potential interference areas and signal blind spots within the target area are identified.

[0021] In some embodiments, a user density distribution map is obtained after preprocessing the collected data based on a geographic information system; the user demand analysis based on the user density data and historical communication data to identify high-demand and low-demand areas within the target region includes:

[0022] Acquire historical communication data within the target area, including communication traffic, communication duration, and peak periods;

[0023] The historical communication data is preprocessed based on a geographic information system to obtain a communication traffic distribution map;

[0024] User demand analysis is performed based on the user density distribution map, the communication traffic distribution map, and the spatial analysis function of the geographic information system to identify high-demand and low-demand areas within the target region.

[0025] In some embodiments, the step of performing user demand analysis based on the user density data and historical communication data to identify high-demand and low-demand areas within the target region includes:

[0026] Acquire historical communication data within the target area, including communication traffic, communication duration, and peak periods;

[0027] The user density data and the communication traffic are merged to form a dataset;

[0028] Feature extraction is performed on the dataset to obtain the target features;

[0029] The target features were clustered using the K-means clustering algorithm.

[0030] Based on the results of cluster analysis, high-demand areas and low-demand areas within the target region are identified.

[0031] In some embodiments, obtaining candidate base station locations based on the terrain data, the building distribution data, the results of the electromagnetic environment survey, and the results of the user demand analysis includes:

[0032] The terrain data, building distribution data, electromagnetic environment survey results, and user demand analysis results are integrated to obtain integrated data.

[0033] Preprocess the integrated data;

[0034] Based on the preprocessed integrated data and the spatial analysis function of the geographic information system, candidate locations for base stations are generated.

[0035] In some embodiments, the selection of the optimal base station location from the candidate base station locations based on a genetic algorithm includes:

[0036] A set of candidate base station locations is randomly generated as the initial population;

[0037] Calculate the fitness value for each candidate location of the base station;

[0038] Reproduction is carried out based on the obtained fitness values;

[0039] Perform a crossover operation on the reproduced candidate base station locations to generate new candidate base station locations;

[0040] Mutation operations are performed on new base station candidate locations to increase population diversity;

[0041] Repeat the above steps of reproduction, crossover and mutation until the preset number of iterations or fitness convergence is reached. The candidate location of the base station obtained at this time is the optimal location of the base station.

[0042] In some embodiments, after selecting the optimal base station location from the candidate base station locations using a genetic algorithm for layout, the method further includes:

[0043] Obtain the coverage map of the base station;

[0044] Obtain the interference distribution map of the base station;

[0045] The layout of the base stations is adjusted based on the coverage map and the interference distribution map.

[0046] Secondly, this application also provides a base station site selection and layout system, the base station site selection and layout system comprising:

[0047] The data acquisition module is used to acquire collected data from the target area, including terrain data, building distribution data, and user density data.

[0048] The preprocessing module is used to preprocess the collected data based on the geographic information system;

[0049] The electromagnetic environment survey and analysis module is used to conduct electromagnetic environment surveys of the target area to identify potential interference areas and signal blind spots within the target area.

[0050] The user demand analysis module is used to perform user demand analysis based on the user density data and historical communication data to identify high-demand areas and low-demand areas within the target area.

[0051] The base station candidate location selection module is used to obtain base station candidate locations based on the terrain data, the building distribution data, the results of the electromagnetic environment survey, and the results of the user demand analysis.

[0052] The base station optimal location selection and layout module is used to select the optimal location of the base station from the candidate locations of the base station based on a genetic algorithm for layout.

[0053] The aforementioned base station site selection and layout method and system acquires data from the target area, including terrain data, building distribution data, and user density data. The acquired data is preprocessed using a geographic information system (GIS). Electromagnetic environment surveys are conducted in the target area to identify potential interference areas and signal blind spots. User demand analysis is performed based on the user density data and historical communication data to identify high-demand and low-demand areas within the target area. Candidate base station locations are obtained based on the terrain data, building distribution data, electromagnetic environment survey results, and user demand analysis results. The optimal base station location is selected from the candidate locations using a genetic algorithm. By comprehensively considering multiple factors such as terrain data, building distribution data, and user density data, and fully utilizing the GIS for accurate electromagnetic environment surveys and user demand analysis, the system accurately identifies potential interference areas and signal blind spots in the electromagnetic environment survey, providing reliable electromagnetic environment data for base station selection. In terms of user demand analysis, it clearly identifies high-density and low-density user areas, ensuring that the distribution of base stations meets the communication needs of users in different areas. This application can also reduce short-range interference between base stations, avoid signal interference problems caused by excessive concentration of base stations, thereby improving network coverage and capacity, enabling users to obtain more stable and faster signals, and improving communication quality; it can also improve the scientificity and accuracy of base station site selection, avoid unnecessary base station construction and resource waste, and effectively reduce network construction and operation costs. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of this application or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a flowchart of a base station site selection and layout method provided in one embodiment of this application;

[0056] Figure 2 This is a structural block diagram of the base station location layout system provided in different embodiments of this application.

[0057] Figure labeling: 10, Data acquisition module; 20, Preprocessing module; 30, Electromagnetic environment survey and analysis module; 40, User requirements analysis module; 50, Base station candidate location selection module; 60, Base station optimal location selection and layout module. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0059] In the field of mobile communication networks, the site selection and layout of microcell base stations play a crucial role in ensuring network coverage and capacity. With accelerating urbanization, buildings in cities are becoming increasingly dense, while users' demand for high-quality mobile communication is constantly increasing. However, traditional base station deployment methods are no longer adequate to meet the evolving needs of modern networks.

[0060] Base station site selection often relies on simple coverage calculations and user density estimates. For example, the number and location of base stations are often planned based solely on the population of an area, neglecting the impact of complex terrain and building distribution on signal propagation. In mountainous areas, for instance, undulating terrain can cause signal obstruction and reflection, resulting in significant differences in signal strength across different locations. In urban areas with numerous high-rise buildings, structures can block signals, creating numerous signal blind spots. This can lead to base stations being overly concentrated in seemingly densely populated areas, such as commercial centers. In these areas, the close proximity of base stations can cause near-field interference, degrading network performance and resulting in unstable signals and slow internet speeds for users.

[0061] Common surveying methods lack systematicity and precision, often involving only simple measurements at a few fixed points, failing to comprehensively reflect the electromagnetic environment of the entire area. For example, in a large industrial park, different factories and equipment generate varying degrees of electromagnetic interference, but existing surveying methods may not accurately capture the distribution and intensity of these interference sources, thus failing to provide comprehensive data support for base station site selection decisions.

[0062] While Geographic Information System (GIS) technology has been applied to base station planning, its application is not yet deep enough. It is merely used for simple map drawing and data visualization, failing to fully utilize its powerful spatial analysis capabilities. For example, when planning base station layout, GIS technology is not used to conduct in-depth analysis of data such as terrain, buildings, and user distribution to optimize the base station layout.

[0063] In one embodiment, see Figure 1 This application provides a base station site selection and layout method, which includes the following steps: S10 to S60.

[0064] S10: Acquire the data collected in the target area, including terrain data, building distribution data, and user density data.

[0065] S20: Preprocess the collected data based on the geographic information system.

[0066] S30: Conduct an electromagnetic environment survey of the target area to identify potential interference areas and signal blind spots within the target area.

[0067] S40: Perform user demand analysis based on the user density data and historical communication data to identify high-demand areas and low-demand areas within the target region.

[0068] S50: Based on the terrain data, the building distribution data, the results of the electromagnetic environment survey, and the results of the user demand analysis, candidate locations for base stations are obtained.

[0069] S60: Select the optimal location of the base station from the candidate locations of the base station based on the genetic algorithm and lay it out.

[0070] The base station site selection and layout method in this application comprehensively considers various factors such as terrain data, building distribution data, and user density data, and fully utilizes geographic information systems for precise electromagnetic environment surveys and user demand analysis. In terms of electromagnetic environment surveys, it can accurately identify potential interference areas and signal blind spots, providing reliable electromagnetic environment data for base station selection. Regarding user demand analysis, it can clearly identify high-density and low-density user areas, ensuring that the distribution of base stations can meet the communication needs of users in different areas. This application can also reduce short-range interference between base stations, avoiding signal interference problems caused by excessive concentration of base stations, thereby improving network coverage and capacity, enabling users to obtain more stable and faster signals, and improving communication quality. It can improve the scientificity and accuracy of base station site selection, avoid unnecessary base station construction and resource waste, and effectively reduce network construction and operation costs. This is not only of significant economic importance to telecommunications operators, but also provides society with higher-quality mobile communication services, resulting in significant economic and social benefits.

[0071] In step S10, please refer to Figure 1 In step S10, the target area is acquired by collecting data, which includes terrain data, building distribution data, and user density data.

[0072] As an example, in step S10, topographic data, building distribution data, and user density data of the target area can be collected, and then data cleaning, format conversion, and spatial analysis can be performed using Geographic Information System (GIS) software to ensure data accuracy and consistency.

[0073] As an example, the terrain data may include: altitude, slope, and landform type. This terrain data can be obtained through satellite remote sensing and LiDAR registers to ensure its accuracy and comprehensiveness. The building distribution data may include: building height, building density, and building materials. This building distribution data can be obtained through urban building databases, drone aerial photography, etc., to accurately reflect the impact of buildings on signal propagation. The user density data includes: the number of users and user distribution; this user density data can be obtained through historical communication records, census data, etc.

[0074] In step S20, please refer to Figure 1 In step S20, the collected data is preprocessed based on the geographic information system.

[0075] As an example, in step S20, the preprocessing of the collected data based on the geographic information system may include the following steps: S201 to S203.

[0076] S201: Clean the collected data.

[0077] S202: Convert the format of the cleaned collected data.

[0078] S203: Perform spatial analysis on the converted data to generate topographic maps, building distribution maps, and user density distribution maps.

[0079] As an example, after data collection is completed, the data is preprocessed using Geographic Information System (GIS) software such as ArcGIS.

[0080] As an example, data cleaning in step S201 is the first step of preprocessing, and its main purpose is to remove outliers and noise from the data. For example, for terrain data, there may be outliers caused by sensor errors or interference during the data acquisition process. These outliers need to be identified and removed using statistical methods or machine learning algorithms.

[0081] As an example, the format conversion in step S202 is the second step of preprocessing, which mainly unifies data from different sources into the same format for subsequent spatial analysis. For example, terrain data may be stored in raster format, while building distribution data may be stored in vector format, and they need to be converted into a unified raster or vector format using GIS software.

[0082] As an example, the spatial analysis in step S203 is the final step of preprocessing. It mainly involves performing spatial interpolation, overlay analysis, and other operations on the data to generate topographic maps, building distribution maps, and user density maps. For instance, discrete terrain data can be converted into continuous terrain maps using spatial interpolation methods (such as Kriging interpolation) to facilitate subsequent base station site selection optimization.

[0083] Specifically, a user density map can visually display the user density in different areas within a target region. The formula for calculating user density is as follows:

[0084]

[0085] Among them, D i Let N be the user density in the i-th region. i Let A be the number of users in the i-th region. i Let be the area of ​​the i-th region. The user density map can identify high-density and low-density user areas; high-density user areas typically include commercial and residential areas, while low-density user areas typically include parks and suburbs.

[0086] In a specific example, taking the site selection and layout optimization of microcell base stations in a certain urban area as an example, firstly, topographic data of the area, including altitude and slope information, can be obtained through satellite remote sensing technology. Next, building distribution data of the area, including building height and density, can be obtained through an urban building database. Then, user density data of the area, including the number of users and user distribution, can be obtained through historical communication records. After data collection, ArcGIS software is used to preprocess the data. First, data cleaning is performed, for example, removing outliers in the topographic data, such as abnormal altitudes caused by sensor errors. Then, format conversion is performed, for example, converting the topographic data from raster format to vector format for overlay analysis with building distribution data. Finally, spatial analysis is performed, for example, using Kriging interpolation to convert discrete topographic data into continuous topographic maps and generate building distribution maps and user density maps. Through these steps, the accuracy and consistency of the data are ensured, providing reliable data support for subsequent base station site selection optimization.

[0087] In step S30, please refer to Figure 1 In step S30, an electromagnetic environment survey is conducted on the target area to identify potential interference areas and signal blind spots within the target area.

[0088] As an example, step S30, which involves conducting an electromagnetic environment survey of the target area to identify potential interference areas and signal blind spots within the target area, may include the following steps: S301 to S303.

[0089] S301: Deploy electromagnetic environment surveying equipment in the target area, and collect signal strength, interference level and propagation characteristics of the target area based on the electromagnetic environment surveying equipment.

[0090] S302: Based on the geographic information system, the signal strength, interference level and propagation characteristics are preprocessed to obtain an electromagnetic environment distribution map.

[0091] S303: Identify potential interference areas and signal blind spots within the target area based on the electromagnetic environment distribution map and the spatial analysis function of the geographic information system.

[0092] As an example, in step S301, the electromagnetic environment surveying equipment deployed within the target area may include a spectrum analyzer, a signal strength meter, and an interference detector. These devices should be evenly distributed in key locations within the target area, such as densely built-up areas, open areas, and areas with complex terrain, to ensure comprehensive data collection.

[0093] As an example, in step S301, a spectrum analyzer can be used to measure the signal strength in the 2.4 GHz and 5 GHz frequency bands, and the signal strength value S at each measurement point can be recorded. i , where i represents the measurement point number. The signal strength formula is as follows:

[0094]

[0095] Among them, P i P0 is the received power at measurement point i, and P0 is the reference power, which can typically be 1mW.

[0096] As an example, in step S301, an interference detector can be used to measure the interference level I at each measurement point. i The formula for the interference level can be as follows:

[0097]

[0098] Among them, P i，j Let P be the power of the j-th interference source. i0 The total received power at the measurement point is the sum of the power of the useful signal and the power of the interfering signal, and n is the total number of interference sources.

[0099] As an example, in step S301, a signal strength meter can be used to record the propagation characteristics C at each measurement point. i The formula for the propagation characteristics can be as follows:

[0100] C i =A i +M i

[0101] Among them, A i M is the signal attenuation rate. i This is the influencing factor of the multipath effect.

[0102] As an example, in step S302, the collected signal strength, interference level, and propagation characteristics data are imported into Geographic Information System (GIS) software (such as ArcGIS) for data cleaning and format conversion. Data cleaning includes removing outliers and noise, such as abnormal measurements caused by equipment malfunction or environmental interference.

[0103] As an example, in step S302, the spatial interpolation function of GIS is used to convert discrete measurement data into a continuous electromagnetic environment distribution map. Commonly used interpolation methods include Kriging interpolation and inverse distance-weighted interpolation. The formula for Kriging interpolation is:

[0104]

[0105] Where Z(x) is the signal strength at the point to be interpolated, Z(x) i Let λ be the signal strength at the known interpolation point i. i is the weighting coefficient, and n is the number of interpolation points.

[0106] As an example, in step S303, the electromagnetic environment distribution map and the spatial analysis function of the geographic information system (e.g., spatial query function) are used to extract signals with strengths below the strength threshold S. th The area is marked as a signal dead zone, and at the same time, interference levels above the interference level threshold I are extracted. th Areas with signal strength below a certain threshold (S) are marked as potential interference areas. Furthermore, areas with signal strength below this threshold are also considered potential interference areas. th And the interference level is higher than the interference level threshold I th Areas with signal strength below a certain threshold S are marked as potential interference areas; th And the propagation characteristics are lower than the propagation characteristic threshold C. th The area is marked as a signal blind zone.

[0107] As an example, in step S303, after identifying potential interference areas and signal blind spots, a further analysis of the causes of these areas is included. Specifically, by overlaying a building distribution map and a topographic map, densely built areas may be signal blind spots caused by signal reflection and obstruction, while undulating terrain may be potential interference areas caused by signal attenuation.

[0108] In step S40, please refer to Figure 1In step S40, user demand analysis is performed based on the user density data and historical communication data to identify high-demand areas and low-demand areas within the target region.

[0109] In one example, step S40, which involves performing user demand analysis based on the user density data and historical communication data to identify high-demand and low-demand areas within the target region, may include the following steps: S401 to S403.

[0110] S401: Obtain historical communication data within the target area, including communication traffic, communication duration, and peak periods.

[0111] S402: Preprocess the historical communication data based on the geographic information system to obtain a communication traffic distribution map.

[0112] S403: Based on the user density distribution map, the communication traffic distribution map, and the spatial analysis function of the geographic information system, perform user demand analysis to identify high-demand areas and low-demand areas within the target area.

[0113] As an example, in step S402, the historical communication data can be cleaned, format converted, and spatially analyzed. Cleaning removes outliers from the historical communication data, format conversion standardizes the data format, and spatial analysis yields a communication traffic map. The communication traffic map visually displays the communication traffic situation in different areas within the target region.

[0114] As an example, in step S402, during the spatial analysis process, communication pattern analysis can be performed on the target area based on the historical communication data. Communication pattern analysis may include peak-hour communication traffic analysis, user distribution analysis, etc.; for example, the communication traffic T of each area during peak hours can be analyzed. i The formula for communication traffic can be as follows:

[0115]

[0116] Among them, t ij Let m be the communication traffic of the i-th region during the j-th time period, and m be the number of time periods.

[0117] As an example, in step S403, the spatial analysis function of the geographic information system can be used to perform spatial analysis of user demand by combining the user density distribution map and the communication traffic distribution map. The purpose of the spatial analysis of user demand is to identify high-demand areas and low-demand areas. High-demand areas typically include high-density user areas and high communication traffic areas, while low-demand areas typically include low-density user areas and low communication traffic areas.

[0118] The results of user demand analysis can be used for subsequent base station site selection decisions. For example, increasing the number of base stations in high-demand areas can improve network coverage and capacity, while reducing the number of base stations in low-demand areas can reduce network construction and operation costs.

[0119] In another example, step S40, which involves performing user demand analysis based on the user density data and historical communication data to identify high-demand and low-demand areas within the target region, may include the following steps: S401 to S405.

[0120] S401: Obtain historical communication data within the target area, including communication traffic, communication duration, and peak periods.

[0121] S402: Merge the user density data and the communication traffic to form a dataset.

[0122] S403: Perform feature extraction on the dataset to obtain the target features.

[0123] S404: Use the K-means clustering algorithm to perform cluster analysis on the target features.

[0124] S405: Based on the results of cluster analysis, identify high-demand areas and low-demand areas within the target region.

[0125] As an example, in step S404, the objective function of the K-means clustering algorithm can be as follows:

[0126]

[0127] Where J is the clustering error, C i For the i-th cluster, μ i Let x be the center of the i-th cluster, x be a data point, and k be the number of clusters.

[0128] In a specific example, taking a city as an example, user density data and historical communication data for that city were collected. User density data includes the number of users in each area, and historical communication data includes communication traffic and peak-hour information for each area. The collected data was preprocessed using ArcGIS software to generate user density distribution maps and communication traffic distribution maps. The user density distribution map shows that commercial and residential areas are high-density user areas, while parks and suburbs are low-density user areas; the communication traffic distribution map shows that peak-hour communication traffic is mainly concentrated in commercial and residential areas. Combining the user density distribution map and the communication traffic distribution map, ArcGIS's spatial analysis function was used to conduct spatial analysis of user demand. The analysis results show that commercial and residential areas are high-demand areas, while parks and suburbs are low-demand areas. K-means clustering was used to perform cluster analysis, dividing the target area into high-demand and low-demand areas. The clustering results show that commercial and residential areas are high-demand areas, while parks and suburbs are low-demand areas. Based on the user demand analysis results, data support was provided for base station site selection.

[0129] In step S50, please refer to Figure 1 Step S50, which obtains candidate locations for base stations based on the terrain data, the building distribution data, the results of the electromagnetic environment survey, and the results of the user demand analysis, may include the following steps: S501 to S503.

[0130] S501: Integrate the terrain data, the building distribution data, the results of the electromagnetic environment survey, and the results of the user demand analysis to obtain integrated data.

[0131] S502: Preprocess the integrated data.

[0132] S503: Based on the pre-processed integrated data and the spatial analysis function of the geographic information system, generate candidate locations for base stations.

[0133] In step S501, the terrain data, building distribution data, electromagnetic environment survey results, and user demand analysis results are integrated into a unified Geographic Information System (GIS) platform. For example, ArcGIS software is used to import these data and perform spatial overlay analysis to generate a comprehensive data layer.

[0134] As an example, in step S502, the integrated data can be cleaned, format converted, and spatially interpolated; wherein, cleaning is used to remove outliers from the integrated data, format conversion is used to unify the data format, and spatial interpolation is used to fill in missing data to ensure the integrity and consistency of the preprocessed integrated data for subsequent analysis.

[0135] As an example, in step S503, when generating candidate locations for base stations based on the preprocessed integrated data and the spatial analysis function of the geographic information system, the following factors need to be considered: terrain factors, building distribution, electromagnetic environment and user needs.

[0136] Specifically, regarding terrain factors, base stations should be avoided on steep slopes or in low-lying areas to ensure stable signal propagation. The formula for terrain suitability scoring can be as follows:

[0137]

[0138] Among them, S t The terrain suitability score is given, where H is the altitude, h is the reference altitude, and k is the adjustment factor.

[0139] Specifically, regarding building distribution, base stations should be avoided in densely built-up areas to reduce signal obstruction. The formula for building density scoring can be as follows:

[0140]

[0141] Among them, S b The building density is scored, where B is the building density, b is the reference density, and k is the adjustment factor.

[0142] Specifically, regarding the electromagnetic environment, base stations can be located in areas with high signal strength and low interference levels. The formula for electromagnetic environment scoring is as follows:

[0143]

[0144] Among them, S e The electromagnetic environment is scored, where E is the signal strength, e is the reference strength, and k is the adjustment factor.

[0145] Specifically, regarding user needs, base stations can be prioritized in high-demand areas to meet user communication requirements. The formula for user demand scoring can be as follows:

[0146]

[0147] Among them, S u User needs are scored, where U is the user density, u is the reference density, and k is the adjustment coefficient.

[0148] Based on the above scores, the following formula for the sum of scores for candidate base station locations can be generated:

[0149] S = w t S t +w b S b +w e Se +w u S u

[0150] Where S is the total score, wt is the weighting coefficient of terrain factors, wb is the weighting coefficient of building distribution, we is the weighting coefficient of electromagnetic environment, and w u The weighting coefficients for user needs, satisfying w t +w b +w e +w u =1.

[0151] Specifically, candidate base station locations can be generated by comprehensively considering the comprehensive scoring formula for candidate base station locations.

[0152] In step S60, please refer to Figure 1 In step S60, the optimal location of the base station is selected from the candidate locations of the base station based on a genetic algorithm for layout.

[0153] As an example, in step S60, the selection of the optimal location of the base station from the candidate locations of the base station based on the genetic algorithm may include the following steps: S601 to S606.

[0154] S601: Randomly generate a set of candidate base station locations as the initial population.

[0155] S602: Calculate the fitness value for each candidate location of a base station.

[0156] S603: Reproduce based on the obtained fitness value.

[0157] S604: Perform a cross-operation on the reproduced candidate base station locations to generate new candidate base station locations.

[0158] S605: Perform mutation operations on new base station candidate locations to increase population diversity.

[0159] S606: Repeat the above steps of reproduction, crossover and mutation until the preset number of iterations or fitness convergence is reached. The candidate location of the base station obtained at this time is the optimal location of the base station.

[0160] Specifically, in step S60, it is recommended to use an optimization method based on a Genetic Algorithm (GA) for base station site selection optimization. A Genetic Algorithm is an optimization algorithm that simulates natural selection and genetic mechanisms, suitable for solving complex multi-objective optimization problems. In this scenario, the goal of the Genetic Algorithm is to minimize interference between base stations while meeting coverage and capacity requirements. The specific steps of the Genetic Algorithm may include: initializing the population, fitness evaluation, selection, crossover, mutation, iteration, and outputting the optimal solution.

[0161] Initialize the population: Randomly generate a set of candidate base station locations as the initial population.

[0162] Fitness evaluation: The fitness value of each candidate base station location is calculated. The fitness value is determined by the comprehensive score S and the interference level I. The fitness function is:

[0163] F=αS-βI

[0164] Where F is the fitness value, α is the weighting coefficient of the overall score S, and β is the weighting coefficient of the interference level I.

[0165] Selection: Based on the fitness value, select excellent base station candidate locations for propagation.

[0166] Crossover: Perform a crossover operation on the selected candidate base station locations to generate new candidate base station locations.

[0167] Mutation: Mutation operations are performed on new base station candidate locations to increase population diversity.

[0168] Iteration: Repeat the selection, crossover, and mutation operations described above until the preset number of iterations or fitness value is reached and convergence is achieved.

[0169] Output the optimal solution: Select the candidate base station location with the highest fitness value as the optimal base station location.

[0170] As an example, after selecting the optimal location of the base station from the candidate locations of the base station based on the genetic algorithm for layout, that is, after step S60, the following steps may also be included: S70 to S90.

[0171] S70: Obtain the coverage map of the base station.

[0172] S80: Obtain the interference distribution map of the base station.

[0173] S90: Adjust the layout of the base stations based on the coverage map and the interference distribution map.

[0174] As an example, in step S70, GIS technology can be used to generate a coverage map, which visually displays the signal coverage area of ​​each base station. Coverage analysis can be based on a ray tracing model, which can accurately simulate the propagation path of signals in complex environments.

[0175] As an example, step S70 may include the following: emitting a ray, ray tracing, signal strength calculation, and coverage map generation.

[0176] Ray emission: Multiple rays are emitted from the location of each base station to simulate signal propagation paths. The number and direction of the rays can be adaptively adjusted according to the base station's transmission power and antenna characteristics.

[0177] Ray tracing: Tracing the propagation path of each ray, taking into account the reflection, refraction, and diffraction effects of terrain and buildings. For example, in densely built-up areas, rays may be blocked or reflected by buildings, creating signal blind spots or multipath effects.

[0178] Signal strength calculation: The signal strength of each ray at the receiving point is calculated using the following formula:

[0179] P r =P t -L fs -L obs

[0180] Among them, P r To receive signal strength, P t For the transmitted signal strength, L fs For free space loss, L obs Obstacle loss. Free space loss L fs It can be obtained through the following formula:

[0181] L fs =20log 10 (d)+20log 10 (f)+32.45

[0182] Where d is the propagation distance (in kilometers) and f is the signal frequency (in MHz). Obstacle loss L obs It can be calculated based on the specific type and thickness of the obstacle.

[0183] Coverage map generation: can be based on the received signal strength P r Generate a coverage map. The coverage map can visually show the signal coverage area of ​​each base station, as well as whether there are any signal blind spots.

[0184] In a specific example, 100 rays can be emitted from each base station location to simulate the signal propagation path. Using a ray tracing model, the signal strength P of each ray at the receiving point can be calculated. r For example, the signal strength of base station A at receiving point 1 is -70dBm, and the signal strength at receiving point 2 is -80dBm. Based on the received signal strength P... r A coverage map is generated. The coverage map shows that base station A has a coverage range of 500 meters, but there are signal blind spots in densely built-up areas.

[0185] As an example, in step S80, GIS technology can be used to generate an interference distribution map to assess the interference level between base stations; specifically, it can include the following steps: signal overlap area identification, interference level calculation, and interference distribution map generation.

[0186] Signal overlap area identification: Identifying overlapping areas covered by signals from different base stations. Users within these overlapping areas may experience signal interference from multiple base stations, leading to a degraded signal quality.

[0187] Interference level calculation: The interference level within the overlapping area can be calculated using the following formula:

[0188]

[0189] Where I represents the interference level, P r,i Let be the received signal strength of the i-th base station in the overlapping area, and n be the number of base stations.

[0190] Interference distribution map generation: An interference distribution map can be generated based on interference level I. The interference distribution map can visually display the interference areas between base stations, as well as the severity of the interference.

[0191] In a specific example, an overlapping area can be identified between the signal coverage of base station A and base station B. Users within this overlapping area may experience signal interference from both base stations, leading to a degraded signal quality. The interference level I within the overlapping area is calculated; for example, the interference level between base station A and base station B in the overlapping area is -60 dBm. An interference distribution map is generated based on the interference level I, showing a severe interference area between base station A and base station B.

[0192] As an example, in step S90, the layout of the base station can be adjusted as necessary based on the coverage analysis and interference assessment results. Specifically, this may include base station location adjustment, antenna angle adjustment, and transmit power adjustment.

[0193] Base station relocation: If a base station has insufficient coverage or suffers from severe interference, its location can be adjusted. For example, the base station can be moved to a higher location to expand its coverage, or it can be moved away from other base stations to reduce interference.

[0194] Antenna angle adjustment: Adjusting the angle of the base station antenna to optimize signal coverage and interference control. For example, adjusting the antenna angle to a horizontal direction can reduce interference to other base stations.

[0195] Transmit power adjustment: Adjusting the base station's transmit power to optimize signal coverage and interference control. For example, increasing transmit power to expand coverage or decreasing transmit power to reduce interference.

[0196] As an example, step S90 may also include verification and iteration. Specifically, the adjusted base station layout is re-verified to ensure optimal network performance. If insufficient coverage or excessive interference still exists, the above steps can be repeated for further adjustments and optimizations.

[0197] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0198] In another embodiment, please refer to Figure 2 This application also provides a base station site selection and layout system, which may include: a data acquisition module 10, a preprocessing module 20, an electromagnetic environment survey and analysis module 30, a user demand analysis module 40, a base station candidate location selection module 50, and a base station optimal location selection and layout module 60; wherein, the data acquisition module 10 is used to acquire collected data of a target area, the collected data including: terrain data, building distribution data, and user density data; the preprocessing module 20 is used to preprocess the collected data based on a geographic information system; the electromagnetic environment survey and analysis module 30 is used to conduct electromagnetic environment surveys of the target area to identify potential interference areas and signal blind spots within the target area; the user demand analysis module 40 is used to perform user demand analysis based on the user density data and historical communication data to identify high-demand areas and low-demand areas within the target area; the base station candidate location selection module 50 is used to obtain base station candidate locations based on the terrain data, the building distribution data, the results of the electromagnetic environment survey, and the results of the user demand analysis; the base station optimal location selection and layout module 60 is used to select the optimal base station location from the base station candidate locations for layout based on a genetic algorithm.

[0199] The base station site selection and layout system of this application can be used to perform, for example... Figure 1 The steps of the base station site selection and layout method in its related embodiments.

[0200] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features of the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0201] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for selecting and laying out base stations, characterized in that, include: Acquire data from the target area, including terrain data, building distribution data, and user density data. The collected data is preprocessed based on a geographic information system; Electromagnetic environment surveys are conducted on the target area to identify potential interference areas and signal blind spots within the target area; User demand analysis is performed based on the user density data and historical communication data to identify high-demand and low-demand areas within the target region. Candidate locations for base stations are obtained based on the terrain data, the building distribution data, the results of the electromagnetic environment survey, and the results of the user demand analysis. The optimal location for a base station is selected from the candidate locations using a genetic algorithm for deployment.

2. The method according to claim 1, characterized in that, The terrain data includes: altitude, slope and landform type; the building distribution data includes: building height, building density and building material; and the user density data includes: number of users and user distribution.

3. The method according to claim 1, characterized in that, The preprocessing of the collected data based on the geographic information system includes: The collected data is cleaned; The cleaned and collected data is then converted to a different format. The collected data, after format conversion, is subjected to spatial analysis to generate topographic maps, building distribution maps, and user density distribution maps.

4. The method according to claim 1, characterized in that, The electromagnetic environment survey of the target area to identify potential interference areas and signal blind spots within the target area includes: Electromagnetic environment surveying equipment is deployed within the target area, and the signal strength, interference level, and propagation characteristics of the target area are collected based on the electromagnetic environment surveying equipment. The electromagnetic environment distribution map is obtained by preprocessing the signal strength, interference level and propagation characteristics based on the geographic information system. Based on the electromagnetic environment distribution map and the spatial analysis function of the geographic information system, potential interference areas and signal blind spots within the target area are identified.

5. The method according to claim 1, characterized in that, After preprocessing the collected data using a geographic information system, a user density distribution map is obtained; the user demand analysis based on the user density data and historical communication data to identify high-demand and low-demand areas within the target region includes: Acquire historical communication data within the target area, including communication traffic, communication duration, and peak periods; The historical communication data is preprocessed based on a geographic information system to obtain a communication traffic distribution map; User demand analysis is performed based on the user density distribution map, the communication traffic distribution map, and the spatial analysis function of the geographic information system to identify high-demand and low-demand areas within the target region.

6. The method according to claim 1, characterized in that, The step of performing user demand analysis based on the user density data and historical communication data to identify high-demand and low-demand areas within the target region includes: Acquire historical communication data within the target area, including communication traffic, communication duration, and peak periods; The user density data and the communication traffic are merged to form a dataset; Feature extraction is performed on the dataset to obtain the target features; The target features were clustered using the K-means clustering algorithm. Based on the results of cluster analysis, high-demand areas and low-demand areas within the target region are identified.

7. The method according to claim 1, characterized in that, The process of obtaining candidate base station locations based on the terrain data, building distribution data, electromagnetic environment survey results, and user demand analysis results includes: The terrain data, building distribution data, electromagnetic environment survey results, and user demand analysis results are integrated to obtain integrated data. Preprocess the integrated data; Based on the preprocessed integrated data and the spatial analysis function of the geographic information system, candidate locations for base stations are generated.

8. The method according to any one of claims 1 to 7, characterized in that, The step of selecting the optimal location of a base station from the candidate locations based on a genetic algorithm includes: A set of candidate base station locations is randomly generated as the initial population; Calculate the fitness value for each candidate location of the base station; Reproduction is carried out based on the obtained fitness values; Perform a crossover operation on the reproduced candidate base station locations to generate new candidate base station locations; Mutation operations are performed on new base station candidate locations to increase population diversity; Repeat the above steps of reproduction, crossover and mutation until the preset number of iterations or fitness convergence is reached. The candidate location of the base station obtained at this time is the optimal location of the base station.

9. The method according to any one of claims 1 to 7, characterized in that, After selecting the optimal base station location from the candidate base station locations using a genetic algorithm for layout, the process further includes: Obtain the coverage map of the base station; Obtain the interference distribution map of the base station; The layout of the base stations is adjusted based on the coverage map and the interference distribution map.

10. A base station site selection and layout system, characterized in that, include: The data acquisition module is used to acquire collected data from the target area, including terrain data, building distribution data, and user density data. The preprocessing module is used to preprocess the collected data based on the geographic information system; The electromagnetic environment survey and analysis module is used to conduct electromagnetic environment surveys of the target area to identify potential interference areas and signal blind spots within the target area. The user demand analysis module is used to perform user demand analysis based on the user density data and historical communication data to identify high-demand areas and low-demand areas within the target area. The base station candidate location selection module is used to obtain base station candidate locations based on the terrain data, the building distribution data, the results of the electromagnetic environment survey, and the results of the user demand analysis. The base station optimal location selection and layout module is used to select the optimal location of the base station from the candidate locations of the base station based on a genetic algorithm for layout.

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