Cell division method and device and electronic equipment
By determining the maximum coverage area and number of users of network equipment and dynamically adjusting the power, the problem of low rationality in cell division caused by static signal strength measurement was solved, thus improving the rationality of cell division and network coverage performance.
Patent Information
- Application Number
- CN202511792934.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-01
- Publication Date
- 2026-02-06
AI Technical Summary
Existing cell division technology relies on static signal strength measurement, which cannot reflect changes in the network environment in real time, resulting in low rationality of cell division.
By determining the maximum coverage area and number of users of network devices, the power of network devices is dynamically adjusted to match the actual coverage area of the cell, thus achieving the rationality of cell division.
It improves the rationality of cell division and the overall performance of network coverage, and enhances the network's dynamic adaptability and user experience.
Smart Images
Figure CN121486841A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of Internet technology, and in particular to a cell division method, apparatus and electronic device. Background Technology
[0002] In mobile communication networks, the cell is the basic unit of network coverage. The accuracy of cell boundary delineation directly affects network performance and user experience. With the increasing prevalence of mobile devices and the growth of data services, the demand for cell boundary delineation technology is growing.
[0003] Currently, cell boundary delineation technology mainly relies on static signal strength measurement methods. In this method, signal strength measurement usually depends on static parameter settings, which cannot reflect the dynamic changes in the network environment in real time. For example, changes in user distribution can affect the actual signal coverage, which can easily lead to low rationality in cell delineation. Summary of the Invention
[0004] This application provides a cell division method, apparatus, and electronic device to address the problem of low rationality in existing cell division methods.
[0005] To solve the above-mentioned technical problems, this application is implemented as follows:
[0006] In a first aspect, embodiments of this application provide a cell division method, the method comprising:
[0007] Identify the first cell that needs network coverage;
[0008] The first cell is divided to obtain a target division result, which includes multiple sub-regions;
[0009] For each of the M network devices in the first cell, determine the maximum coverage area of the network device for the sub-region, where M is a positive integer;
[0010] Based on the number of users within the maximum coverage area of the M network devices for the sub-region, determine the actual coverage area of the M network devices;
[0011] The actual coverage area of the cell is determined based on the actual coverage area of the M network devices.
[0012] Based on the actual coverage area of the cell and the area of the first cell, adjust the power of at least some of the M network devices.
[0013] Secondly, embodiments of this application provide a cell division device, the device comprising:
[0014] The first determining module is used to determine the first cell that needs network coverage;
[0015] The partitioning module is used to partition the first cell to obtain a target partitioning result, wherein the target partitioning result includes multiple sub-regions;
[0016] The second determining module is used to determine the maximum coverage area of each of the M network devices in the first cell for the sub-region, where M is a positive integer;
[0017] The third determining module is used to determine the actual coverage area of the M network devices based on the number of users within the maximum coverage area of the M network devices for the sub-region;
[0018] The fourth determining module is used to determine the actual coverage area of the cell based on the actual coverage area of the M network devices;
[0019] The adjustment module is used to adjust the power of at least some of the M network devices based on the actual coverage area of the cell and the area of the first cell.
[0020] Thirdly, embodiments of this application provide an electronic device, including a transceiver and a processor.
[0021] The processor is used for:
[0022] Identify the first cell that needs network coverage;
[0023] The first cell is divided to obtain a target division result, which includes multiple sub-regions;
[0024] For each of the M network devices in the first cell, determine the maximum coverage area of the network device for the sub-region, where M is a positive integer;
[0025] Based on the number of users within the maximum coverage area of the M network devices for the sub-region, determine the actual coverage area of the M network devices;
[0026] The actual coverage area of the cell is determined based on the actual coverage area of the M network devices.
[0027] Based on the actual coverage area of the cell and the area of the first cell, adjust the power of at least some of the M network devices.
[0028] Fourthly, embodiments of this application provide an electronic device, including: a processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, it implements the steps of the cell division method described in the first aspect.
[0029] Fifthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the cell division method described in the first aspect.
[0030] In a sixth aspect, embodiments of this application provide a computer program product, including computer instructions that, when executed by a processor, implement the steps of the method described in the first aspect above.
[0031] In this embodiment, after determining the target division result of the first cell, the maximum coverage area of the network devices for the divided sub-regions can be determined. Then, using the number of users within the maximum coverage area of the sub-regions for the M network devices, the actual coverage area of the M network devices is determined. That is, in the process of determining the actual coverage area, the distribution of users within the maximum coverage area is considered. Then, using the actual coverage area of the M network devices, the actual coverage area of the cell is determined. Then, based on the actual coverage area of the cell and the area of the first cell, the power of at least some of the M network devices is adjusted, thereby adjusting the actual coverage area of the network devices in the first cell, so that the actual coverage area of the first cell by the M network devices matches the actual area of the first cell, thereby improving the rationality of cell division. Attached Figure Description
[0032] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application 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.
[0033] Figure 1 This is one of the flowcharts of a cell division method provided in the embodiments of this application;
[0034] Figure 2 This is a schematic diagram of a defined first cell provided in an embodiment of this application;
[0035] Figure 3 This is a schematic diagram illustrating a partitioning scheme for dividing a first cell according to an embodiment of this application;
[0036] Figure 4 This is a schematic diagram illustrating another partitioning scheme for dividing the first cell according to an embodiment of this application;
[0037] Figure 5 This is one of the scenarios provided in this application embodiment for adjusting the power of at least some of the M network devices;
[0038] Figure 6 This is a schematic diagram showing a comparison between the actual coverage area of a cell and the area of a first cell, provided in an embodiment of this application.
[0039] Figure 7 This is a second scenario provided in the embodiments of this application for adjusting the power of at least some of the M network devices;
[0040] Figure 8 This is a schematic diagram of the structure of a cell division device provided in an embodiment of this application;
[0041] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0042] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0043] See Figure 1 , Figure 1 This is a flowchart of a cell division method provided in an embodiment of this application, which can be applied to electronic devices, such as... Figure 1 As shown, the cell division method provided in this embodiment includes the following steps:
[0044] Step 101: Determine the first cell that needs network coverage;
[0045] Step 102: Divide the first cell into multiple sub-regions to obtain the target partitioning result.
[0046] Step 103: For each of the M network devices in the first cell, determine the maximum coverage area of the network device for the sub-region, where M is a positive integer;
[0047] Step 104: Based on the number of users within the maximum coverage area of the M network devices for the sub-region, determine the actual coverage area of the M network devices;
[0048] Step 105: Determine the actual coverage area of the cell based on the actual coverage area of the M network devices;
[0049] Step 106: Adjust the power of at least some of the network devices among the M network devices according to the actual coverage area of the cell and the area of the first cell.
[0050] The actual coverage area of a cell can be understood as the area actually covered by M network devices in the first cell, and the area of the first cell can be understood as the actual area of the first cell. The divided sub-regions can be called sub-regions.
[0051] In this embodiment, after determining the target division result of the first cell, the maximum coverage area of the network devices for the divided sub-regions can be determined. Then, using the number of users within the maximum coverage area of the sub-regions for the M network devices, the actual coverage area of the M network devices is determined. That is, in the process of determining the actual coverage area, the distribution of users within the maximum coverage area is considered. Then, using the actual coverage area of the M network devices, the actual coverage area of the cell is determined. Then, based on the actual coverage area of the cell and the area of the first cell, the power of at least some of the M network devices is adjusted, thereby adjusting the actual coverage area of the network devices in the first cell, so that the actual coverage area of the M network devices in the first cell matches the actual area of the first cell, thereby improving the rationality of cell division.
[0052] In some embodiments, the first cell is divided to obtain the target division result, including:
[0053] The first cell is divided into N partitioning schemes, resulting in N partitioning results. Each partitioning result includes multiple sub-regions, where N is an integer greater than 1.
[0054] For each of the N possible partitioning results, determine the intra-regional correlation parameter and inter-regional correlation parameter of the sub-region in the partitioning result;
[0055] Based on the intra-regional correlation parameters and inter-regional correlation parameters of N partitioning results, the N partitioning results are filtered to determine L partitioning results, where L is an integer less than N;
[0056] The target partition result is determined from L partition results.
[0057] The first cell can be divided using various different partitioning schemes, resulting in N different partitioning results. A target partitioning result can be selected from these N results. During the selection process, the intra-regional correlation parameters and inter-regional correlation parameters of each partitioning result can be determined first. The intra-regional correlation parameter is used to assess the road segment correlation within a sub-region, while the inter-regional correlation parameter is used to assess the road segment correlation between sub-regions. A higher intra-regional correlation parameter and a lower inter-regional correlation parameter indicate a better partitioning result. The intra-regional and inter-regional correlation parameters of the N partitioning results can be used to select from these N results. This selection process considers both intra-regional and inter-regional correlation parameters, improving the accuracy and rationality of the L partitioning results obtained. Finally, the target partitioning result is determined from these L results to further improve the accuracy and rationality of the selected target partitioning result.
[0058] As an example, based on the intra-regional correlation parameters and inter-regional correlation parameters of N partitioning results, in the process of filtering N partitioning results to determine L partitioning results, L partitioning results with larger intra-regional correlation parameters and smaller inter-regional correlation parameters can be selected. For example, as an example, the average intra-regional correlation parameter and the average inter-regional correlation parameter of each partitioning result are calculated to obtain the average intra-regional correlation parameter and the average inter-regional correlation parameter of N partitioning results. The average intra-regional correlation parameter of N partitioning results and the average inter-regional correlation parameter of N partitioning results are compared to select L partitioning results with a larger average intra-regional correlation parameter (e.g., greater than a first preset value) and a smaller average inter-regional correlation parameter (e.g., less than a second preset value, where the second preset value is less than the first preset value). Furthermore, as an example, in determining L types of partitioning results, the median of the intra-regional correlation parameter and the median of the inter-regional correlation parameter for each partitioning result can also be determined to obtain the median of the intra-regional correlation parameter and the median of the inter-regional correlation parameter for N types of partitioning results. The median of the intra-regional correlation parameter and the median of the inter-regional correlation parameter for the N types of partitioning results are then compared to select L types of partitioning results with a larger median of the intra-regional correlation parameter (e.g., greater than the third preset value) and a smaller median of the inter-regional correlation parameter (e.g., less than the fourth preset value, where the fourth preset value is less than the third preset value). In other words, there are multiple ways to determine L types of partitioning results, and this application does not limit this approach.
[0059] In some embodiments, M is greater than 1, and the target partitioning result is determined from L partitioning results, including:
[0060] For each of the L partitioning results, determine the internal road segment density homogeneity of each sub-region in the partitioning result. Based on the internal road segment density homogeneity of the sub-region in the partitioning result, determine the average road segment density homogeneity of the partitioning result. The internal road segment density homogeneity is used to characterize the uniformity of density distribution on road segments within the sub-region.
[0061] The division result with the smallest average road segment density homogeneity among the L division results is determined as the target division result.
[0062] It should be understood that the smaller the average road segment density homogeneity of the sub-regions within the division result, the better the overall division effect of the community. Therefore, the division result with the smallest average road segment density homogeneity among the L division results can be determined as the target division result, thus completing the screening of division results and improving the rationality of community division.
[0063] In some embodiments, determining the actual coverage area of the M network devices based on the number of users within the maximum coverage area of the sub-region by the M network devices includes:
[0064] For each of the M network devices, determine the maximum number of users that the network device can support based on the number of users within the maximum coverage area of the sub-region.
[0065] The actual coverage area of the network equipment is determined based on the maximum number of users supported by the network equipment and the user density of the first cell.
[0066] In other words, the maximum number of users and user density supported by the network device are considered in the process of determining the actual coverage area, so as to improve the accuracy of the determination of the actual coverage area.
[0067] In some embodiments, determining the maximum number of users supported by the network device based on the number of users within the maximum coverage area of the sub-region includes:
[0068] The link load factor of the network device is determined based on the number of users within the maximum coverage area of the sub-region.
[0069] The maximum number of users supported by a network device's link is determined based on the network device's link load factor and preset scaling factor.
[0070] In this embodiment, the link load factor of the network device can be calculated. Using the link load factor of the network device and a preset scaling factor, the maximum number of users supported by the link of the network device can be determined. Based on the maximum number of users supported by the network device and the user density of the first cell, the actual coverage area of the network device can be determined. In this way, the actual coverage area of the cell under the constraint of the link load factor can be determined, thereby improving the rationality of the determination of the actual coverage area.
[0071] In some embodiments, determining the intra-regional correlation parameter and inter-regional correlation parameter of the sub-region in the partitioning result includes:
[0072] For each sub-region in the segmentation results, the intra-region correlation coefficient of the sub-region is determined based on the similarity between road segments within the sub-region.
[0073] For each pair of sub-regions in the segmentation result, the interval correlation coefficient between the two sub-regions is determined based on the similarity between road segments in one sub-region and road segments in the other sub-region.
[0074] Based on the intra-regional correlation coefficients and inter-regional correlation coefficients of the sub-regions in the partitioning results, the intra-regional correlation parameters and inter-regional correlation parameters of the sub-regions in the partitioning results are determined.
[0075] In some embodiments, determining the maximum coverage area of the network for a sub-region includes:
[0076] Obtain the maximum path loss supported by the network device;
[0077] Based on the maximum path loss, the attribute information of network devices, and the attribute information of multiple sub-regions, the maximum coverage area of the network for the sub-regions is determined.
[0078] It should be understood that the maximum path loss supported by a network device can be defined by the maximum path loss supported under the network operating parameters of the network device. Using the maximum path loss, the attribute information of the network device, and the attribute information of multiple sub-regions, the maximum coverage area of the network for each sub-region can be determined. For example, the attribute information of the network device may include, but is not limited to, at least one of the following: the operating frequency of the network device, and the antenna height of the network device. The attribute information of the sub-regions may include, but is not limited to, the antenna height of the terminals within the sub-region.
[0079] In some embodiments, the power of at least some of the M network devices is adjusted based on the actual coverage area of the cell and the area of the first cell, including at least one of the following:
[0080] If the actual coverage area of the first cell is smaller than that of the second cell, increase the power of at least some network devices.
[0081] If the actual coverage area of the first cell is larger than the area of the second cell, reduce the power of at least some network devices.
[0082] The method also includes maintaining the power of M network devices unchanged when the actual coverage area of the cell is equal to the area of the first cell.
[0083] If the actual coverage area of a cell is smaller than that of the first cell, the coverage area of the cell can be increased by increasing the power of at least some network devices, making the actual coverage area of the cell more closely match the actual area of the first cell. If the actual coverage area of a cell is larger than that of the first cell, to save coverage costs, the power of at least some network devices can be decreased, making the actual coverage area of the cell more closely match the actual area of the first cell. Alternatively, to save costs, if the actual coverage area of a cell is equal to that of the first cell, the power of the M network devices can be kept constant.
[0084] In some embodiments, the method further includes:
[0085] Determine the evaluation scores of multiple network device selection schemes. The evaluation scores are used to characterize the degree of matching between the actual coverage area of the network device selected from M network devices through the network device selection scheme and the area of the first cell, and the weighted sum of the economic benefit indicators under the power of the selected network device.
[0086] Based on the evaluation scores of multiple network device selection schemes, at least a portion of the network devices are identified.
[0087] Each network device selection scheme has its own corresponding network devices. During the selection process, the matching degree between the actual coverage area of the network device and the area of the first cell is considered, as well as the economic efficiency indicators under the network device's power. These are weighted and summed to form the evaluation score for the network device selection scheme. Using the evaluation scores of multiple network device selection schemes, a target selection scheme is chosen from among them, and the network devices selected by the target selection scheme are determined to be at least a subset of the network devices. For example, as an illustration, the scheme with the higher evaluation score can be used as the target selection scheme, and at least a subset of the network devices are those selected through the target selection scheme.
[0088] The above method will be described in detail below with some specific embodiments.
[0089] In mobile communication networks, the cell is the basic unit of network coverage. The accuracy of cell boundary delineation directly affects network performance and user experience. With the widespread adoption of mobile devices and the growth of data services, the demand for cell boundary delineation technology is increasing. Currently, cell boundary delineation technologies mainly rely on traditional static signal strength measurement methods, preset parameter methods, and manual optimization methods. These methods have limitations in that they cannot dynamically adapt to changes in the network environment and often suffer from low accuracy and efficiency in practical applications. These methods also have limitations in handling dynamically changing network loads and user distributions. Therefore, a novel dynamic adjustment method is urgently needed to improve the accuracy of cell boundary delineation and the overall performance of network coverage.
[0090] This application provides a dynamic cell partitioning scheme that addresses the problems of poor dynamic adaptability, insufficient accuracy, uneven resource allocation, difficulty in interference management, low algorithm efficiency, high implementation difficulty, insufficient service personalization, inadequate data utilization, and high maintenance costs in related cell boundary partitioning techniques. This scheme can improve the service quality and operational efficiency of mobile communication networks. Taking a network device as a base station as an example, the specific process of the scheme provided in this application is as follows:
[0091] (1) Determine the scope and area of the cell that needs network coverage (i.e., the first cell).
[0092] Identify the cells that need network coverage, such as... Figure 2 As shown, for example, the polygon tool can be used to draw the boundary of the first cell ( Figure 2 Within the dashed box, calculate the area S of this cell (the first cell). 小区 .
[0093] (2) Divide the first cell into its target cells to obtain the target cell division results.
[0094] (2.1) Using N partitioning schemes, such as the equidistant partitioning method, the first cell is partitioned to obtain N partitioning results;
[0095] As an example, the neighborhood can be divided into 5x5, 4x4, or nxn square regions using an equidistant partitioning method, resulting in different partitioning outcomes, such as... Figure 3 As shown, this is the result after dividing the image into 5x5 equal intervals. Figure 4 As shown, this is the result after dividing the image into 4*4 equal intervals.
[0096] (2.2) Evaluate the rationality of the N partitioning results to obtain the L partitioning results that are initially reasonable:
[0097] After determining the overall scope of the community, it is necessary to divide the community into regions. After determining N possible division results, the following screening method can be used to select L possible division results from the N results. Selecting the more distinctive features of the community to create a spatially compact cluster effect avoids the creation of sub-regions with few nodes, thus improving computational efficiency. This screening process combines road segment similarity, segmentation coefficients, and correlation of segmentation groups to select the division results. Compared with related techniques based on simple regional density clustering results, this method offers finer granularity, more precise scale, and a grid that better matches the actual situation.
[0098] Here is a specific filtering method for an example:
[0099] Using sub-regions (sub-areas) as nodes, adjacency relationships between sub-regions are established based on spatial relationships, and the community network is constructed as an undirected road network W. Let a and b represent any distinct road segments in the undirected road network W, and set the density values of a and b as follows: and t(a,b) represents the undirected adjacency relationship between road segment a and road segment b. t(a,b)=1 indicates that road segment a and road segment b are directly connected, and t(a,b)=0 indicates that road segment a and road segment b are not directly connected.
[0100] First, use t(a,b) to calculate the shortest path length d(a,b) between road segments a and b. When t(a,b)=1, d(a,b)=1; when t(a,b)=0, d(a,b) is greater than 1. Specifically, it is the number of the shortest road segments between road segments a and b plus 1. For example, if road segment a is directly connected to road segments c and d, road segment c is directly connected to road segment b, road segment d is connected to road segment e, road segment e is directly connected to road segment b, road segment a is directly connected to road segment c, and road segment c is directly connected to road segment b, then the shortest path from road segment a to road segment b via road segment c is the shortest, and the intervening road segment is road segment c. That is, the number of the shortest road segments is 1, then d(a,b)=2.
[0101] Let the preset threshold be th, and th can be 1 as an example. (1) Calculate the similarity s(a,b) between road segment a and road segment b:
[0102] When d(a,b) = th, ;
[0103] When d(a,b) > th, s(a,b) = 0.
[0104] Secondly, based on the similarity s(a,b) between road segment a and road segment b, the segmentation coefficient G(H,I) is calculated;
[0105] In graph W=(E,F), E is the set of points (i.e., the set of subregions), and F is the set of edges. The interval correlation coefficient G(H,I) between any two subregions H and I of the road network W is:
[0106] ;
[0107] Furthermore, G(H,E) is the correlation coefficient between subregion H and point set E (the sum of the intra-regional correlation coefficient of subregion H in point set E and the interval correlation coefficient between subregion H and the remaining subregions in point set E excluding subregion H), G(I,E) is the correlation coefficient between subregion I and point set E (the sum of the intra-regional correlation coefficient of subregion I in point set E and the interval correlation coefficient between subregion I and the remaining subregions in point set E excluding subregion I), and G(H,H) is the intra-group correlation coefficient of subregion H. The correlation coefficient (i.e., intra-regional correlation coefficient) and G(I,I) are the intra-group correlation coefficients (i.e., inter-regional correlation coefficients) of sub-region I. The calculation method is the same as that of G(H,I). For example, for the calculation method of G(H,E), simply replace I with E in the above formula; for the calculation method of G(I,E), simply replace H with E in the above formula; for the calculation method of G(H,H), simply replace I with H in the above formula; and for the calculation method of G(I,I), simply replace H with I in the above formula.
[0108] Then, based on the interval correlation coefficient G(H,I), the correlation coefficient G(H,E) between sub-region H and point set E, and the correlation coefficient G(I,E) between sub-region I and point set E, the inter-group similarity parameter (interval similarity parameter) K(H,I) is calculated:
[0109] ;
[0110] Based on the intragroup correlation coefficients G(H,H) and G(I,I) of subregions H and I, the correlation coefficient G(H,E) between subregion H and point set E, and the correlation coefficient G(I,E) between subregion I and point set E, calculate the intragroup correlation parameter (intraregion correlation parameter) L(H,I):
[0111] ;
[0112] Evaluation process: The smaller the inter-group similarity parameter K(H,I) and the larger the intra-group similarity parameter L(H,I), the better the segmentation result.
[0113] Substituting the above N partitioning results into the above evaluation process, we obtain the inter-group similarity parameter K(H,I) and intra-group similarity parameter L(H,I) for each partitioning result. By comparing the K(H,I) and L(H,I) values of each partitioning result, we select the partitioning result with relatively small K(H,I) and relatively large L(H,I) as the preliminary screening result, that is, we obtain L partitioning results.
[0114] (2.3) Further refine the L reasonable division results in (2.2) and use the innovative new sub-region division evaluation index to judge the division results;
[0115] Based on indicators such as intra-group correlation parameters and inter-group correlation parameters of sub-regions, the partitioning results of the partitioning scheme can be evaluated a second time to obtain the optimal partitioning method. This solves the problem that the partitioning method in the relevant set cannot obtain a better partitioning result, resulting in unreasonable allocation of base station resources. Compared with related technologies, it has the characteristics of flexible allocation and scientific partitioning assisted by accurate data results.
[0116] The specific method is as follows:
[0117] (2.3.1) First, calculate the contour value O between any two sub-regions H and I, using the following formula:
[0118] ;
[0119] Among them, P H P is the number of road segments in sub-region H. I This represents the number of road segments in sub-region I.
[0120] Although the above steps can quantify the density difference between sub-regions H and I, the spatial relationship between the two sub-regions is unrelated, making it difficult to quantify the level of road segment density difference within the two sub-regions. Therefore, it is necessary to further calculate the internal density homogeneity of each sub-region.
[0121] (2.3.2) Further calculate the internal density homogeneity of each sub-region. The formula is as follows:
[0122] ;
[0123] Where t is the sub-region adjacency matrix of road network W; t(N,H) represents the adjacency relationship between sub-region N and sub-region H, t(N,H)=1 means that sub-region N and sub-region H are adjacent. The calculation method of O(H,H) is similar to that of O(H,I) above, except that I is replaced with H. The denominator in the calculation formula can be understood as the minimum value of the interval similarity parameter between sub-region H and the sub-intervals adjacent to sub-region H.
[0124] (2.3.3) When the number of sub-regions in the road network W is J, the uniformity of all sub-regions in the entire road network (i.e., the uniformity of average road segment density) for:
[0125] ;
[0126] in, The smaller the value, the smaller the density variation within each sub-region under the J sub-region division, which means the overall community division is better.
[0127] (2.4) Obtain the target segmentation results.
[0128] Substitute the L partitioning results selected above into (2.3.3) for result verification and compare the results of each partitioning. Value, select The partition result with the smallest value is taken as the target partition result for that cell.
[0129] (3) Obtain all base stations within the area range, and use the self-constructed Rcsa-sbsa sub-area base station area reasonable coverage segmentation algorithm to segment them, and obtain the base station coverage cell area value under load factor constraints. ;
[0130] A reasonable coverage segmentation algorithm for Rcsa-sbsa sub-region base station areas is proposed, which can ensure that the selected base stations can reasonably cover the entire area of the sub-region, improving the rationality of the segmentation and saving coverage costs. The specific process is as follows:
[0131] (3.1) Calculate the maximum coverage area of the base station for the sub-cell.
[0132] (3.1.1) Calculate the path loss PL of the macro sub-region cellular network coverage.
[0133] To ensure that the number and coverage of the selected base stations match the sub-area perfectly, and to obtain a high-quality network with a lower network construction cost, the link budget method is first used to determine the maximum coverage area of the base stations.
[0134] Link budget is used to determine the maximum acceptable (supported) path loss of a 5G network. Using an appropriate propagation model and path loss, the maximum radius of a sub-cell can be calculated.
[0135] For the macro-sub-region cellular network coverage, the path loss calculation formula for this model is as follows:
[0136] ;
[0137] in, It is the operating frequency of the base station. This refers to the effective height of the base station antenna, i.e., the actual altitude of the base station antenna. This refers to the effective height of the terminal antenna, that is, the height of the terminal antenna above the ground. It is the horizontal distance between the base station antenna and the terminal antenna. This is a correction factor for the terminal antenna (which can be preset based on experience, needs, or environment, and is a default value). For example, its value is related to the wireless environment. This is a cell type correction factor (which can be preset based on experience or needs, and is a default value). This is the terrain calibration factor (which can be preset based on experience or needs, and is a default value). Calibration factor for major city centers (can be preset based on experience or needs, etc., and is a default value).
[0138] (3.1.2) Given the operating parameters of the 5G network, the path loss PL of the macro sub-area cellular network coverage can be simplified as follows:
[0139] ;
[0140] in This is the coefficient for the base station radius term. That is , It is a constant, and it is Given the maximum acceptable path loss for the network. back,
[0141] (3.1.3) Using the formula The maximum value of c can then be calculated, which is the maximum coverage radius R of the base station.
[0142] (3.1.4) The formula for calculating the maximum area affected by the base station in the sub-region is as follows:
[0143] ;
[0144] (3.2) Based on the self-designed load factor, estimate the total number of users supported by each base station, and combine this with the maximum area of the cell. Calculate the cell coverage area under load factor constraints. ;
[0145] The load factor calculation method in this application is a newly created method. Based on indicators such as the number of users in a cell, the interference ratio of surrounding cells, the signal energy per bit per user, the bit rate, and the activation factor, it calculates the maximum number of users for the uplink and downlink of a single base station, and comprehensively considers the operational benefits of the 5G network to obtain the cell area under load factor constraints. Customized evaluation is performed according to the actual situation of the cell and the base station conditions, avoiding the problem of formulaic evaluation data not matching the actual situation of the cell, which leads to results that do not meet the requirements for cell base station construction. It has the characteristics of targeted and flexible analysis. The specific process is as follows:
[0146] (3.2.1) Calculate the uplink load factor
[0147] Once the load factor is determined, the number of users supported by a single base station can be determined. Uplink load factor calculation formula:
[0148] ;
[0149] in The value represents the ratio of interference from other cells to interference from this cell (the first cell), where M is the number of users within the maximum coverage area of the base station in this cell for the sub-region. The signal energy per bit for the i-th user in the uplink. It is the sum of the total transmit power and receive power of the base stations within this cell. For the unit energy of the uplink, Let i be the bit rate of the i-th user in the uplink. Let A be the activation factor for the i-th user in the uplink, and let A be the chip rate.
[0150] (3.2.2) According to the uplink load factor calculation formula Calculate the maximum number of users supported by the uplink and downlink. The calculation formula is... Similarly, you can simply replace the uplink with the downlink.
[0151] Assuming that each user has the same signal energy per bit, bit rate, and activation factor, the maximum number of users supported by the uplink can be obtained given the 5G network operating parameters:
[0152] ;
[0153] Similarly, the maximum number of users supported by the downlink:
[0154] ;
[0155] It is a preset scaling factor set in advance based on 5G network operating parameters and user behavior.
[0156] (3.2.3) Since 5G networks use both Frequency Division Duplex (FDD) and Time Division Duplex (TDD) modes for uplink and downlink, the number of users supported by each cell should be min(M). SL M XL Under load factor constraints, the cell coverage area The calculation formula is:
[0157] ;
[0158] Where YM represents user density.
[0159] (3.2.4) Taking into account the operational efficiency of 5G networks, assuming the minimum acceptable user density is YMmin and the maximum supportable user density is YMmax, the signal area of the sub-region to be covered by the base station under load factor constraints should satisfy:
[0160] ;
[0161] (4) Based on the results of steps (2) and (3), analyze the coverage area (S) of the cell base station. 基站 This refers to the matching between the actual coverage area of the cell (determined based on the actual coverage area of the network equipment) and the actual area of the cell:
[0162] When S 基站 <S 小区 At this time, it is necessary to adjust the base station power to expand its coverage area:
[0163] like Figure 5 As shown on the left, when S 基站 <S 小区 This indicates that the current base station configuration is insufficient to meet the cell's needs. In this case, the base station power can be adjusted. For example, adjusting the power of base stations A2 and A5 can expand the coverage area of the two base stations. After adjustment, as shown... Figure 5 As shown on the right, this is to meet the requirements for network coverage in the community;
[0164] If S 基站 =S 小区 When the base station configuration is well-matched with the cell range, this indicates that the configuration is optimal.
[0165] like Figure 6 As shown, when S 基站 =S 小区 When the current base station configuration meets the cell's requirements, this is the optimal situation and no further adjustments are needed.
[0166] When S 基站>S 小区 At the same time, reduce or adjust the coverage area of the base station:
[0167] like Figure 7 As shown on the left, when S 基站 >S 小区 This indicates that the current base station coverage area far exceeds the actual area required by the cell. In this case, the base station power needs to be adjusted, for example, by adjusting the overall power of A1-A7 to reduce the coverage area of each base station. After adjustment, as shown... Figure 7 As shown on the right, this achieves the effect of the base station coverage area being consistent with the cell area, maximizing the utilization of base station resources and avoiding excessive coverage beyond the designated area.
[0168] In addition, (5) a dynamic adjustment model for base station power and power consumption is established to automatically adjust base station power and power consumption based on matching conditions:
[0169] (5.1) Establish an antibody affinity evaluation function (evaluation score) to evaluate the merits of base station site selection schemes;
[0170] In the base station location optimization problem, each candidate base station has only two possibilities: selected or not selected. Therefore, we will use binary encoding to record the base station selection status. There are M base stations in the cell, denoted as: =(i1,i2,...,iM), where ix (x∈M) corresponds to the selection status of the x-th base station, i.e., the x-th base station is selected when ix=1, and the x-th base station is not selected when ix=0.
[0171] The optimization problem of cell area and base station coverage area is transformed into a single base station area optimization problem using a weighted method. The antibody affinity evaluation function is designed as follows:
[0172] ;
[0173] in, for The weighting coefficient, ℷ is The weighting coefficients can all be set in advance based on experience, and , for The degree to which the actual coverage area of the selected network equipment matches the area of the first cell. Indicates in The economic efficiency index of the selected network equipment under its power is given. Based on the above function, the merits of the base station site selection scheme can be evaluated. The value of the evaluation function is... The larger this value is, the better the base station location scheme is.
[0174] (5.2) Based on the value of the evaluation function, the power and power consumption of the base station are dynamically and automatically adjusted.
[0175] Continuous monitoring evaluation function The evaluation function value (i.e., the evaluation score) is used to set a dynamic adjustment threshold. When the evaluation function value falls below this threshold, it indicates that the currently selected base station scheme has room for optimization. By making targeted adjustments to the base station's power and power consumption to restore the evaluation function value to above the threshold, a more optimized configuration of the cell base station can be achieved.
[0176] In the solution of this application embodiment:
[0177] 1) Innovate regional division strategies. Based on the division results, evaluate the rationality of sub-region segmentation to obtain a preliminary reasonable division result.
[0178] After determining the overall scope of the community, it is necessary to divide and select the selected area. During the selection process, the more distinctive features of the community are chosen to create a spatially compact cluster effect, thereby avoiding the creation of sub-regions with few nodes and improving computational efficiency. Compared with related methods, this scheme combines road segment similarity, segmentation coefficients, and correlation of segmentation groups to determine the division results. Compared with existing division methods based on simple regional density clustering results, it has finer granularity, more accurate division scale, and the resulting grid is more adapted to the actual situation.
[0179] 2) Optimize the screening mechanism
[0180] A unique set of evaluation criteria is introduced, namely intra-group correlation and inter-group correlation, to review and refine the reasonable initial division schemes, ensuring that the most superior regional division results are obtained to deeply match the network layout requirements.
[0181] 3) Load prediction model
[0182] All base stations within the area are obtained, and a self-constructed Rcsa-sbsa sub-area base station area reasonable coverage segmentation algorithm is used for segmentation to obtain the base station coverage cell area value under load factor constraints. The proposed Rcsa-sbsa sub-area base station area reasonable coverage segmentation algorithm can ensure that the selected base stations can reasonably cover all areas of the sub-area, improve the rationality of the segmentation, and save coverage costs.
[0183] 4) Dynamic energy efficiency management
[0184] An adaptive adjustment model for base station power and energy consumption is constructed. This model can intelligently adjust the output power and energy consumption level of the base station based on the real-time network matching status, ensuring a dual improvement in network performance and energy efficiency.
[0185] Compared with existing technologies, this proposal demonstrates significant technical advantages in several aspects:
[0186] 1. Dynamic adaptability
[0187] Real-time response to changes: This proposal utilizes real-time signaling data and dynamic adjustment mechanisms to quickly respond to changes in user distribution and network environment. This differs from traditional static boundary delineation methods, which typically require manual adjustment and reconfiguration and cannot adapt to dynamically changing network environments in a timely manner.
[0188] Flexible adjustment: Through optimization algorithms such as dynamic programming and reinforcement learning, cell boundaries can be dynamically adjusted based on real-time data, thereby improving network coverage efficiency and user experience.
[0189] 2. Precise partitioning and optimization
[0190] Multi-technology integration: This proposal combines link budget method and antibody affinity evaluation function to achieve accurate cell boundary delineation. It employs inter-group similarity and intra-group correlation evaluation rules to identify and highlight blocks with prominent features within the cell, promoting the formation of spatially highly aggregated clusters for initial delineation. It introduces intra-group correlation and inter-group similarity to review and refine reasonable initial delineation schemes, ensuring the acquisition of the most superior regional delineation results. Based on a self-designed load factor algorithm, it accurately calculates the cell area value under load factor constraints. Finally, it constructs an adaptive adjustment model for base station power and energy consumption, intelligently adjusting base station output power and energy consumption levels based on real-time network matching status.
[0191] 3. High-efficiency data processing
[0192] Advanced data preprocessing: Techniques such as outlier detection, data normalization, and data fusion are employed to improve data quality and analytical accuracy. These techniques help reduce data noise and enhance the performance of subsequent algorithms.
[0193] Real-time data processing: Through sliding window technology and efficient data storage solutions, signaling data can be processed and analyzed in real time, ensuring that the system can respond and adjust quickly.
[0194] like Figure 8 As shown, Figure 8 This is a schematic diagram of the structure of a cell division device 800 provided in an embodiment of this application, as shown below. Figure 8 As shown, the cell division device 800, applied to electronic devices, includes:
[0195] The first determining module 801 is used to determine the first cell that needs network coverage;
[0196] The partitioning module 802 is used to partition the first cell to obtain the target partitioning result, which includes multiple sub-regions;
[0197] The second determining module 803 is used to determine the maximum coverage area of the network for each of the M network devices in the first cell for the sub-region, where M is a positive integer.
[0198] The third determining module 804 is used to determine the actual coverage area of the M network devices based on the number of users within the maximum coverage area of the M network devices for the sub-region.
[0199] The fourth determination module 805 is used to determine the actual coverage area of the cell based on the actual coverage area of M network devices;
[0200] The adjustment module 806 is used to adjust the power of at least some of the network devices among the M network devices according to the actual coverage area of the cell and the area of the first cell.
[0201] In some embodiments, the first cell is divided to obtain the target division result, including:
[0202] The first cell is divided into N partitioning schemes, resulting in N partitioning results. Each partitioning result includes multiple sub-regions, where N is an integer greater than 1.
[0203] For each of the N possible partitioning results, determine the intra-regional correlation parameter and inter-regional correlation parameter of the sub-region in the partitioning result;
[0204] Based on the intra-regional correlation parameters and inter-regional correlation parameters of N partitioning results, the N partitioning results are filtered to determine L partitioning results, where L is an integer less than N;
[0205] The target partition result is determined from L partition results.
[0206] In some embodiments, M is greater than 1, and the target partitioning result is determined from L partitioning results, including:
[0207] For each of the L partitioning results, determine the internal road segment density homogeneity of each sub-region in the partitioning result. Based on the internal road segment density homogeneity of the sub-region in the partitioning result, determine the average road segment density homogeneity of the partitioning result. The internal road segment density homogeneity is used to characterize the uniformity of density distribution on road segments within the sub-region.
[0208] The division result with the smallest average road segment density homogeneity among the L division results is determined as the target division result.
[0209] In some embodiments, determining the actual coverage area of the M network devices based on the number of users within the maximum coverage area of the sub-region by the M network devices includes:
[0210] For each of the M network devices, determine the maximum number of users that the network device can support based on the number of users within the maximum coverage area of the sub-region.
[0211] The actual coverage area of the network equipment is determined based on the maximum number of users supported by the network equipment and the user density of the first cell.
[0212] In some embodiments, determining the maximum number of users supported by the network device based on the number of users within the maximum coverage area of the sub-region includes:
[0213] The link load factor of the network device is determined based on the number of users within the maximum coverage area of the sub-region.
[0214] The maximum number of users supported by a network device's link is determined based on the network device's link load factor and preset scaling factor.
[0215] In some embodiments, determining the intra-regional correlation parameter and inter-regional correlation parameter of the sub-region in the partitioning result includes:
[0216] For each sub-region in the segmentation results, the intra-region correlation coefficient of the sub-region is determined based on the similarity between road segments within the sub-region.
[0217] For each pair of sub-regions in the segmentation result, the interval correlation coefficient between the two sub-regions is determined based on the similarity between road segments in one sub-region and road segments in the other sub-region.
[0218] Based on the intra-regional correlation coefficients and inter-regional correlation coefficients of the sub-regions in the partitioning results, the intra-regional correlation parameters and inter-regional correlation parameters of the sub-regions in the partitioning results are determined.
[0219] In some embodiments, determining the maximum coverage area of the network for a sub-region includes:
[0220] Obtain the maximum path loss supported by the network device;
[0221] Based on the maximum path loss, the attribute information of network devices, and the attribute information of multiple sub-regions, the maximum coverage area of the network for the sub-regions is determined.
[0222] In some embodiments, the power of at least some of the M network devices is adjusted based on the actual coverage area of the cell and the area of the first cell, including at least one of the following:
[0223] If the actual coverage area of the first cell is smaller than that of the second cell, increase the power of at least some network devices.
[0224] If the actual coverage area of the first cell is larger than the area of the second cell, reduce the power of at least some network devices.
[0225] The device also includes a maintenance module, used to maintain the power of M network devices unchanged when the actual coverage area of the cell is equal to the area of the first cell.
[0226] In some embodiments, the apparatus further includes:
[0227] The score determination module is used to determine the evaluation score of multiple network device selection schemes. The evaluation score is used to characterize the degree of matching between the actual coverage area of the network device selected from M network devices through the network device selection scheme and the area of the first cell, and the weighted sum of the economic benefit index under the power of the selected network device.
[0228] The device determination module is used to determine at least a subset of network devices based on the evaluation scores of multiple network device selection schemes.
[0229] The cell division device 800 provided in this embodiment can implement the various processes of the above-described cell division method embodiments. The technical features are one-to-one and can achieve the same technical effect. To avoid repetition, it will not be described again here.
[0230] This application also provides an electronic device, including: a processor, a memory, and a program stored in the memory and executable on the processor. When the program is executed by the processor, it implements the various processes of the above-described cell division method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.
[0231] For details, see Figure 9 This application also provides an electronic device, which is a relay device, including a bus 901, a transceiver 902, an antenna 903, a bus interface 904, a processor 905, and a memory 906.
[0232] The processor 905 is used for:
[0233] Identify the first cell that needs network coverage;
[0234] The first cell is divided to obtain the target division result, which includes multiple sub-regions;
[0235] For each of the M network devices in the first cell, determine the maximum coverage area of the network for the sub-region, where M is a positive integer;
[0236] Based on the number of users within the maximum coverage area of the M network devices for the sub-region, determine the actual coverage area of the M network devices.
[0237] Determine the actual coverage area of the cell based on the actual coverage area of M network devices;
[0238] Based on the actual coverage area of the cell and the area of the first cell, adjust the power of at least some of the network devices among the M network devices.
[0239] exist Figure 9 In this document, a bus architecture (represented by bus 901) is used. Bus 901 can include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 905 and memory represented by memory 906. Bus 901 can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 904 provides an interface between bus 901 and transceiver 902. Transceiver 902 can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 905 is transmitted over a wireless medium via antenna 903, which further receives data and transmits it to processor 905.
[0240] Processor 905 manages bus 901 and general processing, and also provides various functions, including timing, peripheral interface, voltage regulation, power management, and other control functions. Memory 906 can be used to store data used by processor 905 during operation.
[0241] Optionally, the processor 905 can be a CPU, ASIC, FPGA, or CPLD.
[0242] The processor 905 of the electronic device provided in this embodiment can implement each process of each embodiment of the above-described cell division method. The technical features are one-to-one and can achieve the same technical effect. To avoid repetition, it will not be described again here.
[0243] This application also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the various processes of the above-described cell division method embodiments and achieves the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.
[0244] This application provides a computer program product, including computer instructions. When the computer instructions are executed by a processor, they implement the various processes of the method described in the embodiment. The technical features are one-to-one and can achieve the same technical effect. To avoid repetition, they will not be described again here.
[0245] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0246] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods of the various embodiments of this application.
[0247] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.
Claims
1. A method for dividing residential areas, characterized in that, The method includes: Identify the first cell that needs network coverage; The first cell is divided to obtain a target division result, which includes multiple sub-regions; For each of the M network devices in the first cell, determine the maximum coverage area of the network device for the sub-region, where M is a positive integer; Based on the number of users within the maximum coverage area of the M network devices for the sub-region, determine the actual coverage area of the M network devices; The actual coverage area of the cell is determined based on the actual coverage area of the M network devices. Based on the actual coverage area of the cell and the area of the first cell, adjust the power of at least some of the M network devices.
2. The method according to claim 1, characterized in that, The process of dividing the first cell to obtain the target division result includes: The first cell is divided according to N division schemes, resulting in N division results. Each division result includes multiple sub-regions, where N is an integer greater than 1. For each of the N possible partitioning results, determine the intra-regional correlation parameter and inter-regional correlation parameter of the sub-region in the partitioning result; Based on the intra-regional correlation parameters and inter-regional correlation parameters of the N partitioning results, the N partitioning results are filtered to determine L partitioning results, where L is an integer less than N; The target partitioning result is determined from the L partitioning results.
3. The method according to claim 2, characterized in that, If M is greater than 1, determining the target partitioning result from the L partitioning results includes: For each of the L types of partitioning results, the internal road segment density homogeneity of each sub-region in the partitioning result is determined. Based on the internal road segment density homogeneity of the sub-region in the partitioning result, the average road segment density homogeneity of the partitioning result is determined. The internal road segment density homogeneity is used to characterize the uniformity of density distribution on road segments within the sub-region. The division result with the smallest average road segment density homogeneity among the L division results is determined as the target division result.
4. The method according to any one of claims 1-3, characterized in that, Determining the actual coverage area of the M network devices based on the number of users within the maximum coverage area of the sub-region by the M network devices includes: For each of the M network devices, the maximum number of users supported by the network device is determined based on the number of users within the maximum coverage area of the sub-region. The actual coverage area of the network device is determined based on the maximum number of users supported by the network device and the user density of the first cell.
5. The method according to claim 4, characterized in that, Determining the maximum number of users supported by the network device based on the number of users within the maximum coverage area of the sub-region includes: The link load factor of the network device is determined based on the number of users within the maximum coverage area of the sub-region. The maximum number of users supported by the link of the network device is determined based on the link load factor and the preset scaling factor of the network device.
6. The method according to claim 2, characterized in that, Determining the intra-regional correlation parameters and inter-regional correlation parameters of the sub-regions in the partitioning result includes: For each sub-region in the division result, the intra-region correlation coefficient of the sub-region is determined based on the similarity between road segments within the sub-region; For each pair of sub-regions in the division result, the interval correlation coefficient between the two sub-regions is determined based on the similarity between road segments in one sub-region and road segments in the other sub-region. Based on the intra-regional correlation coefficients of the sub-regions in the partitioning results and the inter-regional correlation coefficients between the sub-regions in the partitioning results, the intra-regional correlation parameters and inter-regional correlation parameters of the sub-regions in the partitioning results are determined.
7. The method according to claim 1, characterized in that, Determining the maximum coverage area of the network for a sub-region includes: Obtain the maximum path loss supported by the network device; Based on the maximum path loss, the attribute information of the network device, and the attribute information of the multiple sub-regions, the maximum coverage area of the network for the sub-regions is determined.
8. The method according to claim 1, characterized in that, Adjusting the power of at least some of the M network devices based on the actual coverage area of the cell and the area of the first cell includes at least one of the following: If the actual coverage area of the cell is smaller than the area of the first cell, increase the power of at least some of the network devices; If the actual coverage area of the cell is greater than the area of the first cell, reduce the power of at least some of the network devices; The method further includes maintaining the power of the M network devices unchanged when the actual coverage area of the cell is equal to the area of the first cell.
9. The method according to claim 1 or 8, characterized in that, The method further includes: Determine the evaluation score of multiple network device selection schemes. The evaluation score is used to characterize the degree of matching between the actual coverage area of the network device selected by the network device selection scheme and the area of the first cell, and the economic benefit index under the power of the selected network device. Based on the evaluation scores of the multiple network device selection schemes, at least some of the network devices are determined.
10. A cell division device, characterized in that, The device includes: The first determining module is used to determine the first cell that needs network coverage; The partitioning module is used to partition the first cell to obtain a target partitioning result, wherein the target partitioning result includes multiple sub-regions; The second determining module is used to determine the maximum coverage area of the network for the sub-region for each of the M network devices in the first cell, where M is a positive integer; The third determining module is used to determine the actual coverage area of the M network devices based on the number of users within the maximum coverage area of the M network devices for the sub-region; The fourth determining module is used to determine the actual coverage area of the cell based on the actual coverage area of the M network devices; The adjustment module is used to adjust the power of at least some of the M network devices based on the actual coverage area of the cell and the area of the first cell.
11. An electronic device, characterized in that, Including transceivers and processors, The processor is used for: Identify the first cell that needs network coverage; The first cell is divided to obtain a target division result, which includes multiple sub-regions; For each of the M network devices in the first cell, determine the maximum coverage area of the network for the sub-region, where M is a positive integer; Based on the number of users within the maximum coverage area of the M network devices for the sub-region, determine the actual coverage area of the M network devices; The actual coverage area of the cell is determined based on the actual coverage area of the M network devices. Based on the actual coverage area of the cell and the area of the first cell, adjust the power of at least some of the M network devices.
12. An electronic device, characterized in that, include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the steps of the method as described in any one of claims 1 to 9.
13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method described in any one of claims 1-9.
14. A computer program product, characterized in that, Includes computer instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1-9.