Network deployment parameter optimization method and device, electronic equipment and storage medium

Abstract

The application provides a network deployment parameter optimization method and device, electronic equipment and storage medium, and relates to the technical field of communication. When network deployment is performed, network deployment basic information corresponding to a target scene is acquired. Then, based on the network deployment basic information, each grid respectively performs voting of a first candidate parameter combination of a first candidate AP and voting of a second candidate parameter combination of a second candidate AP, and a voting result is obtained. The rank of a channel matrix corresponding to the first candidate AP under the first candidate parameter combination is maximum, and the received power corresponding to the second candidate AP under the second candidate parameter combination is maximum. Then, according to the voting result, a parameter optimization result of network deployment under the target scene is determined. In this way, according to the characteristics of the 2B scene, a simple and efficient voting algorithm based on the rank of the channel matrix and the received power is adopted, the complexity of the optimization algorithm in the super-large parameter space is greatly reduced, and the network coverage reliability is ensured.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a method, apparatus, electronic device, and storage medium for optimizing network deployment parameters. Background Technology

[0002] Wireless communication technology is playing an increasingly important role not only in 2C scenarios, but also in a growing number of industrial applications. These include unmanned port control, real-time monitoring and video transmission in factories, machine tool control, robot control, full automation of large warehouses, and large office areas and live streaming platforms, all of which rely on the reliability, full coverage, and high-speed connectivity of wireless networks. Therefore, Wi-Fi and other wireless technologies are increasingly widely used in 2B scenarios within large-scale wireless local area networks. Here, 2C scenarios refer to scenarios using 2C networks, which are primarily for individual users; 2B scenarios refer to scenarios using 2B networks, which are primarily for industries, organizations, enterprises, and other non-personal applications.

[0003] However, the 2B scenario involves a larger space and a more complex environment, resulting in a higher complexity of the optimization algorithm. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus, electronic device, and storage medium for optimizing network deployment parameters, so as to reduce the complexity of optimization algorithms.

[0005] In a first aspect, embodiments of the present invention provide a method for optimizing network deployment parameters, including: Obtain basic network deployment information corresponding to the target scenario; wherein, the basic network deployment information includes gridding results and location information of multiple APs, and the gridding results include multiple grids obtained by gridding the plan view of the target scenario; Based on the network deployment information, each grid votes on the first candidate parameter combination of the first candidate AP and the second candidate parameter combination of the second candidate AP for each parameter combination under each AP, and obtains the voting results; wherein, the rank of the channel matrix obtained by each grid from the corresponding first candidate AP under the corresponding first candidate parameter combination is the largest, and the received power obtained by each grid from the corresponding second candidate AP under the corresponding second candidate parameter combination is the largest. Based on the voting results, the parameter optimization results for network deployment in the target scenario are determined; wherein, the parameter optimization results include the target parameter combination corresponding to each AP.

[0006] Further, based on the network deployment information, each grid performs voting on the first candidate parameter combination of the first candidate AP and the second candidate parameter combination of the second candidate AP for each parameter combination under each AP, and obtains the voting results, including: For each grid, based on the network deployment basic information, determine the rank of the maximum channel matrix under the first parameter combination and the maximum received power value under the second parameter combination for each AP corresponding to that grid; The first parameter combination of the AP corresponding to the maximum value of the rank of each maximum channel matrix corresponding to the grid is determined as the first candidate parameter combination of the first candidate AP corresponding to the grid, and the second parameter combination of the AP corresponding to the maximum value of each maximum received power value corresponding to the grid is determined as the second candidate parameter combination of the second candidate AP corresponding to the grid. The grid is determined to vote for the first candidate parameter combination of the first candidate AP and the second candidate parameter combination of the second candidate AP respectively.

[0007] Furthermore, the network deployment basic information also includes a three-dimensional model of the target scene; the step of determining the rank of the maximum channel matrix under the first parameter combination and the maximum received power value under the second parameter combination for each AP corresponding to the grid based on the network deployment basic information includes: Based on the three-dimensional model, ray tracing technology is used to calculate the ray parameter information corresponding to each parameter combination of each AP for the mesh; wherein, the ray parameter information includes the number of rays, the amplitude of each ray, and the time delay information; Based on the ray parameter information corresponding to each parameter combination of each AP, the rank value and received power value of the channel matrix obtained by the grid from each AP under each parameter combination are calculated. For each AP, based on the rank of the channel matrix obtained by the grid from the AP under each parameter combination and the received power value, the rank of the maximum channel matrix under the first parameter combination and the maximum received power value under the second parameter combination of the AP corresponding to the grid are determined; wherein, the rank of the maximum channel matrix is ​​the maximum value among the rank values ​​of the channel matrix obtained by the grid from the AP under each parameter combination, the first parameter combination is the parameter combination corresponding to the rank of the maximum channel matrix, the maximum received power value is the maximum value among the received power values ​​obtained by the grid from the AP under each parameter combination, and the second parameter combination is the parameter combination corresponding to the maximum received power value.

[0008] Further, determining the parameter optimization results for network deployment in the target scenario based on the voting results includes: Based on the voting results, the total voting score corresponding to each parameter combination of each AP is calculated. For each AP, the target parameter combination corresponding to the AP is determined based on the total voting score corresponding to each parameter combination of the AP.

[0009] Further, the voting results include a first voting score corresponding to the first candidate parameter combination of each first candidate AP and a second voting score corresponding to the second candidate parameter combination of each second candidate AP; the step of calculating the total voting score corresponding to each parameter combination of each AP based on the voting results includes: Based on the voting results, the number of first votes corresponding to the first voting score and the number of second votes corresponding to the second voting score are calculated for each parameter combination of each AP. For each AP, the first voting score and the second voting score are used as weights to perform a weighted sum of the first number of votes and the second number of votes for each parameter combination of the AP, so as to obtain the total voting score corresponding to each parameter combination of the AP.

[0010] Furthermore, both the first voting score and the second voting score are greater than 0 and less than or equal to 1.

[0011] Further, determining the target parameter combination corresponding to the AP based on the total voting score corresponding to each parameter combination of the AP includes: The parameter combination with the highest total voting score is determined as the target parameter combination corresponding to this AP; or... Based on the total votes corresponding to each parameter combination of the AP, a weighted average is performed on each parameter combination of the AP to obtain the target parameter combination corresponding to the AP; or, Cluster the total votes corresponding to each parameter combination of the AP, and use the obtained cluster center values ​​as the target parameter combination corresponding to the AP.

[0012] Secondly, embodiments of the present invention also provide a network deployment parameter optimization device, comprising: The acquisition module is used to acquire basic network deployment information corresponding to the target scenario; wherein, the basic network deployment information includes gridding results and location information of multiple APs, and the gridding results include multiple grids obtained by gridding the plan view of the target scenario; The voting module is used to, based on the network deployment basic information, have each grid vote on the first candidate parameter combination of the first candidate AP and the second candidate parameter combination of the second candidate AP for each parameter combination under each AP, and obtain the voting result; wherein, the rank of the channel matrix obtained by each grid from the corresponding first candidate AP under the corresponding first candidate parameter combination is the largest, and the received power obtained by each grid from the corresponding second candidate AP under the corresponding second candidate parameter combination is the largest. The determining module is used to determine the parameter optimization results of the network deployment under the target scenario based on the voting results; wherein, the parameter optimization results include the target parameter combination corresponding to each AP.

[0013] Thirdly, embodiments of the present invention also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the network deployment parameter optimization method described in the first aspect.

[0014] Fourthly, embodiments of the present invention also provide a storage medium storing a computer program, wherein the computer program is executed by a processor to perform the network deployment parameter optimization method described in the first aspect.

[0015] The network deployment parameter optimization method, apparatus, electronic device, and storage medium provided in this invention, when deploying a network, first obtains basic network deployment information corresponding to the target scenario. This basic information includes a gridded result and the location information of multiple access points (APs). The gridded result includes multiple grids obtained by gridding a plan view of the target scenario. Then, based on the basic network deployment information, each grid votes on the first candidate parameter combination of the first candidate AP and the second candidate parameter combination of the second candidate AP for each parameter combination under each AP, obtaining voting results. The grid obtains the maximum rank of the channel matrix from the corresponding first candidate AP under the corresponding first candidate parameter combination, and the maximum received power from the corresponding second candidate AP under the corresponding second candidate parameter combination. Based on the voting results, the parameter optimization result for the network deployment in the target scenario is determined. The parameter optimization result includes the target parameter combination corresponding to each AP. This approach, tailored to the characteristics of 2B scenarios, employs a simple and efficient voting algorithm based on the rank of the channel matrix and received power, significantly reducing the complexity of the optimization algorithm within a very large parameter space while ensuring network coverage reliability. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating a network deployment parameter optimization method provided in an embodiment of the present invention; Figure 2 A schematic diagram of a meshing result provided in an embodiment of the present invention; Figure 3 A schematic diagram of three three-dimensional models corresponding to the indoor scene provided in an embodiment of the present invention; Figure 4 A schematic diagram of two three-dimensional models corresponding to an outdoor scene provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of a network deployment parameter optimization device provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Currently, wireless technologies such as Wi-Fi are increasingly widely used in 2B scenarios of large-scale wireless LANs. Addressing the network deployment issues in 2B scenarios, this invention provides a network deployment parameter optimization method, apparatus, electronic device, and storage medium. Using the RANK (rank of the channel matrix) value and RSSI (Received Signal Strength Indicator, also known as received power) value extracted from the channel as optimization targets, it employs a simple yet efficient weighted voting algorithm—a network planning optimization technique for 2B scenarios—to optimize network deployment and planning in 2B scenarios. This enables large-scale AP (Access Point) deployment optimization, significantly reducing the complexity of optimization algorithms within a vast parameter space while enhancing network coverage reliability.

[0020] To facilitate understanding of this embodiment, a network deployment parameter optimization method disclosed in this embodiment of the invention will first be described in detail.

[0021] This invention provides a method for optimizing network deployment parameters, which can be executed by an electronic device with data processing capabilities. See also... Figure 1 The diagram shows a flowchart of a network deployment parameter optimization method, which mainly includes the following steps S102 to S106: Step S102: Obtain basic network deployment information corresponding to the target scenario; wherein, the basic network deployment information includes the gridding result and the location information of multiple APs, and the gridding result includes multiple grids obtained by gridding the plan view of the target scenario.

[0022] The target scenario can be, but is not limited to, a B2B scenario. Examples include large office spaces, large factories / mega-factories, ports, steel mills, shopping malls, or coal mines. The placement locations of multiple access points (APs) are known, but their height (i.e., the AP's third-dimensional coordinate z) and angle parameters (including the antenna's downtilt angle) are not. and azimuth The parameter to be optimized is (i.e., the parameter to be optimized is...). Taking indoor Wi-Fi AP deployment as an example, the indoor floor plan can be gridded, such as... Figure 2 As shown, for example, the grid size is L×L There are a total of N Each grid.

[0023] In some embodiments, the aforementioned network deployment basic information, in addition to the meshing results and the location information of multiple APs, may also include a 3D model of the target scene. The 3D model is obtained by performing a 3D modeling of the target scene. 3D modeling software can be used to perform 3D modeling of the indoor scene, and the resulting 3D model includes walls, furniture, tables and chairs, lighting fixtures, green plants, and other objects that can reflect electromagnetic waves, such as... Figure 3 As shown; it is also possible to perform 3D modeling on outdoor scenes, resulting in 3D models including buildings such as houses, etc. Figure 4 As shown.

[0024] It should be noted that the target scenarios mentioned above are not limited to indoor scenarios, and the network devices deployed are not limited to Wi-Fi APs. In other embodiments, they can also be outdoor scenarios, or network planning of cellular small base stations or cellular 4G / 5G base stations in urban scenarios, etc.

[0025] Step S104: Based on the network deployment basic information, each grid votes on the first candidate parameter combination of the first candidate AP and the second candidate parameter combination of the second candidate AP for each parameter combination under each AP, and obtains the voting results; among them, the rank of the channel matrix obtained by each grid from the corresponding first candidate AP under the corresponding first candidate parameter combination is the largest, and the received power obtained by each grid from the corresponding second candidate AP under the corresponding second candidate parameter combination is the largest.

[0026] A higher rank (RANK) of the channel matrix corresponds to higher channel throughput. Therefore, this embodiment uses the rank of the channel matrix instead of channel throughput as an optimization objective. Higher received signal strength (RSSI) results in a larger network coverage area. Therefore, this embodiment uses received signal strength as another optimization objective. Based on these two optimization objectives, the overall network coverage and throughput can be maximized.

[0027] The above parameter combination, i.e., the above parameters to be optimized, is During voting, each grid only votes for the first candidate parameter combination of the first candidate AP that maximizes the rank of the channel matrix, and the second candidate parameter combination of the second candidate AP that maximizes the received power. That is, each grid casts two types of votes, corresponding to the rank of the channel matrix and the received power, respectively. These two types of votes can be cast for the same parameter combination of the same AP, different parameter combinations of the same AP, or parameter combinations of different APs. Similarly, two different grids can cast the same type of vote for the same parameter combination of the same AP, different parameter combinations of the same AP, or parameter combinations of different APs. It should be noted that the voting scores for these two types of votes can be the same or different, depending on the specific requirements.

[0028] In some possible embodiments, step S104 above can be implemented by the following sub-steps 1 to 3: Sub-step 1: For each grid, based on the network deployment information, determine the rank of the maximum channel matrix under the first parameter combination and the maximum received power under the second parameter combination for each AP corresponding to that grid.

[0029] The RANK and RSSI values ​​obtained by each grid from each AP under each parameter combination can be directly read from a mobile terminal through network testing. For example, placing a mobile phone within a grid allows direct reading of the RANK and RSSI values ​​from the corresponding AP. Alternatively, the wireless channel can be reconstructed using 3D reconstruction and ray tracing techniques, from which the two main parameters, RANK and RSSI, can be extracted. Based on this, the aforementioned network deployment infrastructure also includes a 3D model of the target scenario. The rank of the maximum channel matrix under the first parameter combination for each AP corresponding to the grid and the maximum received power under the second parameter combination for each AP can be determined as follows: First, based on the 3D model, ray tracing techniques are used to calculate the ray parameter information corresponding to each AP under each parameter combination for the grid. The ray parameter information includes the number of rays, the amplitude of each ray, and the delay information. Then, based on the ray parameter information corresponding to each AP under each parameter combination for the grid, the rank of the channel matrix and the maximum received power obtained by the grid from each AP under each parameter combination are calculated. Finally, for each AP, based on the rank of the channel matrix obtained from the AP under each parameter combination and the received power value, the rank of the maximum channel matrix under the first parameter combination and the maximum received power value under the second parameter combination of the AP corresponding to the grid are determined; where the rank of the maximum channel matrix is ​​the maximum value among the rank values ​​of the channel matrix obtained from the AP under each parameter combination of the grid, the first parameter combination is the parameter combination corresponding to the rank of the maximum channel matrix, the maximum received power value is the maximum value among the received power values ​​obtained from the AP under each parameter combination of the grid, and the second parameter combination is the parameter combination corresponding to the maximum received power value.

[0030] Sub-step 2: The first parameter combination of the AP corresponding to the maximum value of the rank of each maximum channel matrix corresponding to the grid is determined as the first candidate parameter combination of the first candidate AP corresponding to the grid, and the second parameter combination of the AP corresponding to the maximum value of each maximum received power value corresponding to the grid is determined as the second candidate parameter combination of the second candidate AP corresponding to the grid.

[0031] Sub-step 3: Determine the first candidate parameter combination and the second candidate parameter combination of the corresponding first candidate AP and the second candidate AP, respectively, for the grid to vote for.

[0032] Step S106: Based on the voting results, determine the parameter optimization results for network deployment in the target scenario; wherein, the parameter optimization results include the target parameter combination corresponding to each AP.

[0033] The voting results can reflect the network coverage and throughput corresponding to each parameter combination; the more votes a parameter combination receives, the greater its network coverage and throughput. In some possible embodiments, the total voting score for each parameter combination of each AP can be calculated based on the voting results; for each AP, the target parameter combination for that AP can be determined based on the total voting score for each parameter combination.

[0034] Optionally, the voting results include the first voting score corresponding to the first candidate parameter combination of each first candidate AP and the second voting score corresponding to the second candidate parameter combination of each second candidate AP. That is, the first voting score corresponds to the first type of vote, which is a vote cast based on the rank of the channel matrix; the second voting score corresponds to the second type of vote, which is a vote cast based on the received power. Based on this, the total voting score corresponding to each parameter combination of each AP can be determined by the following process: According to the voting results, the number of first votes corresponding to the first voting score and the number of second votes corresponding to the second voting score are counted for each parameter combination of each AP; for each AP, the first and second voting scores are used as weights to perform a weighted sum of the first and second voting scores for each parameter combination of the AP to obtain the total voting score corresponding to each parameter combination of the AP.

[0035] Optionally, both the first and second voting scores are greater than 0 and less than or equal to 1. For example, the first voting score is 1 and the second voting score is 0.75.

[0036] The present invention provides three specific methods for determining the target parameter combination, as follows: (1) the parameter combination with the highest total voting score is determined as the target parameter combination corresponding to the AP; or, (2) according to the total voting score corresponding to each parameter combination of the AP, the weighted average of each parameter combination of the AP is performed to obtain the target parameter combination corresponding to the AP; or, (3) the total voting score corresponding to each parameter combination of the AP is clustered and the obtained cluster center value is used as the target parameter combination corresponding to the AP.

[0037] In method (1) above, if there are two parameter combinations with the highest total voting scores, either one can be arbitrarily selected as the target parameter combination, or the two can be weighted and averaged based on the total voting scores to obtain the target parameter combination. In method (3) above, clustering algorithms such as K-means and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) can be used during clustering. This embodiment of the invention does not limit the specific clustering algorithm.

[0038] The network deployment parameter optimization method provided in this invention first obtains basic network deployment information corresponding to the target scenario during network deployment. This basic information includes a gridded result and the location information of multiple access points (APs). The gridded result includes multiple grids obtained by gridding a plan view of the target scenario. Then, based on the basic network deployment information, each grid votes on the first candidate parameter combination of the first candidate AP and the second candidate parameter combination of the second candidate AP for each parameter combination under each AP, obtaining voting results. The grid obtains the maximum rank of the channel matrix from the corresponding first candidate AP under the corresponding first candidate parameter combination, and the maximum received power from the corresponding second candidate AP under the corresponding second candidate parameter combination. Based on the voting results, the parameter optimization result for the network deployment in the target scenario is determined. The parameter optimization result includes the target parameter combination corresponding to each AP. This method, tailored to the characteristics of 2B scenarios, employs a simple and efficient voting algorithm based on the rank of the channel matrix and received power, significantly reducing the complexity of the optimization algorithm in a very large parameter space while ensuring network coverage reliability.

[0039] This invention provides a purely software-based simulation solution. Upon receiving the requirements for the target scene, a 3D model of the environment is created. Based on this 3D model and the location of the access points (APs) (where AP locations may only include APs within the model), the simulation proceeds.<x,y,z> The first two coordinates (where height z is used as an optimization parameter) are used to calculate the changes in electromagnetic waves emitted by the AP in three-dimensional space, including direct, reflected, refracted, diffracted, and transmitted rays, as well as the influence of parameters such as the material, reflection coefficient, and refractive index of the surfaces of objects contacted during electromagnetic wave transmission. Ray parameters (including ray amplitude, time delay, and angle of arrival) within each meshed grid are calculated. Based on the rays received by each grid, time-domain channel information is obtained, and the spatial degrees of freedom of the channel are analyzed (note that this assumes a MIMO (multiple input multiple output) channel, i.e., the AP is a multi-antenna, two-dimensional antenna array). Under the constraint of ensuring network coverage, the overall network throughput (the sum of the throughput of all grids) is maximized while minimizing the number of APs, thereby maximizing economic efficiency while ensuring overall network reliability. This invention extracts two main parameters, RANK value and RSSI value, from the channel obtained by software reconstruction as optimization targets, and proposes a simple and efficient weighted voting algorithm, which greatly reduces the complexity of the optimization algorithm in a very large parameter space and provides a feasible implementation plan that guarantees performance. Taking Wi-Fi AP deployment in an indoor scenario as an example, the specific steps of the above network deployment parameter optimization method are as follows: 1. Use 3D modeling software to create a 3D model of the interior scene, including walls, furniture, tables and chairs, lamps, green plants, and other objects that can reflect electromagnetic waves.

[0040] 2. In a known space M Location and angle parameters of each AP (3D coordinates)<x,y,z> and the antenna's downtilt angle and azimuth The optimization objective is to maximize overall network coverage and throughput across the entire region. Parameters to be optimized: .

[0041] 3. Grid the interior floor plan, with a grid size of [size missing]. L×L Assuming there are a total of N Each grid.

[0042] 4. Use ray-tracing technology to calculate the number of rays received on each grid, the amplitude of each ray, the time delay, and the angle of arrival.

[0043] 5. Assume the first i Each grid received from ID [number]. k The amplitude of the AP's rays, after being sorted by time according to the time delay information, is (padding to zero). J (Number of values): , i.e., grid i To AP k The time-domain impulse response (CIR).

[0044] 6. Obtain the grid by performing an IFFT (Inverse Fast Fourier Transform) on the CIR. i Channel Frequency Response (CFR) in the frequency domain: (Column vector).

[0045] 7. For the covariance matrix Matrix decomposition yields nonzero eigenvalues The matrix decomposition can be performed using SVD (Singular Value Decomposition) or ED (Eigen Decomposition), with the number of non-zero eigenvalues ​​corresponding to the number of effective rays.

[0046] 8. Take the middle one such that The condition number is less than The former Each feature value is a RANK value. (A higher RANK value indicates higher channel throughput; therefore, RANK value is used instead of throughput as the optimization target.) The condition number is the ratio of the largest eigenvalue to the smallest eigenvalue. This is a preset threshold value.

[0047] 9. Take The RSSI value is obtained by averaging the diagonal elements of the matrix. (Guaranteed coverage). Among them, J The preset number of rays, diag This involves summing the elements after taking the diagonal elements.

[0048] 10. Assume AP m The antenna can be adjusted downtilt angle The range is and azimuth variable range (For some APs, this can be equivalent to the AP body rotating within this angular range), the height of the AP. z exist Adjustments can be made within the specified range.

[0049] 11. Using step size respectively , as well as Traversing AP m The adjustable range of the antenna's downtilt angle, azimuth angle, and altitude is calculated and recorded for each. RANK value below and RSSI value p .

[0050] 12. Search M In each AP, make The largest AP ID, let's assume it is... k ; make The largest AP ID, let's assume it is... l , ( k and l It may be the same, or it may be different.

[0051] 13. and for AP k , making Largest parameter combination Assuming Then the maximum RANK value is For AP l , making Largest parameter combination Assuming Then the maximum RSSI value is ; 14. Voting method adopted: a) Grid i Vote for ID k Parameter combinations under AP That is, add the number of votes to this parameter combination. ,in .

[0052] b) Grid i Vote for ID l Parameter combinations under AP That is, add the number of votes to this parameter combination. ρ ,in .

[0053] c) For example, take .

[0054] 15. All parameter combinations for each AP will receive a vote from the grid (vote count + ... Or + ρ That is, the voting principle for each grid is to select a certain combination of parameters under the AP that maximizes the RANK or RSSI of that grid.

[0055] 16. For all N Repeat steps 11-13 for each grid.

[0056] 17. Therefore, each parameter combination of each AP will receive a vote from the grid.

[0057] 18. AP sorts the number of votes for all parameter combinations and selects the parameter combination with the most votes as the optimal result.

[0058] 19. Following this method, each AP selects an optimal parameter combination, and the optimization of the entire network is completed.

[0059] In summary, this invention provides a network deployment optimization scheme for 2B scenarios, maximizing network speed and minimizing AP usage while ensuring network coverage. This guarantees network reliability and smoothness, as well as the economic efficiency of network deployment. Two main parameters, RANK value and RSSI value, are extracted from the channel obtained through software reconstruction as optimization targets. A simple and efficient weighted voting algorithm is proposed, significantly reducing the complexity of optimization algorithms in a very large parameter space and providing a feasible implementation scheme that guarantees performance.

[0060] Corresponding to the network deployment parameter optimization method described above, this embodiment of the invention also provides a network deployment parameter optimization device, see [link to device]. Figure 5The diagram shows a structural schematic of a network deployment parameter optimization device, which includes: The acquisition module 501 is used to acquire basic network deployment information corresponding to the target scenario; wherein, the basic network deployment information includes the gridding result and the location information of multiple APs, and the gridding result includes multiple grids obtained by gridding the plan view of the target scenario; The voting module 502 is used to vote on the first candidate parameter combination of the first candidate AP and the second candidate parameter combination of the second candidate AP for each parameter combination under each AP based on the network deployment basic information, and to obtain the voting results; wherein, the rank of the channel matrix obtained by each grid from the corresponding first candidate AP under the corresponding first candidate parameter combination is the largest, and the received power obtained by each grid from the corresponding second candidate AP under the corresponding second candidate parameter combination is the largest. The determination module 503 is used to determine the parameter optimization results of the network deployment under the target scenario based on the voting results; wherein, the parameter optimization results include the target parameter combination corresponding to each AP.

[0061] The network deployment parameter optimization device provided in this embodiment of the invention first acquires basic network deployment information corresponding to the target scenario during network deployment. This basic information includes a gridded result and the location information of multiple access points (APs). The gridded result includes multiple grids obtained by gridding a plan view of the target scenario. Then, based on the basic network deployment information, each grid votes on the first candidate parameter combination of the first candidate AP and the second candidate parameter combination of the second candidate AP for each parameter combination under each AP, obtaining voting results. The grid obtains the maximum rank of the channel matrix from the corresponding first candidate AP under the corresponding first candidate parameter combination, and the maximum received power from the corresponding second candidate AP under the corresponding second candidate parameter combination. Based on the voting results, the parameter optimization result for the network deployment in the target scenario is determined. The parameter optimization result includes the target parameter combination corresponding to each AP. This approach, tailored to the characteristics of 2B scenarios, employs a simple and efficient voting algorithm based on the rank of the channel matrix and received power, significantly reducing the complexity of the optimization algorithm within a very large parameter space while ensuring network coverage reliability.

[0062] Further, the voting module 502 is specifically used for: for each grid, based on the network deployment basic information, determining the rank of the maximum channel matrix under the first parameter combination of each AP corresponding to the grid and the maximum received power value under the second parameter combination of each AP; determining the first parameter combination of the AP corresponding to the maximum value among the rank values ​​of each maximum channel matrix corresponding to the grid as the first candidate parameter combination of the first candidate AP corresponding to the grid, and determining the second parameter combination of the AP corresponding to the maximum value among the maximum received power values ​​corresponding to the grid as the second candidate parameter combination of the second candidate AP corresponding to the grid; determining the first candidate parameter combination and the second candidate parameter combination of the first candidate AP and the second candidate AP to which the grid votes respectively.

[0063] Furthermore, the aforementioned network deployment basic information also includes a three-dimensional model of the target scene; the voting module 502 is also used to: calculate the ray parameter information corresponding to each parameter combination of each AP for the grid based on the three-dimensional model using ray tracing technology; wherein, the ray parameter information includes the number of rays, the amplitude of each ray, and the delay information; calculate the rank value and received power value of the channel matrix obtained by the grid from each AP under each parameter combination based on the ray parameter information corresponding to the grid under each parameter combination; for each AP, determine the rank value of the maximum channel matrix under the first parameter combination and the maximum received power value under the second parameter combination of the AP corresponding to the grid based on the rank value and received power value of the channel matrix obtained by the grid from the AP under each parameter combination; wherein, the rank value of the maximum channel matrix is ​​the maximum value among the rank values ​​of the channel matrix obtained by the grid from the AP under each parameter combination, the first parameter combination is the parameter combination corresponding to the rank value of the maximum channel matrix, the maximum received power value is the maximum value among the received power values ​​obtained by the grid from the AP under each parameter combination, and the second parameter combination is the parameter combination corresponding to the maximum received power value.

[0064] Furthermore, the aforementioned determining module 503 is specifically used to: calculate the total voting score corresponding to each parameter combination of each AP based on the voting results; and for each AP, determine the target parameter combination corresponding to the AP based on the total voting score corresponding to each parameter combination of the AP.

[0065] Furthermore, the voting results include the first voting score corresponding to the first candidate parameter combination of each first candidate AP and the second voting score corresponding to the second candidate parameter combination of each second candidate AP; the determining module 503 is also used to: based on the voting results, count the number of first votes corresponding to the first voting score and the number of second votes corresponding to the second voting score for each parameter combination of each AP; for each AP, using the first voting score and the second voting score as weights, perform a weighted sum of the number of first votes and the number of second votes for each parameter combination of the AP to obtain the total voting score corresponding to each parameter combination of the AP.

[0066] Furthermore, both the first and second vote scores are greater than 0 and less than or equal to 1.

[0067] Furthermore, the aforementioned determining module 503 is also used to: determine the parameter combination with the highest total voting score as the target parameter combination corresponding to the AP; or, perform a weighted average of the various parameter combinations of the AP according to the total voting scores corresponding to the various parameter combinations of the AP to obtain the target parameter combination corresponding to the AP; or, perform clustering processing on the total voting scores corresponding to the various parameter combinations of the AP, and use the obtained cluster center value as the target parameter combination corresponding to the AP.

[0068] The network deployment parameter optimization device provided in this embodiment has the same implementation principle and technical effect as the aforementioned network deployment parameter optimization method embodiment. For the sake of brevity, any parts not mentioned in the network deployment parameter optimization device embodiment can be referred to the corresponding content in the aforementioned network deployment parameter optimization method embodiment.

[0069] like Figure 6 As shown, an electronic device 600 provided in this embodiment of the invention includes: a processor 601, a memory 602 and a bus. The memory 602 stores a computer program that can run on the processor 601. When the electronic device 600 is running, the processor 601 and the memory 602 communicate through the bus. The processor 601 executes the computer program to implement the above-mentioned network deployment parameter optimization method.

[0070] Specifically, the memory 602 and processor 601 mentioned above can be general-purpose memory and processor, without any specific limitations here.

[0071] This invention also provides a storage medium storing a computer program, which, when executed by a processor, performs the network deployment parameter optimization method described in the preceding method embodiments. The storage medium includes various media capable of storing program code, such as a USB flash drive, external hard drive, read-only memory (ROM), RAM, magnetic disk, or optical disk.

[0072] In all examples shown and described herein, any specific values ​​should be interpreted as merely exemplary and not as limitations; therefore, other examples of exemplary embodiments may have different values.

[0073] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing network deployment parameters, characterized in that, include: Obtain basic network deployment information corresponding to the target scenario; wherein, the basic network deployment information includes gridding results and location information of multiple APs, and the gridding results include multiple grids obtained by gridding the plan view of the target scenario; Based on the network deployment information, each grid performs voting on the first candidate parameter combination of the first candidate AP and the second candidate parameter combination of the second candidate AP for each parameter combination under each AP, and obtains the voting results; wherein, the rank of the channel matrix obtained by each grid from the corresponding first candidate AP under the corresponding first candidate parameter combination is the largest, and the received power obtained by each grid from the corresponding second candidate AP under the corresponding second candidate parameter combination is the largest; the parameter combination includes altitude and antenna downtilt angle and azimuth angle; Based on the network deployment information, each grid votes on the first candidate parameter combination of the first candidate AP and the second candidate parameter combination of the second candidate AP for each parameter combination under each AP, to obtain the voting results. This includes: for each grid, based on the network deployment information, determining the rank of the maximum channel matrix under the first parameter combination of each AP and the maximum received power value under the second parameter combination of each AP corresponding to that grid; determining the first parameter combination of the AP corresponding to the maximum rank of each maximum channel matrix of that grid as the first candidate parameter combination of the first candidate AP corresponding to that grid, and determining the second parameter combination of the AP corresponding to the maximum received power value of each maximum received power value of that grid as the second candidate parameter combination of the second candidate AP corresponding to that grid; and determining the first candidate parameter combination of the first candidate AP and the second candidate parameter combination of the second candidate AP that the grid votes for respectively. Based on the voting results, the parameter optimization results for network deployment under the target scenario are determined; wherein, the parameter optimization results include the target parameter combination corresponding to each AP.

2. The network deployment parameter optimization method according to claim 1, characterized in that, The network deployment basic information also includes a three-dimensional model of the target scene; the step of determining the rank of the maximum channel matrix under the first parameter combination and the maximum received power value under the second parameter combination for each AP corresponding to the grid based on the network deployment basic information includes: Based on the three-dimensional model, ray tracing technology is used to calculate the ray parameter information corresponding to each parameter combination of each AP for the mesh; wherein, the ray parameter information includes the number of rays, the amplitude of each ray, and the time delay information; Based on the ray parameter information corresponding to each parameter combination of each AP, the rank value and received power value of the channel matrix obtained by the grid from each AP under each parameter combination are calculated. For each AP, based on the rank of the channel matrix obtained by the grid from the AP under each parameter combination and the received power value, the rank of the maximum channel matrix under the first parameter combination and the maximum received power value under the second parameter combination of the AP corresponding to the grid are determined; wherein, the rank of the maximum channel matrix is ​​the maximum value among the rank values ​​of the channel matrix obtained by the grid from the AP under each parameter combination, the first parameter combination is the parameter combination corresponding to the rank of the maximum channel matrix, the maximum received power value is the maximum value among the received power values ​​obtained by the grid from the AP under each parameter combination, and the second parameter combination is the parameter combination corresponding to the maximum received power value.

3. The network deployment parameter optimization method according to claim 1, characterized in that, The step of determining the parameter optimization results for network deployment in the target scenario based on the voting results includes: Based on the voting results, the total voting score corresponding to each parameter combination of each AP is calculated. For each AP, the target parameter combination corresponding to the AP is determined based on the total voting score corresponding to each parameter combination of the AP.

4. The network deployment parameter optimization method according to claim 3, characterized in that, The voting results include a first voting score corresponding to the first candidate parameter combination of each first candidate AP and a second voting score corresponding to the second candidate parameter combination of each second candidate AP; The step of calculating the total vote score for each parameter combination of each AP based on the voting results includes: Based on the voting results, the number of first votes corresponding to the first voting score and the number of second votes corresponding to the second voting score are calculated for each parameter combination of each AP. For each AP, the first voting score and the second voting score are used as weights to perform a weighted sum of the first number of votes and the second number of votes for each parameter combination of the AP, so as to obtain the total voting score corresponding to each parameter combination of the AP.

5. The network deployment parameter optimization method according to claim 4, characterized in that, Both the first vote score and the second vote score are greater than 0 and less than or equal to 1.

6. The network deployment parameter optimization method according to claim 3, characterized in that, The step of determining the target parameter combination corresponding to the AP based on the total voting score corresponding to each parameter combination of the AP includes: The parameter combination with the highest total voting score is determined as the target parameter combination corresponding to this AP; or... Based on the total votes corresponding to each parameter combination of the AP, a weighted average is performed on each parameter combination of the AP to obtain the target parameter combination corresponding to the AP; or, Cluster the total votes corresponding to each parameter combination of the AP, and use the obtained cluster center values ​​as the target parameter combination corresponding to the AP.

7. A network deployment parameter optimization device, characterized in that, include: The acquisition module is used to acquire basic network deployment information corresponding to the target scenario; wherein, the basic network deployment information includes gridding results and location information of multiple APs, and the gridding results include multiple grids obtained by gridding the plan view of the target scenario; The voting module is used to, based on the network deployment information, have each grid vote on the first candidate parameter combination of the first candidate AP and the second candidate parameter combination of the second candidate AP for each parameter combination under each AP, to obtain the voting result; wherein, the rank of the channel matrix obtained by each grid from the corresponding first candidate AP under the corresponding first candidate parameter combination is the largest, and the received power obtained by each grid from the corresponding second candidate AP under the corresponding second candidate parameter combination is the largest; the parameter combination includes altitude and antenna downtilt angle and azimuth angle; The voting module is specifically used for: for each grid, based on the network deployment basic information, determining the rank of the maximum channel matrix under the first parameter combination of each AP corresponding to the grid and the maximum received power value under the second parameter combination of each AP; determining the first parameter combination of the AP corresponding to the maximum value among the rank values ​​of each maximum channel matrix corresponding to the grid as the first candidate parameter combination of the first candidate AP corresponding to the grid, and determining the second parameter combination of the AP corresponding to the maximum value among the maximum received power values ​​corresponding to the grid as the second candidate parameter combination of the second candidate AP corresponding to the grid; determining the first candidate parameter combination of the first candidate AP and the second candidate parameter combination of the second candidate AP that the grid votes for respectively; The determining module is used to determine the parameter optimization results of the network deployment under the target scenario based on the voting results; wherein, the parameter optimization results include the target parameter combination corresponding to each AP.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the network deployment parameter optimization method according to any one of claims 1-6.

9. A storage medium storing a computer program, characterized in that, The computer program is executed by the processor to perform the network deployment parameter optimization method according to any one of claims 1-6.

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