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

Abstract

The present invention provides a network deployment optimization method, device, electronic device and storage medium, which relate to the field of communication technology. According to the characteristics of the 2B scenario, the present invention first screens out the optimal AP position combination with the least number of APs and the highest grid coverage when performing network deployment optimization, and then determines the target deployment result with the maximum overall network throughput under the optimal AP position combination. That is, under the premise of ensuring network coverage, the overall network throughput is maximized and the number of APs used is minimized, thereby ensuring the reliability and smoothness of network coverage while ensuring the economy of network deployment.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a network deployment optimization method, device, electronic device and storage medium. Background Art

[0002] Wireless communication technology is not only playing an increasingly important role in consumer (B2C) scenarios, but is also increasingly becoming increasingly reliant on it in a growing number of industrial applications. For example, unmanned port control, real-time factory monitoring, video backhaul, machine tool control, robotics control, fully automated large warehouses, large office areas, and large live streaming platforms all rely on the reliability, comprehensive coverage, and high-speed connectivity of wireless networks. Consequently, wireless technologies like Wi-Fi are increasingly being used in B2B scenarios within large-scale wireless local area networks (WLANs). B2C scenarios refer to those using B2B networks, primarily for individual users. B2B scenarios refer to those using B2B networks, primarily for non-personal applications within industries, organizations, enterprises, and other sectors.

[0003] However, in the 2B scenario, the space is large and the environment is complex. How to ensure the reliability and smoothness of network coverage while ensuring the economy of network deployment is an urgent problem to be solved. Summary of the Invention

[0004] The purpose of the present invention is to provide a network deployment optimization method, device, electronic device and storage medium to ensure the reliability and smoothness of network coverage while ensuring the economy of network deployment.

[0005] In a first aspect, an embodiment of the present invention provides a network deployment optimization method, comprising:

[0006] Obtaining basic network deployment information corresponding to the target scenario; wherein the basic network deployment information includes a three-dimensional model, a gridding result, and multiple candidate AP locations, and the gridding result includes multiple grids obtained by network processing the plan view of the target scenario;

[0007] Based on the three-dimensional model and the gridding result, an optimal AP position combination that meets a preset coverage threshold is screened out from the multiple candidate AP positions; wherein the optimal AP position combination has the least number of APs and the grid coverage corresponding to the optimal AP position combination is the highest;

[0008] Based on the optimal AP position combination, the target deployment result corresponding to the target scenario is determined; wherein the target deployment result includes the target parameter combination corresponding to the target AP deployed at each AP position in the optimal AP position combination, and the entire network throughput corresponding to each of the target APs under the corresponding target parameter combination is the largest.

[0009] Furthermore, the selecting, based on the three-dimensional model and the gridding result, an optimal AP position combination that reaches a preset coverage threshold from the multiple candidate AP positions includes:

[0010] Get the current value of the number of initialized APs;

[0011] Determining, based on the multiple candidate AP positions, multiple AP position combinations corresponding to the current value;

[0012] Determining a grid coverage rate corresponding to each AP position combination based on the three-dimensional model and the gridding result;

[0013] Determine the AP position combination with the largest grid coverage as the candidate AP position combination corresponding to the current value;

[0014] When the grid coverage corresponding to the candidate AP position combination does not reach the coverage threshold, updating the current value according to a preset step size;

[0015] When the grid coverage corresponding to the candidate AP position combination reaches the coverage threshold, the candidate AP position combination is determined as the optimal AP position combination.

[0016] Furthermore, determining the grid coverage corresponding to each AP position combination based on the three-dimensional model and the gridding result includes:

[0017] Based on the three-dimensional model and ray tracing technology, calculate and obtain ray parameter information corresponding to each parameter combination of each AP position combination; wherein the ray parameter information includes the number of rays and the amplitude of each ray;

[0018] Based on the ray parameter information corresponding to each parameter combination of each AP position combination, the number of valid grids corresponding to each parameter combination of each AP position combination is obtained by counting; wherein the number of valid grids is the number of grids that receive at least a preset number of valid rays, and the valid rays are rays with amplitudes greater than a preset amplitude threshold;

[0019] Calculate the grid coverage corresponding to each parameter combination of each AP position combination based on the number of valid grids corresponding to each parameter combination of each AP position combination;

[0020] The maximum value among the grid coverage rates corresponding to the parameter combinations of each AP position combination is determined as the grid coverage rate corresponding to each AP position combination.

[0021] Furthermore, determining a target deployment result corresponding to the target scenario based on the optimal AP position combination includes:

[0022] Determining an expected AP position and an expected parameter combination corresponding to each of the grids based on the optimal AP position combination; wherein the channel capacity from the target AP to the grid under the expected AP position and expected parameter combination corresponding to the grid is the largest;

[0023] Determining candidate parameter combinations for each target AP based on the desired AP positions and desired parameter combinations corresponding to the grids; wherein the candidate parameter combinations for the target APs are desired parameter combinations corresponding to the AP positions where the target APs are deployed;

[0024] From the candidate parameter combinations of each target AP, a target parameter combination corresponding to each target AP is determined.

[0025] Furthermore, determining the desired AP position and desired parameter combination corresponding to each grid based on the optimal AP position combination includes:

[0026] For each of the grids, based on the three-dimensional model and ray tracing technology, calculating the channel capacity from each of the target APs to the grid under each parameter combination;

[0027] Determining, based on the channel capacity from each target AP to the grid under each parameter combination, the maximum channel capacity under the optimal parameter combination for each target AP corresponding to the grid; wherein the maximum channel capacity is the maximum value of the channel capacities from each target AP to the grid under each parameter combination, and the optimal parameter combination is the parameter combination corresponding to the maximum channel capacity;

[0028] The AP position and optimal parameter combination of the target AP corresponding to the maximum value among the maximum channel capacities corresponding to the grid are determined as the expected AP position and expected parameter combination corresponding to the grid.

[0029] Furthermore, determining a target parameter combination corresponding to each target AP from each candidate parameter combination of each target AP includes:

[0030] For each target AP, determining the corresponding grids of the target AP; wherein the related grids are grids with the AP position of the target AP as the expected AP position;

[0031] Determining a metric value corresponding to each candidate parameter combination of the target AP under each of the relevant grids; wherein the metric value is a value obtained by normalizing the channel capacity from the target AP to the corresponding relevant grid under the corresponding candidate parameter combination;

[0032] Summing up the metric values ​​corresponding to each candidate parameter combination of the target AP under each of the related grids to obtain a total metric value corresponding to each candidate parameter combination of the target AP;

[0033] The candidate parameter combination corresponding to the maximum value among the total metric values ​​corresponding to the candidate parameter combinations of the target AP is determined as the target parameter combination corresponding to the target AP.

[0034] Furthermore, determining a target parameter combination corresponding to each target AP from each candidate parameter combination of each target AP includes:

[0035] Based on the three-dimensional model and ray tracing technology, calculate the total channel capacity corresponding to each candidate parameter combination of each target AP;

[0036] Based on the total channel capacity corresponding to each candidate parameter combination of each target AP, a target parameter combination corresponding to each target AP is determined by using a Viterbi algorithm.

[0037] In a second aspect, an embodiment of the present invention further provides a network deployment optimization device, comprising:

[0038] An acquisition module is configured to acquire basic network deployment information corresponding to a target scenario; wherein the basic network deployment information includes a three-dimensional model, a gridding result, and a plurality of candidate AP locations, wherein the gridding result includes a plurality of grids obtained by performing a network processing on a plan view of the target scenario;

[0039] a screening module, configured to screen, from the plurality of candidate AP positions, an optimal AP position combination that meets a preset coverage threshold based on the three-dimensional model and the gridding result; wherein the optimal AP position combination has the least number of APs and the grid coverage corresponding to the optimal AP position combination is the highest;

[0040] A determination module is used to determine the target deployment result corresponding to the target scenario based on the optimal AP position combination; wherein the target deployment result includes a target parameter combination corresponding to the target AP deployed at each AP position in the optimal AP position combination, and the entire network throughput corresponding to each of the target APs under the corresponding target parameter combination is the largest.

[0041] In a third aspect, an embodiment of the present invention further provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the network deployment optimization method described in the first aspect is implemented.

[0042] In a fourth aspect, an embodiment of the present invention further provides a storage medium having a computer program stored thereon, and when the computer program is run by a processor, the network deployment optimization method described in the first aspect is executed.

[0043] The network deployment optimization method, device, electronic device and storage medium provided by the embodiments of the present invention first obtain basic network deployment information corresponding to the target scenario when performing network deployment optimization; wherein the basic network deployment information includes a three-dimensional model, a gridding result and multiple alternative AP positions, and the gridding result includes multiple grids obtained by network processing of the plan view of the target scenario; then, based on the three-dimensional model and the gridding result, the optimal AP position combination that reaches a preset coverage threshold is screened out from the multiple alternative AP positions; wherein the number of APs in the optimal AP position combination is the least, and the grid coverage rate corresponding to the optimal AP position combination is the largest; and then, based on the optimal AP position combination, the target deployment result corresponding to the target scenario is determined; wherein the target deployment result includes a target parameter combination corresponding to the target AP deployed at each AP position in the optimal AP position combination, and the entire network throughput corresponding to each target AP under the corresponding target parameter combination is the largest. In this way, based on the characteristics of the 2B scenario, we first screen out the optimal AP position combination with the least number of APs and the highest grid coverage, and then determine the target deployment result with the highest overall network throughput under the optimal AP position combination. That is, while ensuring network coverage, we maximize the overall network throughput and minimize the number of APs used. This ensures the reliability and smoothness of network coverage while ensuring the economic efficiency of network deployment. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 A schematic diagram of a flow chart of a network deployment optimization method provided by an embodiment of the present invention;

[0046] Figure 2 Schematic diagram of three 3D models corresponding to the indoor scene provided by an embodiment of the present invention;

[0047] Figure 3 A schematic diagram of two three-dimensional models corresponding to an outdoor scene provided by an embodiment of the present invention;

[0048] Figure 4 Schematic diagram of two gridding results provided by an embodiment of the present invention;

[0049] Figure 5 A signal coverage heat map provided by an embodiment of the present invention;

[0050] Figure 6 A schematic diagram of the structure of a network deployment optimization device provided by an embodiment of the present invention;

[0051] Figure 7 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0053] Currently, wireless technologies such as Wi-Fi are increasingly being used in 2B scenarios of large-scale wireless local area networks. In response to network deployment issues in 2B scenarios, embodiments of the present invention provide a network deployment optimization method, device, electronic device, and storage medium. These methods utilize network planning optimization technology in 2B scenarios and can be used to optimize network deployment and planning in 2B scenarios, enabling large-scale AP (Access Point, wireless access point) deployment in 2B scenarios and enhancing network coverage reliability. These methods can maximize network speed and minimize the number of APs used while ensuring network coverage, thereby ensuring both the reliability and smoothness of network coverage and the economic efficiency of network deployment.

[0054] To facilitate understanding of this embodiment, a network deployment optimization method disclosed in an embodiment of the present invention is first introduced in detail.

[0055] The embodiment of the present invention provides a network deployment optimization method, which can be executed by an electronic device with data processing capabilities. Figure 1 The flowchart of a network deployment optimization method shown in FIG. 1 mainly includes the following steps S102 to S106:

[0056] Step S102, obtaining basic network deployment information corresponding to the target scene; wherein the basic network deployment information includes a three-dimensional model, a gridding result, and multiple candidate AP positions, and the gridding result includes multiple grids obtained by network processing the plan view of the target scene.

[0057] The target scene can be but not limited to 2B scenes, for example, the target scene includes large office spaces, large factories / super factories, ports, steel mills, shopping malls or coal mines, etc. The 3D model is obtained by 3D modeling of the target scene. 3D modeling software can be used to perform 3D modeling of indoor scenes. The obtained 3D model includes walls, furniture, tables and chairs, lamps, green plants and other objects that can reflect electromagnetic waves, such as Figure 2 As shown; outdoor scenes can also be 3D modeled, and the resulting 3D models include buildings such as houses, such as Figure 3 The gridding result is obtained by processing the plane map of the target scene in a networked manner. Taking the indoor scene as an example, Figure 4 As shown, the indoor floor plan is gridded, for example, the grid size is L×L, and there are N grids in total. Multiple candidate AP positions are known, that is, the two-dimensional coordinates are known<x,y> The purpose of the embodiment of the present invention is to select K optimal location parameters from M candidate AP locations through an optimization algorithm. K should be as small as possible and the network throughput should be maximized. The optimal location parameters include the optimal AP location and its parameter combination (AP's three-dimensional coordinates).<x,y,z> , and the antenna downtilt angle φ, and the azimuth angle ).

[0058] It should be noted that the above-deployed network devices are not limited to Wi-Fi APs. In other embodiments, they may also be cellular small base stations or cellular 4G / 5G base stations for network planning in urban scenarios.

[0059] Step S104: Based on the three-dimensional model and the gridding results, an optimal AP position combination that meets a preset coverage threshold is selected from multiple candidate AP positions; wherein the optimal AP position combination has the least number of APs and the grid coverage corresponding to the optimal AP position combination is the highest.

[0060] The above coverage threshold can be set according to actual needs and is not limited here. In some possible embodiments, the above step S104 can be implemented by the following process: first obtain the current value of the initialized number of APs; then, based on multiple candidate AP positions, determine multiple AP position combinations corresponding to the current value; then, based on the three-dimensional model and the gridding result, determine the grid coverage corresponding to each AP position combination; determine the AP position combination with the largest grid coverage as the candidate AP position combination corresponding to the current value; when the grid coverage corresponding to the candidate AP position combination does not reach the coverage threshold, update the current value according to the preset step size, and then re-execute the step of determining multiple AP position combinations corresponding to the current value based on multiple candidate AP positions; when the grid coverage corresponding to the candidate AP position combination reaches the coverage threshold, determine the candidate AP position combination as the optimal AP position combination.

[0061] You can first initialize a smaller value as the current value of the number of APs, for example, the initial current value is 3. Select the current value of candidate AP positions from multiple candidate AP positions to obtain multiple AP position combinations. Each parameter combination of each AP position combination can obtain a grid coverage rate, such as Figure 5 As shown in the figure, the maximum grid coverage rate among all parameter combinations for each AP location combination can be used as the grid coverage rate corresponding to that AP location combination. The preset step size can be set based on actual needs. For example, if the preset step size is 2, the sum of the current value and 2 is used as the updated current value. By continuously updating the current value, the optimal AP location combination that ensures the grid coverage rate meets the coverage threshold and has the least number of APs can be found.

[0062] Optionally, the grid coverage rate corresponding to each AP position combination can be determined by the following process: first, based on the three-dimensional model and ray tracing technology, calculate the ray parameter information corresponding to each parameter combination of each AP position combination; wherein the ray parameter information includes the number of rays and the amplitude of each ray; then, based on the ray parameter information corresponding to each parameter combination of each AP position combination, statistically obtain the number of valid grids corresponding to each parameter combination of each AP position combination; wherein the number of valid grids is the number of grids that receive at least a preset number of valid rays, and the valid rays are rays with amplitudes greater than a preset amplitude threshold; then, based on the number of valid grids corresponding to each parameter combination of each AP position combination, calculate the grid coverage rate corresponding to each parameter combination of each AP position combination; the maximum value of the grid coverage rates corresponding to each parameter combination of each AP position combination is determined as the grid coverage rate corresponding to each AP position combination. Wherein, the preset number and the preset amplitude threshold can be set according to actual needs and are not limited here.

[0063] Step S106: Determine the target deployment result corresponding to the target scenario based on the optimal AP position combination; wherein the target deployment result includes the target parameter combination corresponding to the target AP deployed at each AP position in the optimal AP position combination, and the entire network throughput corresponding to each target AP under the corresponding target parameter combination is the largest.

[0064] In some possible embodiments, the above step S106 may be implemented by the following sub-steps 1 to 3:

[0065] Sub-step 1: Based on the optimal AP position combination, determine the desired AP position and desired parameter combination corresponding to each grid; wherein the channel capacity from the target AP under the desired AP position and desired parameter combination corresponding to the grid to the grid is the largest.

[0066] The embodiment of the present invention can utilize three-dimensional reconstruction and ray tracing technology to reconstruct wireless channels, calculate the channel capacity based on the channel parameters, and thus determine the expected AP position and expected parameter combination corresponding to each grid. Specifically, for each grid, based on the three-dimensional model and ray tracing technology, the channel capacity from each target AP to the grid under each parameter combination is calculated; based on the channel capacity from each target AP to the grid under each parameter combination, the maximum channel capacity under the optimal parameter combination for each target AP corresponding to the grid is determined; wherein the maximum channel capacity is the maximum value of the channel capacity from each target AP to the grid under each parameter combination, and the optimal parameter combination is the parameter combination corresponding to the maximum channel capacity; the AP position and optimal parameter combination of the target AP corresponding to the maximum value of each maximum channel capacity corresponding to the grid are determined as the expected AP position and expected parameter combination corresponding to the grid.

[0067] Sub-step 2: determining candidate parameter combinations for each target AP based on the desired AP positions and desired parameter combinations corresponding to the grids; wherein the candidate parameter combinations for the target AP are the desired parameter combinations corresponding to the AP positions where the target AP is deployed.

[0068] That is, different desired parameter combinations corresponding to each desired AP position are determined to be candidate parameter combinations corresponding to the target AP deployed at the desired AP position.

[0069] Sub-step 3: Determine a target parameter combination corresponding to each target AP from each candidate parameter combination of each target AP.

[0070] The present invention provides two specific methods for determining target parameter combinations, which are as follows:

[0071] Method 1: For each target AP, first determine the relevant grids corresponding to the target AP; wherein the relevant grid is a grid with the AP position of the target AP as the expected AP position; then determine the metric value corresponding to each candidate parameter combination of the target AP under each relevant grid; wherein the metric value is the value obtained by normalizing the channel capacity from the target AP to the corresponding relevant grid under the corresponding candidate parameter combination; then, sum the metric values ​​corresponding to each candidate parameter combination of the target AP under each relevant grid to obtain the total metric value corresponding to each candidate parameter combination of the target AP; the candidate parameter combination corresponding to the maximum value among the total metric values ​​corresponding to each candidate parameter combination of the target AP is determined as the target parameter combination corresponding to the target AP.

[0072] Method 2: First, using a 3D model and ray tracing technology, calculate the total channel capacity corresponding to each candidate parameter combination for each target AP. Then, based on the total channel capacity corresponding to each candidate parameter combination for each target AP, use the Viterbi algorithm to determine the target parameter combination for each target AP.

[0073] In the above-mentioned method 2, the total channel capacity corresponding to each candidate parameter combination of each target AP can be calculated by the following process: for each candidate parameter combination of each target AP, first, based on the three-dimensional model, use ray tracing technology to calculate the ray parameter information corresponding to each grid under the candidate parameter combination of the target AP; wherein the ray parameter information includes the number of rays, the amplitude and delay information of each ray; then, based on the ray parameter information corresponding to each grid under the candidate parameter combination of the target AP, calculate the channel capacity from the target AP to each grid under the candidate parameter combination; finally, based on the channel capacity from the target AP to each grid under the candidate parameter combination, calculate the total channel capacity corresponding to the candidate parameter combination of the target AP (the total channel capacity is equal to the sum of the corresponding channel capacities).

[0074] In the above-mentioned method 2, determining the target parameter combination corresponding to each target AP by the Viterbi algorithm can be achieved through the following process: sorting each target AP according to the positional relationship between the target APs to obtain the AP sorting result; then, according to the AP sorting result, taking each candidate parameter combination of the first target AP as the first path node, based on the total channel capacity corresponding to each candidate parameter combination of each target AP and the principle of retaining the maximum value, the next path node is selected in sequence to obtain multiple surviving paths; screening the target surviving path among the multiple surviving paths to obtain the target parameter combination corresponding to each target AP; wherein, the target surviving path corresponds to the largest network throughput.

[0075] The network deployment optimization method provided by an embodiment of the present invention first obtains basic network deployment information corresponding to a target scenario during network deployment optimization. The basic network deployment information includes a three-dimensional model, a gridding result, and multiple candidate AP locations. The gridding result includes multiple grids obtained by gridding a plan view of the target scenario. Based on the three-dimensional model and the gridding result, an optimal AP location combination that meets a preset coverage threshold is selected from the multiple candidate AP locations. The optimal AP location combination has the fewest number of APs and the grid coverage corresponding to the optimal AP location combination is maximized. A target deployment result corresponding to the target scenario is then determined based on the optimal AP location combination. The target deployment result includes a target parameter combination corresponding to a target AP deployed at each AP location in the optimal AP location combination, and each target AP under the corresponding target parameter combination has the maximum overall network throughput. In this way, targeting the characteristics of the 2B scenario, the optimal AP location combination with the fewest number of APs and the highest grid coverage is first selected. The target deployment result with the maximum overall network throughput under the optimal AP location combination is then determined. This maximizes overall network throughput and minimizes the number of APs used while ensuring network coverage, thereby ensuring the reliability and smoothness of network coverage while ensuring the economic efficiency of network deployment.

[0076] The embodiment of the present invention is a pure software simulation solution. After receiving the requirements of the target scene, the environment is 3D modeled. Based on the 3D model and the candidate AP position (the candidate AP position here can only include the AP in the model),<x,y,z> Using the first two coordinates in the y-axis, with height z serving as an optimization parameter, ray tracing is used to calculate the direct / reflected / refracted / diffracted / transmitted ray patterns of electromagnetic waves emitted by the AP in three-dimensional space. Ray tracing also calculates the influence of surface material, reflection coefficient, and refraction coefficient on the electromagnetic wave during transmission. Ray parameters (including amplitude, delay, and angle of arrival) are calculated for each gridded cell within the space. Time-domain channel information is obtained based on the rays received by each grid cell. The spatial degrees of freedom of the channel are analyzed (note that this assumes a MIMO (multiple input, multiple output) channel, meaning the AP has multiple antennas and a two-dimensional antenna array) to calculate channel capacity, or throughput. While ensuring network coverage, the overall network throughput (the sum of the throughput of all grid cells) is maximized and the number of APs is minimized, thereby achieving maximum economic efficiency while ensuring overall network reliability. Taking indoor Wi-Fi AP deployment as an example, the specific steps of this network deployment optimization method are as follows:

[0077] 1. Use 3D modeling software to create three-dimensional models of indoor scenes, including walls, furniture, tables and chairs, lamps, green plants, and other objects that can reflect electromagnetic waves.

[0078] 2. Grid the indoor floor plan with a grid size of L×L. Assume there are N grids in total.

[0079] 3. Assume there are M candidate AP locations<x,y> , using ray-tracing technology to calculate the number of rays received on each grid, the amplitude, delay, and arrival angle of each ray.

[0080] 4. Optimization strategy:

[0081] Through the optimization algorithm, the positions of M candidate APs / base stations (two-dimensional coordinates<x,y> ) Select the K best APs (K should be as small as possible) positions (the optimal position parameters should include: AP's three-dimensional coordinates<x,y,z> , and the antenna downtilt angle φ, and the azimuth angle Parameters to be optimized ). That is, the optimization goals are: 1: Minimize the number of K; 2: Maximize the overall network coverage and throughput in the entire area, as shown in the following formula:

[0082]

[0083] stK≤M

[0084]

[0085]

[0086] Where arg represents a variable, which maximizes the variable C and minimizes K. C refers to the throughput C obtained by traversing m from 1 to K and i from 1 to N. i,m The sum of C i,m represents the throughput of candidate AP m to grid i, C i,m is a function of the parameter combination of candidate AP m; st refers to subject to, which indicates the constraint conditions; Indicates any number (i.e. universal quantifier), C i represents the throughput to grid i.

[0087] a) Initialize a smaller K ini , randomly select K from M candidate positions ini There are a total of combinations, assuming D is the set of all combinations.

[0088] b) Based on ray statistics, the signal coverage of all grids is:

[0089] i. Traverse the K in each combination d of the D set ini All parameter combinations of APs (Assuming φ, There are P, Q, and T values ​​respectively. Calculate the number of rays sent by the AP that can reach each grid in all grids under each parameter combination (ray parameters include power / arrival angle / delay information). There are a total of N grids with a total of (P×Q×T×K ini ) ray values ​​under these combinations.

[0090] ii. Assume that the amplitude of the ray received by the i-th grid from the m-th AP is sorted by time according to the delay information (filled with zeros to J values): h i，m (j), j=1, 2, ... J, ie, the time domain impulse response CIR (Channel Impulse Response) from grid i to AP m.

[0091] iii. For each K ini For combination d, {d∈D}, all grids that receive at least η rays with amplitude greater than γ are counted (i.e., valid grid condition: receiving at least η rays with amplitude greater than γ). Assume that the grid coverage for combination d is N times the total number of grids. Each combination d corresponds to a grid coverage

[0092] c) Based on grid coverage Sort the d in set D and select Take the d corresponding to the maximum value, recorded as K ini The optimal coverage under the value.

[0093] d) Increase K by a step size of Δ ini Repeat b) and c) to search for the corresponding K ini +Δn (after n searches) optimal coverage until the threshold is reached So far, the corresponding K=K ini +Δn is the optimal value found, making the grid coverage The optimal d is the optimal position combination of K APs (the optimal position combination of K APs<x,y> Sure).

[0094] e) Optimize the throughput of the entire network:

[0095] i. Transform CIR by IFFT (Inverse Fast Fourier Transform) to obtain the frequency domain channel response CFR (Channel Frequency Response) of grid i: H i，m(j), j = 1, 2, ..., J (column vector).

[0096] ii. Calculate the channel capacity C from AP m to grid i i，m ,

[0097]

[0098] Among them, det represents the determinant, I represents the identity matrix, E s represents the transmit power of the AP, α represents a preset constant, and H* represents the conjugate transposed matrix of H.

[0099] iii.φ range is (φ dn ,φ up ), and azimuth The variable range (For some APs, it can be equivalent to the AP body rotating within this angle range), the height z of the AP / base station is (z low , z high ) range.

[0100] iv. With step size φ0, And z0 traverses the adjustable range of downtilt angle, azimuth angle and height of the antenna of AP m, calculates and records each Channel capacity under

[0101] Note: Assuming 100% coverage is guaranteed, the parameters that make the grid throughput 0 can be set. Remove from the candidate list. In other cases, this operation can be performed when some grids do not have zero throughput.

[0102] v. Search for K target APs so that The ID of the largest target AP, assumed to be max;

[0103] vi. And for AP max, such that The largest Assume that Then the maximum channel capacity is

[0104]

[0105] vii. At downdip angle φ, azimuth Each group of AP height z search range

[0106]

[0107] You will get one viii. Delete 0, or a value lower than the threshold ξ. by Normalized, the obtained value is used as the corresponding The measurement value of ix. Then, the channel capacity is maximized Corresponding

[0108] The other parameter combinations correspond to

[0109]

[0110] The metric values ​​corresponding to different parameter combinations of AP m are shown in Table 1 below:

[0111] Table 1

[0112]

[0113]

[0114]

[0115] x. Traverse each grid to obtain a set of expected AP optimal positions And repeat the above steps.

[0116] xi. For two or more adjacent grids, there may be different Expected value. Select the optimal combination from the different expectations of different grids for different APs to maximize the throughput of the entire network. That is, search for an optimal sequence combination among K target APs:

[0117] xii. Based on the corresponding metric values ​​of each candidate parameter combination of each target AP under each relevant grid, determine the target parameter combination corresponding to each target AP using method 1. Or,

[0118] xiii. Calculate using the Viterbi algorithm under the MLSE criterion Find the optimal downtilt, azimuth, and position combination for the AP (the optimal combination of K combinations of the above parameters)

[0119] xiv. That is: define Γ i (s) is the maximum metric (maximum throughput or maximum channel capacity) corresponding to state s (candidate parameter combination) of AP i (or mapped to time i, considering the overall optimization as a time sequence state), Γ i+1 (s′) is the maximum metric corresponding to the next AP i+1 (or mapped to the i+1 moment) state s′, then according to the optimal principle

[0120]

[0121] in, It represents the number of candidate parameter combinations of AP i+1 for all those s that can be transferred to the s′ state during the maximization operation.

[0122] xv. Specific Viterbi algorithm:

[0123] Add-compare-select (ACS:add-compare-select).

[0124] Add: For all possible transitions to the new state s′, add Γ i (s) and Λ i (s→s′) are added.

[0125] Comparison: Among all valid s, compare the previous step Γ i (s)+Λ i The size of (s→s′).

[0126] Selection: retain the maximum value (select the surviving path); discard other paths; get Γ i+1 The value of (s′).

[0127] The embodiments of the present invention provide a network deployment optimization solution for 2B scenarios, which maximizes network speed and minimizes AP usage while ensuring network coverage. This ensures both network reliability and smoothness as well as the economic efficiency of network deployment.

[0128] Corresponding to the above-mentioned network deployment optimization method, the embodiment of the present invention also provides a network deployment optimization device, see Figure 6 The schematic diagram of a network deployment optimization device shown in FIG. 1 includes:

[0129] Acquisition module 601 is configured to acquire basic network deployment information corresponding to a target scenario; wherein the basic network deployment information includes a three-dimensional model, a gridding result, and multiple candidate AP locations, wherein the gridding result includes multiple grids obtained by gridding a plan view of the target scenario;

[0130] A screening module 602 is configured to screen, based on the three-dimensional model and the gridding result, an optimal AP position combination that meets a preset coverage threshold from a plurality of candidate AP positions; wherein the optimal AP position combination has the least number of APs and the grid coverage corresponding to the optimal AP position combination is the highest;

[0131] Determination module 603 is used to determine the target deployment result corresponding to the target scenario based on the optimal AP position combination; wherein the target deployment result includes the target parameter combination corresponding to the target AP deployed at each AP position in the optimal AP position combination, and the entire network throughput corresponding to each target AP under the corresponding target parameter combination is the largest.

[0132] The network deployment optimization device provided by an embodiment of the present invention, when performing network deployment optimization, first obtains basic network deployment information corresponding to a target scenario. The basic network deployment information includes a three-dimensional model, a gridding result, and multiple candidate AP locations. The gridding result includes multiple grids obtained by gridding a plan view of the target scenario. Then, based on the three-dimensional model and the gridding result, an optimal AP location combination that meets a preset coverage threshold is screened from the multiple candidate AP locations. The optimal AP location combination has the fewest number of APs and the grid coverage corresponding to the optimal AP location combination is maximized. Then, based on the optimal AP location combination, a target deployment result corresponding to the target scenario is determined. The target deployment result includes a target parameter combination corresponding to the target AP deployed at each AP location in the optimal AP location combination, and the target APs under the corresponding target parameter combination have the highest overall network throughput. In this way, based on the characteristics of the 2B scenario, the optimal AP location combination with the fewest number of APs and the highest grid coverage is first screened, and then the target deployment result with the highest overall network throughput under the optimal AP location combination is determined. That is, while ensuring network coverage, the overall network throughput is maximized and the number of APs used is minimized, thereby ensuring the reliability and smoothness of network coverage while ensuring the economic efficiency of network deployment.

[0133] Furthermore, the above-mentioned screening module 602 is specifically used to: obtain the current value of the initialized AP number; determine multiple AP position combinations corresponding to the current value based on multiple alternative AP positions; determine the grid coverage corresponding to each AP position combination based on the three-dimensional model and gridding results; determine the AP position combination with the largest grid coverage as the candidate AP position combination corresponding to the current value; when the grid coverage corresponding to the candidate AP position combination does not reach the coverage threshold, update the current value according to the preset step size; when the grid coverage corresponding to the candidate AP position combination reaches the coverage threshold, determine the candidate AP position combination as the optimal AP position combination.

[0134] Furthermore, the above-mentioned screening module 602 is also used to: calculate the ray parameter information corresponding to each parameter combination of each AP position combination based on the three-dimensional model and ray tracing technology; wherein the ray parameter information includes the number of rays and the amplitude of each ray; based on the ray parameter information corresponding to each parameter combination of each AP position combination, statistically obtain the number of valid grids corresponding to each parameter combination of each AP position combination; wherein the number of valid grids is the number of grids that receive at least a preset number of valid rays, and the valid rays are rays with amplitudes greater than a preset amplitude threshold; based on the number of valid grids corresponding to each parameter combination of each AP position combination, calculate the grid coverage rate corresponding to each parameter combination of each AP position combination; and determine the maximum value of the grid coverage rates corresponding to the various parameter combinations of each AP position combination as the grid coverage rate corresponding to each AP position combination.

[0135] Furthermore, the above-mentioned determination module 603 is specifically used to: determine the expected AP position and expected parameter combination corresponding to each grid based on the optimal AP position combination; wherein, the channel capacity from the target AP under the expected AP position and expected parameter combination corresponding to the grid to the grid is the largest; determine each candidate parameter combination of each target AP according to the expected AP position and expected parameter combination corresponding to each grid; wherein, each candidate parameter combination of the target AP is each expected parameter combination corresponding to the AP position where the target AP is deployed; and determine the target parameter combination corresponding to each target AP from the various candidate parameter combinations of each target AP.

[0136] Furthermore, the above-mentioned determination module 603 is also used to: for each grid, based on the three-dimensional model and ray tracing technology, calculate the channel capacity from each target AP to the grid under each parameter combination; determine the maximum channel capacity under the optimal parameter combination of each target AP corresponding to the grid according to the channel capacity from each target AP to the grid under each parameter combination; wherein the maximum channel capacity is the maximum value of the channel capacity from each target AP to the grid under each parameter combination, and the optimal parameter combination is the parameter combination corresponding to the maximum channel capacity; determine the AP position and optimal parameter combination of the target AP corresponding to the maximum value of each maximum channel capacity corresponding to the grid as the expected AP position and expected parameter combination corresponding to the grid.

[0137] Furthermore, in a possible implementation, the above-mentioned determination module 603 is also used to: for each target AP, determine the relevant grids corresponding to the target AP; wherein the relevant grid is a grid with the AP position of the target AP as the expected AP position; determine the measurement value corresponding to each candidate parameter combination of the target AP under each relevant grid; wherein the measurement value is the value obtained by normalizing the channel capacity from the target AP to the corresponding relevant grid under the corresponding candidate parameter combination; sum the measurement values ​​corresponding to each candidate parameter combination of the target AP under each relevant grid to obtain the total measurement value corresponding to each candidate parameter combination of the target AP; determine the candidate parameter combination corresponding to the maximum value in the total measurement value corresponding to each candidate parameter combination of the target AP as the target parameter combination corresponding to the target AP.

[0138] Furthermore, in another possible implementation, the above-mentioned determination module 603 is also used to: calculate the total channel capacity corresponding to each candidate parameter combination of each target AP based on a three-dimensional model and ray tracing technology; and determine the target parameter combination corresponding to each target AP through the Viterbi algorithm based on the total channel capacity corresponding to each candidate parameter combination of each target AP.

[0139] The network deployment optimization device provided in this embodiment has the same implementation principle and technical effects as those in the aforementioned network deployment optimization method embodiment. For the sake of brief description, for matters not mentioned in the network deployment optimization device embodiment, reference may be made to the corresponding content in the aforementioned network deployment optimization method embodiment.

[0140] like Figure 7 As shown, an embodiment of the present invention provides an electronic device 700, including: a processor 701, a memory 702 and a bus, the memory 702 stores a computer program that can be run on the processor 701, when the electronic device 700 is running, the processor 701 and the memory 702 communicate through the bus, and the processor 701 executes the computer program to implement the above-mentioned network deployment optimization method.

[0141] Specifically, the memory 702 and processor 701 can be general-purpose memories and processors, which are not specifically limited here.

[0142] Embodiments of the present invention also provide a storage medium storing a computer program that, when executed by a processor, executes the network deployment 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, a mobile hard drive, a read-only memory (ROM), RAM, a magnetic disk, or an optical disk.

[0143] In all examples shown and described herein, any specific values ​​should be interpreted as merely exemplary and not limiting, and thus other examples of the exemplary embodiments may have different values.

[0144] The flowcharts and block diagrams in the accompanying drawings show the possible implementation architectures, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of code, and the module, program segment or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0145] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements 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 network deployment optimization method, characterized in that: include: Obtaining basic network deployment information corresponding to the target scenario; wherein the basic network deployment information includes a three-dimensional model, a gridding result, and multiple candidate AP locations, and the gridding result includes multiple grids obtained by network processing the plan view of the target scenario; Based on the three-dimensional model and the gridding result, an optimal AP position combination that meets a preset coverage threshold is screened out from the multiple candidate AP positions; wherein the optimal AP position combination has the least number of APs and the grid coverage corresponding to the optimal AP position combination is the highest; Determining a target deployment result corresponding to the target scenario based on the optimal AP position combination; wherein the target deployment result includes a target parameter combination corresponding to a target AP deployed at each AP position in the optimal AP position combination, and the entire network throughput corresponding to each target AP under the corresponding target parameter combination is the maximum; The determining, based on the optimal AP position combination, a target deployment result corresponding to the target scenario includes: Determining an expected AP position and an expected parameter combination corresponding to each of the grids based on the optimal AP position combination; wherein the channel capacity from the target AP to the grid under the expected AP position and expected parameter combination corresponding to the grid is the largest; Determining candidate parameter combinations for each target AP based on the desired AP positions and desired parameter combinations corresponding to the grids; wherein the candidate parameter combinations for the target APs are desired parameter combinations corresponding to the AP positions where the target APs are deployed; A target parameter combination corresponding to each target AP is determined from each candidate parameter combination of each target AP, where the target parameter combination includes a downtilt angle, an azimuth angle, and an altitude.

2. The network deployment optimization method according to claim 1, characterized in that: The selecting, based on the three-dimensional model and the gridding result, an optimal AP position combination that reaches a preset coverage threshold from the multiple candidate AP positions includes: Get the current value of the number of initialized APs; Determining, based on the multiple candidate AP positions, multiple AP position combinations corresponding to the current value; Determining a grid coverage rate corresponding to each AP position combination based on the three-dimensional model and the gridding result; Determine the AP position combination with the largest grid coverage as the candidate AP position combination corresponding to the current value; When the grid coverage corresponding to the candidate AP position combination does not reach the coverage threshold, updating the current value according to a preset step size; When the grid coverage corresponding to the candidate AP position combination reaches the coverage threshold, the candidate AP position combination is determined as the optimal AP position combination.

3. The network deployment optimization method according to claim 2, characterized in that: The determining, based on the three-dimensional model and the gridding result, a grid coverage rate corresponding to each AP position combination includes: Based on the three-dimensional model and ray tracing technology, calculate and obtain ray parameter information corresponding to each parameter combination of each AP position combination; wherein the ray parameter information includes the number of rays and the amplitude of each ray; Based on the ray parameter information corresponding to each parameter combination of each AP position combination, the number of valid grids corresponding to each parameter combination of each AP position combination is obtained by counting; wherein the number of valid grids is the number of grids that receive at least a preset number of valid rays, and the valid rays are rays with amplitudes greater than a preset amplitude threshold; Calculate the grid coverage corresponding to each parameter combination of each AP position combination based on the number of valid grids corresponding to each parameter combination of each AP position combination; The maximum value among the grid coverage rates corresponding to the parameter combinations of each AP position combination is determined as the grid coverage rate corresponding to each AP position combination.

4. The network deployment optimization method according to claim 1, characterized in that: The determining, based on the optimal AP position combination, an expected AP position and an expected parameter combination corresponding to each grid, includes: For each of the grids, based on the three-dimensional model and ray tracing technology, calculating the channel capacity from each of the target APs to the grid under each parameter combination; Determining, based on the channel capacity from each target AP to the grid under each parameter combination, the maximum channel capacity under the optimal parameter combination for each target AP corresponding to the grid; wherein the maximum channel capacity is the maximum value of the channel capacities from each target AP to the grid under each parameter combination, and the optimal parameter combination is the parameter combination corresponding to the maximum channel capacity; The AP position and optimal parameter combination of the target AP corresponding to the maximum value among the maximum channel capacities corresponding to the grid are determined as the expected AP position and expected parameter combination corresponding to the grid.

5. The network deployment optimization method according to claim 1, characterized in that: The determining, from each candidate parameter combination of each target AP, a target parameter combination corresponding to each target AP includes: For each target AP, determining the corresponding grids of the target AP; wherein the related grids are grids with the AP position of the target AP as the expected AP position; Determining a metric value corresponding to each candidate parameter combination of the target AP under each of the relevant grids; wherein the metric value is a value obtained by normalizing the channel capacity from the target AP to the corresponding relevant grid under the corresponding candidate parameter combination; Summing up the metric values ​​corresponding to each candidate parameter combination of the target AP under each of the related grids to obtain a total metric value corresponding to each candidate parameter combination of the target AP; The candidate parameter combination corresponding to the maximum value among the total metric values ​​corresponding to the candidate parameter combinations of the target AP is determined as the target parameter combination corresponding to the target AP.

6. The network deployment optimization method according to claim 1, characterized in that: The determining, from each candidate parameter combination of each target AP, a target parameter combination corresponding to each target AP includes: Based on the three-dimensional model and ray tracing technology, calculate the total channel capacity corresponding to each candidate parameter combination of each target AP; Based on the total channel capacity corresponding to each candidate parameter combination of each target AP, a target parameter combination corresponding to each target AP is determined by using a Viterbi algorithm.

7. A network deployment optimization device, characterized in that: include: An acquisition module is configured to acquire basic network deployment information corresponding to a target scenario; wherein the basic network deployment information includes a three-dimensional model, a gridding result, and a plurality of candidate AP locations, wherein the gridding result includes a plurality of grids obtained by performing a network processing on a plan view of the target scenario; a screening module, configured to screen, from the plurality of candidate AP positions, an optimal AP position combination that meets a preset coverage threshold based on the three-dimensional model and the gridding result; wherein the optimal AP position combination has the least number of APs and the grid coverage corresponding to the optimal AP position combination is the highest; A determination module is configured to determine a target deployment result corresponding to the target scenario based on the optimal AP position combination; wherein the target deployment result includes a target parameter combination corresponding to a target AP deployed at each AP position in the optimal AP position combination, and the entire network throughput corresponding to each target AP under the corresponding target parameter combination is the maximum; The determination module is specifically used to: determine the expected AP position and expected parameter combination corresponding to each of the grids based on the optimal AP position combination; wherein the channel capacity from the target AP to the grid under the expected AP position and expected parameter combination corresponding to the grid is the largest; determine each candidate parameter combination of each target AP according to the expected AP position and expected parameter combination corresponding to each of the grids; wherein the each candidate parameter combination of the target AP is the each expected parameter combination corresponding to the AP position where the target AP is deployed; and determine the target parameter combination corresponding to each target AP from the each candidate parameter combination of each target AP, wherein the target parameter combination includes downtilt angle, azimuth angle and altitude.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the network deployment optimization method according to any one of claims 1 to 6 is executed.

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