Channel modeling method, device, equipment and storage medium

Abstract

Disclosed are a channel modeling method, apparatus, device, and storage medium, belonging to the field of communication technology. The method includes: acquiring point cloud data of a target area; dividing the point cloud data into first point cloud data and second point cloud data, wherein the second point cloud data is point cloud data of vegetation in the target area, and the first point cloud data is point cloud data other than the second point cloud data in the point cloud data; based on the first point cloud data, determining the direct signal strength, diffracted signal strength, and scattered signal strength from the transmitting point to the receiving point; based on the second point cloud data, determining the direct vegetation attenuation, diffracted vegetation attenuation, and scattered vegetation attenuation, respectively, to correct the direct signal strength, diffracted signal strength, and scattered signal strength; and performing channel modeling on the transmitting point and the receiving point based on the corrected direct signal strength, corrected diffracted signal strength, and corrected scattered signal strength. This method can improve the accuracy of channel modeling.

Description

Technical Field

[0001] The present disclosure relates to the field of communication technology, and in particular to a channel modeling method, apparatus, device, and storage medium. Background Art

[0002] Generally, the higher the signal frequency, the more accurate the scenario model needs to be when modeling the channel. High-frequency millimeter waves require a more detailed and accurate scenario model than ordinary radio waves.

[0003] In related technologies, channel modeling methods include: establishing a bin mesh model from the transmitting point to the receiving point; and performing channel modeling based on the bin mesh model. The bin mesh model is a model that expresses the geometric structure of a building as a planar triangular mesh.

[0004] However, the surface element grid model has problems such as cumbersome establishment process, long processing time, and poor model precision. In actual modeling, it often adopts planar polygon approximation, which leads to differences between the surface element grid model and the actual structure of the building in reality, which in turn leads to errors in the channel modeling results. Summary of the Invention

[0005] The present disclosure provides a channel modeling method, apparatus, device, and storage medium that can improve the accuracy of channel modeling. The technical solution includes at least the following solutions:

[0006] In a first aspect, a channel modeling method is provided, including: acquiring point cloud data of a target area; dividing the point cloud data into first point cloud data and second point cloud data, the second point cloud data being point cloud data of vegetation in the target area, and the first point cloud data being point cloud data other than the second point cloud data in the point cloud data; determining the direct signal strength, diffracted signal strength and scattered signal strength from a transmitting point to a receiving point based on the first point cloud data; determining the direct vegetation attenuation, diffracted vegetation attenuation and scattered vegetation attenuation respectively based on the second point cloud data to correct the direct signal strength, the diffracted signal strength and the scattered signal strength; and performing channel modeling on the transmitting point and the receiving point based on the corrected direct signal strength, the corrected diffracted signal strength and the corrected scattered signal strength.

[0007] Optionally, determining the direct signal strength, diffraction signal strength and scattered signal strength from the transmitting point to the receiving point based on the first point cloud data includes: establishing a first Fresnel zone based on the first point cloud data, the transmitting point and the receiving point; if the density of the point cloud data in the first Fresnel zone is greater than a point cloud density threshold, determining that the direct signal strength does not exist; if the density of the point cloud data in the first Fresnel zone is less than or equal to the point cloud density threshold, calculating the direct signal strength from the transmitting point to the receiving point.

[0008] Optionally, determining the direct signal strength, diffracted signal strength and scattered signal strength from the transmitting point to the receiving point based on the first point cloud data includes: determining the scattering point; determining the scattering surface, the incident angle, the reflection angle and the angle between the scattered wave and the reflected wave direction based on the three points in the first point cloud data that are closest to the scattering point; and determining the scattered signal strength from the transmitting point to the receiving point through the incident angle, the reflection angle and the angle between the scattered wave and the reflected wave direction based on a single-lobe directional scattering model.

[0009] Optionally, determining the intensity of the scattered signal from the transmitting point to the receiving point based on the single-lobe directional scattering model by using the incident angle, the reflection angle, and the angle between the scattered wave and the reflected wave direction includes: determining the intensity of the scattered signal from the transmitting point to the receiving point using the following formula:

[0010]

[0011] in, is the amplitude of the scattered field, is the scattering coefficient, , is the amplitude of the incident field, is the distance from the scattering point to the emission point, is the distance from the scattering point to the receiving point, is the angle between the scattered wave and the reflected wave, Used to express the scattering lobe width, are coefficients, determined by series expansion.

[0012] Optionally, an octree algorithm is used to process the first point cloud data, and the direct signal strength, diffraction signal strength and scattered signal strength from the transmitting point to the receiving point are determined based on the first point cloud data, including: based on the octree algorithm, any one point in each leaf node in the octree algorithm is selected as a seed, and planes of each area are grown based on the seed to obtain multiple planes; an expression equation of each plane in the multiple planes is determined; based on the expression equation of each plane, the intersecting line segments of the intersecting planes are determined; and based on the intersecting line segments of the intersecting planes, the diffraction signal strength is determined.

[0013] Optionally, based on the second point cloud data, the direct vegetation attenuation, diffraction vegetation attenuation and scattered vegetation attenuation are respectively determined to correct the direct signal strength, the diffraction signal strength and the scattered signal strength, including: clustering the point cloud data belonging to the same tree in the second point cloud data into a cluster to obtain multiple clusters; modeling each of the clusters according to an ellipsoid to obtain a three-dimensional model of each cluster; determining the direct vegetation attenuation, the diffraction vegetation attenuation and the scattered vegetation attenuation based on the three-dimensional model of each cluster; and correcting the direct signal strength, the diffraction signal strength and the scattered signal strength based on the direct vegetation attenuation, the diffraction vegetation attenuation and the scattered vegetation attenuation.

[0014] Optionally, the direct vegetation attenuation, the diffraction vegetation attenuation and the scattered vegetation attenuation are determined based on the three-dimensional model of each cluster, including: respectively determining whether each direct path, diffraction path and scattered path passes through at least one three-dimensional model of the cluster; and determining the direct vegetation attenuation, the diffraction vegetation attenuation and the scattered vegetation attenuation based on the distance that each direct path, diffraction path and scattered path passes through the three-dimensional model of the cluster.

[0015] In the second aspect, a channel modeling device is also provided, including: an acquisition module for acquiring point cloud data of a target area; a division module for dividing the point cloud data into first point cloud data and second point cloud data, the second point cloud data being point cloud data of vegetation in the target area, and the first point cloud data being point cloud data other than the second point cloud data in the point cloud data; a signal strength calculation module for determining the direct signal strength, diffracted signal strength and scattered signal strength from the transmitting point to the receiving point based on the first point cloud data; a correction module for determining the direct vegetation attenuation, diffracted vegetation attenuation and scattered vegetation attenuation respectively based on the second point cloud data, so as to correct the direct signal strength, the diffracted signal strength and the scattered signal strength; a channel modeling module for performing channel modeling on the transmitting point and the receiving point based on the corrected direct signal strength, the corrected diffracted signal strength and the corrected scattered signal strength.

[0016] Optionally, the signal strength calculation module is also used to establish a first Fresnel zone based on the first point cloud data, the transmitting point and the receiving point; if the density of the point cloud data in the first Fresnel zone is greater than the point cloud density threshold, it is determined that the direct signal strength does not exist; if the density of the point cloud data in the first Fresnel zone is less than or equal to the point cloud density threshold, the direct signal strength from the transmitting point to the receiving point is calculated.

[0017] Optionally, the signal strength calculation module is also used to determine the scattering point; based on the three points in the first point cloud data that are closest to the scattering point, determine the scattering surface, the incident angle, the reflection angle, and the angle between the scattered wave and the reflected wave direction; based on the single-lobe directional scattering model, determine the scattered signal strength from the transmitting point to the receiving point through the incident angle, the reflection angle, and the angle between the scattered wave and the reflected wave direction.

[0018] Optionally, the signal strength calculation module is further configured to determine the scattered signal strength from the transmitting point to the receiving point using the following formula:

[0019]

[0020] in, is the amplitude of the scattered field, is the scattering coefficient, , is the amplitude of the incident field, is the distance from the scattering point to the emission point, is the distance from the scattering point to the receiving point, is the angle between the scattered wave and the reflected wave, Used to express the scattering lobe width, are coefficients, determined by series expansion.

[0021] Optionally, the signal strength calculation module is also used to select any point from each leaf node in the octree algorithm as a seed based on the octree algorithm, grow planes of each area based on the seed, and obtain multiple planes; determine the expression equation of each plane in the multiple planes; based on the expression equation of each plane, determine the intersecting line segments of the intersecting planes; based on the intersecting line segments of the intersecting planes, determine the diffraction signal strength.

[0022] Optionally, the correction module is also used to cluster the point cloud data belonging to the same tree in the second point cloud data into a cluster to obtain multiple clusters; model each of the clusters according to an ellipsoid to obtain a three-dimensional model of each cluster; determine the direct vegetation attenuation, the diffraction vegetation attenuation and the scattered vegetation attenuation based on the three-dimensional model of each cluster; and correct the direct signal strength, the diffraction signal strength and the scattered signal strength based on the direct vegetation attenuation, the diffraction vegetation attenuation and the scattered vegetation attenuation.

[0023] Optionally, the correction module is also used to determine whether each direct path, diffraction path and scattered path passes through at least one three-dimensional model of the cluster; based on the distance that each of the direct path, the diffraction path and the scattered path passes through the three-dimensional model of the cluster, determine the direct vegetation attenuation, the diffraction vegetation attenuation and the scattered vegetation attenuation.

[0024] In a third aspect, a computer device is also provided, comprising: a memory and a processor, wherein the memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor, thereby executing the channel modeling method described in the above embodiment.

[0025] In a fourth aspect, a computer-readable storage medium is further provided, wherein at least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by a processor, thereby executing the channel modeling method described in the above embodiment.

[0026] In a fifth aspect, a computer program product is provided, comprising a computer program / instruction, which implements the method described in the first aspect when executed by a processor.

[0027] The beneficial effects of the technical solutions provided by the embodiments of the present disclosure include at least:

[0028] Compared with implementing channel modeling based on a surface element grid model, implementing channel modeling based on point cloud data in the embodiment of the present disclosure has the advantages of a simple establishment process, fast processing speed, and high model accuracy.

[0029] During wireless signal propagation, buildings and vegetation can be considered obstacles. High-frequency millimeter waves are generally unable to penetrate buildings. However, due to gaps in vegetation, high-frequency millimeter waves can pass through it, though the signal is attenuated during the process. Based on this, channel modeling directly based on point cloud data is equivalent to treating vegetation point clouds and building point clouds as the same type of point cloud data. This can lead to errors in channel modeling by mistaking vegetation point clouds for buildings.

[0030] In the embodiment of the present disclosure, the point cloud data is divided into first point cloud data and second point cloud data, wherein the second point cloud data is the vegetation point cloud, and the first point cloud data is the point cloud other than the vegetation point cloud in the point cloud data, and the signal strength of direct, diffraction and scattering is determined based on the first point cloud data, and the vegetation attenuation of direct, diffraction and scattering is determined based on the second point cloud data, thereby reducing the error existing when the vegetation point cloud and the building point cloud are processed as the same type of point cloud data, and improving the accuracy of channel modeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0032] Figure 1 A flow chart of a channel modeling method provided by an exemplary embodiment of the present disclosure is shown;

[0033] Figure 2 A flow chart of a channel modeling method provided by another exemplary embodiment of the present disclosure is shown;

[0034] Figure 3 This is a schematic diagram of the first Fresnel zone;

[0035] Figure 4 Schematic diagram of extracting diffraction edges for region growing;

[0036] Figure 5 Schematic diagram of the scattering surface and single-lobe directional scattering model;

[0037] Figure 6 is a schematic diagram of vegetation attenuation of wireless signals;

[0038] Figure 7 A schematic structural diagram of a channel modeling device provided by an exemplary embodiment of the present disclosure is shown;

[0039] Figure 8 It is a structural diagram of a computer device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0040] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning understood by persons of ordinary skill in the field to which the present disclosure belongs. The words “first”, “second”, “third” and similar terms used in the patent application specification and claims of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as “a” or “an” do not indicate a quantity limitation, but rather indicate the presence of at least one. Words such as “include” or “comprise” mean that the elements or objects appearing before “include” or “comprises” include the elements or objects listed after “include” or “comprises” and their equivalents, and do not exclude other elements or objects. Words such as “connect” or “connected” are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Up”, “down”, “left”, “right” and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0041] In order to make the objectives, technical solutions and advantages of the present disclosure more clear, the embodiments of the present disclosure will be further described in detail below with reference to the accompanying drawings.

[0042] Figure 1 A flow chart of a channel modeling method provided by an exemplary embodiment of the present disclosure is shown. The method can be executed by a computer device. Figure 1 , the method comprising:

[0043] In step 101, point cloud data of a target area is obtained.

[0044] In the embodiment of the present disclosure, the target area is an area where channel modeling is required, and there is at least one transmitting point and at least one receiving point in the target area.

[0045] For example, point cloud data of the target area can be acquired through a laser radar, or through multi-view reconstruction using an optical camera. The implementation of laser radar and multi-view reconstruction using an optical camera is well-known in the related art and will not be described in detail here.

[0046] In step 102 , the point cloud data is divided into first point cloud data and second point cloud data.

[0047] The second point cloud data is point cloud data of vegetation in the target area, and the first point cloud data is point cloud data other than the second point cloud data in the point cloud data.

[0048] In step 103, based on the first point cloud data, the direct signal strength, diffracted signal strength and scattered signal strength from the transmitting point to the receiving point are determined.

[0049] In the process of wireless signals being transmitted from the transmitting point and propagating to the receiving point, under the influence of various obstacles in the target area, part of the wireless signals can be directly transmitted to the receiving point. This process is called direct radiation; part of the wireless signals first hit the edge of the irregular protruding surface of the object (such as the edge of the roof, the four corners of the window, etc.) and then propagate to the receiving point. This process is called diffraction; some wireless signals propagate in the medium where there are objects with wavelengths smaller than the wavelength of the wireless signal, and the number of obstructions per unit volume is very large, which makes the wireless signal here.

[0050] In step 104 , based on the second point cloud data, direct vegetation attenuation, diffraction vegetation attenuation, and scattered vegetation attenuation are determined respectively to correct the direct signal strength, diffraction signal strength, and scattered signal strength.

[0051] In step 105 , channel modeling is performed on the transmitting point and the receiving point based on the corrected direct signal strength, the corrected diffracted signal strength, and the corrected scattered signal strength.

[0052] Compared with implementing channel modeling based on a surface element grid model, implementing channel modeling based on point cloud data in the embodiment of the present disclosure has the advantages of a simple establishment process, fast processing speed, and high model accuracy.

[0053] During wireless signal propagation, buildings and vegetation can be considered obstacles. High-frequency millimeter waves are generally unable to penetrate buildings. However, due to gaps in vegetation, high-frequency millimeter waves can pass through it, though the signal is attenuated during the process. Based on this, channel modeling directly based on point cloud data is equivalent to treating vegetation point clouds and building point clouds as the same type of point cloud data. This can lead to errors in channel modeling by mistaking vegetation point clouds for buildings.

[0054] In the embodiment of the present disclosure, the point cloud data is divided into first point cloud data and second point cloud data, wherein the second point cloud data is the vegetation point cloud, and the first point cloud data is the point cloud other than the vegetation point cloud in the point cloud data, and the signal strength of direct, diffraction and scattering is determined based on the first point cloud data, and the vegetation attenuation of direct, diffraction and scattering is determined based on the second point cloud data, thereby reducing the error existing when the vegetation point cloud and the building point cloud are processed as the same type of point cloud data, and improving the accuracy of channel modeling.

[0055] Figure 2 A flow chart of a channel modeling method provided by another exemplary embodiment of the present disclosure is shown. The method can be executed by a computer device. Figure 2 , the method comprising:

[0056] In step 201 , point cloud data of a target area is obtained.

[0057] The relevant contents of step 201 refer to the aforementioned step 101, and detailed description is omitted here.

[0058] In step 202 , the point cloud data is divided into first point cloud data and second point cloud data.

[0059] Optionally, when implementing step 202, the second point cloud data can be extracted using prior information about the vegetation. This prior information includes, for example, the average crown height and crown coverage. Given the height (represented by the average crown height) and crown coverage of a particular tree, a cubic region can be constructed based on this information. The point cloud data within this region constitutes the point cloud data for that tree. This same approach can be applied to all vegetation in the entire target area, resulting in the extraction of point cloud data for all vegetation in the target area. This point cloud data for all vegetation in the target area constitutes the second point cloud data.

[0060] After the second point cloud data is extracted, the remaining point cloud data in the point cloud data is the first point cloud data.

[0061] In step 203, based on the first point cloud data, the direct signal strength, diffracted signal strength and scattered signal strength from the transmitting point to the receiving point are determined.

[0062] In the embodiment of the present disclosure, the point cloud data, the first point cloud data, and the second point cloud data are all managed by an octree algorithm.

[0063] The principle of the octree algorithm is to establish a Cartesian coordinate system in space, divide the space into 8 quadrants through the Cartesian coordinate system, and each quadrant can be divided into 8 quadrants through another Cartesian coordinate system. Similarly, any area in space can be divided by n Cartesian coordinate systems.

[0064] There are many related technologies for implementing the octree algorithm to manage point cloud data, so detailed description is omitted here.

[0065] By managing the point cloud data, the first point cloud data, and the second point cloud data through the octree algorithm, the processing efficiency of the point cloud data can be improved.

[0066] Optionally, determining the direct signal strength from the transmitting point to the receiving point based on the first point cloud data includes the following two steps:

[0067] The first step is to establish the first Fresnel zone based on the first point cloud data, the emission point and the receiving point.

[0068] The Fresnel zone is a cluster of concentric ellipses centered around the transmitting and receiving points. The innermost ellipse in this cluster is the first Fresnel zone. The implementation of the Fresnel zone is well-known in the art and will not be detailed here.

[0069] Figure 3 is a schematic diagram of the first Fresnel zone, as shown in Figure 3 As shown, there are multiple point cloud data in the first Fresnel zone 301 with the transmitting point Tx and the receiving point Rx as the focus.

[0070] The second step is to determine whether the density of the point cloud data in the first Fresnel zone is greater than the point cloud density threshold.

[0071] If the density of the point cloud data in the first Fresnel zone is greater than the point cloud density threshold, it is determined that the direct signal strength does not exist. If the density of the point cloud data in the first Fresnel zone is less than or equal to the point cloud density threshold, the direct signal strength from the transmitting point to the receiving point is calculated.

[0072] The point cloud density threshold is an empirical value, and the embodiment of the present disclosure does not limit the setting method of the point cloud density threshold.

[0073] If the density of the point cloud data in the first Fresnel zone is greater than the point cloud density threshold, it indicates that there is a building blocking the distance between the transmitting point and the receiving point. In this case, the wireless signal cannot be directly transmitted from the transmitting point to the receiving point, and the direct signal strength is 0. If the density of the point cloud data in the first Fresnel zone is less than or equal to the point cloud density threshold, it indicates that there is no building blocking the distance between the transmitting point and the receiving point, and the wireless signal can be directly transmitted from the transmitting point to the receiving point.

[0074] When the signal propagates directly from the transmitting point to the receiving point, the direct path is known and unique: the line connecting the transmitting point to the receiving point. In this case, the free-space attenuation model can be used to determine the path loss of the direct path. This path loss is the direct signal strength.

[0075] There are many methods for implementing the free space attenuation model in related technologies, and detailed description is omitted here.

[0076] Optionally, determining the signal strength of diffraction from the transmitting point to the receiving point based on the first point cloud data includes the following four steps:

[0077] In the first step, based on the octree algorithm, a point is randomly selected from each leaf node in the octree algorithm as a seed, and planes of each region are grown based on the seed to obtain multiple planes.

[0078] In the octree algorithm, there are multiple leaf nodes. A point can be selected from these leaf nodes as a seed. Points with the same normal and a close distance to the seed are then merged. This process is repeated over multiple iterations, gradually growing planes in various regions. This process is called region growing.

[0079] The second step is to determine the expression equation of each plane in the multiple planes.

[0080] After obtaining multiple planes through region growing, the expression equation of each plane can be determined.

[0081] The third step is to determine the intersecting line segments of the intersecting planes based on the expression equation of each plane.

[0082] When the expression equations of multiple planes are known, we can determine which planes intersect and the intersecting line segments of the intersecting planes based on the expression equations. This step is also called diffraction edge extraction based on region growing.

[0083] After determining multiple intersecting line segments, these cannot be directly regarded as diffraction edges. Intersecting surfaces that do not meet the diffraction requirements and their intersecting line segments must be filtered out. This method includes: for any intersecting line segment of a pair of intersecting surfaces, determining the angle of incidence from the transmitting point to the intersecting line segment and the angle of diffraction from the intersecting line segment to the receiving point. Determining whether the angle of incidence is equal to the diffraction angle; if so, the intersecting line segment is a diffraction edge; if not, the intersecting surface and the intersecting line segment are filtered out.

[0084] After filtering is completed, the remaining intersecting line segments are the diffraction edges in the diffraction process.

[0085] Figure 4 Schematic diagram of extracting diffraction edges for region growing. Figure 4 As shown in the figure, after filtering out the intersecting surfaces that do not satisfy the diffraction requirements, multiple diffraction edges can be determined.

[0086] The fourth step is to determine the diffracted signal strength based on the intersecting line segments of the intersecting planes.

[0087] When the transmitting point, receiving point, and multiple diffraction edges are known, multiple diffraction paths can be determined. The path loss of each diffraction path can then be determined based on these multiple diffraction paths. The path loss of each diffraction path is the signal strength of that diffraction path. The diffracted signal strength includes the signal strengths of multiple diffraction paths.

[0088] In the embodiment of the present disclosure, when determining the diffraction path, only one diffraction is considered, that is, each diffraction edge is diffracted only once.

[0089] Optionally, the path loss of each diffraction path is determined by the principle of consistent diffraction. There are many related technologies for determining the diffraction path and implementing the principle of consistent diffraction, and detailed description is omitted here.

[0090] Optionally, determining the signal strength of scattering from the transmitting point to the receiving point based on the first point cloud data includes the following three steps:

[0091] The first step is to determine the scattering points.

[0092] A scattering point is the point where the wireless signal begins to scatter. In the disclosed embodiment, due to the large number of point clouds, only the single scattering generated by each point in the point cloud is considered. In other words, each point in the first point cloud data is treated as a scattering point, and the single scattering of the scattering point is considered.

[0093] In the second step, based on the three points closest to the scattering point in the first point cloud data, the scattering surface, the incident angle, the reflection angle, and the angle between the scattered wave and the reflected wave are determined.

[0094] The three points closest to the scattering point can be used to define a triangular plane, which is the scattering surface. The area of ​​the scattering surface is the scattering surface. Once the scattering surface is determined, the angle of incidence, angle of reflection, and the angle between the scattered and reflected waves can be determined based on the scattering surface's normal vector, the emitting point, and the receiving point. The implementation of determining the angle of incidence, angle of reflection, and the angle between the scattered and reflected waves based on the scattering surface's normal vector, the emitting point, and the receiving point is well-known in the related art and will not be detailed here.

[0095] Figure 5 Schematic diagram of the scattering surface and single-lobe directional scattering model. Figure 5 Part (a) is a schematic diagram of the scattering surface. Figure 5 The three points closest to the scattering point shown in part (a) constitute the scattering surface 501. The vectors from the scattering point to these three points 、 and The normal vector of the scattering surface can be determined.

[0096] The third step is to determine the signal intensity of scattering from the transmitting point to the receiving point based on the single-lobe directional scattering model through the incident angle, reflection angle, and the angle between the scattered wave and the reflected wave direction.

[0097] Optionally, the intensity of the scattered signal from the transmitting point to the receiving point is determined using formula (1). Formula (1) is a formula of a single-lobe directive scattering model.

[0098] (1)

[0099] In formula (1), is the amplitude of the scattered field, is the amplitude of the incident field, is the scattering coefficient, , , is the distance from the scattering point to the emission point, is the distance from the scattering point to the receiving point, is the angle between the scattered wave and the reflected wave, Used to express the scattering lobe width, are coefficients, determined by series expansion.

[0100] in, It can be determined by series expansion as shown in formula (2).

[0101] (2)

[0102] In formula (2), represents an imaginary number, The angular frequency of the wireless signal. The meanings of the other parameters in formula (2) are the same as those in formula (1) and are not described in detail here.

[0103] Figure 5 Part (b) is a schematic diagram of the single-lobe directional scattering model. Figure 5 As shown in part (b) of the figure, the total wireless channel within the target area is the sum of all paths between the transmitting point (Tx) and the receiving point (Rx). In this disclosed embodiment, the single-lobe directional scattering model only considers single scattering, ignoring multiple scattering. This improves channel modeling efficiency. Furthermore, in the case of multiple scattering, the path loss is excessive, making the calculated multiple scattering signal strength almost negligible. Therefore, this disclosed embodiment only considers single scattering.

[0104] There are many methods for implementing the single-lobe directional scattering model in related technologies, so detailed description is omitted here.

[0105] When using the single-lobe directional scattering model, the scattering coefficient and scattering lobe width are pre-set, so they may not be accurate. Alternatively, a brute-force search algorithm can be used to optimize the scattering coefficient and scattering lobe width in the single-lobe directional scattering model by comparing measured and simulated power delay spectrum data. This can improve the accuracy of the scattering coefficient and scattering lobe width.

[0106] Using formulas (1) and (2), we can calculate multiple amplitudes of the scattered field and the multiple scattering paths corresponding to the amplitudes. The amplitude of a scattered field is the signal strength of a scattering path. Since there are multiple scattering paths during scattering, there are multiple scattered signal intensities.

[0107] In step 204 , based on the second point cloud data, direct vegetation attenuation, diffraction vegetation attenuation, and scattered vegetation attenuation are determined respectively to correct the direct signal strength, diffraction signal strength, and scattered signal strength.

[0108] Optionally, step 204 includes the following steps ad:

[0109] In step a, point cloud data belonging to the same tree in the second point cloud data are clustered into one cluster to obtain multiple clusters.

[0110] Optionally, a clustering algorithm is employed to cluster the point cloud data belonging to the same tree in the second point cloud data into a single cluster. For example, a common clustering algorithm such as the k-means algorithm or the DBSCAN algorithm can be employed. Implementations of the k-means algorithm and the DBSCAN algorithm are well-known in the related art and will not be detailed here.

[0111] Step b: Model each cluster according to an ellipsoid to obtain a three-dimensional model of each cluster.

[0112] The shapes of the multiple clusters obtained by the clustering algorithm are usually irregular, which increases the computational complexity. In the embodiment of the present disclosure, each cluster is modeled as an ellipsoid, so that the shape of the three-dimensional model of each cluster is more regular, which facilitates subsequent calculations.

[0113] Step c: determining direct vegetation attenuation, diffraction vegetation attenuation and scattered vegetation attenuation based on the three-dimensional model of each cluster.

[0114] Optionally, step c includes the following two steps:

[0115] In the first step, it is determined whether each direct path, diffraction path and scattered path passes through the three-dimensional model of at least one cluster.

[0116] In the second step, based on the distance that each of the direct path, the diffraction path, and the scattered path passes through the three-dimensional model of the cluster, the vegetation attenuation of the direct radiation, the diffraction vegetation attenuation, and the scattered vegetation attenuation are determined.

[0117] It can be seen from the aforementioned step 203 that if there is direct light from the transmitting point to the receiving point, there is a direct light path; if there is no direct light from the transmitting point to the receiving point, there is no direct light path; in addition, there are multiple diffraction paths and multiple scattering paths between the transmitting point and the receiving point.

[0118] In this case, the second step involves multiplying the distance a direct path passes through the 3D model of at least one cluster by the attenuation coefficient to obtain the direct vegetation attenuation. If a direct path does not exist, direct vegetation attenuation does not exist and no correction is required.

[0119] If any diffraction path passes through the three-dimensional model of at least one cluster, the distance that the diffraction path passes through the three-dimensional model of at least one cluster is multiplied by the attenuation coefficient to obtain the vegetation attenuation of the diffraction path. The diffraction vegetation attenuation includes the vegetation attenuation of all diffraction paths.

[0120] If any scattering path passes through the three-dimensional model of at least one cluster, the distance the scattering path passes through the three-dimensional model of the cluster is multiplied by the attenuation coefficient to obtain the vegetation attenuation of the scattering path. The scattered vegetation attenuation includes the vegetation attenuation of all scattering paths.

[0121] The attenuation coefficient is an empirical value, and the present disclosure does not impose any restrictions on its value. For example, the attenuation coefficient can be calculated by measuring the difference in signal strength before and after a wireless signal passes through a cluster of trees, and then dividing the signal strength difference by the distance along the propagation path through the cluster of trees.

[0122] Figure 6 This is a schematic diagram of vegetation attenuation of wireless signals. Figure 6 As shown, a direct path from the transmitting point to the receiving point passes through a cluster 601, and the distance L of the direct path through the three-dimensional model of the cluster multiplied by the attenuation coefficient is the vegetation attenuation of the direct radiation.

[0123] Step d: correcting the direct signal strength, the diffracted signal strength and the scattered signal strength based on the direct vegetation attenuation, the diffracted vegetation attenuation and the scattered vegetation attenuation.

[0124] Optionally, step d includes: in the presence of a direct signal and direct vegetation attenuation, adding the direct vegetation attenuation to the direct signal strength to obtain a corrected direct signal strength.

[0125] For any diffraction path with vegetation attenuation, the vegetation attenuation of the diffraction path is added to the signal strength of the diffraction path to obtain the corrected signal strength of the diffraction path. In this way, the corrected diffraction signal strength includes the signal strengths of multiple corrected diffraction paths and the signal strengths of multiple uncorrected diffraction paths (the uncorrected diffraction path is the diffraction path that does not pass through at least one cluster, and such diffraction path does not need to be corrected).

[0126] For any scattering path with vegetation attenuation, the vegetation attenuation of the scattering path is added to the signal strength of the scattering path to obtain the corrected signal strength of the scattering path. In this way, the corrected scattering signal strength includes the signal strengths of multiple corrected scattering paths and the signal strengths of multiple uncorrected scattering paths (uncorrected scattering paths are scattering paths that do not pass through at least one cluster, and such scattering paths do not need to be corrected).

[0127] In step 205, channel modeling is performed on the transmitting point and the receiving point based on the corrected direct signal strength, the corrected diffracted signal strength, and the corrected scattered signal strength.

[0128] In the embodiment of the present disclosure, performing channel modeling on the transmitting point and the receiving point includes calculating the receiving power at the receiving point and the power delay spectrum from the transmitting point to the receiving point.

[0129] Optionally, calculating the receiving power at the receiving point includes: in the presence of a corrected direct signal strength, vector superposition of the corrected direct signal strength, multiple corrected diffraction signal strengths, and multiple corrected scattered signal strengths to obtain a total loss from the transmitting point to the receiving point; subtracting the total loss from the transmitting power of the transmitting point is the receiving power of the receiving point.

[0130] Since each propagation path (including direct path, diffraction path, and scattering path) of the wireless signal between the transmitting point and the receiving point and the signal strength of each propagation path are known, the received power at the receiving point along each propagation path can be calculated, thereby constructing the power delay profile from the transmitting point to the receiving point.

[0131] Optionally, when there are multiple receiving points in the target area, the above steps 201 to 205 may be used to perform channel modeling for each receiving point, thereby determining the situation distribution of the target area.

[0132] The following are device embodiments of the present application. For details not described in detail in the device embodiments, reference may be made to the above method embodiments.

[0133] Figure 7 A schematic diagram of the structure of a channel modeling device provided by an exemplary embodiment of the present disclosure is shown. Figure 7 The channel modeling device 700 includes: an acquisition module 701, a division module 702, a signal strength calculation module 703, a correction module 704 and a channel modeling module 705.

[0134] The acquisition module 701 is used to acquire point cloud data of the target area.

[0135] The division module 702 is used to divide the point cloud data into first point cloud data and second point cloud data, where the second point cloud data is point cloud data of vegetation in the target area, and the first point cloud data is point cloud data other than the second point cloud data.

[0136] The signal strength calculation module 703 is used to determine the direct signal strength, diffracted signal strength and scattered signal strength from the transmitting point to the receiving point based on the first point cloud data.

[0137] The correction module 704 is used to determine direct vegetation attenuation, diffraction vegetation attenuation and scattered vegetation attenuation respectively based on the second point cloud data to correct the direct signal strength, diffraction signal strength and scattered signal strength.

[0138] The channel modeling module 705 is used to perform channel modeling on the transmitting point and the receiving point based on the corrected direct signal strength, the corrected diffracted signal strength and the corrected scattered signal strength.

[0139] Optionally, the signal strength calculation module 703 is also used to establish a first Fresnel zone based on the first point cloud data, the transmitting point and the receiving point; if the density of the point cloud data in the first Fresnel zone is greater than the point cloud density threshold, it is determined that the direct signal strength does not exist; if the density of the point cloud data in the first Fresnel zone is less than or equal to the point cloud density threshold, the direct signal strength from the transmitting point to the receiving point is calculated.

[0140] Optionally, the signal strength calculation module 703 is also used to determine the scattering point; based on the three points in the first point cloud data that are closest to the scattering point, the scattering surface, the incident angle, the reflection angle, and the angle between the scattered wave and the reflected wave direction are determined; based on the single-lobe directional scattering model, the signal strength of the scattering from the transmitting point to the receiving point is determined by the incident angle, the reflection angle, and the angle between the scattered wave and the reflected wave direction.

[0141] Optionally, the signal strength calculation module 703 is further configured to use the following formula to determine the signal strength of scattering from the transmitting point to the receiving point:

[0142]

[0143] in, is the amplitude of the scattered field, is the scattering coefficient, , is the amplitude of the incident field, is the distance from the scattering point to the emission point, is the distance from the scattering point to the receiving point, is the angle between the scattered wave and the reflected wave, Used to express the scattering lobe width, are coefficients, determined by series expansion.

[0144] Optionally, the signal strength calculation module 703 is also used to select any point from each leaf node in the octree algorithm as a seed based on the octree algorithm, grow planes in each area based on the seed, and obtain multiple planes; determine the expression equation of each plane in the multiple planes; based on the expression equation of each plane, determine the intersecting line segments of the intersecting planes; based on the intersecting line segments of the intersecting planes, determine the diffraction signal strength.

[0145] Optionally, the correction module 704 is also used to cluster the point cloud data belonging to the same tree in the second point cloud data into a cluster to obtain multiple clusters; model each cluster according to an ellipsoid to obtain a three-dimensional model of each cluster; determine the direct vegetation attenuation, diffraction vegetation attenuation and scattered vegetation attenuation based on the three-dimensional model of each cluster; and correct the direct signal strength, diffraction signal strength and scattered signal strength based on the direct vegetation attenuation, diffraction vegetation attenuation and scattered vegetation attenuation.

[0146] Optionally, the correction module 704 is also used to determine whether each direct path, diffraction path and scattered path passes through the three-dimensional model of at least one cluster; based on the distance that each direct path, diffraction path and scattered path passes through the three-dimensional model of the cluster, determine the direct vegetation attenuation, diffraction vegetation attenuation and scattered vegetation attenuation.

[0147] It should be noted that the channel modeling device provided in the above embodiment only uses the division of the above functional modules as an example to illustrate channel modeling. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the channel modeling device provided in the above embodiment and the channel modeling method embodiment are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0148] The division of modules in the embodiments of the present disclosure is illustrative and represents only a logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the present disclosure may be integrated into a single processor, exist physically as separate modules, or be integrated into a single module. The integrated modules may be implemented in either hardware or software functional modules.

[0149] If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a terminal device (which can be a personal computer, mobile phone, or communication device, etc.) or a processor to execute all or part of the steps of the method of each embodiment of the present disclosure. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0150] Figure 8 Schematic diagram of the structure of the computer device provided by the embodiment of the present disclosure. Figure 8 As shown, the computer device 800 includes a processor 801 and a memory 802 .

[0151] Processor 801 may include one or more processing cores, such as a quad-core processor or an octa-core processor. Processor 801 may be implemented in hardware using at least one of the following: a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), or a PLA (Programmable Logic Array). Processor 801 may also include a main processor and a coprocessor. The main processor is used to process data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 801 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing content required for display. In some embodiments, processor 801 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0152] The memory 802 may include one or more computer-readable storage media, which may be non-transitory. The memory 802 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 802 is used to store at least one instruction, which is executed by the processor 801 to implement the channel modeling method provided in the embodiments of the present disclosure.

[0153] Those skilled in the art will understand that Figure 8 The structure shown in the figure does not constitute a limitation on the computer device 800, and the computer device 800 may include more or fewer components than shown in the figure, or combine some components, or adopt a different arrangement of components.

[0154] The embodiments of the present disclosure further provide a non-transitory computer-readable storage medium. When the instructions in the storage medium are executed by a processor of a computer device, the computer device is enabled to execute the channel modeling method provided in the embodiments of the present disclosure.

[0155] The embodiments of the present disclosure further provide a computer program product, including a computer program / instruction, which implements the channel modeling method provided in the embodiments of the present disclosure when the computer program / instruction is executed by a processor.

[0156] The above description is merely an optional embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present disclosure shall be included in the scope of protection of the present disclosure.

Claims

1. A channel modeling method, characterized in that: The method comprises: Obtain point cloud data of the target area; dividing the point cloud data into first point cloud data and second point cloud data, wherein the second point cloud data is point cloud data of vegetation in the target area, and the first point cloud data is point cloud data in the point cloud data excluding the second point cloud data; Determine, based on the first point cloud data, a direct signal strength, a diffracted signal strength, and a scattered signal strength from a transmitting point to a receiving point; Determining, based on the second point cloud data, direct vegetation attenuation, diffraction vegetation attenuation, and scattered vegetation attenuation, respectively, to correct the direct signal strength, the diffraction signal strength, and the scattered signal strength; Performing channel modeling on the transmitting point and the receiving point based on the corrected direct signal strength, the corrected diffracted signal strength, and the corrected scattered signal strength; The determining of direct vegetation attenuation, diffracted vegetation attenuation, and scattered vegetation attenuation based on the second point cloud data to correct the direct signal strength, the diffracted signal strength, and the scattered signal strength includes: Clustering the point cloud data belonging to the same tree in the second point cloud data into a cluster to obtain multiple clusters; Modeling each of the clusters according to an ellipsoid to obtain a three-dimensional model of each of the clusters; Determining the direct vegetation attenuation, the diffraction vegetation attenuation, and the scattered vegetation attenuation based on the three-dimensional model of each cluster; Correcting the direct signal strength, the diffracted signal strength, and the scattered signal strength based on the direct vegetation attenuation, the diffracted vegetation attenuation, and the scattered vegetation attenuation; The determining, based on the three-dimensional model of each cluster, the direct vegetation attenuation, the diffraction vegetation attenuation, and the scattered vegetation attenuation includes: respectively determining whether each of the direct path, the diffraction path, and the scattered path passes through at least one of the three-dimensional models of the cluster; The direct vegetation attenuation, the diffraction vegetation attenuation and the scattered vegetation attenuation are determined based on a distance that each of the direct path, the diffraction path and the scattered path passes through the three-dimensional model of the cluster.

2. The method according to claim 1, characterized in that The determining, based on the first point cloud data, the direct signal strength, the diffracted signal strength, and the scattered signal strength from the transmitting point to the receiving point includes: establishing a first Fresnel zone based on the first point cloud data, the transmitting point, and the receiving point; If the density of the point cloud data in the first Fresnel zone is greater than the point cloud density threshold, it is determined that the direct signal intensity does not exist; If the density of the point cloud data in the first Fresnel zone is less than or equal to a point cloud density threshold, the direct signal strength from the transmitting point to the receiving point is calculated.

3. The method according to claim 1, characterized in that The determining, based on the first point cloud data, the direct signal strength, the diffracted signal strength, and the scattered signal strength from the transmitting point to the receiving point includes: Determine scattering points; Determine the scattering surface, the incident angle, the reflection angle, and the angle between the scattered wave and the reflected wave based on the three points closest to the scattering point in the first point cloud data; Based on a single-lobe directional scattering model, the intensity of the scattered signal from the transmitting point to the receiving point is determined by the incident angle, the reflection angle, and the angle between the scattered wave and the reflected wave direction.

4. The method according to claim 3, characterized in that The determining, based on a single-lobe directional scattering model, the intensity of the scattered signal from the transmitting point to the receiving point by means of the incident angle, the reflection angle, and the angle between the scattered wave and the reflected wave direction, includes: The following formula is used to determine the scattered signal strength from the transmitting point to the receiving point: in, is the amplitude of the scattered field, is the scattering coefficient, , is the amplitude of the incident field, is the distance from the scattering point to the emission point, is the distance from the scattering point to the receiving point, is the angle between the scattered wave and the reflected wave, Used to express the scattering lobe width, are coefficients, determined by series expansion.

5. The method according to claim 1, characterized in that The octree algorithm is used to process the first point cloud data, and the determining, based on the first point cloud data, the direct signal strength, the diffracted signal strength, and the scattered signal strength from the transmitting point to the receiving point includes: Based on the octree algorithm, any one point in each leaf node in the octree algorithm is selected as a seed, and planes of each region are grown based on the seed to obtain multiple planes; determining an expression equation for each of the plurality of planes; Based on the expression equation of each plane, determining the intersecting line segments of the intersecting planes; The diffraction signal strength is determined based on the intersection line segments of the intersecting planes.

6. A channel modeling device, characterized in that: The device comprises: Acquisition module, used to obtain point cloud data of the target area; a division module, configured to divide the point cloud data into first point cloud data and second point cloud data, wherein the second point cloud data is point cloud data of vegetation in the target area, and the first point cloud data is point cloud data in the point cloud data excluding the second point cloud data; a signal strength calculation module, configured to determine, based on the first point cloud data, a direct signal strength, a diffracted signal strength, and a scattered signal strength from a transmitting point to a receiving point; a correction module, configured to determine, based on the second point cloud data, direct vegetation attenuation, diffraction vegetation attenuation, and scattered vegetation attenuation, respectively, to correct the direct signal strength, the diffraction signal strength, and the scattered signal strength; a channel modeling module, configured to perform channel modeling on the transmitting point and the receiving point based on the corrected direct signal strength, the corrected diffracted signal strength, and the corrected scattered signal strength; The correction module is further configured to cluster the point cloud data belonging to the same tree in the second point cloud data into one cluster, thereby obtaining a plurality of clusters; Modeling each of the clusters according to an ellipsoid to obtain a three-dimensional model of each of the clusters; Determining the direct vegetation attenuation, the diffraction vegetation attenuation, and the scattered vegetation attenuation based on the three-dimensional model of each cluster; Correcting the direct signal strength, the diffracted signal strength, and the scattered signal strength based on the direct vegetation attenuation, the diffracted vegetation attenuation, and the scattered vegetation attenuation; The correction module is further configured to respectively determine whether each direct path, diffraction path, and scattered path passes through at least one of the three-dimensional models of the cluster; The direct vegetation attenuation, the diffraction vegetation attenuation and the scattered vegetation attenuation are determined based on a distance that each of the direct path, the diffraction path and the scattered path passes through the three-dimensional model of the cluster.

7. A computer device, characterized in that: The computer device includes: a memory and a processor, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor to implement the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that At least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by the processor to implement the method according to any one of claims 1 to 5.

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