A method, equipment, and medium for modeling ground cover loss based on environmental statistical characteristics.
By using a method based on environmental statistical characteristics to determine parameters and recursively calculate ground feature loss, the problems of model accuracy and computational efficiency in satellite-terrestrial communication systems are solved, achieving high-precision loss prediction, which is suitable for the deployment and evaluation of future 6G networks.
Patent Information
- Application Number
- CN202610493254.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-15
- Publication Date
- 2026-07-10
AI Technical Summary
In satellite-terrestrial communication systems, existing technologies struggle to construct high-precision channel models and ground object loss models in large-scale, complex, and ever-changing real-world environments. This is especially true in scenarios involving concurrent operation of multiple constellations, dense deployment of ground stations, and high-speed movement of low-Earth orbit satellites. The accuracy of the channel model significantly impacts system reliability and stability, and existing methods suffer from high computational costs or insufficient data accuracy.
By determining environmental statistical parameters, setting the maximum reflection and diffraction numbers, generating environmental samples and recursively calculating propagation loss, an analytical model of ground feature loss is obtained through fitting, and ground feature loss is predicted under different scenarios using environmental statistical parameters.
Maintaining model reliability and computational efficiency under diverse urban structures, this system meets the needs of large-scale deployment of future 6G non-terrestrial networks and cross-regional spectrum coexistence assessment, and provides high-precision loss prediction capabilities.
Smart Images
Figure CN122372121A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of wireless communication technology, and in particular to a method, device, and medium for modeling ground feature loss based on environmental statistical characteristics. Background Technology
[0002] In satellite-to-terrestrial communication systems, the wireless channel constitutes the core bridge connecting the space segment and the ground segment. Its propagation characteristics directly determine the reachability of the link, coverage quality, and the overall system performance ceiling. Therefore, constructing a high-precision satellite-to-terrestrial wireless channel model is not only the foundation for link budgeting, system design, and spectrum planning, but also a key prerequisite for achieving large-scale network deployment, interference management, and performance assurance. Especially in new communication systems with concurrent operation of multiple constellations, dense deployment of ground stations, and high-speed movement of low-Earth orbit satellites, the accuracy of the channel model has a decisive impact on the reliability and stability of the system.
[0003] Meanwhile, ground feature loss modeling depicts the effects of environmental elements such as buildings and vegetation on satellite signals through obstruction, attenuation, and scattering. Accurate ground feature loss models play an irreplaceable role in tasks such as interference coexistence analysis, ground station power limitation setting, on-board transmit power control, and adjacent channel interference assessment. For example, when low-Earth orbit satellites traverse urban environments, the deep fading caused by building obstruction not only affects the reliability of user links but also significantly alters the interference coupling relationship between the space segment and other ground systems. Therefore, accurate modeling of ground feature loss is crucial for ensuring the safe coexistence of systems in complex shared spectrum scenarios.
[0004] However, achieving high-precision channel modeling and ground feature loss characterization simultaneously in large-scale real-world environments remains a significant challenge. First, satellite-to-ground links traverse multi-scale, non-stationary scenarios across space, cities, and rural areas, with highly variable environmental structures, making the construction of a universally applicable unified channel model extremely difficult. Second, while high-precision ray tracing (RT) methods can characterize signal reflection, diffraction, and obstruction effects, their computational cost is extremely high over very large areas, and their accuracy is highly dependent on the completeness and geometric accuracy of the environmental model. If information on building height, material, or layout is missing, RT predictions will deviate significantly from reality. Simultaneously, obtaining high-precision building models covering large areas remains extremely difficult: publicly available geographic data often suffers from outdated updates, insufficient accuracy, or missing 3D structural information, making channel predictions based on this data insufficient for system-level simulation and interference assessment requirements. Therefore, how to balance modeling accuracy, computational efficiency, and data availability in large-scale, complex, and variable real-world environments to construct reliable and scalable satellite-to-ground channel and ground feature loss models has become a critical technical challenge that urgently needs to be addressed in current satellite communication system design, interference coexistence assessment, and power management strategy formulation. Summary of the Invention
[0005] The purpose of this invention is to provide a method, device, and medium for modeling ground feature loss based on environmental statistical characteristics, aiming to solve or improve at least one of the above-mentioned technical problems.
[0006] To achieve the above objectives, the present invention provides the following solution: A method for modeling land cover loss based on environmental statistical characteristics, comprising: Determine environmental statistical parameters and set the maximum number of reflections and the maximum number of diffractions; the environmental statistical parameters include average building width, average building height, average street width, average base station height, signal elevation angle, and frequency; An environmental sample is generated based on the environmental statistical parameters, and the propagation loss of rays generated by each base station is recursively calculated until the ray exceeds the building height threshold or reaches the maximum number of reflections / diffractions. The recursive calculation is repeated a set number of times to obtain the distribution function curve of ground feature loss; Based on the aforementioned ground feature loss distribution function curve, an analytical model for ground feature loss is obtained by fitting the model. Using environmental statistical parameters as variables, the analytical model is then used to predict ground feature loss under different scenarios.
[0007] Optionally, the environmental samples include randomly generated values for building width, building height, and street width, and the distribution of each data is determined based on the environmental statistical parameters.
[0008] Optionally, the calculation process of the distribution function curve specifically includes: Determine if the current ray height is higher than the building threshold; if so, return the current power. Determine whether the current number of reflections and diffractions exceeds the set maximum value; if so, set the return power to 0. Determine whether reflection occurs based on environmental samples: if no reflection occurs, calculate only the diffraction loss; if reflection occurs, calculate both the reflection loss and the diffraction loss. A new environmental sample is generated for the ray at the next moment, and the calculation function is recursively called until all calculations are completed. The power of all rays is accumulated to obtain the ground feature loss distribution function curve.
[0009] Optionally, in the propagation loss, the diffraction loss is calculated based on the ITU-R P.526 single-edge diffraction model, and the reflection loss is calculated based on the ITU-R P.2040 model, and the reflective material type, ray polarization, and incident angle are randomly generated.
[0010] Optionally, the ground feature loss distribution function curve is calculated using the following formula: in,L cl Indicates the wear and tear of ground features. P output This represents the output power on a linear scale.
[0011] Optionally, the analytical model is represented as: in, The parameters to be fitted are functions influenced by environmental parameters; The LosS probability is calculated from environmental parameters. p is the probability distribution.
[0012] The present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the above-described method for modeling ground feature loss based on environmental statistical characteristics.
[0013] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the ground feature loss modeling method based on environmental statistical features as described above.
[0014] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: This invention discloses a method, device, and medium for modeling ground feature loss based on environmental statistical characteristics. The method includes determining environmental statistical parameters and setting the maximum number of reflections and the maximum number of diffractions. The environmental statistical parameters include average building width, average building height, average street width, average base station height, signal elevation angle, and frequency. Based on the environmental statistical parameters, environmental samples are generated, and the propagation loss of rays originating from each base station is recursively calculated until the ray exceeds a building height threshold or reaches the maximum number of reflections / diffractions. This recursive calculation is repeated a set number of times to obtain a ground feature loss distribution function curve. Based on the ground feature loss distribution function curve, an analytical model of ground feature loss is fitted, and the analytical model, using the environmental statistical parameters as variables, is used to predict ground feature loss under different scenarios. This invention can maintain consistent reliability under diverse urban structural conditions, thereby meeting the practical needs of future 6G non-terrestrial networks in large-scale deployment and cross-regional spectrum coexistence assessment. Attached Figure Description
[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0016] Figure 1 This is a flowchart illustrating the ground cover loss modeling method based on environmental statistical characteristics of the present invention. Figure 2 This is a 3D map of the simulated area of Huangdao District, Qingdao City, in this embodiment one; Figure 3 This is a schematic diagram of the cumulative distribution function of land cover loss in Qingdao in this embodiment; where (a) is a comparison diagram between the Monte Carlo model and ITU-R 2402; and (b) is a comparison diagram between the analytical model and ITU-R P.2108. Figure 4 This is a 3D map of the simulated Manhattan area in New York City in this second embodiment; Figure 5 This is a schematic diagram of the cumulative distribution function of Manhattan feature loss in this embodiment 2; where (a) is a comparison diagram between the Monte Carlo model and ITU-R 2402; and (b) is a comparison diagram between the analytical model and ITU-R P.2108. Detailed Implementation
[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0018] The purpose of this invention is to provide a method, device, and medium for modeling ground feature loss based on environmental statistical characteristics, aiming to solve or improve at least one of the above-mentioned technical problems.
[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0020] As a first aspect, the present invention provides, as follows Figure 1 The method for modeling land cover loss based on environmental statistical characteristics, as shown, includes: Step 1: Input parameters: Determine the environmental statistical parameters and set the maximum number of reflections and the maximum number of diffractions; the environmental statistical parameters include average building width, average building height, average street width, average base station height, signal elevation angle, and frequency.
[0021] As one specific implementation method, the specific steps are as follows: Input vector The elements represent, in order: 1 / average building width (m), 1 / average building height (m), 1 / average street width (m), 1 / average base station height (m), elevation angle (°), and frequency (GHz). Additionally, the maximum allowed number of reflections and the maximum allowed number of diffractions are also input. .
[0022] Step 2: Recursively calculate ray loss: Generate environmental samples based on the environmental statistical parameters, and recursively calculate the propagation loss of rays generated by each base station until the ray exceeds the building height threshold or reaches the maximum number of reflections / diffractions; the environmental samples include randomly generated values of building width, building height, and street width, and the distribution of each data is determined according to the environmental statistical parameters.
[0023] As one specific implementation method, the specific steps are as follows: 1) Generate base station height samples for this calculation based on the exponential distribution. and the environmental sample vector of the transmitting base station. This is used as input to the recursive function ComputePower. Here, i and j represent the i-th diffraction and j-th reflection of the ray, respectively; both are 0 when the ray is emitted. These represent the building width, building height, and street width, respectively.
[0024] 2) Generate the initial ray , representing a transmission power of 1.
[0025] 3) Calculate the height of the ray reaching the next building and determine whether the ray height has reached the threshold height. If the height is higher than that, the current ray power is returned.
[0026] 4) Determine whether the number of reflections and diffractions experienced by the radiation is greater than [number missing]. If the value is less than 0, continue; if the value is greater than 0, return to 0 for ray power.
[0027] 5) Based on the generated environment sample: If no reflection occurs, then diffracted rays are generated with a power of: If reflection occurs, reflected rays and diffracted rays are generated, with powers of: in, Let be the power at the i-th diffraction and j-th reflection. These are the diffraction and reflection losses calculated based on environmental samples and the height and elevation angle of the rays. The diffraction loss is calculated according to the single-edge diffraction model of ITU-R P.526-18; the reflection loss is calculated according to ITU-R P.2040. During the calculation, the ray elevation angle is fixed, the horizontal angle is randomly generated and the ray incident angle is calculated, and the reflecting material and ray type (TE, TM) are randomly generated.
[0028] 6) Generate new environmental samples for the newly generated rays, and use the ray information and environmental samples as inputs to a recursive function to continue the calculation. The final function return value is the ray power, and the calculated ground feature loss distribution function curve is as follows: in, L cl Indicates the wear and tear of ground features. P output This represents the output power on a linear scale.
[0029] Step 3: Repeat the calculation to determine the cumulative distribution curve of land cover loss: Repeat the recursive calculation a set number of times to obtain the land cover loss distribution function curve. Specifically, repeat step 2 until a sufficient number of samples are calculated, such as 10,000 times, so that the distribution curve is sufficiently smooth.
[0030] As a specific implementation method, the calculation process of the distribution function curve specifically includes: Determine if the current ray height is higher than the building threshold; if so, return the current power. Determine if the current number of reflections and diffractions exceeds the set maximum value; if so, set the returned power to 0. Determine if reflection occurs based on the environmental sample: if no reflection occurs, calculate only the diffraction loss; if reflection occurs, calculate the reflection loss and diffraction loss respectively. Generate a new environmental sample for the ray at the next moment and recursively call the calculation function until all calculations are completed. Accumulate the power of all rays to obtain the ground feature loss distribution function curve.
[0031] Step 4: Calculate the cumulative distribution function under multiple parameters: Based on the distribution function curve of the ground feature loss, fit the analytical model of ground feature loss, and use the analytical model with environmental statistical parameters as variables to predict ground feature loss under different scenarios.
[0032] As one specific implementation method, the specific steps are as follows: The cumulative distribution function of land cover loss under a large number of parameters is calculated and used to fit the analytical model. The fitted model function is as follows: in, The parameters to be fitted are functions influenced by environmental parameters; The LosS probability is calculated from environmental parameters. p is the probability distribution.
[0033] Based on the above technical solution, the following two embodiments are provided.
[0034] Example 1 Extract urban parameters (such as 3D map) of a certain area in Huangdao District, Qingdao City. Figure 2 (As shown in the image), and input the specific configuration as shown in Table 1. To compare and demonstrate the effectiveness of the models, the ITU model and the ray tracing model are included in the comparison.
[0035] Table 1 Configuration parameters for Example 1
[0036] Finally, the cumulative distribution function curves of ground object loss at various elevation angles are obtained, such as... Figure 3 As shown.
[0037] Example 2 Extracting urban parameters (such as 3D maps) from a specific area in Manhattan, New York City. Figure 4 (As shown in the image), and input the specific configuration as shown in Table 2. To compare and demonstrate the effectiveness of the models, the ITU model and the ray tracing model are included in the comparison.
[0038] Table 2 Configuration parameters for Example 2
[0039] The cumulative distribution function curves of ground object loss at various elevation angles are obtained, such as... Figure 5 As shown.
[0040] As a second aspect, the present invention also provides an electronic device, including a memory and a processor, the memory for storing a computer program, the processor for running the computer program to cause the electronic device to perform the above-described method for modeling ground feature loss based on environmental statistical characteristics.
[0041] As a third aspect, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the ground feature loss modeling method based on environmental statistical features as described above.
[0042] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.
[0043] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.
Claims
1. A method for modeling land cover loss based on environmental statistical characteristics, characterized in that, include: Determine the environmental statistical parameters and set the maximum number of reflections and the maximum number of diffractions; The environmental statistical parameters include average building width, average building height, average street width, average base station height, signal elevation angle, and frequency. An environmental sample is generated based on the environmental statistical parameters, and the propagation loss of rays generated by each base station is recursively calculated until the ray exceeds the building height threshold or reaches the maximum number of reflections / diffractions. The recursive calculation is repeated a set number of times to obtain the distribution function curve of ground feature loss; Based on the aforementioned ground feature loss distribution function curve, an analytical model for ground feature loss is obtained by fitting the model. Using environmental statistical parameters as variables, the analytical model is then used to predict ground feature loss under different scenarios.
2. The method for modeling ground cover loss based on environmental statistical characteristics according to claim 1, characterized in that, The environmental samples include randomly generated values for building width, building height, and street width, and the distribution of each data is determined based on the environmental statistical parameters.
3. The method for modeling land cover loss based on environmental statistical characteristics according to claim 1, characterized in that, The calculation process of the distribution function curve specifically includes: Determine if the current ray height is higher than the building threshold; if so, return the current power. Determine whether the current number of reflections and diffractions exceeds the set maximum value; if so, set the return power to 0. Determine whether reflection occurs based on environmental samples: if no reflection occurs, calculate only the diffraction loss; if reflection occurs, calculate both the reflection loss and the diffraction loss. A new environmental sample is generated for the ray at the next moment, and the calculation function is recursively called until all calculations are completed. The power of all rays is accumulated to obtain the ground feature loss distribution function curve.
4. The method for modeling ground cover loss based on environmental statistical characteristics according to claim 1, characterized in that, In the propagation loss, the diffraction loss is calculated based on the ITU-R P.526 single-edge diffraction model, and the reflection loss is calculated based on the ITU-R P.2040 model, with the reflective material type, ray polarization, and incident angle randomly generated.
5. The method for modeling ground cover loss based on environmental statistical characteristics according to claim 1, characterized in that, The formula for calculating the distribution function curve of ground feature loss is as follows: in, L cl Indicates the wear and tear of ground features. P output This represents the output power on a linear scale.
6. The method for modeling ground cover loss based on environmental statistical characteristics according to claim 1, characterized in that, The analytical model is expressed as: in, The parameters to be fitted are functions influenced by environmental parameters; The LosS probability is calculated from environmental parameters. p is the probability distribution.
7. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform the ground feature loss modeling method based on environmental statistical characteristics according to any one of claims 1-6.
8. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the ground feature loss modeling method based on environmental statistical features as described in any one of claims 1-6.