Channel modeling method, model application method, electronic equipment and storage medium

Abstract

The invention provides a channel modeling method, a model application method, electronic equipment and a storage medium. The channel modeling method comprises the steps that a rainfall type factor and a rainfall rate in a current environment are obtained, the value of the rainfall type factor is larger than or equal to a first preset value and smaller than or equal to a second preset value, the first preset value represents that the rainfall type in the current environment is rain, and the second preset value represents that the rainfall type in the current environment is snow; according to the rainfall rate, determining a rainfall intensity grade in the current environment; and generating a signal attenuation model of the wireless communication channel in the current environment based on the rainfall type factor and the rainfall intensity grade. According to the embodiment of the invention, the rainfall type factor and the rainfall intensity grade are adopted to represent the rain and snow channel attenuation in a unified manner, and the unified channel model based on the equivalent rainfall particles is constructed to describe the mixed rainfall scene in a unified manner, improve the model precision and reduce the error of the signal attenuation prediction result.

Description

Technical Field

[0001] This application relates to the field of wireless communication channel technology, specifically to a channel modeling method, a model application method, an electronic device, and a storage medium. Background Technology

[0002] During wireless communication, electromagnetic waves are susceptible to atmospheric conditions, especially precipitation. To ensure the stability and reliability of communication links under adverse weather conditions, channel modeling techniques can be used to predict signal attenuation caused by precipitation, thereby compensating for the signal loss.

[0003] In related technologies, signal attenuation caused by precipitation particles such as rain and snow is usually modeled separately, which cannot uniformly describe mixed precipitation scenarios, resulting in low model accuracy and excessive error in signal attenuation prediction results. Summary of the Invention

[0004] The embodiments of this application provide a channel modeling method, electronic device, and storage medium, which aim to uniformly describe mixed precipitation scenarios, improve model accuracy, and reduce the error of signal attenuation prediction results.

[0005] In a first aspect, embodiments of this application provide a channel modeling method, the channel modeling method comprising: Obtain the precipitation type factor and precipitation rate under the current environment, wherein the value of the precipitation type factor is greater than or equal to a first preset value and less than or equal to a second preset value, the first preset value indicates that the precipitation type under the current environment is rain, and the second preset value indicates that the precipitation type under the current environment is snow. Based on the precipitation rate, determine the precipitation intensity level under the current environment; Based on the precipitation type factor and the precipitation intensity level, a signal attenuation model for the wireless communication channel under the current environment is generated.

[0006] In the above embodiments, precipitation type factor and precipitation intensity level are used to uniformly characterize rain and snow channel attenuation, and a unified channel model based on equivalent precipitation particles is constructed to uniformly describe mixed precipitation scenarios, improve model accuracy, and reduce the error of signal attenuation prediction results.

[0007] In one embodiment, generating a signal attenuation model for the wireless communication channel under the current environment based on the precipitation type factor and the precipitation intensity level includes: Based on the precipitation type factor and the precipitation intensity level, the equivalent particle size and equivalent density of the equivalent precipitation particles in the current environment are determined. If the value of the precipitation type factor is equal to the first preset value, the equivalent precipitation particles are raindrop particles. If the value of the precipitation type factor is equal to the second preset value, the equivalent precipitation particles are snowflake particles. The signal attenuation model is generated based on the equivalent particle size and the equivalent density.

[0008] In the above embodiments, equivalent particle size and equivalent density are used to realize a modeling method based on microscopic physical parameters, thereby generating a signal attenuation model that highly matches the actual precipitation physical process, significantly improving the model's accuracy and applicability.

[0009] In one embodiment, the equivalent particle size is determined by the following steps: Based on the precipitation intensity level, determine the first particle size of raindrop particles and the second particle size of snowflake particles; Based on the value of the precipitation type factor, the first weight of raindrop particles and the second weight of snowflake particles are determined. Based on the first weight and the second weight, the first particle size and the second particle size are weighted and summed to obtain the equivalent particle size.

[0010] In the above embodiments, by comprehensively considering the first particle size of raindrop particles and the second particle size of snowflake particles in the equivalent particle size, the equivalent particle size can smoothly reflect the particle size change process from raindrop to snowflake, realizing a precise and quantitative calculation path from macroscopic meteorological parameters to microscopic equivalent particle size, making the entire channel modeling process have a stronger physical basis and higher accuracy.

[0011] In one embodiment, the equivalent density is determined by the following steps: Obtain the first preset density of raindrop particles and the second preset density of snowflake particles; Based on the value of the precipitation type factor, the first weight of raindrop particles and the second weight of snowflake particles are determined. Based on the first weight and the second weight, the first preset density and the second preset density are weighted and summed to obtain the equivalent density.

[0012] In the above embodiments, by comprehensively considering the first preset density of raindrop particles and the second preset density of snowflake particles in the equivalent density, the equivalent density can smoothly reflect the density change process from raindrops to snowflakes, realizing a precise and quantitative calculation path from macroscopic meteorological parameters to microscopic equivalent density, making the entire channel modeling process have a stronger physical basis and higher accuracy.

[0013] In one embodiment, generating a signal attenuation model for the wireless communication channel under the current environment based on the precipitation type factor and the precipitation intensity level includes: Obtain the communication link height of the wireless communication channel; Determine the thickness of the precipitation layer under the current environment; The exponential correction factor for signal attenuation is determined based on the ratio of the communication link height to the precipitation layer thickness. Based on the precipitation type factor, the precipitation intensity level, and the exponential correction coefficient for signal attenuation, the precipitation path loss increment of the wireless communication channel is determined. The signal attenuation model is generated based on the precipitation path loss increment.

[0014] In the above embodiments, a more comprehensive and accurate signal attenuation model was constructed. This model not only considers the micro-type and macro-intensity of precipitation, but also introduces the three-dimensional spatial geometric relationship between the communication link and the precipitation layer, enabling the model to better adapt to various complex communication scenarios. In particular, the error of the signal attenuation prediction results is smaller in non-horizontal or high-altitude communication applications.

[0015] In one embodiment, generating the signal attenuation model based on the precipitation path loss increment includes: Obtain the urban multipath effect factor of wireless communication channels; Based on the urban multipath effect factor, the urban multipath effect loss increment of the wireless communication channel is determined. The signal attenuation model is generated based on the free space loss value of the wireless communication channel, the loss increment of the urban multipath effect, and the loss increment of the precipitation path.

[0016] In the above embodiments, by combining the free space loss value of the wireless communication channel, the urban multipath effect loss increment, and the precipitation path loss increment, a more accurate signal attenuation model can be generated, thereby further reducing the error of the signal attenuation prediction results.

[0017] In one embodiment, determining the urban multipath effect loss increment of the wireless communication channel based on the urban multipath effect factor includes: Based on the precipitation path loss increment, the impact coefficient of precipitation on urban multipath effect is determined; Based on the influence coefficient of precipitation on urban multipath effect and the urban multipath effect factor, the incremental loss of urban multipath effect is determined.

[0018] In the above embodiments, the impact of precipitation on urban multipath effects is comprehensively considered, making the determined urban multipath effect loss increment more accurate, and thus the signal attenuation model more precise.

[0019] Secondly, embodiments of this application provide a model application method, the model application method comprising: Using any of the signal attenuation models described above, determine the signal attenuation magnitude of the wireless communication channel under the current environment; Based on the signal attenuation amplitude, signal compensation processing is performed, wherein the signal compensation processing includes at least one of increasing signal power and switching modulation and coding strategies.

[0020] In the above embodiments, by closely integrating the accurate channel attenuation model with the actual adaptive adjustment mechanism of the communication system, the communication system can optimize the configuration of power or modulation and coding strategies, thereby achieving more intelligent and robust wireless communication in complex and ever-changing environments.

[0021] Thirdly, embodiments of this application provide a channel modeling apparatus, the channel modeling apparatus comprising: The acquisition module is used to acquire the precipitation type factor and precipitation rate under the current environment, wherein the value of the precipitation type factor is greater than or equal to a first preset value and less than or equal to a second preset value, the first preset value indicates that the precipitation type under the current environment is rain, and the second preset value indicates that the precipitation type under the current environment is snow. The determination module is used to determine the precipitation intensity level under the current environment based on the precipitation rate; The generation module is used to generate a signal attenuation model of the wireless communication channel under the current environment based on the precipitation type factor and the precipitation intensity level.

[0022] Fourthly, embodiments of this application provide a model application apparatus, the model application apparatus comprising: The prediction module is used to determine the signal attenuation magnitude of the wireless communication channel under the current environment using any of the signal attenuation models described above. The compensation module is used to perform signal compensation processing based on the signal attenuation amplitude, wherein the signal compensation processing includes at least one of increasing signal power and switching modulation and coding strategies.

[0023] Fifthly, embodiments of this application provide an electronic device including a processor and a memory, wherein the memory stores a computer program configured to be executed by the processor to implement the channel modeling method as described in any of the preceding claims, or the model application method as described in any of the preceding claims.

[0024] Sixthly, embodiments of this application provide a computer-readable storage medium storing a computer program configured to be executed by a processor to implement the channel modeling method or the model application method as described in any of the preceding claims.

[0025] In a seventh aspect, embodiments of this application provide a computer program product, including a computer program or instructions, which are executed by a processor to implement the channel modeling method or the model application method as described in any of the preceding claims.

[0026] The beneficial effects of the embodiments of this application are as follows: In the embodiments of this application, precipitation type factor and precipitation intensity level are used to uniformly characterize rain and snow channel attenuation, and a unified channel model based on equivalent precipitation particles is constructed to uniformly describe mixed precipitation scenarios, improve model accuracy, and reduce the error of signal attenuation prediction results. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a schematic flowchart of an embodiment of the channel modeling method provided in this application; Figure 2 This is a schematic flowchart of another embodiment of the channel modeling method provided in this application; Figure 3 This is a schematic flowchart of another embodiment of the channel modeling method provided in this application; Figure 4 This is a schematic flowchart of an embodiment of the model application method provided in this application; Figure 5 This is a schematic flowchart of one embodiment of the channel modeling method and model application method provided in this application; Figure 6 This is an example diagram illustrating the power loss at different distances under different weather conditions, based on the channel modeling method provided in the embodiments of this application. Figure 7 This is a schematic diagram of an embodiment of the channel modeling apparatus provided in this application; Figure 8 This is a schematic diagram of an embodiment of the model application device provided in this application; Figure 9 This is a schematic diagram of an embodiment of the electronic device provided in this application.

[0029] Among them, the appendix Figure 6 The images are in color so that different objects can be distinguished by different colors. Detailed Implementation

[0030] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. In addition, in the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0031] In related technologies, precipitation channel models have the following shortcomings: (1) Model fragmentation: Rain and snow need to be modeled independently (such as ITU-R P.838 rain attenuation model and ITU-R P.840 snow attenuation model), which cannot uniformly describe mixed precipitation scenarios; (2) Poor environmental generalization: Traditional models rely on fixed precipitation rate parameters and do not consider the differential effects of different precipitation particle physical properties (such as snowflake density and raindrop size distribution) on multi-band signals; (3) Lack of time-domain dynamism: Channel fluctuations caused by the failure to take into account the time-varying characteristics of precipitation intensity (such as the suddenness of rainstorms).

[0032] To address this, this application provides a channel modeling method, a model application method, an electronic device, and a storage medium. It employs a precipitation type factor and precipitation intensity level to uniformly characterize rain and snow channel attenuation, constructing a unified channel model based on equivalent precipitation particles to uniformly describe mixed precipitation scenarios, improve model accuracy, and reduce errors in signal attenuation prediction results. For specific details, please refer to the following description.

[0033] Firstly, embodiments of this application provide a channel modeling method that can accurately simulate the signal attenuation characteristics of wireless communication channels under different precipitation weather conditions (especially complex weather conditions such as rain, snow, and mixed rain and snow). Specifically, refer to... Figure 1 , Figure 1 This is a schematic flowchart of an embodiment of a channel modeling method. Figure 1 In this context, the modeling method for this channel may include: 101. Obtain the precipitation type factor and precipitation rate under the current environment. The precipitation type factor is greater than or equal to a first preset value and less than or equal to a second preset value. The first preset value indicates that the precipitation type under the current environment is rain, and the second preset value indicates that the precipitation type under the current environment is snow.

[0034] In the embodiments of this application, the precipitation type factor is a numerical parameter whose core significance lies in quantitatively characterizing the specific form of precipitation. In related technologies, precipitation can be simply divided into "rain" or "snow," while the "precipitation type factor" introduced in the embodiments of this application can describe the continuous transition state from pure rain to pure snow, thereby accurately characterizing mixed or transitional precipitation types such as "sleet," "wet snow," and "freezing rain."

[0035] In the embodiments of this application, the range of the precipitation type factor is limited to a closed interval, that is, greater than or equal to a first preset value and less than or equal to a second preset value. If the value of the precipitation type factor is equal to the first preset value, it indicates that the precipitation type under the current environment is rain, that is, the precipitation under the current environment is pure rain. If the value of the precipitation type factor is equal to the second preset value, it indicates that the precipitation type under the current environment is snow, that is, the precipitation under the current environment is pure snow.

[0036] In some embodiments of this application, the first preset value can be set to 0, and the second preset value can be set to 1. Therefore, when the precipitation type factor is 0, it indicates that the current environment is purely rainy; when the precipitation type factor is 1, it indicates that the current environment is purely snowy; and when the precipitation type factor is greater than 0 and less than 1 (e.g., 0.3, 0.5, 0.8, etc.), it indicates mixed rain and snow weather with different proportions. The closer the precipitation type factor value is to 1, the higher the proportion of snow in the precipitation. This setting allows the model to smoothly and continuously handle various complex precipitation types.

[0037] In some embodiments of this application, the precipitation type factor can be determined using data collected by meteorological sensors. For example, a laser raindrop spectrometer or a two-dimensional video raindrop spectrometer can be used to analyze data such as the morphology and falling velocity of precipitation particles. Based on relevant data processing rules, a quantitative factor characterizing the precipitation type, i.e., the precipitation type factor, can be calculated. Alternatively, the precipitation type factor can be determined based on a preset empirical model or machine learning model (e.g., decision tree, neural network). For instance, multidimensional meteorological parameters such as temperature and humidity under the current environment can be input into a preset empirical model or machine learning model, and the precipitation type factor output by the preset empirical model or machine learning model can be received.

[0038] In the embodiments of this application, precipitation rate is a meteorological parameter characterizing the depth of precipitation per unit area per unit time, with its standard unit being millimeters per hour (mm / h). Precipitation rate directly reflects the intensity of precipitation. Since the more intense the precipitation, the more severe the signal power attenuation, precipitation rate can be used to generate a signal attenuation model for wireless communication channels in the current environment. Precipitation rate can also be determined using data collected by corresponding meteorological sensors (such as rain gauges, weather radars, etc.), which will not be elaborated upon here.

[0039] 102. Determine the precipitation intensity level under the current environment based on the precipitation rate.

[0040] In the embodiments of this application, the precipitation intensity level is a qualitative or semi-quantitative classification of precipitation rate, such as "light rain", "moderate rain", "heavy rain" or "light snow", "moderate snow", "heavy snow", etc. The classification of precipitation intensity levels can simplify the modeling process because the precipitation particle spectrum distribution (particle size and number density) of different precipitation intensity levels have different statistical characteristics.

[0041] In the embodiments of this application, a mapping relationship is pre-set between precipitation rate and precipitation intensity level, thereby mapping the precipitation rate in the current environment to the precipitation intensity level in the current environment, so as to facilitate subsequent model lookup or segmented calculation and simplify the modeling process.

[0042] In some embodiments of this application, if the precipitation type factor is equal to a first preset value, and the precipitation type under the current environment is rain, the mapping relationship between precipitation rate and precipitation intensity level may include, for example: If the precipitation rate R < 2.5 mm / h, the precipitation intensity level is determined to be 1, which represents "light rain"; If the rainfall rate is 2.5 mm / h ≤ R < 10 mm / h, the rainfall intensity level is determined to be 2, which represents "moderate rain". When R ≥ 10 mm / h, the precipitation intensity level is determined to be 3, which represents "heavy rain".

[0043] In some embodiments of this application, if the precipitation type factor is equal to a second preset value, and the precipitation type under the current environment is snow, the mapping relationship between precipitation rate and precipitation intensity level may include, for example: If the precipitation rate R < 0.5 mm / h, the precipitation intensity level is determined to be 1, representing "light snow"; If 0.5 mm / h ≤ R < 1.5 mm / h, the precipitation intensity level is determined to be 2, indicating "moderate snow"; With a precipitation rate of R ≥ 1.5 mm / h, the precipitation intensity level is determined to be 3, representing "heavy snow".

[0044] 103. Based on precipitation type factors and precipitation intensity levels, generate a signal attenuation model for wireless communication channels under the current environment.

[0045] In the embodiments of this application, the signal attenuation model is used to predict the signal power loss (usually in dB) of the wireless signal in the wireless communication channel under the current environment. Since the signal attenuation amplitude caused by precipitation particles such as rain and snow may vary depending on the type of precipitation, and the precipitation intensity level also affects the signal attenuation amplitude, a unified signal attenuation model for the wireless communication channel under the current environment can be generated by combining precipitation type factors and precipitation intensity levels. This improves the model accuracy and reduces the error in the signal attenuation prediction results.

[0046] In the embodiments of this application, the signal attenuation model of the wireless communication channel under the current environment can be referred to... Figure 2 or Figure 3 The method described in the illustrated embodiment can also be used to generate the data using a machine learning model (such as a neural network), and this is not a limitation. The machine learning model takes as input the precipitation type factor and precipitation intensity level under the current environment, and outputs the signal attenuation amplitude of the wireless communication channel under the current environment. This model requires training with a large amount of experimental or simulation data.

[0047] As can be seen from the above embodiments of this application, precipitation type factor and precipitation intensity level are used to uniformly characterize rain and snow channel attenuation, and a unified channel model based on equivalent precipitation particles is constructed to uniformly describe mixed precipitation scenarios, improve model accuracy, and reduce the error of signal attenuation prediction results. Furthermore, this signal attenuation model is a dynamic channel model that integrates precipitation type (rain / snow) and intensity (small / medium / large), achieving high-precision modeling in multiple environments through a unified parameterization framework. It is suitable for link budget and anti-interference design in 5G / 6G, satellite communication, and IoT systems.

[0048] In some embodiments of this application, reference is made to Figure 2 ,exist Figure 1 Based on the illustrated embodiment, a signal attenuation model for the wireless communication channel under the current environment is generated based on precipitation type factors and precipitation intensity levels, which may include: 201. Based on the precipitation type factor and precipitation intensity level, determine the equivalent particle size and equivalent density of the equivalent precipitation particles under the current environment. If the value of the precipitation type factor is equal to the first preset value, the equivalent precipitation particles are raindrop particles. If the value of the precipitation type factor is equal to the second preset value, the equivalent precipitation particles are snowflake particles.

[0049] In the embodiments of this application, the equivalent precipitation particle does not refer to a specific particle that actually exists in the atmosphere, but rather to a theoretically constructed abstract particle used for calculation. Its physical properties (such as size, density, dielectric constant, etc.) are the result of abstracting thousands of real precipitation particles (which may be raindrops, snowflakes, or a mixture of both) in the current environment. The significance of introducing this concept is that it greatly simplifies the calculation of the interaction between electromagnetic waves and complex precipitation environments, making accurate modeling based on physical scattering theories (such as Mie scattering theory) possible. Therefore, through the equivalent precipitation particle, mixed or transitional types of precipitation particles such as "sleet," "wet snow," and "freezing rain" can be accurately characterized.

[0050] In some embodiments of this application, similar to raindrop particles and snowflake particles, equivalent precipitation particles also possess physical properties such as particle size and density. The particle size of equivalent precipitation particles is called the equivalent particle size, and the density of equivalent precipitation particles is called the equivalent density. The equivalent particle size is one of the key factors determining the electromagnetic wave scattering and absorption cross-section of equivalent precipitation particles, and the equivalent density directly affects the dielectric constant of equivalent precipitation particles, thereby affecting their interaction with electromagnetic waves. Therefore, when generating a signal attenuation model, the influence of equivalent particle size and equivalent density can be considered to improve model accuracy and reduce the error of signal attenuation prediction results.

[0051] In some embodiments of this application, if the precipitation type factor is equal to a first preset value (e.g., a value of 0, representing pure rain), the equivalent precipitation particles are physically equivalent to raindrop particles, and the equivalent particle size, equivalent density, and other parameters used in subsequent calculations all adopt the physical characteristics of raindrop particles. If the precipitation type factor is equal to a second preset value (e.g., a value of 1, representing pure snow), the equivalent precipitation particles are physically equivalent to snowflake particles, and the equivalent particle size, equivalent density, and other parameters used in subsequent calculations all adopt the physical characteristics of snowflake particles.

[0052] In some embodiments of this application, the equivalent particle size can be determined by the following steps: determining the first particle size of raindrop particles and the second particle size of snowflake particles according to the precipitation intensity level; determining the first weight of raindrop particles and the second weight of snowflake particles based on the value of the precipitation type factor; and performing a weighted summation of the first particle size and the second particle size based on the first weight and the second weight to obtain the equivalent particle size.

[0053] The first particle size refers to the typical or characteristic particle size of raindrops in a pure rain environment at the current precipitation intensity level, while the second particle size refers to the typical or characteristic particle size of snowflakes in a pure snow environment at the same precipitation intensity level. Here, "particle size" does not refer to the size of any single particle, but rather a statistical quantity that represents the overall size distribution characteristics of the particle swarm at the current precipitation intensity level, such as the median volume diameter or average diameter. The first particle size of raindrop particles and the second particle size of snowflake particles differ at different precipitation intensity levels; for example, the higher the precipitation intensity level, the larger the first and second particle sizes. The mapping relationship between precipitation intensity level and the first and second particle sizes can be obtained based on prior measurements and statistics, and will not be elaborated upon here.

[0054] Since the value of the precipitation type factor can characterize whether the current equivalent precipitation particles are more biased towards raindrop particles or snowflake particles, the same value of the precipitation type factor can be used to determine the equivalent particle size of the equivalent precipitation particles, and whether it is more biased towards the first particle size of raindrop particles or the second particle size of snowflake particles. Taking a first preset value of 0 and a second preset value of 1 as an example, the value of the precipitation type factor α can be directly used as the second weight of snowflake particles, and (1-α) can be used as the first weight of raindrop particles. It can be seen that the first weight of raindrop particles characterizes the degree of bias of the equivalent particle size of the equivalent precipitation particles towards the first particle size of raindrop particles; the second weight of snowflake particles characterizes the degree of bias of the equivalent particle size of the equivalent precipitation particles towards the second particle size of snowflake particles. Thus, by comprehensively considering the first particle size of raindrop particles and the second particle size of snowflake particles in the equivalent particle size, the determined equivalent particle size is more accurate.

[0055] Accordingly, the formula for weighted summation of the first and second particle sizes may include, for example: d_e(α,β)=d_rain(β)*(1-α)+d_snow(β)*α Where α is the precipitation type factor, β is the precipitation intensity level, d_e(α,β) is the equivalent particle size, d_rain(β) is the first particle size of raindrop particles, and d_snow(β) is the second particle size of snowflake particles.

[0056] It can be seen that the equivalent particle size can smoothly reflect the particle size change process from raindrops to snowflakes, realizing a precise and quantitative calculation path from macroscopic meteorological parameters to microscopic equivalent particle size, making the entire channel modeling process have a stronger physical basis and higher accuracy.

[0057] In some embodiments of this application, similar to equivalent particle size, equivalent density can be determined by the following steps: obtaining a first preset density of raindrop particles and a second preset density of snowflake particles; determining a first weight of raindrop particles and a second weight of snowflake particles based on the value of precipitation type factor; and performing a weighted summation of the first preset density and the second preset density based on the first weight and the second weight to obtain the equivalent density.

[0058] The first preset density refers to the mass density of raindrops in pure rain, which is essentially the density of liquid water, for example, 1000 kg / m³. 3 The second preset density refers to the equivalent mass density of pure snowflakes or snow crystals, for example, 150 kg / m³. 3 The first and second preset densities can be obtained based on prior measurements and statistics, and will not be elaborated here.

[0059] Since the value of the precipitation type factor can characterize whether the current equivalent precipitation particles are more biased towards raindrop particles or snowflake particles, the same value of the precipitation type factor can be used to determine whether the equivalent density of the equivalent precipitation particles is more biased towards the first preset density of raindrop particles or the second preset density of snowflake particles. Taking a first preset value of 0 and a second preset value of 1 as an example, the value of the precipitation type factor α can be directly used as the second weight of snowflake particles, and (1-α) can be used as the first weight of raindrop particles. It can be seen that the first weight of raindrop particles characterizes the degree of bias of the equivalent density of equivalent precipitation particles towards the first preset density of raindrop particles; the second weight of snowflake particles characterizes the degree of bias of the equivalent density of equivalent precipitation particles towards the second preset density of snowflake particles. Thus, the equivalent density comprehensively considers the first preset density of raindrop particles and the second preset density of snowflake particles, making the determined equivalent density more accurate.

[0060] Accordingly, taking a first preset density of 1000 kg / m³ and a second preset density of 150 kg / m³ as an example, the formula for weighted summation of the first and second preset densities can include: ρ_e(α)=1000*(1-α)+150*α Where α is the precipitation type factor and ρ_e(α) is the equivalent density.

[0061] It can be seen that the equivalent density can smoothly reflect the density change process from raindrops to snowflakes, realizing a precise and quantitative calculation path from macroscopic meteorological parameters to microscopic equivalent density, which makes the entire channel modeling process have a stronger physical basis and higher accuracy.

[0062] 202. Based on equivalent particle size and equivalent density, a signal attenuation model is generated.

[0063] In the embodiments of this application, since both equivalent particle size and equivalent density affect the power attenuation of wireless signals, generating a signal attenuation model based on equivalent particle size and equivalent density can achieve higher modeling accuracy. The input to the signal attenuation model may include equivalent particle size and equivalent density, and the output may include the signal attenuation amplitude of the wireless communication channel under the current environment. The signal attenuation model can be referenced... Figure 3 The method shown in the embodiment can also be used to generate the data, but it is not limited to using a machine learning model (such as a neural network).

[0064] As can be seen, in the above embodiments of this application, equivalent particle size and equivalent density are used to achieve a modeling method based on microscopic physical parameters, thereby generating a signal attenuation model that highly matches the actual precipitation physical process, significantly improving the model's accuracy and applicability (especially under complex weather conditions such as mixed rain and snow). Furthermore, this signal attenuation model is a unified channel model based on equivalent precipitation particles, and through dynamic parameter mapping and multi-physics coupling, it achieves high-precision modeling of all scenarios, including light rain / heavy rain and light snow / heavy snow.

[0065] In some embodiments of this application, reference is made to Figure 3 ,exist Figure 1 or Figure 2 Based on the illustrated embodiment, a signal attenuation model for the wireless communication channel under the current environment is generated based on precipitation type factors and precipitation intensity levels, which may include: 301. Obtain the communication link height of the wireless communication channel.

[0066] In the embodiments of this application, the communication link altitude refers to the characteristic elevation or relative ground elevation of the propagation path of the wireless signal between the transmitting and receiving ends, thereby providing a vertical dimension for the signal path. The specific definition may differ for different communication scenarios. For example, for a horizontal point-to-point terrestrial microwave link, it can be the average elevation of the link; for satellite communication or UAV communication, it can be the characteristic elevation of the connection between the ground station and the airborne terminal.

[0067] In some embodiments of this application, for fixed communication links, such as fixed microwave relay stations and satellite ground stations, the communication link altitude is known and constant. This altitude value can be pre-configured and stored as a system parameter and read directly when needed. However, for mobile communication links, such as those of drones, aircraft, or vehicle-mounted terminals, the altitude changes in real time. In this case, the communication link altitude can be obtained in real time through onboard sensors, such as altitude data obtained through a Global Positioning System (GPS) module, or altitude data measured and converted using a barometric altimeter.

[0068] 302. Determine the thickness of the precipitation layer under the current environment.

[0069] In embodiments of this application, precipitation layer thickness refers to the vertical distance from the height at which precipitation particles form (cloud base or higher) to the ground, or more specifically, the vertical thickness of a precipitation region that significantly attenuates radio waves.

[0070] In some embodiments of this application, the precipitation layer thickness can be determined based on a precipitation type factor under the current environment. For example, when the precipitation type factor equals a first preset value, the precipitation layer is a rain layer, and its thickness is a preset thickness (e.g., 3 km). Alternatively, when the precipitation type factor equals a second preset value, the precipitation layer is a snow layer, and its thickness is a preset thickness (e.g., 1.5 km). When the precipitation type factor is greater than the first preset value but less than the second preset value, a first weight (e.g., 1-α) for the rain layer and a second weight (e.g., α) for the snow layer can be determined based on the value α of the precipitation type factor. Then, based on the first and second weights, the preset thicknesses of the rain layer and the snow layer are weighted and summed to obtain the precipitation layer thickness. In this way, by dynamically adjusting the precipitation layer thickness, the thickness change process from the rain layer to the snow layer can be smoothly reflected, greatly improving the accuracy and applicability of the model in three-dimensional space, especially for communication links that need to traverse the entire atmosphere.

[0071] 303. Determine the signal attenuation correction coefficient based on the ratio of the communication link height to the precipitation layer thickness.

[0072] In the embodiments of this application, the signal attenuation correction coefficient is a dimensionless adjustment factor used to correct the signal attenuation amplitude. Since the attenuation effect of the precipitation layer is small when the communication link is above the precipitation layer, but large when the communication link is within the precipitation layer, the value of the signal attenuation correction coefficient can be negatively correlated with the ratio of the communication link height to the precipitation layer thickness.

[0073] In embodiments of this application, the formula for calculating the signal attenuation correction coefficient can be an exponential function formula to better fit the attenuation effect of precipitation on the signal in the actual environment. The formula for calculating the signal attenuation correction coefficient may include, for example: C=exp(-h / H(a)) Where C is the signal attenuation correction coefficient, exp is the natural exponential function, h is the communication link height, and H(a) is the precipitation layer thickness. It can be seen that the signal attenuation correction coefficient takes into account the vertical position of the communication link. For example, a high-altitude link may only pass through the upper part of the precipitation layer (usually snow or ice crystals, with relatively low attenuation). Even if the ground rainfall intensity is high, its actual signal attenuation should be corrected downwards. Therefore, it is particularly suitable for non-terrestrial links such as satellite communication or drone communication.

[0074] 304. Based on precipitation type factor, precipitation intensity level, and exponential correction coefficient of signal attenuation, determine the precipitation path loss increment of wireless communication channel.

[0075] In the embodiments of this application, the precipitation path loss increment of the wireless communication channel refers to the additional loss value of the wireless signal along the entire propagation path due to the presence of precipitation, compared to sunny weather, and is typically measured in decibels (dB). The precipitation path loss increment directly reflects the degree of impact of current precipitation on communication quality.

[0076] In some embodiments of this application, determining the precipitation path loss increment of the wireless communication channel based on precipitation type factor, precipitation intensity level, and exponential correction coefficient for signal attenuation may include: determining the equivalent particle size and equivalent density of equivalent precipitation particles in the current environment based on precipitation type factor and precipitation intensity level; and comprehensively determining the precipitation path loss increment of the wireless communication channel based on equivalent particle size, equivalent density, and exponential correction coefficient for signal attenuation. Specifically, the equivalent density and exponential correction coefficient for signal attenuation are positively correlated with the precipitation path loss increment, while the equivalent particle size is negatively correlated with the precipitation path loss increment.

[0077] In some embodiments of this application, the formula for calculating the precipitation path loss increment may include, for example: ΔL=K*R γ *f δ *[ρ_e(α) / d_e(α,β)] η *exp(-h / H(a)) Where ΔL represents the precipitation path loss increment. K, γ, δ, and η are fitting parameters that can be optimized using measured data, for example, K=0.01. R is the precipitation rate under the current environment. f is the signal frequency in GHz. It can be seen that the calculation of the precipitation path loss increment considers not only the effects of equivalent particle size, equivalent density, communication link height, and precipitation layer thickness, but also the effects of precipitation rate and signal frequency under the current environment, making the determined precipitation path loss increment more accurate.

[0078] 305. Generate a signal attenuation model based on the precipitation path loss increment.

[0079] In the embodiments of this application, the calculation formula of precipitation path loss increment can be directly used as the signal attenuation model of wireless communication channel in the current environment, or the signal attenuation model can be further determined based on precipitation path loss increment, which is not limited here.

[0080] As can be seen from the above embodiments of this application, a more comprehensive and accurate signal attenuation model is constructed. This model not only considers the micro-type and macro-intensity of precipitation, but also introduces the three-dimensional spatial geometric relationship between the communication link and the precipitation layer, so that the model can better adapt to various complex communication scenarios. In particular, the error of the signal attenuation prediction result is smaller in non-horizontal or high-altitude communication applications.

[0081] In some embodiments of this application, the signal attenuation model may also consider the urban multipath effect. The multipath effect refers to the interference caused by the different arrival times of the component fields of an electromagnetic wave after propagating along different paths. These components superimpose their phases, resulting in signal distortion or errors. Therefore, the multipath effect is a significant cause of signal attenuation. Accordingly, generating a signal attenuation model based on the precipitation path loss increment may include: obtaining the urban multipath effect factor of the wireless communication channel; determining the urban multipath effect loss increment of the wireless communication channel based on the urban multipath effect factor; and generating the signal attenuation model based on the free space loss value of the wireless communication channel, the urban multipath effect loss increment, and the precipitation path loss increment.

[0082] The urban multipath effect factor refers to a quantitative description of the severity of signal attenuation caused by urban multipath effects. The urban multipath effect factor can be determined based on pre-configured static settings or dynamic queries based on a Geographic Information System (GIS). Taking static configuration as an example, for wireless links deployed in fixed locations, the environmental type is determined, so static parameters can be pre-configured as the urban multipath effect factor through preliminary experiments. Taking dynamic queries based on a GIS as an example, for mobile terminals or redeployable links, their current geographic coordinates can be obtained (e.g., via GPS). Then, by accessing a geographic information database, the corresponding region type can be queried (e.g., building density, street canyon features, etc.), thereby dynamically determining the urban multipath effect factor matching the region type.

[0083] The urban multipath effect loss increment refers to the actual attenuation of signal power amplitude caused by urban multipath effects. The urban multipath effect loss increment can also vary depending on the urban multipath effect factor.

[0084] In some embodiments of this application, determining the urban multipath effect loss increment of a wireless communication channel based on the urban multipath effect factor includes: determining the influence coefficient of precipitation on the urban multipath effect based on the precipitation path loss increment; and determining the urban multipath effect loss increment based on the influence coefficient of precipitation on the urban multipath effect and the urban multipath effect factor.

[0085] Understandably, precipitation alters the reflection, scattering, and diffraction characteristics of signals in the urban environment. For example, rainwater wets building facades and roads, changing the dielectric constant and conductivity of these surfaces, thus affecting the reflection coefficient of wireless signals and influencing the increase in urban multipath loss. Therefore, based on the increase in precipitation path loss, the influence coefficient of precipitation on urban multipath effects can be determined, thereby identifying a more accurate increase in urban multipath loss.

[0086] In some embodiments of this application, the formula for calculating the increase in urban multipath effect loss may include, for example: L p =M*(1+ΔL / 10) Among them, L p ΔL represents the increment of urban multipath effect loss, M represents the urban multipath effect factor, ΔL represents the increment of precipitation path loss, and ΔL / 10 represents the influence coefficient of precipitation on urban multipath effect.

[0087] Free-space loss refers to the fundamental energy loss that occurs when a wireless signal propagates in an ideal, unobstructed, vacuum environment due to energy diffusion. It is determined by the signal frequency and propagation distance and serves as the theoretical basis and starting point for any wireless link loss calculation; its calculation method will not be elaborated upon here.

[0088] In some embodiments of this application, the calculation formula for the signal attenuation model may include, for example: L total =L free +ΔL+M*(1+ΔL / 10) Among them, L total L represents the signal attenuation level of the wireless communication channel under the current environment. free ΔL represents the free space loss value, M*(1+ΔL / 10) represents the precipitation path loss increment, and M*(1+ΔL / 10) represents the urban multipath effect loss increment.

[0089] It can be seen that by combining the free space loss value of the integrated wireless communication channel, the urban multipath effect loss increment, and the precipitation path loss increment, a more accurate signal attenuation model can be generated, thereby further reducing the error of the signal attenuation prediction results.

[0090] In some embodiments of this application, the second aspect is that... Figures 1 to 3Based on any of the embodiments shown, a model application method is provided. (Refer to...) Figure 4 Model application methods may include: 401. Using any one of the signal attenuation models, determine the signal attenuation magnitude of the wireless communication channel under the current environment.

[0091] In the embodiments of this application, the function value L of the signal attenuation model can be determined by using the signal attenuation model in any of the above embodiments, combined with the precipitation type factor and precipitation rate under the current environment. total And as a measure of signal attenuation in the current environment of wireless communication channels.

[0092] 402. Based on the signal attenuation amplitude, perform signal compensation processing, wherein the signal compensation processing includes at least one of increasing signal power and switching modulation and coding strategies.

[0093] In the embodiments of this application, increasing the signal power is a direct compensation method, also known as Transmit Power Control (TPC). Specifically, the signal attenuation amplitude can be used as input, and the signal output power can be calculated and adjusted through relevant control algorithms. The basic principle is that the greater the signal attenuation amplitude, the higher the signal output power should be to compensate for the signal attenuation.

[0094] In the embodiments of this application, switching the modulation and coding scheme (MCS) is a method of compensation by changing the inherent robustness of the signal. The modulation and coding scheme determines the number of data bits carried by each symbol in the signal (modulation order) and the proportion of redundant bits used for error correction (coding rate). Specifically, a pre-set MCS level list is provided, which includes multiple modulation and coding schemes. These schemes, from high to low, have decreasing data transmission rates but increasing robustness and resistance to interference and fading. Examples of the multiple modulation and coding schemes include, for example, 256-QAM (Quadrature Amplitude Modulation) and QPSK (Quadrature Phase Shift Keying). 256-QAM has a high data rate but requires a very high signal-to-noise ratio (SNR); while QPSK has a low data rate but can operate reliably at very low SNRs. Therefore, a suitable modulation and coding scheme can be selected based on the determined signal attenuation amplitude. For example, when the signal attenuation is less than the first amplitude threshold, indicating low signal attenuation, the modulation and coding scheme can be switched to 256-QAM to pursue higher throughput. Conversely, when the signal attenuation is greater than the second amplitude threshold, indicating high signal attenuation, the modulation and coding scheme can be switched to QPSK, sacrificing some data rate for improved communication reliability.

[0095] As can be seen, in the above embodiments of this application, by closely combining the accurate channel attenuation model with the actual adaptive adjustment mechanism of the communication system, the communication system can optimize the configuration of power or modulation and coding strategies, thereby achieving more intelligent and robust wireless communication in complex and ever-changing environments.

[0096] In some embodiments of this application, reference is made to Figure 5 ,based on Figures 1 to 4 Any embodiment of the present invention provides an example description of the channel modeling method and the model application method, which may include the following steps: 501. Input uniform parameters for rain and snow; 502. Multiphysics coupling; 503. Perform time-domain adaptive updates based on meteorological information; 504. Based on the city's compatibility with multipath effects, it is compatible with LOS / MLOS scenarios; 505. Output total path loss (L) total ).

[0097] For a more detailed explanation of the above steps, please refer to [link / reference]. Figures 1 to 4 The content of any of the embodiments, and may further include: (1) Define the equivalent precipitation particle parameter set: Precipitation type factor α: a value of 0 indicates pure rain; a value of 1 indicates pure snow.

[0098] Precipitation intensity level β: a value of 1 indicates low precipitation; a value of 2 indicates medium precipitation; and a value of 3 indicates high precipitation.

[0099] Equivalent particle size d_e(α,β): The calculation formula includes: d_e(α,β)=d_rain(β)*(1-α)+d_snow(β)*α Equivalent density ρ_e(α): The calculation formula includes: ρ_e(α)=1000*(1-α)+150*α (2) Unified formula for channel attenuation: The path loss increment ΔL is caused by precipitation: ΔL=K*R γ *f δ *[ρ_e(α) / d_e(α,β)] η *exp(-h / H(a)) (3) Innovative technological features: Unified parameterization: Rain / snow type and intensity are dynamically described using (α,β), replacing the discrete rain and snow models in related technologies; Multiphysics coupling: Integrating precipitation particle dynamics (Mie scattering model) with meteorological tomography data to improve accuracy in the millimeter wave / terahertz band; Time-domain adaptive: Based on real-time meteorological data updates (α,β), it supports dynamic switching of precipitation type and intensity, such as supporting sudden precipitation scenarios (e.g., rainstorm turning into heavy snow), with a sudden weather response latency of <100ms; Environmental Scalability: Overlays urban multipath effect factors, compatible with LOS (Line of Sight) / NLOS (Non-Line of Sight) scenarios: L total =L free +ΔL+M*(1+ΔL / 10) (4) Validation and advantages: Accuracy comparison: In the 28GHz band, the measured signal attenuation under heavy rain (α,β) is 12.3dB / km, while the model output is 12.1dB / km, with an error of <2%; Generality verification: Under mixed precipitation (rain and snow, (α,β)), the model prediction error is reduced by 47% compared with the ITU model; Computational efficiency: The single link budget takes 3ms (15ms for switching between rain and snow models).

[0100] Reference Figure 6 This is an example diagram illustrating power loss at different distances under different weather conditions, illustrating a channel modeling method. Figure 6 The diagram shows the power loss (dB) of (α,β) at different distances (km) under light rain, heavy rain, light snow, heavy snow, and free space (clear weather) when the signal frequency is 1 GHz. It can be seen that the signal attenuation model based on adaptive precipitation type and intensity level in this embodiment can achieve high-precision modeling of all scenarios, including light rain / heavy rain and light snow / heavy snow.

[0101] The relevant hardware deployment includes: Weather sensor: Real-time acquisition of (R, α), where rain and snow are distinguished by an optical particle size analyzer to determine α; Channel estimator integrated with FPGA: dynamic computation L total This information is then fed back to the base station beamforming algorithm.

[0102] Parameter optimization: (1) Based on relevant historical data training, the Levenberg-Marquardt method (LM) is used for nonlinear fitting to determine the values ​​of fitting parameters such as K, γ, δ, and η in the calculation formula of the signal attenuation model; (2) Define the intensity level threshold: Rain: Light rain (R<2.5mm / h), precipitation intensity level is determined as 1; moderate rain (2.5~10mm / h), precipitation intensity level is determined as 2; heavy rain (R>10mm / h), precipitation intensity level is determined as 3; Snow: Light snow (R<0.5mm / h), precipitation intensity level is determined as 1; moderate snow (0.5~1.5mm / h), precipitation intensity level is determined as 2; heavy snow (>1.5mm / h), precipitation intensity level is determined as 3.

[0103] Communication system adaptation: (1) In the adaptive pre-equalizer at the receiver, based on L total (2) Perform signal compensation processing; (3) Dynamically adjust MCS, such as automatically downgrading to QPSK during heavy rain.

[0104] As can be seen, the embodiments of this application realize unified modeling of rain and snow channels, with an average error of less than 3% in the signal attenuation prediction results in the 28 to 100 GHz frequency band, and a 5-fold improvement in computational efficiency. It can provide high-precision channel prediction for dynamic environments such as low-orbit satellites and vehicle-to-everything (V2X) networks, and reduce the probability of communication interruption in rain and snow weather by more than 60%.

[0105] Thirdly, referring to Figure 7Based on the channel modeling method of the above embodiments, embodiments of this application provide a channel modeling apparatus 700, which is used to execute the steps of any embodiment of the channel modeling method described above. For example, the channel modeling apparatus 700 may include: The acquisition module 701 is used to acquire the precipitation type factor and precipitation rate under the current environment. The precipitation type factor is greater than or equal to a first preset value and less than or equal to a second preset value. The first preset value indicates that the precipitation type under the current environment is rain and the second preset value indicates that the precipitation type under the current environment is snow. The determination module 702 is used to determine the precipitation intensity level under the current environment based on the precipitation rate; The generation module 703 is used to generate a signal attenuation model of the wireless communication channel under the current environment based on the precipitation type factor and precipitation intensity level.

[0106] Fourthly, refer to Figure 8 Based on the model application method of the above embodiments, embodiments of this application provide a model application apparatus 800, which is used to execute the steps of any embodiment of the above model application method. For example, the model application apparatus 800 may include: The prediction module 801 is used to determine the signal attenuation magnitude of the wireless communication channel under the current environment using any of the signal attenuation models. The compensation module 802 is used to perform signal compensation processing based on the signal attenuation amplitude, wherein the signal compensation processing includes at least one of increasing signal power and switching modulation and coding strategies.

[0107] Fifthly, embodiments of this application provide an electronic device that integrates any of the channel modeling apparatus or model application apparatus provided in the embodiments of this application. The electronic device includes a processor and a memory, the memory storing a computer program configured to be executed by the processor to implement the channel modeling method or model application method as described in any of the above embodiments.

[0108] Channel modeling methods include: obtaining the precipitation type factor and precipitation rate in the current environment, wherein the precipitation type factor is greater than or equal to a first preset value and less than or equal to a second preset value, the first preset value representing the precipitation type in the current environment as rain, and the second preset value representing the precipitation type in the current environment as snow; determining the precipitation intensity level in the current environment based on the precipitation rate; and generating a signal attenuation model of the wireless communication channel in the current environment based on the precipitation type factor and precipitation intensity level.

[0109] Model application methods include: using any signal attenuation model to determine the signal attenuation magnitude of the wireless communication channel under the current environment; and performing signal compensation processing based on the signal attenuation magnitude, wherein the signal compensation processing includes at least one of increasing signal power and switching modulation and coding strategies.

[0110] Sixthly, embodiments of this application provide an electronic device that integrates any of the channel modeling devices or model application devices provided in embodiments of this application. For example... Figure 9 As shown, it illustrates a structural schematic diagram of the electronic device involved in the embodiments of this application, specifically: The electronic device may include components such as a processor 901 with one or more processing cores, a memory 902 with one or more computer-readable storage media, a power supply 903, and an input unit 904. Those skilled in the art will understand that... Figure 9 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 901 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 902, and by calling data stored in the memory 902, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. Optionally, the processor 901 may include one or more processing cores; preferably, the processor 901 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 901.

[0111] The memory 902 can be used to store software programs and modules. The processor 901 executes various functional applications and data processing by running the software programs and modules stored in the memory 902. The memory 902 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 902 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 902 may also include a memory controller to provide the processor 901 with access to the memory 902.

[0112] The electronic device also includes a power supply 903 that supplies power to various components. Preferably, the power supply 903 can be logically connected to the processor 901 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 903 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0113] The electronic device may also include an input unit 904, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0114] Although not shown, the electronic device may also include a display unit, etc., which will not be described in detail here. Specifically, in the embodiments of this application, the processor 901 in the electronic device loads the executable files corresponding to the processes of one or more application programs into the memory 902 according to the following instructions, and the processor 901 runs the application programs stored in the memory 902 to realize various functions, such as: Obtain the precipitation type factor and precipitation rate under the current environment. The precipitation type factor is greater than or equal to a first preset value and less than or equal to a second preset value. The first preset value indicates that the precipitation type under the current environment is rain, and the second preset value indicates that the precipitation type under the current environment is snow. Determine the precipitation intensity level under the current environment based on the precipitation rate. Generate a signal attenuation model of the wireless communication channel under the current environment based on the precipitation type factor and precipitation intensity level.

[0115] For example: using any signal attenuation model, determine the signal attenuation magnitude of the wireless communication channel under the current environment; based on the signal attenuation magnitude, perform signal compensation processing, wherein the signal compensation processing includes at least one of increasing signal power and switching modulation and coding strategies.

[0116] In a seventh aspect, embodiments of this application provide a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk, etc. The computer-readable storage medium stores a computer program configured to be executed by a processor to implement the channel modeling method or model application method as described in any of the preceding claims.

[0117] Channel modeling methods include: obtaining the precipitation type factor and precipitation rate in the current environment, wherein the precipitation type factor is greater than or equal to a first preset value and less than or equal to a second preset value, the first preset value representing the precipitation type in the current environment as rain, and the second preset value representing the precipitation type in the current environment as snow; determining the precipitation intensity level in the current environment based on the precipitation rate; and generating a signal attenuation model of the wireless communication channel in the current environment based on the precipitation type factor and precipitation intensity level.

[0118] Model application methods include: using any signal attenuation model to determine the signal attenuation magnitude of the wireless communication channel under the current environment; and performing signal compensation processing based on the signal attenuation magnitude, wherein the signal compensation processing includes at least one of increasing signal power and switching modulation and coding strategies.

[0119] Eighthly, embodiments of this application provide a computer program product, including a computer program or instructions, which are executed by a processor to implement the channel modeling method or model application method as described in any of the preceding claims.

[0120] Channel modeling methods include: obtaining the precipitation type factor and precipitation rate in the current environment, wherein the precipitation type factor is greater than or equal to a first preset value and less than or equal to a second preset value, the first preset value representing the precipitation type in the current environment as rain, and the second preset value representing the precipitation type in the current environment as snow; determining the precipitation intensity level in the current environment based on the precipitation rate; and generating a signal attenuation model of the wireless communication channel in the current environment based on the precipitation type factor and precipitation intensity level.

[0121] Model application methods include: using any signal attenuation model to determine the signal attenuation magnitude of the wireless communication channel under the current environment; and performing signal compensation processing based on the signal attenuation magnitude, wherein the signal compensation processing includes at least one of increasing signal power and switching modulation and coding strategies.

[0122] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method of modeling a channel, the method comprising: The modeling method of the channel comprises: obtaining a precipitation type factor and a precipitation rate in a current environment, wherein the precipitation type factor has a value greater than or equal to a first preset value and less than or equal to a second preset value, the first preset value represents that the precipitation type in the current environment is rain, and the second preset value represents that the precipitation type in the current environment is snow; determining a precipitation intensity level in the current environment according to the precipitation rate; generating a signal attenuation model of a wireless communication channel in the current environment based on the precipitation type factor and the precipitation intensity level.

2. The method of modeling a channel of claim 1, wherein, The generating of the signal attenuation model of the wireless communication channel in the current environment based on the precipitation type factor and the precipitation intensity level comprises: determining equivalent particle diameter and equivalent density of an equivalent precipitation particle in the current environment based on the precipitation type factor and the precipitation intensity level, wherein if the value of the precipitation type factor is equal to the first preset value, the equivalent precipitation particle is a raindrop particle, and if the value of the precipitation type factor is equal to the second preset value, the equivalent precipitation particle is a snowflake particle; generating the signal attenuation model based on the equivalent particle diameter and the equivalent density.

3. The method of modeling a channel of claim 2, wherein, The equivalent particle diameter is determined by the following steps: determining first particle diameter of the raindrop particle and second particle diameter of the snowflake particle according to the precipitation intensity level; determining first weight of the raindrop particle and second weight of the snowflake particle based on the value of the precipitation type factor; performing weighted sum processing on the first particle diameter and the second particle diameter based on the first weight and the second weight to obtain the equivalent particle diameter.

4. The method of modeling a channel of claim 2, wherein, The equivalent density is determined by the following steps: obtaining first preset density of the raindrop particle and second preset density of the snowflake particle; determining first weight of the raindrop particle and second weight of the snowflake particle based on the value of the precipitation type factor; performing weighted sum processing on the first preset density and the second preset density based on the first weight and the second weight to obtain the equivalent density.

5. The method of modeling a channel of claim 1, wherein, The generating of the signal attenuation model of the wireless communication channel in the current environment based on the precipitation type factor and the precipitation intensity level comprises: obtaining communication link height of the wireless communication channel; determining precipitation layer thickness in the current environment; determining signal attenuation correction coefficient according to the ratio of the communication link height to the precipitation layer thickness; determining precipitation path loss increment of the wireless communication channel based on the precipitation type factor, the precipitation intensity level and the signal attenuation correction coefficient; generating the signal attenuation model according to the precipitation path loss increment.

6. The method of modeling a channel of claim 5, wherein, The generating of the signal attenuation model according to the precipitation path loss increment comprises: obtaining urban multipath effect factor of the wireless communication channel; determining urban multipath effect loss increment of the wireless communication channel based on the urban multipath effect factor; generating the signal attenuation model based on free space loss value of the wireless communication channel, the urban multipath effect loss increment and the precipitation path loss increment.

7. The method of modeling a channel of claim 6, wherein, The determining of the urban multipath effect loss increment of the wireless communication channel based on the urban multipath effect factor comprises: determine an influence coefficient of the precipitation on the urban multipath effect based on the precipitation path loss increment; determine the urban multipath effect loss increment based on the influence coefficient of the precipitation on the urban multipath effect and the urban multipath effect factor.

8. A model application method characterized by, The model application method comprises: determining a signal attenuation amplitude of the wireless communication channel in the current environment by using the signal attenuation model in any one of claims 1 to 7; performing signal compensation processing based on the signal attenuation amplitude, wherein the signal compensation processing comprises at least one of increasing signal power and switching a modulation and coding strategy.

9. An electronic device, comprising: The electronic device comprises a processor and a memory, and the memory stores a computer program configured to be executed by the processor to implement the channel modeling method in any one of claims 1 to 7 or the model application method in claim 8.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program configured to be executed by a processor to implement the channel modeling method in any one of claims 1 to 7 or the model application method in claim 8.

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