Low earth orbit satellite propagation channel modeling method

Abstract

The invention discloses a low-orbit satellite propagation channel modeling method. The method comprises the following steps: generating an optimal signal propagation path from a satellite to a target receiving point by adopting an RRT algorithm; simulating propagation of a direct path component and a multipath component according to a Corazza model; attenuation simulation is carried out on the direct path component according to Lognormal distribution; time delay distribution simulation is carried out on the multipath component, and reflection, refraction and scattering conditions in an actual space environment are simulated to determine amplitude and phase changes of the multipath component; synthesizing the direct path component and the plurality of multipath components; doppler frequency shift is calculated, and distribution of the Doppler frequency shift is simulated by using Gaussian distribution; and constructing a low-orbit satellite propagation channel model based on a synthesis result of the direct path component and the plurality of multipath components and additive Gaussian white noise. According to the method, by accurately simulating the multi-path effect and Doppler frequency shift, important theoretical basis and practical guidance are provided for design and optimization of a wireless communication system, and the performance and reliability of the system in a complex environment are remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of low-orbit satellite communications, and in particular relates to a method for modeling a low-orbit satellite propagation channel. Background Art

[0002] In modern satellite communication systems, low-orbit satellites (LEO) are increasingly used, especially in the fields of global communication coverage, remote sensing monitoring and navigation. Due to their lower orbital altitude, low-orbit satellites can provide higher signal strength and lower transmission delay, but they also face complex signal propagation environments. These environments include atmospheric inhomogeneities, terrain obstacles (such as buildings and mountains), and the relative motion between the satellite and the receiver, all of which have a significant impact on signal propagation.

[0003] Traditional satellite communication channel models usually assume that the signal propagation environment is idealized, ignoring complex factors such as multipath effects and Doppler shift. These models can provide a certain degree of accuracy in simple environments, but in actual applications, especially in complex environments such as cities and mountainous areas, the propagation characteristics of signals are significantly different from the ideal model. Multipath effects can cause reflection, refraction and scattering of signals, while the relative motion between the satellite and the receiver causes Doppler shift, all of which affect the quality and reliability of the signal.

[0004] Although existing channel models can describe the basic propagation characteristics of signals to a certain extent, they are insufficient in dealing with multipath effects and Doppler shift. Especially in dynamic environments and multi-task scenarios, these models cannot accurately predict the propagation behavior of signals, leading to challenges in the design and optimization of communication systems. Therefore, developing a model that can accurately simulate the propagation channel of low-orbit satellites is of great significance to improving the performance and reliability of satellite communication systems. Summary of the invention

[0005] In view of this, the present invention proposes a low-orbit satellite propagation channel modeling method. By accurately simulating the multipath effect and Doppler frequency shift, the present invention provides an important theoretical basis and practical guidance for the design and optimization of wireless communication systems, and significantly improves the performance and reliability of the system in complex environments.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention provides a low-orbit satellite propagation channel modeling method, comprising:

[0008] Step S1, using the RRT algorithm for path planning to generate an optimal signal propagation path from the satellite to the target receiving point;

[0009] Step S2, simulating the propagation of direct path components and multipath components according to the Corazza model;

[0010] Step S3, performing attenuation simulation on the direct path component according to Lognormal distribution;

[0011] Step S4: for the multipath component, a delay distribution simulation is performed based on the propagation distance and propagation speed of the signal, and the reflection, refraction and scattering conditions in the actual space environment are simulated to determine the amplitude and phase changes thereof;

[0012] Step S5, synthesizing the direct path component and multiple multipath components according to their respective time delays, amplitudes and phases;

[0013] Step S6, calculating the Doppler frequency shift according to the relative motion speed between the satellite and the receiver, and using Gaussian distribution to simulate the distribution of the Doppler frequency shift;

[0014] Step S7: construct a low-orbit satellite propagation channel model based on the synthesis results of the direct path component and multiple multipath components and additive Gaussian white noise.

[0015] Preferably, step S1 comprises:

[0016] Step S11, taking the current position of the satellite as the starting point of path planning, taking the position of the target receiving point as the end point, and taking the starting point as the root node of the random tree;

[0017] Step S12: randomly generate a target point in the space area between the satellite and the receiving point;

[0018] Step S13, finding the node closest to the target point in the existing random tree, and generating a new node within a preset range by linear interpolation or an interpolation method based on a physical model to connect the nearest node and the target point;

[0019] Step S14: Check whether the new node intersects with obstacles, where obstacles include uneven areas of the atmosphere, mountains, and ground buildings;

[0020] Step S15: When the new node intersects with the obstacle, return to step S12; if not, add the new node to the random tree to form a new path;

[0021] Step S16: Use polynomial interpolation or spline interpolation method to smooth the path to reduce path jitter and unnecessary turns, and optimize the path length and shape based on the rewiring operation of the RRT algorithm to find the optimal path.

[0022] Preferably, step S3 comprises:

[0023] According to the propagation of the direct path component simulated by the Corazza model, the shadow effect on the direct path component is considered, and the attenuation simulation is performed according to the Lognormal distribution to determine its corresponding probability density function f Z (z):

[0024]

[0025] Where μ represents the mean of the logarithmic values ​​in the Lognormal distribution, σ represents the standard deviation of the logarithmic values ​​in the Lognormal distribution, and z represents the amplitude of the direct path component.

[0026] Preferably, step S4 comprises:

[0027] Generate multiple multipath components according to the obstacle distribution and signal propagation characteristics in the actual environment;

[0028] The time delay distribution of multipath components is approximated by exponential distribution or Rayleigh distribution, and the reflection, refraction and scattering in the actual space environment are simulated to determine its amplitude and phase changes;

[0029] Construct a representation model for multipath components:

[0030]

[0031] Where, d(t) represents the multipath component, α n (t) is a random variable determined by path loss and shadow fading, τ n (t) is the signal path transmission delay of different paths, e is the natural logarithm, j is the imaginary unit, r n (t) represents the signal propagation distance of the nth path, c represents the speed of light, u(t) represents the equivalent baseband signal, φ n (t) represents the Doppler phase shift:

[0032]

[0033] Among them, f c represents the carrier frequency of the signal, represents the Doppler phase shift of the nth path, is the Doppler shift of the nth path.

[0034] Preferably, the low-orbit satellite propagation channel model is expressed as:

[0035]

[0036] Where r(t) represents the received signal at the receiving end, z(t) represents the direct path component, N(t) represents the number of resolvable multipath components at time t, n(t) represents additive white Gaussian noise, and f D Indicates Doppler shift Where v represents the relative speed between the satellite and the receiver, c represents the speed of light, and f0 represents the frequency of the transmitted signal;

[0037] The distribution of Doppler frequency shift is approximately Gaussian distribution, and its probability density function is:

[0038]

[0039] in, Expressed as the probability density function of Doppler shift, represents the mean value of Doppler frequency shift, is the standard deviation of the Doppler shift;

[0040] Additive Gaussian white noise n(t) obeys Gaussian distribution, and its probability density function is:

[0041]

[0042] Among them, f N (n) represents the probability density function of additive white Gaussian noise, σ N is the standard deviation of the noise.

[0043] Preferably, the satellite communication signal probability density function of the signal reaching the receiving end is determined to be expressed as:

[0044] f RS (r, s) = f R (r)·f S (s)

[0045] Among them, f RS (r, s) represents the probability density function of the satellite communication signal reaching the receiving end, f R (r) represents the probability density function of the received signal envelope, which obeys the Rician distribution and:

[0046]

[0047] Where r represents the envelope of the received signal, ρ represents the amplitude of the direct path component of the received signal, and σ 2 represents the average multipath power, i0 is the first kind of zero-order corrected Bessel function;

[0048] f S (s) is the probability density function of the received signal phase, which obeys uniform distribution and:

[0049]

[0050] Where s is the phase of the received signal, s∈[0, 2π].

[0051] The present invention has achieved at least the following beneficial effects:

[0052] 1. The model can fully simulate the multipath effect, shadowing effect, Doppler effect and additive white Gaussian noise in the low-orbit satellite communication channel, providing a reference for the performance evaluation and optimization of the low-orbit satellite communication system.

[0053] 2. Based on the gradual construction and optimization of random trees, it ensures that the generated path is not only feasible but also efficient and smooth by detecting and avoiding obstacles in real time, which significantly improves the efficiency and reliability of path planning, and is particularly suitable for dynamic path planning in complex environments.

[0054] Other advantages, objectives and features of the present invention will be described in the following description and will be apparent to those skilled in the art to some extent, or those skilled in the art may be taught from the practice of the present invention. The objectives and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to make the purpose, technical solution and beneficial effects of the present invention clearer, the present invention provides the following drawings for illustration:

[0056] Figure 1 A flowchart of a method for modeling a low-orbit satellite propagation channel in an embodiment of the present invention;

[0057] Figure 2 This is a flow chart of the steps of an optimal signal propagation path generation algorithm in an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0059] The present invention provides a low-orbit satellite propagation channel modeling method, referring to Figure 1 ,include:

[0060] Step S1, using the RRT algorithm for path planning to generate an optimal signal propagation path from the satellite to the target receiving point;

[0061] Step S2, simulating the propagation of direct path components and multipath components according to the Corazza model;

[0062] Step S3, performing attenuation simulation on the direct path component according to Lognormal distribution;

[0063] Step S4: for the multipath component, a delay distribution simulation is performed based on the propagation distance and propagation speed of the signal, and the reflection, refraction and scattering conditions in the actual space environment are simulated to determine the amplitude and phase changes thereof;

[0064] Step S5, synthesizing the direct path component and multiple multipath components according to their respective time delays, amplitudes and phases;

[0065] Step S6, calculating the Doppler frequency shift according to the relative motion speed between the satellite and the receiver, and using Gaussian distribution to simulate the distribution of the Doppler frequency shift;

[0066] Step S7: construct a low-orbit satellite propagation channel model based on the synthesis results of the direct path component and multiple multipath components and additive Gaussian white noise.

[0067] The working principle and beneficial effects of the above technical solution are as follows: the RRT algorithm is used for path planning to generate the optimal signal propagation path from the satellite to the target receiving point. The RRT algorithm can quickly generate a path in a complex environment, taking into account factors such as the orbital characteristics of the satellite, the curvature of the earth, the influence of the atmosphere, and possible ground obstacles to ensure the feasibility and efficiency of the path. The propagation of the direct path component and the multipath component is simulated according to the Corazza model. The Corazza model is a full shadow model that can effectively describe the direct and multipath effects of the signal during propagation. The attenuation of the direct path component is simulated according to the Lognormal distribution. The direct path component is affected by the shadow effect, and the Lognormal distribution can accurately describe this attenuation characteristic and improve the accuracy of the model. The delay distribution is simulated based on the propagation distance and propagation speed of the signal. The delay of the multipath component can be calculated by dividing the propagation distance by the speed of light. The reflection, refraction and scattering in the actual space environment are simulated to determine the amplitude and phase changes of the multipath component. These changes will affect the strength and phase of the signal, thereby affecting the quality of the received signal. The direct path component and multiple multipath components are synthesized according to their respective delays, amplitudes and phases. The synthesized signal can fully reflect the various effects of the signal in the propagation process, improving the comprehensiveness and accuracy of the model. The Doppler shift is calculated according to the relative speed of the satellite and the receiver, and the Gaussian distribution is used to simulate the distribution of the Doppler shift. The Doppler shift will affect the frequency of the signal, and the Gaussian distribution can accurately describe this change and improve the accuracy of the model. Based on the synthesis results of the direct path component and the multipath component and the additive Gaussian white noise, a low-orbit satellite propagation channel model is constructed. The model can fully simulate the multipath effect, shadow effect, Doppler effect and additive Gaussian white noise in the low-orbit satellite communication channel, providing a reference for the performance evaluation and optimization of the low-orbit satellite communication system.

[0068] In a preferred embodiment, referring to Figure 2 , step S1 comprises:

[0069] Step S11, taking the current position of the satellite as the starting point of path planning, taking the position of the target receiving point as the end point, and taking the starting point as the root node of the random tree;

[0070] Step S12: randomly generate a target point in the space area between the satellite and the receiving point;

[0071] Step S13, finding the node closest to the target point in the existing random tree, and generating a new node within a preset range by linear interpolation or an interpolation method based on a physical model to connect the nearest node and the target point;

[0072] Step S14: Check whether the new node intersects with obstacles, where obstacles include uneven areas of the atmosphere, mountains, and ground buildings;

[0073] Step S15: When the new node intersects with the obstacle, return to step S12; if not, add the new node to the random tree to form a new path;

[0074] Step S16: Use polynomial interpolation or spline interpolation method to smooth the path to reduce path jitter and unnecessary turns, and optimize the path length and shape based on the rewiring operation of the RRT algorithm to find the optimal path.

[0075] The working principle and beneficial effects of the above technical solution are as follows: Step S11 sets the current position of the satellite as the starting point of the path planning, the position of the target receiving point as the end point, and the starting point as the root node of the random tree. This step lays the foundation for path planning and clarifies the starting and ending positions of the path. Next, in step S12, a target point is randomly generated in the space area between the satellite and the receiving point as an intermediate guide point in the path planning process. Then, in step S13, the node closest to the target point is found in the existing random tree, and a new node is generated within a preset range by linear interpolation or an interpolation method based on a physical model to connect the nearest node and the target point. After the new node is generated, in step S14, the system checks whether the node intersects with any obstacles, which may include uneven areas of the atmosphere, mountains, and ground buildings. If the new node intersects with the obstacle, step S15 returns to step S12, regenerates the target point and repeats the above process; if it does not intersect, the new node is added to the random tree to form a new path. Finally, in step S16, the path is smoothed using polynomial interpolation or spline interpolation methods to reduce jitter and unnecessary turns in the path, and the length and shape of the path are further optimized through the rewiring operation of the RRT algorithm to find the optimal path. The working principle of this technical solution is based on the gradual construction and optimization of random trees. By detecting and avoiding obstacles in real time, it ensures that the generated path is not only feasible, but also efficient and smooth, which significantly improves the efficiency and reliability of path planning, and is particularly suitable for dynamic path planning in complex environments. In this way, satellite communications, navigation, and other application scenarios that require precise path planning can be effectively responded to, providing strong support for the development of modern aerospace and communication technologies.

[0076] In a preferred embodiment, step S3 comprises:

[0077] According to the propagation of the direct path component simulated by the Corazza model, the shadow effect on the direct path component is considered, and the attenuation simulation is performed according to the Lognormal distribution to determine its corresponding probability density function f Z (z):

[0078]

[0079] Where μ represents the mean of the logarithmic values ​​in the Lognormal distribution, σ represents the standard deviation of the logarithmic values ​​in the Lognormal distribution, and z represents the amplitude of the direct path component.

[0080] The working principle and beneficial effects of the above technical solution are as follows: Step S3 focuses on simulating the propagation of the direct path component and the shadow effect it is subjected to. Specifically, this step simulates the propagation characteristics of the direct path component based on the Corazza model, and takes into account the shadow effect that the direct path component may be subjected to during the propagation process. In order to accurately describe this attenuation phenomenon, the step uses the Lognormal distribution for simulation, which is a statistical distribution commonly used to describe the attenuation of signal strength. By determining the probability density function of the Lognormal distribution, the attenuation characteristics of the amplitude of the direct path component can be quantified, where the parameters μ and σ represent the mean and standard deviation of the logarithmic values ​​in the Lognormal distribution, respectively, and A represents the amplitude of the direct path component. The working principle of the technical solution is based on the simulation of the propagation of the direct path component by the Corazza model, combined with the Lognormal distribution to describe the signal attenuation caused by the shadow effect. The Corazza model provides a theoretical basis for understanding and predicting the propagation behavior of the direct path component in a complex environment. The Lognormal distribution is used to simulate the random attenuation of signal strength caused by obstacles such as buildings and terrain. This attenuation usually presents nonlinear characteristics and is related to the distribution of obstacles on the signal propagation path. By calculating the probability density function of the Lognormal distribution, the probability distribution of the attenuation of the direct path component amplitude under specific environmental conditions can be evaluated, thus providing an important reference for the design and optimization of wireless communication systems. By combining the Corazza model and the Lognormal distribution, the propagation characteristics of the direct path component in a complex environment can be more accurately predicted, including the attenuation of signal strength. This provides a more accurate signal attenuation model for the design of wireless communication systems, helps optimize system parameters, and improves communication quality and reliability. By accurately predicting signal attenuation, network layout can be planned more reasonably, base station location and power configuration can be optimized, and the utilization efficiency of spectrum resources can be improved.

[0081] In a preferred embodiment, step S4 comprises:

[0082] Generate multiple multipath components according to the obstacle distribution and signal propagation characteristics in the actual environment;

[0083] The time delay distribution of multipath components is approximated by exponential distribution or Rayleigh distribution, and the reflection, refraction and scattering in the actual space environment are simulated to determine its amplitude and phase changes;

[0084] Construct a representation model for multipath components:

[0085]

[0086] Where, d(t) represents the multipath component, α n(t) is a random variable determined by path loss and shadow fading, τ n (t) is the signal path transmission delay of different paths, e is the natural logarithm, j is the imaginary unit, r n (t) represents the signal propagation distance of the nth path, c represents the speed of light, u(t) represents the equivalent baseband signal, φ n (t) represents the Doppler phase shift:

[0087]

[0088] Among them, f c represents the carrier frequency of the signal, represents the Doppler phase shift of the nth path, is the Doppler shift of the nth path.

[0089] The working principle and beneficial effects of the above technical solution are as follows: Step S4 focuses on simulating the multipath effect, which is an important phenomenon in wireless signal propagation. Specifically, step S4 first generates multiple multipath components according to the obstacle distribution and signal propagation characteristics in the actual environment. These multipath components are reflection, refraction and scattering phenomena caused by the signal encountering obstacles such as buildings and terrain during propagation. Then, the time delay distribution of the multipath component is approximated by exponential distribution or Rayleigh distribution, which helps to simulate the signal propagation characteristics in the actual space environment and determine the amplitude and phase changes of the multipath component. These changes are caused by the difference in propagation distance and speed of the signal on different paths. In order to more accurately describe the multipath components, the representation model is constructed, in which each multipath component consists of parameters such as random variables determined by path loss and shadow fading, signal path transmission delay, natural logarithm, imaginary unit, signal propagation distance, speed of light, equivalent baseband signal and Doppler phase shift. The Doppler phase shift is further determined by the carrier frequency of the signal and the Doppler frequency shift of each path, which reflects the frequency change of the signal on different paths.

[0090] In a specific embodiment, the low-orbit satellite propagation channel model is expressed as:

[0091]

[0092] Where r(t) represents the received signal at the receiving end, z(t) represents the direct path component, N(t) represents the number of resolvable multipath components at time t, n(t) represents additive white Gaussian noise, and f D Indicates Doppler shift Where v represents the relative speed between the satellite and the receiver, c represents the speed of light, and f0 represents the frequency of the transmitted signal;

[0093] The distribution of Doppler frequency shift is approximately Gaussian distribution, and its probability density function is:

[0094]

[0095] in, Expressed as the probability density function of Doppler shift, represents the mean value of Doppler frequency shift, is the standard deviation of the Doppler shift;

[0096] Additive Gaussian white noise n(t) obeys Gaussian distribution, and its probability density function is:

[0097]

[0098] Among them, f N (n) represents the probability density function of additive white Gaussian noise, σ N is the standard deviation of the noise.

[0099] The working principle and beneficial effects of the above technical solution are:

[0100] In a specific embodiment, the satellite communication signal probability density function of the signal arriving at the receiving end is determined as:

[0101] f RS (r, s) = f R (r)·f S (s)

[0102] Among them, f RS (r, s) represents the probability density function of the satellite communication signal reaching the receiving end, f R (r) represents the probability density function of the received signal envelope, which obeys the Rician distribution and:

[0103]

[0104] Where r represents the envelope of the received signal, ρ represents the amplitude of the direct path component of the received signal, and σ 2 represents the average multipath power, I0 is the first kind of zero-order corrected Bessel function;

[0105] f S (s) is the probability density function of the received signal phase, which obeys uniform distribution and:

[0106]

[0107] Where s is the phase of the received signal, s∈[0, 2π].

[0108] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A method for modeling a low-orbit satellite propagation channel, characterized in that: include: Step S1: Use the RRT algorithm to perform path planning and generate the optimal signal propagation path from the satellite to the target receiving point; Step S2, simulating the propagation of direct path components and multipath components according to the Corazza model; Step S3, performing attenuation simulation on the direct path component according to Lognormal distribution; Step S4: for the multipath component, a delay distribution simulation is performed based on the propagation distance and propagation speed of the signal, and the reflection, refraction and scattering conditions in the actual space environment are simulated to determine the amplitude and phase changes thereof; Step S5, synthesizing the direct path component and multiple multipath components according to their respective time delays, amplitudes and phases; Step S6, calculating the Doppler frequency shift according to the relative motion speed between the satellite and the receiver, and using Gaussian distribution to simulate the distribution of the Doppler frequency shift; Step S7: construct a low-orbit satellite propagation channel model based on the synthesis results of the direct path component and multiple multipath components and additive Gaussian white noise.

2. A low-orbit satellite propagation channel modeling method according to claim 1, characterized in that: Step S1 includes: Step S11, taking the current position of the satellite as the starting point of path planning, taking the position of the target receiving point as the end point, and taking the starting point as the root node of the random tree; Step S12: randomly generate a target point in the space area between the satellite and the receiving point; Step S13, finding the node closest to the target point in the existing random tree, and generating a new node within a preset range by linear interpolation or an interpolation method based on a physical model to connect the nearest node and the target point; Step S14: Check whether the new node intersects with obstacles, where obstacles include uneven areas of the atmosphere, mountains, and ground buildings; Step S15: When the new node intersects with the obstacle, return to step S12; if not, add the new node to the random tree to form a new path; Step S16: Use polynomial interpolation or spline interpolation method to smooth the path to reduce path jitter and unnecessary turns, and optimize the path length and shape based on the rewiring operation of the RRT algorithm to find the optimal path.

3. A low-orbit satellite propagation channel modeling method according to claim 1, characterized in that: Step S3 includes: According to the propagation of the direct path component simulated by the Corazza model, the shadow effect on the direct path component is considered, and the attenuation simulation is performed according to the Lognormal distribution to determine its corresponding probability density function f Z (z): Where μ represents the mean of the logarithmic values ​​in the Lognormal distribution, σ represents the standard deviation of the logarithmic values ​​in the Lognormal distribution, and z represents the amplitude of the direct path component.

4. A low-orbit satellite propagation channel modeling method according to claim 1, characterized in that: Step S4 includes: Generate multiple multipath components according to the obstacle distribution and signal propagation characteristics in the actual environment; The time delay distribution of multipath components is approximated by exponential distribution or Rayleigh distribution, and the reflection, refraction and scattering in the actual space environment are simulated to determine its amplitude and phase changes; Construct a representation model for multipath components: Where, d(t) represents the multipath component, α n (t) is a random variable determined by path loss and shadow fading, τ n (t) is the signal path transmission delay of different paths, e is the natural logarithm, j is the imaginary unit, r n (t) represents the signal propagation distance of the nth path, c represents the speed of light, u(t) represents the equivalent baseband signal, φ n (t) represents the Doppler phase shift: Where fc represents the carrier frequency of the signal, represents the Doppler phase shift of the nth path, is the Doppler shift of the nth path.

5. A method for modeling a low-orbit satellite propagation channel according to any one of claims 1 to 4, characterized in that: The low-orbit satellite propagation channel model is expressed as: Where r(t) represents the received signal at the receiving end, z(t) represents the direct path component, N(t) represents the number of resolvable multipath components at time t, n(t) represents additive white Gaussian noise, and fD represents the Doppler frequency shift. Where v represents the relative speed between the satellite and the receiver, c represents the speed of light, and f0 represents the frequency of the transmitted signal; The distribution of Doppler frequency shift is approximately Gaussian distribution, and its probability density function is: in, Expressed as the probability density function of Doppler shift, represents the mean value of Doppler frequency shift, is the standard deviation of the Doppler shift; Additive Gaussian white noise n(t) obeys Gaussian distribution, and its probability density function is: Among them, f N (n) represents the probability density function of additive white Gaussian noise, σ N is the standard deviation of the noise.

6. A low-orbit satellite propagation channel modeling method according to claim 5, characterized in that: The probability density function of the satellite communication signal that determines the signal reaching the receiving end is expressed as: fRs(r,s)=fR(r)·fs(s) Among them, f RS (r, s) represents the probability density function of the satellite communication signal reaching the receiving end, f R (r) represents the probability density function of the received signal envelope, which obeys the Rician distribution and: Where r represents the envelope of the received signal, ρ represents the amplitude of the direct path component of the received signal, and σ 2 represents the average multipath power, I0 is the first kind of zero-order corrected Bessel function; fS(s) is the probability density function of the received signal phase, which obeys uniform distribution and: Where s is the phase of the received signal, s∈[0, 2π].

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