Over-the-horizon unmanned aerial vehicle communication geometric random channel modeling method

Abstract

The invention discloses a beyond-visual-range unmanned aerial vehicle communication geometric random channel modeling method, an unmanned aerial vehicle body uses a cylindrical conformal antenna array, a channel model contains specific troposphere scattering characteristics so as to support beyond-visual-range communication, and the modeling method comprises the following steps: 1) establishing a beyond-visual-range unmanned aerial vehicle communication scene, setting receiving and transmitting end antenna parameters and environment parameters; 2) determining the position of a troposphere scattering public volume according to troposphere scattering characteristics and calculating path loss, and performing link budget to judge the feasibility of a communication link of the beyond-visual-range unmanned aerial vehicle; 3) initializing a troposphere scattering cluster, and jointly modeling evolution of the troposphere scattering cluster in a time domain and an array domain to generate a channel matrix; and 4) calculating channel statistical characteristics according to the channel matrix, and performing simulation analysis. According to the method, the accurate and flexible beyond-visual-range unmanned aerial vehicle communication channel model is constructed, and the long-distance unmanned aerial vehicle communication requirement can be met.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and in particular relates to a method for modeling geometric random channels for beyond-visual-range unmanned aerial vehicle (UAV) communications. Background Art

[0002] Drone communications are a key enabling technology for building integrated air, land, and sea networks and achieving deep global coverage. For long-distance drone communications applications such as reconnaissance, surveillance, and disaster relief, the effectiveness of line-of-sight paths can be affected by the Earth's curvature or obstacles. Tropospheric scatter (BTH) communications can create reliable links, preventing interruptions due to natural limitations.

[0003] Currently, UAV channel models are mainly divided into deterministic and random channel models. Deterministic channel models rely on the channel environment and have high accuracy and computational complexity. Random channel models balance complexity and versatility, making them more flexible in application. Existing UAV channel models fully account for channel time-varying and time-domain nonstationarity caused by the high mobility of UAVs, such as arbitrary three-dimensional trajectories and three-dimensional rotations. However, these models are only suitable for short-range communications and cannot meet the future wireless communication requirements for reliable long-range communications, such as higher capacity and wider coverage. Existing UAV communication channel measurements indicate that long-range UAV communication is possible due to the influence of the tropospheric scatter propagation mechanism. Therefore, to meet the future 6G integrated sky-ground-sea network requirements for reliable long-range communications, such as higher capacity and wider coverage, and to further realize long-range UAV communications, it is necessary to construct accurate and flexible beyond-line-of-sight UAV communication channel models based on geometric random channel modeling methods. Summary of the Invention

[0004] Purpose of the invention: The technical problem to be solved by the present invention is to propose a geometric random channel modeling method for beyond-line-of-sight UAV communication for long-distance UAV communication applications, wherein the UAV fuselage uses a cylindrical conformal antenna array to better meet the needs of long-distance UAV communication. At the same time, the channel model includes specific tropospheric scattering characteristics to support beyond-line-of-sight communication and meet the long-distance UAV communication needs such as large capacity and wide coverage.

[0005] Technical solution, in order to solve the above technical problems, the present invention proposes a geometric random channel modeling method for beyond-visual-range UAV communication, which includes the following steps:

[0006] Step S1: Establish a beyond-visual-range UAV communication scenario and set the antenna parameters and environmental parameters of the transmitter and receiver.

[0007] Step S2: Determine the location of the tropospheric scatter common volume based on the tropospheric scatter characteristics, calculate the path loss, and obtain a loss threshold based on the link budget. When the path loss is less than the loss threshold, the beyond-visual-range UAV communication link is determined to be feasible, and proceed to step S3.

[0008] Step S3: Modeling the tropospheric scatter cluster as a single-hop scatter cluster located in a tropospheric scatter common volume, initializing the tropospheric scatter cluster, and jointly modeling the evolution of the tropospheric scatter cluster in the time domain and array domain to generate a channel matrix;

[0009] Step S4: Calculate the space-time-frequency correlation function and channel capacity of the beyond-visual-range UAV communication based on the channel matrix, and perform simulation based on this.

[0010] Furthermore, the S1 step includes:

[0011] S1.1. The UAV side is the communication receiving end, the ground station side is the communication transmitting end, and the communication distance D between the transmitting and receiving ends satisfies When establishing a beyond-visual-range UAV communication scenario, is the effective line-of-sight communication distance, h R is the height of the receiving end, h T is the transmitter height, R e is the effective Earth radius;

[0012] S1.2. The antennas of the communication receiving end and the communication transmitting end are both multi-transmit multi-receive antenna arrays, and the antenna elevation angle variation of the receiving end and the transmitting end is θ. The transmitting end is equipped with a circular antenna array containing P antenna elements, and the receiving end is equipped with a cylindrical conformal antenna array containing Q antenna elements;

[0013] S1.3, set the carrier frequency f c , transmitter moving speed v T , receiving end moving speed v R , the height of the obstacle at the transmitting end h1, the distance between the obstacle at the transmitting end and the transmitting end d1, the height of the obstacle at the receiving end h2, and the distance between the obstacle at the receiving end and the receiving end d2.

[0014] Furthermore, the S2 step includes:

[0015] S2.1. Considering obstacles at the transmitting and receiving ends and the influence of the earth's curvature, calculate the minimum elevation angle β0 and maximum elevation angle β1 of the receiving end tropospheric scatter path, and the minimum elevation angle α0 and maximum elevation angle α1 of the transmitting end tropospheric scatter path:

[0016]

[0017] Calculate the maximum scattering angle θ based on the elevation angle of the tropospheric scatter path at the transmitting and receiving ends max and the minimum scattering angle θ min :

[0018]

[0019] Calculate the bottom height H of the tropospheric scatter common volume min and top height H max To further determine the location of the common volume of tropospheric scatterers:

[0020] H min =θ min 2 R e / 8

[0021] H max =θ max D / 4

[0022] S2.2. Calculate the path loss L based on climate parameters and tropospheric scatter characteristics p :

[0023] L p =M+30logf c +10logD+30logθ min +20log(5+γH max )+4.34γH min +180

[0024] Where M is the meteorological factor, γ is the exponential decay coefficient of tropospheric inhomogeneity intensity with height;

[0025] S2.3. Perform link budgeting to obtain the loss threshold L th :

[0026] L th =P T +G T +G R -P Rmin -L sys

[0027] Among them, P T is the transmission power, G T is the antenna gain at the transmitting end, G R is the receiving antenna gain, L sys is the system loss, P Rmin is the receiver sensitivity;

[0028] S2.4. Compare path loss L p and loss threshold L th , when L p <L th The beyond-visual-range UAV communication link is determined to be feasible.

[0029] Furthermore, the S3 step includes:

[0030] S3.1. Initialize the tropospheric scatter cluster: The center of the tropospheric scatter cluster is located in the tropospheric scatter common volume, and the moving speed of the tropospheric scatter cluster is v n Equal to the average wind speed v wind , the departure azimuth of the troposcatter cluster and departure pitch angle They all obey the truncated Gaussian distribution with the lower bound of α0 and the upper bound of α1. The arrival azimuth of the tropospheric scatter cluster is and arrival pitch angle They all obey a truncated Gaussian distribution with a lower truncation bound of β0 and an upper truncation bound of β1, and the subpath range offset within the tropospheric scatter cluster obeys a standard normal distribution;

[0031] S3.2 Jointly modeling the evolution of tropospheric scatter clusters in the time and array domains: The generation and extinction of tropospheric scatter clusters are characterized by the birth and death process. The joint survival probability of the tropospheric scatter cluster between the transmitter and receiver is expressed as:

[0032] P S (Δt,Δξ T ,Δξ R )=P T (Δt,Δξ T )·P R (Δt,Δξ R )

[0033] Among them, P T (Δt,Δξ T ) indicates that at the transmitting end, the tropospheric scatter cluster passes through the time interval Δt and the antenna spacing Δξ T The survival probability after R (Δt,Δξ R ) indicates that at the receiving end, the tropospheric scatter cluster passes through the time interval Δt and the antenna spacing Δξ R After the survival probability is Δt, new tropospheric scatter clusters are generated in the channel. According to the Poisson process, the expected number of newly generated tropospheric scatter clusters is expressed as:

[0034]

[0035] Among them, λ G is the generation rate of troposcatter clusters, λ R is the extinction rate of the troposcatter cluster;

[0036] S3.3. Calculate the channel impulse response of the pth transmitting antenna element and the qth receiving antenna element at time t and time delay τ as follows:

[0037]

[0038] Where p = 1,…,P, q = 1,…,Q, n = 1,…,N qp (t), N qp (t) represents the number of troposcatter clusters at time t, M n is the number of subpaths of the nth troposcatter cluster, the matrix F q The matrix F is the receiving antenna pattern including horizontal and vertical directions. p is the transmitting antenna pattern including horizontal and vertical directions, matrix M is the antenna polarization along the troposcatter propagation path, is the delay of the mth subpath in the nth tropospheric scatter cluster at time t, including the transmission delay The delay difference caused by the position of the antenna units at the transmitting and receiving ends satisfy is the power of the mth subpath in the nth tropospheric scatter cluster at time t, which obeys a single-slope exponential distribution;

[0039] S3.4. Generate channel matrix H = [h qp (t,τ)] Q×P .

[0040] Furthermore, the S4 step includes:

[0041] S4.1. Calculate the space-time-frequency correlation function of beyond-visual-range UAV communication:

[0042]

[0043] Where f is the frequency, Δf is the frequency offset, and denote the power and delay of the mth subpath in the nth troposcatter cluster between the p′th transmitting antenna element and the q′th receiving antenna element at time t+Δt, respectively, p′=1,…,P,q′=1,…,Q;

[0044] S4.2. Calculate the channel capacity for beyond-visual-range UAV communication:

[0045]

[0046] Where ρ is the signal-to-noise ratio, I is the P×Q identity matrix;

[0047] S4.3. Simulate and analyze the space-time-frequency correlation function and channel capacity of beyond-line-of-sight UAV communication, and compare the channel characteristics of line-of-sight UAV communication and beyond-line-of-sight UAV communication.

[0048] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0049] The beneficial effect of the present invention is that for long-distance UAV communication, a cylindrical conformal antenna array is equipped on the UAV fuselage to better meet the long-distance UAV communication needs. At the same time, considering the unique three-dimensional mobility characteristics and specific tropospheric scattering characteristics of the UAV, a beyond-line-of-sight UAV communication geometric random channel modeling method is provided, which makes up for the deficiency of the existing UAV channel model that only uses short-range communication, so as to accurately characterize the channel characteristics in the long-distance UAV communication scenario. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 Flow chart of the method of the present invention;

[0051] Figure 2 Schematic diagram of the geometric relationship of tropospheric scatter beyond-horizon communication paths;

[0052] Figure 3 This is the budget result diagram of the beyond-visual-range UAV communication link;

[0053] Figure 4 This is the simulation result diagram of the time autocorrelation function of a 50km communication distance;

[0054] Figure 5 This is a comparison chart of the time autocorrelation function simulation results for communication distances of 32km and 50km;

[0055] Figure 6 This is the channel capacity simulation result diagram. DETAILED DESCRIPTION

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0057] Example 1:

[0058] See also Figure 1 This embodiment provides a method for modeling a geometric random channel for beyond-visual-range UAV communication, which specifically includes the following steps:

[0059] Step S1: Establish a beyond-visual-range UAV communication scenario and set the antenna parameters and environmental parameters of the transmitter and receiver.

[0060] Specifically, in this embodiment, step 1 specifically includes:

[0061] S1.1. The UAV side is the communication receiving end, and the ground station side is the communication transmitting end. The heights of the receiving end and the transmitting end are set to hR =50m and h T =5m, effective line-of-sight communication distance The communication distance between the transmitter and receiver is set to D = 50km, which satisfies the communication distance between the transmitter and receiver. Establish beyond-line-of-sight drone communication scenarios;

[0062] S1.2. The antennas of the communication receiving and transmitting ends are both multi-transmitting, multi-receiving antenna arrays. The antenna elevation angles of both the receiving and transmitting ends vary by θ. To better meet the needs of long-range UAV communication, the transmitting end is equipped with a circular antenna array containing P antenna elements, and the receiving end is equipped with a cylindrical conformal antenna array containing Q antenna elements to reduce the effects of flight resistance and fuselage obstruction.

[0063] S1.3, set the carrier frequency f c , transmitter moving speed v T , receiving end moving speed v R , the height of the obstacle at the transmitting end h1, the distance between the obstacle at the transmitting end and the transmitting end d1, the height of the obstacle at the receiving end h2, and the distance between the obstacle at the receiving end and the receiving end d2.

[0064] Step S2: Determine the location of the tropospheric scatter common volume based on the tropospheric scatter characteristics and calculate the path loss. Obtain a loss threshold based on the link budget. When the path loss is less than the loss threshold, determine that the beyond-visual-range UAV communication link is feasible, and proceed to step S3.

[0065] Specifically, in this embodiment, step 2 specifically includes:

[0066] S2.1、Consider the influence of obstacles at the transmitting and receiving ends and the curvature of the earth, such as Figure 2 As shown, calculate the minimum elevation angle β0 and maximum elevation angle β1 of the receiving end tropospheric scatter path, and the minimum elevation angle α0 and maximum elevation angle α1 of the transmitting end tropospheric scatter path:

[0067]

[0068] Calculate the maximum scattering angle θ based on the elevation angle of the tropospheric scatter path at the transmitting and receiving ends max and the minimum scattering angle θ min :

[0069]

[0070] Calculate the bottom height H of the tropospheric scatter common volume min and top height H max To further determine the location of the common volume of tropospheric scatterers:

[0071] H min =θ min2 R e / 8

[0072] H max =θ max D / 4

[0073] S2.2. Calculate the path loss L based on climate parameters and tropospheric scatter characteristics p :

[0074] L p =M+30logf c +10logD+30logθ min +20log(5+γH max )+4.34γH min +180

[0075] The climate zone where the communication link is located is set to continental subtropical climate, and the exponential attenuation coefficient of tropospheric inhomogeneity intensity with height is obtained as γ = 0.27 km-1, and the meteorological factor M = 298.73 dB;

[0076] S2.3. Set the transmit power P T =70dBm, transmitter antenna gain G T =45dB, receiving end antenna gain G R =45dB, system loss L sys =10dB, receiver sensitivity P Rmin =-90dBm, according to L th =P T +G T +G R -P Rmin -L sys Calculate the path loss threshold as L th =240dB;

[0077] S2.4. Comparison of path loss L p and loss threshold L th , when L p <L th The beyond-visual-range UAV communication link is determined to be feasible. Figure 3 The figure shows the path loss under different carrier frequencies and transceiver elevation angles. It can be seen that tropospheric scattering introduces higher path loss. The smaller the transceiver elevation angle changes, the lower the path loss at the same communication distance. This is because when the transceiver elevation angle changes slightly, the scattering angle of the tropospheric scattering common volume is small and the height is low, so the transmission loss is small. At the path loss threshold L thUnder certain limitations, lower frequencies can support longer communication distances. When the elevation angle of the transmitter and receiver varies from θ∈[0°,10°], 4.7GHz can achieve beyond-line-of-sight UAV communication over a distance of more than 300 kilometers.

[0078] Step S3: Modeling the tropospheric scatter cluster as a single-hop scatter cluster located in a tropospheric scatter common volume, initializing the tropospheric scatter cluster, and jointly modeling the evolution of the tropospheric scatter cluster in the time domain and array domain to generate a channel matrix;

[0079] Specifically, in this embodiment, step 3 specifically includes:

[0080] S3.1. Initialize the tropospheric scatter cluster: The center of the tropospheric scatter cluster is located in the tropospheric scatter common volume, and the moving speed of the tropospheric scatter cluster is v n Equal to the average wind speed v wind , the departure azimuth of the troposcatter cluster and departure pitch angle They all obey the truncated Gaussian distribution with the lower bound of α0 and the upper bound of α1. The arrival azimuth of the tropospheric scatter cluster is and arrival pitch angle They all obey a truncated Gaussian distribution with a lower truncation bound of β0 and an upper truncation bound of β1, and the subpath range offset within the tropospheric scatter cluster obeys a standard normal distribution;

[0081] S3.2 Jointly modeling the evolution of tropospheric scatter clusters in the time and array domains: The generation and extinction of tropospheric scatter clusters are characterized by the birth and death process. The joint survival probability of the tropospheric scatter cluster between the transmitter and receiver is expressed as:

[0082] P S (Δt,Δξ T ,Δξ R )=P T (Δt,Δξ T )·P R (Δt,Δξ R )

[0083] Among them, P T (Δt,Δξ T ) indicates that at the transmitting end, the tropospheric scatter cluster passes through the time interval Δt and the antenna spacing Δξ T The survival probability after R (Δt,Δξ R ) indicates that at the receiving end, the tropospheric scatter cluster passes through the time interval Δt and the antenna spacing Δξ R At the same time, after a time interval Δt, new tropospheric scatter clusters will be generated in the channel. According to the Poisson process, the expected number of newly generated tropospheric scatter clusters is expressed as:

[0084]

[0085] Among them, λ G is the generation rate of troposcatter clusters, λ R is the extinction rate of the troposcatter cluster;

[0086] S3.3. Calculate the channel impulse response of the pth transmitting antenna element and the qth receiving antenna element at time t and time delay τ as follows:

[0087]

[0088] Where p = 1,…,P, q = 1,…,Q, n = 1,…,N qp (t), N qp (t) represents the number of troposcatter clusters at time t, M n is the number of subpaths of the nth troposcatter cluster, the matrix F q The matrix F is the receiving antenna pattern including horizontal and vertical directions. p is the transmitting antenna pattern including horizontal and vertical directions, matrix M is the antenna polarization along the troposcatter propagation path, is the delay of the mth subpath in the nth tropospheric scatter cluster at time t, including the transmission delay The delay difference caused by the position of the antenna units at the transmitting and receiving ends satisfy is the power of the mth subpath in the nth tropospheric scatter cluster at time t, which obeys a single-slope exponential distribution;

[0089] S3.4. Generate channel matrix H = [h qp (t,τ)] Q×P .

[0090] Step S4: Calculate the space-time-frequency correlation function and channel capacity of the beyond-horizon UAV communication based on the channel matrix, and perform simulation based on this;

[0091] Specifically, in this embodiment, step 4 specifically includes:

[0092] Calculate the space-time-frequency correlation function for beyond-line-of-sight UAV communications:

[0093]

[0094] Where f is the frequency, Δf is the frequency offset, and denote the power and delay of the mth subpath in the nth troposcatter cluster between the p′th transmitting antenna element and the q′th receiving antenna element at time t+Δt, respectively, p′=1,…,P,q′=1,…,Q;

[0095] S4.2. Calculate the channel capacity for beyond-visual-range UAV communication:

[0096]

[0097] Where ρ is the signal-to-noise ratio, I is the P×Q identity matrix;

[0098] S4.3. Simulate and analyze the space-time-frequency correlation function and channel capacity of beyond-line-of-sight UAV communication, and compare the channel characteristics of line-of-sight UAV communication and beyond-line-of-sight UAV communication. Figure 4 It represents the time autocorrelation function when the communication distance D = 50km. It can be seen that with the increase of the average wind speed and the elevation angle of the transmitting and receiving ends, the time autocorrelation function decreases faster, which shows that the tropospheric scattering conditions have a great influence on the time correlation of beyond-visual-range UAV communication. Figure 5 The simulation results show that the time autocorrelation function of the communication distance D = 1km and D = 32km is compared. The results show that the decrease rate of the time autocorrelation function becomes smaller with the increase of the communication distance, which indicates that the channel non-stationarity of beyond-line-of-sight UAV communication is lower in the time domain compared with line-of-sight UAV communication.

[0099] Figure 6 This figure compares the channel capacity of line-of-sight (LOS) UAV communications with a communication distance of D = 1 km, and beyond-line-of-sight (BLOS) UAV communications with distances of D = 32 km and D = 50 km. The simulation adjusts the transmit power to ensure consistent signal-to-noise ratios at different communication distances. The results show that LOS UAV communications with D = 1 km consistently provide a channel capacity exceeding 8 bps / Hz; beyond-line-of-sight UAV communications with D = 32 km provide a channel capacity exceeding 8 bps / Hz 70% of the time; and beyond-line-of-sight UAV communications with D = 50 km provide a channel capacity exceeding 8 bps / Hz 60% of the time. This indicates that at the same SNR, the channel capacity of beyond-line-of-sight (BLOS) communications is lower than that of LOS communications. Therefore, it is necessary to further increase the transmit power and transmit / receive antenna gain to meet the requirements of beyond-line-of-sight UAV communications. Furthermore, when the transceiver's multi-transmitter and multi-receiver antenna array is 4×4, beyond-line-of-sight UAV communications with D = 50 km provide a channel capacity exceeding 8 bps / Hz 80% of the time. This indicates that both the median channel capacity and link reliability of beyond-line-of-sight UAV communications increase with the addition of a multi-transmitter and multi-receiver antenna array configuration.

[0100] The above describes in detail the preferred embodiments of the present invention. It should be understood that those skilled in the art can make numerous modifications and variations based on the concepts of the present invention without inventive effort. Therefore, any technical solutions that can be derived by those skilled in the art through logical analysis, reasoning, or limited experimentation based on the concepts of the present invention and the prior art should be within the scope of protection defined by the claims.

Claims

1. A geometric random channel modeling method for beyond-visual-range UAV communication, characterized by: The method comprises the following steps: Step S1: Establish a beyond-visual-range UAV communication scenario and set the antenna parameters and environmental parameters of the transmitter and receiver. Step S2: Determine the location of the tropospheric scatter common volume based on the tropospheric scatter characteristics, calculate the path loss, and obtain a loss threshold based on the link budget. When the path loss is less than the loss threshold, the beyond-visual-range UAV communication link is determined to be feasible, and proceed to step S3. Step S3: Modeling the tropospheric scatter cluster as a single-hop scatter cluster located in a tropospheric scatter common volume, initializing the tropospheric scatter cluster, and jointly modeling the evolution of the tropospheric scatter cluster in the time domain and array domain to generate a channel matrix; Step S4: Calculate the space-time-frequency correlation function and channel capacity of the beyond-visual-range UAV communication based on the channel matrix, and perform simulation based on this.

2. A geometric random channel modeling method for beyond-visual-range UAV communication according to claim 1, characterized in that: The S1 step includes: S1.

1. The UAV side is the communication receiving end, the ground station side is the communication transmitting end, and the communication distance D between the transmitting and receiving ends satisfies When establishing a beyond-visual-range UAV communication scenario, is the effective line-of-sight communication distance, h R is the height of the receiving end, h T is the transmitter height, R e is the effective Earth radius; S1.

2. The antennas of the communication receiving end and the communication transmitting end are both multi-transmit multi-receive antenna arrays, and the antenna elevation angle variation of the receiving end and the transmitting end is θ. The transmitting end is equipped with a circular antenna array containing P antenna elements, and the receiving end is equipped with a cylindrical conformal antenna array containing Q antenna elements; S1.3, set the carrier frequency f c , transmitter moving speed v T , receiving end moving speed v R , the height of the obstacle at the transmitting end h1, the distance between the obstacle at the transmitting end and the transmitting end d1, the height of the obstacle at the receiving end h2, and the distance between the obstacle at the receiving end and the receiving end d2.

3. The geometric random channel modeling method for beyond-visual-range UAV communication according to claim 2 is characterized in that: The S2 step includes: S2.

1. Considering obstacles at the transmitting and receiving ends and the influence of the earth's curvature, calculate the minimum elevation angle β0 and maximum elevation angle β1 of the receiving end tropospheric scatter path, and the minimum elevation angle α0 and maximum elevation angle α1 of the transmitting end tropospheric scatter path: Calculate the maximum scattering angle θ based on the elevation angle of the tropospheric scatter path at the transmitting and receiving ends max and the minimum scattering angle θ min : Calculate the bottom height H of the tropospheric scatter common volume min and top height H max To further determine the location of the common volume of tropospheric scatterers: H min =θ min 2 R e / 8 H max =θ max D / 4 S2.

2. Calculate the path loss L based on climate parameters and tropospheric scatter characteristics p : L p =M+30logf c +10logD+30logθ min +20log(5+γH max )+4.34γH min +180 Where M is the meteorological factor, γ is the exponential decay coefficient of tropospheric inhomogeneity intensity with height; S2.

3. Perform link budgeting to obtain the loss threshold L th : L th =P T +G T +G R -P Rmin -L sys Among them, P T is the transmission power, G T is the antenna gain at the transmitting end, G R is the receiving antenna gain, L sys is the system loss, P Rmin is the receiver sensitivity; S2.

4. Compare path loss L p and loss threshold L th , when L p <L th The beyond-visual-range UAV communication link is determined to be feasible.

4. The geometric random channel modeling method for beyond-visual-range UAV communication according to claim 3 is characterized in that: The S3 step includes: S3.

1. Initialize the tropospheric scatter cluster: The center of the tropospheric scatter cluster is located in the tropospheric scatter common volume, and the moving speed of the tropospheric scatter cluster is v n Equal to the average wind speed v wind , the departure azimuth of the troposcatter cluster and departure pitch angle They all obey the truncated Gaussian distribution with the lower bound of α0 and the upper bound of α1. The arrival azimuth of the tropospheric scatter cluster is and arrival pitch angle They all obey a truncated Gaussian distribution with a lower truncation bound of β0 and an upper truncation bound of β1, and the subpath range offset within the tropospheric scatter cluster obeys a standard normal distribution; S3.2 Jointly modeling the evolution of tropospheric scatter clusters in the time and array domains: The generation and extinction of tropospheric scatter clusters are characterized by the birth and death process. The joint survival probability of the tropospheric scatter cluster between the transmitter and receiver is expressed as: P S (Δt,Δξ T ,Dx R )=P T (Δt,Δξ T )·P R (Δt,Δξ R ) Among them, P T (Δt,Δξ T ) indicates that at the transmitting end, the tropospheric scatter cluster passes through the time interval Δt and the antenna spacing Δξ T The survival probability after R (Δt,Δξ R ) indicates that at the receiving end, the tropospheric scatter cluster passes through the time interval Δt and the antenna spacing Δξ R After the survival probability is Δt, new tropospheric scatter clusters are generated in the channel. According to the Poisson process, the expected number of newly generated tropospheric scatter clusters is expressed as: Among them, λ G is the generation rate of troposcatter clusters, λ R is the extinction rate of the troposcatter cluster; S3.

3. Calculate the channel impulse response of the pth transmitting antenna element and the qth receiving antenna element at time t and time delay τ as follows: Where p = 1,…,P, q = 1,…,Q, n = 1,…,N qp (t), N qp (t) represents the number of troposcatter clusters at time t, M n is the number of subpaths of the nth troposcatter cluster, the matrix F q The matrix F is the receiving antenna pattern including horizontal and vertical directions. p is the transmitting antenna pattern including horizontal and vertical directions, matrix M is the antenna polarization along the troposcatter propagation path, is the delay of the mth subpath in the nth tropospheric scatter cluster at time t, including the transmission delay The delay difference caused by the position of the antenna units at the transmitting and receiving ends satisfy is the power of the mth subpath in the nth tropospheric scatter cluster at time t, which obeys a single-slope exponential distribution; S3.

4. Generate channel matrix H = [h qp (t,τ)] Q×P .

5. The geometric random channel modeling method for beyond-visual-range UAV communication according to claim 4 is characterized in that: The S4 step includes: S4.

1. Calculate the space-time-frequency correlation function of beyond-visual-range UAV communication: Where f is the frequency, Δf is the frequency offset, and denote the power and delay of the mth subpath in the nth troposcatter cluster between the p′th transmitting antenna element and the q′th receiving antenna element at time t+Δt, respectively, p′=1,…,P,q′=1,…,Q; S4.

2. Calculate the channel capacity for beyond-visual-range UAV communication: Where ρ is the signal-to-noise ratio, I is the P×Q identity matrix; S4.

3. Simulate and analyze the space-time-frequency correlation function and channel capacity of beyond-line-of-sight UAV communication, and compare the channel characteristics of line-of-sight UAV communication and beyond-line-of-sight UAV communication.

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