Air-to-ground communication channel modeling method based on random trajectory of unmanned aerial vehicle

Abstract

The invention discloses an air-to-ground communication channel modeling method based on a random trajectory of an unmanned aerial vehicle, and the method comprises the steps: generating the random trajectory of the unmanned aerial vehicle through employing a smooth steering random movement model; a grid-based large-scale parameter generation method is adopted to generate large-scale parameters, and then small-scale parameters such as time delay, power, departure azimuth angle, arrival azimuth angle, departure pitch angle, arrival pitch angle and cross polarization ratio are generated; performing space-time evolution analysis of the cluster; calculating a channel impulse response CIR and a channel matrix; simulating and calculating a space-time correlation function STCF, a root-mean-square delay spread RMS DS, a stationary time interval SI and a channel capacity, and judging the influence of different unmanned aerial vehicle trajectories on a channel. Based on the random trajectory of the unmanned aerial vehicle, the influence of different unmanned aerial vehicle trajectories on the channel is considered, the characteristics of the spatial domain and the time domain of the channel are comprehensively analyzed, and the method has important significance for guiding the design of an air-to-ground communication system of the unmanned aerial vehicle.

Description

Technical Field

[0001] This invention relates to the technical field of channel modeling, and in particular to a method for modeling air-to-ground communication channels based on the random trajectory of a UAV. Background Technology

[0002] In recent years, UAV technology has been increasingly widely used in fields such as communication relay, disaster monitoring, border patrol, and agricultural plant protection, and UAV communication channels have exhibited some new channel characteristics. Channel modeling and analysis are crucial for effective system design, performance evaluation, link budget analysis, antenna design, interference analysis, system planning, and standardization. Air-to-ground communication, as a core capability of UAV systems, directly affects the reliability of communication links, resource allocation efficiency, and overall system performance through the accuracy of its channel modeling. To meet the communication needs of UAVs, 6G wireless communication networks have demonstrated enormous potential due to their wide coverage and large bandwidth. 6G channel research lays the foundation for future 6G networks and is one of the most important fundamental research projects in the 6G field. However, traditional models are mostly based on UAV hovering or pre-defined trajectories, failing to fully consider the dynamic and random maneuvering characteristics of UAVs in actual missions. This significantly reduces the applicability of the models in real random motion scenarios. Time-varying non-stationary effects caused by high-speed UAV movement, such as Doppler shift spread and angle spread, are often simplified in existing models. Especially under random trajectories, the statistical characteristics of channel parameters such as delay, fading, and spatial correlation are difficult to accurately characterize.

[0003] The invention disclosed in CN119299028A presents a method and implementation system for simulating channel characteristics based on a UAV-to-unmanned surface vessel (USV) wireless channel model. The method includes: constructing a UAV-to-USV wireless channel model; the USV wireless channel model includes: generating channel impulse response, generating UAV random trajectory, generating USV random trajectory, generating an evaporating waveguide model, generating small-scale parameters, and updating the small-scale parameters; calculating channel statistical characteristics; the channel statistical characteristics include time delay power spectral density, space-time correlation function, stationary interval, and root mean square time delay spread. However, this channel model fails to effectively analyze the impact of UAV random trajectories on channel characteristics, cannot correspond to trajectories in the real environment, and is difficult to reflect the impact of trajectory randomness on channel characteristics.

[0004] Meanwhile, existing methods rarely systematically integrate the interaction between 3D random trajectories and complex ground object scatterers, resulting in significant prediction errors in path loss, shadow fading, and multipath structure. Although geometrically deterministic modeling-based schemes offer high accuracy, they rely on detailed digital maps and are computationally time-consuming, making it difficult to support the online communication and decision-making needs of UAVs. Summary of the Invention

[0005] The purpose of this invention is to provide a method for modeling air-to-ground communication channels based on UAV random trajectories. This method can characterize the special channel characteristics of UAVs to ground channels, consider the impact of different UAV trajectories on the channel, and comprehensively analyze the characteristics of the channel in both the spatial and temporal domains. It establishes an accurate and appropriately complex geometric random channel model for UAV air-to-ground scenarios.

[0006] To achieve the above-mentioned technical objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for modeling air-to-ground communication channels based on UAV random trajectories, the method comprising the following steps:

[0008] S1 uses a smooth steering random movement model to generate random trajectories for the UAV;

[0009] S2, based on the generated UAV random trajectory, adopts a grid-based large-scale parameter generation method to generate large-scale parameters for describing the macroscopic characteristics of the channel in any spatial layer and on any altitude plane. On this basis, small-scale parameters including time delay, power, departure azimuth, arrival azimuth, departure pitch, arrival pitch, and cross-polarization ratio are generated.

[0010] S3, initialize the cluster generation and destruction rates, and combine them with the small-scale parameters calculated in step S2 to calculate the clusters corresponding to the antenna array at the transmitting end in trajectory segment δ. d The distance between the inner and outer antennas is Δd T The probability of continued existence under the condition, and the clusters corresponding to the antenna array at the receiving end within the time interval Δt and the antenna spacing Δd. R The probability of continued existence under certain conditions; calculate the joint survival probability of clusters with integrated spatiotemporal evolution and the mathematical expectation of new clusters, and perform spatiotemporal evolution analysis of clusters;

[0011] S4. Combining the small-scale parameters calculated in step S2 and the spatiotemporal evolution results of the cluster in step S3, calculate the channel impulse response (CIR) and the channel matrix.

[0012] S5 combines the channel impulse response (CIR) and the channel matrix to simulate and calculate the space-time correlation function (STCF), root mean square delay spread (RMSDS), stationary time interval (SI), and channel capacity, thereby determining the impact of different UAV trajectories on the channel.

[0013] Step S1 further includes:

[0014] The random trajectory of the UAV is modeled using a smooth steering random movement model:

[0015] a xut (t)=0

[0016]

[0017] l x (t)=v x (t)=v xy (t)cos(φ(t))

[0018] l y (t)=v y (t)=v xy (t)sin(φ(t))

[0019] l z (t)=v z (t)

[0020] Among them, a xyt (t) and a xyn (t) represents the horizontal tangential acceleration and centripetal acceleration of the UAV at time t, l x (t), l y (t) and l z (t) represents the coordinates of the UAV in the x, y, and z directions at time t, φ xy θ(t), θ(t), and ω(t) represent the heading, turning angle, and angular velocity of the UAV at time t, respectively; v xy (t), v x (t) and v y (t) represent the velocity of the UAV in the horizontal plane at time t, the velocity in the x direction, and the velocity in the y direction, respectively;

[0021] In a time period [T] i ,T i+1 Within ) the drone circled the turning center (c x (T i ),c y (T i With turning radius r(T) i )sports, Follows the variance σ 2 Gaussian distribution, r(T) i )>0 indicates a right turn, r(T) i <0 indicates a left turn; the drone's movement time interval τ i =T i+1 -T i Follow the mean The exponential distribution.

[0022] Furthermore, in step S2, a grid-based large-scale parameter generation method is used to generate large-scale parameters:

[0023]

[0024] in, and l x,y Let A and B represent the large-scale parameters of grid points (a,b) and (x,y) within a certain spatial layer, respectively, where A and B are the grid length and width, and f(·) represents the exponential spatial filtering function. This represents the large-scale parameter distribution on the height h plane. and The large-scale parameter representing the highly stratified boundary, where h b With base, h t Top;

[0025] Further, in step S2, the time delay and power are generated using the following formulas:

[0026]

[0027] in, and This represents the time delay and power of the m-th ray passing through the n-th reflection cluster from the p-th antenna element at the transmitter to the q-th antenna element at the receiver at time t. denoted by , representing the time-varying distance between the p / q-th antenna element at the transmitter / receiver end and the m-th scatterer in the first-hop cluster / last-hop cluster, where c represents the speed of light; This represents the virtual link delay of the m-th scatterer between the first-hop cluster and the last-hop cluster. N represents the non-normalized sub-path power. qp (t) represents the number of time-varying clusters, M n (t) represents the number of rays in the nth path;

[0028] Furthermore, in step S2, the generated azimuth and elevation angles follow a uniform distribution, and the cross-polarization ratio follows a log-normal distribution.

[0029] Step S3 further includes:

[0030] Initialization cluster generation rate λ R , mortality rate λ G For the antenna array at the transmitting end, the cluster is in the trajectory segment δ d The distance between the inner and outer antennas is Δd T The probability of continued existence under certain circumstances Represented as:

[0031]

[0032] in, v UAV f represents the speed of the drone. T This represents the number of samples per second of the drone's trajectory. Represents the scene dependency coefficient in the time domain. The scene dependency coefficient representing the spatial domain;

[0033] For the antenna array at the receiving end, the clusters are spaced Δd apart by the antenna spacing within the time interval Δt. R The probability of continued existence under certain circumstances Represented as:

[0034]

[0035] in, This represents the distance difference caused by the evolution of the receiver array, where q is the q-th antenna element. The elevation angle of the receiving antenna array. v represents the distance difference caused by time evolution at the receiving end. R For the speed of the receiving end, This indicates the azimuth angle of the movement of the receiving antenna array. Indicates the azimuth angle of the receiving antenna array;

[0036] Calculate the joint survival probability P of clusters in the context of integrated spatiotemporal evolution. survival (δ d ,Δd T ,Δt,Δd R ):

[0037]

[0038] The mathematical expectation value E(N) of the new cluster new )for:

[0039]

[0040] Step S4 further includes:

[0041] The channel impulse response (CIR) is calculated using the following formula:

[0042]

[0043] Among them, K Ri (t) represents the Rice K-factor, the channel impulse response of line-of-sight (LoS) path transmission. for:

[0044]

[0045] Among them, f c F represents the carrier frequency. q(p),V and F q(p),H This indicates the vertical / horizontal polarization pattern of the UAV / ground station antenna. and This indicates the arrival pitch angle and arrival azimuth angle of the LosS path. and This represents a random phase uniformly distributed in (0, 2π]. and The loS path represents the departure pitch and departure azimuth angles, and δ(·) represents the unit impulse function. Indicates the delay of the Loss path;

[0046] Channel impulse response for non-line-of-sight (NLoS) path transmission for:

[0047]

[0048] in, and Indicates the arrival pitch angle and arrival azimuth angle of the NLoS path. and This represents the vertical, horizontal, and cross-polarized random phase of the NLoS component. Indicates the cross-polarization power ratio. and Indicates the departure pitch angle and departure azimuth angle of the NLoS path. and This represents the time-varying power and time delay of each sub-path in NLoS, where N... qp (t) represents the number of time-varying clusters, M n (t) represents the number of rays in the nth path;

[0049] Step S402: Calculate the channel matrix. The channel matrix is ​​calculated as follows:

[0050]

[0051] Where PL represents path loss, SH represents shadow fading, and H represents path loss. s Let a represent the small-scale fading matrix. R M represents the antenna element number in the receiving antenna array. T and N T This indicates the row and column dimensions of the transmitting antenna array.

[0052] Step S5 further includes:

[0053] The space-time correlation function STCF is calculated using the following formula:

[0054]

[0055] in, It is the space-time correlation function of the Loss path. K is the space-time correlation function of the NLoS path. Ri (t) represents Rice's K-factor, N qp (t) represents the number of time-varying clusters;

[0056] The root mean square delay spread (RMSDS) is calculated using the following formula:

[0057]

[0058]

[0059] Among them, M n (t) represents the number of rays in the nth path. and This represents the time delay and power of the m-th ray passing through the n-th reflection cluster from the p-th antenna element at the transmitter to the q-th antenna element at the receiver at time t.

[0060] The stationary time interval SI is calculated using the following formula:

[0061] SI(t) = max{Δt|χ(t,Δt)≥c t}

[0062] Where χ(t,Δt) represents the correlation coefficient of the power spectral density between two time delays from time t to time t+Δt, and c t It is the set coefficient threshold;

[0063]

[0064] Among them, Ω qp (t,τ) represents the time-varying time-delay power spectral density:

[0065]

[0066] Step S504: Calculate the channel capacity. The channel capacity is calculated as follows:

[0067]

[0068] in, Indicates a size of M R The identity matrix, H represents the normalized channel matrix, ρ s M represents the signal-to-noise ratio. T and N T This indicates the row and column dimensions of the transmitting antenna array.

[0069] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0070] This invention proposes a method for modeling air-to-ground communication channels based on UAV random trajectories. It considers the channel characteristics of the UAV air-to-ground channel and models the channel using a geometry-based stochastic model framework and the UAV's random trajectory. This invention comprehensively analyzes the impact of the UAV's random trajectory on the channel. Furthermore, the model provides theoretical derivations and simulation verifications for statistical properties reflecting channel accuracy and consistency, such as channel STCF, RMSDS, SI, and channel capacity. Attached Figure Description

[0071] Figure 1 This is a flowchart of the air-to-ground communication channel modeling method based on UAV random trajectory in an embodiment of the present invention;

[0072] Figure 2 This is a schematic diagram of the UAV air-to-ground channel model in an embodiment of the present invention;

[0073] Figure 3 This is a simulation diagram of the random trajectory of the UAV in an embodiment of the present invention;

[0074] Figure 4 This is a schematic diagram of the simulation results of the cumulative distribution function of SI under different UAV trajectories in an embodiment of the present invention. Detailed Implementation

[0075] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0076] See Figure 1 This embodiment provides a method for modeling air-to-ground communication channels based on the random trajectory of a UAV. The method specifically includes:

[0077] Step S1: Use a smooth steering random movement model to generate a random trajectory for the UAV.

[0078] Specifically, step S1 includes:

[0079] Step S101: Model the random trajectory of the UAV using a smooth steering random movement model:

[0080] a xyt (t)=0

[0081]

[0082] l x (t)=v x (t)=v xy (t)cos(φ(t))

[0083] l y (t)=v y (t)=v xy(t)sin(φ(t))

[0084] l z (t)=v z (t)

[0085] Among them, a xyt (t) and a xyn (t) represents the horizontal tangential acceleration and centripetal acceleration of the UAV at time t, l x (t), l y (t) and l z (t) represents the coordinates of the UAV, and φ(t), θ(t) and ω(t) represent the heading, turning angle and angular velocity of the UAV.

[0086] Step S102: Set a time period [T] i ,T i+1 Within ) the drone circled the turning center (c x (T i ),c y (T i With turning radius r(T) i )sports, Follows the variance σ 2 Gaussian distribution, r(T) i )>0 indicates a right turn, r(T) i <0 indicates a left turn. The drone's movement time interval τ i =T i+1 -T i Follow the mean The exponential distribution. In this embodiment, the initial coordinates of the UAV are set to (0,0,20) (m), and the initial coordinates of the ground station are set to (20,10,0) (m). A total of 5 UAV trajectories are generated, denoted as trajectories A, B, C, D, and E. The acceleration is set to 0, and the ground station is considered to be stationary.

[0087] Trajectory A is a straight line moving in the direction of (1,1,0) (m) at a speed of 5m / s or 15m / s; trajectory B is a circle with a speed of 15m / s and a radius of 16.88m; trajectory C is a circle with a speed of 15m / s and a radius of 50m; trajectory D uses the smooth turning random movement model described above, where λ = 1m. -1 σ = 0.05s -1 The vertical velocity along the positive z-axis is 2 m / s; trajectory D adopts the above-mentioned smooth turning random movement model, where λ = 1 m -1 σ = 0.2s -1 The vertical velocity along the positive z-axis is 2 m / s;

[0088] Figure 3A schematic diagram of a randomly generated trajectory E is shown.

[0089] Step S2: Generate the large-scale and small-scale parameters of the channel.

[0090] Specifically, step S2 includes:

[0091] Step S201: Generate large-scale parameters using a grid-based large-scale parameter generation method:

[0092]

[0093] in, and l x,y Let f(·) represent the large-scale parameters of grid points (a,b) and (x,y) within a certain spatial layer, and let f(·) represent the exponential spatial filtering function. This represents the large-scale parameter distribution on the height h plane. and The large-scale parameter representing the highly stratified boundary, where h b With base, h t Top;

[0094] Step S202: Generate small-scale parameters, first generating time delay and power:

[0095]

[0096] Where, τ qp,mn (t) and P qp,mn (t) represents the time delay and power of the m-th ray passing through the n-th reflection cluster from the p-th antenna element at the transmitter to the q-th antenna element at the receiver at time t. denoted by , represents the time-varying distance between the p / q-th antenna element at the transmitter / receiver and the m-th scatterer in the first-hop cluster / last-hop cluster, where c represents the speed of light. This represents the virtual link delay of the m-th scatterer between the first-hop cluster and the last-hop cluster. N represents the non-normalized sub-path power. qp (t) represents the number of time-varying clusters, M n (t) represents the number of rays in the nth path.

[0097] Step S3: Perform spatiotemporal evolution analysis of the cluster.

[0098] Specifically, step S3 includes:

[0099] Step S301: Initialize the cluster generation rate λ R , mortality rate λ G For the antenna array at the transmitting end, the cluster is in the trajectory segment δ d The distance between the inner and outer antennas is Δd TThe probability of it continuing under the given condition can be expressed as:

[0100]

[0101] in, v UAV f represents the speed of the drone. T This represents the number of samples per second of the drone's trajectory. This represents the scene dependency coefficient in the time domain, with a value of 20m. The scene dependency coefficient representing the spatial domain has a value of 20m;

[0102] For the antenna array at the receiving end, the clusters are spaced Δd apart by the antenna spacing within the time interval Δt. R The probability of it continuing under the given condition can be expressed as:

[0103]

[0104] in, This represents the distance difference caused by the evolution of the receiver array, where q is the q-th antenna element. The elevation angle of the receiving antenna array. v represents the distance difference caused by time evolution at the receiving end. R For the speed of the receiving end, This indicates the azimuth angle of the movement of the receiving antenna array. Indicates the azimuth angle of the receiving antenna array;

[0105] Step S302: Calculate the joint survival probability of the clusters based on the integrated spatiotemporal evolution:

[0106]

[0107] The expected value of the new cluster is:

[0108]

[0109] Step S4: Calculate the channel impulse response (CIR) and the channel matrix.

[0110] Specifically, step S4 includes:

[0111] Step S401: Calculate the channel CIR. The channel CIR is calculated as follows:

[0112]

[0113] Among them, K Ri (t) represents the Rice K-factor, and the CIR of line-of-sight (LoS) path transmission is calculated as follows:

[0114]

[0115] Among them, F q(p),V and F q(p),H This indicates the vertical / horizontal polarization pattern of the UAV / ground station antenna. and This indicates the arrival pitch angle and arrival azimuth angle of the LosS path. and This represents a random phase uniformly distributed in (0, 2π]. and This indicates the departure pitch angle and departure azimuth angle of the Los path. This indicates the delay of the Loss of Path (LoS).

[0116] The CIR calculation for non-line-of-sight (NLoS) path transmission is as follows:

[0117]

[0118]

[0119] in, and Indicates the arrival pitch angle and arrival azimuth angle of the NLoS path. and κ represents the vertical, horizontal, and cross-polarized random phase of the NLoS component. mn (t) represents the cross-polarization power ratio. and P represents the departure pitch and departure azimuth angles of the NLoS path. qp,mn (t) and τ qp,mn (t) represents the time-varying power and time delay of each sub-path of NLoS;

[0120] Step S402: Calculate the channel matrix. The channel matrix is ​​calculated as follows:

[0121]

[0122] Where PL represents path loss, SH represents shadow fading, and H represents path loss. s Let a represent the small-scale fading matrix. R M represents the antenna element number in the receiving antenna array. T and N T This indicates the row and column dimensions of the transmitting antenna array.

[0123] Step S5: Simulate and calculate the space-time correlation function STCF, root mean square delay spread RMSDS, stationary time interval SI, and channel capacity to determine the impact of different UAV trajectories on the channel.

[0124] Specifically, step S5 includes:

[0125] Step S501: Calculate STCF. STCF is calculated as follows:

[0126]

[0127] in, It is the STCF of the Loss path. The STCF of the NLoS path, if Δd is set to 0, the STCF will simplify to the time autocorrelation function TACF; if Δt is set to 0, the STCF will simplify to the spatial cross-correlation function SCCF.

[0128] Step S502: Calculate RMSDS. RMSDS is calculated as follows:

[0129]

[0130] Figure 4 The cumulative distribution function (SI) of five UAV trajectories is shown. Trajectory B has the smallest SI, indicating that the UAV air-to-ground channel under this trajectory is the most unstable. Circular trajectories may obstruct the signal propagation path, thus increasing signal volatility. Trajectories A and C have similar and large SI values, with trajectory A having no turns and trajectory C having a very gentle turn. SI decreases as the UAV's speed increases. For trajectory D, SI is initially small but gradually increases because trajectory D initially experienced a sharp turn, and later, due to the UAV's vertical speed, the vertical distance between the UAV and the ground station gradually increased. Trajectory E has a smaller SI than trajectory D, indicating that the greater the randomness of the trajectory, the more unstable the channel may be. This randomness is reflected in the rapid changes in the UAV's departure angle, arrival angle, and direction of travel.

[0131] Step S503: Calculate SI. SI is calculated as follows:

[0132] SI(t) = max{Δt|χ(t,Δt)≥c t}

[0133] Where χ(t,Δt) represents the correlation coefficient of the power spectral density between two time delays from time t to time t+Δt, and c t It is the set coefficient threshold;

[0134]

[0135] Among them, Ω qp (t,τ) represents the time-varying time-delay power spectral density:

[0136]

[0137] Step S504: Calculate the channel capacity. The channel capacity is calculated as follows:

[0138]

[0139] in, Indicates a size of M R The identity matrix, H represents the normalized channel matrix, ρ s This indicates the signal-to-noise ratio.

[0140] In summary, this invention proposes a method for modeling air-to-ground communication channels based on UAV random trajectories. It has the following advantages: it considers the channel characteristics of the UAV air-to-ground channel and models the channel using a geometry-based stochastic model framework and UAV random trajectories; it considers and comprehensively analyzes the impact of UAV random trajectories on the channel; the model provides theoretical derivation and simulation verification of statistical characteristics reflecting channel accuracy and consistency, such as channel STCF, RMSDS, SI, and channel capacity, thus validating the effectiveness of the channel modeling method. This invention is of great significance for guiding the design of UAV air-to-ground communication systems.

[0141] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0142] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, produce instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0143] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0144] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment, causing a series of operational steps to be executed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that run on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0145] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0146] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for modeling air-to-ground communication channels based on the random trajectory of an unmanned aerial vehicle (UAV), characterized in that, The method Includes the following steps: S1 uses a smooth steering random movement model to generate random trajectories for the UAV; S2, based on the generated UAV random trajectory, adopts a grid-based large-scale parameter generation method to generate large-scale parameters for describing the macroscopic characteristics of the channel in any spatial layer and on any altitude plane. On this basis, small-scale parameters including time delay, power, departure azimuth, arrival azimuth, departure pitch, arrival pitch, and cross-polarization ratio are generated. S3, initialize the cluster generation and destruction rates, and combine them with the small-scale parameters calculated in step S2 to calculate the clusters corresponding to the antenna array at the transmitting end in trajectory segment δ. d The distance between the inner and outer antennas is Δd T The probability of continued existence under the condition, and the clusters corresponding to the antenna array at the receiving end within the time interval Δt and the antenna spacing Δd. R The probability of continued existence under certain conditions; calculate the joint survival probability of clusters with integrated spatiotemporal evolution and the mathematical expectation of new clusters, and perform spatiotemporal evolution analysis of clusters; S4. Combining the small-scale parameters calculated in step S2 and the spatiotemporal evolution results of the cluster in step S3, calculate the channel impulse response (CIR) and the channel matrix. S5 combines the channel impulse response (CIR) and the channel matrix to simulate and calculate the space-time correlation function (STCF), root mean square delay spread (RMSDS), stationary time interval (SI), and channel capacity, thereby determining the impact of different UAV trajectories on the channel.

2. The air-to-ground communication channel modeling method based on UAV random trajectory according to claim 1, characterized in that, Step S1 further includes: The random trajectory of the UAV is modeled using a smooth steering random movement model: a xyt (t)=0 l x (t)=v x (t)=v xy (t)cos(φ(t)) l y (t)=v y (t)=v xy (t)sin(φ(t)) l z (t)=v z (t) Among them, a xyt (t) and a xyn (t) represents the horizontal tangential acceleration and centripetal acceleration of the UAV at time t, l x (t), l y (t) and l z (t) represents the coordinates of the UAV in the x, y, and z directions at time t, φ xy θ(t), θ(t), and ω(t) represent the heading, turning angle, and angular velocity of the UAV at time t, respectively; v xy (t), v x (t) and v y (t) represent the velocity of the UAV in the horizontal plane at time t, the velocity in the x direction, and the velocity in the y direction, respectively; In a time period [T] i ,T i+1 Within ) the drone circled the turning center (c x (T i ),c y (T i With turning radius r(T) i )sports, Follows the variance σ 2 Gaussian distribution, r(T) i )>0 indicates a right turn, r(T) i <0 indicates a left turn; the drone's movement time interval τ i =T i+1 -T i Follow the mean The exponential distribution.

3. The air-to-ground communication channel modeling method based on UAV random trajectory according to claim 1, characterized in that, In step S2, a grid-based large-scale parameter generation method is used to generate large-scale parameters: in, and l x,y Let A and B represent the large-scale parameters of grid points (a,b) and (x,y) within a certain spatial layer, respectively, where A and B are the grid length and width, and f(·) represents the exponential spatial filtering function. This represents the large-scale parameter distribution on the height h plane. and The large-scale parameter representing the highly stratified boundary, where h b With base, h t Top.

4. The air-to-ground communication channel modeling method based on UAV random trajectory according to claim 1, characterized in that, In step S2, the time delay and power are generated using the following formulas: in, and This represents the time delay and power of the m-th ray passing through the n-th reflection cluster from the p-th antenna element at the transmitter to the q-th antenna element at the receiver at time t. denoted by , representing the time-varying distance between the p / q-th antenna element at the transmitter / receiver end and the m-th scatterer in the first-hop cluster / last-hop cluster, where c represents the speed of light; This represents the virtual link delay of the m-th scatterer between the first-hop cluster and the last-hop cluster. N represents the non-normalized sub-path power. qp (t) represents the number of time-varying clusters, M n (t) represents the number of rays in the nth path.

5. The air-to-ground communication channel modeling method based on UAV random trajectory according to claim 1, characterized in that, In step S2, the generated azimuth and elevation angles follow a uniform distribution, and the cross-polarization ratio follows a log-normal distribution.

6. The air-to-ground communication channel modeling method based on UAV random trajectory according to claim 1, characterized in that, Step S3 further includes: Initialization cluster generation rate λ R , mortality rate λ G For the antenna array at the transmitting end, the cluster is in the trajectory segment δ d The distance between the inner and outer antennas is Δd T The probability of continued existence under certain circumstances Represented as: in, v UAV f represents the speed of the drone. T This represents the number of samples per second of the drone's trajectory. Represents the scene dependency coefficient in the time domain. The scene dependency coefficient representing the spatial domain; For the antenna array at the receiving end, the clusters are spaced Δd apart by the antenna spacing within the time interval Δt. R The probability of continued existence under certain circumstances Represented as: in, This represents the distance difference caused by the evolution of the receiver array, where q is the q-th antenna element. The elevation angle of the receiving antenna array. v represents the distance difference caused by time evolution at the receiving end. R For the speed of the receiving end, This indicates the azimuth angle of the movement of the receiving antenna array. Indicates the azimuth angle of the receiving antenna array; Calculate the joint survival probability P of clusters in the context of integrated spatiotemporal evolution. survival (δ d ,Δd T ,Δt,Δd R ): The mathematical expectation value E(N) of the new cluster new )for:

7. The air-to-ground communication channel modeling method based on UAV random trajectory according to claim 1, characterized in that, Step S4 further includes: The channel impulse response (CIR) is calculated using the following formula: Among them, K Ri (t) represents the Rice K-factor, the channel impulse response of line-of-sight (LoS) path transmission. for: Among them, f c F represents the carrier frequency. q(p),V and F q(p),H This indicates the vertical / horizontal polarization pattern of the UAV / ground station antenna. and This indicates the arrival pitch angle and arrival azimuth angle of the LosS path. and This represents a random phase uniformly distributed in (0, 2π]. and The loS path represents the departure pitch and departure azimuth angles, and δ(·) represents the unit impulse function. Indicates the delay of the Loss path; Channel impulse response for non-line-of-sight (NLoS) path transmission for: in, and Indicates the arrival pitch angle and arrival azimuth angle of the NLoS path. and This represents the vertical, horizontal, and cross-polarized random phase of the NLoS component. Indicates the cross-polarization power ratio. and Indicates the departure pitch angle and departure azimuth angle of the NLoS path. and This represents the time-varying power and time delay of each sub-path in NLoS, where N... qp (t) represents the number of time-varying clusters, M n (t) represents the number of rays in the nth path; Step S402: Calculate the channel matrix. The channel matrix is ​​calculated as follows: Where PL represents path loss, SH represents shadow fading, and H represents path loss. s Let a represent the small-scale fading matrix. R M represents the antenna element number in the receiving antenna array. T and N T This indicates the row and column dimensions of the transmitting antenna array.

8. The air-to-ground communication channel modeling method based on UAV random trajectory according to claim 1, characterized in that, Step S5 further includes: The space-time correlation function STCF is calculated using the following formula: in, It is the space-time correlation function of the Loss path. K is the space-time correlation function of the NLoS path. Ri (t) represents Rice's K-factor, N qp (t) represents the number of time-varying clusters; The root mean square delay spread (RMSDS) is calculated using the following formula: Among them, M n (t) represents the number of rays in the nth path. and This represents the time delay and power of the m-th ray passing through the n-th reflection cluster from the p-th antenna element at the transmitter to the q-th antenna element at the receiver at time t. The stationary time interval SI is calculated using the following formula: SI(t)=max{Δt|χ(t,Δt)≥c t } Where χ(t,Δt) represents the correlation coefficient of the power spectral density between two time delays from time t to time t+Δt, and c t It is the set coefficient threshold; Among them, Ω qp (t,τ) represents the time-varying time-delay power spectral density: Step S504: Calculate the channel capacity. The channel capacity is calculated as follows: in, Indicates a size of M R The identity matrix, H represents the normalized channel matrix, ρ s M represents the signal-to-noise ratio. T and N T This indicates the row and column dimensions of the transmitting antenna array.

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