A Simulation Method, Medium and Device for a Non-Stationary MIMO Channel of an Ultra-Wideband Drone

Abstract

The present invention proposes a simulation method, medium and device for a non-stationary MIMO channel of an ultra-wideband unmanned aerial vehicle, specifically: a user inputs configuration parameters; channel simulation parameters related to bandwidth are calculated, including the Rice factor, the number of scattering clusters and the number of sub-paths within the scattering clusters; channel simulation parameters related to geometry are calculated, including the delay and angle parameters of the line-of-sight path, the delay, angle parameters and power parameters of the non-line-of-sight path, the power correction factor related to the attitude and the Doppler phase; finally, the channel simulation output result is obtained by using the calculated channel simulation parameters related to bandwidth and geometry. The present invention proposes a channel simulation method for an unmanned aerial vehicle supporting an ultra-wideband communication system, introducing parameters related to bandwidth, thus being more consistent with the real scenario; the present invention also proposes an accurate calculation method for channel parameters and supports the continuous evolution of a dynamic scenario, and can reflect the channel non-stationarity caused by attitude and frequency changes.

Description

Technical Field

[0001] The present invention belongs to the field of wireless information transmission, and particularly relates to a simulation method for an unmanned aerial vehicle (UAV) non-stationary massive multiple-input-multiple-output (MIMO) channel, which is specifically adapted to the ultra-wideband communication scenario. Background Art

[0002] UAVs are widely used in fields such as aerial base stations and relay communications due to their advantages of high mobility, easy operation, and low cost. A stable and reliable UAV communication system is an important support for its rapid development. At present, the development of UAV communication technology shows a trend of high rate and large bandwidth, and UAVs improve the efficiency of flight missions through fast data transmission. Building a model for the UAV channel and conducting numerical simulation are the theoretical basis for designing and optimizing the UAV communication system. Therefore, the modeling and simulation method of the UAV channel has important engineering significance.

[0003] Different from terrestrial channels, the UAV air-to-ground channel usually has more obvious non-stationary characteristics. Factors such as three-dimensional motion and fuselage attitude changes will affect the channel characteristics and need to be considered in the modeling. With the development of the sixth-generation mobile communication technology, the transmission bandwidth requirement for UAV communication is increasing day by day. However, traditional UAV channel modeling and simulation methods usually approximate the bandwidth and only consider the characteristics at the center frequency point, resulting in distortion of the channel model output and making it difficult to meet the bandwidth requirements of the new generation of communication systems. At the same time, the ultra-wideband communication scenario introduces additional frequency non-stationarity, which poses a great challenge to the research of the UAV wireless communication system. Summary of the Invention

[0004] Aiming at the deficiencies in the prior art, the present invention comprehensively considers the ultra-wideband and frequency non-stationary characteristics in the UAV communication channel, and provides a simulation method, medium, and device for an ultra-wideband UAV non-stationary MIMO channel, which can output the channel coefficients between any antenna pairs in the UAV communication scenario and provide a method for generating and evolving channel parameters.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A simulation method for an ultra-wideband UAV non-stationary MIMO channel, characterized by comprising the following steps:

[0007] Input configuration parameters;

[0008] Calculate channel simulation parameters related to the bandwidth, including the Rice factor, the number of scattering clusters, and the number of sub-paths within the scattering clusters;

[0009] Calculate the channel simulation parameters related to computational geometry, including the delay and angle parameters of the line-of-sight path, the delay, angle parameters and power parameters of the non-line-of-sight path, the power correction factor related to the attitude, and the Doppler phase;

[0010] Use the calculated channel simulation parameters related to bandwidth and geometry to obtain the channel simulation output results.

[0011] To optimize the above technical solution, the specific measures taken also include:

[0012] Further, the configuration parameters include the type of UAV communication scenario, carrier frequency, antenna type, signal bandwidth, and the initial position vectors and velocity vectors of the UAV, receiving end, and scatterers.

[0013] Further, the calculation processes of the Rice factor, the number of scattering clusters, and the number of sub-paths within the scattering clusters are as follows:

[0014] 1) Model the Rice factor as a Gaussian random variable with a mean c and a standard deviation at a given frequency point f Given the positions of the transmitter and receiver as p = (x T (t), y T (t), z T (t), x R (t), y R (t), z R (t)), with the superscript T representing the transmitter Tx and the superscript R representing the receiver Rx, the Rice factor is expressed as:

[0015]

[0016] where K μ is the mean at a carrier frequency of 1 GHz, K γ is the frequency-dependent correction coefficient, ω ref is the frequency offset in GHz, K ε is the bandwidth-dependent correction coefficient, K σ is the standard deviation at a carrier frequency of 1 GHz, K δ is the frequency-dependent standard deviation, K κ is the bandwidth-dependent standard deviation, X K is a spatially correlated Gaussian distributed zero-mean random variable, t represents time, and B represents the signal bandwidth;

[0017] 2) For each scattering cluster, use the K-nearest neighbor algorithm to calculate the kernel power density ρ x :

[0018]

[0019] In the formula, K x represents the set of the K multipath components closest to x, and P y is the power of another arbitrary sub-path, and τ x / y is the time delay of the sub-path, and σ τ 、 respectively represent the standard deviations of the time delay, the azimuth angle of the departure angle, and the azimuth angle of the arrival angle, and respectively represent the azimuth angles of the departure angle and the arrival angle during the clustering process, and the subscripts x and y respectively represent the sub-paths x and y;

[0020] Using the clustering algorithm based on kernel power density, the rays are divided into different clusters, so as to determine the total number N(t,B) of non-line-of-sight paths, that is, the number of scattering clusters;

[0021] The number M(B) of sub-paths in the scattering cluster is determined by the statistical method as follows:

[0022]

[0023] In the formula, M max is the upper limit of M(B), the parameter k represents the sparsity factor determined by the environment, and c DS 、c ASD and c ZSD respectively represent the intra-cluster delay spread, the intra-cluster angular spread in the horizontal direction, and the intra-cluster angular spread in the elevation direction, D h and D v respectively represent the array sizes in the horizontal and vertical dimensions, and λ is the wavelength.

[0024] Furthermore, the calculation process of the time delay and angle parameters of the line-of-sight path is as follows:

[0025] 1) Calculate the position vectors of the transmitter and receiver at time t:

[0026]

[0027] In the formula, L T / R (t 0 ) represents the initial positions of the transmitter and receiver, v T / R (t′) represents the instantaneous velocity, and the superscript T represents the transmitter Tx, and the superscript R represents the receiver Rx;

[0028] 2) According to the calculated position vectors of the transmitter and receiver, calculate the time delay of the line-of-sight path

[0029]

[0030] In the formula, e p(t) is the vector pointing from the Tx center to the p-th antenna element, e q (t) is the vector pointing from the Rx center to the q-th antenna element, and c is the propagation speed of the electromagnetic wave;

[0031] 3) Calculate the azimuth and elevation angles of the departure angle and arrival angle of the line-of-sight path based on the calculated position vectors of the transmitter and receiver:

[0032]

[0033] In the formula, and are the azimuth and elevation angles of the departure angle respectively, and are the azimuth and elevation angles of the arrival angle respectively, e x 、e y and e z are the unit vectors of the x-axis, y-axis, and z-axis.

[0034] Furthermore, the calculation process of the delay, angle parameters, and power parameters of the non-line-of-sight path is as follows:

[0035] 1) Specify as the vector pointing from the q-th antenna element of Rx to the last-hop scatterer, as the vector pointing from the p-th antenna element of Tx to the first-hop scatterer, as the vector connecting the first-hop scatterer to the last-hop scatterer, and the three vectors together form the signal propagation path:

[0036]

[0037] In the formula, is the delay of the non-line-of-sight path, and c is the propagation speed of the electromagnetic wave; the p-th antenna element of Tx, the first-hop scatterer, and the last-hop scatterer form a vector triangle, and at the same time, the p-th antenna element of Tx, the q-th antenna element of Rx, and the last-hop scatterer form another vector triangle; by minimizing the length of to infer the vectors and lengths, the problem is transformed into the following optimization task:

[0038]

[0039] Constraint conditions:

[0040] In the formula, the minimum distance d min is a variable customized according to the specific scenario; assign d min to the vector Then, the cosine theorem is used to determine the mutual relationship of the remaining vectors as follows:

[0041]

[0042] 2) Determine the angle parameters of the non-line-of-sight path through the geometric relationship of vectors:

[0043]

[0044] In the formula, is the azimuth angle of the departure angle, is the elevation angle of the departure angle, is the azimuth angle of the arrival angle, is the elevation angle of the arrival angle;

[0045] 3) After normalizing the power of the line-of-sight path, the power distribution of the m-th sub-path in the n-th non-line-of-sight path is:

[0046]

[0047] In the formula, r τ represents the delay scalar, σ τ represents the delay spread, SF c represents the cluster shadow fading that follows a Gaussian distribution;

[0048] The relative power of the non-line-of-sight path is determined through the normalization process as:

[0049]

[0050] In the formula, N(t,B) and M(B) represent the number of scattering clusters and the number of sub-paths within the scattering cluster, respectively.

[0051] Furthermore, the attitude-related power correction factor is modeled as:

[0052]

[0053] In the formula, represents the power correction factor, θ E (t) is obtained through calculation. The vector set Θ E contains all the direction vectors from the edge of the fuselage to the antenna element, and represent the line-of-sight direction vector and the departure angle, respectively, is the vector that matches the azimuth angle of , is the base vector of the z-axis, θ P (t) is obtained through obtained, RP (t) is the attitude matrix.

[0054] Furthermore, the calculation process of the Doppler phase is as follows:

[0055] 1) Convert the azimuth angle of the departure angle The azimuth angle of the arrival angle The elevation angle of the departure angle and the elevation angle of the arrival angle to the Cartesian coordinate system, and the angular unit vectors of the LOS paths at the transmitter and receiver are respectively and The angular unit vectors of the NLOS paths at the transmitter and receiver are and

[0056] 2) Based on the Doppler effect principle, calculate the time-frequency varying traditional Doppler phase components of the LOS path and the NLOS path and as follows:

[0057]

[0058] In the formula, and respectively represent the instantaneous moving speeds of the transmitter and the receiver, and f c represents the carrier frequency;

[0059] 3) Calculate the attitude change phase components of the LOS path and the NLOS path and as follows:

[0060]

[0061] In the formula, the velocity rotation matrix R R (t) is used to modify the position vector according to the change in the direction of motion, and the attitude matrix is defined as:

[0062]

[0063] In the formula, ω is the roll angle, is the yaw angle, and γ is the pitch angle;

[0064] 4) Calculate the equivalent Doppler phases of the LOS path and the NLOS path and as follows:

[0065]

[0066] In the formula, U(0, 2π) is a random initial component, following a uniform distribution on 0 to 2π.

[0067] Further, the process of obtaining the channel simulation output result is as follows:

[0068] The channel matrix including P transmit antenna elements and Q receive antenna elements is expressed as:

[0069]

[0070] In the formula, H represents channel fading, f c ′ represents the center frequency of the signal, L PL represents path propagation loss, L SF represents shadow fading, t represents time, τ represents time delay, B represents signal bandwidth, f c represents carrier frequency, represents the combination of channel impulse responses between P transmit antennas and Q receive antennas, and the expression is as follows:

[0071]

[0072] In the formula, represents the Rice factor, N(t,B) represents the number of scattering clusters, M(B) represents the number of sub-paths within the scattering cluster, represents the time delay of the line-of-sight path, represents the time delay of the non-line-of-sight path, δ(·) represents the Dirac function, and the coefficients and are modeled as:

[0073]

[0074] In the formula, represents the power correction factor caused by the blockage of the fuselage structure, represents the power correction factor related to the path time delay, and represent the equivalent Doppler phases of the line-of-sight path and the non-line-of-sight path caused by motion, and represent the antenna pattern functions of the line-of-sight path and the non-line-of-sight path considering attitude changes.

[0075] Correspondingly, the present invention proposes a computer-readable storage medium storing a computer program, characterized in that the computer program enables a computer to execute the simulation method of the ultra-wideband unmanned aerial vehicle non-stationary MIMO channel as described above.

[0076] Correspondingly, the present invention proposes an electronic device, characterized by including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the simulation method of the ultra-wideband unmanned aerial vehicle non-stationary MIMO channel as described above is implemented.

[0077] The beneficial effects of the present invention are as follows:

[0078] 1. The present invention proposes a method for simulating the UAV channel in a UWB communication system, introducing bandwidth-related parameters, solving the problem of output distortion caused by approximating the bandwidth in traditional methods, and thus being more consistent with the real scenario;

[0079] 2. The present invention proposes an accurate calculation method for channel parameters and supports the continuous evolution of dynamic scenarios, being able to reflect the channel non-stationarity caused by attitude and frequency changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 It is a schematic flowchart of a method for simulating a non-stationary MIMO channel of an ultra-wideband UAV proposed by the present invention.

[0081] Figure 2 It is a scatter cluster distribution diagram under different bandwidths in the present invention.

[0082] Figure 3 It is a result diagram of the normalized channel coefficient output by the antenna pair Tx1 and Rx1 in the present invention.

[0083] Figure 4 It is a result diagram of the normalized channel coefficient output by the antenna pair Tx1 and Rx2 in the present invention.

[0084] Figure 5 It is a result diagram of the normalized channel coefficient output by the antenna pair Tx2 and Rx1 in the present invention.

[0085] Figure 6 It is a result diagram of the normalized channel coefficient output by the antenna pair Tx2 and Rx2 in the present invention.

[0086] Figure 7 It is a result diagram of the channel output when the simulation time is 1 s in the present invention.

[0087] Figure 8 It is a result diagram of the channel output when the simulation time is 3 s in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0088] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0089] In one embodiment, the present invention proposes a method for simulating a non-stationary MIMO channel of an ultra-wideband UAV, and its process is asFigure 1 As shown in the figure, it specifically includes the following steps.

[0090] Step 1: Input user-defined parameters, that is, configuration parameters, including parameters such as the type of UAV communication scenario, carrier frequency, antenna type, signal bandwidth, and the initial position vectors and velocity vectors of the UAV / receiver / scatterer. For details, see Table 1.

[0091] Table 1 User Input Parameters

[0092]

[0093]

[0094] Step 2: Calculate the channel parameters related to the bandwidth. Specifically, it includes: generating the Rice factor parameter using a bandwidth-corrected random distribution; calculating the number of scattering clusters and the number of sub-paths within the scattering clusters using ray-tracing technology.

[0095] Step 2.1: Calculate the Rice factor in the UAV ultra-wideband channel as follows:

[0096] In this embodiment, the Rice factor is modeled as a Gaussian random variable with a mean c at a given frequency point f and a standard deviation Given the positions of the transmitter (Tx) and receiver (Rx) as p = (x T (t), y T (t), z T (t), x R (t), y R (t), z R (t)), the Rice factor can be expressed as:

[0097]

[0098] where K μ takes the value of 15, K γ takes the value of 2.8, ω ref takes the value of 1 GHz, K ε takes the value of -8.3, K σ takes the value of 9.85, K δ takes the value of 1.15, K κ takes the value of 1.4, X K is a spatially correlated Gaussian distributed zero-mean random variable. The values of the Rice factor are shown in Table 2.

[0099] Table 2 Example Values of the Rice Factor in the Simulation

[0100] Quasi-steady section Steady section 1 Steady section 2 Steady section 3 Steady section 4 Value taken 9.6372 15.6094 7.6055 3.6072

[0101] Step 2.2: Calculate the number of scattering clusters and the number of sub-paths within the scattering clusters as follows:

[0102] Use ray tracing technology to generate the power and delay information of a large number of sub-paths, and use a clustering algorithm based on kernel power density to determine the number of scattering clusters and the number of sub-paths within the scattering clusters. For each cluster sample, use the K-nearest neighbor algorithm to calculate the density:

[0103]

[0104] where K x represents the set of K multi-path components closest to the multi-path component x, P y is the power of another arbitrary sub-path, τ x / y is the delay of the sub-path, and σ (·) represents the standard deviation of the sub-path; and represent the azimuth angles of the departure angle and the arrival angle during the clustering process. Using the clustering algorithm based on kernel power density, the rays can be divided into different clusters, thereby determining the total number N(t,B) of non-line-of-sight paths. The number of sub-paths M(B) is determined by statistical methods, and the number of sub-paths affected by the bandwidth within each cluster is modeled as:

[0105]

[0106] where M max is the upper limit of M(B) input by the user, the parameter k represents the sparsity factor determined by the environment, c DS , c ASD and c ZSD are the intra-cluster delay spread and the intra-cluster angular spreads in the horizontal and elevation directions respectively, D h and D v are the array sizes in the horizontal and vertical dimensions in m, λ is the wavelength in m, and B is the bandwidth in MHz.

[0107] Refer to Figure 2 shown. In the case of a narrowband of 10M, all the calculated rays are grouped into the same cluster. As the bandwidth increases to 1 GHz, the number of clusters N(t,B) is 10, and the number of sub-paths within each cluster is shown in Table 3 correspondingly.

[0108] Table 3 Ray clustering results

[0109]

[0110] Step 3: Calculate the geometrically related channel simulation parameters, including position vectors, delay parameters, angle parameters, power parameters, and Doppler phase parameters.

[0111] Step 3.1: Calculate the delay and angle parameters of the line-of-sight (LoS) path of the UAV air-ground channel as follows:

[0112] Step 3.1.1: Calculate the position vectors of the receiver, transmitter, and scatterer at time t as follows:

[0113] In this embodiment, the delay of the LoS path is determined based on the geometric relationship between the receiver and the transmitter. The channel parameters of the MIMO system need to be calculated separately for each element in the antenna array. The instantaneous position vector of Tx or Rx can be expressed as:

[0114]

[0115] where,

[0116]

[0117] where, ||v T / R / S || represents the modulus of the moving speed of the transceiver and the scatterer, φ T / R / S represents the azimuth angle of the moving speed of the transceiver and the scatterer, ψ T / R / S represents the elevation angle of the moving speed of the transceiver and the scatterer, and the values are shown in Table 1.

[0118] Step 3.1.2: Calculate the delay of the LoS path at time t according to the position vectors of the transceiver calculated in Step 3.1.1 as follows:

[0119]

[0120] where, e p (t) is the vector pointing from the center of Tx to the p-th antenna element, e q (t) is the vector pointing from the center of Rx to the q-th antenna element, c is the speed of electromagnetic waves, with a value of 3×10 8 m / s. When the simulation time is 1 s, has a value of 0.504 us.

[0121] Step 3.1.3: Calculate the azimuth and elevation angles of the angle of departure and the angle of arrival of the LoS path from the positions of the transceiver calculated in Step 3.1.1 as follows:

[0122]

[0123] In the formula, and are the azimuth and elevation angles of the angle of departure respectively, and are the azimuth and elevation angles of the angle of arrival respectively, ex , e y and e z are the unit vectors of the x-axis, y-axis, and z-axis.

[0124] Step 3.2: Calculate the delay, angle, and power parameters of the non-line-of-sight (NLoS) path of the UAV-airground channel as follows:

[0125] Step 3.2.1: According to the position information of the transceiver calculated in Step 3.1.1, calculate the distance vector and delay at time t as follows:

[0126] In this embodiment, it is specified that is the vector from the q-th antenna element of Rx to the last-hop scatterer, is the vector from the p-th antenna element of Tx to the first-hop scatterer, is the vector connecting the first-hop scatterer to the last-hop scatterer, and the signal propagation path is composed of the above three vectors.

[0127] Calculate the vectors and through the following optimization tasks:

[0128]

[0129] Constraint conditions:

[0130] where the minimum distance d min is a variable customized according to the specific scenario, and assign d min to the vector Then use the cosine theorem to determine the mutual relationship of the remaining vectors:

[0131]

[0132] According to the distance vector of the m-th sub-path among the n NLoS paths calculated above, calculate the delay of the NLoS path as follows:

[0133]

[0134] When the simulation time is 1 s, select the first 5 NLoS paths, The values of

[0135] Step 3.2.2: From the positions of the transceiver calculated in Step 3.1.1, calculate the azimuth and elevation angles of the departure angle and arrival angle of the NLoS path as follows:

[0136] During the calculation of the path delay in step 3.2.1, the position vectors of the transmitter, the first-hop, and the last-hop scatterers are defined, and the geometric relationships of these vectors determine the angles corresponding to the NLoS paths:

[0137]

[0138] In the formula, is the azimuth angle of the angle of departure, is the elevation angle of the angle of departure, is the azimuth angle of the angle of arrival, is the elevation angle of the angle of arrival.

[0139] Step 3.2.3: Using the propagation delay obtained in step 3.2.1, calculate the normalized power of the LoS path and the nth NLoS path as follows:

[0140] After normalizing the power of the LoS path, the power distribution of the NLoS path is:

[0141]

[0142] Among them, r τ represents the delay scalar, with a value of 2.5, and σ τ represents the delay spread, with a value of 9.81×10 -8 , and SF c represents the cluster shadow fading that follows a Gaussian distribution, with a mean of 0 and a variance of 3. Subsequently, the relative power of the NLoS component is finally determined through the normalization process:

[0143]

[0144] Step 3.3: Calculate the attitude-related power correction factor as follows:

[0145] According to the positions of the UAV and the ground terminal, the line-of-sight direction vector and the departure angle can be determined. In addition, in this embodiment, a vector set Θ E is introduced, which contains all the direction vectors from the edge of the aircraft to the antenna unit. is the vector that matches the azimuth angle of , which determines the angle θ E (t). The angle θ P (t) combines the attitude changes of the UAV, which depends on the attitude matrix R P (t). Therefore, in this embodiment, the power correction factor is modeled as:

[0146]

[0147] Among them, θE (t) is calculated through and is the basis vector of the z-axis, and θ P (t) is obtained through .

[0148] Step 3.4: Calculate the Doppler phase as follows:

[0149] Step 3.4.1: Convert the azimuth angle of the angle of departure the azimuth angle of the angle of arrival the elevation angle of the angle of departure and the elevation angle of the angle of arrival into Cartesian coordinates to obtain the angular unit vectors of the LoS and NLoS paths between the transmitter and receiver and

[0150] Step 3.4.2: Calculate the time-frequency varying traditional Doppler phase components of the LoS and NLoS paths as follows:

[0151]

[0152] Step 3.4.3: Calculate the attitude change phase components of the LoS and NLoS propagation paths as follows:

[0153]

[0154] When the roll angle of the UAV is -30°, the yaw angle is -45°, and the pitch angle is 10°, the corresponding ω = -30°, γ = 10°, and the calculated attitude matrix is:

[0155]

[0156] Step 3.4.4: Calculate the equivalent Doppler phases of the LoS path and the NLoS path according to the traditional Doppler effect components and attitude change Doppler components in Steps 3.4.2 and 3.4.3. That is, the frequency-dependent path phase consists of a random initial component, a traditional Doppler effect component, and an attitude change component, as follows:

[0157]

[0158] where U(0, 2π) is the random initial component, which follows a uniform distribution on 0 to 2π.

[0159] Step Four: Use the calculated Rice factor, time delay, phase, and power coefficient obtained above as inputs to the UAV air-to-ground channel model to obtain the channel simulation output results.

[0160] The ultra-wideband UAV MIMO channel is modeled as:

[0161]

[0162] where H represents the channel fading, B is the actual bandwidth of the signal, and f c represents the carrier frequency, and f c ′ represents the center frequency of the signal, L PL represents the path propagation loss, and L SF represents the shadow fading. represents the combination of the channel impulse responses between P transmitting antennas and Q receiving antennas, and the specific expression is as follows:

[0163]

[0164] where the coefficients and are expressed as follows:

[0165]

[0166] In the formula, represents the power correction factor caused by the obstruction of the fuselage structure, represents the power correction factor related to the path delay, and represent the equivalent Doppler phases of the line-of-sight path and the non-line-of-sight path caused by movement, and represent the antenna pattern functions of the line-of-sight path and the non-line-of-sight path considering the attitude change.

[0167] In this embodiment, both the receiving end and the transmitting end use ideal omnidirectional antennas, that is, the antenna pattern functions and can be approximated as the constant 1. The finally output channel coefficients include the results of different antenna pairs (as shown in Figures 3 - 6 ), and the results at different times (as shown in Figures 7 - 8 ).

[0168] In another embodiment, the present invention proposes a computer-readable storage medium storing a computer program, and the computer program causes a computer to execute the simulation method of the ultra-wideband UAV non-stationary MIMO channel as described in Embodiment 1.

[0169] In another embodiment, the present invention proposes an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the simulation method of the ultra-wideband UAV non-stationary MIMO channel as described in Embodiment 1 is implemented.

[0170] In the embodiments disclosed in the present application, the computer storage medium may be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. The computer storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of the computer storage medium would include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0171] Those of ordinary skill in the art will appreciate that the units and algorithm steps of the examples described in connection with the embodiments disclosed in the present application can be implemented in electronic hardware or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present application.

[0172] The above are only the preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art in the technical field, several improvements and refinements made without departing from the principle of the present invention should be regarded as within the protection scope of the present invention.

Claims

1. A simulation method for non-stationary MIMO channels of ultra-wideband unmanned aerial vehicles, characterized in that: The steps include: Enter configuration parameters; Calculate the bandwidth-related channel simulation parameters, including the Rice factor, the number of scattering clusters, and the number of subpaths within a scattering cluster. The process is as follows: 1) Rice factor Modeled as a Gaussian random variable, at a given frequency point f c has a mean With standard deviation Given the positions of the transmitter and receiver as p = (x T (t),y T (t),z T (t),x R (t),y R (t),z R (t)), the superscript T represents the transmitter Tx, the superscript R represents the receiver Rx, and the Rice factor is expressed as: In the formula, K μ is the average value at a carrier frequency of 1 GHz, K γ is the frequency-dependent correction factor, ω ref is the frequency offset in GHz, K ε is the bandwidth-dependent correction factor, K σ is the standard deviation at a carrier frequency of 1 GHz, K δ is the frequency-dependent standard deviation, K κ is the bandwidth-dependent standard deviation, X K is a spatially correlated Gaussian distribution zero-mean random variable, t represents the time, and B represents the signal bandwidth; 2) For each scattering cluster, use the K-nearest neighbor algorithm to calculate the kernel power density ρ x : In the formula, K x represents the set of K multipath components closest to x, P y is the power of another arbitrary subpath, τ x / y is the delay of the subpath, They represent the standard deviation of the time delay, the azimuth of the departure angle, and the azimuth of the arrival angle, respectively. and They represent the azimuths of the departure angle and arrival angle in the clustering process, respectively. The subscripts x and y represent subpaths x and y, respectively. The rays are divided into different clusters using a clustering algorithm based on kernel power density, so as to determine the total number of non-line-of-sight paths N(t,B), which is the number of scattering clusters; The number of subpaths M(B) within a scattering cluster is determined by statistical methods as follows: Where M max is the upper limit of M(B), the parameter k represents the sparse factor determined by the environment, and c DS 、c ASD and c ZSD They are intra-cluster delay spread, intra-cluster angle spread in the horizontal direction, and intra-cluster angle spread in the elevation direction, respectively. h and D v are the array sizes in the horizontal and vertical dimensions, respectively, and λ is the wavelength; Calculate geometry-related channel simulation parameters, including the delay and angle parameters of the line-of-sight path, the delay, angle parameters and power parameters of the non-line-of-sight path, the attitude-related power correction factor and Doppler phase; Using the calculated bandwidth-related and geometry-related channel simulation parameters, the channel simulation output results are obtained. The process is as follows: The channel matrix containing P transmitting antenna elements and Q receiving antenna elements is expressed as: Where H represents channel fading, f c ′ represents the center frequency of the signal, L PL represents the path propagation loss, L SF represents shadow fading, t represents time, τ represents delay, B represents signal bandwidth, and f c Indicates the carrier frequency, represents the channel impulse response combination between P transmitting antennas and Q receiving antennas, and the expression is as follows: In the formula, represents the Rice factor, N(t,B) represents the number of scattering clusters, M(B) represents the number of subpaths within a scattering cluster, represents the delay of the line-of-sight path, represents the delay of the non-line-of-sight path, δ(·) represents the Dirac function, and the coefficient and is modeled as: In the formula, represents the power correction factor due to the obstruction of the fuselage structure, represents the power correction factor related to the path delay, and represents the equivalent Doppler phase of the line-of-sight path and the non-line-of-sight path caused by motion, and Represents the antenna pattern function for line-of-sight and non-line-of-sight paths taking into account attitude changes.

2. The method for simulating a non-stationary MIMO channel of an ultra-wideband unmanned aerial vehicle according to claim 1, characterized in that: The configuration parameters include the UAV communication scenario type, carrier frequency, antenna type, signal bandwidth, and the initial position vector and velocity vector of the UAV, the receiving end, and the scatterer.

3. The method for simulating a non-stationary MIMO channel of an ultra-wideband unmanned aerial vehicle according to claim 1, characterized in that: The calculation process of the delay and angle parameters of the line-of-sight path is as follows: 1) Calculate the position vector of the transmitter and receiver at time t: Where, L T / R (t0) represents the initial position of the transmitting and receiving ends, v T / R (t′) represents the instantaneous speed, the superscript T represents the transmitting end Tx, and the superscript R represents the receiving end Rx; 2) Calculate the delay of the line-of-sight path based on the calculated position vector of the transmitting and receiving ends In the formula, e p (t) is the vector pointing from the Tx center to the pth antenna unit, e q (t) is the vector pointing from the center of Rx to the qth antenna element, and c is the propagation speed of electromagnetic waves; 3) Based on the calculated position vector of the transmitting and receiving end, calculate the azimuth and elevation angles of the departure angle and arrival angle of the line-of-sight path: In the formula, and are the azimuth and elevation angles of the departure angle, and are the azimuth and elevation angles of the arrival angle, e x 、e y and e z are the unit vectors of the x-axis, y-axis, and z-axis.

4. The method for simulating a non-stationary MIMO channel of an ultra-wideband unmanned aerial vehicle according to claim 3, characterized in that: The calculation process of the delay, angle parameter and power parameter of the non-line-of-sight path is as follows: 1)Specify is the vector from the qth antenna unit of Rx to the last hop scatterer, is the vector pointing from the pth antenna unit of Tx to the first hop scatterer, The three vectors that connect the first hop scatterer to the last hop scatterer together form the signal propagation path: In the formula, is the delay of the non-line-of-sight path, c is the propagation speed of electromagnetic waves; the pth antenna unit of Tx, the first hop scatterer and the last hop scatterer form a vector triangle, while the pth antenna unit of Tx, the qth antenna unit of Rx and the last hop scatterer form another vector triangle; by minimizing The length of the vector and The length of , transforms the problem into the following optimization task: In the formula, the minimum distance d min It is a variable customized according to the specific scenario; the value is assigned to d min Give vector The law of cosines is then used to determine the relationship between the remaining vectors as follows: 2) Determine the angle parameters of the non-line-of-sight path through the geometric relationship of the vectors: In the formula, is the azimuth of the departure angle, is the pitch angle of the departure angle, is the azimuth of the angle of arrival, is the pitch angle of the arrival angle; 3) After normalizing the power of the line-of-sight path, the power distribution of the mth subpath in the nth non-line-of-sight path is for: In the formula, r τ represents the delay scalar, σ τ represents delay spread, SF c represents cluster shadow fading that follows a Gaussian distribution; Relative power on non-line-of-sight paths Determined by the normalization process: Where N(t,B) and M(B) represent the number of scattering clusters and the number of subpaths within a scattering cluster, respectively.

5. The method for simulating a non-stationary MIMO channel of an ultra-wideband unmanned aerial vehicle according to claim 4, characterized in that: The attitude-related power correction factor is modeled as: In the formula, represents the power correction factor, θ E (t)Through Calculated, the vector set Θ E Contains all direction vectors from the edge of the fuselage to the antenna element, and They represent the angular unit vector of the line-of-sight path and the pitch angle of the departure angle, is with The azimuth angle of the vector matches, is the basis vector of the z-axis, θ P (t)Through Get, R P (t) is the posture matrix.

6. The method for simulating a non-stationary MIMO channel of an ultra-wideband unmanned aerial vehicle according to claim 5, characterized in that: The calculation process of the Doppler phase is as follows: 1) Convert the azimuth of the departure angle Azimuth of the angle of arrival Pitch angle of departure angle and the pitch angle of the arrival angle In the Cartesian coordinate system, the unit vectors of the line-of-sight paths at the transmitter and receiver are and The angular unit vectors of the non-line-of-sight paths between the transmitter and the receiver are: and 2) Based on the principle of Doppler effect, the time-frequency-varying traditional Doppler phase components of line-of-sight and non-line-of-sight paths are calculated and as follows: In the formula, and Respectively represent the instantaneous moving speed of the transmitter and the receiver, f c Indicates the carrier frequency; 3) Calculate the phase component of attitude change for line-of-sight and non-line-of-sight paths and as follows: Where, the velocity rotation matrix R R (t) is used to modify the position vector according to the change of motion direction, and define the attitude matrix as: Where ω is the roll angle, is the yaw angle, γ is the pitch angle; 4) Calculate the equivalent Doppler phase of the line-of-sight path and the non-line-of-sight path and as follows: Where U(0,2π) is a random initial component that obeys a uniform distribution from 0 to 2π.

7. A computer-readable storage medium storing a computer program, characterized in that: The computer program enables a computer to execute the simulation method for an ultra-wideband unmanned aerial vehicle non-stationary MIMO channel as described in any one of claims 1-6.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the simulation method for the ultra-wideband unmanned aerial vehicle non-stationary MIMO channel as described in any one of claims 1 to 6 is implemented.

Images