CN114567400B - Unmanned aerial vehicle space MIMO channel modeling method based on geometric random - Google Patents

Unmanned aerial vehicle space MIMO channel modeling method based on geometric random Download PDF

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CN114567400B
CN114567400B CN202210199505.XA CN202210199505A CN114567400B CN 114567400 B CN114567400 B CN 114567400B CN 202210199505 A CN202210199505 A CN 202210199505A CN 114567400 B CN114567400 B CN 114567400B
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CN114567400A (en
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燕锋
侯琳
夏玮玮
沈连丰
胡静
宋铁成
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Southeast University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • H04B7/185—Space-based or airborne stations; Stations for satellite systems
    • H04B7/18502—Airborne stations
    • H04B7/18506—Communications with or from aircraft, i.e. aeronautical mobile service
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    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract

The invention provides an unmanned aerial vehicle space-based MIMO channel modeling method based on geometric randomness. Compared with the prior art, the model provided by the modeling method comprehensively considers the scattering of the unmanned aerial vehicle side, the reflection and the scattering of the ground station side, the mobility of the unmanned aerial vehicle transmitting end and the ground station receiving end, and particularly the rotation of the unmanned aerial vehicle side and the influence of the three-dimensional moving track of the unmanned aerial vehicle, so that the model accords with various actual communication scenes; the time-varying distance and the time-varying angle are calculated, the complex envelope signal is received, the actual communication condition and the nonstationary statistical characteristic of the unmanned aerial vehicle space MIMO channel can be accurately described, based on the modeling method, some general rules of the unmanned aerial vehicle communication scheme can be summarized through actual simulation, and the idea is provided for building a robust unmanned aerial vehicle wireless communication system.

Description

Unmanned aerial vehicle space MIMO channel modeling method based on geometric random
Technical Field
The invention belongs to the field of wireless channel modeling, and particularly relates to an unmanned aerial vehicle space MIMO channel modeling method based on geometric randomness.
Background
With the development of unmanned aerial vehicles in the military and civil fields, unmanned aerial vehicle communication networks formed by information exchange between unmanned aerial vehicles and unmanned aerial vehicles or between unmanned aerial vehicles and other receiving stations form an important component of unmanned aerial vehicle systems. Meanwhile, the unmanned aerial vehicle plays an important role in the application of a fifth generation (5G) mobile communication system, the unmanned aerial vehicle communication technology has become one of the important points of the wireless communication technology research, and it is particularly important to establish an accurate and reliable unmanned aerial vehicle communication channel model.
At present, an unmanned aerial vehicle air-ground channel modeling method is mainly divided into a deterministic model, a statistical model and a geometric stochastic channel model, but the deterministic model modeling process is complex and completely depends on detailed information of a channel environment, and has no universality; the statistical model has a certain gap from an actual scene according to random parameters obtained empirically, the accuracy is low, some unique characteristics of unmanned aerial vehicle communication cannot be captured, and the geometric random channel model balances complexity and accuracy, so that the model becomes a mainstream unmanned aerial vehicle space-ground channel modeling method. However, the existing unmanned aerial vehicle air-ground channel model based on geometric randomness mostly assumes that the unmanned aerial vehicle does uniform linear motion in a three-dimensional space and ignores the influence caused by the position change of an array antenna, so that the model does not conform to the characteristics of any flight track and rotating antenna of the actual unmanned aerial vehicle three-dimensional space, and meanwhile ignores the channel non-stationarity caused by the movement of the unmanned aerial vehicle and a ground station.
Disclosure of Invention
The invention aims to provide a geometric-random-based unmanned aerial vehicle space MIMO channel modeling method, which aims to solve the technical problems that the existing geometric-random-based unmanned aerial vehicle space channel model mostly assumes that an unmanned aerial vehicle does uniform linear motion in a three-dimensional space and ignores the influence caused by the position change of an array antenna, the method is not in line with the characteristics that the actual unmanned aerial vehicle three-dimensional space has any flight track and rotary antenna, and meanwhile ignores the channel non-stationarity caused by the movement of the unmanned aerial vehicle and a ground station.
In order to solve the technical problems, the specific technical scheme of the invention is as follows:
A geometric-random-based unmanned aerial vehicle space-based MIMO channel modeling method comprises the following steps:
Step 1, constructing a three-dimensional model of an unmanned aerial vehicle space MIMO channel, wherein the three-dimensional model of the unmanned aerial vehicle space MIMO channel comprises an unmanned aerial vehicle serving as a transmitting end, a ground station serving as a receiving end and two right cylinders positioned on the side of the unmanned aerial vehicle and the ground station, wherein the unmanned aerial vehicle moves at a high speed in a low-altitude three-dimensional space.
The three-dimensional movement of the unmanned aerial vehicle is characterized by a speed v T, a speed horizontal angle gamma T and a speed pitch angle zeta T, the three-dimensional movement of the unmanned aerial vehicle meets a Gaussian Markov movement model, and the initial position is moved to O T' at the moment of O T,ti;
The moving speed of the ground station is v R, the speed horizontal angle is gamma R, and the initial position is O R,ti and moves to O R' at the moment;
the radius of the right circular cylinder of the unmanned plane is R T, the surface distribution is N 1 effective scatterers, wherein the nth 1 effective scatterers can be expressed as N 1＝1,2,…,N1; the right circular cylinder on the ground station side has a radius of R R and a surface distribution of N 2 effective scatterers, wherein the nth 2 effective scatterers can be expressed as/>N 2＝1,2,…,N2, bottom surface distribution N 3 ground reflection effective scatterers, where the nth 3 effective scatterer can be expressed as/>n3＝1,2,…,N3；
The initial horizontal distance between the unmanned aerial vehicle and the ground station is D, the vertical distance is H, the pitch angle of the unmanned aerial vehicle position meets beta 0 =arctan (H/D), and the height of the ground station is H 0.
Step 2, describing an unmanned aerial vehicle space MIMO channel by using an L T×LR antenna array matrix, wherein the antenna array is a uniform linear antenna array, the antenna rotation pitch angle psi T and the rotation azimuth angle theta T of the unmanned aerial vehicle side antenna array T X are time-varying angles, and the antenna pitch angle psi R and the azimuth angle theta R of the ground station side antenna array R X are fixed;
step 3, deducing an unstable time-varying distance and a time-varying angle of the unmanned aerial vehicle space MIMO channel caused by unmanned aerial vehicle antenna rotation and unmanned aerial vehicle three-dimensional movement according to a geometric relation;
and 4, calculating the total received complex envelope signal of the unmanned aerial vehicle space MIMO channel according to different path component signals of the unmanned aerial vehicle side antenna propagation signals which are propagated to the ground station side antenna through different scatterers.
Further, the gaussian markov movement model satisfied by the three-dimensional movement of the unmanned aerial vehicle in step 1 is:
The initial value of the speed at the moment t 0 is v T(t0), the initial value of the pitch angle at the moment t 0 is ζ T(t0), and the initial value of the horizontal angle at the moment t 0 is gamma T(t0); the value of the speed at the time t i is v T(ti), the value of the pitch angle at the time t i is ζ T(ti), and the value of the horizontal angle at the time t i is gamma T(ti); when i → infinity, the asymptotic mean value of the velocity is The asymptotic mean of the pitch angle is/>The asymptotic mean of the horizontal angles is/>Ρ v、ρξ and ρ γ are tuning values within [0,1], ρ v characterizes the randomness of the speed magnitude, ρ ξ characterizes the randomness of the pitch angle, ρ γ characterizes the randomness of the horizontal angle; l, M and N are variables that follow a gaussian distribution; in order to ensure that the speed, the horizontal angle and the pitch angle are in reasonable ranges, the maximum value of the speed is v Tmax, the maximum value of the pitch angle is ζ Tmax and the maximum value of the horizontal angle is gamma Tmax.
Further, in step 2, the antenna rotation time-varying angle of the unmanned aerial vehicle side antenna array T X satisfies the cosine process:
ψT(ti)＝ψT(t0)+ψTmcos(πti)
θT(ti)＝θT(t0)+θTmcos(πti)
The initial value of the antenna rotation pitch angle at time t 0 is ψ T(t0), and the initial value of the antenna rotation horizontal angle at time t 0 is θ T(t0); the value of the antenna rotation pitch angle at time t i is ψ T(ti), and the value of the antenna rotation horizontal angle at time t i is θ T(ti); the maximum amplitude of the antenna rotation pitch angle variation is psi Tm, and the maximum amplitude of the antenna rotation horizontal angle variation is theta Tm.
Further, the antenna rotation time-varying angle of the unmanned aerial vehicle side antenna array T X satisfies the gaussian markov process:
The initial value of the antenna rotation pitch angle at the time t 0 is phi T(t0), and the initial value of the antenna rotation pitch angle at the time t 0 is theta T(t0); the value of the antenna rotation pitch angle at time t i is ψ T(ti), and the value of the antenna rotation horizontal angle at time t i is θ T(ti); when i → infinity, the asymptotic mean value of the antenna rotation pitch angle is The asymptotic mean of the antenna rotation horizontal angle is/>Ρ ψ and ρ θ are tuning values within [0,1], ρ ψ represents the randomness of the antenna rotation pitch angle, ρ θ represents the randomness of the antenna rotation horizontal angle; x and Y are variables that follow a Gaussian distribution; to ensure that the antenna rotation pitch angle and the horizontal angle are within a reasonable range, the maximum value of the antenna rotation pitch angle is set to be phi Tmax, and the maximum value of the line rotation horizontal angle is set to be theta Tmax.
Further, in step 3, the non-stationary time-varying distance is a link T p-Rq formed by the p-th antenna in the unmanned aerial vehicle side antenna array to the q-th antenna in the ground station side antenna array via different effective scatterers,And/>Time-varying distance epsilon pq(ti),And/>The path components formed by each link are LOS, SB1, SB2, SB3 and DB, the time-varying distance is calculated by the position coordinates of the unmanned aerial vehicle and the ground station at the time t i through a distance formula, and the time t i And/>Representing three-dimensional position coordinates of a transmitting end and a receiving end of a ground station of an unmanned aerial vehicle, wherein the expression of the coordinates is
Wherein delta T is the distance between the p-th antenna of the unmanned aerial vehicle and the center of the antenna array of the unmanned aerial vehicle, delta R is the distance between the q-th antenna of the ground station and the center of the antenna array of the ground station, and the requirements are met
Delta T represents the antenna spacing of the drone and delta R represents the antenna spacing of the ground station.
Further, in step 3, the non-stationary time-varying angle includes the departure azimuth angle, departure pitch angle, arrival azimuth angle and arrival pitch angle of the LOS path at time t i, and the departure azimuth angle of the LOS path at time t i is defined byThe exit pitch angle of the LOS path at time t i is represented by/>The arrival azimuth of the LOS path at time t i is represented by/>The arrival pitch angle of the LOS path at time t i is represented by/>Representation of/>The departure azimuth of the link is defined by/>Representation of/>The departure pitch angle of the link is defined by >Representation of/>The arrival azimuth of the link is defined by/>Representation of/>The arrival pitch angle of the link is defined by >Represents k=1, 2,3, where
Further, in step 4, the expressions of the path component signals of the received complex envelope are obtained by considering the non-stationary time-varying angle and the time-varying distance:
the complex envelope expression of the LoS path component is
The complex envelope expression of SB1 path components is
The complex envelope expression of SB2 path components is
The complex envelope expression of SB3 path component is
The complex envelope expression of the DB path component is
Where Ω pq denotes the total received power, K denotes the rice factor, η SB1、ηSB2、ηSB3 and η DB denote the ratio of the respective path components to the total scattered power Ω pq/(k+1), respectively, and η SB1+ηSB2+ηSB2+ηDB =1 is satisfied,AndRepresenting the phase of each path component produced via the scatterer, as an independent random variable subject to uniform distribution [ -pi, pi ]), f Tm represents the maximum Doppler frequency of the drone, and f Rm represents the maximum Doppler frequency of the ground station.
The unmanned aerial vehicle space-based MIMO channel modeling method based on geometric randomness has the following advantages:
The invention provides an unmanned aerial vehicle space MIMO channel modeling method based on geometric randomness, wherein the model is a three-dimensional double-cylinder model, and compared with the prior art, the model comprehensively considers the scattering of an unmanned aerial vehicle side, the reflection and the scattering of a ground station side, the mobility of an unmanned aerial vehicle transmitting end and a ground station receiving end, in particular the rotation of the unmanned aerial vehicle side and the influence of a three-dimensional moving track of the unmanned aerial vehicle, and accords with various actual communication scenes; the time-varying distance and the time-varying angle are calculated, the complex envelope signal is received, the actual communication condition and the nonstationary statistical characteristic of the unmanned aerial vehicle space MIMO channel can be accurately described, based on the modeling method, some general rules of the unmanned aerial vehicle communication scheme can be summarized through actual simulation, and the idea is provided for building a robust unmanned aerial vehicle communication wireless communication system.
Drawings
Fig. 1 is a schematic diagram of a space-time MIMO channel model of an unmanned aerial vehicle based on geometric randomness in the present invention;
fig. 2 is a schematic rotation diagram of an antenna array of the unmanned aerial vehicle according to the present invention;
fig. 3 is a schematic diagram of the path components formed in the channel model of the present invention.
Detailed Description
For better understanding of the purpose, structure and function of the present invention, the following describes in further detail a geometric random-based unmanned aerial vehicle space MIMO channel modeling method with reference to the accompanying drawings.
The technical scheme adopted by the invention is as follows: a geometric-random-based unmanned aerial vehicle space-based MIMO channel modeling method comprises the following steps:
Step 1, constructing a three-dimensional model of an unmanned aerial vehicle space MIMO channel, wherein the three-dimensional model of the unmanned aerial vehicle space MIMO channel comprises an unmanned aerial vehicle serving as a transmitting end, a ground station serving as a receiving end and two right cylinders positioned on the side of the unmanned aerial vehicle and the ground station, wherein the unmanned aerial vehicle is moved at a high speed in a low-altitude three-dimensional space, and the ground station is moved in a ground two-dimensional space, as shown in fig. 1.
The three-dimensional movement of the unmanned aerial vehicle is characterized by a speed v T, a speed horizontal angle gamma T and a speed pitch angle zeta T, the three-dimensional movement of the unmanned aerial vehicle meets a Gaussian Markov movement model, and the initial position is moved to O T' at the moment of O T,ti;
The moving speed of the ground station is v R, the speed horizontal angle is gamma R, and the initial position is O R,ti and moves to O R' at the moment;
the radius of the right circular cylinder of the unmanned plane is R T, the surface distribution is N 1 effective scatterers, wherein the nth 1 effective scatterers can be expressed as N 1＝1,2,…,N1; the right circular cylinder on the ground station side has a radius of R R and a surface distribution of N 2 effective scatterers, wherein the nth 2 effective scatterers can be expressed as/>N 2＝1,2,…,N2, bottom surface distribution N 3 ground reflection effective scatterers, where the nth 3 effective scatterer can be expressed as/>n3＝1,2,…,N3；
The initial horizontal distance between the unmanned aerial vehicle and the ground station is D, the vertical distance is H, the pitch angle of the unmanned aerial vehicle position meets beta 0 =arctan (H/D), and the height of the ground station is H 0.
In order to better characterize the influence of three-dimensional movement of the unmanned aerial vehicle on the non-stationary characteristic of the channel, the three-dimensional movement of the unmanned aerial vehicle is modeled as a Gaussian Markov movement model expressed as:
Wherein, the initial value of the speed is v T(t0 at the time t 0), the initial value of the pitch angle is ζ T(t0 at the time t 0), the initial value of the horizontal angle is gamma T(t0 at the time t 0), the value of the speed is v T(ti at the time t i), the value of the pitch angle is ζ T(ti at the time t i), and the value of the horizontal angle is gamma T(ti at the time t i); when i → infinity, the asymptotic mean value of the velocity is The asymptotic mean of the pitch angle is/>The asymptotic mean of the horizontal angles is/>Ρ v、ρξ and ρ γ are tuning values within [0,1], ρ v characterizes the randomness of the speed magnitude, ρ ξ characterizes the randomness of the pitch angle, ρ γ characterizes the randomness of the horizontal angle; l, M and N are variables that follow a gaussian distribution; in order to ensure that the speed, the horizontal angle and the pitch angle are in reasonable ranges, the maximum value of the speed is v Tmax, the maximum value of the pitch angle is ζ Tmax and the maximum value of the horizontal angle is gamma Tmax.
Step 2, describing an unmanned aerial vehicle space MIMO channel by using an L T×LR antenna array matrix, wherein the antenna array is a uniform linear antenna array, and assuming that L T＝LR =2, as shown in fig. 2, considering the roll and pitch motions of the unmanned aerial vehicle, the antenna rotation pitch angle psi T and the rotation azimuth angle theta T of the unmanned aerial vehicle side antenna array T X are time-varying angles, and the antenna pitch angle psi R and the azimuth angle theta R of the ground station side antenna array R X are fixed;
The antenna rotation time-varying angle of the unmanned aerial vehicle side antenna array T X is modeled as a cosine process:
ψT(ti)＝ψT(t0)+ψTmcos(πti)
θT(ti)＝θT(t0)+θTmcos(πti)
The initial value of the antenna rotation pitch angle at time t 0 is ψ T(t0), and the initial value of the antenna rotation horizontal angle at time t 0 is θ T(t0); the value of the antenna rotation pitch angle at time t i is ψ T(ti), and the value of the antenna rotation horizontal angle at time t i is θ T(ti); the maximum amplitude of the antenna rotation pitch angle variation is psi Tm, and the maximum amplitude of the antenna rotation horizontal angle variation is theta Tm.
When the unmanned aerial vehicle antenna rotation is random and unpredictable, the antenna rotation time-varying angle of the unmanned aerial vehicle side antenna array T X is modeled as a gaussian markov process:
The initial value of the antenna rotation pitch angle at the time t 0 is phi T(t0), and the initial value of the antenna rotation pitch angle at the time t 0 is theta T(t0); the value of the antenna rotation pitch angle at time t i is ψ T(ti), and the value of the antenna rotation horizontal angle at time t i is θ T(ti); when i → infinity, the asymptotic mean value of the antenna rotation pitch angle is The asymptotic mean of the antenna rotation horizontal angle is/>Ρ ψ and ρ θ are tuning values within [0,1], ρ ψ represents the randomness of the antenna rotation pitch angle, ρ θ represents the randomness of the antenna rotation horizontal angle; x and Y are variables that follow a Gaussian distribution; to ensure that the antenna rotation pitch angle and the horizontal angle are within a reasonable range, the maximum value of the antenna rotation pitch angle is set to be phi Tmax, and the maximum value of the line rotation horizontal angle is set to be theta Tmax.
And step 3, deducing an unstable time-varying distance and a time-varying angle of the unmanned aerial vehicle space MIMO channel caused by unmanned aerial vehicle antenna rotation and unmanned aerial vehicle three-dimensional movement according to the geometric relation.
The non-stationary time-varying distance is a link T p-Rq formed by the p-th antenna in the unmanned aerial vehicle side antenna array to the q-th antenna in the ground station side antenna array through different effective scatterers, And/>Time-varying distance epsilon pq(ti),/> And/>As shown in fig. 3, path components formed by the links are LOS, SB1, SB2, SB3 and DB, and the time-varying distance is calculated by a distance formula from the position coordinates of the unmanned aerial vehicle and the ground station at time t i, time t i/>And/>Representing three-dimensional position coordinates of a transmitting end and a receiving end of a ground station of an unmanned aerial vehicle, wherein the expression of the coordinates is
Wherein delta T is the distance between the p-th antenna of the unmanned aerial vehicle and the center of the antenna array of the unmanned aerial vehicle, delta R is the distance between the q-th antenna of the ground station and the center of the antenna array of the ground station, and the requirements are met
Delta T represents the antenna spacing of the drone and delta R represents the antenna spacing of the ground station.
The non-stationary time-varying angle generated by the movement of the drone and the ground station includes the departure azimuth angle, departure pitch angle, arrival azimuth angle and arrival pitch angle of the LOS path at time t i, the departure azimuth angle of the LOS path at time t i is determined byThe exit pitch angle of the LOS path at time t i is represented by/>The arrival azimuth of the LOS path at time t i is represented by/>The arrival pitch angle of the LOS path at time t i is represented by/>Representation of/>The departure azimuth of the link is defined by/>Representation of/>The departure pitch angle of the link is defined by >Representation of/>The arrival azimuth of the link is defined by/>The representation is made of a combination of a first and a second color,The arrival pitch angle of the link is defined by >Represents k=1, 2,3, where
And 4, calculating the total received complex envelope signal of the unmanned aerial vehicle space MIMO channel according to different path component signals of the unmanned aerial vehicle side antenna propagation signals which are propagated to the ground station side antenna through different scatterers.
The non-stationary time-varying angle and time-varying distance are considered to obtain an expression of each path component signal of the received complex envelope:
(1) The complex envelope expression of the LoS path component is
(2) The complex envelope expression of SB1 path components is
(3) The complex envelope expression of SB2 path components is
(4) The complex envelope expression of SB3 path component is
(5) The complex envelope expression of the DB path component is
Where Ω pq denotes the total received power, K denotes the rice factor, η SB1、ηSB2、ηSB3 and η DB denote the ratio of the respective path components to the total scattered power Ω pq/(k+1), respectively, and η SB1+ηSB2+ηSB2+ηDB =1 is satisfied,AndRepresenting the phase of each path component produced via the scatterer, as an independent random variable subject to uniform distribution [ -pi, pi ]), f Tm represents the maximum Doppler frequency of the drone, and f Rm represents the maximum Doppler frequency of the ground station.
It will be understood that the application has been described in terms of several embodiments, and that various changes and equivalents may be made to these features and embodiments by those skilled in the art without departing from the spirit and scope of the application. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the application without departing from the essential scope thereof. Therefore, it is intended that the application not be limited to the particular embodiment disclosed, but that the application will include all embodiments falling within the scope of the appended claims.

Claims (1)

1. The unmanned aerial vehicle space-based MIMO channel modeling method based on geometric randomness is characterized by comprising the following steps of:
Step 1, constructing a three-dimensional model of an unmanned aerial vehicle space MIMO channel, wherein the three-dimensional model of the unmanned aerial vehicle space MIMO channel comprises an unmanned aerial vehicle serving as a transmitting end, a ground station serving as a receiving end and two right cylinders positioned on the side of the unmanned aerial vehicle and the ground station, wherein the unmanned aerial vehicle moves at a high speed in a low-altitude three-dimensional space;
The three-dimensional movement of the unmanned aerial vehicle is characterized by a speed v T, a speed horizontal angle gamma T and a speed pitch angle zeta T, the three-dimensional movement of the unmanned aerial vehicle meets a Gaussian Markov movement model, and the initial position is moved to O T' at the moment of O T,ti;
The moving speed of the ground station is v R, the speed horizontal angle is gamma R, and the initial position is O R,ti and moves to O R' at the moment;
the radius of the right circular cylinder of the unmanned plane is R T, the surface distribution is N 1 effective scatterers, wherein the nth 1 effective scatterers are expressed as N 1＝1,2,…,N1; the right circular cylinder on the ground station side has a radius of R R and a surface distribution of N 2 effective scatterers, wherein the nth 2 effective scatterers are expressed as/>N 2＝1,2,…,N2, bottom surface distribution N 3 ground reflection effective scatterers, where the nth 3 effective scatterers are denoted as/>，n3＝1,2,…,N3；
The initial horizontal distance between the unmanned aerial vehicle and the ground station is D, the vertical distance is H, the pitch angle of the unmanned aerial vehicle position meets beta 0 = arctan (H/D), and the height of the ground station is H 0;
Step 2, describing an unmanned aerial vehicle space MIMO channel by using an L T×LR antenna array matrix, wherein the antenna array is a uniform linear antenna array, the antenna rotation pitch angle psi T and the rotation azimuth angle theta T of the unmanned aerial vehicle side antenna array T X are time-varying angles, and the antenna pitch angle psi R and the azimuth angle theta R of the ground station side antenna array R X are fixed;
step 3, deducing an unstable time-varying distance and a time-varying angle of the unmanned aerial vehicle space MIMO channel caused by unmanned aerial vehicle antenna rotation and unmanned aerial vehicle three-dimensional movement according to a geometric relation;
step 4, calculating the total received complex envelope signal of the unmanned aerial vehicle space MIMO channel according to the component signals of different paths of the unmanned aerial vehicle side antenna propagation signals which are propagated to the ground station side antenna through different scatterers;
the gaussian markov movement model satisfied by the three-dimensional movement of the unmanned aerial vehicle in step 1 is:
The initial value of the speed at the time t 0 is v T(t0), and the initial value of the pitch angle at the time t 0 is ζ T(t0); the initial value of the horizontal angle at time t 0 is gamma T(t0), the value of the speed at time t i is v T(ti), the value of the pitch angle at time t i is ζ T(ti), and the value of the horizontal angle at time t i is gamma T(ti); when i → infinity, the asymptotic mean value of the velocity is The asymptotic mean of the pitch angle is/>The asymptotic mean of the horizontal angles is/>Ρ v、ρξ and ρ γ are tuning values within [0,1], ρ v characterizes the randomness of the speed magnitude, ρ ξ characterizes the randomness of the pitch angle, ρ γ characterizes the randomness of the horizontal angle; l, M and N are variables that follow a gaussian distribution; in order to ensure that the speed, the horizontal angle and the pitch angle are in a reasonable range, the maximum value of the speed is v Tmax, the maximum value of the pitch angle is ζ Tmax and the maximum value of the horizontal angle is gamma Tmax;
In step 2, the antenna rotation time-varying angle of the unmanned aerial vehicle side antenna array T X satisfies the cosine process:
ψT(ti)＝ψT(t0)+ψTmcos(πti)
θT(ti)＝θT(t0)+θTmcos(πti)
The initial value of the antenna rotation pitch angle at time t 0 is ψ T(t0), and the initial value of the antenna rotation horizontal angle at time t 0 is θ T(t0); the value of the antenna rotation pitch angle at time t i is ψ T(ti), and the value of the antenna rotation horizontal angle at time t i is θ T(ti); the maximum amplitude of the antenna rotation pitching angle change is phi Tm, and the maximum amplitude of the antenna rotation horizontal angle change is theta Tm;
The antenna rotation time-varying angle of the unmanned aerial vehicle side antenna array T X satisfies the gaussian markov process:
The initial value of the antenna rotation pitch angle at the time t 0 is phi T(t0), and the initial value of the antenna rotation pitch angle at the time t 0 is theta T(t0); the value of the antenna rotation pitch angle at time t i is ψ T(ti), and the value of the antenna rotation horizontal angle at time t i is θ T(ti); when i → infinity, the asymptotic mean value of the antenna rotation pitch angle is The asymptotic mean of the antenna rotation horizontal angle is/>Ρ ψ and ρ θ are tuning values within [0,1], ρ ψ represents the randomness of the antenna rotation pitch angle, ρ θ represents the randomness of the antenna rotation horizontal angle; x and Y are variables that follow a Gaussian distribution; in order to ensure that the antenna rotation pitch angle and the horizontal angle are in a reasonable range, the maximum value of the antenna rotation pitch angle is set as phi Tmax, and the maximum value of the line rotation horizontal angle is set as theta Tmax;
In step 3, the non-stationary time-varying distance is a link T p-Rq formed by the p-th antenna in the unmanned aerial vehicle side antenna array to the q-th antenna in the ground station side antenna array via different effective scatterers, AndTime-varying distance epsilon pq(ti),/> And/>The path components formed by each link are LOS, SB1, SB2, SB3 and DB, the time-varying distance is calculated by the position coordinates of the unmanned aerial vehicle and the ground station at the moment t i through a distance formula, and the moment t i/>And/>Representing three-dimensional position coordinates of a transmitting end and a receiving end of a ground station of an unmanned aerial vehicle, wherein the expression of the coordinates is
Wherein delta T is the distance between the p-th antenna of the unmanned aerial vehicle and the center of the antenna array of the unmanned aerial vehicle, delta R is the distance between the q-th antenna of the ground station and the center of the antenna array of the ground station, and the requirements are met
Delta T represents the antenna spacing of the drone, delta R represents the antenna spacing of the ground station;
In step 3, the non-stationary time-varying angle includes the departure azimuth angle, departure pitch angle, arrival azimuth angle and arrival pitch angle of the LOS path at time t i, the departure azimuth angle of the LOS path at time t i is determined by The exit pitch angle of the LOS path at time t i is represented by/>The arrival azimuth of the LOS path at time t i is represented by/>The arrival pitch angle of the LOS path at time t i is represented by/>Representation of/>The departure azimuth of the link is defined by/>Representation of/>The departure pitch angle of the link is defined by >Representation of/>The arrival azimuth of the link is defined by/>Representation of/>The arrival pitch angle of the link is defined byRepresents k=1, 2,3, where
In step 4, the expressions of the received complex envelope respective path component signals are derived taking into account the non-stationary time-varying angles and time-varying distances:
the complex envelope expression of the LoS path component is
The complex envelope expression of SB1 path components is
The complex envelope expression of SB2 path components is
The complex envelope expression of SB3 path component is
The complex envelope expression of the DB path component is
Where Ω pq denotes the total received power, K denotes the rice factor, η SB1、ηSB2、ηSB3 and η DB denote the ratio of the respective path components to the total scattered power Ω pq/(k+1), respectively, and η SB1+ηSB2+ηSB2+ηDB =1 is satisfied,AndRepresenting the phase of each path component produced via the scatterer, as an independent random variable subject to uniform distribution [ -pi, pi ]), f Tm represents the maximum Doppler frequency of the drone, and f Rm represents the maximum Doppler frequency of the ground station.
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