CN114567400A - Unmanned aerial vehicle air-ground MIMO channel modeling method based on geometric randomness - Google Patents

Unmanned aerial vehicle air-ground MIMO channel modeling method based on geometric randomness Download PDF

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CN114567400A
CN114567400A CN202210199505.XA CN202210199505A CN114567400A CN 114567400 A CN114567400 A CN 114567400A CN 202210199505 A CN202210199505 A CN 202210199505A CN 114567400 A CN114567400 A CN 114567400A
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unmanned aerial
aerial vehicle
antenna
time
angle
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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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Abstract

The invention provides an unmanned aerial vehicle air-ground MIMO channel modeling method based on geometric randomness. Compared with the prior art, the model provided by the modeling method is a three-dimensional double-cylinder model, the model comprehensively considers the scattering of the unmanned aerial vehicle side, the reflection and scattering of the ground station side, the mobility of the unmanned aerial vehicle transmitting end and the ground station receiving end, particularly the influence of the rotation of the unmanned aerial vehicle side and the 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 situation and the non-stable statistical characteristics 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 an idea is provided for building a robust unmanned aerial vehicle wireless communication system.

Description

Unmanned aerial vehicle air-ground MIMO channel modeling method based on geometric randomness
Technical Field
The invention belongs to the field of wireless channel modeling, and particularly relates to an unmanned aerial vehicle air-ground MIMO channel modeling method based on geometric randomness.
Background
Along with the development of unmanned aerial vehicles in military and civil fields, the unmanned aerial vehicle communication network that the information exchange formed between unmanned aerial vehicle and the unmanned aerial vehicle or between unmanned aerial vehicle and other receiving stations constitutes the important component part of unmanned aerial vehicle system. Meanwhile, the unmanned aerial vehicle plays an important role in the application of the fifth generation (5G) mobile communication system, the unmanned aerial vehicle communication technology has become one of the key points of the research of the wireless communication technology, and the establishment of an accurate and reliable unmanned aerial vehicle communication channel model is particularly important.
At present, an unmanned aerial vehicle air-ground channel modeling method mainly comprises a deterministic model, a statistical model and a geometric random channel model, but the deterministic model modeling process is complex, completely depends on detailed information of a channel environment, and has no universality; the random parameters obtained by the statistical model according to experience have certain difference with the actual scene, the accuracy is low, some unique characteristics of unmanned aerial vehicle communication cannot be captured, and the geometric random channel model balances the complexity and the accuracy, and becomes the mainstream unmanned aerial vehicle air-ground channel modeling method. However, most of the existing unmanned aerial vehicle air-ground channel models based on geometric randomness assume that an unmanned aerial vehicle does uniform linear motion in a three-dimensional space and neglects the influence caused by the position change of an array antenna, so that the unmanned aerial vehicle three-dimensional space model does not conform to the characteristics of any flight track and rotating antenna, and meanwhile neglects channel non-stationarity caused by the movement of the unmanned aerial vehicle and a ground station.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle air-ground MIMO channel modeling method based on geometric randomness, which aims to solve the technical problems that the existing unmanned aerial vehicle air-ground channel model based on geometric randomness 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 unmanned aerial vehicle air-ground channel model does not conform to the characteristics that the actual unmanned aerial vehicle three-dimensional space has any flight track and rotating antenna, and meanwhile, the channel instability caused by the movement of the unmanned aerial vehicle and a ground station is ignored.
In order to solve the technical problems, the specific technical scheme of the invention is as follows:
an unmanned aerial vehicle air-ground MIMO channel modeling method based on geometric randomness comprises the following steps:
step 1, constructing a three-dimensional model of an unmanned aerial vehicle air-ground MIMO channel, wherein the three-dimensional model of the unmanned aerial vehicle air-ground MIMO channel comprises an unmanned aerial vehicle which moves at a high speed in a low-altitude three-dimensional space and serves as a transmitting end, a ground station which moves in a ground two-dimensional space and serves as a receiving end, and two right cylinders which are positioned on the unmanned aerial vehicle side and the ground station side.
The three-dimensional movement of the unmanned aerial vehicle is determined by the speed vTHorizontal angle of velocity gammaTAnd speed pitch angle ξTCharacterizing, and satisfying the Gauss Markov motion model with an initial position of OT，tiTime shift to OT’；
The moving speed of the ground station is vRHorizontal angle of velocity of gammaRInitial position is OR，tiTime shift to OR’；
The radius of the right cylinder at the unmanned aerial vehicle side is RTSurface distribution N1An effective scatterer, wherein the n-th1An effective scatterer can be represented as
Figure BDA0003528716940000021
The radius of the right cylinder at the side of the ground station is RRSurface distribution N2An effective scatterer, wherein the n-th2An effective scatterer can be represented as
Figure BDA0003528716940000022
Figure BDA0003528716940000023
Bottom surface distribution N3A ground reflection effective scatterer, wherein3An effective scatterer can be expressed as
Figure BDA0003528716940000024
Wherein, the initial horizontal distance between unmanned aerial vehicle and the ground station is D, the vertical distance is H, and the pitch angle of unmanned aerial vehicle position satisfies beta0H/D, the ground station is at a height H0。
Step 2, use of LT×LRThe antenna array matrix describes an unmanned aerial vehicle air-ground MIMO channel, the antenna array is a uniform linear antenna array, and an unmanned aerial vehicle side antenna array TXAntenna rotation pitch angle psiTAnd the rotation azimuth angle thetaTAre all time-varying angles, and the ground station side antenna array RXAntenna pitch angle psiRAnd azimuth angle thetaRFixing;
step 3, deducing nonstationary time-varying distance and time-varying angle of the unmanned aerial vehicle air-ground MIMO channel caused by unmanned aerial vehicle antenna rotation and unmanned aerial vehicle three-dimensional movement according to the geometric relationship;
and 4, calculating the total receiving complex envelope signal of the unmanned aerial vehicle space-ground MIMO channel according to different path component signals of the unmanned aerial vehicle side antenna propagation signal propagated to the ground station side antenna through different scatterers.
Further, the gaussian markov motion model satisfied by the three-dimensional motion of the unmanned aerial vehicle in step 1 is:
Figure BDA0003528716940000031
Figure BDA0003528716940000032
Figure BDA0003528716940000033
wherein the velocity is t0The initial value of the time is vT(t0) At a pitch angle t0The initial value of the time is xiT(t0) Horizontal angle at t0The initial value of time is gammaT(t0) (ii) a Velocity magnitude at tiThe value of the time is vT(ti) Elevation angle at tiThe value of time is xiT(ti) Horizontal angle at tiThe value of time is gammaT(ti) (ii) a When i → infinity, the asymptotic mean of the velocity magnitudes is
Figure BDA0003528716940000034
Asymptotic mean value of pitch angle of
Figure BDA0003528716940000035
Asymptotic mean of horizontal angle is
Figure BDA0003528716940000036
ρv、 ρξAnd ργIs [0,1 ]]Inner tuning value, pvRandomness, p, characterizing the magnitude of the velocityξCharacterizing the randomness of the pitch angle, pγCharacterizing randomness of horizontal angles; 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 set as vTmaxThe maximum value of the pitch angle is set to xiTmaxThe maximum horizontal angle is set to gammaTmax。
Further, in step 2, the unmanned aerial vehicle side antenna array TXThe rotation time-varying angle of the antenna meets the cosine-remaining process:
ψT(ti)＝ψT(t0)+ψTm cos(πti)
θT(ti)＝θT(t0)+θTm cos(πti)
wherein the antenna rotates at a pitch angle t0The initial value of the time is psiT(t0) The antenna is rotated at a horizontal angle t0The initial value of the time is thetaT(t0) (ii) a The antenna rotates at a pitch angle tiThe value of time is psiT(ti) The antenna is rotated by a horizontal angle tiValue of time of dayIs thetaT(ti) (ii) a The maximum amplitude of the variation of the antenna rotation pitch angle is psiTmThe maximum amplitude of the variation of the horizontal angle of rotation of the antenna is thetaTm。
Further, unmanned aerial vehicle side antenna array TXThe time-varying angle of rotation of the antenna satisfies a Gaussian Markov process:
Figure BDA0003528716940000041
Figure BDA0003528716940000042
wherein the antenna has a rotation pitch angle of t0The initial value of the time is psiT(t0) At the antenna rotation pitch angle t0The initial value of the time is thetaT(t0) (ii) a The antenna rotates at a pitch angle tiThe value of time is psiT(ti) The antenna is rotated by a horizontal angle tiThe value of time is thetaT(ti) (ii) a When i → ∞, the asymptotic mean of the antenna rotation pitch angle is
Figure BDA0003528716940000043
The asymptotic mean value of the antenna rotation horizontal angle is
Figure BDA0003528716940000044
ρψAnd ρθIs [0,1 ]]Inner tuning value, pψCharacterizing the randomness, rho, of the rotational pitch angle of the antennaθCharacterizing randomness of a horizontal angle of rotation of the antenna; x and Y are variables subject to Gaussian distribution; in order to ensure that the rotation pitch angle and the horizontal angle of the antenna are within a reasonable range, the maximum value of the rotation pitch angle of the antenna is set to psiTmaxThe maximum value of the horizontal angle of the line rotation is set to thetaTmax。
Further, in step 3, the non-stationary time-varying distance is a distance from the p-th antenna in the antenna array of the unmanned aerial vehicle side to the q-th antenna in the antenna array of the ground station side through different effective scatterersFormed link
Figure BDA0003528716940000045
And
Figure BDA0003528716940000046
time-varying distance epsilonpq(ti)、
Figure BDA0003528716940000047
And
Figure BDA0003528716940000048
the path components formed by the links are LOS, SB1, SB2, SB3 and DB, and the time-varying distance is tiThe position coordinates of the unmanned aerial vehicle and the ground station at the moment are calculated by a distance formula, tiTime of day
Figure BDA0003528716940000049
And
Figure BDA00035287169400000410
representing the three-dimensional position coordinates of the transmitting end of the unmanned aerial vehicle and the receiving end of the ground station, wherein the expression of the coordinates is
Figure BDA00035287169400000411
Figure BDA00035287169400000412
Figure BDA00035287169400000413
Figure BDA00035287169400000414
Figure BDA0003528716940000051
Wherein, DeltaTIs the distance, delta, between the p-th antenna of the drone and the center of the drone antenna arrayRIs the distance between the q-th antenna of the ground station and the center of the ground station antenna array, and meets the requirement
Figure BDA0003528716940000052
Figure RE-GDA00035721138500000521
δTIndicating the antenna spacing of the drone, deltaRRepresenting the antenna spacing of the ground station.
Further, in step 3, the non-stationary time-varying angle includes tiThe departure azimuth, departure pitch, arrival azimuth and arrival pitch of the LOS path at time tiThe departure azimuth angle of the LOS path at the moment
Figure BDA0003528716940000054
Denotes, tiThe angle of departure and pitch of the LOS path at the moment
Figure BDA0003528716940000055
Denotes, tiAzimuth of arrival of time LOS path
Figure BDA0003528716940000056
Denotes, tiThe angle of elevation of arrival of the LOS path at time
Figure BDA0003528716940000057
It is shown that,
Figure BDA0003528716940000058
azimuth of departure of link
Figure BDA0003528716940000059
It is shown that,
Figure BDA00035287169400000510
the departure and pitch angle of the link is set by
Figure BDA00035287169400000511
It is shown that,
Figure BDA00035287169400000512
link arrival azimuth angle of
Figure BDA00035287169400000513
It is shown that,
Figure BDA00035287169400000514
angle of arrival of link from
Figure BDA00035287169400000515
Is represented by, k is 1, 2, 3, wherein
Figure BDA00035287169400000516
Figure BDA00035287169400000517
Figure BDA00035287169400000518
Figure BDA00035287169400000519
Figure BDA00035287169400000520
Figure BDA00035287169400000521
Figure BDA0003528716940000061
Figure BDA0003528716940000062
Further, in step 4, an expression of each path component signal of the receiving complex packet is obtained by considering the non-stationary time-varying angle and the time-varying distance:
the complex envelope expression of the LoS path component is
Figure BDA0003528716940000063
The complex envelope of the SB1 path component is expressed as
Figure BDA0003528716940000064
The complex envelope of the SB2 path component is expressed as
Figure BDA0003528716940000065
The complex envelope of the SB3 path component is expressed as
Figure BDA0003528716940000071
The complex envelope expression of the DB path component is
Figure BDA0003528716940000072
Wherein omegapqRepresenting the total received power, K representing the Rice factor, etaSB1、ηSB2、ηSB3And ηDBRespectively representing the total scattered power omega of each path componentpqA ratio of/(K +1) and satisfies ηSB1+ηSB2+ ηSB2+ηDB＝1，
Figure BDA0003528716940000073
And
Figure BDA0003528716940000074
representing the phase produced by each path component through the scatterer as an independent random variable obeying a uniform distribution over [ - π, π ]TmRepresenting the maximum Doppler frequency, f, of the droneRmRepresenting the maximum doppler frequency of the ground station.
The invention discloses an unmanned aerial vehicle air-ground MIMO channel modeling method based on geometric randomness, which has the following advantages:
the invention provides an unmanned aerial vehicle air-ground 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 scattering of a ground station side, the mobility of an unmanned aerial vehicle transmitting end and a ground station receiving end, particularly the rotation of the unmanned aerial vehicle side and the influence of the 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 situation and the non-stable 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 an idea is provided for building a robust unmanned aerial vehicle communication wireless communication system.
Drawings
FIG. 1 is a schematic diagram of an unmanned aerial vehicle space-ground MIMO channel model based on geometric randomness according to the present invention;
fig. 2 is a schematic diagram of the rotation of the drone antenna array of the present invention;
fig. 3 is a diagram illustrating the components of each path formed in the channel model of the present invention.
Detailed Description
In order to better understand the purpose, structure and function of the present invention, the following describes the unmanned aerial vehicle air-ground MIMO channel modeling method based on geometric randomness in further detail with reference to the accompanying drawings.
The technical scheme adopted by the invention is as follows: an unmanned aerial vehicle air-ground MIMO channel modeling method based on geometric randomness comprises the following steps:
step 1, constructing a three-dimensional model of the unmanned aerial vehicle air-ground MIMO channel, wherein the three-dimensional model of the unmanned aerial vehicle air-ground MIMO channel comprises an unmanned aerial vehicle which moves at a high speed in a low-altitude three-dimensional space and serves as a transmitting end, a ground station which moves in a ground two-dimensional space and serves as a receiving end, and two right cylinders which are positioned on the side of the unmanned aerial vehicle and the side of the ground station, as shown in FIG. 1.
The three-dimensional movement of the unmanned aerial vehicle is controlled by the speed vTHorizontal angle of velocity gammaTAnd speed pitch angle ξTCharacterizing, and satisfying the Gaussian Markov motion model with an initial position of OT，tiTime of day shifts to OT’；
The moving speed of the ground station is vRHorizontal angle of velocity of gammaRInitial position is OR，tiTime shift to OR’；
The radius of the right cylinder at the unmanned aerial vehicle side is RTSurface distribution N1An effective scatterer, wherein the n-th1An effective scatterer can be represented as
Figure BDA0003528716940000081
The radius of the right cylinder at the side of the ground station is RRSurface distribution N2An effective scatterer, wherein2An effective scatterer can be represented as
Figure BDA0003528716940000082
Figure BDA0003528716940000083
Bottom surface distribution N3A ground reflection effective scatterer, wherein3Effective powderThe projectile may be represented as
Figure BDA0003528716940000084
Wherein, the initial horizontal distance between unmanned aerial vehicle and the ground station is D, the vertical distance is H, and the pitch angle of unmanned aerial vehicle position satisfies beta0H/D, the ground station is at a height H0。
In order to better represent the influence of the three-dimensional movement of the unmanned aerial vehicle on the non-stationary characteristics of the channel, the three-dimensional movement modeling of the unmanned aerial vehicle is a Gaussian Markov movement model and is expressed as follows:
Figure BDA0003528716940000091
Figure BDA0003528716940000092
Figure BDA0003528716940000093
wherein the velocity is t0The initial value of the time is vT(t0) At a pitch angle t0The initial value of the time is xiT(t0) Horizontal angle at t0The initial value of time is gammaT(t0) The velocity is tiThe value of the time is vT(ti) Elevation angle at tiThe value of time is xiT(ti) Horizontal angle at tiThe value of time is gammaT(ti) (ii) a When i → ∞ is reached, the asymptotic mean value of the velocity magnitude is μvTThe asymptotic mean value of the pitch angle is
Figure BDA0003528716940000094
Asymptotic mean of horizontal angle of
Figure BDA0003528716940000095
ρv、ρξAnd ργIs [0,1 ]]Inner tuning value, pvRandomness, p, characterizing the magnitude of the velocityξCharacterizing randomness of pitch angle, pγCharacterizing randomness of horizontal angles; 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 set as vTmaxThe maximum value of the pitch angle is set to xiTmaxThe maximum horizontal angle is set to gammaTmax。
Step 2, use LT×LRThe antenna array matrix describes the unmanned aerial vehicle air-ground MIMO channel, which is a uniform linear antenna array, assuming LT＝LRAs shown in fig. 2, the drone side antenna array T takes into account the roll and pitch motion of the droneXAntenna rotation pitch angle psiTAnd rotation azimuth angle thetaTAre all time-varying angles, and the ground station side antenna array RXAntenna pitch angle psiRAnd azimuth angle thetaRFixing;
the unmanned aerial vehicle side antenna array TXThe antenna rotation time-varying angle is modeled as a cosine process:
ψT(ti)＝ψT(t0)+ψTm cos(πti)
θT(ti)＝θT(t0)+θTm cos(πti)
wherein the antenna rotates at a pitch angle t0The initial value of the time is psiT(t0) The antenna is rotated at a horizontal angle t0The initial value of the time is thetaT(t0) (ii) a The antenna rotates at a pitch angle tiThe value of time is psiT(ti) The antenna is rotated at a horizontal angle tiThe value of time is thetaT(ti) (ii) a The maximum amplitude of the variation of the antenna rotation pitch angle is psiTmThe maximum amplitude of the variation of the horizontal angle of rotation of the antenna is thetaTm。
When the rotation of the unmanned aerial vehicle antenna is random and unpredictable, the unmanned aerial vehicle side antenna array T is arrangedXThe antenna rotation time-varying angle is modeled as a Gauss horseThe Erkov process:
Figure BDA0003528716940000101
Figure BDA0003528716940000102
wherein the antenna rotates at a pitch angle t0The initial value of the time is psiT(t0) At the antenna rotation pitch angle t0The initial value of the time is thetaT(t0) (ii) a The antenna rotates at a pitch angle tiThe value of time is psiT(ti) The antenna is rotated at a horizontal angle tiThe value of time is thetaT(ti) (ii) a When i → ∞, the asymptotic mean of the antenna rotation pitch angle is
Figure BDA0003528716940000103
The asymptotic mean value of the antenna rotation horizontal angle is
Figure BDA0003528716940000104
ρψAnd ρθIs [0,1 ]]Inner tuning value, pψCharacterizing the randomness, rho, of the rotational pitch angle of the antennaθCharacterizing randomness of a horizontal angle of rotation of the antenna; x and Y are variables subject to Gaussian distribution; in order to ensure that the rotation pitch angle and the horizontal angle of the antenna are in a reasonable range, the maximum value of the rotation pitch angle of the antenna is set to be psiTmaxThe maximum value of the horizontal angle of linear rotation is set to thetaTmax。
And 3, deducing non-stationary time-varying distance and time-varying angle of the unmanned aerial vehicle air-ground MIMO channel caused by rotation of an unmanned aerial vehicle antenna and three-dimensional movement of the unmanned aerial vehicle according to the geometric relation.
The non-stationary time-varying distance is a link T formed by a pth antenna in the antenna array of the unmanned aerial vehicle side to a qth antenna in the antenna array of the ground station side through different effective scatterersp-Rq、
Figure BDA0003528716940000105
Figure BDA0003528716940000106
And
Figure BDA0003528716940000107
time-varying distance epsilonpq(ti)、
Figure BDA0003528716940000108
Figure BDA0003528716940000109
And
Figure BDA00035287169400001010
as shown in FIG. 3, the path components formed by the links are LOS, SB1, SB2, SB3 and DB, and the time-varying distance is tiThe position coordinates of the unmanned aerial vehicle and the ground station at the moment are calculated by a distance formula, tiTime of day
Figure BDA00035287169400001011
And
Figure BDA00035287169400001012
representing the three-dimensional position coordinates of the transmitting end of the unmanned aerial vehicle and the receiving end of the ground station, wherein the expression of the coordinates is
Figure BDA00035287169400001013
Figure BDA0003528716940000111
Figure BDA0003528716940000112
Figure BDA0003528716940000113
Figure BDA0003528716940000114
Wherein, DeltaTIs the distance, delta, between the p-th antenna of the drone and the center of the drone antenna arrayRIs the distance between the qth antenna of the ground station and the center of the antenna array of the ground station, and satisfies
Figure BDA0003528716940000115
Figure RE-GDA0003572113850000117
δTIndicating the antenna spacing, delta, of the droneRIndicating the antenna spacing of the ground station.
The non-stationary time-varying angles due to the movement of the drone and the ground station include tiThe departure azimuth, departure pitch angle, arrival azimuth and arrival pitch angle, t, of the LOS path at timeiAzimuth of departure of time LOS path
Figure BDA0003528716940000117
Denotes tiThe departure-pitch angle of the LOS path at that moment
Figure BDA0003528716940000118
Denotes, tiAzimuth of arrival of time LOS path
Figure BDA0003528716940000119
Denotes, tiThe angle of elevation of arrival of the LOS path at time
Figure BDA00035287169400001110
It is shown that,
Figure BDA00035287169400001111
the departure azimuth angle of the link is determined by
Figure BDA00035287169400001112
It is shown that the process of the present invention,
Figure BDA00035287169400001113
the departure and pitch angle of the link is set by
Figure BDA00035287169400001114
It is shown that,
Figure BDA00035287169400001115
link arrival azimuth angle of
Figure BDA00035287169400001116
It is shown that,
Figure BDA00035287169400001117
angle of arrival pitch of link
Figure BDA00035287169400001118
Is represented by, k is 1, 2, 3, wherein
Figure BDA00035287169400001119
Figure BDA00035287169400001120
Figure BDA00035287169400001121
Figure BDA00035287169400001122
Figure BDA00035287169400001123
Figure BDA0003528716940000121
Figure BDA0003528716940000122
Figure BDA0003528716940000123
And 4, calculating the total receiving complex envelope signal of the unmanned aerial vehicle space-ground MIMO channel according to different path component signals of the unmanned aerial vehicle side antenna propagation signal propagated to the ground station side antenna through different scatterers.
And obtaining an expression of each path component signal of the received complex envelope by considering the non-stationary time-varying angle and the time-varying distance:
(1) the complex envelope expression of the LoS path component is
Figure BDA0003528716940000124
(2) The complex envelope of the SB1 path component is expressed as
Figure BDA0003528716940000125
(3) The complex envelope of the SB2 path component is expressed as
Figure BDA0003528716940000126
(4) The complex envelope of the SB3 path component is expressed as
Figure BDA0003528716940000131
(5) The complex envelope expression of the DB path component is
Figure BDA0003528716940000132
Wherein omegapqRepresenting the total received power, K representing the Rice factor, etaSB1、ηSB2、ηSB3And ηDBRespectively representing the total scattered power omega of each path componentpqA ratio of (K +1) and satisfies ηSB1+ηSB2+ ηSB2+ηDB＝1，
Figure BDA0003528716940000133
And
Figure BDA0003528716940000134
representing the phase generated by each path component through the scatterer as an independent random variable subject to uniform distribution over [ - π, π [ ], fTmRepresenting the maximum Doppler frequency, f, of the droneRmRepresenting the maximum doppler frequency of the ground station.
It is to be understood that the present invention has been described with reference to certain embodiments, and that various changes in the features and embodiments, or equivalent substitutions may be made therein by those skilled in the art without departing from the spirit and scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.

Claims (7)

1. An unmanned aerial vehicle air-ground MIMO channel modeling method based on geometric randomness is characterized by comprising the following steps:
step 1, constructing a three-dimensional model of an unmanned aerial vehicle air-ground MIMO channel, wherein the three-dimensional model of the unmanned aerial vehicle air-ground MIMO channel comprises an unmanned aerial vehicle which moves at a high speed in a low-altitude three-dimensional space and serves as a transmitting end, a ground station which moves in a ground two-dimensional space and serves as a receiving end, and two right cylinders which are positioned at the side of the unmanned aerial vehicle and the side of the ground station;
the three-dimensional movement of the unmanned aerial vehicle is controlled by the speed vTHorizontal angle of velocity gammaTAnd velocity pitch angle ξTCharacterizing, and satisfying the Gaussian Markov motion model with an initial position of OT，tiTime shift to OT’；
The moving speed of the ground station is vRHorizontal angle of velocity of gammaRInitial position is OR，tiTime shift to OR’；
The radius of the right cylinder at the unmanned aerial vehicle side is RTSurface distribution N1An effective scatterer, wherein1An effective scatterer is represented as
Figure FDA0003528716930000011
The radius of the right cylinder at the side of the ground station is RRSurface distribution N2An effective scatterer, wherein2An effective scatterer is represented as
Figure FDA0003528716930000012
Figure FDA0003528716930000013
Bottom surface distribution N3A ground reflection effective scatterer, wherein3An effective scatterer is represented as
Figure FDA0003528716930000014
Wherein, the initial horizontal distance between unmanned aerial vehicle and the ground station is D, the vertical distance is H, and the pitch angle of unmanned aerial vehicle position satisfies beta0H/D, the ground station is at a height H0；
Step 2, use of LT×LRAntenna array matrix descriptionAn unmanned aerial vehicle air-ground MIMO channel, wherein the antenna array is a uniform linear antenna array, and an unmanned aerial vehicle side antenna array TXAntenna rotation pitch angle psiTAnd rotation azimuth angle thetaTAll of which are time-varying angles, ground station side antenna array RXAntenna pitch angle psiRAnd azimuth angle thetaRFixing;
step 3, deducing nonstationary time-varying distance and time-varying angle of the unmanned aerial vehicle air-ground MIMO channel caused by unmanned aerial vehicle antenna rotation and unmanned aerial vehicle three-dimensional movement according to the geometric relationship;
and 4, calculating the total receiving complex envelope signal of the unmanned aerial vehicle space-ground MIMO channel according to different path component signals of the unmanned aerial vehicle side antenna propagation signal propagated to the ground station side antenna through different scatterers.
2. The method for modeling the unmanned aerial vehicle air-ground MIMO channel based on geometric randomness according to claim 1, wherein the Gaussian Markov motion model satisfied by the three-dimensional motion of the unmanned aerial vehicle in step 1 is:
Figure FDA0003528716930000021
Figure FDA0003528716930000022
Figure FDA0003528716930000023
wherein the velocity is t0The initial value of the time is vT(t0) At a pitch angle t0The initial value of the time is xiT(t0) (ii) a Horizontal angle at t0The initial value of time is gammaT(t0) The velocity is tiThe value of the time is vT(ti) At a pitch angle tiThe value of time is xiT(ti) Water, waterStraight angle is at tiThe value of time is gammaT(ti) (ii) a When i → ∞ the asymptotic mean value of the velocity magnitude is
Figure FDA0003528716930000024
Asymptotic mean value of pitch angle of
Figure FDA0003528716930000025
Asymptotic mean of horizontal angle is
Figure FDA0003528716930000026
ρv、ρξAnd ργIs [0,1 ]]Inner tuning value, pvRandomness, p, characterizing the magnitude of the velocityξCharacterizing the randomness of the pitch angle, pγCharacterizing randomness of horizontal angles; 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 set as vTmaxThe maximum value of the pitch angle is set to xiTmaxThe maximum horizontal angle is set to gammaTmax。
3. The method of claim 2, wherein in step 2, the drone-side antenna array TXThe antenna rotation time variation angle satisfies the cosine process:
ψT(ti)＝ψT(t0)+ψTmcos(πti)
θT(ti)＝θT(t0)+θTmcos(πti)
wherein the antenna rotates at a pitch angle t0The initial value of the time is psiT(t0) The antenna is rotated at a horizontal angle t0The initial value of the time is thetaT(t0) (ii) a The antenna rotates at a pitch angle tiThe value of time is psiT(ti) The antenna is rotated at a horizontal angle tiThe value of time is thetaT(ti) (ii) a Maximum change in antenna rotation pitch angleAmplitude of psiTmThe maximum amplitude of the change of the antenna rotation horizontal angle is thetaTm。
4. The method of claim 2, wherein the drone side antenna array T is a space MIMO channel modeling method based on geometric randomnessXSatisfies the gaussian markov process:
Figure FDA0003528716930000031
Figure FDA0003528716930000032
wherein the antenna rotates at a pitch angle t0The initial value of the time is psiT(t0) At a rotational pitch angle t of the antenna0The initial value of the time is thetaT(t0) (ii) a The antenna rotates at a pitch angle tiThe value of time is psiT(ti) The antenna is rotated at a horizontal angle tiThe value of time is thetaT(ti) (ii) a When i → ∞, the asymptotic mean of the antenna rotation pitch angle is
Figure FDA0003528716930000033
Asymptotic mean of the horizontal angle of rotation of the antenna is
Figure FDA0003528716930000034
ρψAnd ρθIs [0,1 ]]Inner tuning value, pψCharacterizing the randomness, rho, of the rotational pitch angle of the antennaθCharacterizing randomness of a horizontal angle of rotation of the antenna; x and Y are variables subject to a Gaussian distribution; in order to ensure that the rotation pitch angle and the horizontal angle of the antenna are in a reasonable range, the maximum value of the rotation pitch angle of the antenna is set to be psiTmaxThe maximum value of the horizontal angle of linear rotation is set to thetaTmax。
5. Root of herbaceous plantThe method as claimed in claim 3 or 4, wherein in step 3, the non-stationary time-varying distance is a link T formed by a p-th antenna in the unmanned aerial vehicle side antenna array to a q-th antenna in the ground station side antenna array via different effective scatterersp-Rq、
Figure FDA0003528716930000035
And
Figure FDA0003528716930000036
time-varying distance epsilonpq(ti)、
Figure FDA0003528716930000037
And
Figure FDA0003528716930000038
the path components formed by the links are LOS, SB1, SB2, SB3 and DB, and the time-varying distance is tiThe position coordinates of the unmanned aerial vehicle and the ground station at the moment are calculated by a distance formula, tiTime of day
Figure FDA0003528716930000039
And
Figure FDA00035287169300000310
representing the three-dimensional position coordinates of the transmitting end of the unmanned aerial vehicle and the receiving end of the ground station, wherein the expression of the coordinates is
Figure FDA00035287169300000311
Figure FDA0003528716930000041
Figure FDA0003528716930000042
Figure FDA0003528716930000043
Figure FDA0003528716930000044
Wherein, DeltaTIs the distance, delta, between the p-th antenna of the drone and the center of the drone antenna arrayRIs the distance between the q-th antenna of the ground station and the center of the ground station antenna array, and meets the requirement
Figure FDA0003528716930000045
Figure FDA0003528716930000046
δTIndicating the antenna spacing, delta, of the droneRRepresenting the antenna spacing of the ground station.
6. The method of claim 5, wherein in step 3, the non-stationary time-varying angle comprises tiThe departure azimuth, departure pitch angle, arrival azimuth and arrival pitch angle, t, of the LOS path at timeiAzimuth of departure of time LOS path
Figure FDA0003528716930000047
Denotes, tiThe angle of departure and pitch of the LOS path at the moment
Figure FDA0003528716930000048
Denotes, tiOf the LOS path at that momentArrival azimuth angle of
Figure FDA0003528716930000049
Denotes, tiThe angle of elevation of arrival of the LOS path at time
Figure FDA00035287169300000410
It is shown that,
Figure FDA00035287169300000411
azimuth of departure of link
Figure FDA00035287169300000412
It is shown that,
Figure FDA00035287169300000413
the departure and pitch angle of the link is set by
Figure FDA00035287169300000414
It is shown that the process of the present invention,
Figure FDA00035287169300000415
link arrival azimuth angle of
Figure FDA00035287169300000416
It is shown that,
Figure FDA00035287169300000417
angle of arrival pitch of link
Figure FDA00035287169300000418
Is represented by, k is 1, 2, 3, wherein
Figure FDA00035287169300000419
Figure FDA00035287169300000420
Figure FDA00035287169300000421
Figure FDA00035287169300000422
Figure FDA0003528716930000051
Figure FDA0003528716930000052
Figure FDA0003528716930000053
Figure FDA0003528716930000054
7. The unmanned aerial vehicle space-earth MIMO channel modeling method based on geometric randomness as claimed in claim 6, wherein in step 4, an expression of each path component signal of the received complex envelope is obtained by considering non-stationary time-varying angle and time-varying distance:
the complex envelope expression of the LoS path component is
Figure FDA0003528716930000055
The complex envelope of the SB1 path component is expressed as
Figure FDA0003528716930000056
The complex envelope of the SB2 path component is expressed as
Figure FDA0003528716930000061
The complex envelope of the SB3 path component is expressed as
Figure FDA0003528716930000062
The complex envelope expression of the DB path component is
Figure FDA0003528716930000063
Wherein omegapqRepresenting the total received power, K representing the Rice factor, etaSB1、ηSB2、ηSB3And ηDBRespectively representing the total scattered power omega of each path componentpqA ratio of (K +1) and satisfies ηSB1+ηSB2+ηSB2+ηDB＝1，
Figure FDA0003528716930000064
And
Figure FDA0003528716930000065
representing the phase generated by each path component through the scatterer as an independent random variable subject to uniform distribution over [ - π, π [ ], fTmRepresenting the maximum Doppler frequency, f, of the droneRmRepresenting the maximum doppler frequency of the ground station.
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US20150270885A1 (en) * 2015-06-08 2015-09-24 Donald C.D. Chang MIMO Systems with Active Scatters and their Performance Evaluation
CN109951213A (en) * 2017-12-21 2019-06-28 上海交通大学 High altitude platform MIMO three-dimensional geometry stochastic model method for building up and communication means
CN113489560A (en) * 2021-05-12 2021-10-08 东南大学 Geometric random modeling method for non-stationary air-ground MIMO channel of unmanned aerial vehicle
CN113938233A (en) * 2021-11-18 2022-01-14 重庆邮电大学 Geometric random modeling method for non-stationary air-to-air MIMO channel of unmanned aerial vehicle

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150270885A1 (en) * 2015-06-08 2015-09-24 Donald C.D. Chang MIMO Systems with Active Scatters and their Performance Evaluation
CN109951213A (en) * 2017-12-21 2019-06-28 上海交通大学 High altitude platform MIMO three-dimensional geometry stochastic model method for building up and communication means
CN113489560A (en) * 2021-05-12 2021-10-08 东南大学 Geometric random modeling method for non-stationary air-ground MIMO channel of unmanned aerial vehicle
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