CN114124264B - Unmanned aerial vehicle channel model building method based on intelligent reflection surface time-varying reflection phase - Google Patents
Unmanned aerial vehicle channel model building method based on intelligent reflection surface time-varying reflection phase Download PDFInfo
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Abstract
The invention discloses an unmanned aerial vehicle channel model building method based on an intelligent reflection surface time-varying reflection phase, which comprises the steps of firstly, building an unmanned aerial vehicle channel model based on the intelligent reflection surface time-varying reflection phase; secondly, according to an unmanned aerial vehicle channel model, taking into consideration whether an intelligent reflection surface IRS is applied or not and whether direct signals are contained or not, obtaining complex channel gains of all components; thirdly, designing an optimization problem according to a power maximization principle of the received signal, and simplifying the optimization problem; then, according to the intensity of the LoS component and the NLoS component, the optimal time-varying reflection phase of the intelligent reflection surface IRS is obtained; and finally, solving a space-time correlation function of the unmanned aerial vehicle channel model based on the intelligent reflection surface time-varying reflection phase, and determining the influence of the intelligent reflection surface IRS on the unmanned aerial vehicle channel characteristic through correlation analysis. The invention can provide help and support for the exploration of key technologies of the next generation mobile communication system.
Description
Technical Field
The invention relates to a wireless communication technology, in particular to an unmanned aerial vehicle channel model building method based on an intelligent reflection surface time-varying reflection phase.
Background
With the comprehensive deployment of the fifth generation communication technology, the rapid development of the technology of the internet of things, and the rapid expansion of power consumption inevitably become a problem in the future. How to achieve high-rate low-power data transmission will become a key to future network development. The intelligent reflector technology with the characteristics of low power consumption, low complexity and low cost can well solve the difficult problem due to the high-speed development of the related emerging technology. The unmanned aerial vehicle (Unmanned Aerial Vehicle, UAV) relay communication system is a communication system taking the UAV as a mobile relay, and has the advantages of long transmission distance, convenient deployment, flexibility, wide coverage range, rapid system architecture, high economic benefit and the like by virtue of high mobility, so that the UAV realizes high-speed wireless communication and plays an important role in future communication systems.
In conventional mobile communication, the signal transmission environment between the transmitting end and the receiving end is uncontrollable, and thus the problem of adverse communication quality is also difficult to solve. However, after the intelligent reflecting surface is added in the system, the transmission environment between the transmitter and the receiver can be changed by adjusting the reflecting phase of the intelligent reflecting surface, so that the transmission quality can be improved. In order to design an intelligent reflection surface-assisted unmanned aerial vehicle multiple-input multiple-output communication system, a channel model capable of accurately describing communication characteristics is indispensable. The present invention is based on the principle of optimizing the power of the received signal, by making a new design of the reflection phase, which is a key point of the communication system with the intelligent transmitting surface.
In summary, the method for establishing the unmanned aerial vehicle channel model based on the intelligent reflection surface time-varying reflection phase is in an initial stage, and the statistical characteristics of the intelligent reflection surface IRS on the unmanned aerial vehicle UAV channel are yet to be explored, so that an accurate unmanned aerial vehicle channel model based on the intelligent reflection surface time-varying reflection phase is very necessary.
Disclosure of Invention
The invention aims to: the invention aims to provide an accurate unmanned aerial vehicle channel model building method based on an intelligent reflection surface time-varying reflection phase, and the building of the model can provide basis for system performance analysis and precoding algorithm design in the future.
The technical scheme is as follows: the invention discloses an unmanned aerial vehicle channel model building method based on an intelligent reflection surface time-varying reflection phase, which comprises the following steps:
s1, establishing an unmanned aerial vehicle channel model based on an intelligent reflection surface time-varying reflection phase according to the position relation among an unmanned aerial vehicle UAV, an intelligent reflection surface IRS and a receiving end;
the model adopts a three-dimensional cylinder with the bottom radius of R to simulate the receiving end R x Surrounding scatterers, N 1 Individual scatterersIs arranged on the surface of the three-dimensional cylinder; adopting a focal length of 2f at the bottom 0 To simulate the intelligent reflection surface IRS and the scattering body near the receiving end, and the intelligent reflection surface IRS and the receiving end R x Arranged at the focus of the bottom surface of the three-dimensional ellipse-cylinder, N 2 Individual scatterers->Is arranged on the surface of a three-dimensional elliptical-cylinder; be provided with N on unmanned aerial vehicle UAV U Uniform linear antenna array, receiving end R x On which N is arranged R The intelligent reflection surface IRS is provided with M rows and N columns of uniform plane antenna arrays;
s2, according to the unmanned aerial vehicle channel model established in the step S1, taking into consideration whether an intelligent reflection surface IRS is applied or not and whether direct signals are contained or not, obtaining complex channel gains of all components;
the intelligent reflection surface IRS is configured on the floor surface of the cell edge, and when the distance between the intelligent reflection surface IRS and the receiving end Rx is far, the channel between the intelligent reflection surface IRS and the receiving end Rx is mainly a Rayleigh fading channel; the complex channel gain of the unmanned aerial vehicle channel model comprises a LoS component, an NLoS component and a reflection component passing through the intelligent reflection surface IRS;
s3, designing an optimization problem according to a power maximization principle of the received signal, and simplifying the optimization problem;
s4, considering the simplified optimization problem in two cases; case 1: the LoS component is stronger than the NLoS component; case 2: the NLoS component is stronger than the LoS component; the time-varying reflection phases of the optimal intelligent reflection surfaces IRS of the case 1 and the case 2 are respectively obtained;
S5, carrying out statistical averaging on complex channel gains of all components obtained in the step S2 and complex conjugates thereof according to definition of correlation functions through time-varying reflection phases of the optimal intelligent reflection surface IRS obtained in the step S4, solving a space-time correlation function of an unmanned aerial vehicle channel model based on the time-varying reflection phases of the intelligent reflection surface, further solving space-time correlation functions of the LoS component, the NLoS component and the IRS component, and determining influence of the intelligent reflection surface IRS on unmanned aerial vehicle channel characteristics through correlation analysis;
further, the complex channel gain obtained in step S2 is expressed as follows:
wherein ,hpq (t) represents the complex channel gain between the unmanned aerial vehicle UAV antenna unit p and the receiving-end antenna unit q, t represents the time variable,complex channel gain representing LoS component between unmanned aerial vehicle UAV antenna unit p and receiving end antenna unit q, +.>A complex channel gain representing the NLoS component between the unmanned aerial vehicle UAV antenna unit p and the receiving-end antenna unit q,the complex channel gain of IRS component between UAV antenna unit p and receiving end antenna unit q is represented, and the calculation formula is:
wherein ,Gu and Gr The gains of the unmanned aerial vehicle UAV and the receiving end respectively,is an unmanned aerial vehicle UAV to a receiving end R X K is the Lais factorLambda is the carrier wavelength, ζ pq (t) represents a time-varying distance between the unmanned aerial vehicle UAV antenna unit p and the receiving-end antenna unit q, f LoS (t) represents the Doppler shift of the LoS component; />Representing unmanned aerial vehicle UAV antenna unit p and diffuser->Time-varying distance between->Representing scatterer->And a time-varying distance, f, between receiving-end antenna elements q NLoS (t) represents Doppler shift of NLoS component, G is gain of intelligent reflection surface IRS, +.>Is a link UAV-IRS-R X Path loss, ζ pmn (t) represents the time-varying distance, ζ, between the unmanned aerial vehicle UAV antenna unit p and the (m, n) -th smart reflection unit mnq (t) represents the time-varying distance between the (m, n) -th smart reflection unit and the user antenna unit q, < >>Representing (m, n) -th smart reflection unit and scatterer +.>Time-varying distance between->Representing scatterer->And the time-varying distance, θ, of the receiving-end antenna unit q mn (t) represents (m, n) -th intelligent inverseTime-varying reflection phase of the radiation unit, f IRS (t) is the Doppler shift of the NLoS component reflected by the intelligent reflection surface IRS.
Further, the optimization problem in step S3 is expressed as follows:
wherein t represents a time variable, θ mn (t) represents the time-varying reflection phase of the (m, n) -th smart reflection unit, Represents statistical mean calculation, h pq (t) represents the complex channel gain between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q.
Further, considering the received signal power in the comparison set, the optimization problem simplifies the formula as follows:
wherein ,representing statistical mean operation, t representing time variable, θ mn (t) represents the time-varying reflection phase of the (m, n) -th smart reflection unit, < -> and />Pulse amplitudes respectively representing LoS component, IRS component, NLoS component, cos (·) representing cosine function, ++>Time-varying phase representing direct component between unmanned aerial vehicle UAV antenna unit p and receiving-end antenna unit q, +.>Representing the time-varying phase of the multipath component between unmanned aerial vehicle UAV antenna unit p and receiving end antenna unit q after (m, n) -th intelligent reflecting unit, +.>A time-varying phase representing a scattering component between the unmanned aerial vehicle UAV antenna unit p and the receiving-end antenna unit q;
wherein ,
wherein ,Gu Represents unmanned aerial vehicle UAV gain, G represents intelligent reflection surface IRS gain, G r Indicating the gain of the antenna at the receiving end,representing the path loss of an unmanned aerial vehicle UAV to an intelligent reflective surface IRS, +.>Represents the path loss from the UAV to the receiving end, K represents the Lais factor, lambda represents the carrier wavelength, pi represents the circumference ratio, and xi pq (t) represents a time-varying distance between the unmanned aerial vehicle UAV antenna unit p and the receiving-end antenna unit q, f LoS (t) Doppler shift of LoS component, < ->Representing unmanned aerial vehicle UAV antenna unit p and diffuser->Time-varying distance between->Representing scatterer->And the time-varying distance between the user side antenna units q, f NLoS (t) is the Doppler shift, ζ, of the NLoS component pmn (t) represents the time-varying distance between the unmanned aerial vehicle UAV antenna unit p and the (m, n) -th smart reflection unit,/->Representing (m, n) -th smart reflection unit and scatterer +.>Time-varying distance between->Representing scatterer->And the time-varying distance, θ, of the receiving-end antenna unit q mn (t) represents the time-varying reflection phase of the (m, n) -th smart reflection unit, f IRS (t) is the Doppler shift of the NLoS component reflected by the intelligent reflection surface IRS.
Further, the optimization problem is further simplified by simplifying the formula (3), specifically:
assuming that the auxiliary variable is and />And assume the time-varying reflection phase of the intelligent reflection surface IRS as
(1) When the LoS component is dominant, the power of the NLoS component is ignored, and the received signal power is dominated by the LoS component and the intelligent reflection surface IRS reflection component, so the optimization problem is further reduced to:
(2) When the NLoS component is dominant, the power of the LoS component is ignored, the received signal power is dominated by the NLoS component and the intelligent reflection surface IRS reflection component, and therefore, the optimization problem is reduced to:
When NLoS component is dominant, due to intelligent reflection surface IRS and receiving end R X The surrounding scatterers are randomly distributed, and the reflection phase theta affected by these scatterers mn (t) is a random variable, and it is difficult for the intelligent reflection surface IRS to acquire an accurate reflection phase; to increase the power of the received signal, the statistical reflection phase of the intelligent reflection surface IRS is studied instead of the precise random variable phase; thus, a formula (6) is constructed which is identical to formula (5) and whose expression is as follows:
further, the solving method of the time-varying reflection phase of the optimal intelligent reflection surface IRS in step S4 is as follows:
when the LoS component is stronger than the NLoS component:
setting up wherein ,/>(m, n) position reflection unit and diffuser representing intelligent reflection surface IRS +.>Time-varying distance between->Representing scatterer->And the time-varying distance of the receiving end antenna unit q, f IRS (t) is the doppler shift of the NLoS component reflected by the intelligent reflective surface IRS, then the optimal time-varying reflection phase of the intelligent reflective surface IRS is:
wherein ,time-varying phase representing direct component between unmanned aerial vehicle UAV antenna unit p and receiving end antenna unit q, t representing time variable, χ A 、χ B All represent auxiliary variables, +.>Representing the optimal time-varying reflection phase of the (m, n) -th intelligent reflection unit;
When the NLoS component is stronger than the LoS component:
easy to know theta mn (t) is a random variable, it is difficult for the intelligent reflection surface IRS to obtain an accurate reflection phase, and in order to improve the reflection power of the received signal, the statistical reflection phase of the intelligent reflection surface IRS is studied; assume thatThe time-varying reflection phase of the intelligent reflection surface IRS is obtained as follows:
wherein ,a time-varying phase representing a scattering component between the unmanned aerial vehicle UAV antenna unit p and the receiving-end antenna unit q;
further optimizing to obtain the optimal time-varying reflection phase of the intelligent reflection surface IRS as follows:
wherein ,χC and χD Representing the auxiliary variable.
Further, in step S5, the calculation formula of the unmanned aerial vehicle channel space-time correlation function based on the intelligent reflection surface time-varying reflection phase is as follows:
wherein ,ρpq,p′q′ (t,d U ,d R τ) represents two time-varying transfer functions h pq(t) and hp′q′ Normalized space-time correlation function between (t+τ), t representing the time variable, d U Representing antenna spacing, d, between unmanned aerial vehicle UAV antenna units U ＝(p′-p)δ U P', p denote UAV terminal antenna elements, d R Representing receptionAntenna spacing, d, between end antenna elements R ＝(q′-q)δ R Q and q' respectively represent user terminal antenna elements, τ represents transmission delay, E [. Cndot.]Represents a statistical average operation, h pq (t) represents the complex channel gain, h, between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q p′q′ (t+τ) represents the complex channel gain between the unmanned aerial vehicle UAV antenna unit p 'and the user antenna unit q' (-) * Representing complex conjugate operations, |·| represents absolute value functions.
And determining the influence of the intelligent reflection surface IRS, the number of the intelligent reflection units and the size of the intelligent reflection units on the unmanned aerial vehicle UAV channel statistical characteristics by using the obtained unmanned aerial vehicle channel space-time correlation function based on the intelligent reflection surface time-varying reflection phase.
Furthermore, according to the unmanned aerial vehicle channel space-time correlation function based on the intelligent reflection surface time-varying reflection phase, the space-time correlation function of the LoS component is further obtained as follows:
wherein ,is a space-time dependent function of the LoS component, K represents the rice factor, λ represents the carrier wavelength, pi is the circumference ratio, exp (·) represents the exponential function, ζ pq (t) represents the time-varying distance, ζ, between the unmanned aerial vehicle UAV antenna unit p and the receiving-end antenna unit q p′q′ (t) represents the time-varying distance, f, between the unmanned aerial vehicle UAV antenna unit p' and the user antenna unit q LoS (t) represents the Doppler shift of the LoS component.
Furthermore, according to the unmanned aerial vehicle channel space-time correlation function based on the intelligent reflection surface time-varying reflection phase, the space-time correlation function of the NLoS component is further obtained as follows:
wherein ,is a space-time dependent function of the NLoS component, K represents the Leis factor, λ represents the carrier wavelength, pi is the circumference rate, exp (·) represents an exponential function, +.>Representing unmanned aerial vehicle UAV antenna unit p and diffuser->Time-varying distance between->Representing scatterer->And a time-varying distance between the receiver antenna elements q,/->Representing unmanned aerial vehicle UAV antenna unit p' and diffuser->Time-varying distance between->Representing scatterer->And the time-varying distance between the user side antenna elements q', f NLoS And (t) represents the Doppler shift of the NLoS component.
Furthermore, according to the unmanned aerial vehicle channel space-time correlation function based on the intelligent reflection surface time-varying reflection phase, the space-time correlation function of the IRS component is further obtained as follows:
wherein ,is a space-time correlation function of the reflection component of the IRS through the intelligent reflection surface, K represents a Leis factor, lambda represents a carrier wavelength, pi is a circumference ratio, exp (·) represents an exponential function, M represents the number of IRS row reflection units of the IRS, N represents the number of IRS column reflection units of the IRS, and xi pmn (t) represents the time-varying distance between the unmanned aerial vehicle UAV antenna unit p and the (m, n) -th smart reflection unit,/->Representing scatterer->And the time-varying distance, ζ, between the receiving-end antenna elements q p′mn (t) represents the time-varying propagation distance of the link between the unmanned aerial vehicle UAV antenna unit p' and the (m, n) -th smart reflection unit, +.>Representing scatterer->And the time-varying propagation distance, f, of the link between the user side antenna elements q IRS (t) represents the Doppler shift of the NLoS component reflected by the intelligent reflection surface IRS.
The beneficial effects are that: compared with the prior art, the invention has the following advantages:
(1) In the prior art, the situation that no intelligent reflecting surface direct component exists in reality is not considered. However, in the real communication situation, the situation that the signal emitted by the intelligent reflecting surface cannot directly reach the receiving end is likely to occur, and the invention fills the gap.
(2) According to the unmanned aerial vehicle channel model based on the intelligent reflection surface time-varying reflection phase, the time-varying reflection phase of the intelligent reflection surface is researched and designed by the intensity of the LoS component and the NLoS component respectively, and the real and complex communication situation is more comprehensively described. The obtained reflection phase of the optimized intelligent reflection surface is more accurate and reliable.
(3) When NLoS component is dominant, due to intelligent reflection surface IRS and receiving end R X The surrounding scatterers are randomly distributed, and the reflection phase theta affected by these scatterers mn (t) is a random variable, and it is difficult for the intelligent reflection surface IRS to acquire an accurate reflection phase. The invention researches the statistical reflection phase of the intelligent reflection surface IRS instead of the accurate random variable phase, which can lead the subsequent numerical simulation verification model to be more accurate and reliable.
Drawings
Fig. 1 is a schematic diagram of a unmanned aerial vehicle channel model based on an intelligent reflection surface time-varying reflection phase;
FIG. 2 is a graph showing the comparison of absolute envelope amplitudes at different reflection phases of an intelligent reflection surface IRS;
FIG. 3 is a graph showing the comparison of absolute envelope amplitudes for different numbers of reflection units of the intelligent reflection surface IRS when the channel is a rice channel;
FIG. 4 is a graph showing the comparison of absolute envelope amplitudes for different numbers of reflection units of the intelligent reflection surface IRS when the channel is a Rayleigh channel;
FIG. 5 is a graph comparing unmanned aerial vehicle channel spatial correlation functions for different numbers of intelligent reflection surface IRS reflection units;
fig. 6 is a graph comparing the time correlation functions of unmanned aerial vehicle channels based on intelligent reflection surface time-varying reflection phases at different unmanned aerial vehicle movement speeds.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the present invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications could be made by those skilled in the art without departing from the inventive concept. These are all within the scope of the present invention.
According to the invention, the intelligent reflection surface IRS is adopted to control the propagation environment of the unmanned aerial vehicle UAV channel, the optimal received signal power is respectively considered according to the intensity of LoS and NLoS components, the optimal intelligent reflection surface IRS reflection phase is obtained, and the time-varying parameters are adopted to describe the channel characteristics of the unmanned aerial vehicle UAV channel. And the capability of the intelligent reflection surface IRS for changing the unmanned aerial vehicle channel propagation environment is considered, namely the influence of the number of the intelligent reflection surface IRS reflection units and the sizes of the reflection units on the unmanned aerial vehicle channel statistical characteristics (Doppler frequency shift and multipath fading phenomenon). And the unmanned aerial vehicle channel assisted by the intelligent reflection surface IRS is also considered, the influence of the intelligent reflection surface IRS on the unmanned aerial vehicle channel statistical characteristic is explored, and the basis is better provided for system performance analysis and precoding algorithm design in the future.
The unmanned aerial vehicle channel model building method based on the intelligent reflection surface time-varying reflection phase comprises the following steps of: designing an optimization problem by taking the maximization of the received signal power as a target, and solving the optimization problem to obtain an optimal time-varying reflection phase; a time-varying distance parameter design step: obtaining a time-varying distance parameter and a time-varying Doppler frequency shift parameter among the unmanned aerial vehicle, the receiving end and the intelligent reflecting surface according to the geometrical model assisted by the intelligent reflecting surface; and a channel statistical characteristic analysis step: and analyzing the statistical characteristics of the unmanned aerial vehicle channel model based on the assistance of the intelligent reflecting surface according to the time-varying reflecting phase and the time-varying distance parameters of the intelligent reflecting surface. In the invention, the communication system adopting the intelligent reflecting surface can obviously improve the receiving power of the signal and reduce the multipath fading phenomenon of the received signal, so that the model building method can provide powerful support for exploring the key technology of the 6G communication system. The method specifically comprises the following steps:
S1, establishing an unmanned aerial vehicle channel model based on an intelligent reflection surface time-varying reflection phase according to the position relation among an unmanned aerial vehicle UAV, an intelligent reflection surface IRS and a receiving end;
the invention adopts a three-dimensional ellipse-cylinder to simulate an intelligent reflection surface IRS, an unmanned aerial vehicle UAV and a scatterer around a receiving end. The intelligent reflecting surface adopts a uniform plane reflecting array unit, the number of reflecting units in each row is assumed to be M, the number of reflecting units in each column is assumed to be N, and the intelligent reflecting surface IRS is assumed to be configured on the surface of a building. The height of the unmanned aerial vehicle UAV is obviously higher than that of a ground building, no building shielding exists between the unmanned aerial vehicle UAV and the intelligent reflection surface IRS, and a direct link is assumed between the unmanned aerial vehicle UAV and the intelligent reflection surface IRS.
The unmanned aerial vehicle channel model based on the intelligent reflection surface time-varying reflection phase of the embodiment is shown in fig. 1, and the model uses a bottom surface focal length of 2f 0 To simulate a smart reflecting surface and a diffuser near the receiving end, smart reflecting surface and receiving end R x At the focal point of the oval-cylindrical bottom surface. R is R x The surrounding scatterers were modeled by a three-dimensional cylinder with a bottom radius of R, N 1 A plurality of scatterers arranged on the surface of the cylinder, wherein the nth 1 The individual scatterers are denoted asIs positioned on the intelligent reflecting surface and the receiving end R x N of the surroundings 2 The scattering bodies are arranged on the surface of the elliptic cylinder, wherein the nth 2 The individual scatterers are denoted->N U and NR A uniform linear antenna array is arranged at the UAV and the receiving end R x And (3) upper part. A uniform planar antenna array of M rows and N columns is arranged on the intelligent reflective surface IRS.
S2, according to the established unmanned aerial vehicle channel model, taking into consideration whether an intelligent reflection surface IRS is applied and whether direct signals are contained or not, obtaining complex channel gains of all components;
to serve all users within the cell, the intelligent reflective surface IRS is arranged on the floor surface at the cell edge. When the distance between the smart reflection surface IRS and the receiver Rx is far, the channel between the smart reflection surface IRS and the receiver Rx is mainly a Rayleigh fading channel. The complex channel gain of the model channel of the invention thus comprises a LoS component, an NLoS component and a reflection component via the intelligent reflection surface IRS.
The invention considers the change of the IRS of the intelligent reflecting surface to the channel propagation environment, and provides an unmanned aerial vehicle channel model based on the time-varying reflection phase of the intelligent reflecting surface, wherein the complex gain of the channel is expressed as follows:
wherein ,hpq (t) represents the complex channel gain between the unmanned aerial vehicle UAV antenna unit p and the receiving-end antenna unit q, t represents the time variable,complex channel gain representing LoS component between unmanned aerial vehicle UAV antenna unit p and receiving end antenna unit q, +.>A complex channel gain representing the NLoS component between the unmanned aerial vehicle UAV antenna unit p and the receiving-end antenna unit q,the complex channel gain of IRS component between UAV antenna unit p and receiving end antenna unit q is represented, and the calculation formula is:
the calculation formula is as follows:
wherein ,Gu and Gr The gains of the unmanned aerial vehicle UAV and the receiving end respectively,is an unmanned aerial vehicle UAV to a receiving end R X Path loss, ζ pq (t) represents the time-varying distance between the unmanned aerial vehicle UAV antenna unit p and the user side antenna unit q, K is the Laise factor, λ is the carrier wavelength, pi represents the circumference ratio, ζ pq (t) represents a time-varying distance between the unmanned aerial vehicle UAV antenna unit p and the receiving-end antenna unit q, f LoS (t) represents the Doppler shift of the LoS component; />Representing unmanned aerial vehicle UAV antenna unit p and diffuser->Time-varying distance between->Representing scatterer->And a time-varying distance, f, between receiving-end antenna elements q NLoS (t) represents the doppler shift of the NLoS component; g is the gain of the intelligent reflector IRS, < > >Is a link UAV-IRS-R X Path loss, delta M Representing the spacing, delta, between adjacent antennas of a row of reflecting elements N Representing the antenna spacing, ζ, between adjacent column-reflecting elements pmn (t) represents the time-varying distance, ζ, between the unmanned aerial vehicle UAV antenna unit p and the (m, n) -th smart reflection unit mnq (t) represents the time-varying distance between the (m, n) -th smart reflection unit and the user antenna unit q, < >>Representing (m, n) -th smart reflection unit and scatterer +.>Time-varying distance, ζ between nq (t) represents scatterers->And the time-varying distance, θ, of the receiving-end antenna unit q mn (t) represents the time-varying reflection phase of the (m, n) -th smart reflection unit, f IRS (t) is the Doppler shift of the NLoS component reflected by the intelligent reflection surface IRS.
wherein ,
wherein ,θmn (t) represents the time-varying reflection phase, v, of the (m, n) -th smart reflection unit U Representing unmanned aerial vehicle UAV velocity vector, alpha UR Representing azimuth angle of unmanned aerial vehicle UAV antenna unit relative to user, gamma U Representing the azimuth angle of the unmanned aerial vehicle UAV movement direction, beta UR Representing the elevation angle of the unmanned aerial vehicle UAV antenna unit relative to the user's end,representing the elevation angle, v, of the movement direction of the UAV of the unmanned aerial vehicle R Representing the velocity vector of the user terminal, gamma R Indicating the azimuth angle of the movement direction of the user side.
S3, designing an optimization problem according to a power maximization principle of the received signal, and simplifying the optimization problem;
The optimization problem is expressed as follows:
wherein t represents a time variable, θ mn (t) represents the time-varying reflection phase of the (m, n) -th smart reflection unit,represents statistical mean calculation, h pq (t) represents the complex channel gain between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q.
The invention further considers the influence of the intelligent reflection surface IRS on the unmanned aerial vehicle channel propagation environment, wherein the optimization problem is further expressed as follows:
wherein ,representing statistical mean operation, t representing time variable, θ mn (t) represents the time-varying reflection phase of the (m, n) -th smart reflection unit, < -> and />Pulse amplitudes respectively representing LoS component, IRS component, NLoS component, cos (·) representing cosine function, ++>Time-varying phase representing direct component between unmanned aerial vehicle UAV antenna unit p and receiving-end antenna unit q, +.>Representing the time-varying phase of the multipath component between unmanned aerial vehicle UAV antenna unit p and receiving end antenna unit q after (m, n) -th intelligent reflecting unit, +.>Representing scattering fraction between unmanned aerial vehicle UAV antenna unit p and receiving end antenna unit qTime-varying phase of the quantity.
wherein ,
wherein ,Gu Represents unmanned aerial vehicle UAV gain, G represents intelligent reflection surface IRS gain, G r Indicating the gain of the antenna at the receiving end, Representing the path loss of an unmanned aerial vehicle UAV to an intelligent reflective surface IRS, +.>Represents the path loss from the UAV to the receiving end, K represents the Lais factor, lambda represents the carrier wavelength, pi represents the circumference ratio, and xi pq (t) represents a time-varying distance between the unmanned aerial vehicle UAV antenna unit p and the receiving-end antenna unit q, f LoS (t) Doppler shift of LoS component, < ->Representing unmanned aerial vehicle UAV antenna unit p and diffuser->Time-varying distance between->Representing scatterer->And the time-varying distance between the user side antenna units q, f NLoS (t) is the Doppler shift, ζ, of the NLoS component pmn (t) represents the time-varying distance between the unmanned aerial vehicle UAV antenna unit p and the (m, n) -th smart reflection unit,/->Representing (m, n) -th smart reflection unit and scatterer +.>Time-varying distance between->Representing scatterer->And the time-varying distance of the receiving end antenna unit q, f IRS (t) is the Doppler shift, θ, of the NLoS component reflected by the smart reflective surface IRS mn And (t) represents the time-varying reflection phase of the (m, n) -th intelligent reflection unit.
Assuming that the auxiliary variable is and />Assume that the time-varying reflection phase of the smart reflective surface is +.>The optimization problem is further simplified as follows:
(1) When the LoS component is stronger than the NLoS component, the LoS component is dominant, the power of the NLoS component can be ignored, and the received signal power is mainly governed by the LoS component and the intelligent reflection surface IRS reflection component, so that the optimization problem can be simplified as:
(2) When the NLoS component is stronger than the LoS component, the NLoS component is dominant, the power of the LoS component can be ignored, and the power of the received signal is mainly governed by the NLoS component and the IRS reflection component of the intelligent reflection surface, so that the optimization problem can be simplified as follows:
/>
when NLoS component is dominant, due to intelligent reflection surface IRS and receiving end R X The surrounding scatterers are randomly distributed, and the reflection phase theta affected by these scatterers mn (t) is a random variable, and it is difficult for the intelligent reflection surface IRS to acquire an accurate reflection phase; in order to improve the power of the received signal, the invention researches the statistical reflection phase of the intelligent reflection surface IRS instead of the accurate random variable phase; thus, a formula (6) is constructed which is identical to formula (5) and whose expression is as follows:
s4, in order to optimize the power of the received signal and reduce Doppler frequency shift, obtaining the optimal IRS reflection phase of the intelligent reflection surface;
the simplified optimization problem is considered in two cases; case 1: the LoS component is stronger than the NLoS component; case 2: the NLoS component is stronger than the LoS component; the time-varying reflection phases of the optimal intelligent reflection surfaces IRS of the case 1 and the case 2 are respectively obtained;
because the Rayleigh fading channel is between the intelligent reflection surface IRS and the receiving end Rx, it is difficult to design an accurate reflection phase according to a certain reflection component passing through the intelligent reflection surface IRS, so that the reflection phase of the intelligent reflection surface IRS is designed by adopting statistical channel information.
When the LoS component is stronger than the NLoS component:
setting up wherein ,/>Representing (m, n) -th smart reflection unit and scatterer +.>Time-varying distance between->Representing scatterer->And the time-varying distance of the receiving end antenna unit q, f IRS (t) is the doppler shift of the NLoS component reflected by the intelligent reflective surface IRS, then the optimal time-varying reflection phase of the intelligent reflective surface IRS is:
wherein ,representing the optimal time-varying reflection phase of the (m, n) -th smart reflection unit,/for>Time-varying phase representing direct component between unmanned aerial vehicle UAV antenna unit p and receiving end antenna unit q, t representing time variable, χ A 、χ B All represent assistanceThe variables, the calculation formula is:
wherein ,representing scatterer->Probability density function of azimuth angle +.>Representing scatterer->Probability density function of elevation angle of +.>Representing scatterer->Azimuth angle of->Representing scatterer->Is a standard for a large number of different angles of elevation.
When the NLoS component is stronger than the LoS component:
easy to know theta mn (t) is a random variable, it is difficult for the intelligent reflection surface IRS to obtain an accurate reflection phase, and in order to improve the reflection power of the received signal, the statistical reflection phase of the intelligent reflection surface IRS is studied; assume thatThe time-varying reflection phase of the intelligent reflection surface IRS is obtained as follows: />
wherein ,Representing the time-varying phase of the scattering component between the unmanned aerial vehicle UAV antenna unit p and the receiving-end antenna unit q.
Further optimizing to obtain the optimal time-varying reflection phase of the intelligent reflection surface IRS as follows:
wherein ,χC and χD Representing the auxiliary variable, the calculation formula is:
wherein ,representing the optimal time-varying reflection phase of the (m, n) -th smart reflection unit,/for>Representing scatterer->Probability density function of azimuth angle +.>Representing scatterer->Probability density function of elevation angle of +.>Representing scatterer->Azimuth angle of->Representing scatterer->Is a standard for a large number of different angles of elevation.
S5, through the time-varying reflection phase of the optimal intelligent reflection surface IRS obtained in the step S4, according to the definition of a correlation function, the complex channel gain of each component obtained in the step S2 and the complex conjugate thereof are statistically averaged, the space-time correlation function of the unmanned aerial vehicle channel model based on the time-varying reflection phase of the intelligent reflection surface is solved, the space-time correlation functions of the LoS component, the NLoS component and the IRS component are further solved, and the influence of the intelligent reflection surface IRS on the unmanned aerial vehicle channel characteristic is determined through correlation analysis. Specific:
unmanned aerial vehicle channel space-time correlation function based on intelligent reflecting surface time-varying reflection phase, the calculation formula is as follows:
wherein ,ρpq,p′q′ (t,d U ,d R τ) represents two time-varying transfer functions h pq(t) and hp′q′ Normalized space-time correlation function between (t+τ), t representing the time variable, d U Representing antenna spacing, d, between unmanned aerial vehicle UAV antenna units U ＝(p′-p)δ U P', p denote UAV terminal antenna elements, d R Representing the antenna spacing between receiving end antenna elements, d R ＝(q′-q)δ R Q and q' respectively represent user terminal antenna elements, τ represents transmission delay, E [. Cndot.]Represents a statistical average operation, h pq (t) represents the complex channel gain, h, between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q p′q′ (t+τ) represents the complex channel gain between the unmanned aerial vehicle UAV antenna unit p 'and the user antenna unit q' (-) * Representing complex conjugate operations, |·| represents absolute value functions.
The spatial correlation function of the LoS component is:
wherein ,is a space-time dependent function of the LoS component, K represents the rice factor, λ represents the carrier wavelength, pi is the circumference ratio, exp (·) represents the exponential function, ζ pq (t) represents the time-varying distance, ζ, between the unmanned aerial vehicle UAV antenna unit p and the receiving-end antenna unit q p′q′ (t) represents the time-varying distance, f, between the unmanned aerial vehicle UAV antenna unit p' and the user antenna unit q LoS (t) represents the Doppler shift of the LoS component.
The spatial correlation function of the NLoS component is:
wherein ,is a space-time dependent function of the NLoS component, K represents the Leis factor, λ represents the carrier wavelength, pi is the circumference rate, exp (·) represents an exponential function, +.>Representing unmanned aerial vehicle UAV antenna unit p and diffuser->Time-varying distance between->Representing scatterer->And a time-varying distance between the receiver antenna elements q,/->Representing unmanned aerial vehicle UAV antenna unit p' and diffuser->Time-varying distance between->Representing scatterer->And the time-varying distance between the user side antenna elements q', f NLoS And (t) represents the Doppler shift of the NLoS component.
The spatial correlation function of the IRS component is:
wherein ,is a space-time correlation function of the reflection component of the IRS through the intelligent reflection surface, K represents a Leis factor, lambda represents a carrier wavelength, pi is a circumference ratio, exp (·) represents an exponential function, M represents the number of IRS row reflection units of the IRS, N represents the number of IRS column reflection units of the IRS, and xi pmn (t) represents unmanned aerial vehicle UAV antenna units p and (m, n)-th time-varying distance between smart reflective units, < >>Representing scatterer->And the time-varying distance, ζ, between the receiving-end antenna elements q p′mn (t) represents the time-varying propagation distance of the link between the unmanned aerial vehicle UAV antenna unit p' and the (m, n) -th smart reflection unit, +. >Scattering body->And the time-varying propagation distance, f, of the link between the user side antenna elements q IRS (t) represents the Doppler shift of the NLoS component reflected by the intelligent reflection surface IRS.
And finally, determining the influence of the intelligent reflection surface IRS, the number of the intelligent reflection units and the size of the intelligent reflection units on the unmanned aerial vehicle UAV channel statistical characteristics by using the obtained unmanned aerial vehicle channel space-time correlation function based on the intelligent reflection surface time-varying reflection phase.
Fig. 2 is a graph comparing absolute envelope amplitudes of a conventional unmanned aerial vehicle channel model and an unmanned aerial vehicle channel model based on an intelligent reflection surface time-varying reflection phase under different IRS reflection phases. In fig. 2, the time-varying phase of phase 1 of the IRS reflection unit is: θ mn (t) =0; the time-varying phase of phase 2 of the IRS reflection unit is:the time-varying phase of phase 3 of the IRS reflection unit is the optimal reflection phase proposed by the present invention. It can be seen from fig. 2 that the absolute envelope amplitude of the received signal can be obviously improved by adopting the intelligent reflection surface IRS, and meanwhile, the absolute envelope amplitude of the received signal can be enhanced by adjusting the time-varying phase of the intelligent reflection surface, so that it is verified that the model of the invention can effectively change the propagation environment between the unmanned aerial vehicle and the receiving end.
Fig. 3 is a graph showing the comparison of absolute envelope amplitudes of different numbers of reflection units of the intelligent reflection surface IRS when the channel is rice channel. Fig. 4 is a graph comparing absolute envelope amplitudes of different numbers of reflection units of the intelligent reflection surface IRS when the channel is a rayleigh channel. It can be seen from fig. 3 and 4 that increasing the number of smart reflecting units under the rayleigh and rice channels significantly enhances the absolute envelope amplitude of the received signal. The phases obtained by formulae (11) and (17) are correct at the same time
FIG. 5 is a graph comparing unmanned aerial vehicle channel spatial correlation functions for different numbers of intelligent reflection surface IRS reflection units; from fig. 5 it can be seen that the spatial correlation of the drone channels based on the smart reflection surface time-varying reflection phases is related to the number of smart reflection units. Spatial correlation follows delta U Increasing and decreasing. The initial value of the spatial correlation is reduced along with the increase of the intelligent reflection unit, and meanwhile, the non-stationary characteristic of the space of the intelligent reflection surface IRS-assisted unmanned aerial vehicle channel model is also displayed.
FIG. 6 is a graph comparing the time correlation functions of unmanned aerial vehicle channels based on intelligent reflection surface time-varying reflection phases at different unmanned aerial vehicle movement speeds; it can be seen from fig. 6 that the time dependence gradually decreases as the time delay τ increases. From fig. 6, it can be seen that the time correlation function of the unmanned aerial vehicle channel is obviously enhanced after the intelligent reflection surface IRS is adopted.
In summary, in the unmanned aerial vehicle channel model, the unmanned aerial vehicle sending signal is reflected by the intelligent reflecting surface and finally received by the user side. Wherein the reflection phase of the intelligent reflection surface is adjustable. In order to obtain the optimal received signal power, considering the situation that no intelligent reflecting surface direct component exists, the time-varying reflecting phase of the intelligent reflecting surface is respectively researched and designed by the strength of a Line of Sight (LoS) component and a non-Line of Sight (Non Line of Sight, loS) component. The numerical results show that the power of the received signal can be increased by increasing the number of intelligent reflecting surface reflecting units. The spatial correlation decreases as the number of intelligent reflective surface reflecting units increases. And the time dependence increases as the number of intelligent reflective surface reflecting units increases. The model building method can provide help and support for exploring key technologies of the next generation mobile communication system.
Claims (10)
1. The unmanned aerial vehicle channel model building method based on the intelligent reflection surface time-varying reflection phase is characterized by comprising the following steps of:
s1, establishing an unmanned aerial vehicle channel model based on an intelligent reflection surface time-varying reflection phase according to the position relation among an unmanned aerial vehicle UAV, an intelligent reflection surface IRS and a receiving end;
The model adopts a three-dimensional cylinder with the bottom radius of R to simulate the receiving end R x Surrounding scatterers, N 1 Individual scatterersIs arranged on the surface of the three-dimensional cylinder; adopting a focal length of 2f at the bottom 0 To simulate the intelligent reflection surface IRS and the scattering body near the receiving end, and the intelligent reflection surface IRS and the receiving end R x Arranged at the focus of the bottom surface of the three-dimensional ellipse-cylinder, N 2 Individual scatterers->Is arranged on the surface of a three-dimensional elliptical-cylinder; be provided with N on unmanned aerial vehicle UAV U Uniform linear antenna array, receiving end R x On which N is arranged R The intelligent reflection surface IRS is provided with M rows and N columns of uniform plane antenna arrays;
s2, according to the unmanned aerial vehicle channel model established in the step S1, taking into consideration whether an intelligent reflection surface IRS is applied or not and whether direct signals are contained or not, obtaining complex channel gains of all components;
the intelligent reflection surface IRS is configured on the floor surface of the cell edge, and when the distance between the intelligent reflection surface IRS and the receiving end Rx is far, the channel between the intelligent reflection surface IRS and the receiving end Rx is mainly a Rayleigh fading channel; the complex channel gain of the unmanned aerial vehicle channel model comprises a LoS component, an NLoS component and a reflection component passing through the intelligent reflection surface IRS;
S3, designing an optimization problem according to a power maximization principle of the received signal, and simplifying the optimization problem;
s4, considering the simplified optimization problem in two cases; case 1: the LoS component is stronger than the NLoS component; case 2: the NLoS component is stronger than the LoS component; the time-varying reflection phases of the optimal intelligent reflection surfaces IRS of the case 1 and the case 2 are respectively obtained;
s5, through the time-varying reflection phase of the optimal intelligent reflection surface IRS obtained in the step S4, according to the definition of a correlation function, the complex channel gain of each component obtained in the step S2 and the complex conjugate thereof are statistically averaged, the space-time correlation function of the unmanned aerial vehicle channel model based on the time-varying reflection phase of the intelligent reflection surface is solved, the space-time correlation functions of the LoS component, the NLoS component and the IRS component are further solved, and the influence of the intelligent reflection surface IRS on the unmanned aerial vehicle channel characteristic is determined through correlation analysis.
2. The unmanned aerial vehicle channel model building method based on the intelligent reflection surface time-varying reflection phase according to claim 1, wherein the complex channel gain obtained in step S2 is expressed as follows:
wherein ,hpq (t) represents the complex channel gain between the unmanned aerial vehicle UAV antenna unit p and the receiving-end antenna unit q, t represents the time variable, A complex channel gain representing the LoS component between the UAV antenna element p and the receiving end antenna element q,complex channel gain representing NLoS component between unmanned aerial vehicle UAV antenna unit p and receiving end antenna unit q, +.>Representing unmanned aerial vehicle UAV antenna unit pAnd the complex channel gain of the IRS component between the receiving end antenna unit q, the calculation formula is as follows:
wherein ,Gu and Gr The gains of the unmanned aerial vehicle UAV and the receiving end respectively,is an unmanned aerial vehicle UAV to a receiving end R X K is the Lais factor, lambda is the carrier wavelength, xi pq (t) represents a time-varying distance between the unmanned aerial vehicle UAV antenna unit p and the receiving-end antenna unit q, f LoS (t) represents the Doppler shift of the LoS component; />Representing unmanned aerial vehicle UAV antenna unit p and scattererTime-varying distance between->Representing scatterer->And a time-varying distance, f, between receiving-end antenna elements q NLoS (t) represents Doppler shift of NLoS component, G is gain of intelligent reflection surface IRS, +.>Is a link UAV-IRS-R X Path loss, ζ pmn (t) represents the time-varying distance, ζ, between the unmanned aerial vehicle UAV antenna unit p and the (m, n) -th smart reflection unit mnq (t) represents the time-varying distance between the (m, n) -th smart reflection unit and the user antenna unit q, < >>Representing (m, n) -th smart reflection unit and scatterer +. >Time-varying distance between->Representing scatterer->And the time-varying distance, θ, of the receiving-end antenna unit q mn (t) represents the time-varying reflection phase of the (m, n) -th smart reflection unit, f IRS (t) is the Doppler shift of the NLoS component reflected by the intelligent reflection surface IRS.
3. The unmanned aerial vehicle channel model building method based on the intelligent reflection surface time-varying reflection phase according to claim 1, wherein the optimization problem in step S3 is expressed as follows:
wherein t represents a time variable, θ mn (t) represents the time-varying reflection phase of the (m, n) -th smart reflection unit,represents statistical mean calculation, h pq (t) represents the complex channel gain between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q.
4. The unmanned aerial vehicle channel model building method based on the intelligent reflection surface time-varying reflection phase according to claim 3, wherein the optimization problem is simplified by considering the concentrated received signal power as follows:
wherein ,representing statistical mean operation, t representing time variable, θ mn (t) represents the time-varying reflection phase of the (m, n) -th smart reflection unit, < -> and />Pulse amplitudes respectively representing LoS component, IRS component, NLoS component, cos (·) representing cosine function, ++>Time-varying phase representing direct component between unmanned aerial vehicle UAV antenna unit p and receiving-end antenna unit q, +. >Representing the time-varying phase of the multipath component between unmanned aerial vehicle UAV antenna unit p and receiving end antenna unit q after (m, n) -th intelligent reflecting unit, +.>A time-varying phase representing a scattering component between the unmanned aerial vehicle UAV antenna unit p and the receiving-end antenna unit q;
wherein ,
wherein ,Gu Represents unmanned aerial vehicle UAV gain, G represents intelligent reflection surface IRS gain, G r Indicating the gain of the antenna at the receiving end,representing the path loss of an unmanned aerial vehicle UAV to an intelligent reflective surface IRS, +.>Represents the path loss from the UAV to the receiving end, K represents the Lais factor, lambda represents the carrier wavelength, pi represents the circumference ratio, and xi pq (t) represents a time-varying distance between the unmanned aerial vehicle UAV antenna unit p and the receiving-end antenna unit q, f LoS (t) Doppler shift of LoS component, < ->Representing unmanned aerial vehicle UAV antenna unit p and diffuser->Time-varying distance between->Representing scatterer->And the time-varying distance between the user side antenna units q, f NLoS (t) is the Doppler shift, ζ, of the NLoS component pmn (t) represents the time-varying distance between the unmanned aerial vehicle UAV antenna unit p and the (m, n) -th smart reflection unit,/->Representing (m, n) -th smart reflection unit and scatterer +.>The time-varying distance between the two,representing scatterer->And the time-varying distance, θ, of the receiving-end antenna unit q mn (t) represents the time-varying reflection phase of the (m, n) -th smart reflection unit, f IRS (t) is the Doppler shift of the NLoS component reflected by the intelligent reflection surface IRS.
5. The unmanned aerial vehicle channel model building method based on the intelligent reflection surface time-varying reflection phase according to claim 4, wherein the optimization problem simplification formula (3) is further simplified, specifically:
assuming that the auxiliary variable is and />And assuming a time-varying reflection phase of the intelligent reflection surface IRS of +.>
(1) When the LoS component is dominant, the power of the NLoS component is ignored, and the received signal power is dominated by the LoS component and the intelligent reflection surface IRS reflection component, so the optimization problem is further reduced to:
(2) When the NLoS component is dominant, the power of the LoS component is ignored, the received signal power is dominated by the NLoS component and the intelligent reflection surface IRS reflection component, and therefore, the optimization problem is reduced to:
when NLoS component is dominant, due to intelligent reflection surface IRS and receiving end R X The surrounding scatterers are randomly distributed, and the reflection phase theta affected by these scatterers mn (t) is a random variable, and it is difficult for the intelligent reflection surface IRS to acquire an accurate reflection phase; to increase the power of the received signal, the statistical reflection phase of the intelligent reflection surface IRS is studied instead of the precise random variable phase; thus, a formula (6) is constructed which is identical to formula (5) and whose expression is as follows:
6. The unmanned aerial vehicle channel model building method based on the intelligent reflection surface time-varying reflection phase according to claim 1, wherein the solving method of the time-varying reflection phase of the optimal intelligent reflection surface IRS in step S4 is as follows:
when the LoS component is stronger than the NLoS component:
setting up wherein ,/>(m, n) position reflection unit and diffuser representing intelligent reflection surface IRS +.>Time-varying distance between->Representing scatterer->And the time-varying distance of the receiving end antenna unit q, f IRS (t) is the doppler shift of the NLoS component reflected by the intelligent reflective surface IRS, then the optimal time-varying reflection phase of the intelligent reflective surface IRS is:
wherein ,time-varying phase representing direct component between unmanned aerial vehicle UAV antenna unit p and receiving end antenna unit q, t representing time variable, χ A 、χ B All represent auxiliary variables, +.>Representing the optimal time-varying reflection phase of the (m, n) -th intelligent reflection unit;
when the NLoS component is stronger than the LoS component:
easy to know theta mn (t) is a random variable, it is difficult for the intelligent reflection surface IRS to obtain an accurate reflection phase, and in order to improve the reflection power of the received signal, the statistical reflection phase of the intelligent reflection surface IRS is studied; assume thatThe time-varying reflection phase of the intelligent reflection surface IRS is obtained as follows:
wherein ,a time-varying phase representing a scattering component between the unmanned aerial vehicle UAV antenna unit p and the receiving-end antenna unit q;
further optimizing to obtain the optimal time-varying reflection phase of the intelligent reflection surface IRS as follows:
wherein ,χC and χD Representing the auxiliary variable.
7. The unmanned aerial vehicle channel model building method based on the intelligent reflection surface time-varying reflection phase according to claim 1, wherein in step S5, the unmanned aerial vehicle channel space-time correlation function based on the intelligent reflection surface time-varying reflection phase is calculated according to the following formula:
wherein ,ρpq,p′q′ (t,d U ,d R τ) represents two time-varying transfer functions h pq(t) and hp′q′ Normalized space-time correlation function between (t+τ), t representing the time variable, d U Representing antenna spacing, d, between unmanned aerial vehicle UAV antenna units U ＝(p′-p)δ U P', p denote UAV terminal antenna elements, d R Representing the antenna spacing between receiving end antenna elements, d R ＝(q′-q)δ R Q and q' respectively represent user terminal antenna elements, τ represents transmission delay, E [. Cndot.]Represents a statistical average operation, h pq (t) represents the complex channel gain, h, between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q p′q′ (t+τ) represents a droneComplex channel gain between UAV antenna unit p 'and user antenna unit q' (·) * Representing complex conjugate operations, |·| representing absolute value functions;
and determining the influence of the intelligent reflection surface IRS, the number of the intelligent reflection units and the size of the intelligent reflection units on the unmanned aerial vehicle UAV channel statistical characteristics by using the obtained unmanned aerial vehicle channel space-time correlation function based on the intelligent reflection surface time-varying reflection phase.
8. The method for building the unmanned aerial vehicle channel model based on the intelligent reflection plane time-varying reflection phase according to claim 7, wherein the space-time correlation function of the unmanned aerial vehicle channel based on the intelligent reflection plane time-varying reflection phase is further obtained as follows:
wherein ,is a space-time dependent function of the LoS component, K represents the rice factor, λ represents the carrier wavelength, pi is the circumference ratio, exp (·) represents the exponential function, ζ pq (t) represents the time-varying distance, ζ, between the unmanned aerial vehicle UAV antenna unit p and the receiving-end antenna unit q p′q′ (t) represents the time-varying distance, f, between the unmanned aerial vehicle UAV antenna unit p' and the user antenna unit q LoS (t) represents the Doppler shift of the LoS component.
9. The method for building the unmanned aerial vehicle channel model based on the intelligent reflection plane time-varying reflection phase according to claim 7, wherein the space-time correlation function of the unmanned aerial vehicle channel based on the intelligent reflection plane time-varying reflection phase is further obtained as follows:
wherein ,is a space-time dependent function of the NLoS component, K represents the Leis factor, λ represents the carrier wavelength, pi is the circumference rate, exp (·) represents an exponential function, +.>Representing unmanned aerial vehicle UAV antenna unit p and diffuser->Time-varying distance between->Representing scatterer->And a time-varying distance between the receiver antenna elements q,/->Representing unmanned aerial vehicle UAV antenna unit p' and diffuser->Time-varying distance between->Representing scatterer->And the time-varying distance between the user side antenna elements q', f NLoS And (t) represents the Doppler shift of the NLoS component.
10. The method for building the unmanned aerial vehicle channel model based on the intelligent reflection plane time-varying reflection phase according to claim 7, wherein the space-time correlation function of the IRS component is further obtained according to the unmanned aerial vehicle channel space-time correlation function based on the intelligent reflection plane time-varying reflection phase, which is:
wherein ,is a space-time correlation function of the reflection component of the IRS through the intelligent reflection surface, K represents a Leis factor, lambda represents a carrier wavelength, pi is a circumference ratio, exp (·) represents an exponential function, M represents the number of IRS row reflection units of the IRS, N represents the number of IRS column reflection units of the IRS, and xi pmn (t) represents the time-varying distance between the unmanned aerial vehicle UAV antenna unit p and the (m, n) -th smart reflection unit,/- >Representing scatterer->And the time-varying distance, ζ, between the receiving-end antenna elements q p′mn (t) represents the time-varying propagation distance of the link between the unmanned aerial vehicle UAV antenna unit p' and the (m, n) -th smart reflection unit, +.>Representing scatterer->And the time-varying propagation distance, f, of the link between the user side antenna elements q IRS (t) represents the Doppler shift of the NLoS component reflected by the intelligent reflection surface IRS.
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