CN114124263A - Unmanned aerial vehicle channel model building method based on large-scale intelligent reflection unit - Google Patents
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Abstract
The invention discloses an unmanned aerial vehicle channel model building method based on a large-scale intelligent reflection unit, which comprises the steps of firstly building an unmanned aerial vehicle channel model based on the large-scale intelligent reflection unit according to an actual ground communication scene of an unmanned aerial vehicle, and obtaining the complex channel gain of a signal; designing an optimization problem according to an unmanned aerial vehicle channel model and a received signal power maximization criterion; because the power of the received signal is mainly concentrated on the direct component reflected by the intelligent reflecting surface IRS, the process of solving the reflection phase is simplified; solving an optimal IRS reflection phase of the intelligent reflection surface according to a simplified optimization problem; and solving a space-time correlation function based on the assistance of the intelligent reflector IRS according to the complex channel gain and the optimal intelligent reflector IRS reflection phase, and determining the influence of different parameters on the channel characteristics of the unmanned aerial vehicle through correlation analysis. The method provides certain help for exploring the influence of the intelligent reflector IRS on the channel statistical characteristics of the unmanned aerial vehicle.
Description
Technical Field
The invention relates to a wireless communication technology, in particular to an unmanned aerial vehicle channel model building method based on a large-scale intelligent reflection unit.
Background
In recent years, with the rapid development of unmanned aerial vehicle technology, communication technology based on Unmanned Aerial Vehicles (UAVs) has attracted great interest to researchers in various fields. However, due to the rapid movement of the drone, the communication scene of the drone is a non-stationary process, and the received signal power of the communication system is seriously affected by the doppler shift caused by the movement of the drone and the receiving end. Therefore, unmanned aerial vehicle communication based on the assistance of the intelligent reflector IRS attracts people's extensive attention. The influence of Doppler frequency shift and multipath fading on the received signal can be counteracted only by adjusting the reflection phase of the intelligent reflecting surface IRS, so that the power of the received signal is maximized, and meanwhile, the communication quality is improved. The reflecting units in the intelligent reflecting surface IRS are uniformly arranged and equal in size, and each reflecting unit can independently change the phase or amplitude of an incident signal.
With the increase of the scale of the reflection unit of the intelligent reflection surface IRS, the performance of the communication system assisted by the intelligent reflection surface IRS will also increase, but the computational complexity thereof will also increase a lot, so that a method for reducing the computational complexity is required to be found. The invention adopts a second-order approximation method of spherical wavefront to simulate the near-field effect of the large-scale intelligent reflector IRS, so as to reduce the calculation complexity.
In the prior art, some research researches on far-field path loss models and reflection phases of intelligent reflecting surface IRS auxiliary propagation environments, wherein the reflection phases are aligned with the propagation phases of LoS components, and the power of received signals is improved. Some researches research on statistical characteristics such as a spatial correlation matrix and a correlation matrix distance under an isotropic scattering environment, when the size of an intelligent reflecting surface IRS reflecting unit is increased, the correlation matrix distance is gradually increased, and the fact that a channel model based on the assistance of the intelligent reflecting surface IRS has spatial non-stationarity on the IRS reflecting unit is shown. In some models, the influence of the time-varying propagation phase on the channel statistical characteristics is considered, but the influence of the doppler shift due to the movement motion of the unmanned aerial vehicle UAV is ignored. Some have studied a non-stationary geometric model based on intelligent reflective surface IRS, but neglect the time-varying reflective phase of the intelligent reflective surface IRS. Some researches are carried out on the time correlation of an IRS auxiliary model based on the IRS time-varying reflection phase of the intelligent reflection surface, but the spatial non-stationarity of the IRS reflection unit is ignored.
In summary, the unmanned UAV channel modeling based on the intelligent reflector IRS assistance is also in the launch phase, where there are many aspects that are not well considered and have shortcomings that require further exploration and optimization.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide an accurate unmanned aerial vehicle channel model building method based on a large-scale intelligent reflection unit, and the model building method can provide powerful support for the exploration of the key technology of a 6G communication system.
The technical scheme is as follows: the invention discloses an unmanned aerial vehicle channel model building method based on a large-scale intelligent reflection unit, which comprises the following steps:
s1, configuring the intelligent reflecting surface IRS on the surface of a building at the edge of a served cell, simulating a vertical building structure around a receiving end by using a three-dimensional cylinder, simulating the near field effect of the large-scale intelligent reflecting surface IRS by using second-order approximation of spherical wavefront, assuming that a scatterer is positioned on the surface of the three-dimensional cylinder, and establishing an unmanned aerial vehicle channel model based on the large-scale intelligent reflecting unit, wherein the intelligent reflecting surface IRS comprises intelligent reflecting units which are uniformly arranged; obtaining the complex channel gain of the channel according to the model;
s2, obtaining the complex channel gain according to the unmanned aerial vehicle channel model based on the large-scale intelligent reflection unit, wherein the complex channel gain comprises two components: the complex channel gain of the Unmanned Aerial Vehicle (UAV) which is directly transmitted with the receiving end without the Intelligent Reflector (IRS) and the complex channel gain of the UAV which is transmitted with the receiving end by the Intelligent Reflector (IRS);
s3, designing an optimization problem by maximizing the received signal power according to the unmanned aerial vehicle channel model based on the large-scale intelligent reflection unit;
s4, simplifying optimization problems: the optimization problem proposed in S3 is computationally very complex, so to reduce complexity, the problem needs to be further simplified; when the scale of the intelligent reflection unit in the intelligent reflection surface IRS is larger, the power of the received signal is mainly controlled by the reflection signal passing through the intelligent reflection surface IRS, and the complex channel gain of the reflection signal is mainly the direct component of the reflection signal, so that the process of solving the reflection phase is simplified;
s5, according to the simplified optimization problem, considering the time-varying Doppler frequency shift of the multipath component between the UAV antenna unit p and the receiving end antenna unit q after passing through the (m, n) -th intelligent reflection unit
When the optimal IRS reflection phase of the intelligent reflection surface is solved, the Doppler frequency shift of the direct component is subtracted to enhance the power of the received signal;
s6, solving a space-time correlation function based on the intelligent reflector IRS assistance through the complex channel gain obtained in the step S2 and the optimal intelligent reflector IRS reflection phase obtained in the step S5, and determining the influence of different parameters on the unmanned aerial vehicle channel characteristics through correlation analysis.
Further, the complex channel gain h obtained in step S1pq(t, τ), which is expressed as follows:
where t represents a time variable, L represents the number of taps, L represents the total number of taps, clDenotes the gain of the first tap, hl,pq(t) represents the complex channel gain between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q directly without the smart reflector IRS,
represents the complex channel gain, τ, between the UAV antenna unit p and the receiving-end antenna unit q through the intelligent reflector IRSl(t) represents the propagation delay of the ith tap, and δ (·) represents the impulse function.
Further, in step S2, the unmanned aerial vehicle UAV antenna unit p directly connects to the receiving-end antenna without passing through the intelligent reflection surface IRSComplex channel gain h between units ql,pq(t) is represented as follows:
wherein ,
representing the complex channel gain of the direct component between the unmanned aerial vehicle UAV antenna element p and the receiving end antenna element q,
a complex channel gain representing a scattering component between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q;
wherein ,GtDenotes the transmit antenna gain, GrRepresenting the receiving-end antenna gain, gammaTRRepresents the path loss, K, of the UAV to the receiving end1Representing the rice factor, λ the carrier wavelength, t the time variable, π the circumference ratio, ξpq(t) represents the time-varying distance between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q,
the time-varying Doppler frequency shift of a direct component between an Unmanned Aerial Vehicle (UAV) antenna unit p and a receiving end antenna unit q is shown, delta (l-1) represents a delay impact function after l taps, and NlRepresenting scatterers
Number of (1), xipnl(t) denotes unmanned aerial vehicle UAV antenna unit p and scatterers
The time-varying distance between them,
representing scatterers
And the time-varying distance between the receiving-end antenna unit q,
representing the time-varying doppler shift of the scattered component between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q.
Further, in step S2, the complex channel gain of the unmanned aerial vehicle UAV transmitted through the intelligent reflector IRS and the receiving end
Is represented as follows:
wherein ,
represents the complex channel gain of the direct component between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q after being reflected by the intelligent reflecting surface IRS,
the complex channel gain of a scattering component between an Unmanned Aerial Vehicle (UAV) antenna unit p and a receiving end antenna unit q after being scattered by an intelligent reflecting surface IRS and a scatterer is represented;
wherein M represents a row position index of the intelligent reflection unit, N represents a column position index of the intelligent reflection unit, M represents a row reflection unit number of the intelligent reflection surface, N represents a column reflection unit number of the intelligent reflection surface, GtRepresenting the gain of the transmitting antenna, G representing the gain of the IRS reflecting element, GrRepresenting the receiving-end antenna gain, gammaTIRRepresents the path loss, K, from the UAV to the IRS and then to the receiving end2Representing the rice factor, λ the carrier wavelength, t the time variable, π the circumference ratio, ξpmn(t) represents the time-varying distance, ξ, between the unmanned aerial vehicle UAV antenna units p and the (m, n) -th smart reflector unitmnq(t) represents the time-varying distance between the (m, n) -th intelligent reflection unit and the receiving-end antenna unit q, thetamn(t) denotes the reflection phase of the intelligent reflective surface IRS at time t,
the time-varying Doppler frequency shift of multipath components between an Unmanned Aerial Vehicle (UAV) antenna unit p and a receiving end antenna unit q after passing through an (m, n) -th intelligent reflection unit is represented; n is a radical oflRepresenting scatterers
The number of taps, l represents the number of taps,
expressing (m, n) -th intelligent reflection unit and scatterer
The time-varying distance between them,
representing scatterers
And the time-varying distance between the receiving-end antenna unit q,
representing multipath components between an Unmanned Aerial Vehicle (UAV) antenna unit p and a receiving end antenna unit q via an intelligent reflecting surface IRS and a scatterer
The latter time-varying doppler shift.
Further, the optimization problem in step S3 is expressed as follows:
where t represents a time variable, θmn(t) denotes the reflection phase of the intelligent reflective surface IRS at time t,
represents a statistical mean operation, hpq(t) represents the complex channel gain of the multipath component between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q.
Further, step S4 includes the following steps:
s41, since the power of the received signal is mainly concentrated on the direct component reflected by the intelligent reflective surface IRS, the optimization problem in step S3 is simplified as follows:
wherein, t represents a time variable,
the method comprises the steps that complex channel gain of a direct component between an Unmanned Aerial Vehicle (UAV) antenna unit p and a receiving end antenna unit q after being reflected by an intelligent reflecting surface IRS is represented, and | represents an absolute value function;
s42, handle
In (1)The phase relationship is substituted into the formula (5), and the optimization problem is further simplified as follows:
wherein ,
the method is characterized in that the time-varying Doppler frequency shift of multipath components between an Unmanned Aerial Vehicle (UAV) antenna unit p and a receiving end antenna unit q after passing through an (M, N) -th intelligent reflection unit is represented, M represents the number of row reflection units of an intelligent reflection surface, M represents the row position index of the intelligent reflection unit, N represents the number of column reflection units of the intelligent reflection surface, N represents the column position index of the intelligent reflection unit, lambda represents the carrier wavelength, pi represents the circumferential ratio, xi represents the circumferential ratiopmn(t) represents the time-varying distance, ξ, between the unmanned aerial vehicle UAV antenna units p and the (m, n) -th smart reflector unitmnq(t) represents the time-varying distance between the (m, n) -th intelligent reflection unit and the receiving-end antenna unit q, thetamn(t) represents the reflection phase of the intelligent reflective surface IRS at time t.
Further, the step of solving the optimal IRS reflection phase of the intelligent reflection surface in step S5 includes the following steps:
s51, solving the optimal IRS reflection phase of the intelligent reflection surface according to the optimal IRS reflection phase optimization problem obtained by the formula (6)
The expression of (a) is as follows:
wherein ,
to represent
And 2 pi two numberThe remainder of the division;
s52, because formula (7) does not consider the time-varying Doppler frequency shift of the multipath component between the UAV antenna unit p and the receiving end antenna unit q after passing through the (m, n) -th intelligent reflection unit
Therefore, will
Further rewritten as:
wherein ,
the time-varying Doppler frequency shift of multipath components between an Unmanned Aerial Vehicle (UAV) antenna unit p and a receiving end antenna unit q after passing through an (m, n) -th intelligent reflection unit is shown.
Further, step S6 includes the following steps:
s61, solving a space-time correlation function based on the intelligent reflecting surface IRS assistance according to the definition by using the complex channel gain obtained in the step S2 and the optimal reflecting phase of the intelligent reflecting surface IRS obtained in the step S5, and calculating the formula as follows:
firstly, according to the definition formula of the space-time correlation function:
wherein ,
representing a spatio-temporal correlation function, δ, between two time-varying transfer functionsTRepresenting the antenna spacing, δ, between the unmanned aerial vehicle UAV antenna unitsRRepresents the antenna spacing between the antenna units at the user end, tau represents the propagation delay, t represents the time variable,
representing statistical mean operations (·)*Denotes a complex conjugate operation, hpq(t) represents the complex channel gain between the unmanned aerial vehicle UAV antenna unit p and the user side antenna unit q, hp′q′(t + τ) represents the complex channel gain between the unmanned aerial vehicle UAV antenna unit p 'and the user-side antenna unit q' after a time delay τ; | represents an absolute value function;
the expressions of the complex channel gain functions obtained in step S2 are respectively substituted to obtain specific spatio-temporal correlation functions as follows:
wherein ,
represents the space-time correlation of the direct component between the UAV antenna unit and the receiving end antenna unit, wherein lambda represents the carrier wavelength, pi represents the circumferential ratio, xipq(t) represents the time-varying distance, ξ, between the unmanned aerial vehicle UAV antenna unit p and the user side antenna unit qp′q′(t + τ) represents the time-varying distance between the human-machine UAV antenna unit p 'and the user-side antenna unit q' after a time delay τ,
indicating unmanned aerial vehicleTime-varying doppler shift of the direct component between UAV antenna unit p and user side antenna unit q;
represents the space-time correlation of the scattering component between the UAV antenna unit and the receiving-end antenna unit, NlRepresenting scatterers
The number of taps, l, xipnl(t) denotes unmanned aerial vehicle UAV antenna unit p and scatterers
Time-varying distance between, xip′nl(t + τ) denotes the unmanned aerial vehicle UAV antenna unit p' and scatterers after a time delay τ
The time-varying distance between them,
representing scatterers
And the time-varying distance between the subscriber-side antenna unit q,
showing scatterers after a time delay of tau
And the time varying distance between the subscriber side antenna unit q',
a time-varying doppler frequency shift representing a scattering component between the unmanned aerial vehicle UAV antenna unit p and the user-side antenna unit q;
representation of Unmanned Aerial Vehicle (UAV) skyThe space-time correlation of the direct component reflected by the intelligent reflector IRS between the line unit and the receiving end antenna unit, wherein M represents the number of row reflecting units of the intelligent reflector IRS, N represents the number of column reflecting units of the intelligent reflector IRS, and xipmn(t) represents the time-varying distance, ξ, between the unmanned aerial vehicle UAV antenna units p and the (m, n) -th smart reflector unitp′m′n′(t + τ) represents the time-varying distance, ξ, between the unmanned aerial vehicle UAV antenna units p ' and the (m ', n ') -th smart reflector unit after a time delay τ has elapsedmnq(t) represents the time-varying distance between the (m, n) -th intelligent reflection unit and the user terminal antenna unit q, xim′n′q′(t + τ) represents the time-varying distance between the (m ', n ') -th intelligent reflection unit and the user side antenna unit q ' after the time delay τ;
indicating that the space between the UAV antenna unit and the receiving end antenna unit passes through the intelligent reflector IRS and the scatterer
Space-time dependence of the reflected scattered component, δNIndicating the distance, δ, between the column reflective elements of the intelligent reflective surface IRSMRepresents the distance between the column reflective elements of the intelligent reflective surface IRS;
and S62, when the number of reflection units of the intelligent reflection IRS, the reflection phase of the intelligent reflection surface IRS and the flight track of the unmanned aerial vehicle UAV are changed, analyzing the influence of the parameter changes on the channel characteristics of the unmanned aerial vehicle through the correlation according to the space-time correlation function obtained above.
In one embodiment of the invention: an apparatus comprising a memory and a processor, wherein:
a memory for storing a computer program capable of running on the processor;
and the processor is used for executing the steps of the unmanned aerial vehicle channel model building method based on the large-scale intelligent reflection unit when the computer program is run.
In another embodiment of the invention: a storage medium having stored thereon a computer program that, when executed by at least one processor, performs the steps of the above-described method for channel model building for a large-scale intelligent reflection unit-based drone.
Has the advantages that: compared with the prior art, the unmanned aerial vehicle channel model building method based on the large-scale intelligent reflection unit adopts the intelligent reflection surface IRS to assist the unmanned aerial vehicle communication compared with the traditional unmanned aerial vehicle communication technology, and proves that the signal power of the received signal can be improved by adopting the intelligent reflection surface IRS. The reflection phase of the intelligent reflection surface IRS is the key to increase the received signal power. Therefore, the main contribution of the invention is to provide the optimal reflection phase of the intelligent reflecting surface, and the power of the received signal can be maximized. Meanwhile, the invention also solves the space-time correlation function based on the assistance of the intelligent reflector IRS, and researches the influence of the parameter changes on the channel characteristics of the unmanned aerial vehicle by analyzing the changes of the space-time correlation function when the number of reflection units of the intelligent reflector IRS, the reflection phase of the intelligent reflector IRS and the flight track of the unmanned aerial vehicle UAV are changed. Therefore, the method provides certain help for exploring the influence of the intelligent reflector IRS on the channel statistical characteristics of the unmanned aerial vehicle.
Drawings
FIG. 1 is a schematic diagram of a channel model of an unmanned aerial vehicle based on a large-scale intelligent reflection unit;
fig. 2 is a schematic diagram of three different flight trajectories of an unmanned aerial vehicle UAV;
FIG. 3 is a comparison graph of absolute envelope magnitudes of UAV-MIMO models for three different UAV flight trajectories;
FIG. 4 is a comparison graph of absolute envelope amplitudes of a broadband UAV-MIMO model in which an intelligent reflector IRS is adopted or not at different reflection unit scales and reflection phases;
FIG. 5 is a comparison graph of absolute transmission spatial correlations of UAV-MIMO models of whether intelligent reflector IRS is employed for different UAV flight trajectories;
fig. 6 is a comparison diagram of absolute transmission spatial correlation of a broadband drone-MIMO model whether to employ an intelligent reflector IRS for different drone flight trajectories.
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 invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
According to the invention, the intelligent reflector IRS is adopted to control the propagation environment of the UAV channel, the near field effect of the large intelligent reflector IRS is considered, the second-order approximation of spherical wavefront is utilized for modeling, the optimal received signal power is considered, and the optimal intelligent reflector IRS reflection phase is obtained. The invention considers the capability of the intelligent reflector IRS to change the unmanned aerial vehicle channel propagation environment, namely the influence of the number of the intelligent reflector IRS reflection units and the size of the reflection units on the unmanned aerial vehicle channel statistical characteristics. The invention considers the influence of different flight trajectories of the unmanned aerial vehicle UAV on the channel statistical characteristics of the unmanned aerial vehicle. The invention considers the influence of the intelligent reflector IRS on the channel statistical characteristics of the unmanned aerial vehicle under different time-varying reflection phases. The invention considers the unmanned aerial vehicle channel based on the intelligent reflector IRS, explores the influence of the intelligent reflector IRS on the unmanned aerial vehicle channel statistical characteristics, and better provides a basis for system performance analysis and precoding algorithm design in the future.
The invention discloses an unmanned aerial vehicle channel model building method based on a large-scale intelligent reflection unit, which comprises the following steps:
s1, according to an actual ground communication scene of the unmanned aerial vehicle UAV, determining the position relation among the unmanned aerial vehicle UAV, the intelligent reflector IRS and the receiving end, establishing an unmanned aerial vehicle channel model based on a large-scale intelligent reflector unit, and obtaining the complex channel gain of a channel;
the established unmanned aerial vehicle channel model based on the large-scale intelligent reflection unit is used for serving all users in the cell, and the intelligent reflection surface is configured on the surface of a building at the edge of the served cell. Then using a three-dimensional cylinder toAnd simulating a vertical building structure around the receiving end, and simulating the near-field effect of the large-scale intelligent reflecting surface by utilizing second-order approximation of spherical wavefront. The scatterers are assumed to be positioned on the surfaces of the three-dimensional cylinders, and the intelligent reflecting units on the intelligent reflecting surface are uniformly arranged; considering the change of the intelligent reflector IRS to the channel propagation environment, the established large-scale intelligent reflector-unit-based unmanned aerial vehicle channel model is shown in fig. 1, the invention adopts a three-dimensional cylinder to simulate the intelligent reflector IRS, the unmanned aerial vehicle UAV, and scatterers around the receiving end, the intelligent reflector adopts a uniform planar reflection array unit, the number of reflection units in each row is assumed to be M, the number of reflection units in each column is assumed to be N, and the intelligent reflector IRS is assumed to be configured on the surface of the building, so that all users in the local cell can be served. The height of the Unmanned Aerial Vehicle (UAV) is obviously higher than that of a ground building, no building is shielded between the UAV and the intelligent reflecting surface IRS, and a direct link is assumed between the UAV and the intelligent reflecting surface IRS. Wherein, in FIG. 1, HIRSIndicating the height of the intelligent reflecting surface, HTIndicating the altitude, R, of the unmanned aerial vehicle UAVlRadius, xi, of the ith three-dimensional cylinderpq(t) represents the time-varying distance between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q,
representing unmanned aerial vehicle UAV antenna unit p and scatterers
The time-varying distance between them,
representing scatterers
And time-varying distance xi between the antenna unit q and the receiving endTRRepresents the distance, ξ, between the unmanned aerial vehicle UAV and the receiving endpmn(t) represents the time-varying distance, ξ, between the unmanned aerial vehicle UAV antenna units p and the (m, n) -th smart reflector unitmnq(t) denotes (m, n) -th intelligenceThe time-varying distance between the reflecting unit and the receiving-end antenna unit q,
expressing (m, n) -th intelligent reflection unit and scatterer
Time-varying distance between, xiIRSRIndicating the distance, ξ, between the intelligent reflector IRS and the receiving endIRSTRepresenting the distance, θ, between the unmanned aerial vehicle UAV and the Intelligent Reflector IRSIRSIndicating the direction of the intelligent reflecting surface IRS in the x-y plane, alphaIRSTRepresents the direction, θ, of the unmanned aerial vehicle UAV relative to the Intelligent reflective surface IRS in the x-y planeRRepresenting the direction of the receiving end in the x-y plane, alphaTRRepresenting the orientation of the unmanned UAV relative to the receiver in the x-y plane, vTRepresenting the speed of movement, gamma, of the unmanned aerial vehicle UAVTAzimuth, theta, representing unmanned aerial vehicle UAV motion, elevation, representing unmanned aerial vehicle UAV motionTRepresenting the direction, v, of the receiving end of the unmanned UAV in the x-y planeRIndicating the speed of movement, gamma, of the receiving endRIndicating the azimuth angle of the receiving end,
representing scatterers
Is reaching an azimuth angle (AAoA),
representing scatterers
Is in the direction of departure (AAoD),
representing scatterers
Is the angle of elevation of arrival (EAoA),
representing scatterers
Elevation of departure (EAoD).
Obtaining the complex channel gain h of the channel according to the unmanned aerial vehicle channel model based on the large-scale intelligent reflection unitpqThe formula for the calculation of (t, τ) is:
where t represents a time variable, L represents the number of taps, L represents the total number of taps, clDenotes the gain of the first tap, hl,pq(t) represents the complex channel gain between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q directly without the smart reflector IRS,
represents the complex channel gain, τ, between the UAV antenna unit p and the receiving-end antenna unit q through the intelligent reflector IRSl(t) represents the propagation delay of the ith tap, and δ (·) represents the impulse function.
S2, obtaining the complex channel gain according to the unmanned aerial vehicle channel model based on the large-scale intelligent reflection unit, wherein the complex channel gain comprises two components: the complex channel gain of the Unmanned Aerial Vehicle (UAV) which is directly transmitted with the receiving end without the Intelligent Reflector (IRS) and the complex channel gain of the UAV which is transmitted with the receiving end by the Intelligent Reflector (IRS);
s21, complex channel gain h between Unmanned Aerial Vehicle (UAV) antenna unit p and receiving end antenna unit q directly without Intelligent Reflector (IRS)l,pq(t) is represented as follows:
wherein ,
representing the complex channel gain of the direct component between the unmanned aerial vehicle UAV antenna element p and the receiving end antenna element q,
a complex channel gain representing a scattering component between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q;
wherein ,GtDenotes the transmit antenna gain, GrRepresenting the receiving-end antenna gain, gammaTRRepresents the path loss, K, of the UAV to the receiving end1Representing the rice factor, λ the carrier wavelength, t the time variable, π the circumference ratio, ξpq(t) represents the time-varying distance between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q,
the time-varying Doppler frequency shift of a direct component between an Unmanned Aerial Vehicle (UAV) antenna unit p and a receiving end antenna unit q is shown, delta (l-1) represents a delay impact function after l taps, and NlRepresenting scatterers
The number of the (c) component (a),
representing unmanned aerial vehicle UAV antenna unit p and scatterers
The time-varying distance between them,
representing scatterers
And the time-varying distance between the receiving-end antenna unit q,
representing the time-varying doppler shift of the scattered component between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q.
S22, complex channel gain of unmanned aerial vehicle UAV through transmission between intelligent reflector IRS and receiving end
Is represented as follows:
wherein ,
represents the complex channel gain of the direct component between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q after being reflected by the intelligent reflecting surface IRS,
the complex channel gain of a scattering component between an Unmanned Aerial Vehicle (UAV) antenna unit p and a receiving end antenna unit q after being scattered by an intelligent reflecting surface IRS and a scatterer is represented;
wherein m represents the row position index of the intelligent reflection unit, and n represents the column bit of the intelligent reflection unitIndex, M represents the number of row reflective elements of the intelligent reflective surface, N represents the number of column reflective elements of the intelligent reflective surface, GtRepresenting the gain of the transmitting antenna, G representing the gain of the IRS reflecting element, GrRepresenting the receiving-end antenna gain, gammaTIRRepresents the path loss, K, from the UAV to the IRS and then to the receiving end2Representing the rice factor, λ the carrier wavelength, t the time variable, π the circumference ratio, ξpmn(t) represents the time-varying distance, ξ, between the unmanned aerial vehicle UAV antenna units p and the (m, n) -th smart reflector unitmnq(t) represents the time-varying distance between the (m, n) -th intelligent reflection unit and the receiving-end antenna unit q, thetamn(t) denotes the reflection phase of the intelligent reflective surface IRS at time t,
represents the time-varying Doppler shift between the multipath components of the UAV antenna unit p and the receiving end antenna unit q after passing through the (m, N) -th intelligent reflection unit, NlRepresenting scatterers
The number of taps, l represents the number of taps,
expressing (m, n) -th intelligent reflection unit and scatterer
The time-varying distance between them,
representing scatterers
And the time-varying distance between the receiving-end antenna unit q,
representing multipath components between an Unmanned Aerial Vehicle (UAV) antenna unit p and a receiving end antenna unit q via an intelligent reflecting surface IRS and a scatterer
The latter time-varying doppler shift.
S3, designing an optimization problem according to the maximum power of the received signal according to the unmanned aerial vehicle channel model based on the large-scale intelligent reflection unit, wherein the design purpose is to maximize the power of the received signal;
thus, the optimization problem is represented as follows:
where t represents a time variable, θmn(t) denotes the reflection phase of the intelligent reflective surface IRS at time t,
represents a statistical mean operation, hpq(t) represents the complex channel gain of the multipath component between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q.
S4, simplifying optimization problems: the received signal is composed of a direct component, a scattered component, a direct component via the intelligent reflecting surface IRS and a scattered component via the intelligent reflecting surface, which results in a computationally significant complexity of the optimization problem proposed in step S3, so that further simplification of the problem is required in order to reduce the complexity. The invention discovers that when the scale of the intelligent reflection unit in the intelligent reflection surface IRS is larger, the power of the received signal is mainly controlled by the reflection signal passing through the intelligent reflection surface IRS, and the complex channel gain of the reflection signal is mainly determined by the direct component, so that the process of solving the reflection phase can be simplified;
the simplifying process comprises the following steps:
s41, since the power of the received signal is mainly concentrated on the direct component reflected by the intelligent reflective surface IRS, the optimization problem in step S3 is simplified as follows:
wherein, t represents a time variable,
the method comprises the steps that complex channel gain of a direct component between an Unmanned Aerial Vehicle (UAV) antenna unit p and a receiving end antenna unit q after being reflected by an intelligent reflecting surface IRS is represented, and | represents an absolute value function;
s42, handle
The phase relation in (3) is substituted into the formula (5), and the optimization problem is further simplified into:
wherein ,
the time-varying Doppler frequency shift of multipath components between an Unmanned Aerial Vehicle (UAV) antenna unit p and a receiving end antenna unit q after passing through an (M, N) -th intelligent reflection unit is represented, M represents the number of row reflection units of an intelligent reflection surface, M represents the row position index of the intelligent reflection unit, N represents the number of column reflection units of the intelligent reflection surface, N represents the column position index of the intelligent reflection unit, lambda represents the carrier wavelength, and xipmn(t) represents the time-varying distance, ξ, between the unmanned aerial vehicle UAV antenna units p and the (m, n) -th smart reflector unitmnq(t) represents the time-varying distance between the (m, n) -th intelligent reflection unit and the receiving-end antenna unit q, thetamn(t) represents the reflection phase of the intelligent reflective surface IRS at time t.
And S5, solving the optimal IRS reflection phase of the intelligent reflection surface according to the simplified optimization problem. It should be noted that when solving the reflected phase, the doppler shift of the direct component needs to be subtracted, and only the doppler shift is removed to enhance the received signal power, and the existence of the doppler shift is often ignored in the previous research.
Which comprises the following steps:
S51、according to the optimization problem of the optimal IRS reflection phase of the intelligent reflecting surface obtained by the formula (6), the optimal IRS reflection phase of the intelligent reflecting surface can be solved
The expression of (a) is as follows:
where t represents a time variable, λ represents the carrier wavelength, ξpmn(t) represents the time-varying distance, ξ, between the unmanned aerial vehicle UAV antenna units p and the (m, n) -th smart reflector unitmnq(t) represents the time-varying distance between the (m, n) -th intelligent reflection unit and the receiving-end antenna unit q, pi represents the circumferential ratio, thetamn(t) denotes the reflection phase of the intelligent reflective surface IRS at time t, and mod (a, b) denotes the remainder of the division of the two numbers a and b.
S52, because formula (7) does not consider the time-varying Doppler frequency shift of the multipath component between the UAV antenna unit p and the receiving end antenna unit q after passing through the (m, n) -th intelligent reflection unit
Therefore, the invention will
Further rewritten as:
where t represents a time variable, λ represents the carrier wavelength, ξpmn(t) represents the time-varying distance, ξ, between the unmanned aerial vehicle UAV antenna units p and the (m, n) -th smart reflector unitmnq(t) represents the time-varying distance between the (m, n) -th intelligent reflection unit and the receiving-end antenna unit q, pi represents the circumferential ratio, thetamn(t) denotes the reflection bit of the intelligent reflective surface IRS at time t,
the time-varying Doppler frequency shift of multipath components between an Unmanned Aerial Vehicle (UAV) antenna unit p and a receiving end antenna unit q after passing through an (m, n) -th intelligent reflection unit is shown, and mod (a, b) shows a remainder of division of a number and a number b.
S6, solving a space-time correlation function based on the assistance of the intelligent reflecting surface IRS according to the definition by the complex channel gain obtained in the step S2 and the optimal reflecting phase of the intelligent reflecting surface IRS obtained in the step S5, and determining the influence of different parameters on the channel characteristics of the unmanned aerial vehicle through correlation analysis.
Which comprises the following steps:
s61, firstly, according to the definition formula of the space-time correlation function:
wherein ,
representing a spatio-temporal correlation function, δ, between two time-varying transfer functionsTRepresenting the antenna spacing, δ, between the unmanned aerial vehicle UAV antenna unitsRDenotes the antenna spacing between antenna elements at the receiving end, τ denotes the propagation delay, t denotes a time variable,
representing statistical mean operations (·)*Denotes a complex conjugate operation, hpq(t) represents the complex channel gain between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q, hp′q′(t + τ) represents the complex channel gain between the unmanned aerial vehicle UAV antenna unit p 'and the receiving end antenna unit q' after a time delay τ; | represents an absolute value function;
the expressions of the complex channel gain functions obtained in step S2 are respectively substituted to obtain specific spatio-temporal correlation functions as follows:
wherein ,
represents the space-time correlation of the direct component between the UAV antenna unit and the receiving end antenna unit, wherein lambda represents the carrier wavelength, pi represents the circumferential ratio, xipq(t) represents the time-varying distance, ξ, between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit qp′q′(t + τ) represents the time-varying distance between the unmanned aerial vehicle UAV antenna unit p 'and the receiving end antenna unit q' after a time delay τ,
a time-varying doppler frequency shift representing a direct component between an unmanned aerial vehicle UAV antenna unit p and a receiving end antenna unit q;
represents the space-time correlation of the scattering component between the UAV antenna unit and the receiving-end antenna unit, NlRepresenting scatterers
The number of taps, l, xipnl(t) denotes unmanned aerial vehicle UAV antenna unit p and scatterers
Time-varying distance between, xip′nl(t + τ) denotes the unmanned aerial vehicle UAV antenna unit p' and scatterers after a time delay τ
The time-varying distance between them,
representing scatterers
And the time-varying distance between the receiving-end antenna unit q,
showing scatterers after a time delay of tau
And the time-varying distance between the receiving-end antenna unit q',
a time-varying doppler frequency shift representing a scattering component between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q;
the space-time correlation of the direct component reflected by the intelligent reflecting surface IRS between the unmanned aerial vehicle UAV antenna unit and the receiving end antenna unit is represented, M represents the number of row reflecting units of the intelligent reflecting surface IRS, N represents the number of column reflecting units of the intelligent reflecting surface IRS, and xi representspmn(t) represents the time-varying distance, ξ, between the unmanned aerial vehicle UAV antenna units p and the (m, n) -th smart reflector unitp′m′n′(t + τ) represents the time-varying distance, ξ, between the unmanned aerial vehicle UAV antenna units p ' and the (m ', n ') -th smart reflector unit after a time delay τ has elapsedmnq(t) represents the time-varying distance, ξ, between the (m, n) -th intelligent reflection unit and the receiving-end antenna unit qm′n′q′(t + τ) represents a time-varying distance between the (m ', n ') -th intelligent reflection unit and the receiving-end antenna unit q ' after a time delay τ;
indicating that the space between the UAV antenna unit and the receiving end antenna unit passes through the intelligent reflector IRS and the scatterer
Space-time dependence of the reflected scattered component, δNIndicating the distance, δ, between the column reflective elements of the intelligent reflective surface IRSMThe distance between the column reflection elements of the intelligent reflection surface IRS is indicated and τ represents the propagation delay.
And S62, when the number of reflection units of the intelligent reflection IRS, the reflection phase of the intelligent reflection surface IRS and the flight track of the unmanned aerial vehicle UAV are changed, analyzing the influence of the parameter changes on the channel characteristics of the unmanned aerial vehicle through the correlation according to the space-time correlation function obtained above.
The invention also provides an apparatus comprising a memory and a processor, wherein:
a memory for storing a computer program capable of running on the processor;
and the processor is used for executing the steps of the unmanned aerial vehicle channel model building method based on the large-scale intelligent reflection unit when the computer program is run.
The invention also provides a storage medium, wherein the storage medium is stored with a computer program, and the computer program is executed by at least one processor to realize the steps of the unmanned aerial vehicle channel model building method based on the large-scale intelligent reflection unit.
Fig. 2 is a schematic diagram of three different flight trajectories of an unmanned aerial vehicle UAV. In fig. 2, the drone under path I is far from the intelligent reflector IRS and the receiving end, parameters
and γTSet to 0 and 90, respectively, the drone under path II is in 0 to 5.2 seconds
and γTMoving towards the receiving end in the-30 ° direction, the drone under path III is in 0 to 5.2 seconds
and γTMoves towards the intelligent reflection IRS reflection unit in the direction of-120 °. Wherein,
elevation angle, gamma, representing the direction of movement of an Unmanned Aerial Vehicle (UAV)TRepresenting the unmanned aerial vehicle UAV direction of motion azimuth.
Fig. 3 is a comparison graph of absolute envelope amplitudes of a traditional unmanned aerial vehicle channel model and an unmanned aerial vehicle channel model based on the assistance of an intelligent reflector IRS in different unmanned aerial vehicle flight trajectories. As can be seen from fig. 3, as the unmanned aerial vehicle UAV moves towards the intelligent reflective surface IRS, i.e., path III, the envelope size received by the unmanned aerial vehicle model employing the intelligent reflective surface IRS gradually increases. Conversely, when the unmanned aerial vehicle UAV is far away from the intelligent reflective surface IRS, the size of the envelope of the unmanned aerial vehicle model using the intelligent reflective surface IRS gradually decreases, i.e., the path I and the path II. And when the intelligent reflector IRS is not adopted, when the unmanned aerial vehicle UAV gradually moves to the receiving end, the absolute envelope amplitude of the unmanned aerial vehicle model is not obviously increased in the path II. This shows that the propagation environment between unmanned aerial vehicle and the receiving terminal can be effectively changed to intelligent plane of reflection IRS.
FIG. 4 shows a conventional UAV channel model and an intelligent reflector IRS-based broadband UAV channel model in different IRS reflection phases θmn(t) absolute envelope size comparison of the broadband UAV-MIMO model. In phase 1, the IRS reflection phase is aligned with the time-varying phase and the doppler shift of the LoS component, and in phase 2, the IRS reflection phase, i.e., the optimal reflection phase of the intelligent reflective surface IRS, is set according to equation (8). As can be seen from fig. 6, when the size of the reflection unit of the intelligent reflector IRS is increased, the performance and the multipath fading phenomenon of the UAV-MIMO communication system employing the intelligent reflector IRS can be significantly improved. Meanwhile, when the number of the intelligent reflecting surface IRS reflecting units is greater than 100 × 100, the performance of method 2 is the same as that of method 1. The results show that the method has the advantages of high yield,the provided IRS reflection phase is suitable for research of a broadband unmanned aerial vehicle communication system based on intelligent reflector IRS assistance.
Fig. 5 is a comparison graph of the emission spatial correlation curves of the conventional unmanned aerial vehicle channel model and the unmanned aerial vehicle channel model based on the assistance of the intelligent reflector IRS in three different unmanned aerial vehicle trajectories. It can be seen from fig. 5 that the spatial correlation of the drone-MIMO model without the assistance of the intelligent reflector IRS is affected by the drone trajectory, and that the normalized antenna spacing δTThe spatial correlation gradually decreases as/λ increases. While the spatial correlation of the unmanned aerial vehicle channel model based on the assistance of the intelligent reflector IRS is constant. This also means that the intelligent reflective surface IRS can reduce the non-stationarity of the transmitting antenna elements in the spatial domain.
Fig. 6 is a comparison graph of emission spatial correlation curves of a traditional unmanned aerial vehicle channel model and a broadband unmanned aerial vehicle channel model based on the assistance of an intelligent reflector IRS under three different unmanned aerial vehicle flight trajectories. As can be seen in FIG. 6, the spatial correlation of the broadband drone channel model without the assistance of the intelligent reflector IRS is subject to the drone trajectory and the normalized antenna spacing δTThe effect of/λ, which is consistent with the results obtained in fig. 5.
To sum up, the unmanned aerial vehicle channel model building method based on the large-scale intelligent reflection unit is a non-stable three-dimensional broadband channel model building method of an unmanned aerial vehicle multi-input multi-output communication system based on a large-scale intelligent reflection surface, and comprises the steps of intelligent reflection surface time-varying reflection phase design: the received signal power maximization is taken as a target design optimization problem, and the optimization problem is solved to obtain an optimal time-varying reflection phase; designing time-varying Doppler frequency shift parameters: obtaining time-varying Doppler frequency shift parameters among the unmanned aerial vehicle, the receiving end and the intelligent reflecting surface according to the intelligent reflecting surface assisted geometric model; analyzing the statistical characteristics of the channel: and analyzing the statistical characteristics of the unmanned aerial vehicle MIMO channel model based on the assistance of the intelligent reflector according to the time-varying reflection phase and the time-varying parameters of the intelligent reflector. In the invention, the communication system adopting the intelligent reflecting surface IRS has better signal propagation environment, can obviously improve the power of received signals and reduce the influence of multipath fading and Doppler frequency shift on the received signals. Therefore, the model building method can provide powerful support for the exploration of the key technology of the 6G communication system.
Claims (10)
1. An unmanned aerial vehicle channel model building method based on a large-scale intelligent reflection unit is characterized by comprising the following steps:
s1, configuring the intelligent reflecting surface IRS on the surface of a building at the edge of a served cell, simulating a vertical building structure around a receiving end by using a three-dimensional cylinder, simulating the near field effect of the large-scale intelligent reflecting surface IRS by using second-order approximation of spherical wavefront, assuming that a scatterer is positioned on the surface of the three-dimensional cylinder, and establishing an unmanned aerial vehicle channel model based on the large-scale intelligent reflecting unit, wherein the intelligent reflecting surface IRS comprises intelligent reflecting units which are uniformly arranged; obtaining the complex channel gain of the channel according to the model;
s2, obtaining the complex channel gain according to the unmanned aerial vehicle channel model based on the large-scale intelligent reflection unit, wherein the complex channel gain comprises two components: the complex channel gain of the Unmanned Aerial Vehicle (UAV) which is directly transmitted with the receiving end without the Intelligent Reflector (IRS) and the complex channel gain of the UAV which is transmitted with the receiving end by the Intelligent Reflector (IRS);
s3, according to the unmanned aerial vehicle channel model based on the large-scale intelligent reflection unit, the near field effect of the intelligent reflection surface IRS is simulated by considering the second-order approximation of spherical wavefront, and because the reflection phase of the intelligent reflection surface can improve the influence of Doppler frequency shift and multipath fading on the received signal power, the optimization problem is designed based on the received signal power maximization criterion;
s4, simplifying optimization problems: the optimization problem proposed in S3 is computationally very complex, so to reduce complexity, the problem needs to be further simplified; when the scale of the intelligent reflection unit in the intelligent reflection surface IRS is larger, the power of the received signal is mainly controlled by the reflection signal passing through the intelligent reflection surface IRS, and the complex channel gain of the reflection signal is mainly the direct component of the reflection signal, so that the process of solving the reflection phase is simplified;
s5, Excellent according to the simplificationConsidering the time-varying Doppler shift of the multipath component between the UAV antenna unit p and the receiving end antenna unit q after passing through the (m, n) -th intelligent reflection unit
When the optimal IRS reflection phase of the intelligent reflection surface is solved, the Doppler frequency shift of the direct component is subtracted to enhance the power of the received signal;
s6, solving a space-time correlation function based on the intelligent reflector IRS assistance through the complex channel gain obtained in the step S2 and the optimal intelligent reflector IRS reflection phase obtained in the step S5, and determining the influence of different parameters on the unmanned aerial vehicle channel characteristics through correlation analysis.
2. The method for building the UAV channel model based on LSU according to claim 1, wherein the complex channel gain h obtained in step S1pq(t, τ), which is expressed as follows:
where t represents a time variable, L represents the number of taps, L represents the total number of taps, clDenotes the gain of the first tap, hl,pq(t) represents the complex channel gain between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q directly without the smart reflector IRS,
represents the complex channel gain, τ, between the UAV antenna unit p and the receiving-end antenna unit q through the intelligent reflector IRSl(t) represents the propagation delay of the ith tap, and δ (·) represents the impulse function.
3. The method for building UAV channel model based on LSU according to claim 1, wherein the steps are as followsIn S2, complex channel gain h between Unmanned Aerial Vehicle (UAV) antenna unit p and receiving end antenna unit q directly without Intelligent Reflector (IRS)l,pq(t) is represented as follows:
wherein ,
representing the complex channel gain of the direct component between the unmanned aerial vehicle UAV antenna element p and the receiving end antenna element q,
a complex channel gain representing a scattering component between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q;
wherein ,GtDenotes the transmit antenna gain, GrRepresenting the receiving-end antenna gain, gammaTRRepresents the path loss, K, of the UAV to the receiving end1Representing the rice factor, λ the carrier wavelength, t the time variable, π the circumference ratio, ξpq(t) represents the time-varying distance between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q,
the time-varying Doppler frequency shift of a direct component between an Unmanned Aerial Vehicle (UAV) antenna unit p and a receiving end antenna unit q is shown, delta (l-1) represents a delay impact function after l taps, and NlRepresenting scatterers
Number of (1), xipnl(t) denotes unmanned aerial vehicle UAV antenna unit p and scatterers
The time-varying distance between them,
representing scatterers
And the time-varying distance between the receiving-end antenna unit q,
representing the time-varying doppler shift of the scattered component between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q.
4. The method as claimed in claim 1, wherein the step S2 is implemented by using complex channel gain of the UAV transmitting with the receiving end via the IRS
Is represented as follows:
wherein ,
represents the complex channel gain of the direct component between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q after being reflected by the intelligent reflecting surface IRS,
the complex channel gain of a scattering component between an Unmanned Aerial Vehicle (UAV) antenna unit p and a receiving end antenna unit q after being scattered by an intelligent reflecting surface IRS and a scatterer is represented;
wherein M represents a row position index of the intelligent reflection unit, N represents a column position index of the intelligent reflection unit, M represents a row reflection unit number of the intelligent reflection surface, N represents a column reflection unit number of the intelligent reflection surface, GtRepresenting the gain of the transmitting antenna, G representing the gain of the IRS reflecting element, GrRepresenting the receiving-end antenna gain, gammaTIRRepresents the path loss, K, from the UAV to the IRS and then to the receiving end2Representing the rice factor, λ the carrier wavelength, t the time variable, π the circumference ratio, ξpmn(t) represents the time-varying distance, ξ, between the unmanned aerial vehicle UAV antenna units p and the (m, n) -th smart reflector unitmnq(t) represents the time-varying distance between the (m, n) -th intelligent reflection unit and the receiving-end antenna unit q, thetamn(t) denotes the reflection phase of the intelligent reflective surface IRS at time t,
the time-varying Doppler frequency shift of multipath components between an Unmanned Aerial Vehicle (UAV) antenna unit p and a receiving end antenna unit q after passing through an (m, n) -th intelligent reflection unit is represented; n is a radical oflRepresenting scatterers
The number of taps, l represents the number of taps,
expressing (m, n) -th intelligent reflection unit and scatterer
The time-varying distance between them,
representing scatterers
And the time-varying distance between the receiving-end antenna unit q,
representing multipath components between an Unmanned Aerial Vehicle (UAV) antenna unit p and a receiving end antenna unit q via an intelligent reflecting surface IRS and a scatterer
The latter time-varying doppler shift.
5. The method for building the unmanned aerial vehicle channel model based on the large-scale intelligent reflection unit according to claim 1, wherein the optimization problem in the step S3 is represented as follows:
where t represents a time variable, θmn(t) denotes the reflection phase of the intelligent reflective surface IRS at time t,
represents a statistical mean operation, hpq(t) represents the complex channel gain of the multipath component between the unmanned aerial vehicle UAV antenna unit p and the receiving end antenna unit q.
6. The method for building the unmanned aerial vehicle channel model based on the large-scale intelligent reflection unit according to claim 1, wherein the step S4 comprises the following steps:
s41, since the power of the received signal is mainly concentrated on the direct component reflected by the intelligent reflective surface IRS, the optimization problem in step S3 is simplified as follows:
wherein, t represents a time variable,
the method comprises the steps that complex channel gain of a direct component between an Unmanned Aerial Vehicle (UAV) antenna unit p and a receiving end antenna unit q after being reflected by an intelligent reflecting surface IRS is represented, and | represents an absolute value function;
s42, handle
The specific function of (2) is substituted into the formula (5), and the optimization problem is further simplified into:
wherein ,
the method is characterized in that the time-varying Doppler frequency shift of multipath components between an Unmanned Aerial Vehicle (UAV) antenna unit p and a receiving end antenna unit q after passing through an (M, N) -th intelligent reflection unit is represented, M represents the number of row reflection units of an intelligent reflection surface, M represents the row position index of the intelligent reflection unit, N represents the number of column reflection units of the intelligent reflection surface, N represents the column position index of the intelligent reflection unit, lambda represents the carrier wavelength, pi represents the circumferential ratio, xi represents the circumferential ratiopmn(t) represents the time-varying distance, ξ, between the unmanned aerial vehicle UAV antenna units p and the (m, n) -th smart reflector unitmnq(t) represents the time-varying distance between the (m, n) -th intelligent reflection unit and the receiving-end antenna unit q, thetamn(t) represents the reflection phase of the intelligent reflective surface IRS at time t.
7. The method for building the UAV channel model based on the LSU according to claim 6, wherein the step S5 for solving the optimal IRS reflection phase of the intelligent reflection surface comprises the following steps:
s51, solving the optimal IRS reflection phase of the intelligent reflection surface according to the optimal IRS reflection phase optimization problem obtained by the formula (6)
The expression of (a) is as follows:
wherein ,
to represent
And the remainder of the division of two numbers 2 pi;
s52, because formula (7) does not consider the time-varying Doppler frequency shift of the multipath component between the UAV antenna unit p and the receiving end antenna unit q after passing through the (m, n) -th intelligent reflection unit
Therefore, will
Further rewritten as:
wherein ,
the time-varying Doppler frequency shift of multipath components between an Unmanned Aerial Vehicle (UAV) antenna unit p and a receiving end antenna unit q after passing through an (m, n) -th intelligent reflection unit is shown.
8. The method for building the unmanned aerial vehicle channel model based on the large-scale intelligent reflection unit according to claim 1, wherein the step S6 comprises the following steps:
s61, solving a space-time correlation function based on the intelligent reflecting surface IRS assistance according to the definition by using the complex channel gain obtained in the step S2 and the optimal reflecting phase of the intelligent reflecting surface IRS obtained in the step S5, and calculating the formula as follows:
firstly, according to the definition formula of the space-time correlation function:
wherein ,
representing a spatio-temporal correlation function, δ, between two time-varying transfer functionsTRepresenting the antenna spacing, δ, between the unmanned aerial vehicle UAV antenna unitsRRepresents the antenna spacing between the antenna units at the user end, tau represents the propagation delay, t represents the time variable,
representing statistical mean operations (·)*Denotes a complex conjugate operation, hpq(t) represents the complex channel gain between the unmanned aerial vehicle UAV antenna unit p and the user side antenna unit q, hp′q′(t + τ) represents the complex channel gain between the unmanned aerial vehicle UAV antenna unit p 'and the user-side antenna unit q' after a time delay τ; | represents an absolute value function;
the expressions of the complex channel gain functions obtained in step S2 are respectively substituted to obtain specific spatio-temporal correlation functions as follows:
wherein ,
represents the space-time correlation of the direct component between the UAV antenna unit and the receiving end antenna unit, wherein lambda represents the carrier wavelength, pi represents the circumferential ratio, xipq(t) represents the time-varying distance, ξ, between the unmanned aerial vehicle UAV antenna unit p and the user side antenna unit qp′q′(t + τ) represents the time-varying distance between the human-machine UAV antenna unit p 'and the user-side antenna unit q' after a time delay τ,
a time-varying doppler shift representing the direct component between the unmanned aerial vehicle UAV antenna unit p and the user-side antenna unit q;
represents the space-time correlation of the scattering component between the UAV antenna unit and the receiving-end antenna unit, NlRepresenting scatterers
The number of taps, l, represents，ξpnl(t) denotes unmanned aerial vehicle UAV antenna unit p and scatterers
Time-varying distance between, xip′nl(t + τ) denotes the human-machine UAV antenna unit p' and scatterers after a time delay τ
The time-varying distance between them,
representing scatterers
And the time-varying distance between the subscriber-side antenna unit q,
showing scatterers after a time delay of tau
And the time varying distance between the subscriber side antenna unit q',
a time-varying doppler frequency shift representing a scattering component between the unmanned aerial vehicle UAV antenna unit p and the user-side antenna unit q;
the space-time correlation of the direct component reflected by the intelligent reflecting surface IRS between the unmanned aerial vehicle UAV antenna unit and the receiving end antenna unit is represented, M represents the number of row reflecting units of the intelligent reflecting surface IRS, N represents the number of column reflecting units of the intelligent reflecting surface IRS, and xi representspmn(t) represents the time-varying distance, ξ, between the unmanned aerial vehicle UAV antenna units p and the (m, n) -th smart reflector unitp′m′n′(t + τ) indicates that after a time delay τ, the UAV aerial sheetTime-varying distance, ξ, between the elements p ' and (m ', n ') -th intelligent reflection unitmnq(t) represents the time-varying distance between the (m, n) -th intelligent reflection unit and the user terminal antenna unit q, xim′n′q′(t + τ) represents the time-varying distance between the (m ', n ') -th intelligent reflection unit and the user side antenna unit q ' after the time delay τ;
indicating that the space between the UAV antenna unit and the receiving end antenna unit passes through the intelligent reflector IRS and the scatterer
Space-time dependence of the reflected scattered component, δNIndicating the distance, δ, between the column reflective elements of the intelligent reflective surface IRSMRepresents the distance between the column reflective elements of the intelligent reflective surface IRS;
and S62, when the number of reflection units of the intelligent reflection IRS, the reflection phase of the intelligent reflection surface IRS and the flight track of the unmanned aerial vehicle UAV are changed, analyzing the influence of the parameter changes on the channel characteristics of the unmanned aerial vehicle through the correlation according to the space-time correlation function obtained above.
9. An apparatus comprising a memory and a processor, wherein:
a memory for storing a computer program capable of running on the processor;
a processor for executing the steps of the method for unmanned aerial vehicle channel model building based on large scale intelligent reflection units according to any one of claims 1 to 8 when running the computer program.
10. A storage medium having stored thereon a computer program which, when executed by at least one processor, performs the steps of the method for large scale intelligent reflection unit based drone channel model building according to any one of claims 1-8.
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Cited By (4)
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| CN114584238A (en) * | 2022-03-07 | 2022-06-03 | 东南大学 | Ray tracing channel modeling method for intelligent super-surface wireless communication |
| CN114938498A (en) * | 2022-03-29 | 2022-08-23 | 成都理工大学 | Intelligent reflector-assisted unmanned aerial vehicle-enabled wireless sensor network data collection method |
| CN115396912A (en) * | 2022-08-10 | 2022-11-25 | 河海大学 | Tunnel wireless relay communication system based on dual IRS assistance |
| CN115580364A (en) * | 2022-10-14 | 2023-01-06 | 东南大学 | Intelligent super-surface technology assisted unmanned aerial vehicle channel modeling method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5973638A (en) * | 1998-01-30 | 1999-10-26 | Micronetics Wireless, Inc. | Smart antenna channel simulator and test system |
| US20180115065A1 (en) * | 2016-10-26 | 2018-04-26 | International Business Machines Corporation | In-field millimeter-wave phased array radiation pattern estimation and validation |
| CN112968743A (en) * | 2021-02-25 | 2021-06-15 | 中国人民解放军陆军工程大学 | Time-varying de-cellular large-scale MIMO channel modeling method based on visible region division |
| CN113645635A (en) * | 2021-08-12 | 2021-11-12 | 大连理工大学 | Design method of intelligent reflector-assisted high-energy-efficiency unmanned aerial vehicle communication system |
-
2021
- 2021-11-25 CN CN202111415453.7A patent/CN114124263B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5973638A (en) * | 1998-01-30 | 1999-10-26 | Micronetics Wireless, Inc. | Smart antenna channel simulator and test system |
| US20180115065A1 (en) * | 2016-10-26 | 2018-04-26 | International Business Machines Corporation | In-field millimeter-wave phased array radiation pattern estimation and validation |
| CN112968743A (en) * | 2021-02-25 | 2021-06-15 | 中国人民解放军陆军工程大学 | Time-varying de-cellular large-scale MIMO channel modeling method based on visible region division |
| CN113645635A (en) * | 2021-08-12 | 2021-11-12 | 大连理工大学 | Design method of intelligent reflector-assisted high-energy-efficiency unmanned aerial vehicle communication system |
Non-Patent Citations (1)
| Title |
|---|
| 秦岭;郝雅楠;杜永兴;巨永锋: "LED交通灯的光通信系统信道建模与分析", 大气与环境光学学报, vol. 12, no. 3 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114584238A (en) * | 2022-03-07 | 2022-06-03 | 东南大学 | Ray tracing channel modeling method for intelligent super-surface wireless communication |
| CN114584238B (en) * | 2022-03-07 | 2024-02-02 | 东南大学 | Intelligent super-surface wireless communication-oriented ray tracing channel modeling method |
| CN114938498A (en) * | 2022-03-29 | 2022-08-23 | 成都理工大学 | Intelligent reflector-assisted unmanned aerial vehicle-enabled wireless sensor network data collection method |
| CN114938498B (en) * | 2022-03-29 | 2023-10-27 | 成都理工大学 | Intelligent reflection-surface-assisted unmanned aerial vehicle enabled wireless sensor network data collection method |
| CN115396912A (en) * | 2022-08-10 | 2022-11-25 | 河海大学 | Tunnel wireless relay communication system based on dual IRS assistance |
| CN115396912B (en) * | 2022-08-10 | 2023-06-30 | 河海大学 | Tunnel wireless relay communication system based on double IRS assistance |
| CN115580364A (en) * | 2022-10-14 | 2023-01-06 | 东南大学 | Intelligent super-surface technology assisted unmanned aerial vehicle channel modeling method |
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