CN114124263A - Method for establishing channel model of UAV based on large-scale intelligent reflection unit - Google Patents

Method for establishing channel model of UAV based on large-scale intelligent reflection unit Download PDF

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CN114124263A
CN114124263A CN202111415453.7A CN202111415453A CN114124263A CN 114124263 A CN114124263 A CN 114124263A CN 202111415453 A CN202111415453 A CN 202111415453A CN 114124263 A CN114124263 A CN 114124263A
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CN114124263B (en
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王宇浩
练柱先
解志斌
苏胤杰
王亚军
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Guizhou Bonakang Environmental Protection Technology Co ltd
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Abstract

本发明公开了一种基于大规模智能反射单元的无人机信道模型建立方法，首先根据无人机对地实际通信场景，建立了基于大规模智能反射单元的无人机信道模型，并得到信号的复信道增益；根据无人机信道模型，以接收信号功率最大化准则来设计优化问题；由于接收信号的功率主要集中在经智能反射面IRS反射的直射分量上，以此简化求解反射相位的过程；根据化简的优化问题，求解最优的智能反射面IRS反射相位；根据复信道增益和最优的智能反射面IRS反射相位，求解基于智能反射面IRS辅助的时空相关性函数，通过相关性分析来确定不同参数对无人机信道特性的影响。本发明方法对于探索智能反射面IRS对无人机信道统计特性的影响提供了一定帮助。

Figure 202111415453

The invention discloses a method for establishing a channel model of an unmanned aerial vehicle based on a large-scale intelligent reflection unit. First, according to the actual communication scene of the unmanned aerial vehicle to the ground, a channel model of the unmanned aerial vehicle based on the large-scale intelligent reflection unit is established, and a signal is obtained. According to the UAV channel model, the optimization problem is designed with the principle of maximizing the power of the received signal; since the power of the received signal is mainly concentrated on the direct component reflected by the intelligent reflective surface IRS, it simplifies the calculation of the reflection phase Process: According to the simplified optimization problem, solve the optimal IRS reflection phase of the smart reflector; The characteristic analysis is carried out to determine the influence of different parameters on the channel characteristics of the UAV. The method of the invention provides certain help for exploring the influence of the intelligent reflecting surface IRS on the statistical characteristics of the UAV channel.

Figure 202111415453

Description

基于大规模智能反射单元的无人机信道模型建立方法Method for establishing channel model of UAV based on large-scale intelligent reflection unit

技术领域technical field

本发明涉及无线通信技术，具体涉及一种基于大规模智能反射单元的无人机信道模型建立方法。The invention relates to wireless communication technology, in particular to a method for establishing a channel model of an unmanned aerial vehicle based on a large-scale intelligent reflection unit.

背景技术Background technique

近年来，随着无人机技术的快速发展，基于无人机UAV的通信技术便引起了各界研究人员的极大兴趣。但是由于无人机的快速移动，无人机的通信场景是一个非平稳的过程，由于无人机和接收端的移动引起的多普勒频移严重影响了通信系统的接收信号功率。因此基于智能反射面IRS辅助的无人机通信便引起了人们的广泛关注。只需要通过调整智能反射面IRS的反射相位，便可以抵消多普勒频移和多径衰落对接收信号的影响，使得接收信号功率最大化，同时提高通信质量。其中，智能反射面IRS中的反射单元都是均匀排列，且大小相等的，并且每一个反射单元能够独立地改变入射信号的相位或幅度。In recent years, with the rapid development of UAV technology, the communication technology based on UAV UAV has attracted great interest of researchers from all walks of life. However, due to the rapid movement of the UAV, the communication scene of the UAV is a non-stationary process, and the Doppler frequency shift caused by the movement of the UAV and the receiver seriously affects the received signal power of the communication system. Therefore, the UAV communication based on the intelligent reflector IRS has attracted extensive attention. Only by adjusting the reflection phase of the intelligent reflective surface IRS, the influence of Doppler frequency shift and multipath fading on the received signal can be offset, so as to maximize the received signal power and improve the communication quality. Among them, the reflective units in the intelligent reflective surface IRS are uniformly arranged and of equal size, and each reflective unit can independently change the phase or amplitude of the incident signal.

随着智能反射面IRS反射单元规模的增加，智能反射面IRS辅助的通信系统的性能也会增加，但是其计算复杂度也会提高很多，因此就需要寻找出能够降低计算复杂度的方法。本发明便采用球形波前的二阶近似方法来模拟大规模智能反射面IRS的近场效应，来降低计算复杂度。With the increase of the size of the reflection unit of the intelligent reflector IRS, the performance of the communication system assisted by the intelligent reflector IRS will also increase, but its computational complexity will also increase a lot, so it is necessary to find a method that can reduce the computational complexity. The present invention adopts the second-order approximation method of spherical wavefront to simulate the near-field effect of the large-scale intelligent reflecting surface IRS, so as to reduce the computational complexity.

在现有的公开技术内容中，有些研究了智能反射面IRS辅助传播环境的远场路径损失模型和反射相位，该反射相位与LoS分量的传播相位对齐，提高了接收信号的功率。有些研究了在各向同性散射环境下的空间相关矩阵和相关矩阵距离等统计特性，当智能反射面IRS反射单元的尺寸增大时，相关矩阵距离逐渐增大，说明基于智能反射面IRS辅助的信道模型在IRS反射单元上具有空间非平稳性。在有些模型中考虑了时变传播相位对信道统计特性的影响，但忽略了由于无人机UAV移动运动引起的多普勒位移的影响。有些研究了一种基于智能反射面IRS的非平稳几何模型，但却忽略了智能反射面IRS的时变反射相位。有些研究了基于智能反射面IRS时变反射相位的IRS辅助模型的时间相关性，却忽略了IRS反射单元的空间非平稳性。In the existing disclosed technical contents, some studies have studied the far-field path loss model and the reflection phase of the IRS-assisted propagation environment of the intelligent reflective surface. The reflection phase is aligned with the propagation phase of the LoS component, and the power of the received signal is improved. Some studies have studied statistical properties such as spatial correlation matrix and correlation matrix distance in an isotropic scattering environment. When the size of the IRS reflection unit of the smart reflector increases, the correlation matrix distance gradually increases, indicating that the IRS assisted by the smart reflector The channel model has spatial non-stationarity on the IRS reflection unit. In some models, the effect of time-varying propagation phase on channel statistics is considered, but the effect of Doppler shift caused by the moving motion of UAV UAV is ignored. Some studies have studied a non-stationary geometric model based on the IRS of the smart reflector, but ignore the time-varying reflection phase of the IRS of the smart reflector. Some studies have studied the temporal correlation of the IRS-aided model based on the time-varying reflection phase of the IRS of the intelligent reflector, but ignored the spatial non-stationarity of the IRS reflection unit.

综上所述，基于智能反射面IRS辅助的无人UAV信道建模还在起步阶段，其中在很多方面存在着考虑不周和缺点，需要进一步探索和优化。To sum up, the modeling of unmanned UAV channel based on intelligent reflector IRS is still in its infancy, and there are ill-considered and shortcomings in many aspects, which need to be further explored and optimized.

发明内容SUMMARY OF THE INVENTION

发明目的：本发明的目的是提供一种精确的基于大规模智能反射单元的无人机信道模型建立方法，该模型建立方法可以为6G通信系统关键技术的探索提供有力的支撑。Purpose of the invention: The purpose of the present invention is to provide an accurate method for establishing a UAV channel model based on a large-scale intelligent reflection unit, which can provide strong support for the exploration of key technologies of 6G communication systems.

技术方案：本发明的基于大规模智能反射单元的无人机信道模型建立方法，包括以下步骤：Technical solution: The method for establishing a channel model of an unmanned aerial vehicle based on a large-scale intelligent reflection unit of the present invention includes the following steps:

S1、将智能反射面IRS配置在所服务小区边缘的建筑物表面，然后使用三维圆柱体来模拟接收端周围的垂直建筑结构，并利用球面波前的二阶近似来模拟大规模智能反射面IRS的近场效应，假设散射体位于三维圆柱体的表面，且智能反射面IRS包括均匀排列的智能反射单元，建立基于大规模智能反射单元的无人机信道模型；并根据该模型得到信道的复信道增益；S1. Arrange the smart reflective surface IRS on the building surface at the edge of the serving cell, then use a three-dimensional cylinder to simulate the vertical building structure around the receiving end, and use the second-order approximation of the spherical wavefront to simulate the large-scale smart reflective surface IRS It is assumed that the scatterer is located on the surface of a three-dimensional cylinder, and the intelligent reflecting surface IRS includes intelligent reflecting units arranged uniformly, and a UAV channel model based on large-scale intelligent reflecting units is established; and the complex channel model is obtained according to the model. channel gain;

S2、根据基于大规模智能反射单元的无人机信道模型得到该复信道增益有两个分量组成：无人机UAV不通过智能反射面IRS直接与接收端进行传输的复信道增益以及无人机UAV通过智能反射面IRS与接收端进行传输的复信道增益；S2. According to the UAV channel model based on the large-scale intelligent reflection unit, the complex channel gain is composed of two components: the complex channel gain of the UAV UAV directly transmitting to the receiving end without the intelligent reflecting surface IRS, and the complex channel gain of the UAV. The complex channel gain that UAV transmits with the receiver through the intelligent reflective surface IRS;

S3、根据基于大规模智能反射单元的无人机信道模型，以接收信号功率最大化来设计优化问题；S3. According to the UAV channel model based on the large-scale intelligent reflection unit, the optimization problem is designed to maximize the received signal power;

S4、简化优化问题：在S3中提出的优化问题在计算上具有很大的复杂度，所以为了降低复杂度，需要进一步化简问题；当智能反射面IRS中的智能反射单元的规模较大时，接收信号的功率主要由通过智能反射面IRS的反射信号控制，且该反射信号的复信道增益又由其直射分量为主，以此简化求解反射相位的过程；S4. Simplify the optimization problem: The optimization problem proposed in S3 has a lot of computational complexity, so in order to reduce the complexity, it is necessary to further simplify the problem; when the scale of the intelligent reflecting unit in the intelligent reflecting surface IRS is large , the power of the received signal is mainly controlled by the reflected signal passing through the intelligent reflecting surface IRS, and the complex channel gain of the reflected signal is dominated by its direct component, which simplifies the process of solving the reflected phase;

S5、根据化简的优化问题，考虑无人机UAV天线单元p和接收端天线单元q之间多径分量经(m,n)-th智能反射单元后的时变多普勒频移

Figure BDA0003375168710000021
对接收信号功率的影响，在求解最优的智能反射面IRS反射相位时，减去直射分量的多普勒频移，以增强接收信号功率；S5. According to the simplified optimization problem, consider the time-varying Doppler frequency shift of the multipath component between the UAV UAV antenna unit p and the receiving end antenna unit q after the (m,n)-th intelligent reflection unit
Figure BDA0003375168710000021
Influence on the received signal power, when solving the optimal IRS reflection phase of the intelligent reflector, subtract the Doppler frequency shift of the direct component to enhance the received signal power;

S6、通过步骤S2得到的复信道增益和步骤S5得到的最优的智能反射面IRS反射相位，求解基于智能反射面IRS辅助的时空相关性函数，通过相关性分析来确定不同参数对无人机信道特性的影响。S6, through the complex channel gain obtained in step S2 and the optimal reflection phase of the intelligent reflector IRS obtained in step S5, solve the spatiotemporal correlation function based on the assistance of the intelligent reflector IRS, and determine the effect of different parameters on the UAV through correlation analysis The effect of channel characteristics.

进一步的，步骤S1中得到的复信道增益hpq(t,τ)，其表示如下：Further, the complex channel gain h pq (t, τ) obtained in step S1 is expressed as follows:

Figure BDA0003375168710000031
Figure BDA0003375168710000031

其中，t表示时间变量，l表示抽头数，L表示总的抽头数量，cl表示第l次抽头的增益，hl,pq(t)表示无人机UAV天线单元p不通过智能反射面IRS直接与接收端天线单元q之间的复信道增益，

Figure BDA0003375168710000032
表示无人机UAV天线单元p通过智能反射面IRS与接收端天线单元q之间的复信道增益，τl(t)表示第l次抽头的传播延迟，δ(·)表示冲激函数。Among them, t is the time variable, l is the number of taps, L is the total number of taps, c l is the gain of the lth tap, h l, pq (t) means that the UAV antenna unit p of the UAV does not pass the intelligent reflector IRS is the complex channel gain directly between the receiver antenna element q,
Figure BDA0003375168710000032
Represents the complex channel gain between the UAV antenna unit p through the intelligent reflector IRS and the antenna unit q at the receiving end, τ l (t) represents the propagation delay of the lth tap, and δ( ) represents the impulse function.

进一步的，步骤S2中无人机UAV天线单元p不通过智能反射面IRS直接与接收端天线单元q之间的复信道增益hl,pq(t)表示如下：Further, in step S2, the complex channel gain h l, pq (t) between the UAV antenna unit p of the unmanned aerial vehicle and the antenna unit q of the receiving end directly without the intelligent reflecting surface IRS is expressed as follows:

Figure BDA0003375168710000033
Figure BDA0003375168710000033

其中，

Figure BDA0003375168710000034
表示无人机UAV天线单元p和接收端天线单元q之间直射分量的复信道增益，
Figure BDA0003375168710000035
表示无人机UAV天线单元p和接收端天线单元q之间散射分量的复信道增益；in,
Figure BDA0003375168710000034
Represents the complex channel gain of the direct component between the UAV UAV antenna unit p and the receiver antenna unit q,
Figure BDA0003375168710000035
Represents the complex channel gain of the scattering component between the UAV UAV antenna unit p and the receiver antenna unit q;

Figure BDA0003375168710000036
Figure BDA0003375168710000036

Figure BDA0003375168710000037
Figure BDA0003375168710000037

其中，Gt表示发射天线增益，Gr表示接收端天线增益，γTR表示无人机UAV到接收端的路径损耗，K1表示莱斯因子，λ表示载波波长，t表示时间变量，π表示圆周率，ξpq(t)表示无人机UAV天线单元p和接收端天线单元q之间的时变距离，

Figure BDA0003375168710000038
表示无人机UAV天线单元p和接收端天线单元q之间直射分量的时变多普勒频移，δ(l-1)表示经过l次抽头后的延迟冲击函数，Nl表示散射体
Figure BDA0003375168710000039
的数目，ξpnl(t)表示无人机UAV天线单元p和散射体
Figure BDA00033751687100000310
之间的时变距离，
Figure BDA00033751687100000311
表示散射体
Figure BDA00033751687100000312
和接收端天线单元q之间的时变距离，
Figure BDA00033751687100000313
表示无人机UAV天线单元p和接收端天线单元q之间散射分量的时变多普勒频移。where G t is the transmit antenna gain, G r is the receiver antenna gain, γ TR is the path loss from the UAV to the receiver, K 1 is the Rice factor, λ is the carrier wavelength, t is the time variable, and π is the pi , ξ pq (t) represents the time-varying distance between the UAV UAV antenna unit p and the receiver antenna unit q,
Figure BDA0003375168710000038
represents the time-varying Doppler frequency shift of the direct component between the UAV antenna unit p and the receiving antenna unit q, δ(l-1) represents the delay impulse function after l taps, and N l represents the scatterer
Figure BDA0003375168710000039
, ξ pnl (t) denotes the UAV UAV antenna unit p and the scatterer
Figure BDA00033751687100000310
the time-varying distance between
Figure BDA00033751687100000311
Represents a scatterer
Figure BDA00033751687100000312
and the time-varying distance between the receiver antenna element q,
Figure BDA00033751687100000313
Represents the time-varying Doppler shift of the scattered component between the UAV UAV antenna unit p and the receiver antenna unit q.

进一步的，步骤S2中无人机UAV通过智能反射面IRS与接收端进行传输的复信道增益

Figure BDA0003375168710000041
表示如下：Further, in step S2, the complex channel gain of the unmanned aerial vehicle UAV transmitted to the receiving end through the intelligent reflecting surface IRS
Figure BDA0003375168710000041
It is expressed as follows:

Figure BDA0003375168710000042
Figure BDA0003375168710000042

其中，

Figure BDA0003375168710000043
表示无人机UAV天线单元p和接收端天线单元q之间的直射分量经智能反射面IRS反射后的复信道增益，
Figure BDA0003375168710000044
表示无人机UAV天线单元p和接收端天线单元q之间的散射分量经智能反射面IRS和散射体散射后的复信道增益；in,
Figure BDA0003375168710000043
Represents the complex channel gain of the direct component between the UAV UAV antenna unit p and the receiving end antenna unit q after being reflected by the intelligent reflecting surface IRS,
Figure BDA0003375168710000044
Represents the complex channel gain after the scattering component between the UAV UAV antenna unit p and the receiving end antenna unit q is scattered by the intelligent reflector IRS and the scatterer;

Figure BDA0003375168710000045
Figure BDA0003375168710000045

Figure BDA0003375168710000046
Figure BDA0003375168710000046

其中，m表示智能反射单元的行位置索引，n表示智能反射单元的列位置索引，M表示智能反射面的行反射单元数目，N表示智能反射面的列反射单元数目，Gt表示发射天线增益，G表示IRS反射单元的增益，Gr表示接收端天线增益，γTIR表示无人机UAV到IRS再到接收端的路径损耗，K2表示莱斯因子，λ表示载波波长，t表示时间变量，π表示圆周率，ξpmn(t)表示无人机UAV天线单元p和(m,n)-th智能反射单元之间的时变距离，ξmnq(t)表示(m,n)-th智能反射单元和接收端天线单元q之间的时变距离，θmn(t)表示智能反射面IRS在t时刻的反射相位，

Figure BDA0003375168710000047
表示无人机UAV天线单元p和接收端天线单元q之间多径分量经(m,n)-th智能反射单元后的时变多普勒频移；Nl表示散射体
Figure BDA0003375168710000048
的数目，l表示抽头数，
Figure BDA0003375168710000049
表示(m,n)-th智能反射单元和散射体
Figure BDA00033751687100000410
之间的时变距离，
Figure BDA00033751687100000411
表示散射体
Figure BDA00033751687100000412
和接收端天线单元q之间的时变距离，
Figure BDA00033751687100000413
表示无人机UAV天线单元p和接收端天线单元q之间多径分量经智能反射面IRS和散射体
Figure BDA00033751687100000414
后的时变多普勒频移。Among them, m represents the row position index of the smart reflective unit, n represents the column position index of the smart reflective unit, M represents the number of row reflective units on the smart reflective surface, N represents the number of column reflective units on the smart reflective surface, and G t represents the transmit antenna gain , G represents the gain of the IRS reflection unit, G r represents the antenna gain at the receiving end, γ TIR represents the path loss from the UAV to the IRS and then to the receiving end, K 2 represents the Rice factor, λ represents the carrier wavelength, t represents the time variable, π denotes the pi, ξ pmn (t) denotes the time-varying distance between the UAV UAV antenna unit p and the (m,n)-th smart reflection unit, and ξ mnq (t) denotes the (m,n)-th smart reflection is the time-varying distance between the unit and the receiving antenna unit q, θ mn (t) represents the reflection phase of the intelligent reflector IRS at time t,
Figure BDA0003375168710000047
represents the time-varying Doppler frequency shift of the multipath component between the UAV antenna unit p and the receiving antenna unit q after the (m,n)-th intelligent reflection unit; N l represents the scatterer
Figure BDA0003375168710000048
, l represents the number of taps,
Figure BDA0003375168710000049
Represents (m,n)-th smart reflectors and scatterers
Figure BDA00033751687100000410
the time-varying distance between
Figure BDA00033751687100000411
Represents a scatterer
Figure BDA00033751687100000412
and the time-varying distance between the receiver antenna element q,
Figure BDA00033751687100000413
Represents the multipath component between the UAV UAV antenna unit p and the receiver antenna unit q through the intelligent reflector IRS and the scatterer
Figure BDA00033751687100000414
The time-varying Doppler frequency shift.

进一步的，步骤S3中优化问题表示如下：Further, the optimization problem in step S3 is expressed as follows:

Figure BDA00033751687100000415
Figure BDA00033751687100000415

其中，t表示时间变量，θmn(t)表示智能反射面IRS在t时刻的反射相位，

Figure BDA0003375168710000051
表示统计性均值运算，hpq(t)表示无人机UAV天线单元p和接收端天线单元q之间多径分量的复信道增益。Among them, t represents the time variable, θ mn (t) represents the reflection phase of the intelligent reflective surface IRS at time t,
Figure BDA0003375168710000051
represents the statistical mean operation, and h pq (t) represents the complex channel gain of the multipath component between the UAV UAV antenna unit p and the receiver antenna unit q.

进一步的，步骤S4包括以下步骤：Further, step S4 includes the following steps:

S41、由于接收信号的功率主要集中在经智能反射面IRS反射的直射分量上，所以步骤S3中的优化问题化简为：S41. Since the power of the received signal is mainly concentrated on the direct component reflected by the intelligent reflecting surface IRS, the optimization problem in step S3 is simplified as:

Figure BDA0003375168710000052
Figure BDA0003375168710000052

其中，t表示时间变量，

Figure BDA0003375168710000053
表示无人机UAV天线单元p和接收端天线单元q之间的直射分量经智能反射面IRS反射后的复信道增益，|·|表示绝对值函数；where t represents the time variable,
Figure BDA0003375168710000053
Represents the complex channel gain of the direct component between the UAV UAV antenna unit p and the receiving end antenna unit q after being reflected by the intelligent reflecting surface IRS, |·| represents the absolute value function;

S42、把

Figure BDA0003375168710000054
中的相位关系带入公式(5)中，优化问题进一步化简为：S42, put
Figure BDA0003375168710000054
The phase relationship in is brought into formula (5), and the optimization problem is further simplified as:

Figure BDA0003375168710000055
Figure BDA0003375168710000055

其中，

Figure BDA0003375168710000056
表示无人机UAV天线单元p和接收端天线单元q之间多径分量经(m,n)-th智能反射单元后的时变多普勒频移，M表示智能反射面的行反射单元数目，m表示智能反射单元的行位置索引，N表示智能反射面的列反射单元数目，n表示智能反射单元的列位置索引，λ表示载波波长，π表示圆周率，ξpmn(t)表示无人机UAV天线单元p和(m,n)-th智能反射单元之间的时变距离，ξmnq(t)表示(m,n)-th智能反射单元和接收端天线单元q之间的时变距离，θmn(t)表示智能反射面IRS在t时刻的反射相位。in,
Figure BDA0003375168710000056
Represents the time-varying Doppler frequency shift of the multipath component between the UAV antenna unit p and the receiving antenna unit q after the (m,n)-th intelligent reflection unit, M represents the number of line reflection units of the intelligent reflection surface , m represents the row position index of the smart reflective unit, N represents the number of column reflective units on the smart reflective surface, n represents the column position index of the smart reflective unit, λ represents the carrier wavelength, π represents the pi, and ξ pmn (t) represents the UAV The time-varying distance between the UAV antenna unit p and the (m,n)-th smart reflection unit, ξ mnq (t) represents the time-varying distance between the (m,n)-th smart reflection unit and the receiver antenna unit q , θ mn (t) represents the reflection phase of the intelligent reflective surface IRS at time t.

进一步的，步骤S5中求解最优的智能反射面IRS反射相位包括以下步骤：Further, in step S5, solving the optimal IRS reflection phase of the intelligent reflecting surface includes the following steps:

S51、根据公式(6)得到的最优智能反射面IRS反射相位的优化问题，求解出最优的智能反射面IRS反射相位

Figure BDA0003375168710000057
的表达式如下所示：S51. According to the optimization problem of the IRS reflection phase of the optimal intelligent reflecting surface obtained by formula (6), the optimal IRS reflection phase of the intelligent reflecting surface is solved.
Figure BDA0003375168710000057
The expression looks like this:

Figure BDA0003375168710000058
Figure BDA0003375168710000058

其中，

Figure BDA0003375168710000059
表示
Figure BDA00033751687100000510
和2π两数相除的余数；in,
Figure BDA0003375168710000059
express
Figure BDA00033751687100000510
The remainder of the division of two numbers with 2π;

S52、由于公式(7)没有考虑无人机UAV天线单元p和接收端天线单元q之间多径分量经(m,n)-th智能反射单元后的时变多普勒频移

Figure BDA0003375168710000061
因此将
Figure BDA0003375168710000062
进一步改写为：S52. Since 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 the (m,n)-th intelligent reflection unit
Figure BDA0003375168710000061
Therefore will
Figure BDA0003375168710000062
Further rewritten as:

Figure BDA0003375168710000063
Figure BDA0003375168710000063

其中，

Figure BDA0003375168710000064
表示无人机UAV天线单元p和接收端天线单元q之间多径分量经(m,n)-th智能反射单元后的时变多普勒频移。in,
Figure BDA0003375168710000064
Represents the time-varying Doppler frequency shift of the multipath component between the UAV UAV antenna unit p and the receiving end antenna unit q after the (m,n)-th intelligent reflection unit.

进一步的，步骤S6包括以下步骤：Further, step S6 includes the following steps:

S61、利用步骤S2得到的复信道增益和步骤S5得到的智能反射面IRS的最优反射相位，根据定义求解基于智能反射面IRS辅助的时空相关性函数，计算公式如下：S61, use the complex channel gain obtained in step S2 and the optimal reflection phase of the intelligent reflecting surface IRS obtained in step S5 to solve the spatiotemporal correlation function assisted by the intelligent reflecting surface IRS according to the definition, and the calculation formula is as follows:

首先根据时空相关函数的定义式：First, according to the definition of the space-time correlation function:

Figure BDA0003375168710000065
Figure BDA0003375168710000065

其中，

Figure BDA0003375168710000066
表示两个时变传递函数之间的时空相关函数，δT表示无人机UAV天线单元之间的天线间距，δR表示用户端天线单元之间的天线间距，τ表示传播时延，t表示时间变量，
Figure BDA0003375168710000067
表示统计性均值运算，(·)*表示复共轭运算，hpq(t)表示无人机UAV天线单元p与用户端天线单元q之间的复信道增益，hp′q′(t+τ)表示经过时间延迟τ后，无人机UAV天线单元p’与用户端天线单元q’之间的复信道增益；|·|表示绝对值函数；in,
Figure BDA0003375168710000066
Represents the space-time correlation function between two time-varying transfer functions, δ T represents the antenna spacing between UAV antenna units, δ R represents the antenna spacing between the user-end antenna units, τ represents the propagation delay, and t represents time variable,
Figure BDA0003375168710000067
represents the statistical mean operation, ( ) * represents the complex conjugate operation, h pq (t) represents the complex channel gain between the UAV UAV antenna unit p and the user-end antenna unit q, h p′q′ (t+ τ) represents the complex channel gain between the UAV UAV antenna unit p' and the user-end antenna unit q' after the time delay τ; |·| represents the absolute value function;

再把步骤S2中得到的复信道增益函数的表达式分别带入，得到具体的时空相关性函数如下所示：Then, the expressions of the complex channel gain function obtained in step S2 are respectively brought in, and the specific space-time correlation function is obtained as follows:

Figure BDA0003375168710000068
Figure BDA0003375168710000068

Figure BDA0003375168710000069
Figure BDA0003375168710000069

Figure BDA00033751687100000610
Figure BDA00033751687100000610

Figure BDA0003375168710000071
Figure BDA0003375168710000071

其中，

Figure BDA0003375168710000072
表示无人机UAV天线单元和接收端天线单元之间直射分量的空时相关性，λ表示载波波长，π表示圆周率，ξpq(t)表示无人机UAV天线单元p和用户端天线单元q之间的时变距离，ξp′q′(t+τ)表示经过时间延迟τ后，人机UAV天线单元p’和用户端天线单元q’之间的时变距离，
Figure BDA0003375168710000073
表示无人机UAV天线单元p和用户端天线单元q之间直射分量的时变多普勒频移；
Figure BDA0003375168710000074
表示无人机UAV天线单元和接收端天线单元之间散射分量的空时相关性，Nl表示散射体
Figure BDA0003375168710000075
的数目，l表示抽头数，ξpnl(t)表示无人机UAV天线单元p和散射体
Figure BDA0003375168710000076
之间的时变距离，ξp′nl(t+τ)表示经过时间延迟τ后，无人机UAV天线单元p’和散射体
Figure BDA0003375168710000077
之间的时变距离，
Figure BDA0003375168710000078
表示散射体
Figure BDA0003375168710000079
和用户端天线单元q之间的时变距离，
Figure BDA00033751687100000710
表示经过时间延迟τ后，散射体
Figure BDA00033751687100000711
和用户端天线单元q’之间的时变距离，
Figure BDA00033751687100000712
表示无人机UAV天线单元p和用户端天线单元q之间散射分量的时变多普勒频移；
Figure BDA00033751687100000713
表示表示无人机UAV天线单元和接收端天线单元之间经智能反射面IRS反射的直射分量的空时相关性，M表示智能反射面IRS的行反射单元数目，N表示智能反射面IRS的列反射单元数目，ξpmn(t)表示无人机UAV天线单元p和(m,n)-th智能反射单元之间的时变距离，ξp′m′n′(t+τ)表示经过时间延迟τ后，无人机UAV天线单元p’和(m’,n’)-th智能反射单元之间的时变距离，ξmnq(t)表示(m,n)-th智能反射单元和用户端天线单元q之间的时变距离，ξm′n′q′(t+τ)表示经过时间延迟τ后,(m’,n’)-th智能反射单元和用户端天线单元q’之间的时变距离；
Figure BDA00033751687100000714
表示无人机UAV天线单元和接收端天线单元之间经智能反射面IRS和散射体
Figure BDA00033751687100000715
反射的散射分量的空时相关性，δN表示智能反射面IRS的列反射单元之间的距离，δM表示智能反射面IRS的列反射单元之间的距离；in,
Figure BDA0003375168710000072
Represents the space-time correlation of the direct component between the UAV UAV antenna unit and the receiver antenna unit, λ represents the carrier wavelength, π represents the pi, ξ pq (t) represents the UAV UAV antenna unit p and the user-end antenna unit q The time-varying distance between ξ p′q′ (t+τ) represents the time-varying distance between the human-machine UAV antenna unit p' and the user-end antenna unit q' after the time delay τ,
Figure BDA0003375168710000073
represents the time-varying Doppler shift of the direct component between the UAV antenna unit p and the user-end antenna unit q of the UAV;
Figure BDA0003375168710000074
represents the space-time correlation of the scattering components between the UAV antenna unit and the receiving antenna unit, and N l represents the scatterer
Figure BDA0003375168710000075
, l denotes the number of taps, ξ pnl (t) denotes the UAV UAV antenna unit p and the scatterer
Figure BDA0003375168710000076
The time-varying distance between, ξ p′nl (t+τ) represents the time delay τ between the UAV antenna unit p' and the scatterer
Figure BDA0003375168710000077
the time-varying distance between
Figure BDA0003375168710000078
Represents a scatterer
Figure BDA0003375168710000079
and the time-varying distance between the user-end antenna element q,
Figure BDA00033751687100000710
represents that after a time delay τ, the scatterer
Figure BDA00033751687100000711
and the time-varying distance between the user-end antenna unit q',
Figure BDA00033751687100000712
represents the time-varying Doppler shift of the scattering component between the UAV antenna unit p and the user-end antenna unit q;
Figure BDA00033751687100000713
Represents the space-time correlation between the UAV antenna unit and the receiving end antenna unit of the direct component reflected by the smart reflective surface IRS, M represents the number of row reflective units of the smart reflective surface IRS, and N represents the column of the smart reflective surface IRS Number of reflection units, ξ pmn (t) represents the time-varying distance between the UAV UAV antenna unit p and the (m,n)-th smart reflection unit, ξ p′m′n′ (t+τ) represents the elapsed time After delay τ, the time-varying distance between the UAV antenna unit p' and the (m',n')-th smart reflection unit, ξ mnq (t) represents the (m,n)-th smart reflection unit and the user is the time-varying distance between the end antenna elements q, ξ m′n′q′ (t+τ) indicates that after the time delay τ, the distance between the (m′,n′)-th intelligent reflection element and the user end antenna element q′ time-varying distance;
Figure BDA00033751687100000714
Indicates the intelligent reflective surface IRS and scatterer between the UAV antenna unit and the receiver antenna unit
Figure BDA00033751687100000715
The space-time correlation of the reflected scattering components, δ N represents the distance between the column reflection units of the smart reflective surface IRS, δ M represents the distance between the column reflective units of the smart reflective surface IRS;

S62、当改变智能反射IRS的反射单元数目，智能反射面IRS的反射相位、无人机UAV飞行轨迹时，根据上面得到的时空相关性函数，通过其相关性来分析这些参数变化对无人机信道特性的影响。S62. When changing the number of reflective units of the smart reflective IRS, the reflective phase of the smart reflective surface IRS, and the UAV flight trajectory of the UAV, according to the spatiotemporal correlation function obtained above, analyze the effect of these parameter changes on the UAV through the correlation. The effect of channel characteristics.

本发明的一个实施例中：一种设备，包括存储器和处理器，其中：In one embodiment of the present invention: an apparatus including a memory and a processor, wherein:

存储器，用于存储能够在处理器上运行的计算机程序；memory for storing computer programs capable of running on the processor;

处理器，用于在运行所述计算机程序时，执行上述基于大规模智能反射单元的无人机信道模型建立方法的步骤。The processor is configured to execute the steps of the above-mentioned method for establishing a channel model of an unmanned aerial vehicle based on a large-scale intelligent reflection unit when the computer program is executed.

本发明的另一个实施例中：一种存储介质，所述存储介质上存储有计算机程序，所述计算机程序被至少一个处理器执行时实现上述基于大规模智能反射单元的无人机信道模型建立方法的步骤。In another embodiment of the present invention: a storage medium, on which a computer program is stored, and when the computer program is executed by at least one processor, the above-mentioned establishment of a UAV channel model based on a large-scale intelligent reflection unit is realized steps of the method.

有益效果：与现有技术相比，本发明的基于大规模智能反射单元的无人机信道模型建立方法，相比于传统的无人机通信技术，采用了智能反射面IRS辅助来辅助无人机通信，并证明了采用智能反射面IRS可以提高接收信号的信号功率。其中，智能反射面IRS的反射相位是提高接收信号功率的关键。因此本发明主要贡献就是提供了一种智能反射面的最优反射相位，可以让接收信号的功率最大化。同时，本发明还求解了求解基于智能反射面IRS辅助的时空相关性函数，研究了当改变智能反射IRS的反射单元数目，智能反射面IRS的反射相位、无人机UAV飞行轨迹时，通过分析时空相关性函数的变化，了解这些参数变化对无人机信道特性的影响。因此本发明对于探索智能反射面IRS对无人机信道统计特性的影响提供了一定帮助。Beneficial effects: Compared with the prior art, the method for establishing a channel model of an unmanned aerial vehicle based on a large-scale intelligent reflection unit of the present invention, compared with the traditional unmanned aerial vehicle communication technology, adopts the intelligent reflection surface IRS to assist the unmanned aerial vehicle. It is proved that the signal power of the received signal can be improved by using the intelligent reflective surface IRS. Among them, the reflection phase of the intelligent reflective surface IRS is the key to improve the received signal power. Therefore, the main contribution of the present invention is to provide an optimal reflection phase of the smart reflection surface, which can maximize the power of the received signal. At the same time, the invention also solves the time-space correlation function based on the assistance of the intelligent reflecting surface IRS, and studies when changing the number of reflecting units of the intelligent reflecting surface IRS, the reflection phase of the intelligent reflecting surface IRS, and the UAV flight trajectory of the unmanned aerial vehicle. Changes in the spatiotemporal correlation function to understand the impact of these parameter changes on the UAV channel characteristics. Therefore, the present invention provides certain help for exploring the influence of the intelligent reflecting surface IRS on the statistical characteristics of the UAV channel.

附图说明Description of drawings

图1为基于大规模智能反射单元的无人机信道模型示意图；Fig. 1 is a schematic diagram of a UAV channel model based on a large-scale intelligent reflection unit;

图2为无人机UAV的三种不同飞行轨迹示意图；Figure 2 is a schematic diagram of three different flight trajectories of the unmanned aerial vehicle UAV;

图3为三种不同无人机飞行轨迹的无人机-MIMO模型的绝对包络幅度比较图；Figure 3 is a comparison chart of the absolute envelope amplitudes of the UAV-MIMO models of three different UAV flight trajectories;

图4为是否采用智能反射面IRS在不同反射单元规模和反射相位下的宽带无人机-MIMO模型的绝对包络幅度比较图；Figure 4 is a comparison chart of the absolute envelope amplitude of the broadband UAV-MIMO model under different reflection unit scales and reflection phases with or without the intelligent reflective surface IRS;

图5为是否采用智能反射面IRS的无人机-MIMO模型对于不同无人机飞行轨迹的绝对传输空间相关性的比较图；Figure 5 is a comparison diagram of the absolute transmission spatial correlation of the UAV-MIMO model with or without the intelligent reflective surface IRS for different UAV flight trajectories;

图6为是否采用智能反射面IRS的宽带无人机-MIMO模型对于不同无人机飞行轨迹的绝对传输空间相关性的比较图。Figure 6 is a comparison diagram of the absolute transmission spatial correlation of the broadband UAV-MIMO model with or without the intelligent reflective surface IRS for different UAV flight trajectories.

具体实施方式Detailed ways

下面结合具体实施例对本发明进行详细说明。以下实施例将有助于本领域的技术人员进一步理解本发明，但不以任何形式限制本发明。应当指出的是，对本领域的普通技术人员来说，在不脱离本发明构思的前提下，还可以做出若干变化和改进。这些都属于本发明的保护范围。The present invention will be described in detail below with reference to specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that, for those skilled in the art, several changes and improvements can be made without departing from the inventive concept. These all belong to the protection scope of the present invention.

本发明采用智能反射面IRS控制无人机UAV信道的传播环境，考虑了大型智能反射面IRS的近场效应，利用球形波前的二阶近似进行建模，考虑了最优的接收信号功率，并得到了最优的智能反射面IRS反射相位。本发明考虑了智能反射面IRS对无人机信道传播环境的改变能力，即智能反射面IRS反射单元的数目和反射单元尺寸对无人机信道统计特性的影响。本发明考虑了无人机UAV不同飞行轨迹下对无人机信道统计特性的影响。本发明考虑了智能反射面IRS在不同时变反射相位下，对无人机信道统计特性的影响。本发明考虑了基于智能反射面IRS的无人机信道，探索智能反射面IRS对无人机信道统计特性的影响，更好的为今后的系统性能分析以及预编码算法设计提供依据。The invention adopts the intelligent reflecting surface IRS to control the propagation environment of the UAV channel of the unmanned aerial vehicle, considers the near-field effect of the large intelligent reflecting surface IRS, uses the second-order approximation of the spherical wavefront for modeling, and considers the optimal received signal power, And the optimal IRS reflection phase of the intelligent reflecting surface is obtained. The present invention considers the ability of the intelligent reflecting surface IRS to change the channel propagation environment of the UAV, that is, the influence of the number of reflecting units of the intelligent reflecting surface IRS and the size of the reflecting unit on the statistical characteristics of the UAV channel. The present invention considers the influence on the statistical characteristics of the UAV channel under different flight trajectories of the UAV. The invention considers the influence of the intelligent reflecting surface IRS on the statistical characteristics of the UAV channel under different time-varying reflection phases. The invention considers the UAV channel based on the intelligent reflecting surface IRS, explores the influence of the intelligent reflecting surface IRS on the statistical characteristics of the UAV channel, and better provides a basis for future system performance analysis and precoding algorithm design.

本发明的基于大规模智能反射单元的无人机信道模型建立方法，包括以下步骤：The method for establishing a UAV channel model based on a large-scale intelligent reflection unit of the present invention comprises the following steps:

S1、根据实际的无人机UAV对地通信场景，确定无人机UAV、智能反射面IRS和接收端三者之间的位置关系，建立建立基于大规模智能反射单元的无人机信道模型，并得到信道的复信道增益；S1. According to the actual UAV-to-ground communication scenario, determine the positional relationship between the UAV UAV, the intelligent reflective surface IRS and the receiving end, and establish a UAV channel model based on a large-scale intelligent reflective unit. And get the complex channel gain of the channel;

建立的基于大规模智能反射单元的无人机信道模型为了服务本小区内的所有用户，将智能反射面配置在所服务小区的边缘的建筑物表面。然后使用三维圆柱体来模拟接收端周围的垂直建筑结构，并利用球面波前的二阶近似来模拟大规模智能反射面的近场效应。假设散射体位于这些三维圆柱体的表面，且智能反射面上的智能反射单元都是均匀排列；考虑智能反射面IRS对信道传播环境的改变，建立的基于大规模智能反射单元的无人机信道模型如图1所示，在图1中本发明采用三维-圆柱体模拟智能反射面IRS、无人机UAV和接收端周围的散射体，智能反射面采用均匀平面反射阵列单元，假定每行的反射单元数目为M，每列的反射单元数目为N，并假定智能反射面IRS配置在建筑物的表面，以至于可以服务本小区的所有用户。无人机UAV的高度明显高于地面建筑物的高度，无人机UAV和智能反射面IRS之间没有建筑物的遮挡，本发明假定无人机UAV和智能反射面IRS之间为直射链路。其中，图1中，HIRS表示智能反射面的高度，HT表示无人机UAV的高度，Rl第l个三维圆柱体的半径，ξpq(t)表示无人机UAV天线单元p和接收端天线单元q之间的时变距离，

Figure BDA0003375168710000101
表示无人机UAV天线单元p和散射体
Figure BDA0003375168710000102
之间的时变距离，
Figure BDA0003375168710000103
表示散射体
Figure BDA0003375168710000104
和接收端天线单元q之间的时变距离，ξTR表示无人机UAV与接收端之间的距离，ξpmn(t)表示无人机UAV天线单元p和(m,n)-th智能反射单元之间的时变距离，ξmnq(t)表示(m,n)-th智能反射单元和接收端天线单元q之间的时变距离，
Figure BDA0003375168710000105
表示(m,n)-th智能反射单元和散射体
Figure BDA0003375168710000106
之间的时变距离，ξIRSR表示智能反射面IRS与接收端之间的距离，ξIRST表示无人机UAV与智能反射面IRS之间的距离，θIRS表示智能反射面IRS在x-y平面上的方向，αIRST表示无人机UAV相对于智能反射面IRS在x-y平面上的方向，θR表示接收端在x-y平面上的方向，αTR表示无人机UAV相对于接收机在x-y平面上的方向，vT表示无人机UAV的移动速度，γT表示无人机UAV运动的方位角，表示无人机UAV运动的仰角，θT表示无人机UAV接收端在x-y平面上的方向，vR表示接收端的移动速度，γR表示接收端的方位角，
Figure BDA0003375168710000107
表示散射体
Figure BDA0003375168710000108
的到达方位角(AAoA)，
Figure BDA0003375168710000109
表示散射体
Figure BDA00033751687100001010
的出发方位角(AAoD)，
Figure BDA00033751687100001011
表示散射体
Figure BDA00033751687100001012
的到达仰角(EAoA)，
Figure BDA00033751687100001013
表示散射体
Figure BDA00033751687100001014
的出发仰角(EAoD)。In order to serve all users in the cell, the established UAV channel model based on large-scale intelligent reflection unit configures the intelligent reflection surface on the building surface at the edge of the served cell. A 3D cylinder is then used to simulate the vertical building structure around the receiving end, and a second-order approximation of the spherical wavefront is used to simulate the near-field effect of a large-scale smart reflector. It is assumed that the scatterers are located on the surfaces of these three-dimensional cylinders, and the intelligent reflection units on the intelligent reflection surface are uniformly arranged; considering the change of the channel propagation environment of the intelligent reflection surface IRS, a large-scale intelligent reflection unit-based UAV channel is established. The model is shown in Figure 1. In Figure 1, the present invention uses a three-dimensional-cylinder to simulate the intelligent reflecting surface IRS, UAV UAV and the scatterers around the receiving end. The intelligent reflecting surface adopts a uniform plane reflection array unit. The number of reflection units is M, the number of reflection units in each column is N, and it is assumed that the intelligent reflection surface IRS is configured on the surface of the building, so that it can serve all users of the cell. The height of the UAV UAV is obviously higher than the height of the buildings on the ground, and there is no building block between the UAV UAV and the intelligent reflecting surface IRS. The present invention assumes a direct link between the UAV UAV and the intelligent reflecting surface IRS . Among them, in Figure 1, H IRS represents the height of the intelligent reflective surface, HT represents the height of the UAV UAV, R l is the radius of the lth three-dimensional cylinder, ξ pq (t) represents the UAV UAV antenna unit p and the time-varying distance between the antenna elements q at the receiving end,
Figure BDA0003375168710000101
Represents the drone UAV antenna unit p and scatterer
Figure BDA0003375168710000102
the time-varying distance between
Figure BDA0003375168710000103
Represents a scatterer
Figure BDA0003375168710000104
and the time-varying distance between the receiving end antenna unit q, ξ TR represents the distance between the UAV UAV and the receiving end, ξ pmn (t) represents the UAV UAV antenna unit p and (m,n)-th intelligence The time-varying distance between the reflection units, ξ mnq (t) represents the time-varying distance between the (m,n)-th intelligent reflection unit and the receiving antenna unit q,
Figure BDA0003375168710000105
Represents (m,n)-th smart reflectors and scatterers
Figure BDA0003375168710000106
The time-varying distance between, ξ IRSR represents the distance between the smart reflective surface IRS and the receiving end, ξ IRST represents the distance between the UAV UAV and the smart reflective surface IRS, θ IRS represents the smart reflective surface IRS on the xy plane α IRST represents the direction of the UAV UAV relative to the intelligent reflective surface IRS on the xy plane, θ R represents the direction of the receiving end on the xy plane, α TR represents the UAV UAV relative to the receiver on the xy plane direction, v T represents the moving speed of the UAV UAV, γ T represents the azimuth angle of the UAV UAV movement, represents the elevation angle of the UAV UAV movement, θ T represents the direction of the receiving end of the UAV UAV on the xy plane , v R is the moving speed of the receiver, γ R is the azimuth of the receiver,
Figure BDA0003375168710000107
Represents a scatterer
Figure BDA0003375168710000108
Azimuth of Arrival (AAoA) of ,
Figure BDA0003375168710000109
Represents a scatterer
Figure BDA00033751687100001010
Azimuth of Departure (AAoD) of ,
Figure BDA00033751687100001011
Represents a scatterer
Figure BDA00033751687100001012
the elevation angle of arrival (EAoA),
Figure BDA00033751687100001013
Represents a scatterer
Figure BDA00033751687100001014
The elevation angle of departure (EAoD).

根据基于大规模智能反射单元的无人机信道模型得到信道的复信道增益，复信道增益hpq(t,τ)的计算公式为：According to the UAV channel model based on the large-scale intelligent reflection unit, the complex channel gain of the channel is obtained. The calculation formula of the complex channel gain h pq (t,τ) is:

Figure BDA00033751687100001015
Figure BDA00033751687100001015

其中，t表示时间变量，l表示抽头数，L表示总的抽头数量，cl表示第l次抽头的增益，hl,pq(t)表示无人机UAV天线单元p不通过智能反射面IRS直接与接收端天线单元q之间的复信道增益，

Figure BDA00033751687100001016
表示无人机UAV天线单元p通过智能反射面IRS与接收端天线单元q之间的复信道增益，τl(t)表示第l次抽头的传播延迟，δ(·)表示冲激函数。Among them, t is the time variable, l is the number of taps, L is the total number of taps, c l is the gain of the lth tap, h l, pq (t) means that the UAV antenna unit p of the UAV does not pass the intelligent reflector IRS is the complex channel gain directly between the receiver antenna element q,
Figure BDA00033751687100001016
Represents the complex channel gain between the UAV antenna unit p through the intelligent reflector IRS and the antenna unit q at the receiving end, τ l (t) represents the propagation delay of the lth tap, and δ( ) represents the impulse function.

S2、根据基于大规模智能反射单元的无人机信道模型得到该复信道增益有两个分量组成：无人机UAV不通过智能反射面IRS直接与接收端进行传输的复信道增益以及无人机UAV通过智能反射面IRS与接收端进行传输的复信道增益；S2. According to the UAV channel model based on the large-scale intelligent reflection unit, the complex channel gain is composed of two components: the complex channel gain of the UAV UAV directly transmitting to the receiving end without the intelligent reflecting surface IRS, and the complex channel gain of the UAV. The complex channel gain that UAV transmits with the receiver through the intelligent reflective surface IRS;

S21、无人机UAV天线单元p不通过智能反射面IRS直接与接收端天线单元q之间的复信道增益hl,pq(t)表示如下：S21. The complex channel gain h l,pq (t) between the UAV antenna unit p of the UAV and the antenna unit q of the receiving end directly without passing through the intelligent reflecting surface IRS is expressed as follows:

Figure BDA0003375168710000111
Figure BDA0003375168710000111

其中，

Figure BDA0003375168710000112
表示无人机UAV天线单元p和接收端天线单元q之间直射分量的复信道增益，
Figure BDA0003375168710000113
表示无人机UAV天线单元p和接收端天线单元q之间散射分量的复信道增益；in,
Figure BDA0003375168710000112
Represents the complex channel gain of the direct component between the UAV UAV antenna unit p and the receiver antenna unit q,
Figure BDA0003375168710000113
Represents the complex channel gain of the scattering component between the UAV UAV antenna unit p and the receiver antenna unit q;

Figure BDA0003375168710000114
Figure BDA0003375168710000114

Figure BDA0003375168710000115
Figure BDA0003375168710000115

其中，Gt表示发射天线增益，Gr表示接收端天线增益，γTR表示无人机UAV到接收端的路径损耗，K1表示莱斯因子，λ表示载波波长，t表示时间变量，π表示圆周率，ξpq(t)表示无人机UAV天线单元p和接收端天线单元q之间的时变距离，

Figure BDA0003375168710000116
表示无人机UAV天线单元p和接收端天线单元q之间直射分量的时变多普勒频移，δ(l-1)表示经过l次抽头后的延迟冲击函数，Nl表示散射体
Figure BDA0003375168710000117
的数目，
Figure BDA0003375168710000118
表示无人机UAV天线单元p和散射体
Figure BDA0003375168710000119
之间的时变距离，
Figure BDA00033751687100001110
表示散射体
Figure BDA00033751687100001111
和接收端天线单元q之间的时变距离，
Figure BDA00033751687100001112
表示无人机UAV天线单元p和接收端天线单元q之间散射分量的时变多普勒频移。where G t is the transmit antenna gain, G r is the receiver antenna gain, γ TR is the path loss from the UAV to the receiver, K 1 is the Rice factor, λ is the carrier wavelength, t is the time variable, and π is the pi , ξ pq (t) represents the time-varying distance between the UAV UAV antenna unit p and the receiver antenna unit q,
Figure BDA0003375168710000116
represents the time-varying Doppler frequency shift of the direct component between the UAV antenna unit p and the receiving antenna unit q, δ(l-1) represents the delay impulse function after l taps, and N l represents the scatterer
Figure BDA0003375168710000117
Number of,
Figure BDA0003375168710000118
Represents the drone UAV antenna unit p and scatterer
Figure BDA0003375168710000119
the time-varying distance between
Figure BDA00033751687100001110
Represents a scatterer
Figure BDA00033751687100001111
and the time-varying distance between the receiver antenna element q,
Figure BDA00033751687100001112
Represents the time-varying Doppler shift of the scattered component between the UAV UAV antenna unit p and the receiver antenna unit q.

S22、无人机UAV通过智能反射面IRS与接收端进行传输的复信道增益

Figure BDA00033751687100001113
表示如下：S22. The complex channel gain of the UAV transmitted through the intelligent reflective surface IRS and the receiving end
Figure BDA00033751687100001113
It is expressed as follows:

Figure BDA0003375168710000121
Figure BDA0003375168710000121

其中，

Figure BDA0003375168710000122
表示无人机UAV天线单元p和接收端天线单元q之间的直射分量经智能反射面IRS反射后的复信道增益，
Figure BDA0003375168710000123
表示无人机UAV天线单元p和接收端天线单元q之间的散射分量经智能反射面IRS和散射体散射后的复信道增益；in,
Figure BDA0003375168710000122
Represents the complex channel gain of the direct component between the UAV UAV antenna unit p and the receiving end antenna unit q after being reflected by the intelligent reflecting surface IRS,
Figure BDA0003375168710000123
Represents the complex channel gain after the scattering component between the UAV UAV antenna unit p and the receiving end antenna unit q is scattered by the intelligent reflector IRS and the scatterer;

Figure BDA0003375168710000124
Figure BDA0003375168710000124

Figure BDA0003375168710000125
Figure BDA0003375168710000125

其中，m表示智能反射单元的行位置索引，n表示智能反射单元的列位置索引，M表示智能反射面的行反射单元数目，N表示智能反射面的列反射单元数目，Gt表示发射天线增益，G表示IRS反射单元的增益，Gr表示接收端天线增益，γTIR表示无人机UAV到IRS再到接收端的路径损耗，K2表示莱斯因子，λ表示载波波长，t表示时间变量，π表示圆周率，ξpmn(t)表示无人机UAV天线单元p和(m,n)-th智能反射单元之间的时变距离，ξmnq(t)表示(m,n)-th智能反射单元和接收端天线单元q之间的时变距离，θmn(t)表示智能反射面IRS在t时刻的反射相位，

Figure BDA0003375168710000126
表示无人机UAV天线单元p和接收端天线单元q之间多径分量经(m,n)-th智能反射单元后的时变多普勒频移，Nl表示散射体
Figure BDA0003375168710000127
的数目，l表示抽头数，
Figure BDA0003375168710000128
表示(m,n)-th智能反射单元和散射体
Figure BDA0003375168710000129
之间的时变距离，
Figure BDA00033751687100001210
表示散射体
Figure BDA00033751687100001211
和接收端天线单元q之间的时变距离，
Figure BDA00033751687100001212
表示无人机UAV天线单元p和接收端天线单元q之间多径分量经智能反射面IRS和散射体
Figure BDA00033751687100001213
后的时变多普勒频移。Among them, m represents the row position index of the smart reflective unit, n represents the column position index of the smart reflective unit, M represents the number of row reflective units on the smart reflective surface, N represents the number of column reflective units on the smart reflective surface, and G t represents the transmit antenna gain , G represents the gain of the IRS reflection unit, G r represents the antenna gain at the receiving end, γ TIR represents the path loss from the UAV to the IRS and then to the receiving end, K 2 represents the Rice factor, λ represents the carrier wavelength, t represents the time variable, π denotes the pi, ξ pmn (t) denotes the time-varying distance between the UAV UAV antenna unit p and the (m,n)-th smart reflection unit, and ξ mnq (t) denotes the (m,n)-th smart reflection is the time-varying distance between the unit and the receiving antenna unit q, θ mn (t) represents the reflection phase of the intelligent reflector IRS at time t,
Figure BDA0003375168710000126
Represents the time-varying Doppler frequency shift of the multipath component between the UAV antenna unit p and the receiving antenna unit q after the (m,n)-th intelligent reflection unit, N l represents the scatterer
Figure BDA0003375168710000127
, l represents the number of taps,
Figure BDA0003375168710000128
Represents (m,n)-th smart reflectors and scatterers
Figure BDA0003375168710000129
the time-varying distance between
Figure BDA00033751687100001210
Represents a scatterer
Figure BDA00033751687100001211
and the time-varying distance between the receiver antenna element q,
Figure BDA00033751687100001212
Represents the multipath component between the UAV UAV antenna unit p and the receiver antenna unit q through the intelligent reflector IRS and the scatterer
Figure BDA00033751687100001213
The time-varying Doppler frequency shift.

S3、根据基于大规模智能反射单元的无人机信道模型，设计目的是为了让接收信号功率最大，因此根据接收信号功率最大化来设计优化问题；S3. According to the UAV channel model based on the large-scale intelligent reflection unit, the design purpose is to maximize the received signal power, so the optimization problem is designed according to the maximum received signal power;

因此，优化问题表示如下：Therefore, the optimization problem is expressed as follows:

Figure BDA00033751687100001214
Figure BDA00033751687100001214

其中，t表示时间变量，θmn(t)表示智能反射面IRS在t时刻的反射相位，

Figure BDA0003375168710000131
表示统计性均值运算，hpq(t)表示无人机UAV天线单元p和接收端天线单元q之间多径分量的复信道增益。Among them, t represents the time variable, θ mn (t) represents the reflection phase of the intelligent reflective surface IRS at time t,
Figure BDA0003375168710000131
represents the statistical mean operation, and h pq (t) represents the complex channel gain of the multipath component between the UAV UAV antenna unit p and the receiver antenna unit q.

S4、简化优化问题：接收信号由直射分量、散射分量、经智能反射面IRS的直射分量和经智能反射面的散射分量组成，导致了步骤S3中提出的优化问题在计算上具有很大的复杂度，所以为了降低复杂度，需要进一步化简问题。本发明发现，当智能反射面IRS中的智能反射单元的规模较大时，接收信号的功率主要由通过智能反射面IRS的反射信号控制，且该反射信号的复信道增益又由其直射分量为主，因此可以简化求解反射相位的过程；S4. Simplify the optimization problem: the received signal is composed of the direct component, the scattered component, the direct component passing through the intelligent reflecting surface IRS, and the scattering component passing through the intelligent reflecting surface, resulting in the optimization problem proposed in step S3 being computationally complex. Therefore, in order to reduce the complexity, the problem needs to be further simplified. The present invention finds that when the scale of the intelligent reflecting unit in the intelligent reflecting surface IRS is relatively large, the power of the received signal is mainly controlled by the reflected signal passing through the intelligent reflecting surface IRS, and the complex channel gain of the reflected signal is determined by its direct component: main, so the process of solving the reflection phase can be simplified;

其化简过程包括以下步骤：Its simplification process includes the following steps:

S41、由于接收信号的功率主要集中在经智能反射面IRS反射的直射分量上，所以步骤S3中的优化问题化简为：S41. Since the power of the received signal is mainly concentrated on the direct component reflected by the intelligent reflecting surface IRS, the optimization problem in step S3 is simplified as:

Figure BDA0003375168710000132
Figure BDA0003375168710000132

其中，t表示时间变量，

Figure BDA0003375168710000133
表示无人机UAV天线单元p和接收端天线单元q之间的直射分量经智能反射面IRS反射后的复信道增益，|·|表示绝对值函数；where t represents the time variable,
Figure BDA0003375168710000133
Represents the complex channel gain of the direct component between the UAV UAV antenna unit p and the receiving end antenna unit q after being reflected by the intelligent reflecting surface IRS, |·| represents the absolute value function;

S42、把

Figure BDA0003375168710000134
中的相位关系带入公式(5)中，优化问题进一步化简为：S42, put
Figure BDA0003375168710000134
The phase relationship in is brought into formula (5), and the optimization problem is further simplified as:

Figure BDA0003375168710000135
Figure BDA0003375168710000135

其中，

Figure BDA0003375168710000136
表示无人机UAV天线单元p和接收端天线单元q之间多径分量经(m,n)-th智能反射单元后的时变多普勒频移，M表示智能反射面的行反射单元数目，m表示智能反射单元的行位置索引，N表示智能反射面的列反射单元数目，n表示智能反射单元的列位置索引，λ表示载波波长，ξpmn(t)表示无人机UAV天线单元p和(m,n)-th智能反射单元之间的时变距离，ξmnq(t)表示(m,n)-th智能反射单元和接收端天线单元q之间的时变距离，θmn(t)表示智能反射面IRS在t时刻的反射相位。in,
Figure BDA0003375168710000136
Represents the time-varying Doppler frequency shift of the multipath component between the UAV antenna unit p and the receiving antenna unit q after the (m,n)-th intelligent reflection unit, M represents the number of line reflection units of the intelligent reflection surface , m represents the row position index of the smart reflective unit, N represents the number of column reflective units of the smart reflective surface, n represents the column position index of the smart reflective unit, λ represents the carrier wavelength, ξ pmn (t) represents the UAV UAV antenna unit p and the time-varying distance between the (m,n)-th smart reflective unit, ξ mnq (t) represents the time-varying distance between the (m,n)-th smart reflective unit and the receiver antenna unit q, θ mn ( t) represents the reflection phase of the intelligent reflective surface IRS at time t.

S5、根据化简的优化问题，求解最优的智能反射面IRS反射相位。需要注意，在求解反射相位时，还需要减去直射分量的多普勒频移，只有消除多普勒频移才能增强接收信号功率，之前的研究中经常忽略多普勒频移的存在。S5. According to the simplified optimization problem, the optimal IRS reflection phase of the intelligent reflecting surface is solved. It should be noted that when solving the reflection phase, the Doppler frequency shift of the direct component also needs to be subtracted. Only by eliminating the Doppler frequency shift can the received signal power be enhanced. In previous studies, the existence of the Doppler frequency shift was often ignored.

其包括以下步骤：It includes the following steps:

S51、根据公式(6)得到的最优智能反射面IRS反射相位的优化问题，可以求解出最优的智能反射面IRS反射相位

Figure BDA0003375168710000141
的表达式如下所示：S51. According to the optimization problem of the IRS reflection phase of the optimal smart reflective surface obtained according to formula (6), the optimal IRS reflection phase of the smart reflective surface can be solved
Figure BDA0003375168710000141
The expression looks like this:

Figure BDA0003375168710000142
Figure BDA0003375168710000142

其中，t表示时间变量，λ表示载波波长，ξpmn(t)表示无人机UAV天线单元p和(m,n)-th智能反射单元之间的时变距离，ξmnq(t)表示(m,n)-th智能反射单元和接收端天线单元q之间的时变距离，π表示圆周率，θmn(t)表示智能反射面IRS在t时刻的反射相位，mod(a,b)表示a和b两数相除的余数。Among them, t represents the time variable, λ represents the carrier wavelength, ξ pmn (t) represents the time-varying distance between the UAV UAV antenna unit p and the (m,n)-th intelligent reflection unit, ξ mnq (t) represents ( m,n)-th time-varying distance between the smart reflective unit and the receiver antenna unit q, π represents the pi, θ mn (t) represents the reflection phase of the smart reflective surface IRS at time t, mod(a,b) represents The remainder of the division of two numbers a and b.

S52、由于公式(7)没有考虑无人机UAV天线单元p和接收端天线单元q之间多径分量经(m,n)-th智能反射单元后的时变多普勒频移

Figure BDA0003375168710000143
因此本发明将
Figure BDA0003375168710000146
进一步改写为：S52. Since 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 the (m,n)-th intelligent reflection unit
Figure BDA0003375168710000143
Therefore, the present invention will
Figure BDA0003375168710000146
Further rewritten as:

Figure BDA0003375168710000144
Figure BDA0003375168710000144

其中，t表示时间变量，λ表示载波波长，ξpmn(t)表示无人机UAV天线单元p和(m,n)-th智能反射单元之间的时变距离，ξmnq(t)表示(m,n)-th智能反射单元和接收端天线单元q之间的时变距离，π表示圆周率，θmn(t)表示智能反射面IRS在t时刻的反射位,

Figure BDA0003375168710000145
表示无人机UAV天线单元p和接收端天线单元q之间多径分量经(m,n)-th智能反射单元后的时变多普勒频移，mod(a,b)表示a和b两数相除的余数。Among them, t represents the time variable, λ represents the carrier wavelength, ξ pmn (t) represents the time-varying distance between the UAV UAV antenna unit p and the (m,n)-th intelligent reflection unit, ξ mnq (t) represents ( m,n)-th the time-varying distance between the smart reflective unit and the receiver antenna unit q, π represents the pi, θ mn (t) represents the reflection position of the smart reflective surface IRS at time t,
Figure BDA0003375168710000145
Represents the time-varying Doppler frequency shift of the multipath component between the UAV antenna unit p and the receiving antenna unit q after the (m,n)-th intelligent reflection unit, mod(a,b) represents a and b The remainder of the division of two numbers.

S6、通过步骤S2得到的复信道增益和步骤S5得到的智能反射面IRS的最优反射相位，根据定义求解基于智能反射面IRS辅助的时空相关性函数，通过相关性分析来确定不同参数对无人机信道特性的影响。S6. According to the complex channel gain obtained in step S2 and the optimal reflection phase of the intelligent reflecting surface IRS obtained in step S5, solve the spatiotemporal correlation function based on the assistance of the intelligent reflecting surface IRS according to the definition. The influence of human-machine channel characteristics.

其包括以下步骤：It includes the following steps:

S61、首先根据时空相关函数的定义式：S61. First, according to the definition of the space-time correlation function:

Figure BDA0003375168710000151
Figure BDA0003375168710000151

其中，

Figure BDA0003375168710000152
表示两个时变传递函数之间的时空相关函数，δT表示无人机UAV天线单元之间的天线间距，δR表示接收端天线单元之间的天线间距，τ表示传播时延，t表示时间变量，
Figure BDA0003375168710000153
表示统计性均值运算，(·)*表示复共轭运算，hpq(t)表示无人机UAV天线单元p与接收端天线单元q之间的复信道增益，hp′q′(t+τ)表示经过时间延迟τ后，无人机UAV天线单元p’与接收端天线单元q’之间的复信道增益；|·|表示绝对值函数；in,
Figure BDA0003375168710000152
Represents the space-time correlation function between two time-varying transfer functions, δ T represents the antenna spacing between UAV antenna units, δ R represents the antenna spacing between the receiving antenna units, τ represents the propagation delay, and t represents time variable,
Figure BDA0003375168710000153
represents the statistical mean operation, ( ) * represents the complex conjugate operation, h pq (t) represents the complex channel gain between the UAV UAV antenna unit p and the receiver antenna unit q, h p′q′ (t+ τ) represents the complex channel gain between the UAV UAV antenna unit p' and the receiving end antenna unit q' after the time delay τ; |·| represents the absolute value function;

再把步骤S2中得到的复信道增益函数的表达式分别带入，得到具体的时空相关性函数如下所示：Then, the expressions of the complex channel gain function obtained in step S2 are respectively brought in, and the specific space-time correlation function is obtained as follows:

Figure BDA0003375168710000154
Figure BDA0003375168710000154

Figure BDA0003375168710000155
Figure BDA0003375168710000155

Figure BDA0003375168710000156
Figure BDA0003375168710000156

Figure BDA0003375168710000157
Figure BDA0003375168710000157

其中，

Figure BDA0003375168710000158
表示无人机UAV天线单元和接收端天线单元之间直射分量的空时相关性，λ表示载波波长，π表示圆周率，ξpq(t)表示无人机UAV天线单元p和接收端天线单元q之间的时变距离，ξp′q′(t+τ)表示经过时间延迟τ后，无人机UAV天线单元p’和接收端天线单元q’之间的时变距离，
Figure BDA0003375168710000159
表示无人机UAV天线单元p和接收端天线单元q之间直射分量的时变多普勒频移；
Figure BDA00033751687100001510
表示无人机UAV天线单元和接收端天线单元之间散射分量的空时相关性，Nl表示散射体
Figure BDA00033751687100001511
的数目，l表示抽头数，ξpnl(t)表示无人机UAV天线单元p和散射体
Figure BDA00033751687100001512
之间的时变距离，ξp′nl(t+τ)表示经过时间延迟τ后，无人机UAV天线单元p’和散射体
Figure BDA0003375168710000161
之间的时变距离，
Figure BDA0003375168710000162
表示散射体
Figure BDA0003375168710000163
和接收端天线单元q之间的时变距离，
Figure BDA0003375168710000164
表示经过时间延迟τ后,散射体
Figure BDA0003375168710000165
和接收端天线单元q’之间的时变距离，
Figure BDA0003375168710000166
表示无人机UAV天线单元p和接收端天线单元q之间散射分量的时变多普勒频移；
Figure BDA0003375168710000167
表示表示无人机UAV天线单元和接收端天线单元之间经智能反射面IRS反射的直射分量的空时相关性，M表示智能反射面IRS的行反射单元数目，N表示智能反射面IRS的列反射单元数目，ξpmn(t)表示无人机UAV天线单元p和(m,n)-th智能反射单元之间的时变距离，ξp′m′n′(t+τ)表示经过时间延迟τ后，无人机UAV天线单元p’和(m’,n’)-th智能反射单元之间的时变距离，ξmnq(t)表示(m,n)-th智能反射单元和接收端天线单元q之间的时变距离，ξm′n′q′(t+τ)表示经过时间延迟τ后，(m’,n’)-th智能反射单元和接收端天线单元q’之间的时变距离；
Figure BDA0003375168710000168
表示无人机UAV天线单元和接收端天线单元之间经智能反射面IRS和散射体
Figure BDA0003375168710000169
反射的散射分量的空时相关性，δN表示智能反射面IRS的列反射单元之间的距离，δM表示智能反射面IRS的列反射单元之间的距离，τ表示传播时延。in,
Figure BDA0003375168710000158
represents the space-time correlation of the direct component between the UAV UAV antenna unit and the receiving end antenna unit, λ represents the carrier wavelength, π represents the pi, ξ pq (t) represents the UAV UAV antenna unit p and the receiving end antenna unit q The time-varying distance between, ξ p′q′ (t+τ) represents the time-varying distance between the UAV UAV antenna unit p' and the receiving end antenna unit q' after the time delay τ,
Figure BDA0003375168710000159
Represents the time-varying Doppler shift of the direct component between the UAV antenna unit p and the receiver antenna unit q;
Figure BDA00033751687100001510
represents the space-time correlation of the scattering components between the UAV antenna unit and the receiving antenna unit, and N l represents the scatterer
Figure BDA00033751687100001511
, l denotes the number of taps, ξ pnl (t) denotes the UAV UAV antenna unit p and the scatterer
Figure BDA00033751687100001512
The time-varying distance between, ξ p′nl (t+τ) represents the time delay τ between the UAV antenna unit p' and the scatterer
Figure BDA0003375168710000161
the time-varying distance between
Figure BDA0003375168710000162
Represents a scatterer
Figure BDA0003375168710000163
and the time-varying distance between the receiver antenna element q,
Figure BDA0003375168710000164
represents that after a time delay τ, the scatterer
Figure BDA0003375168710000165
and the time-varying distance between the receiver antenna element q',
Figure BDA0003375168710000166
Represents the time-varying Doppler shift of the scattering component between the UAV antenna unit p and the receiver antenna unit q;
Figure BDA0003375168710000167
Represents the space-time correlation between the UAV antenna unit and the receiving end antenna unit of the direct component reflected by the smart reflective surface IRS, M represents the number of row reflective units of the smart reflective surface IRS, and N represents the column of the smart reflective surface IRS Number of reflection units, ξ pmn (t) represents the time-varying distance between the UAV UAV antenna unit p and the (m,n)-th smart reflection unit, ξ p′m′n′ (t+τ) represents the elapsed time After delay τ, the time-varying distance between the UAV antenna unit p' and the (m',n')-th smart reflection unit, ξ mnq (t) represents the (m,n)-th smart reflection unit and the receiver is the time-varying distance between the end antenna units q, ξ m'n'q' (t+τ) represents the distance between the (m',n')-th intelligent reflection unit and the receiving end antenna unit q' after the time delay τ time-varying distance;
Figure BDA0003375168710000168
Indicates the intelligent reflective surface IRS and scatterer between the UAV antenna unit and the receiver antenna unit
Figure BDA0003375168710000169
The space-time correlation of the reflected scattering components, δ N represents the distance between the column reflection units of the smart reflective surface IRS, δ M represents the distance between the column reflective units of the smart reflective surface IRS, and τ represents the propagation delay.

S62、当改变智能反射IRS的反射单元数目，智能反射面IRS的反射相位、无人机UAV飞行轨迹时，根据上面得到的时空相关性函数，通过其相关性来分析这些参数变化对无人机信道特性的影响。S62. When changing the number of reflective units of the smart reflective IRS, the reflective phase of the smart reflective surface IRS, and the UAV flight trajectory of the UAV, according to the spatiotemporal correlation function obtained above, analyze the effect of these parameter changes on the UAV through the correlation. The effect of channel characteristics.

本发明还提供了一种设备，包括存储器和处理器，其中：The present invention also provides a device including a memory and a processor, wherein:

存储器，用于存储能够在处理器上运行的计算机程序；memory for storing computer programs capable of running on the processor;

处理器，用于在运行所述计算机程序时，执行上述基于大规模智能反射单元的无人机信道模型建立方法的步骤。The processor is configured to execute the steps of the above-mentioned method for establishing a channel model of an unmanned aerial vehicle based on a large-scale intelligent reflection unit when the computer program is executed.

本发明还提供了一种存储介质，所述存储介质上存储有计算机程序，所述计算机程序被至少一个处理器执行时实现上述基于大规模智能反射单元的无人机信道模型建立方法的步骤。The present invention also provides a storage medium on which a computer program is stored, and when the computer program is executed by at least one processor, implements the steps of the above-mentioned method for establishing a channel model of an unmanned aerial vehicle based on a large-scale intelligent reflection unit.

图2为无人机UAV的三种不同飞行轨迹示意图。在图2中，在路径I下的无人机远离智能反射面IRS和接收端，参数

Figure BDA0003375168710000171
和γT分别设置为0°和90°，在路径II下的无人机在0到5.2秒内在
Figure BDA0003375168710000172
和γT＝-30°的方向上向接收端移动，在路径III下的无人机在0到5.2秒内在
Figure BDA0003375168710000173
和γT＝-120°的方向上向智能反射IRS反射单元移动。其中，
Figure BDA0003375168710000174
表示无人机UAV运动方向仰角，γT表示无人机UAV运动方向方位角。Figure 2 is a schematic diagram of three different flight trajectories of the UAV. In Figure 2, the UAV under path I is far away from the intelligent reflector IRS and the receiver, the parameter
Figure BDA0003375168710000171
and γT are set to 0° and 90°, respectively, the UAV under Path II within 0 to 5.2 seconds
Figure BDA0003375168710000172
and γ T = -30° towards the receiver, the UAV under path III in 0 to 5.2 seconds
Figure BDA0003375168710000173
and γ T = -120° toward the smart reflective IRS reflective unit. in,
Figure BDA0003375168710000174
Represents the elevation angle of the UAV motion direction of the UAV, and γ T represents the azimuth angle of the UAV UAV motion direction.

图3为传统无人机信道模型和基于智能反射面IRS辅助的无人机信道模型在不同无人机飞行轨迹下的绝对包络幅度比较图。从图3可以看出，当无人机UAV向智能反射面IRS移动时，即路径III，采用智能反射面IRS的无人机模型接收到的包络大小逐渐增加。反之，当无人机UAV远离智能反射面IRS时，采用智能反射面IRS的无人机模型的包络大小逐渐减少，即路径I和路径II。而当没有采用智能反射面IRS时，当无人机UAV逐渐向接收端移动时，路径II，无人机模型的绝对包络幅度的增加并不明显。这说明智能反射面IRS可以有效的改变无人机和接收端之间的传播环境。Figure 3 is a comparison chart of the absolute envelope amplitude of the traditional UAV channel model and the UAV channel model assisted by the IRS based on the intelligent reflector under different UAV flight trajectories. As can be seen from Figure 3, when the UAV UAV moves towards the smart reflector IRS, i.e., path III, the size of the envelope received by the UAV model using the smart reflector IRS gradually increases. On the contrary, when the UAV UAV is far away from the smart reflector IRS, the envelope size of the UAV model using the smart reflector IRS gradually decreases, namely path I and path II. However, when the IRS is not used, when the UAV UAV gradually moves to the receiving end, the increase of the absolute envelope amplitude of the UAV model in Path II is not obvious. This shows that the intelligent reflective surface IRS can effectively change the propagation environment between the UAV and the receiver.

图4为传统无人机信道模型和基于智能反射面IRS辅助的宽带无人机信道模型在不同IRS反射相位θmn(t)的宽带UAV-MIMO模型的绝对包络大小比较图。其中，在相位1中，我们将IRS反射相位与LoS分量的时变相位和多普勒位移相对准，在相位2中，我们根据公式(8)设置IRS反射相位，即智能反射面IRS的最优反射相位。从图6中可以看出，当智能反射面IRS反射单元的尺寸增大时，可以显著改善采用智能反射面IRS的UAV-MIMO通信系统的性能和多路径衰落现象。同时，当智能反射面IRS反射单元的数量大于100×100时，方法2的性能与方法1的性能相同。结果表明，所提出的IRS反射相位适用于基于智能反射面IRS辅助的宽带无人机通信系统的研究。Figure 4 is a comparison chart of the absolute envelope size of the broadband UAV-MIMO model with different IRS reflection phases θ mn (t) between the traditional UAV channel model and the broadband UAV channel model assisted by the intelligent reflector IRS. Among them, in phase 1, we align the IRS reflection phase with the time-varying phase and Doppler shift of the LoS component, and in phase 2, we set the IRS reflection phase according to formula (8), that is, the maximum IRS of the smart reflector. Excellent reflection phase. It can be seen from FIG. 6 that when the size of the reflection unit of the smart reflective surface IRS increases, the performance and multipath fading phenomenon of the UAV-MIMO communication system using the smart reflective surface IRS can be significantly improved. Meanwhile, when the number of IRS reflective units on the intelligent reflective surface is greater than 100×100, the performance of method 2 is the same as that of method 1. The results show that the proposed IRS reflection phase is suitable for the research of the broadband UAV communication system based on the intelligent reflector IRS assisted.

图5为传统无人机信道模型和基于智能反射面IRS辅助的无人机信道模型在三种不同的无人机轨迹下的发射空间相关性曲线比较图。从图5中可以看出没有智能反射面IRS辅助的无人机-MIMO模型的空间相关性受无人机轨迹的影响，以及归一化天线间距δT/λ增加时，其空间相关性逐渐降低。而基于智能反射面IRS辅助的无人机信道模型的空间相关性却是恒定的。这也意味着智能反射面IRS可以降低空间域上发射天线元件的非平稳性。Figure 5 is a comparison chart of the launch space correlation curves of the traditional UAV channel model and the UAV channel model assisted by the intelligent reflector IRS under three different UAV trajectories. From Fig. 5, it can be seen that the spatial correlation of the UAV-MIMO model without the assistance of the intelligent reflector IRS is affected by the trajectory of the UAV, and its spatial correlation gradually increases as the normalized antenna spacing δT /λ increases reduce. However, the spatial correlation of the UAV channel model assisted by the intelligent reflector IRS is constant. This also means that the smart reflector IRS can reduce the non-stationarity of the transmit antenna element in the spatial domain.

图6为传统无人机信道模型和基于智能反射面IRS辅助的宽带无人机信道模型在三种不同的无人机飞行轨迹下的发射空间相关性曲线比较图。从图6中可以看出，没有智能反射面IRS辅助的宽带无人机信道模型的空间相关性受到无人机轨迹和归一化天线间距δT/λ的影响，这与图5中得到的结果一致。Figure 6 is a comparison chart of the launch space correlation curves of the traditional UAV channel model and the broadband UAV channel model assisted by the intelligent reflector IRS under three different UAV flight trajectories. It can be seen from Fig. 6 that the spatial correlation of the broadband UAV channel model without intelligent reflector IRS assistance is affected by the UAV trajectory and the normalized antenna spacing δT /λ, which is consistent with that obtained in Fig. 5 The results are consistent.

综上，本发明的基于大规模智能反射单元的无人机信道模型建立方法，是一种基于大型智能反射面的无人机多输入多输出通信系统的非平稳三维宽带信道模型建立方法，包括智能反射面时变反射相位设计步骤：以接收信号功率最大化为目标设计优化问题，求解优化问题得到最优的时变反射相位；时变多普勒频移参数设计步骤：根据智能反射面辅助的几何模型得到无人机、接收端和智能反射面之间的时变多普勒频移参数；信道统计特性分析步骤：根据智能反射面时变反射相位和时变参数分析基于智能反射面辅助的无人机MIMO信道模型的统计特性。在本发明中，采用智能反射面IRS的通信系统具有更好的信号传播环境，可以显著提高接收信号的功率以及降低多径衰落和多普勒频移对接收信号的影响。因此，该模型建立方法可以为6G通信系统关键技术的探索提供有力的支撑。To sum up, the method for establishing a channel model of an unmanned aerial vehicle based on a large-scale intelligent reflection unit of the present invention is a method for establishing a non-stationary three-dimensional broadband channel model of a multi-input and multiple-output communication system of an unmanned aerial vehicle based on a large-scale intelligent reflecting surface, comprising: The design steps of the time-varying reflection phase of the smart reflector: design the optimization problem with the goal of maximizing the received signal power, and solve the optimization problem to obtain the optimal time-varying reflection phase; the design steps of the time-varying Doppler frequency shift parameters: assist the smart reflector The time-varying Doppler frequency shift parameters between the UAV, the receiving end and the smart reflector are obtained from the geometric model of the Statistical properties of the UAV MIMO channel model. In the present invention, the communication system using the intelligent reflecting surface IRS has a better signal propagation environment, which can significantly improve the power of the received signal and reduce the influence of multipath fading and Doppler frequency shift on the received signal. Therefore, this model establishment method can provide strong support for the exploration of key technologies of 6G communication system.

Claims (10)

1.基于大规模智能反射单元的无人机信道模型建立方法，其特征在于，包括以下步骤：1. a method for establishing a channel model of an unmanned aerial vehicle based on a large-scale intelligent reflection unit, is characterized in that, comprises the following steps: S1、将智能反射面IRS配置在所服务小区边缘的建筑物表面，然后使用三维圆柱体来模拟接收端周围的垂直建筑结构，并利用球面波前的二阶近似来模拟大规模智能反射面IRS的近场效应，假设散射体位于三维圆柱体的表面，且智能反射面IRS包括均匀排列的智能反射单元，建立基于大规模智能反射单元的无人机信道模型；并根据该模型得到信道的复信道增益；S1. Arrange the smart reflective surface IRS on the building surface at the edge of the serving cell, then use a three-dimensional cylinder to simulate the vertical building structure around the receiving end, and use the second-order approximation of the spherical wavefront to simulate the large-scale smart reflective surface IRS It is assumed that the scatterer is located on the surface of a three-dimensional cylinder, and the intelligent reflecting surface IRS includes intelligent reflecting units arranged uniformly, and a UAV channel model based on large-scale intelligent reflecting units is established; and the complex channel model is obtained according to the model. channel gain; S2、根据基于大规模智能反射单元的无人机信道模型得到该复信道增益有两个分量组成：无人机UAV不通过智能反射面IRS直接与接收端进行传输的复信道增益以及无人机UAV通过智能反射面IRS与接收端进行传输的复信道增益；S2. According to the UAV channel model based on the large-scale intelligent reflection unit, the complex channel gain is composed of two components: the complex channel gain of the UAV UAV directly transmitting to the receiving end without the intelligent reflecting surface IRS, and the complex channel gain of the UAV. The complex channel gain that UAV transmits with the receiver through the intelligent reflective surface IRS; S3、根据基于大规模智能反射单元的无人机信道模型，考虑用球形波前的二阶近似来模拟智能反射面IRS的近场效应，由于智能反射面的反射相位能够改善多普勒频移和多径衰落对接收信号功率的影响，因此基于接收信号功率最大化准则来设计优化问题；S3. According to the UAV channel model based on the large-scale intelligent reflection unit, consider using the second-order approximation of the spherical wavefront to simulate the near-field effect of the IRS of the intelligent reflection surface. Since the reflection phase of the intelligent reflection surface can improve the Doppler frequency shift and the influence of multipath fading on the received signal power, so the optimization problem is designed based on the received signal power maximization criterion; S4、简化优化问题：在S3中提出的优化问题在计算上具有很大的复杂度，所以为了降低复杂度，需要进一步化简问题；当智能反射面IRS中的智能反射单元的规模较大时，接收信号的功率主要由通过智能反射面IRS的反射信号控制，且该反射信号的复信道增益又由其直射分量为主，以此简化求解反射相位的过程；S4. Simplify the optimization problem: The optimization problem proposed in S3 has a lot of computational complexity, so in order to reduce the complexity, it is necessary to further simplify the problem; when the scale of the intelligent reflecting unit in the intelligent reflecting surface IRS is large , the power of the received signal is mainly controlled by the reflected signal passing through the intelligent reflecting surface IRS, and the complex channel gain of the reflected signal is dominated by its direct component, which simplifies the process of solving the reflected phase; S5、根据化简的优化问题，考虑无人机UAV天线单元p和接收端天线单元q之间多径分量经(m,n)-th智能反射单元后的时变多普勒频移
Figure FDA0003375168700000011
对接收信号功率的影响，在求解最优的智能反射面IRS反射相位时，减去直射分量的多普勒频移，以增强接收信号功率；S5. According to the simplified optimization problem, consider the time-varying Doppler frequency shift of the multipath component between the UAV UAV antenna unit p and the receiving end antenna unit q after the (m,n)-th intelligent reflection unit
Figure FDA0003375168700000011
Influence on the received signal power, when solving the optimal IRS reflection phase of the intelligent reflector, subtract the Doppler frequency shift of the direct component to enhance the received signal power; S6、通过步骤S2得到的复信道增益和步骤S5得到的最优的智能反射面IRS反射相位，求解基于智能反射面IRS辅助的时空相关性函数，通过相关性分析来确定不同参数对无人机信道特性的影响。S6, through the complex channel gain obtained in step S2 and the optimal reflection phase of the intelligent reflector IRS obtained in step S5, solve the spatiotemporal correlation function based on the assistance of the intelligent reflector IRS, and determine the effect of different parameters on the UAV through correlation analysis The effect of channel characteristics. 2.根据权利要求1所述的基于大规模智能反射单元的无人机信道模型建立方法，其特征在于，步骤S1中得到的复信道增益hpq(t,τ)，其表示如下：2. the unmanned aerial vehicle channel model building method based on large-scale intelligent reflection unit according to claim 1, is characterized in that, the complex channel gain h pq (t, τ) that obtains in step S1, it is expressed as follows:
Figure FDA0003375168700000012
Figure FDA0003375168700000012
 其中，t表示时间变量，l表示抽头数，L表示总的抽头数量，cl表示第l次抽头的增益，hl,pq(t)表示无人机UAV天线单元p不通过智能反射面IRS直接与接收端天线单元q之间的复信道增益，
Figure FDA0003375168700000021
表示无人机UAV天线单元p通过智能反射面IRS与接收端天线单元q之间的复信道增益，τl(t)表示第l次抽头的传播延迟，δ(·)表示冲激函数。Among them, t is the time variable, l is the number of taps, L is the total number of taps, c l is the gain of the lth tap, h l, pq (t) means that the UAV antenna unit p of the UAV does not pass the intelligent reflector IRS is the complex channel gain directly between the receiver antenna element q,
Figure FDA0003375168700000021
Represents the complex channel gain between the UAV antenna unit p through the intelligent reflector IRS and the antenna unit q at the receiving end, τ l (t) represents the propagation delay of the lth tap, and δ( ) represents the impulse function. 3.根据权利要求1所述的基于大规模智能反射单元的无人机信道模型建立方法，其特征在于，步骤S2中无人机UAV天线单元p不通过智能反射面IRS直接与接收端天线单元q之间的复信道增益hl,pq(t)表示如下：3. the unmanned aerial vehicle channel model establishment method based on large-scale intelligent reflection unit according to claim 1, is characterized in that, in step S2, unmanned aerial vehicle UAV antenna unit p does not directly and receiving end antenna unit by intelligent reflection surface IRS The complex channel gain h l,pq (t) between q is expressed as follows:
Figure FDA0003375168700000022
Figure FDA0003375168700000022
 其中，
Figure FDA0003375168700000023
表示无人机UAV天线单元p和接收端天线单元q之间直射分量的复信道增益，
Figure FDA0003375168700000024
表示无人机UAV天线单元p和接收端天线单元q之间散射分量的复信道增益；in,
Figure FDA0003375168700000023
Represents the complex channel gain of the direct component between the UAV UAV antenna unit p and the receiver antenna unit q,
Figure FDA0003375168700000024
Represents the complex channel gain of the scattering component between the UAV UAV antenna unit p and the receiver antenna unit q;
Figure FDA0003375168700000025
Figure FDA0003375168700000025
Figure FDA0003375168700000026
Figure FDA0003375168700000026
 其中，Gt表示发射天线增益，Gr表示接收端天线增益，γTR表示无人机UAV到接收端的路径损耗，K1表示莱斯因子，λ表示载波波长，t表示时间变量，π表示圆周率，ξpq(t)表示无人机UAV天线单元p和接收端天线单元q之间的时变距离，
Figure FDA0003375168700000027
表示无人机UAV天线单元p和接收端天线单元q之间直射分量的时变多普勒频移，δ(l-1)表示经过l次抽头后的延迟冲击函数，Nl表示散射体
Figure FDA0003375168700000028
的数目，ξpnl(t)表示无人机UAV天线单元p和散射体
Figure FDA0003375168700000029
之间的时变距离，
Figure FDA00033751687000000210
表示散射体
Figure FDA00033751687000000211
和接收端天线单元q之间的时变距离，
Figure FDA00033751687000000212
表示无人机UAV天线单元p和接收端天线单元q之间散射分量的时变多普勒频移。where G t is the transmit antenna gain, G r is the receiver antenna gain, γ TR is the path loss from the UAV to the receiver, K 1 is the Rice factor, λ is the carrier wavelength, t is the time variable, and π is the pi , ξ pq (t) represents the time-varying distance between the UAV UAV antenna unit p and the receiver antenna unit q,
Figure FDA0003375168700000027
represents the time-varying Doppler frequency shift of the direct component between the UAV antenna unit p and the receiving antenna unit q, δ(l-1) represents the delay impulse function after l taps, and N l represents the scatterer
Figure FDA0003375168700000028
, ξ pnl (t) denotes the UAV UAV antenna unit p and the scatterer
Figure FDA0003375168700000029
the time-varying distance between
Figure FDA00033751687000000210
Represents a scatterer
Figure FDA00033751687000000211
and the time-varying distance between the receiver antenna element q,
Figure FDA00033751687000000212
Represents the time-varying Doppler shift of the scattered component between the UAV UAV antenna unit p and the receiver antenna unit q. 4.根据权利要求1所述的基于大规模智能反射单元的无人机信道模型建立方法，其特征在于，步骤S2中无人机UAV通过智能反射面IRS与接收端进行传输的复信道增益
Figure FDA0003375168700000031
表示如下：4. the unmanned aerial vehicle channel model establishment method based on large-scale intelligent reflection unit according to claim 1, is characterized in that, in step S2, unmanned aerial vehicle UAV carries out the complex channel gain that transmits through intelligent reflecting surface IRS and receiving end
Figure FDA0003375168700000031
It is expressed as follows:
Figure FDA0003375168700000032
Figure FDA0003375168700000032
 其中，
Figure FDA0003375168700000033
表示无人机UAV天线单元p和接收端天线单元q之间的直射分量经智能反射面IRS反射后的复信道增益，
Figure FDA0003375168700000034
表示无人机UAV天线单元p和接收端天线单元q之间的散射分量经智能反射面IRS和散射体散射后的复信道增益；in,
Figure FDA0003375168700000033
Represents the complex channel gain of the direct component between the UAV UAV antenna unit p and the receiving end antenna unit q after being reflected by the intelligent reflecting surface IRS,
Figure FDA0003375168700000034
Represents the complex channel gain after the scattering component between the UAV UAV antenna unit p and the receiving end antenna unit q is scattered by the intelligent reflector IRS and the scatterer;
Figure FDA0003375168700000035
Figure FDA0003375168700000035
Figure FDA0003375168700000036
Figure FDA0003375168700000036
 其中，m表示智能反射单元的行位置索引，n表示智能反射单元的列位置索引，M表示智能反射面的行反射单元数目，N表示智能反射面的列反射单元数目，Gt表示发射天线增益，G表示IRS反射单元的增益，Gr表示接收端天线增益，γTIR表示无人机UAV到IRS再到接收端的路径损耗，K2表示莱斯因子，λ表示载波波长，t表示时间变量，π表示圆周率，ξpmn(t)表示无人机UAV天线单元p和(m,n)-th智能反射单元之间的时变距离，ξmnq(t)表示(m,n)-th智能反射单元和接收端天线单元q之间的时变距离，θmn(t)表示智能反射面IRS在t时刻的反射相位，
Figure FDA0003375168700000037
表示无人机UAV天线单元p和接收端天线单元q之间多径分量经(m,n)-th智能反射单元后的时变多普勒频移；Nl表示散射体
Figure FDA0003375168700000038
的数目，l表示抽头数，
Figure FDA0003375168700000039
表示(m,n)-th智能反射单元和散射体
Figure FDA00033751687000000310
之间的时变距离，
Figure FDA00033751687000000311
表示散射体
Figure FDA00033751687000000312
和接收端天线单元q之间的时变距离，
Figure FDA00033751687000000313
表示无人机UAV天线单元p和接收端天线单元q之间多径分量经智能反射面IRS和散射体
Figure FDA00033751687000000314
后的时变多普勒频移。Among them, m represents the row position index of the smart reflective unit, n represents the column position index of the smart reflective unit, M represents the number of row reflective units on the smart reflective surface, N represents the number of column reflective units on the smart reflective surface, and G t represents the transmit antenna gain , G represents the gain of the IRS reflection unit, G r represents the antenna gain at the receiving end, γ TIR represents the path loss from the UAV to the IRS and then to the receiving end, K 2 represents the Rice factor, λ represents the carrier wavelength, t represents the time variable, π denotes the pi, ξ pmn (t) denotes the time-varying distance between the UAV UAV antenna unit p and the (m,n)-th smart reflection unit, and ξ mnq (t) denotes the (m,n)-th smart reflection is the time-varying distance between the unit and the receiving antenna unit q, θ mn (t) represents the reflection phase of the intelligent reflector IRS at time t,
Figure FDA0003375168700000037
represents the time-varying Doppler frequency shift of the multipath component between the UAV antenna unit p and the receiving antenna unit q after the (m,n)-th intelligent reflection unit; N l represents the scatterer
Figure FDA0003375168700000038
, l represents the number of taps,
Figure FDA0003375168700000039
Represents (m,n)-th smart reflectors and scatterers
Figure FDA00033751687000000310
the time-varying distance between
Figure FDA00033751687000000311
Represents a scatterer
Figure FDA00033751687000000312
and the time-varying distance between the receiver antenna element q,
Figure FDA00033751687000000313
Represents the multipath component between the UAV UAV antenna unit p and the receiver antenna unit q through the intelligent reflector IRS and the scatterer
Figure FDA00033751687000000314
The time-varying Doppler frequency shift. 5.根据权利要求1所述的基于大规模智能反射单元的无人机信道模型建立方法，其特征在于，步骤S3中优化问题表示如下：5. the unmanned aerial vehicle channel model establishment method based on large-scale intelligent reflection unit according to claim 1, is characterized in that, in step S3, optimization problem is expressed as follows:
Figure FDA0003375168700000041
Figure FDA0003375168700000041
 其中，t表示时间变量，θmn(t)表示智能反射面IRS在t时刻的反射相位，
Figure FDA0003375168700000042
表示统计性均值运算，hpq(t)表示无人机UAV天线单元p和接收端天线单元q之间多径分量的复信道增益。Among them, t represents the time variable, θ mn (t) represents the reflection phase of the intelligent reflective surface IRS at time t,
Figure FDA0003375168700000042
represents the statistical mean operation, and h pq (t) represents the complex channel gain of the multipath component between the UAV UAV antenna unit p and the receiver antenna unit q. 6.根据权利要求1所述的基于大规模智能反射单元的无人机信道模型建立方法，其特征在于，步骤S4包括以下步骤：6. the unmanned aerial vehicle channel model establishment method based on large-scale intelligent reflection unit according to claim 1, is characterized in that, step S4 comprises the following steps: S41、由于接收信号的功率主要集中在经智能反射面IRS反射的直射分量上，所以步骤S3中的优化问题化简为：S41. Since the power of the received signal is mainly concentrated on the direct component reflected by the intelligent reflecting surface IRS, the optimization problem in step S3 is simplified as:
Figure FDA0003375168700000043
Figure FDA0003375168700000043
 其中，t表示时间变量，
Figure FDA0003375168700000044
表示无人机UAV天线单元p和接收端天线单元q之间的直射分量经智能反射面IRS反射后的复信道增益，|·|表示绝对值函数；where t represents the time variable,
Figure FDA0003375168700000044
Represents the complex channel gain of the direct component between the UAV UAV antenna unit p and the receiving end antenna unit q after being reflected by the intelligent reflecting surface IRS, |·| represents the absolute value function; S42、把
Figure FDA0003375168700000045
的具体函数带入公式(5)中，优化问题进一步化简为：S42, put
Figure FDA0003375168700000045
The specific function of is brought into formula (5), and the optimization problem is further simplified as:
Figure FDA0003375168700000046
Figure FDA0003375168700000046
 其中，
Figure FDA0003375168700000047
表示无人机UAV天线单元p和接收端天线单元q之间多径分量经(m,n)-th智能反射单元后的时变多普勒频移，M表示智能反射面的行反射单元数目，m表示智能反射单元的行位置索引，N表示智能反射面的列反射单元数目，n表示智能反射单元的列位置索引，λ表示载波波长，π表示圆周率，ξpmn(t)表示无人机UAV天线单元p和(m,n)-th智能反射单元之间的时变距离，ξmnq(t)表示(m,n)-th智能反射单元和接收端天线单元q之间的时变距离，θmn(t)表示智能反射面IRS在t时刻的反射相位。in,
Figure FDA0003375168700000047
Represents the time-varying Doppler frequency shift of the multipath component between the UAV antenna unit p and the receiving antenna unit q after the (m,n)-th intelligent reflection unit, M represents the number of line reflection units of the intelligent reflection surface , m represents the row position index of the smart reflective unit, N represents the number of column reflective units on the smart reflective surface, n represents the column position index of the smart reflective unit, λ represents the carrier wavelength, π represents the pi, and ξ pmn (t) represents the UAV The time-varying distance between the UAV antenna unit p and the (m,n)-th smart reflection unit, ξ mnq (t) represents the time-varying distance between the (m,n)-th smart reflection unit and the receiver antenna unit q , θ mn (t) represents the reflection phase of the intelligent reflective surface IRS at time t. 7.根据权利要求6所述的基于大规模智能反射单元的无人机信道模型建立方法，其特征在于，步骤S5中求解最优的智能反射面IRS反射相位包括以下步骤：7. the unmanned aerial vehicle channel model establishment method based on large-scale intelligent reflection unit according to claim 6, is characterized in that, in step S5, solving optimal intelligent reflection surface IRS reflection phase comprises the following steps: S51、根据公式(6)得到的最优智能反射面IRS反射相位的优化问题，求解出最优的智能反射面IRS反射相位
Figure FDA0003375168700000048
的表达式如下所示：S51. According to the optimization problem of the IRS reflection phase of the optimal intelligent reflecting surface obtained by formula (6), the optimal IRS reflection phase of the intelligent reflecting surface is solved.
Figure FDA0003375168700000048
The expression looks like this:
Figure FDA0003375168700000051
Figure FDA0003375168700000051
 其中，
Figure FDA0003375168700000052
表示
Figure FDA0003375168700000053
和2π两数相除的余数；in,
Figure FDA0003375168700000052
express
Figure FDA0003375168700000053
The remainder of the division of two numbers with 2π; S52、由于公式(7)没有考虑无人机UAV天线单元p和接收端天线单元q之间多径分量经(m,n)-th智能反射单元后的时变多普勒频移
Figure FDA0003375168700000054
因此将
Figure FDA0003375168700000055
进一步改写为：S52. Since 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 the (m,n)-th intelligent reflection unit
Figure FDA0003375168700000054
Therefore will
Figure FDA0003375168700000055
Further rewritten as:
Figure FDA0003375168700000056
Figure FDA0003375168700000056
 其中，
Figure FDA0003375168700000057
表示无人机UAV天线单元p和接收端天线单元q之间多径分量经(m,n)-th智能反射单元后的时变多普勒频移。in,
Figure FDA0003375168700000057
Represents the time-varying Doppler frequency shift of the multipath component between the UAV UAV antenna unit p and the receiving end antenna unit q after the (m,n)-th intelligent reflection unit. 8.根据权利要求1所述的基于大规模智能反射单元的无人机信道模型建立方法，其特征在于，步骤S6包括以下步骤：8. the unmanned aerial vehicle channel model establishment method based on large-scale intelligent reflection unit according to claim 1, is characterized in that, step S6 comprises the following steps: S61、利用步骤S2得到的复信道增益和步骤S5得到的智能反射面IRS的最优反射相位，根据定义求解基于智能反射面IRS辅助的时空相关性函数，计算公式如下：S61, use the complex channel gain obtained in step S2 and the optimal reflection phase of the intelligent reflecting surface IRS obtained in step S5 to solve the spatiotemporal correlation function assisted by the intelligent reflecting surface IRS according to the definition, and the calculation formula is as follows: 首先根据时空相关函数的定义式：First, according to the definition of the space-time correlation function:
Figure FDA0003375168700000058
Figure FDA0003375168700000058
 其中，
Figure FDA0003375168700000059
表示两个时变传递函数之间的时空相关函数，δT表示无人机UAV天线单元之间的天线间距，δR表示用户端天线单元之间的天线间距，τ表示传播时延，t表示时间变量，
Figure FDA00033751687000000510
表示统计性均值运算，(·)*表示复共轭运算，hpq(t)表示无人机UAV天线单元p与用户端天线单元q之间的复信道增益，hp′q′(t+τ)表示经过时间延迟τ后，无人机UAV天线单元p’与用户端天线单元q’之间的复信道增益；|·|表示绝对值函数；in,
Figure FDA0003375168700000059
Represents the space-time correlation function between two time-varying transfer functions, δ T represents the antenna spacing between UAV antenna units, δ R represents the antenna spacing between the user-end antenna units, τ represents the propagation delay, and t represents time variable,
Figure FDA00033751687000000510
represents the statistical mean operation, ( ) * represents the complex conjugate operation, h pq (t) represents the complex channel gain between the UAV UAV antenna unit p and the user-end antenna unit q, h p′q′ (t+ τ) represents the complex channel gain between the UAV UAV antenna unit p' and the user-end antenna unit q' after the time delay τ; |·| represents the absolute value function; 再把步骤S2中得到的复信道增益函数的表达式分别带入，得到具体的时空相关性函数如下所示：Then, the expressions of the complex channel gain function obtained in step S2 are respectively brought in, and the specific space-time correlation function is obtained as follows:
Figure FDA0003375168700000061
Figure FDA0003375168700000061
Figure FDA0003375168700000062
Figure FDA0003375168700000062
Figure FDA0003375168700000063
Figure FDA0003375168700000063
Figure FDA0003375168700000064
Figure FDA0003375168700000064
 其中，
Figure FDA0003375168700000065
表示无人机UAV天线单元和接收端天线单元之间直射分量的空时相关性，λ表示载波波长，π表示圆周率，ξpq(t)表示无人机UAV天线单元p和用户端天线单元q之间的时变距离，ξp′q′(t+τ)表示经过时间延迟τ后，人机UAV天线单元p’和用户端天线单元q’之间的时变距离，
Figure FDA0003375168700000066
表示无人机UAV天线单元p和用户端天线单元q之间直射分量的时变多普勒频移；
Figure FDA0003375168700000067
表示无人机UAV天线单元和接收端天线单元之间散射分量的空时相关性，Nl表示散射体
Figure FDA0003375168700000068
的数目，l表示抽头数，ξpnl(t)表示无人机UAV天线单元p和散射体
Figure FDA0003375168700000069
之间的时变距离，ξp′nl(t+τ)表示经过时间延迟τ后，人机UAV天线单元p’和散射体
Figure FDA00033751687000000610
之间的时变距离，
Figure FDA00033751687000000611
表示散射体
Figure FDA00033751687000000612
和用户端天线单元q之间的时变距离，
Figure FDA00033751687000000613
表示经过时间延迟τ后，散射体
Figure FDA00033751687000000614
和用户端天线单元q’之间的时变距离，
Figure FDA00033751687000000615
表示无人机UAV天线单元p和用户端天线单元q之间散射分量的时变多普勒频移；
Figure FDA00033751687000000616
表示表示无人机UAV天线单元和接收端天线单元之间经智能反射面IRS反射的直射分量的空时相关性，M表示智能反射面IRS的行反射单元数目，N表示智能反射面IRS的列反射单元数目，ξpmn(t)表示无人机UAV天线单元p和(m,n)-th智能反射单元之间的时变距离，ξp′m′n′(t+τ)表示经过时间延迟τ后，无人机UAV天线单元p’和(m’,n’)-th智能反射单元之间的时变距离，ξmnq(t)表示(m,n)-th智能反射单元和用户端天线单元q之间的时变距离，ξm′n′q′(t+τ)表示经过时间延迟τ后，(m’,n’)-th智能反射单元和用户端天线单元q’之间的时变距离；
Figure FDA0003375168700000071
表示无人机UAV天线单元和接收端天线单元之间经智能反射面IRS和散射体
Figure FDA0003375168700000072
反射的散射分量的空时相关性，δN表示智能反射面IRS的列反射单元之间的距离，δM表示智能反射面IRS的列反射单元之间的距离；in,
Figure FDA0003375168700000065
Represents the space-time correlation of the direct component between the UAV UAV antenna unit and the receiver antenna unit, λ represents the carrier wavelength, π represents the pi, ξ pq (t) represents the UAV UAV antenna unit p and the user-end antenna unit q The time-varying distance between ξ p′q′ (t+τ) represents the time-varying distance between the human-machine UAV antenna unit p' and the user-end antenna unit q' after the time delay τ,
Figure FDA0003375168700000066
represents the time-varying Doppler shift of the direct component between the UAV antenna unit p and the user-end antenna unit q of the UAV;
Figure FDA0003375168700000067
represents the space-time correlation of the scattering components between the UAV antenna unit and the receiving antenna unit, and N l represents the scatterer
Figure FDA0003375168700000068
, l denotes the number of taps, ξ pnl (t) denotes the UAV UAV antenna unit p and the scatterer
Figure FDA0003375168700000069
The time-varying distance between, ξ p′nl (t+τ) represents the time delay τ between the human-machine UAV antenna unit p' and the scatterer
Figure FDA00033751687000000610
the time-varying distance between
Figure FDA00033751687000000611
Represents a scatterer
Figure FDA00033751687000000612
and the time-varying distance between the user-end antenna element q,
Figure FDA00033751687000000613
represents that after a time delay τ, the scatterer
Figure FDA00033751687000000614
and the time-varying distance between the user-end antenna unit q',
Figure FDA00033751687000000615
represents the time-varying Doppler shift of the scattering component between the UAV antenna unit p and the user-end antenna unit q;
Figure FDA00033751687000000616
Represents the space-time correlation between the UAV antenna unit and the receiving end antenna unit of the direct component reflected by the smart reflective surface IRS, M represents the number of row reflective units of the smart reflective surface IRS, and N represents the column of the smart reflective surface IRS Number of reflection units, ξ pmn (t) represents the time-varying distance between the UAV UAV antenna unit p and the (m,n)-th smart reflection unit, ξ p′m′n′ (t+τ) represents the elapsed time After delay τ, the time-varying distance between the UAV antenna unit p' and the (m',n')-th smart reflection unit, ξ mnq (t) represents the (m,n)-th smart reflection unit and the user is the time-varying distance between the end antenna units q, ξ m'n'q' (t+τ) represents the difference between the (m',n')-th intelligent reflection unit and the user end antenna unit q' after the time delay τ time-varying distance;
Figure FDA0003375168700000071
Indicates the intelligent reflective surface IRS and scatterer between the UAV antenna unit and the receiver antenna unit
Figure FDA0003375168700000072
The space-time correlation of the reflected scattering components, δ N represents the distance between the column reflection units of the smart reflective surface IRS, δ M represents the distance between the column reflective units of the smart reflective surface IRS; S62、当改变智能反射IRS的反射单元数目，智能反射面IRS的反射相位、无人机UAV飞行轨迹时，根据上面得到的时空相关性函数，通过其相关性来分析这些参数变化对无人机信道特性的影响。S62. When changing the number of reflective units of the smart reflective IRS, the reflective phase of the smart reflective surface IRS, and the UAV flight trajectory of the UAV, according to the spatiotemporal correlation function obtained above, analyze the effect of these parameter changes on the UAV through the correlation. The effect of channel characteristics. 9.一种设备，其特征在于，包括存储器和处理器，其中：9. A device comprising a memory and a processor, wherein: 存储器，用于存储能够在处理器上运行的计算机程序；memory for storing computer programs capable of running on the processor; 处理器，用于在运行所述计算机程序时，执行如权利要求1-8任一项所述基于大规模智能反射单元的无人机信道模型建立方法的步骤。The processor is configured to, when running the computer program, execute the steps of the method for establishing a channel model for a UAV based on a large-scale intelligent reflection unit according to any one of claims 1-8. 10.一种存储介质，其特征在于，所述存储介质上存储有计算机程序，所述计算机程序被至少一个处理器执行时实现如权利要求1-8任一项所述基于大规模智能反射单元的无人机信道模型建立方法的步骤。10. A storage medium, characterized in that, a computer program is stored on the storage medium, and when the computer program is executed by at least one processor, the large-scale intelligent reflection unit according to any one of claims 1-8 is realized The steps of the UAV channel model building method.
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