Unmanned aerial vehicle millimeter wave channel model establishment method based on intelligent reflecting surface modularization

Abstract

The invention discloses an unmanned aerial vehicle millimeter wave channel model establishment method based on intelligent reflecting surface modularization, and the method comprises the steps: building an intelligent reflecting surface-assisted unmanned aerial vehicle millimeter wave near-field channel model, and obtaining the complex channel gain of the intelligent reflecting surface-assisted unmanned aerial vehicle millimeter wave near-field channel model; establishing an unmanned aerial vehicle millimeter wave near-field channel model with a modularized intelligent reflecting surface to obtain a complex channel gain of the unmanned aerial vehicle millimeter wave near-field channel model; the method comprises the following steps: simplifying an unmanned aerial vehicle millimeter wave near-field channel model based on intelligent reflecting surface modularization, establishing a far-field channel model, and obtaining a complex channel gain of the far-field channel model; designing a reflection phase after intelligent reflection surface modularization based on a reflection unit gain maximization criterion of a far-field channel model; calculating an approximate closed expression of the received signal power; and calculating a power loss ratio, and exploring the influence of the intelligent reflecting surface partitioning scheme on the performance gain of the millimeter wave system of the unmanned aerial vehicle. The method provided by the invention can provide a thought for exploring the application prospect and exploration of intelligent reflecting surface modularization in the millimeter wave communication system of the unmanned aerial vehicle, and provides more possibilities for the next generation of wireless communication technology.

Description

Technical Field

[0001] The present invention relates to wireless communication technology, and in particular to a method for establishing a millimeter wave channel model of an unmanned aerial vehicle based on intelligent reflective surface modularization. Background Art

[0002] The advent of the 5G era has brought about a series of technological revolutions, and 5G-related technical research and applications have emerged in an endless stream. The smart reflector is a two-dimensional plane composed of a low-cost passive reflector unit. As an emerging technology, it is regarded as a reliable solution to improve channel performance and is generally considered to be one of the key technologies for future 6G mobile communications. Each reflector unit of the smart reflector can independently control the amplitude or phase change of the incident signal to achieve effective signal transmission. The smart reflector is controlled by an external controller and can freely adjust the reflection phase shift according to the incident signal to maximize the spectrum utilization. Therefore, establishing an accurate and appropriate channel model can provide a theoretical basis for the design of wireless communication systems assisted by smart reflectors, which can be used to improve the transmission environment of the channel. Millimeter waves, which can provide a very large frequency range, are also considered to be a key factor in significantly improving communication quality in the future.

[0003] In the existing public technical content, the MIMO channel model containing smart reflective surfaces is comparable to the large-scale MIMO channel model without smart reflective surfaces; by adjusting the reflection phase in real time, the impact of multipath fading caused by the movement of the transmitter and the receiver can be reduced or even offset, and the signal reception power can be increased; some studies have shown that only when the smart reflective surface is placed around the transmitter and the receiver will it have a significant impact on the channel, and some studies have found that the wireless communication system assisted by the smart reflective surface not only has a higher data rate than the deployment of repeaters, but also reduces the cost; some research results claim that the reflection phase of the smart reflective surface in the near field area needs to be reconsidered.

[0004] The existing technology discloses the document "Channel estimation for extremely large-scale MIMO: Far-field or near-field?" by M. Cui and L. Dai, IEEE Trans. Commun., vol. 70, no. 4, pp. 2663-2677, April 2022. The study shows that when the antenna scale is large enough, plane waves can no longer accurately simulate the propagation characteristics of electromagnetic waves, and spherical waves should be used instead of plane waves to model the channel model of extremely large-scale MIMO communication systems. W. Tang, X. Chen, M. Z. Chen, J. Y. Dai, Y. Han, M. D. Renzon, S. Jin, Q. Cheng, and T. J. Cui's paper "Path loss modeling and measurements for reconfigurable intelligent surface in millimeter-wave frequency band," IEEE Trans. Commun., vol. 70, no. 9, pp. 6259-6276, Sep. 2022, establishes a path loss model for a millimeter-wave near-field system assisted by an intelligent reflector and designs the reflection phase of the intelligent reflector. However, the reflection phase requires knowing the precise distance between each intelligent reflector unit and the antenna unit at the transceiver end. For a passively reflecting intelligent reflector, the precise distance between the intelligent reflector unit and the transceiver end is difficult to achieve. H. Jiang, B. Xiong, H. Zhang, and E. Basar's paper "Hybrid far-and near-field modeling for reconfigurable intelligent surface assisted V2V channels: A sub-array partition based approach," IEEE Trans. Wireless. Commun., vol. 22, no. 11, pp. 8290-8303, Nov. 2023. In order to reduce the complexity of near-field system modeling, this study uses an intelligent reflective surface partitioning method to establish a channel model for the vehicle-to-vehicle system. However, this model ignores the consideration of the reflection phase of the intelligent reflective unit.

[0005] In summary, the UAV millimeter-wave channel modeling based on the modularization of intelligent reflective surfaces is in its initial stage, and the statistical characteristics of the UAV channel based on the intelligent reflective surface blocking scheme IRS have yet to be explored. Therefore, a UAV millimeter-wave channel model based on the modularization of intelligent reflective surfaces is very necessary. The establishment of this model can provide a basis for future system performance analysis and precoding algorithm design. Summary of the invention

[0006] Purpose of the invention: The purpose of the present invention is to provide an accurate method for establishing a UAV millimeter-wave channel model based on modular intelligent reflective surfaces. The model establishment method can provide strong support for the exploration of key technologies of 6G communication systems.

[0007] Technical solution: The method for establishing a UAV millimeter wave channel model based on intelligent reflective surface modularization of the present invention comprises the following steps:

[0008] S1. A UAV millimeter wave communication system assisted by an intelligent reflective surface is established, and a UAV millimeter wave near-field channel model assisted by an intelligent reflective surface is established, and the complex channel gain of the UAV millimeter wave near-field channel model assisted by an intelligent reflective surface is obtained;

[0009] S2, dividing the smart reflective surface into multiple identical sub-modules, and renumbering the reflective units in each sub-module, establishing a UAV millimeter-wave near-field channel model based on the modularization of the smart reflective surface, and obtaining the complex channel gain of the UAV millimeter-wave near-field channel model based on the modularization of the smart reflective surface;

[0010] S3, simplifying the UAV millimeter wave near-field channel model based on the modularization of the intelligent reflection surface in step S2, using plane waves instead of spherical waves to simulate the propagation characteristics of electromagnetic waves, establishing a UAV millimeter wave far-field channel model based on the modularization of the intelligent reflection surface, and obtaining the complex channel gain of the far-field channel model; designing the reflection phase after the modularization of the intelligent reflection surface based on the maximization criterion of the reflection unit gain of the far-field channel model, the designed reflection phase includes the reflection phase related to the reflection unit and the reflection phase related to the submodule; the reflection phase related to the reflection unit is determined by the plane wave assumption, and the reflection phase related to the submodule is determined by the spherical wave assumption;

[0011] S4. Using the reflection phase after modularization of the designed intelligent reflector, solve the approximate closed-form expression of the received signal power of the UAV millimeter-wave near-field channel model based on the modularization of the intelligent reflector;

[0012] S5. According to the approximate closed-form expression of the received signal power, the reflection phase after the designed intelligent reflector is modularized is used to calculate the power loss ratio of the UAV millimeter-wave near-field channel model based on the modularization of the intelligent reflector. Using the obtained power loss ratio, the influence of the intelligent reflector block scheme on the performance gain of the UAV millimeter-wave system is explored.

[0013] Furthermore, the complex channel gain h of the UAV millimeter wave near-field channel model based on the intelligent reflector in step S1 is 1,p It is expressed as:

[0014]

[0015] Where π represents the circumference of a circle, δ1 and δ2 represent the spacing between adjacent row reflection units and adjacent column reflection units on the smart reflection surface, λ represents the wavelength of the electromagnetic wave emitted by the drone, N1 and N2 represent the number of row reflection units and column reflection units on the smart reflection surface, and γ a and γ d They represent the path losses from the UAV to the smart reflector and from the smart reflector to the receiving end, It represents the projection of the electromagnetic wave emitted by the drone on the normal line of the smart reflective surface. represents the projection of the electromagnetic wave signal reflected by the smart reflective surface on the normal line of the smart reflective surface, e represents the exponential function, represents the distance from the pth transmitting antenna of the drone to the (n2,n1)th reflecting unit, represents the distance from the (n2,n1)th reflection unit to the receiving antenna, Represents the reflection phase of the (n2,n1)th reflection unit on the smart reflection surface.

[0016] Furthermore, the complex channel gain h of the UAV millimeter wave near-field channel model with intelligent reflective surface modularization in step S2 is 2,p It is expressed as:

[0017]

[0018] Where π represents the circumference of a circle, δ1 and δ2 represent the spacing between adjacent row reflection units and adjacent column reflection units on the smart reflection surface, λ represents the wavelength of the electromagnetic wave emitted by the drone, and γ a and γ d They represent the path losses from the UAV to the smart reflector and from the smart reflector to the receiving end, It represents the projection of the electromagnetic wave emitted by the drone on the normal line of the smart reflective surface. represents the projection of the electromagnetic wave signal reflected by the smart reflective surface on the normal line of the smart reflective surface, Q represents the number of submodules of the smart reflective surface, q represents the subscript index of the submodule of the smart reflective surface, M1 and M2 represent the number of row reflection units and column reflection units in each submodule, respectively, m1 and m2 represent the subscript index of the row reflection unit and column reflection unit in each submodule, e represents the exponential function, is the reflection phase of the qth submodule, represents the distance from the pth transmitting antenna of the drone to the (m2, m1)th reflector unit in the qth submodule, Represents the distance from the receiving antenna to the (m2, m1)th reflection unit in the qth submodule.

[0019] Furthermore, the complex channel gain h of the UAV millimeter wave far-field channel model based on the intelligent reflector modularization established in step S3 is 3,p , expressed as:

[0020]

[0021] Where π represents the circumference of a circle, δ1 and δ2 represent the spacing between adjacent row reflection units and adjacent column reflection units on the smart reflection surface, λ represents the wavelength of the electromagnetic wave emitted by the drone, and γ a and γ d They represent the path losses from the UAV to the smart reflector and from the smart reflector to the receiving end, It represents the projection of the electromagnetic wave emitted by the drone on the normal line of the smart reflective surface. represents the projection of the electromagnetic wave signal reflected by the smart reflective surface on the normal line of the smart reflective surface, Q represents the number of submodules of the smart reflective surface, q represents the subscript index of the submodule of the smart reflective surface, M1 and M2 represent the number of row reflection units and column reflection units in each submodule, respectively, m1 and m2 represent the subscript index of the row reflection unit and column reflection unit in each submodule, e represents the exponential function, is the reflection phase of the qth submodule, and represents an auxiliary variable, p represents the subscript index of the transmitting antenna unit, P represents the number of transmitting antenna units, δ represents the distance between adjacent transmitting antenna units, Ψ a Represents the angle between the line connecting the drone to the center of the intelligent reflective surface and the positive direction of the x-axis, ε a,q represents the distance from the UAV to the center of the qth submodule on the intelligent reflective surface, ε d,q represents the distance from the receiving antenna to the center of the qth submodule on the smart reflector, Φ a,q represents the angle between the line connecting the drone and the center of the qth submodule of the intelligent reflective surface and the positive direction of the x-axis, Φ d,qIt represents the angle between the line connecting the receiving antenna to the center of the qth submodule of the smart reflector and the positive direction of the x-axis, Ω a,q represents the angle between the line connecting the drone and the center of the qth submodule on the intelligent reflective surface and the positive direction of the z-axis, Ω d,q It represents the angle between the line from the receiving antenna to the center of the qth submodule of the smart reflector and the positive direction of the z-axis.

[0022] Furthermore, based on the maximization criterion of the reflection unit gain of the UAV millimeter-wave far-field channel model under the modular solution, the reflection phase of the modularized smart reflection surface is designed, including:

[0023] Complex channel gain h of the UAV millimeter-wave far-field channel model based on the modularization of the intelligent reflector 3,p , the reflection unit gain g(Q,M1,M2) of the smart reflection surface is expressed as:

[0024]

[0025] In order to achieve the maximum reflective unit gain of the smart reflector, the modular reflection phase of the smart reflector is expressed as:

[0026]

[0027] Among them, α q represents the constant phase associated with submodule q, represents the expected angle between the line connecting the drone and the center of the qth submodule of the intelligent reflective surface and the positive direction of the x-axis, It represents the expected angle between the line connecting the receiving antenna to the center of the qth submodule of the smart reflector and the positive direction of the x-axis. represents the expected angle between the line connecting the drone and the center of the qth submodule on the intelligent reflective surface and the positive direction of the z-axis, represents the expected angle between the line connecting the receiving antenna to the center of the qth submodule of the smart reflector and the positive direction of the z-axis; Substituting the reflection unit gain g(Q,M1,M2), we have:

[0028]

[0029] Among them, sin represents the sine function, θ q , △1 and △2 represent auxiliary variables, which are respectively expressed as:

[0030]

[0031] According to the reflection unit gain, when △1=0, △2=0 and When , the reflection unit gain g(Q,M1,M2) has a maximum value g(Q,M1,M2)=QM1M2; and α q The following conditions are met:

[0032] At this time and α q Substitute θ m1m2,q Expression, the reflection phase of the designed intelligent reflector module is:

[0033]

[0034] Further, step S4 includes:

[0035] S41, using the second-order Taylor approximation, the distance from the transmitter to the (m2, m1)th reflector unit of the qth submodule The distance from the receiving end to the (m2, m1)th reflector unit of the qth submodule It can be expressed approximately as:

[0036]

[0037] Among them, ε a,q represents the distance from the UAV to the center of the qth submodule on the intelligent reflective surface, ε d,q represents the distance from the receiving antenna to the center of the qth submodule on the smart reflector, Ψ a represents the angle between the line connecting the drone to the center of the smart reflective surface and the positive direction of the x-axis, δ represents the spacing between adjacent antenna units at the transmitting end, δ1 and δ2 represent the spacing between adjacent row reflective units and adjacent column reflective units on the smart reflective surface, respectively. and represents an auxiliary variable, p represents the subscript index of the transmitting antenna unit, P represents the number of transmitting antenna units, M1 and M2 represent the number of row reflection units and column reflection units in each submodule, respectively, m1 and m2 represent the subscript index of the row reflection unit and column reflection unit in each submodule, Φ a,q represents the angle between the line connecting the drone and the center of the qth submodule of the intelligent reflective surface and the positive direction of the x-axis, Φ d,q It represents the angle between the line connecting the receiving antenna to the center of the qth submodule of the smart reflector and the positive direction of the x-axis, Ω a,q represents the angle between the line connecting the drone and the center of the qth submodule on the intelligent reflective surface and the positive direction of the z-axis, Ω d,q Represents the angle between the line connecting the receiving antenna to the center of the qth submodule of the smart reflector and the positive direction of the z-axis;

[0038] S42, approximate distance and After substituting the reflection phase designed in step S3 into the complex channel gain of the UAV millimeter wave near-field channel model with intelligent reflector modularization in step S2, the received signal power is expressed as:

[0039]

[0040] Among them, P r is the received signal power, It represents the projection of the electromagnetic wave emitted by the drone on the normal line of the smart reflective surface. represents the projection of the electromagnetic wave signal reflected by the smart reflective surface on the normal line of the smart reflective surface, ε a represents the distance from the drone to the center of the smart reflective surface, ε d represents the distance from the receiving antenna to the center of the smart reflector, Q represents the number of submodules of the smart reflector, q represents the subscript index of the submodule of the smart reflector, f(η1,η2) is an auxiliary variable, is represented as an auxiliary variable, It is represented as an auxiliary variable, λ represents the wavelength of electromagnetic waves emitted by the UAV;

[0041] S43, because and The auxiliary variable f(η1,η2) is expressed as:

[0042]

[0043] Among them, x and y are integration variables;

[0044] S44, make f(η1,η2) is further expressed as:

[0045]

[0046] Among them, t and τ are integration variables;

[0047] S45. Using the Fresnel integral function, the received signal power is approximately expressed as:

[0048]

[0049] Among them, P t represents the transmitted signal power, C(·) and S(·) are Fresnel functions, and Represented as auxiliary variables.

[0050] Furthermore, in step S5, the power loss ratio Γ of the UAV millimeter wave near-field channel model based on the modularization of the intelligent reflective surface is:

[0051]

[0052] Wherein, Q represents the number of submodules of the smart reflective surface, q represents the subscript index of the submodule of the smart reflective surface, C(·) and S(·) represent Fresnel functions, and is represented as an auxiliary variable, M1 and M2 represent the number of row reflection units and column reflection units in each submodule, respectively, δ1 and δ2 represent the spacing between adjacent row reflection units and adjacent column reflection units on the smart reflection surface, respectively, η1 and η2 are represented as auxiliary variables, and λ represents the wavelength of the electromagnetic wave emitted by the UAV.

[0053] The system corresponding to the method includes:

[0054] A complex channel gain calculation unit is used for the UAV millimeter wave communication system assisted by the intelligent reflective surface, establishes a UAV millimeter wave near-field channel model assisted by the intelligent reflective surface, and obtains the complex channel gain of the UAV millimeter wave near-field channel model assisted by the intelligent reflective surface;

[0055] The post-modular complex channel gain calculation unit is used to divide the intelligent reflection surface into multiple identical sub-modules, renumber the reflection units in each sub-module, establish a UAV millimeter-wave near-field channel model based on the modularization of the intelligent reflection surface, and obtain the complex channel gain of the UAV millimeter-wave near-field channel model based on the modularization of the intelligent reflection surface;

[0056] The reflection phase design unit is used to simplify the UAV millimeter wave near-field channel model based on the modularization of the intelligent reflection surface, use plane waves instead of spherical waves to simulate the propagation characteristics of electromagnetic waves, establish the UAV millimeter wave far-field channel model based on the modularization of the intelligent reflection surface, and obtain the complex channel gain of the far-field channel model; based on the maximization criterion of the reflection unit gain of the far-field channel model, the reflection phase after the modularization of the intelligent reflection surface is designed, and the designed reflection phase includes the reflection phase related to the reflection unit and the reflection phase related to the submodule; the reflection phase related to the reflection unit is determined by the plane wave assumption, and the reflection phase related to the submodule is determined by the spherical wave assumption;

[0057] The received signal power approximation unit is used to solve the approximate closed-form expression of the received signal power of the UAV millimeter-wave near-field channel model based on the modularization of the intelligent reflector by using the reflection phase after the modularization of the designed intelligent reflector;

[0058] The power loss ratio calculation unit is used to calculate the power loss ratio of the UAV millimeter-wave near-field channel model based on the modularization of the intelligent reflector according to the obtained approximate closed-form expression of the received signal power and the reflection phase after the modularization of the designed intelligent reflector; using the obtained power loss ratio, the influence of the intelligent reflector block scheme on the performance gain of the UAV millimeter-wave system is explored.

[0059] An electronic device for storing and executing the method, the device comprising:

[0060] A memory storing executable program code;

[0061] a processor coupled to the memory;

[0062] The processor calls the executable program code stored in the memory to execute the steps of the method for establishing a UAV millimeter wave channel model based on modularization of intelligent reflecting surfaces.

[0063] A computer-readable storage medium for storing and executing the method, wherein the computer-readable storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the steps of the method for establishing a UAV millimeter wave channel model based on modularization of intelligent reflecting surfaces.

[0064] Beneficial effects: Compared with the prior art, the significant technical effects of the present invention are: in order to reduce the complexity of the UAV millimeter-wave near-field channel model based on the assistance of intelligent reflecting surfaces, the present invention constructs a modular UAV millimeter-wave near-field channel model of intelligent reflecting surfaces, and obtains its complex channel gain function; a composite near-field and far-field reflection phase is designed under the condition of the maximization criterion of the intelligent reflecting unit gain function; at the same time, the present invention takes into account the influence of the number and size of the reflecting units of the intelligent reflecting surface on the channel receiving signal power and performance gain of the cellular system; therefore, the present invention can better explore the influence of the intelligent reflecting surface blocking on the performance gain of the UAV millimeter-wave system. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 is a flow chart of the method of the present invention;

[0066] Figure 2 This is a schematic diagram of the UAV millimeter wave near-field channel model based on the assistance of intelligent reflective surface;

[0067] Figure 3 A schematic diagram comparing the near-field channel model proposed by the present invention and the traditional far-field channel model;

[0068] Figure 4 This is a schematic diagram of the comparison of the received signal power between the reflection phase designed under the intelligent reflection surface block solution and the ideal reflection phase;

[0069] Figure 5 Schematic diagram of the power loss ratio of the UAV millimeter wave system under different smart reflector block schemes. DETAILED DESCRIPTION

[0070] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0071] The method for establishing a UAV millimeter wave channel model based on the modularization of an intelligent reflecting surface described in the present invention comprises: establishing a UAV millimeter wave near-field channel model assisted by an intelligent reflecting surface, and obtaining the complex channel gain of the UAV millimeter wave near-field channel model; obtaining the complex channel gain of the UAV millimeter wave near-field channel model modularized by the intelligent reflecting surface based on the modularization of the UAV millimeter wave near-field channel model of the intelligent reflecting surface; designing the reflection phase of the intelligent reflecting surface, wherein the reflection phase of the intelligent reflecting surface is composed of the reflection phase related to the reflection unit and the reflection phase related to the module; using the designed reflection phase, an approximate closed-form expression of the received signal power of the modularized UAV millimeter wave near-field channel model of the intelligent reflecting surface is obtained; designing the power loss ratio, and reflecting the influence of the designed reflection phase of the intelligent reflecting surface on the received signal power; the method of the present invention can provide ideas for exploring the application prospects and explorations of the modularization of intelligent reflecting surfaces in UAV millimeter wave communication systems, and provide more possibilities for the next generation of wireless communication technologies.

[0072] like Figure 1 As shown, the method for establishing a UAV millimeter wave channel model based on intelligent reflective surface modularization of the present invention comprises the following steps:

[0073] S1. Consider the UAV millimeter wave communication system assisted by the intelligent reflective surface. The transmitting end is a uniform linear antenna array, which is deployed at the bottom of the UAV, and the receiving end is a single antenna user. The direct link between the transmitter and the receiver is blocked by the ground buildings. The intelligent reflective surface is deployed on the surface of the building, and its main function is to establish a virtual direct link between the transmitter and the receiver. When the intelligent reflective surface is used to assist millimeter wave communication, the receiver and the transmitter are in the near field area of ​​the intelligent reflective surface. Figure 2 As shown, therefore, the present invention first establishes a UAV millimeter wave near-field channel model based on the assistance of an intelligent reflector, and obtains the complex channel gain of the established UAV millimeter wave near-field channel model. The complex channel gain h 1,p It is expressed as follows:

[0074]

[0075] Where π represents the circumference of a circle and is taken as 3.14. δ1 and δ2 represent the spacing between adjacent row reflection units and adjacent column reflection units on the smart reflection surface, respectively. λ represents the wavelength of the electromagnetic wave emitted by the drone. N1 and N2 represent the number of row reflection units and column reflection units on the smart reflection surface, respectively.a and γ d They represent the path losses from the UAV to the smart reflector and from the smart reflector to the receiving end, It represents the projection of the electromagnetic wave emitted by the drone on the normal line of the smart reflective surface. represents the projection of the electromagnetic wave signal reflected by the smart reflective surface on the normal line of the smart reflective surface, e represents the exponential function, represents the distance from the pth transmitting antenna of the drone to the (n2,n1)th reflecting unit, represents the distance from the (n2,n1)th reflection unit to the receiving antenna, Represents the reflection phase of the (n2,n1)th reflection unit on the smart reflection surface.

[0076] S2. In the UAV millimeter-wave near-field channel model assisted by the intelligent reflecting surface in step S1, the reflection phase design of the intelligent reflecting surface needs to obtain the precise distance between the transceiver and each reflecting unit. Since no sensor equipment is deployed on the passive intelligent reflecting surface, it is impossible to obtain the precise distance between the transceiver and each reflecting unit on the intelligent reflecting surface through channel estimation. Therefore, the present invention divides the intelligent reflecting surface into multiple identical sub-modules, and renumbers the reflecting units in each sub-module. On the basis of step S1, a UAV millimeter-wave near-field channel model based on the modularization of the intelligent reflecting surface is established, and the complex channel gain of the near-field channel model is obtained. The complex channel gain function in step S2 is equivalent to the complex channel gain function of the UAV millimeter-wave near-field channel model in step S1. The complex channel gain h of the UAV millimeter-wave near-field channel model with modularization of the intelligent reflecting surface 2,p It can be expressed as:

[0077]

[0078] Wherein, Q represents the number of submodules of the smart reflective surface, M1 and M2 represent the number of row reflective units and column reflective units in each submodule, respectively, and m1 and m2 represent the subscript index of the row reflective unit and column reflective unit in each submodule. represents the distance from the pth transmitting antenna of the drone to the (m2, m1)th reflector unit in the qth submodule, represents the distance from the receiving antenna to the (m2, m1)th reflector unit in the qth submodule, is the reflection phase of the qth submodule.

[0079] S3. The reflection phase design of the modularized smart reflection surface in step S2 still needs to obtain the precise distance between the transceiver and each smart reflection unit. Therefore, in order to reduce the complexity of the modularization scheme of the smart reflection surface for modeling the UAV millimeter wave system, the present invention designs a composite near-field and far-field reflection phase. First, the present invention simplifies the UAV millimeter wave near-field channel model of the modularized smart reflection surface in step S2, uses plane waves instead of spherical waves to simulate the propagation characteristics of electromagnetic waves, establishes a UAV millimeter wave far-field channel model based on the modularization of the smart reflection surface, and obtains the complex channel gain of the UAV millimeter wave far-field channel model under the modularization scheme. Secondly, the reflection phase of the modularized smart reflection surface is designed based on the maximization criterion of the reflection unit gain of the UAV millimeter wave far-field channel model under the modularization scheme. The designed reflection phase consists of two parts: the reflection phase related to the reflection unit and the reflection phase related to the sub-module. The reflection phase related to the reflection unit is determined by the plane wave assumption, and only the angle of the transceiver relative to the center of each sub-module needs to be obtained. The reflection phase related to the submodule is determined by the spherical wave assumption, and only the propagation distance of the transceiver relative to the center of each module needs to be obtained. Therefore, the composite near-field and far-field reflection phase designed by the present invention does not need to obtain the precise distance between the transceiver and each intelligent reflection unit, and the reflection phase is easy to implement for the intelligent reflection surface. Specifically, it includes:

[0080] S31, complex channel gain h of the simplified far-field channel model 3,p It can be expressed as:

[0081]

[0082] in, and represents an auxiliary variable, p represents the subscript index of the transmitting antenna unit, P represents the number of transmitting antenna units, δ represents the distance between adjacent transmitting antenna units, Ψ a Represents the angle between the line connecting the drone to the center of the intelligent reflective surface and the positive direction of the x-axis, ε a,q represents the distance from the UAV to the center of the qth submodule on the intelligent reflective surface, ε d,q represents the distance from the receiving antenna to the center of the qth submodule on the smart reflector, Φ a,q represents the angle between the line connecting the drone and the center of the qth submodule of the intelligent reflective surface and the positive direction of the x-axis, Φ d,q It represents the angle between the line connecting the receiving antenna to the center of the qth submodule of the smart reflector and the positive direction of the x-axis, Ω a,q represents the angle between the line connecting the drone and the center of the qth submodule on the intelligent reflective surface and the positive direction of the z-axis, Ω d,q It represents the angle between the line from the receiving antenna to the center of the qth submodule of the smart reflector and the positive direction of the z-axis.

[0083] S32, according to the complex channel gain h in step S31 2,p , the reflection unit gain of the smart reflection surface can be written as:

[0084]

[0085] Among them, g(Q,M1,M2) is the reflection unit gain of the smart reflection surface;

[0086] In order to achieve the maximum reflective unit gain of the smart reflector, the present invention assumes that the modularized reflective phase of the smart reflector can be expressed as:

[0087]

[0088] Among them, α q represents the constant phase associated with submodule q, represents the expected angle between the line connecting the drone and the center of the qth submodule of the intelligent reflective surface and the positive direction of the x-axis, It represents the expected angle between the line connecting the receiving antenna to the center of the qth submodule of the smart reflector and the positive direction of the x-axis. represents the expected angle between the line connecting the drone and the center of the qth submodule on the intelligent reflective surface and the positive direction of the z-axis, represents the expected angle between the line connecting the receiving antenna to the center of the qth submodule of the smart reflector and the positive direction of the z-axis. Substituting the reflection unit gain g(Q,M1,M2), we have:

[0089]

[0090] Among them, sin represents the sine function, θ q , △1 and △2 represent auxiliary variables, which can be expressed as:

[0091]

[0092] According to the reflection unit gain, when △1=0, △2=0 and When , the reflection unit gain g(Q,M1,M2) reaches its maximum value g(Q,M1,M2)=QM1M2. and α q The following conditions are met:

[0093]

[0094] At this time and α q Substitute The reflection phase of the designed submodule is:

[0095]

[0096] From the above formula, we can see that the reflection phase of the designed submodule is It consists of two parts: the reflection phase related to the reflection unit and the reflection phase related to the module. The reflection phase related to the reflection unit is determined by the plane wave assumption. It only needs to obtain the angle of the transceiver relative to the center of each submodule, such as Φ a,q , Φ d,q ,Ω a,q and Ω d,q The module-related reflection phase is determined by the spherical wave assumption, and only the propagation distance of the transceiver relative to the center of each submodule needs to be obtained, such as ε a,q and ε d,q .

[0097] S4. Using the designed composite near-field and far-field reflection phase Explore the approximate closed-form expression of the received signal power of the UAV millimeter-wave near-field channel model based on the modularization of intelligent reflective surfaces;

[0098] S41, using the second-order Taylor approximation, the distance from the transmitter to the (m2, m1)th reflector unit of the qth submodule The distance from the receiving end to the (m2, m1)th reflector unit of the qth submodule It can be approximately expressed as:

[0099]

[0100] S42, approximate distance and Substitute the reflection phase of the submodule designed in step S3 into the complex channel gain h of the UAV millimeter wave near-field channel model with intelligent reflector modularization in step S2 2,p After that, the received signal power is expressed as:

[0101]

[0102] Among them, P r is the received signal power, ε a represents the distance from the drone to the center of the smart reflective surface, ε d represents the distance from the receiving antenna to the center of the smart reflector, f(η1,η2) is an auxiliary variable, is represented as an auxiliary variable, Represented as auxiliary variables.

[0103] S43, because and The auxiliary variable f(η1,η2) can be expressed as:

[0104]

[0105] Here, x and y are the integration variables.

[0106] S44, make f(η1,η2) can be further expressed as:

[0107]

[0108] Among them, t and τ are integration variables.

[0109] S45. Using the Fresnel integral function, the received signal power can be approximately expressed as:

[0110]

[0111] Among them, P t represents the transmitted signal power, C(·) and S(·) are Fresnel functions, and Represented as auxiliary variables.

[0112] S5. According to the obtained approximate expression of the received signal power, the designed reflection phase is adopted, and the power loss ratio Γ of the UAV millimeter wave near-field channel model based on the modularization of the intelligent reflection surface is:

[0113]

[0114] Using the obtained power loss ratio, the influence of the intelligent reflector segmentation scheme on the performance gain of the UAV millimeter wave system is explored.

[0115] The system corresponding to the method includes:

[0116] A complex channel gain calculation unit is used for the UAV millimeter wave communication system assisted by the intelligent reflective surface, establishes a UAV millimeter wave near-field channel model assisted by the intelligent reflective surface, and obtains the complex channel gain of the UAV millimeter wave near-field channel model assisted by the intelligent reflective surface;

[0117] The post-modular complex channel gain calculation unit is used to divide the intelligent reflection surface into multiple identical sub-modules, renumber the reflection units in each sub-module, establish a UAV millimeter-wave near-field channel model based on the modularization of the intelligent reflection surface, and obtain the complex channel gain of the UAV millimeter-wave near-field channel model based on the modularization of the intelligent reflection surface;

[0118] The reflection phase design unit is used to simplify the UAV millimeter wave near-field channel model based on the modularization of the intelligent reflection surface, use plane waves instead of spherical waves to simulate the propagation characteristics of electromagnetic waves, establish the UAV millimeter wave far-field channel model based on the modularization of the intelligent reflection surface, and obtain the complex channel gain of the far-field channel model; based on the maximization criterion of the reflection unit gain of the far-field channel model, the reflection phase after the modularization of the intelligent reflection surface is designed, and the designed reflection phase includes the reflection phase related to the reflection unit and the reflection phase related to the submodule; the reflection phase related to the reflection unit is determined by the plane wave assumption, and the reflection phase related to the submodule is determined by the spherical wave assumption;

[0119] The received signal power approximation unit is used to solve the approximate closed-form expression of the received signal power of the UAV millimeter-wave near-field channel model based on the modularization of the intelligent reflector by using the reflection phase after the modularization of the designed intelligent reflector;

[0120] The power loss ratio calculation unit is used to calculate the power loss ratio of the UAV millimeter-wave near-field channel model based on the modularization of the intelligent reflector according to the obtained approximate closed-form expression of the received signal power and the reflection phase after the modularization of the designed intelligent reflector; using the obtained power loss ratio, the influence of the intelligent reflector block scheme on the performance gain of the UAV millimeter-wave system is explored.

[0121] An electronic device for storing and executing the method, the device comprising:

[0122] A memory storing executable program code;

[0123] a processor coupled to the memory;

[0124] The processor calls the executable program code stored in the memory to execute the steps of the method for establishing a UAV millimeter wave channel model based on modularization of intelligent reflecting surfaces.

[0125] A computer-readable storage medium for storing and executing the method, wherein the computer-readable storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the steps of the method for establishing a UAV millimeter wave channel model based on modularization of intelligent reflecting surfaces.

[0126] Figure 3The figure shows a comparison of the received signal power of the near-field model proposed by the present invention and the traditional far-field model. When the size of the smart reflection surface reaches 180*180 and 240*240, the received signal power under the model proposed by the present invention is 4.9dBm and 9.9dBm higher, while when the size of the smart reflection surface is 80*80, the advantage of the model of the present invention is not obvious. This is because when the size is small, the Rayleigh distance distinguishing the near and far fields is also small. When the size becomes larger, the original far-field area will be transformed into the respective near-field area.

[0127] Figure 4 What is shown is the comparison between the received signal power calculated by the reflection phase designed by the present invention and the optimal received signal power. It can be seen that when the size of the intelligent reflection surface is 80*80, 180*180 and 240*240, the power corresponding to the designed reflection phase is very consistent with the optimal received signal power, which shows that the reflection phase designed by the present invention is very effective.

[0128] Figure 5 The power loss ratio Γ is plotted as a function of the number of subsurfaces. It can be observed that the power loss ratio decreases as the number of subsurfaces increases. The reason is that the Rayleigh distance becomes shorter as the number of subsurfaces increases, resulting in a more accurate far-field model. It can be observed that when the size of the smart reflector is reasonable, that is, the far-field model of 240×240 is sufficient to describe the radiation field characteristics of the electromagnetic wave.

Claims

1. A method for establishing a UAV millimeter wave channel model based on intelligent reflective surface modularization, characterized in that: The following steps are involved: S1. A UAV millimeter wave communication system assisted by an intelligent reflective surface is established, and a UAV millimeter wave near-field channel model assisted by an intelligent reflective surface is established, and the complex channel gain of the UAV millimeter wave near-field channel model assisted by an intelligent reflective surface is obtained; S2, dividing the smart reflective surface into multiple identical sub-modules, and renumbering the reflective units in each sub-module, establishing a UAV millimeter-wave near-field channel model based on the modularization of the smart reflective surface, and obtaining the complex channel gain of the UAV millimeter-wave near-field channel model based on the modularization of the smart reflective surface; S3, simplifying the UAV millimeter wave near-field channel model based on the modularization of the intelligent reflection surface in step S2, using plane waves instead of spherical waves to simulate the propagation characteristics of electromagnetic waves, establishing a UAV millimeter wave far-field channel model based on the modularization of the intelligent reflection surface, and obtaining the complex channel gain of the far-field channel model; designing the reflection phase after the modularization of the intelligent reflection surface based on the maximization criterion of the reflection unit gain of the far-field channel model, the designed reflection phase includes the reflection phase related to the reflection unit and the reflection phase related to the submodule; the reflection phase related to the reflection unit is determined by the plane wave assumption, and the reflection phase related to the submodule is determined by the spherical wave assumption; S4. Using the reflection phase after modularization of the designed intelligent reflector, solve the approximate closed-form expression of the received signal power of the UAV millimeter-wave near-field channel model based on the modularization of the intelligent reflector; S5. According to the approximate closed-form expression of the received signal power, the reflection phase after the designed intelligent reflector is modularized is used to calculate the power loss ratio of the UAV millimeter-wave near-field channel model based on the modularization of the intelligent reflector. Using the obtained power loss ratio, the influence of the intelligent reflector block scheme on the performance gain of the UAV millimeter-wave system is explored.

2. The method for establishing a UAV millimeter wave channel model based on intelligent reflective surface modularization according to claim 1 is characterized in that: The complex channel gain h of the UAV millimeter wave near-field channel model based on the intelligent reflector in step S1 is 1,p It is expressed as: Where π represents the circumference of a circle, δ1 and δ2 represent the spacing between adjacent row reflection units and adjacent column reflection units on the smart reflection surface, λ represents the wavelength of the electromagnetic wave emitted by the drone, N1 and N2 represent the number of row reflection units and column reflection units on the smart reflection surface, and γ a and γ d They represent the path losses from the UAV to the smart reflector and from the smart reflector to the receiving end, It represents the projection of the electromagnetic wave emitted by the drone on the normal line of the smart reflective surface. represents the projection of the electromagnetic wave signal reflected by the smart reflective surface on the normal line of the smart reflective surface, e represents the exponential function, represents the distance from the pth transmitting antenna of the drone to the (n2,n1)th reflecting unit, represents the distance from the (n2,n1)th reflection unit to the receiving antenna, Represents the reflection phase of the (n2,n1)th reflection unit on the smart reflection surface.

3. The method for establishing a UAV millimeter wave channel model based on intelligent reflective surface modularization according to claim 1 is characterized in that: The complex channel gain h of the UAV millimeter wave near-field channel model with intelligent reflective surface modularization in step S2 2,p It is expressed as: Where π represents the circumference of a circle, δ1 and δ2 represent the spacing between adjacent row reflection units and adjacent column reflection units on the smart reflection surface, λ represents the wavelength of the electromagnetic wave emitted by the drone, and γ a and γ d They represent the path losses from the UAV to the smart reflector and from the smart reflector to the receiving end, It represents the projection of the electromagnetic wave emitted by the drone on the normal line of the smart reflective surface. represents the projection of the electromagnetic wave signal reflected by the smart reflective surface on the normal line of the smart reflective surface, Q represents the number of submodules of the smart reflective surface, q represents the subscript index of the submodule of the smart reflective surface, M1 and M2 represent the number of row reflection units and column reflection units in each submodule, respectively, m1 and m2 represent the subscript index of the row reflection unit and column reflection unit in each submodule, e represents the exponential function, is the reflection phase of the qth submodule, represents the distance from the pth transmitting antenna of the drone to the (m2, m1)th reflector unit in the qth submodule, Represents the distance from the receiving antenna to the (m2, m1)th reflection unit in the qth submodule.

4. The method for establishing a UAV millimeter wave channel model based on intelligent reflective surface modularization according to claim 1 is characterized in that: The complex channel gain h of the UAV millimeter wave far-field channel model based on the intelligent reflector modularization established in step S3 3,p , expressed as: Where π represents the circumference of a circle, δ1 and δ2 represent the spacing between adjacent row reflection units and adjacent column reflection units on the smart reflection surface, λ represents the wavelength of the electromagnetic wave emitted by the drone, and γ a and γ d They represent the path losses from the UAV to the smart reflector and from the smart reflector to the receiving end, It represents the projection of the electromagnetic wave emitted by the drone on the normal line of the smart reflective surface. represents the projection of the electromagnetic wave signal reflected by the smart reflective surface on the normal line of the smart reflective surface, Q represents the number of submodules of the smart reflective surface, q represents the subscript index of the submodule of the smart reflective surface, M1 and M2 represent the number of row reflection units and column reflection units in each submodule, respectively, m1 and m2 represent the subscript index of the row reflection unit and column reflection unit in each submodule, e represents the exponential function, is the reflection phase of the qth submodule, and represents an auxiliary variable, p represents the subscript index of the transmitting antenna unit, P represents the number of transmitting antenna units, δ represents the distance between adjacent transmitting antenna units, Ψ a Represents the angle between the line connecting the drone to the center of the intelligent reflective surface and the positive direction of the x-axis, ε a,q represents the distance from the UAV to the center of the qth submodule on the intelligent reflective surface, ε d,q represents the distance from the receiving antenna to the center of the qth submodule on the smart reflector, Φ a,q represents the angle between the line connecting the drone and the center of the qth submodule of the intelligent reflective surface and the positive direction of the x-axis, Φ d,q It represents the angle between the line connecting the receiving antenna to the center of the qth submodule of the smart reflector and the positive direction of the x-axis, Ω a,q represents the angle between the line connecting the drone and the center of the qth submodule on the intelligent reflective surface and the positive direction of the z-axis, Ω d,q It represents the angle between the line from the receiving antenna to the center of the qth submodule of the smart reflector and the positive direction of the z-axis.

5. The method for establishing a UAV millimeter wave channel model based on intelligent reflective surface modularization according to claim 4 is characterized in that: The reflection phase of the modularized smart reflector is designed based on the maximization criterion of the reflection unit gain of the UAV millimeter-wave far-field channel model under the modular solution, including: Complex channel gain h of the UAV millimeter-wave far-field channel model based on the modularization of the intelligent reflector 3,p , the reflection unit gain g(Q,M1,M2) of the smart reflection surface is expressed as: In order to achieve the maximum reflective unit gain of the smart reflector, the modular reflection phase of the smart reflector is expressed as: Among them, α q represents the constant phase associated with submodule q, represents the expected angle between the line connecting the drone and the center of the qth submodule of the intelligent reflective surface and the positive direction of the x-axis, It represents the expected angle between the line connecting the receiving antenna to the center of the qth submodule of the smart reflector and the positive direction of the x-axis. represents the expected angle between the line connecting the drone and the center of the qth submodule on the intelligent reflective surface and the positive direction of the z-axis, represents the expected angle between the line connecting the receiving antenna to the center of the qth submodule of the smart reflector and the positive direction of the z-axis; Substituting the reflection unit gain g(Q,M1,M2), we have: Among them, sin represents the sine function, θ q , △1 and △2 represent auxiliary variables, which are respectively expressed as: According to the reflection unit gain, when △1=0, △2=0 and When , the reflection unit gain g(Q,M1,M2) has a maximum value g(Q,M1,M2)=QM1M2; and α q The following conditions are met: At this time and α q Substitution Expression, the reflection phase of the designed intelligent reflector module is:

6. The method for establishing a UAV millimeter wave channel model based on intelligent reflective surface modularization according to claim 1 is characterized in that: Step S4 includes: S41, using the second-order Taylor approximation, the distance from the transmitter to the (m2, m1)th reflector unit of the qth submodule The distance from the receiving end to the (m2, m1)th reflector unit of the qth submodule It can be expressed approximately as: Among them, ε a,q represents the distance from the UAV to the center of the qth submodule on the intelligent reflective surface, ε d,q represents the distance from the receiving antenna to the center of the qth submodule on the smart reflector, Ψ a represents the angle between the line connecting the drone to the center of the smart reflective surface and the positive direction of the x-axis, δ represents the spacing between adjacent antenna units at the transmitting end, δ1 and δ2 represent the spacing between adjacent row reflective units and adjacent column reflective units on the smart reflective surface, respectively. and represents an auxiliary variable, p represents the subscript index of the transmitting antenna unit, P represents the number of transmitting antenna units, M1 and M2 represent the number of row reflection units and column reflection units in each submodule, respectively, m1 and m2 represent the subscript index of the row reflection unit and column reflection unit in each submodule, Φ a,q represents the angle between the line connecting the drone and the center of the qth submodule of the intelligent reflective surface and the positive direction of the x-axis, Φ d,q It represents the angle between the line connecting the receiving antenna to the center of the qth submodule of the smart reflector and the positive direction of the x-axis, Ω a,q represents the angle between the line connecting the drone and the center of the qth submodule on the intelligent reflective surface and the positive direction of the z-axis, Ω d,q Represents the angle between the line connecting the receiving antenna to the center of the qth submodule of the smart reflector and the positive direction of the z-axis; S42, approximate distance and After substituting the reflection phase designed in step S3 into the complex channel gain of the UAV millimeter wave near-field channel model with intelligent reflector modularization in step S2, the received signal power is expressed as: Among them, P r is the received signal power, It represents the projection of the electromagnetic wave emitted by the drone on the normal line of the smart reflective surface. represents the projection of the electromagnetic wave signal reflected by the smart reflective surface on the normal line of the smart reflective surface, ε a represents the distance from the drone to the center of the smart reflective surface, ε d represents the distance from the receiving antenna to the center of the smart reflector, Q represents the number of submodules of the smart reflector, q represents the subscript index of the submodule of the smart reflector, f(η1,η2) is an auxiliary variable, is represented as an auxiliary variable, It is represented as an auxiliary variable, λ represents the wavelength of electromagnetic waves emitted by the UAV; S43, because and The auxiliary variable f(η1,η2) is expressed as: Among them, x and y are integration variables; S44, make f(η1,η2) is further expressed as: Among them, t and τ are integration variables; S45. Using the Fresnel integral function, the received signal power is approximately expressed as: Among them, P t represents the transmitted signal power, C(·) and S(·) are Fresnel functions, and Represented as auxiliary variables.

7. The method for establishing a UAV millimeter wave channel model based on intelligent reflective surface modularization according to claim 1 is characterized in that: The power loss ratio Γ of the UAV millimeter wave near-field channel model based on the modularization of the intelligent reflector in step S5 is: Wherein, Q represents the number of submodules of the smart reflective surface, q represents the subscript index of the submodule of the smart reflective surface, C(·) and S(·) represent Fresnel functions, and is represented as an auxiliary variable, M1 and M2 represent the number of row reflection units and column reflection units in each submodule, respectively, δ1 and δ2 represent the spacing between adjacent row reflection units and adjacent column reflection units on the smart reflection surface, respectively, η1 and η2 are represented as auxiliary variables, and λ represents the wavelength of the electromagnetic wave emitted by the UAV.

8. A UAV millimeter wave channel model establishment system based on intelligent reflective surface modularization, characterized in that: include: A complex channel gain calculation unit is used for the UAV millimeter wave communication system assisted by the intelligent reflective surface, establishes a UAV millimeter wave near-field channel model assisted by the intelligent reflective surface, and obtains the complex channel gain of the UAV millimeter wave near-field channel model assisted by the intelligent reflective surface; The post-modular complex channel gain calculation unit is used to divide the intelligent reflection surface into multiple identical sub-modules, renumber the reflection units in each sub-module, establish a UAV millimeter-wave near-field channel model based on the modularization of the intelligent reflection surface, and obtain the complex channel gain of the UAV millimeter-wave near-field channel model based on the modularization of the intelligent reflection surface; The reflection phase design unit is used to simplify the UAV millimeter wave near-field channel model based on the modularization of the intelligent reflection surface, use plane waves instead of spherical waves to simulate the propagation characteristics of electromagnetic waves, establish the UAV millimeter wave far-field channel model based on the modularization of the intelligent reflection surface, and obtain the complex channel gain of the far-field channel model; based on the maximization criterion of the reflection unit gain of the far-field channel model, the reflection phase after the modularization of the intelligent reflection surface is designed, and the designed reflection phase includes the reflection phase related to the reflection unit and the reflection phase related to the submodule; the reflection phase related to the reflection unit is determined by the plane wave assumption, and the reflection phase related to the submodule is determined by the spherical wave assumption; The received signal power approximation unit is used to solve the approximate closed-form expression of the received signal power of the UAV millimeter-wave near-field channel model based on the modularization of the intelligent reflector by using the reflection phase after the modularization of the designed intelligent reflector; The power loss ratio calculation unit is used to calculate the power loss ratio of the UAV millimeter-wave near-field channel model based on the modularization of the intelligent reflector according to the obtained approximate closed-form expression of the received signal power and the reflection phase after the modularization of the designed intelligent reflector; using the obtained power loss ratio, the influence of the intelligent reflector block scheme on the performance gain of the UAV millimeter-wave system is explored.

9. An electronic device, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the steps of the method for establishing a UAV millimeter wave channel model based on intelligent reflective surface modularization as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, which, when called, are used to execute the steps of the method for establishing a UAV millimeter-wave channel model based on modular intelligent reflecting surfaces as described in any one of claims 1 to 7.

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