Aerial intelligent reflecting surface coverage expansion method and device under building constraint

Abstract

The embodiment of the invention provides an air intelligent reflecting surface coverage expansion method and device under building constraint, and the method comprises the steps: calculating parameters of quantified topographic features according to the height distribution, the building proportion and the building density of buildings in a deployment region; establishing an association rule of the intelligent reflector-user in the air under the constraint of the urban building; establishing a gain model of the aerial intelligent reflecting surface for assisting the urban users to perform coverage expansion; determining a channel model according to a channel environment, and deriving to obtain a signal to interference plus noise ratio of a user; setting of optimal deployment parameters under the constraint of urban buildings is determined by giving an expression of the coverage rate under the model; and according to different requirements and different deployment environments, re-optimizing deployment parameters of the aerial intelligent reflecting surface. According to the embodiment of the invention, the problem of how to obtain the searching equipment of the deployment parameters of the air intelligent reflecting surface in different environments constrained by the building so as to maximize the network performance is solved.

Description

Technical Field

[0001] The present invention relates to the field of 5G and 6G wireless communication technologies, and in particular to a parameter configuration method for considering aerial intelligent reflective surface-assisted coverage expansion in a cellular network. Background Art

[0002] At present, aerial intelligent reflective surfaces assisting ground cellular networks have become a research hotspot in the field of wireless communications. They have the following advantages: high probability line-of-sight transmission, flexible deployment, large-scale coverage expansion, and reduced signal reflection attenuation:

[0003] Since the aerial intelligent reflective surface is deployed at a high position, it can avoid most ground obstructions, especially in urban areas where buildings are densely distributed. If the ground is not an intelligent reflective surface, it will be severely obstructed and cannot give full play to its advantages. The deployment of aerial intelligent reflective surfaces can effectively improve the signal strength received by users.

[0004] Since aerial smart reflective surfaces are usually deployed on hot air balloons or drones, they can more flexibly optimize the position or trajectory of the aerial platform to further improve communication performance, thus providing a new degree of freedom for improving performance through three-dimensional network design;

[0005] Due to deployment issues, the traditional deployment method of smart reflective surfaces usually only has half of the coverage range, while the aerial smart reflective surface can be deployed horizontally on the drone to form a 360-degree coverage range, greatly expanding the coverage range and fully utilizing the advantages of the smart reflective surface;

[0006] In actual situations, there are usually ground obstructions such as buildings in the deployment area. Therefore, when the smart reflective surface is deployed on the ground or on the surface of a building, the signal may be reflected by the smart reflective surface multiple times when it reaches the user side from the base station side. By deploying aerial smart reflective surfaces, the number of reflections can be reduced, thereby reducing signal loss.

[0007] Existing studies have proposed some aerial intelligent reflector design optimization algorithms to improve user performance, as well as theoretical performance analysis of a single base station and a single aerial intelligent reflector. However, there are still some urgent problems to be solved in the specific implementation of aerial intelligent reflector-assisted network communication: considering the interference caused by different base stations to users, how to determine the deployment of aerial intelligent reflectors according to different deployment environments; after deploying aerial intelligent reflectors, what impact do base stations of different densities have on user performance; under the influence of urban buildings, what impact do building characteristics have on system performance; in actual deployment environments, there is usually ground obstruction in air-to-ground links. In addition, since the aerial intelligent reflector has a certain deployment height, the communication link fluctuates with the deployment height. On the one hand, changes in the deployment height of the aerial intelligent reflector lead to changes in the communication distance. On the other hand, changes in height lead to different probabilities of forming line-of-sight links and non-line-of-sight links. Therefore, how to optimize the deployment height of the aerial intelligent reflector and how to set the deployment parameters of the aerial intelligent reflector in different environments. Summary of the invention

[0008] The purpose of the present invention is to provide a method and device for expanding coverage of an aerial intelligent reflective surface under building constraints, so as to reduce the impact of building shading on ground user performance and improve resource utilization. The specific technical solution is as follows:

[0009] Step 200, calculating parameters for quantifying terrain features based on the height distribution, building ratio, and building density of buildings in the deployment area;

[0010] Step 210, establishing an association rule between an aerial intelligent reflective surface and a user under the constraints of urban buildings;

[0011] Step 220, establishing a gain model for using an aerial intelligent reflector to assist urban users in extending coverage;

[0012] Step 230, determining a channel model according to the channel environment, and obtaining the signal to interference and noise ratio of the user by derivation;

[0013] Step 240, by providing an expression for coverage under the model, determining the setting of optimal deployment parameters under the constraints of urban buildings;

[0014] Step 250, re-optimize the deployment parameters of the aerial intelligent reflective surface according to different requirements and deployment environments.

[0015] Optionally, the calculating of parameters for quantifying terrain features according to the height distribution, building proportion, and building density of buildings in the deployment area specifically includes:

[0016] Based on map data, with typical users as the center, calculate the average height, building space ratio, and building density of buildings within a fixed radius area;

[0017] Based on Monte Carlo simulation, the characteristics of urban buildings are simulated, in which the building heights obey the Rayleigh distribution. Based on the Poisson point process, the horizontal position of the aerial intelligent reflector, the ground user position and the ground base station position are randomly generated.

[0018] Optionally, the establishing of association rules between the aerial intelligent reflective surface and the user under the constraints of urban buildings specifically includes:

[0019] Due to the shading effect of urban buildings, the association strategy between smart reflective surfaces and users cannot be determined based on distance alone. If the smart reflective surface closest to a typical user is blocked, it will not be selected as the associated smart reflective surface. A typical user will choose the closest smart reflective surface that guarantees link visibility for association.

[0020] First, the probability of whether the link is blocked by a building needs to be quantified. The probability of not being blocked can be expressed as:

[0021] P LoS (R) = e - η(β R+p)

[0022] Where R represents the horizontal distance between the nodes at both ends of the link, It represents the probability of link being blocked when there are buildings in the horizontal direction of the link. p=λ BU EL·EW.λ BU , EL, EW represent the average length and average width of building density respectively.

[0023] According to the distribution characteristics of the aerial intelligent reflective surface, the cumulative distribution function of the horizontal distance from a typical user to the associated aerial intelligent reflective surface can be obtained as follows:

[0024]

[0025] Among them, N k It represents the number of aerial intelligent reflective surfaces within a circular range with a typical user as the center and x as the radius. Event A indicates that there is no visible aerial intelligent reflective surface node within the range.

[0026] Therefore, the probability density function of the horizontal distance can be expressed as

[0027]

[0028] Optionally, the establishing of a gain model for using an aerial intelligent reflector to assist urban users in extending coverage specifically includes:

[0029] Due to the edgeless characteristics of the smart reflective surface, it is divided into three categories according to the distance for subsequent calculation:1 Within the range, the intelligent reflector provides beamforming gain for its associated users, and the rest are within D 2 The intelligent reflective surface within the range provides random scattering gain; if the range D 1 If there is no intelligent reflective surface within the range D 2 The smart reflective surfaces inside only provide random scattering gain; if D 2 If there is no smart reflective surface within the range, the smart reflective surface will not generate any gain for the user.

[0030] Optionally, determining a channel model according to a channel environment and obtaining a signal to interference and noise ratio of a user by derivation specifically includes:

[0031] Assume that the base station and the user are equipped with only a single antenna, and the aerial intelligent reflector is equipped with N reflective units. m Indicates the mth base station, AIRS j represents the jth AIRS, Indicates AIRS j The nth reflection unit of . The channel is modeled by large-scale path loss and small-scale fading. Therefore, BS m -UE channel power gain can be expressed as:

[0032]

[0033] Where g represents the average channel power gain, represents the average channel power gain coefficient, α represents the path loss exponent, and f c is the carrier frequency, c is the speed of light, ξ represents small-scale fading and is a gamma random variable, whose probability density function is expressed as Therefore, BS m -UE cascade link can be expressed as

[0034] Furthermore, according to the properties of gamma random variables, the first-order moment and second-order moment of the direct link amplitude can be expressed as

[0035]

[0036] Therefore, it is easy to get The expectation and variance of

[0037]

[0038] Therefore, the average signal power after the associated aerial smart reflector beamforming gain can be obtained as:

[0039]

[0040] The average signal power of the random scattering gain of the intelligent reflector in the air is:

[0041]

[0042] Considering the building shielding effect, for the convenience of analysis, the aerial intelligent reflector is divided into two categories according to the visibility of the base station-user link, so the signal power can be expressed as:

[0043]

[0044] Among them, S i,m,j and S i,j Indicates BS m -AIRS j and AIRS j -UE link penetration loss. To simplify the deduction, the building is set as a signal that cannot penetrate. Therefore, the value of the penetration loss can only be 0 or 1, obeying the Bernoulli distribution. The interference power can be expressed as:

[0045]

[0046] According to the central limit theorem, ξ m,0 and m,1 denote gamma and cyclically symmetric complex Gaussian distributions, respectively.

[0047] Therefore, the signal-to-interference-to-noise ratio at the user can be expressed as

[0048]

[0049] Where W represents the normalized additive white Gaussian noise.

[0050] Optionally, the method of determining the setting of optimal deployment parameters under the constraints of urban buildings by providing an expression of coverage under the model specifically includes:

[0051] According to the definition, coverage can be expressed as

[0052]

[0053] The coverage can be expressed as

[0054]

[0055] in, k s and θ s are shape and scale parameters respectively. Combining the probability density function of the distance, the final expression of coverage can be obtained as:

[0056]

[0057] in, P BS,NLOS =1-P BS,LOS .

[0058] Optionally, re-optimizing the deployment parameters of the aerial intelligent reflective surface according to different requirements and deployment environments specifically includes:

[0059] The optimal deployment height of the aerial intelligent reflective surface is determined based on the relationship between network coverage and the height of the aerial intelligent reflective surface deployment plane under different conditions. When the number of aerial intelligent reflective surfaces or the number of reflective units or the deployment scenario changes, the optimal deployment height of the aerial intelligent reflective surface needs to be re-determined.

[0060] Optionally, the aerial intelligent reflective surface coverage expansion device under building constraints specifically includes: a determination module, a network side device, an intelligent reflective surface device and a terminal device.

[0061] Optionally, the determination module is used to determine the number of base stations and channel environment parameters in the deployed area;

[0062] Network side equipment: includes a transceiver, a memory, a processor and a computer program stored in the memory and executable on the processor; when the processor executes the program, the step of confirming the optimal deployment parameters described in any one of claims 1 to 7 is implemented.

[0063] Beneficial Effects

[0064] The present invention proposes a deployment method for aerial intelligent reflective surfaces by considering the air-to-ground link model. First, the impact of the cumulative interference generated by different base stations on the user side through aerial intelligent reflective surfaces on network performance (user coverage) is considered. At the same time, due to different network requirements, aerial intelligent reflective surfaces can be deployed at different heights, and a network model of three-dimensional aerial intelligent reflective surfaces assisting ground users is proposed. The spatial distribution characteristics of urban buildings are further quantified to construct an intelligent reflective surface association strategy and its channel model in an urban environment. The model is more in line with the actual deployment situation in an urban environment to set deployment parameters.

[0065] When deployed in different urban environments, the spatial characteristics of buildings are different, and they have different environmental parameters, which cause different probabilities of forming line-of-sight links and non-line-of-sight links. The interference caused by multiple base stations and the gain of the aerial intelligent reflector to the signal are analyzed, and a network coverage model is established based on this. The deployment parameters of the aerial intelligent reflector are selected according to the coverage performance at the user, and the optimal deployment height in this environment is determined to improve the performance of the aerial intelligent reflector-assisted network.

[0066] In addition, when the deployment environment changes (the spatial characteristics of the building or the number of base stations change), the optimal aerial intelligent reflective surface deployment parameters can be reselected based on the obtained network coverage to achieve the purpose of optimizing performance and saving resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a system model diagram of a method and device for extending coverage of an aerial intelligent reflective surface under building constraints of the present invention;

[0068] Figure 2 It is an implementation flow chart of the present invention;

[0069] Figure 3 It is a graph showing the relationship between user coverage and deployment height under different aerial smart reflective surface densities and building heights under building constraints;

[0070] Figure 4 It is a graph showing the relationship between user coverage and base station density under building constraints at different deployment heights and densities of aerial intelligent reflectors; DETAILED DESCRIPTION

[0071] In order to clarify the purpose, implementation scheme and technical advantages of the present invention, the present invention is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0072] Figure 1 This is a practical application scenario of the method and device for extending coverage of an aerial intelligent reflective surface under building constraints of the present invention: the aerial intelligent reflective surface serves ground users as a network intermediate node, reconstructs the line-of-sight link through passive reflection signals, and reduces the impact of building shielding on ground user performance. The present invention aims to configure the deployment parameters of the aerial intelligent reflective surface in different urban environments by quantifying the spatial distribution characteristics of buildings.

[0073] Figure 2 A schematic flow chart of a method and device for extending coverage of an aerial intelligent reflective surface under building constraints provided by the present invention includes:

[0074] Step 200, calculating parameters for quantifying terrain features based on the height distribution, building ratio, and building density of buildings in the deployment area.

[0075] Step 210: Establish association rules between aerial intelligent reflective surfaces and users under the constraints of urban buildings.

[0076] Step 220: Establish a gain model for using an aerial intelligent reflector to assist urban users in extending coverage.

[0077] Step 230: determine a channel model according to the channel environment, and obtain the signal to interference and noise ratio of the user by derivation.

[0078] Step 240, by providing an expression for coverage under the model, determine the setting of optimal deployment parameters under the constraints of urban buildings.

[0079] Step 250, re-optimize the deployment parameters of the aerial intelligent reflective surface according to different requirements and deployment environments.

[0080] In step 200, the quantitative parameters of the terrain characteristics need to utilize the spatial characteristics of the buildings in the area, which are specifically: height distribution, spatial density, duty cycle and geometric shape, which can be extracted through map information.

[0081] In step 210, the association rules between the aerial intelligent reflective surface and the user under the constraints of urban buildings are established, which needs to be further established based on the quantitative characteristics of the building distribution. Specifically:

[0082] Due to the shading effect of urban buildings, the association strategy between smart reflective surfaces and users cannot be determined based on distance alone. If the smart reflective surface closest to a typical user is blocked, it will not be selected as the associated smart reflective surface. A typical user will choose the closest smart reflective surface that guarantees link visibility for association.

[0083] First, the probability of whether the link is blocked by a building needs to be quantified. The probability of not being blocked can be expressed as:

[0084] P LoS (R) = e - η(β R+p)

[0085] Where R represents the horizontal distance between the nodes at both ends of the link, It represents the probability of link being blocked when there are buildings in the horizontal direction of the link. p=λ BU EL·EW.λ BU , EL, EW represent the average length and average width of building density respectively.

[0086] According to the distribution characteristics of the aerial intelligent reflective surface, the cumulative distribution function of the horizontal distance from a typical user to the associated aerial intelligent reflective surface can be obtained as follows:

[0087]

[0088] Among them, N k It represents the number of aerial intelligent reflective surfaces within a circular range with a typical user as the center and x as the radius. Event A indicates that there is no visible aerial intelligent reflective surface node within the range.

[0089] Therefore, the probability density function of the horizontal distance can be expressed as

[0090]

[0091] In step 220, in order to clearly construct the gain model of the smart reflective surface, its service gain type needs to be classified first. Specifically:

[0092] Due to the edgeless characteristics of the smart reflective surface, it is divided into three categories according to the distance for subsequent calculation: 1 Within the range, the intelligent reflector provides beamforming gain for its associated users, and the rest are within D 2 The intelligent reflective surface within the range provides random scattering gain; if the range D 1 If there is no intelligent reflective surface within the range D 2 The smart reflective surfaces inside only provide random scattering gain; if D 2 If there is no smart reflective surface within the range, the smart reflective surface will not generate any gain for the user.

[0093] Step 230, after selecting a suitable model to model the channel environment, the service type of the smart reflective surface needs to be classified to accurately derive the useful and interference signal power distribution, and then derive the user's signal to noise ratio, specifically:

[0094] Assume that the base station and the user are equipped with only a single antenna, and the aerial intelligent reflector is equipped with N reflective units. m Indicates the mth base station, AIRS j represents the jth AIRS, Indicates AIRS j The nth reflection unit of . The channel is modeled by large-scale path loss and small-scale fading. Therefore, BS m -UE channel power gain can be expressed as:

[0095]

[0096] Where g represents the average channel power gain, represents the average channel power gain coefficient, α represents the path loss exponent, and f c is the carrier frequency, c is the speed of light, ξ represents small-scale fading and is a gamma random variable, whose probability density function is expressed as Therefore, BS m -UE cascade link can be expressed as

[0097] Furthermore, according to the properties of gamma random variables, the first-order moment and second-order moment of the direct link amplitude can be expressed as

[0098]

[0099] E{h 2 =g

[0100] Therefore, it is easy to get The expectation and variance of

[0101]

[0102] Therefore, the average signal power after the associated aerial smart reflector beamforming gain can be obtained as:

[0103]

[0104] The average signal power of the random scattering gain of the intelligent reflector in the air is:

[0105]

[0106] Considering the building shielding effect, for the convenience of analysis, the aerial intelligent reflector is divided into two categories according to the visibility of the base station-user link, so the signal power can be expressed as:

[0107]

[0108] Among them, S i,m,j and S i,j Indicates BS m -AIRS j and AIRS j -UE link penetration loss. To simplify the deduction, the building is set as a signal that cannot penetrate. Therefore, the value of the penetration loss can only be 0 or 1, obeying the Bernoulli distribution. The interference power can be expressed as:

[0109]

[0110] According to the central limit theorem, ξ m,0 and m,1 denote gamma and cyclically symmetric complex Gaussian distributions, respectively.

[0111] Therefore, the signal-to-interference-to-noise ratio at the user can be expressed as

[0112]

[0113] Where W represents the normalized additive white Gaussian noise.

[0114] Step 240, based on the definition of coverage and combined with the power distribution characteristics of useful and interfering signals, derive a theoretical expression for coverage. Specifically:

[0115] According to the definition, coverage can be expressed as

[0116]

[0117] The coverage can be expressed as

[0118]

[0119] in, k s and θ s are shape and scale parameters respectively. Combining the probability density function of the distance, the final expression of coverage can be obtained as:

[0120]

[0121] in, P BS,NLOS =1-P BS,LOS .

[0122] Step 250: Re-optimize the deployment parameters of the aerial intelligent reflective surface according to different requirements and deployment environments. Specifically:

[0123] Due to changes in the environment, the required optimal deployment parameters will also change accordingly. Therefore, it is necessary to determine the optimal deployment height of the aerial intelligent reflective surface based on the relationship between the network coverage and the height of the aerial intelligent reflective surface deployment plane under different conditions. When the number of aerial intelligent reflective surfaces or the number of reflective units or the deployment scenario changes, the optimal deployment height of the aerial intelligent reflective surface needs to be re-determined.

[0124] Figure 3 This is a schematic diagram showing how the coverage of ground users changes with the deployment height of the aerial intelligent reflector. The coverage rate represents the statistical probability that the user service link SIR meets the standard under a certain SIR threshold requirement. The horizontal axis represents the deployment height of the aerial intelligent reflector. The simulation results show the impact of adjusting the aerial intelligent reflector on the performance of ground users at different building heights. It can be seen that at different building heights, the aerial intelligent reflector is deployed in two optimal height intervals; for higher scenarios, a higher deployment height is required to ensure the basic performance of ground users.

[0125] Figure 4 This is a schematic diagram showing how the coverage of ground users changes with the density of base stations. Since the existing base stations have been deployed, the density of base stations in a certain area is determined. As can be seen from the figure, the optimal deployment height and deployment density of the aerial intelligent reflector are different under different base station density environments. Under different deployment conditions, there is an optimal base station density that maximizes network performance. Therefore, it is necessary to re-optimize the deployment parameters according to different environments.

Claims

1. A method and device for extending coverage of an aerial intelligent reflective surface under building constraints, characterized in that: include: Intelligent reflective surfaces are dynamically deployed in the air as passive devices to expand the coverage of ground cellular networks. The parameters of quantitative terrain characteristics are calculated based on the height distribution, building proportion, and building density of buildings in the deployment area. Association rules between aerial intelligent reflective surfaces and users under the constraints of urban buildings are established. A gain model for aerial intelligent reflective surfaces to assist urban users in expanding coverage is established. The channel model is determined based on the channel environment, and the user's signal-to-interference-noise ratio is obtained by deduction. The optimal deployment parameter setting under the constraints of urban buildings is determined by giving an expression for the coverage rate under the model. The deployment parameters of aerial intelligent reflective surfaces are re-optimized according to different needs and deployment environments.

2. The method according to claim 1, characterized in that: Calculate the parameters for quantifying terrain characteristics based on the height distribution, building proportion, and building density of buildings in the deployment area, including: Based on map data, with typical users as the center, calculate the average height, building space ratio, and building density of buildings within a fixed radius area; Based on Monte Carlo simulation, the characteristics of urban buildings are simulated, in which the building heights obey the Rayleigh distribution. Based on the Poisson point process, the horizontal position of the aerial intelligent reflector, the ground user position and the ground base station position are randomly generated.

3. The method according to claim 1, characterized in that The three-dimensional air-to-ground system model for establishing an aerial intelligent reflective surface to assist urban users in extending coverage specifically includes: Due to the shading effect of urban buildings, the association strategy between smart reflective surfaces and users cannot be determined based on distance alone. If the smart reflective surface closest to a typical user is blocked, it will not be selected as the associated smart reflective surface. The typical user will choose the closest smart reflective surface that guarantees link visibility for association; First, we need to quantify the probability of whether the link is blocked by a building. The probability of not being blocked can be expressed as: P LoS (R)=e - h(b R+p) Where R represents the horizontal distance between the nodes at both ends of the link, It represents the probability of link being blocked when there are buildings in the horizontal direction of the link. p=λ BU EL·EW.λ BU , EL, EW represent the average length and average width of building density, respectively; According to the distribution characteristics of the aerial intelligent reflective surface, the cumulative distribution function of the horizontal distance from a typical user to the associated aerial intelligent reflective surface can be obtained as follows: Among them, N k represents the number of aerial intelligent reflective surfaces within a circular range with a typical user as the center and x as the radius. Event A indicates that there is no visible aerial intelligent reflective surface node within the range. Therefore, the probability density function of the horizontal distance can be expressed as 4. The method according to claim 1, characterized in that: The gain model of establishing an aerial intelligent reflector to assist urban users in extending coverage specifically includes: Due to the edgeless characteristics of the smart reflective surface, it is divided into three categories according to the distance for the convenience of subsequent calculation: within the range D1, the smart reflective surface provides beamforming gain for its associated users, and the rest of the smart reflective surfaces within the range D2 provide random scattering gain; if there is no smart reflective surface within the range D1, all smart reflective surfaces within the range D2 only provide random scattering gain; if there is no smart reflective surface within the range D2, the smart reflective surface does not generate any gain for the user.

5. The method according to claim 1, characterized in that Determining a channel model according to a channel environment and obtaining a signal to interference noise ratio of a user by derivation specifically includes: Assume that the base station and the user are equipped with only a single antenna, and the aerial intelligent reflector is equipped with N reflective units. m Indicates the mth base station, AIRS j represents the jth AIRS, Indicates AIRS j The nth reflection unit of . The channel is modeled by large-scale path loss and small-scale fading. Therefore, BS m -UE channel power gain can be expressed as: Where g represents the average channel power gain, represents the average channel power gain coefficient, α represents the path loss exponent, and f c is the carrier frequency, c is the speed of light, ξ represents small-scale fading and is a gamma random variable, whose probability density function is expressed as Therefore, BS m -UE cascade link can be expressed as Furthermore, according to the properties of gamma random variables, the first-order moment and second-order moment of the direct link amplitude can be expressed as E{h 2 }=g Therefore, it is easy to get The expectation and variance of Therefore, the average signal power after the associated aerial smart reflector beamforming gain can be obtained as: The average signal power of the random scattering gain of the intelligent reflector in the air is: Considering the building shielding effect, for the convenience of analysis, the aerial intelligent reflector is divided into two categories according to the visibility of the base station-user link, so the signal power can be expressed as: Among them, S i,m,j and S i,j Indicates BS m -AIRS j and AIRS j -UE link penetration loss. To simplify the deduction, the building is set as a signal that cannot penetrate. Therefore, the value of the penetration loss can only be 0 or 1, and it obeys the Bernoulli distribution. The interference power can be expressed as: According to the central limit theorem, ξ m,0 and m,1 denote gamma and cyclically symmetric complex Gaussian distributions, respectively; Therefore, the signal-to-interference-to-noise ratio at the user can be expressed as Where W represents the normalized additive white Gaussian noise.

6. The method according to claim 1, characterized in that The optimal deployment parameter setting under the constraints of urban buildings is determined by giving an expression of coverage under the model, which specifically includes: According to the definition, coverage can be expressed as The coverage can be expressed as in, k s and θ s are shape and scale parameters respectively. Combining the probability density function of the distance, the final expression of coverage can be obtained as: in, P BS,NLOS =1-P BS,LOS .

7. The method according to claim 1, characterized in that The deployment parameters of the aerial intelligent reflective surface are re-optimized according to different requirements and deployment environments, specifically including: The optimal deployment height of the aerial intelligent reflective surface is determined based on the relationship between network coverage and the height of the aerial intelligent reflective surface deployment plane under different conditions. When the number of aerial intelligent reflective surfaces or the number of reflective units or the deployment scenario changes, the optimal deployment height of the aerial intelligent reflective surface needs to be re-determined.

8. The method according to claim 1, characterized in that The aerial intelligent reflective surface coverage expansion device under building constraints specifically includes: a determination module, a network side device, an intelligent reflective surface device and a terminal device.

9. The method according to claim 8, characterized in that The determination module is used to determine the number of base stations and channel environment parameters in the deployed area; Network side equipment: includes a transceiver, a memory, a processor and a computer program stored in the memory and executable on the processor; when the processor executes the program, the step of confirming the optimal deployment parameters described in any one of claims 1 to 7 is implemented.

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