A backhaul constraint-based intelligent reflecting surface assisted network parameter configuration method and device

Abstract

The application provides a method and equipment for configuring parameters of an intelligent reflecting surface assisted network under backhaul constraints. In the method, fixed parameters of an intelligent reflecting surface network considering backhaul constraints are statistically considered, including base station deployment density λ b , connection node density λ c , IRS service distance D1 and D2, signal path loss index α, carrier frequency f c , transmission rate threshold τ, and serving as input parameters of a network coverage probability estimation algorithm. The interference signal from other connection nodes scattered by the IRS is considered, the capacity distribution of the backhaul link under the influence of the IRS deployment is recalculated, the influence of the backhaul link capacity factor on the user received signal strength is considered, the theoretical expression of the typical user SINR and the conditional coverage probability of the IRS network under the backhaul constraints is calculated, the typical user coverage probability in the IRS network under the backhaul constraints is calculated, and according to the change relationship of the coverage probability index with the network IRS deployment density and the number of IRS units, the intelligent reflecting surface network configuration parameters under the condition of maximum coverage probability are obtained.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a method and device for configuring parameters of an intelligent reflector-assisted network under backhaul constraints. Background Technology

[0002] In recent years, the surge in wireless connectivity and mobile data traffic has presented challenges to system design, demanding high backhaul capacity, reliability, and connectivity. While fiber optic cables offer high capacity, their deployment in certain locations is costly and impractical. Therefore, high-density small cells with wireless backhaul capabilities have emerged as a solution. However, compared to fiber optics, wireless backhaul faces challenges including relatively lower capacity, susceptibility to interference, and severe attenuation of high-frequency signals. Against this backdrop, the emergence of Intelligent Reflecting Surfaces (IRS) offers new possibilities for meeting these needs. IRS manipulates the propagation path of electromagnetic waves to control wireless signals, thereby improving the performance and efficiency of wireless networks. This technology can effectively increase achievable data rates, especially in areas where traditional technologies struggle to provide coverage. Therefore, IRS can effectively address the challenges of backhaul capacity, reliability, and connectivity, providing a promising solution where traditional wireless backhaul technologies fall short.

[0003] Research on IRS-assisted networks typically assumes ideal backhaul capacity, neglecting common backhaul-constrained scenarios in real-world networks, where users usually connect to the nearest base station. The passive reflection characteristics of IRS affect the base station's wireless backhaul link, thus necessitating research into backhaul-aware IRS-assisted networks to study the impact of IRS cascaded channels on access and backhaul link capacity, and to match these capacities. However, considering the costs associated with establishing backhaul connections and irregularly deployed sites, users must consider the backhaul capacity of sites when making selections. Currently, however, there is a lack of optimal parameter configuration methods for intelligent reflector-assisted networks under backhaul constraints to achieve optimal coverage performance. Summary of the Invention

[0004] This invention proposes a method and device for configuring parameters of an intelligent reflector-assisted network under backhaul constraints. The method first involves statistically analyzing fixed parameters of the intelligent reflector-assisted network under backhaul constraints, including the base station deployment density λ. b Node density λ c IRS service distances D1 and D2, signal road loss index α, carrier frequency f cFirst, the transmission rate threshold τ is determined. Then, considering the random scattering of interference signals from other connected nodes by the IRS, the capacity distribution of the backhaul link under the influence of IRS deployment is recalculated. Considering the influence of the backhaul link capacity factor on the strength of the received signal by the user, the theoretical expression of the typical user signal to interference plus noise ratio under the backhaul constraint of the IRS network is calculated. The coverage probability of typical users in the IRS network under the backhaul constraint is calculated. Based on the relationship between the index and the network IRS deployment density and the number of IRS units, the configuration parameters of the intelligent reflector network under the condition of maximizing the coverage probability are obtained.

[0005] The present invention provides a method for configuring parameters of an intelligent reflector-assisted network under return-flow constraints, comprising the following steps:

[0006] Step 200: Calculate the fixed parameters of the intelligent reflector-assisted network under backhaul constraints, including the base station deployment density λ. b Node density λ c IRS service distances D1 and D2, signal road loss index α, carrier frequency f c Transmission rate threshold τ.

[0007] Consider a smart reflector-assisted cellular network with backhaul awareness, as shown in the attached diagram. Figure 1 As shown, the deployed Connector Nodes (CNs) provide backhaul links to the Base Station (BS), and their capacity is far greater than the user access link capacity. The set of CNs is Φ. c BS set Φ b IRS set Φ I They respectively obey density λ c , λ b , λ I The two-dimensional Homogenous Poisson Point Process (HPPP) is described. A typical user, UE0, is located at the origin, and both the base station and the user have single antennas. The IRS provides beamforming and random scattering services over finite distances, D1 and D2 respectively. The channel path loss exponent in the network is α, and the carrier frequency is f. c The above parameters serve as input parameters for the coverage probability estimation algorithm.

[0008] Based on the above system parameters, the signal power received by a typical user from BSi is: Where P0 is the base station transmit power, and the biased average received power (BRSRP_IRS) at a typical user location considering IRS reflectbeamforming of the channel is... Bias Bi Indicates base station BS i The return capacity value, h d,i For BS i -UE0 direct channel For BS i Path loss related to distance between UE0, G i For small-scale fading, assume Rayleigh fading, i.e., G i ~exp(1). β=(4πf c / c) 2 Let represent the average signal power gain at a reference distance of 1m in the free-space path loss model, where c represents the speed of light. Amplitude |h d,i | Follows the scale parameter as The Rayleigh distribution, and h d,i It follows a pattern with a mean of zero and a covariance of g. d,i The circularly symmetric complex Gaussian (CSCG) distribution, in which BS i -IRS j -UE0 cascade channel Follows a CSCG distribution, with amplitudes BS0-IRS0-UE0. It follows a normal Gaussian distribution, that is...

[0009]

[0010] in With q i,j BS respectively i -IRS j Average channel gain and distance of the channel With r j IRS respectively j - Average channel gain and distance of UE0 channel.

[0011] Step 210: Considering the interference signals randomly scattered by the IRS from other connected nodes, recalculate the capacity distribution of the backhaul link under the influence of IRS deployment.

[0012] CN all have the same power P c Send a signal, s m CN m With BS o The distance between them, CN m -IRS j -BS o Cascaded Channels and CN nst -IRS nst -BS o The amplitude distribution is as follows:

[0013]

[0014] Where X nst X represents the case where the data is closest to a typical business unit (BS) and a typical user. With c m,j CN m -IRS j Average channel gain and distance of the channel With q j IRS respectively j -BS o The average channel gain and distance of the channel. The signal-to-interference-plus-noise ratio (SINR) at a typical base station on the backhaul link can be expressed as:

[0015]

[0016] Among them W bkl =σ 2 / P c , for P c Normalized receiver noise, assuming its power is σ 2 Additive white Gaussian noise (AWGN) q j Indicates typical base station to IRS j Distance, considering the signal enhancement characteristics of IRS in local areas, therefore only the influence of IRS within the D2 range is considered, thus CN can be... m -IRS j The distance is approximately CN m -BS o The distance, i.e., c m,j ≈s m Useful signal S for backhaul link bkl The expression is:

[0017]

[0018] Among them G nst This is Rayleigh fading, i.e., G nst The expression for the interference signal power of the backhaul link is ~exp(1):

[0019]

[0020] in Therefore, the conditional probability distribution of SINR for the backhaul link is:

[0021]

[0022] Where θ is SINR bkl Threshold, and It can be represented as

[0023]

[0024] in 2F1 represents the Gaussian hypergeometry function. Further, it represents the distribution of the backhaul link SINR as follows:

[0025]

[0026] in The backhaul link capacity distribution is represented as follows:

[0027]

[0028] in Derivation of the maximum offset received signal strength (BRSRP) for typical users and IRS reconstructed channels IRS The probability density function of the distance d0 between the base station (i.e., the serving base station) and the value is:

[0029]

[0030] Where ζ can be represented as:

[0031]

[0032] The intermediate variables are as follows:

[0033]

[0034] in

[0035] Step 220: Considering the impact of the backhaul link capacity factor on the received signal strength of users, calculate the theoretical expressions for the SINR and conditional coverage probability of typical users in the IRS network under backhaul constraints.

[0036] The SINR at a typical user location on an access link can be expressed as:

[0037]

[0038] Among them W ac =σ 2 / P0 is the receiver noise normalized to P0, and its power is assumed to be σ. 2 AWGN, S ac The total signal power from BS0 to UE0 (normalized by P0), Iac Let P0 be the total interference power received by UE0 (also normalized to P0). Given the distance d0 from the serving base station BS0 to the typical user UE0 and the distance r0 between the nearest smart reflector IRS0 to UE0 and the typical user, using S... ac The first and second moments are matched with Gamma moments to obtain S. ac Approximately Gamma distribution Γ(k) S ,θ S Shape parameter k S and scale parameter θ S It can be represented as:

[0039]

[0040] in and It can be represented as:

[0041]

[0042] in And |h1| 2 The first and second moments can be expressed as:

[0043]

[0044] |h2| 2 The first and second moments can be expressed as:

[0045]

[0046] When k S When the integer value is used, the SINR conditional probability distribution for a typical user on the access link can be transformed as follows:

[0047]

[0048] Where T is the SINR threshold, Y = T(I ac +W ac ) / θ S For integer k S ,satisfy It can be represented as The following is about the total interference power I. ac Solve using the Laplace transform.

[0049] Given the distance d0 from the serving base station BS0 to the typical user UE0 and the distance r0 between the nearest smart reflector IRS0 to the typical user, the interference power I ac The Laplace transform can be expressed as:

[0050]

[0051] intermediate variables It can be represented as:

[0052]

[0053] in

[0054] Step 230: Calculate the coverage probability of a typical user in the IRS network under backhaul constraints. Based on the relationship between the index and the network IRS deployment density and the number of IRS units, obtain the intelligent reflector network configuration parameters under the condition of maximizing the coverage probability.

[0055] For non-integer k S SINR is obtained through linear interpolation. ac Conditional probability distribution:

[0056]

[0057] in and These represent rounding down and rounding up, respectively. The interpolation weighting coefficients can be represented as:

[0058]

[0059] Where M > 0 represents SINR ac The coefficients of the nonlinear conditional probability distribution.

[0060] During transmission, the user's actual transmission rate is affected by the combined capacity of the access link and the backhaul link, which we define as the smaller of the two.

[0061] Ω=min(R,B)min[log2(1+SINR ac ),log2(1+SINR bkl (27)

[0062] Define the minimum transmission rate threshold as τ, where its relationship with the SINR threshold can be expressed as T = 2. τ -1, if the rate is less than this threshold, it is considered a transmission interruption. If the base station currently serving the user is unable to provide the user with a transmission rate greater than the minimum threshold τ at a certain moment, it is considered an interruption. Therefore, the coverage probability can be expressed as:

[0063]

[0064] in

[0065]

[0066] SINR ac Substituting the conditional probability distribution into formula (28), and then integrating the distributions of d0 and r0, we can obtain the expression for the network coverage probability as follows:

[0067]

[0068] The probability density function of B0 is shown in formula (14), and the probability density distribution function of d0 is shown in formula (10). i (x)=βx -α .

[0069] This study investigates the relationship between the average network coverage probability index and the network IRS deployment density and IRS unit number parameters. Taking coverage probability as a key indicator of network performance, the study explores the intelligent reflector network configuration parameter scheme under the condition of maximizing coverage probability.

[0070] The intelligent reflector-assisted network parameter configuration device under return constraint includes:

[0071] The first determining module is used to determine the backhaul capacity distribution of typical base stations based on the fixed parameters of the intelligent reflector-assisted network under backhaul constraints.

[0072] The second determining module is used to determine the distribution of useful signal strength and interference signal strength of typical users based on the fixed parameters of the intelligent reflector-assisted network under backhaul constraints.

[0073] The third determining module is used to determine the user coverage probability in the network and the deployment density and number of smart reflectors under the condition of maximizing the coverage probability, based on the distribution of backhaul capacity of typical base stations, the distribution of useful signal strength and interference signal strength received by typical users.

[0074] Core 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, it implements the steps of the intelligent reflector-assisted network parameter configuration method under backhaul constraints as described above.

[0075] 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, it implements the steps of the intelligent reflector-assisted network parameter configuration method under backhaul constraints as described above.

[0076] Intelligent reflective surface device: includes a unit array, a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the program, it implements the steps of the intelligent reflective surface auxiliary network parameter configuration method under hysteresis as described above;

[0077] Terminal device: 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, it implements the steps of the intelligent reflector-assisted network parameter configuration method under return constraint as described above.

[0078] Beneficial effects

[0079] This invention fixes parameters in a smart reflector-assisted network under statistical backhaul constraints, including base station deployment density λ. b Node density λ c IRS service distances D1 and D2, signal road loss index α, carrier frequency f c The transmission rate threshold τ is used as the input parameter for the network coverage probability estimation algorithm. Considering the interference signals randomly scattered by the IRS from other connected nodes, the capacity distribution of the backhaul link under the influence of IRS deployment is recalculated. Considering the influence of the backhaul link capacity factor on the received signal strength of users, the theoretical expressions of the typical user SINR and conditional coverage probability under the backhaul constraint of the IRS network are calculated. The coverage probability of typical users in the IRS network under the backhaul constraint is calculated. Based on the relationship between the index and the network IRS deployment density and the number of IRS units, the configuration parameters of the smart reflector network under the condition of maximizing the coverage probability are obtained. Attached Figure Description

[0080] To clearly explain the technical steps of this invention, all the accompanying drawings used in this description will be briefly described below. It should be noted that the drawings described below are merely examples of embodiments of this invention, and those skilled in the art can still obtain other drawings in different scenarios based on these drawings.

[0081] Figure 1 This is a model diagram of an intelligent reflective surface-assisted network system under return constraint according to the present invention;

[0082] Figure 2 This is a flowchart illustrating the algorithm implementation of the present invention;

[0083] Figure 3 This is a schematic diagram illustrating the relationship between network coverage probability and IRS density when the SINR threshold is -3dB according to the present invention.

[0084] Figure 4 This is a schematic diagram illustrating the relationship between the network coverage probability and the number of IRS units according to the present invention;

[0085] Figure 5 This is a structural diagram of a communication system for configuring intelligent reflector-assisted network parameters under return-flow constraints, according to the present invention.

[0086] Figure 6 This is a schematic diagram of the first determining module of a smart reflective surface auxiliary network parameter configuration device under return constraint according to the present invention;

[0087] Figure 7 This is a schematic diagram of the second determining module of a smart reflective surface auxiliary network parameter configuration device under return constraint according to the present invention;

[0088] Figure 8 This is a schematic diagram of the third determining module of a smart reflective surface auxiliary network parameter configuration device under return constraint according to the present invention;

[0089] Figure 9 This is a schematic diagram of the core network side device of an intelligent reflector-assisted network parameter configuration device under return constraint according to the present invention;

[0090] Figure 10 This is a schematic diagram of the network side device of an intelligent reflective surface-assisted network parameter configuration device under return constraint according to the present invention;

[0091] Figure 11 This is a schematic diagram of an intelligent reflector device for configuring intelligent reflector auxiliary network parameters under return constraints, according to the present invention.

[0092] Figure 12 This is a schematic diagram of a terminal device for a smart reflector-assisted network parameter configuration device under return constraint according to the present invention. Detailed Implementation

[0093] The steps and processes of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the examples described in this application are merely one application scenario of the present invention, and other results based on the content of the present invention without making substantial changes are within the protection scope of the present invention.

[0094] Appendix Figure 1 For a smart reflector-assisted cellular network system model under backhaul constraints, consider a smart reflector-assisted cellular network with backhaul awareness. The deployed CNs provide backhaul links to the terrestrial BS, and their capacity is much greater than the user access link capacity. The set of CNs is Φ. c BS set Φ b IRS set Φ I They respectively obey density λ c , λ b , λ IHPPP. Typical user UE0 is located at the origin, and both the base station and the user have single antennas; considering that the IRS provides beamforming and random scattering services over finite distances, D1 and D2 respectively; the channel path loss exponent in the network is α, and the carrier frequency f... c Consider the base station where the user will connect to the base station with the maximum signal strength, taking into account the impact of backhaul links and IRS cascade links.

[0095] The algorithm flow for this case is attached. Figure 2 As shown, the specific implementation steps are as follows:

[0096] Step 200: Calculate the fixed parameters of the intelligent reflector-assisted network under backhaul constraints, including the base station deployment density λ. b Node density λ c IRS service distances D1 and D2, signal road loss index α, carrier frequency f c The transmission rate threshold τ; the fixed parameter is the input parameter of the network average coverage probability estimation algorithm.

[0097] Step 210: Considering the interference signals randomly scattered by the IRS from other connected nodes, recalculate the capacity distribution of the backhaul link under the influence of IRS deployment.

[0098] The formula for calculating backhaul link capacity distribution is:

[0099]

[0100] Where B is the backhaul link capacity.

[0101]

[0102] P c Let λ be the CN transmit power, α be the path loss exponent, and λ be the path loss index. c For CN density, λ I The density of IRS is N, and the number of IRS units is N. f c q is the carrier frequency. nst and s nst d represents the distance from a typical base station to the nearest IRS and the nearest CN, respectively. j D represents the distance from a typical base station to the j-th IRS, D1 and D2 are the beamforming service distance and random scattering service distance provided by the IRS, respectively, 2F1 is the Gaussian hypergeometry function, and σ 2 For noise power,

[0103] Step 220: Considering the impact of the backhaul link capacity factor on the received signal strength of users, calculate the theoretical expressions for the SINR and conditional coverage probability of typical users in the IRS network under backhaul constraints.

[0104] The SINR at a typical user location on an access link can be expressed as:

[0105]

[0106] Among them W ac =σ 2 / P0 is the receiver noise normalized to P0, and its power is assumed to be σ. 2 AWGN, S ac I represents the total signal power from BS0 to UE0. ac This represents the total interference power received by UE0. Gamma moment matching will... ac Approximately Gamma distribution Γ(k) S ,θ S Shape parameter k S and scale parameter θ S It can be represented as:

[0107]

[0108] Where d0 is the distance between a typical user and the serving base station with the maximum offset received signal strength under IRS reconstructed channel, and r0 is the distance from the typical user to the serving IRS.

[0109] And |h1| 2 The first moment is The second moment is expressed as The first moment is The second moment is

[0110] When k S When the integer value is used, the SINR conditional probability distribution for a typical user on the access link can be transformed as follows:

[0111]

[0112] Where Y = T(I) ac +W ac ) / θ S T is the SINR threshold, for integer k S ,satisfy Γ() is an incomplete Gamma function. It can be represented as Total interference power I acLaplace transform

[0113]

[0114] intermediate variables for: in

[0115] Step 230: Calculate the coverage probability of a typical user in the IRS network under backhaul constraints. Based on the relationship between the index and the network IRS deployment density and the number of IRS units, obtain the intelligent reflector network configuration parameters under the condition of maximizing the coverage probability.

[0116] The expression for network coverage probability is:

[0117]

[0118] in and These represent rounding down and rounding up, respectively. Indicates the interpolation weighting coefficients: The probability density function of the return capacity B0 is:

[0119] This study investigates the relationship between the average network coverage probability index and the network IRS deployment density and IRS unit number parameters. Taking coverage probability as a key indicator of network performance, the study explores the intelligent reflector network configuration parameter scheme under the condition of maximizing coverage probability.

[0120] Simulation results are attached. Figure 3 and 4 As shown.

[0121] Appendix Figure 3 This is a schematic diagram illustrating the relationship between network coverage probability and IRS density when the SINR threshold is -3dB. (BRSRP) IRS The site selection criterion is based on the maximum offset received signal strength under IRS channel reconstruction, where N is the number of IRS units. As shown in the figure, the network coverage probability initially increases and then decreases with IRS deployment density, indicating the existence of an optimal IRS deployment density that maximizes the network coverage probability. (BRSRP) IRS Under the site selection criteria, increasing the number of IRS units from 100 to 1000 increases the maximum network coverage probability from 0.55 to 0.78, and BRSRP... IRS The network coverage probability under the site selection criterion is higher than that under the distance-based site selection criterion.

[0122] Appendix Figure 4This diagram illustrates the relationship between network coverage probability and the number of IRS cells, where Nλ represents the number of IRS cells per unit area. I =1; As shown in the figure, under different IRS / BS density ratios, the coverage rate first increases and then decreases with the number of IRS reflective units N. For a fixed base station density and IRS / BS density ratio, there exists an optimal number of IRS units that maximizes the coverage probability. When the BS density is a fixed parameter of 10... -5 / km 2 When the IRS / BS density ratio is set to 50, the optimal number of IRS units is 100.

[0123] The aforementioned intelligent reflector-assisted network parameter configuration device under return-travel constraints, such as Figure 5 As shown, it includes: the core network side device and the network side device are simultaneously connected to the first determining module; the network side device and the first determining module are connected to the second determining module; the terminal side device is simultaneously connected to the second determining module and the third determining module; the second determining module is connected to the third determining module; and the third determining module sends the deployment density and unit number configuration information to the intelligent reflective surface device.

[0124] First Determined Module: Such as Figure 6 As shown, this is used to determine the backhaul capacity distribution of a typical base station based on the fixed parameters of the intelligent reflector-assisted network under backhaul constraints.

[0125] Second determination module: such as Figure 7 As shown, this method is used to determine the distribution of useful signal strength and interference signal strength of typical users based on fixed parameters of the intelligent reflector-assisted network under backhaul constraints.

[0126] The third determining module: such as Figure 8 As shown, this is used to determine the user coverage probability in the network and the deployment density and number of smart reflectors under the condition of maximizing the coverage probability, based on the distribution of backhaul capacity of typical base stations, the distribution of useful signal strength received by typical users, and the distribution of interference signal strength.

[0127] Core network side equipment: such as Figure 9 As shown, it 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, it implements the steps of the intelligent reflector-assisted network parameter configuration method under return constraint as described above;

[0128] Network-side devices: such as Figure 10 As shown, it 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, it implements the steps of the intelligent reflector-assisted network parameter configuration method under return constraint as described above;

[0129] Intelligent reflective surface devices: such as Figure 11 As shown, it includes a unit array, a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the program, it implements the steps of the intelligent reflector-assisted network parameter configuration method under return constraint as described above;

[0130] Terminal equipment: such as Figure 12 As shown, it 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, it implements the steps of the intelligent reflective surface-assisted network parameter configuration method under return constraint as described above.

Claims

1. A method for intelligent reflecting surface assisted network parameter configuration under backhaul constraint, characterized in that, include: By statistically considering the fixed parameters of the intelligent reflector network with backhaul constraints, the base station deployment density is determined. Node density IRS service distance and Road damage index carrier frequency Transmission rate threshold , and serve as input parameters for the network coverage probability estimation algorithm; Considering that the IRS randomly scatters interference signals from other connected nodes, the capacity distribution of the backhaul link under the influence of the IRS deployment is recalculated; the backhaul link capacity distribution The calculation formula is: wherein for backhaul link capacity, , , , , For CN transmission power, This is the road damage index. CN density, The IRS density is given by the number of IRS units. , , For carrier frequency, and These represent the distances from a typical base station to the nearest IRS and to the nearest CN, respectively. Indicates typical base station to the th The distance between IRS, and They provide beamforming service range and random scattering service range for the IRS, respectively. It is a Gaussian hypergeometry function. For noise power, ; Considering the impact of the backhaul link capacity factor on the received signal strength of users, the theoretical expressions for the SINR and conditional coverage probability of typical users in an IRS network under backhaul constraints are calculated; the SINR calculation formula at the typical user location on the access link is: in Base station transmit power Normalized receiver noise, assuming its power is AWGN, for arrive Total signal power for The total received interference power, Gamma moment matching will Approximately Gamma distribution Shape parameters and scale parameters It can be represented as: in This represents the distance between a typical user and the serving base station with the maximum offset received signal strength under IRS reconstructed channel conditions. The distance from a typical user to the service IRS. , , , ,and The first moment is The second moment is expressed as , The first moment is The second moment is , , and They are respectively - The average channel gain of the channel and the distance, when When the integer value is used, the SINR conditional probability distribution for a typical user on the access link can be transformed as follows: in , For the SINR threshold, on integers ,satisfy , For an incomplete Gamma function, It can be represented as Total interference power Laplace transform intermediate variable is: wherein ; Calculate the typical user coverage probability expression in the IRS network under backhaul constraints; the average network coverage probability calculation formula is: in , and These represent rounding down and rounding up, respectively. Indicates the interpolation weighting coefficients: , , Return capacity The probability density function is: , , , For backhaul link capacity distribution, , , , , ; Based on the relationship between the coverage probability index and the network IRS deployment density and the number of IRS units, the configuration parameters of the intelligent reflector network under the condition of maximizing the coverage probability are obtained.

2. The method of claim 1, wherein, This study investigates the relationship between the average network coverage probability index and the network IRS deployment density and IRS unit number parameters. Taking coverage probability as a key indicator of network performance, the study explores the intelligent reflector network configuration parameter scheme under the condition of maximizing coverage probability.

3. A system for implementing the method for configuring parameters of an intelligent reflector-assisted network under return constraint as described in claim 1, comprising: Core network side equipment, network side equipment, intelligent reflector equipment, and terminal equipment.

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