Intelligent reflector auxiliary network parameter configuration method and device under backhaul constraint

Abstract

The invention provides an intelligent reflecting surface auxiliary network parameter configuration method and equipment under backhaul constraint, in the method, intelligent reflecting surface network fixed parameters considering the backhaul constraint are counted, and comprise base station deployment density lambda b, connection node density lambda c, IRS service distances D1 and D2, a channel loss index alpha, a carrier frequency fc and a transmission rate threshold value tau, and the base station deployment density lambda b, the connection node density lambda c, the IRS service distances D1 and D2, the channel loss index alpha, the carrier frequency fc and the transmission rate threshold value tau are matched with the base station deployment density lambda b and the connection node density lambda c. Taking the parameters as input parameters of a network coverage probability estimation algorithm; considering that the IRS randomly scatters interference signals from other connection nodes, and recalculating the capacity distribution of the backhaul link under the influence of IRS deployment; considering the influence of a backhaul link capacity factor on the user received signal strength, and calculating a theoretical expression of a typical user SINR and a conditional coverage probability under the IRS network under the backhaul constraint; and calculating the typical user coverage probability in the IRS network under the backhaul constraint, and obtaining the intelligent reflector network configuration parameters under the condition of maximum coverage probability according to the change relation of the coverage probability index along with the network IRS deployment density and the IRS unit number.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a method and device for configuring network parameters of an intelligent reflective surface under backhaul constraints. Background Art

[0002] In recent years, the surge in wireless connections and mobile data traffic has challenged system design, requiring high backhaul capacity, reliability, and connectivity. Although optical fiber has high capacity, it is costly and impractical to deploy in specific locations. Therefore, high-density small base stations with wireless backhaul capabilities have become a solution. However, compared with optical fiber, wireless backhaul faces challenges including relatively low capacity, susceptibility to interference, and severe attenuation of high-frequency signals. In this context, the emergence of intelligent reflecting surfaces (IRS) provides new possibilities to meet 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 the achievable data rate, especially in areas where traditional technologies have difficulty providing coverage. Therefore, IRS can effectively address the challenges of backhaul capacity, reliability, and connectivity, providing a promising solution where traditional wireless backhaul technology is insufficient.

[0003] Research on IRS-assisted networks usually assumes an ideal backhaul capacity, ignoring the common backhaul-constrained scenarios in actual networks, and users are usually connected to the nearest base station. The passive reflection characteristics of IRS affect the base station wireless backhaul link, so a backhaul-aware IRS-assisted network is needed to study the impact of IRS cascade channels on access and backhaul link capacities and match these capacities. However, given the costs associated with establishing backhaul connections and irregularly deployed sites, users must consider the backhaul capacity of the site when making a choice. However, there is currently a lack of intelligent reflector-assisted network parameter configuration methods under backhaul constraints with optimal coverage performance. Summary of the invention

[0004] The present invention proposes a method and device for configuring parameters of an intelligent reflector-assisted network under backhaul constraints. In the method, fixed parameters of the intelligent reflector-assisted network under backhaul constraints are first counted, 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; then consider the interference signals randomly scattered by IRS from other connected nodes, and recalculate the capacity distribution of the backhaul link under the influence of IRS deployment; consider the impact of the backhaul link capacity factor on the user's received signal strength, and calculate the theoretical expression of the signal to interference plus noise ratio of a typical user in the IRS network under the backhaul constraint; calculate the coverage probability of a typical user in the IRS network under the backhaul constraint, and according to the relationship between the indicator and the network IRS deployment density and the number of IRS units, obtain the network configuration parameters of the intelligent reflector when the coverage probability is maximized.

[0005] A method for configuring network parameters of an intelligent reflector-assisted network under backhaul constraints of the present invention comprises the following steps:

[0006] Step 200: Count the fixed parameters of the intelligent reflector-assisted network under the backhaul constraint, including the 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.

[0007] Consider a smart reflector-assisted cellular network with backhaul awareness, as shown in the following figure. Figure 1 As shown in Figure 1, the deployed Connector Nodes (CNs) provide backhaul links to the ground base stations (BS), and their capacity is much larger than the user access link capacity. c , BS set Φ b 、IRS set Φ I They are subject to density λ c , b , I The two-dimensional Homogenous Poisson Point Process (HPPP) is a two-dimensional Poisson Point Process. The typical user UE0 is located at the origin, and both the base station and the user have a single antenna. Considering that the IRS provides beamforming and random scattering services at limited distances, D1 and D2 respectively; the channel path loss index in the network is α, and the carrier frequency f c ; The above parameters are used as input parameters of the coverage probability estimation algorithm.

[0008] Based on the above system parameters, the signal power received by a typical user from BS i is Where P0 is the base station transmit power, and the biased average received power at a typical user considering the IRS reconstructed channel (Biased Reference Signal Received Power with IRS reflect beamforming, BRSRP_IRS) is Bias Bi Base Station BS i The return capacity value, h d,i For BS i -UE0 direct channel, For BS i Path loss related to the distance between UE0, G i is small-scale fading, assuming Rayleigh fading, that is, G i ~exp(1). β=(4πf c / c) 2 represents 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 |Subject to the scale parameter Rayleigh distribution, and h d,i Subject to mean zero and covariance g d,i The circularly symmetric complex Gaussian (CSCG) distribution of BS i -IRS j -UE0 cascade channel Obey CSCG distribution, BS0-IRS0-UE0 amplitude It obeys the normal Gaussian distribution, that is,

[0009]

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

[0012] CN all have the same power P c Send signal, s m For CN m With BS o The distance between m -IRS j -BS o Cascade Channel and CN nst -IRS nst -BS o The distribution of amplitude is:

[0013]

[0014] Where X nst represents X when the distance to a typical BS and a typical user is the shortest. With c m,j CN m -IRS j Average channel gain vs. distance of the channel, With q j IRS 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 of the backhaul link can be expressed as:

[0015]

[0016] Where W bkl =σ 2 / P c , is P c Normalized receiver noise, assuming its power is σ 2 Additive White Gaussian Noise (AWGN), q j Indicates the typical base station to IRS j distance, considering the characteristic of IRS signal enhancement in local areas, only the influence of IRS within D2 is considered, so that CN m -IRS j The distance is approximately CN m -BS o The distance, that is: c m,j ≈s m . Backhaul link useful signal S bkl The expression is:

[0017]

[0018] Among them G nst is Rayleigh fading, that is, G nst ~exp(1), the interference signal power expression of the backhaul link is:

[0019]

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

[0021]

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

[0023]

[0024] in 2F1 represents the Gaussian hypergeometric function. Further, the distribution of the backhaul link SINR is

[0025]

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

[0027]

[0028] in Derivation of the maximum biased received signal strength (BRSRP) for a typical user and IRS reconstructed channel IRS The probability density function of the distance d0 of the base station (i.e., the serving base station) with a value is:

[0029]

[0030] where ζ can be expressed 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 user received signal strength, calculate the theoretical expression of the typical user SINR and conditional coverage probability in the IRS network under the backhaul constraint.

[0036] The SINR at a typical user of the access link can be expressed as:

[0037]

[0038] Where W ac =σ 2 / P0 is the receiver noise normalized by P0, assuming its power is σ 2 AWGN, S acis the total signal power from BS0 to UE0 (normalized by P0), I ac is the total interference power received by UE0 (also normalized by P0). Given the distance d0 from the serving base station BS0 to the typical user UE0 and the distance r0 from the smart reflector IRS0 closest to UE0 to the typical user, using S ac The first and second order moments of S are matched by Gamma moment matching. ac Approximately Gamma distribution Γ(k S ,θ S ), shape parameter k S and the scale parameter θ S It can be expressed as:

[0039]

[0040] in and It can be expressed as:

[0041]

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

[0043]

[0044] |h2| 2 The first-order moment and the second-order moment of can be expressed as:

[0045]

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

[0047]

[0048] Where T is the SINR threshold, Y = T (I ac +W ac ) / θ S , for integer k S ,satisfy It can be expressed as The total interference power I ac The Laplace transform solution.

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

[0050]

[0051] Intermediate variables It can be expressed as:

[0052]

[0053] in

[0054] Step 230, calculate the coverage probability of typical users in the IRS network under the backhaul constraint, and obtain the intelligent reflector network configuration parameters under the condition of maximizing the coverage probability according to the relationship between the indicator and the network IRS deployment density and the number of IRS units.

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

[0056]

[0057] in and Respectively represent floor and ceiling, represents the interpolation weight coefficient, which can be expressed as:

[0058]

[0059] Where M>0 represents SINR ac Coefficients for the nonlinearity of the conditional probability distribution.

[0060] During the transmission process, the actual transmission rate of the user is affected by the capacity of the access link and the backhaul link, which we define as the smaller value of the two, that is,

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

[0062] The minimum transmission rate threshold is defined as τ, where the relationship with the SINR threshold can be expressed as T = 2 τ -1, if it is less than this rate threshold, it is considered as a transmission interruption. If the base station currently serving the user cannot provide the user with a transmission rate greater than the minimum threshold τ at a certain moment, it is considered as 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 integrating the distribution of d0 and r0, we can obtain the network coverage probability expression:

[0067]

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

[0069] The relationship between the average network coverage probability index and the network IRS deployment density and the number of IRS units is studied. Taking the coverage probability as the key indicator of network performance, the intelligent reflector network configuration parameter scheme is explored to maximize the coverage probability.

[0070] The intelligent reflective surface-assisted network parameter configuration device under the backhaul constraint includes:

[0071] The first determination module is used to determine the typical base station backhaul capacity distribution according to the fixed parameters of the smart reflector-assisted network under the backhaul constraints;

[0072] The second determination module is used to determine the strength distribution of the useful signal received by the typical user and the strength distribution of the interference signal received according to the fixed parameters of the intelligent reflector-assisted network under the backhaul constraint;

[0073] The third determination module is used to determine the user coverage probability in the network and the deployment density and number of smart reflective surfaces under the condition of maximizing the coverage probability according to the distribution of typical base station backhaul capacity, the distribution of the useful signal strength and the interference signal strength received by typical users;

[0074] Core network side equipment: including 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 steps of the method for configuring network parameters of the intelligent reflector-assisted network under the backhaul constraint as described above are implemented;

[0075] The network side 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, the steps of the method for configuring network parameters of the intelligent reflector-assisted network under the backhaul constraint as described above are implemented;

[0076] Intelligent reflective surface device: comprising 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, the steps of the method for configuring network parameters of an intelligent reflective surface auxiliary under backhaul constraints as described above are implemented;

[0077] Terminal equipment: including 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 steps of the method for configuring network parameters of an intelligent reflector-assisted network under a backhaul constraint as described above are implemented.

[0078] Beneficial Effects

[0079] The present invention uses an intelligent reflector under statistical backhaul constraints to assist in fixing network parameters, 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 use them as input parameters of the network coverage probability estimation algorithm; consider the interference signals randomly scattered by IRS from other connected nodes, and recalculate the capacity distribution of the backhaul link under the influence of IRS deployment; consider the impact of the backhaul link capacity factor on the user's received signal strength, and calculate the theoretical expressions of SINR and conditional coverage probability of typical users in the IRS network under backhaul constraints; calculate the coverage probability of typical users in the IRS network under backhaul constraints, and according to the relationship between the indicators and the network IRS deployment density and the number of IRS units, obtain the network configuration parameters of the intelligent reflector when the coverage probability is maximized. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] In order to clearly explain the technical steps of the present invention, all the drawings used in the description of the present invention are briefly described below. It should be noted that the drawings described below are only some examples of the implementation of the present invention, and other ordinary technicians in this field can still obtain other drawings in other different scenarios based on these drawings.

[0081] Figure 1 It is a model diagram of an intelligent reflector-assisted network system under backhaul constraints of the present invention;

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

[0083] Figure 3 It is a schematic diagram of the relationship between the network coverage probability and the IRS density when the SINR threshold is -3dB in the present invention;

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

[0085] Figure 5 It is a communication system structure diagram of intelligent reflective surface assisted network parameter configuration under backhaul constraints of the present invention;

[0086] Figure 6 A schematic diagram of a first determination module of a device for configuring network parameters assisted by an intelligent reflective surface under backhaul constraints according to the present invention;

[0087] Figure 7 A schematic diagram of a second determination module of a device for configuring network parameters assisted by an intelligent reflective surface under backhaul constraints according to the present invention;

[0088] Figure 8 A schematic diagram of a third determination module of a device for configuring network parameters assisted by an intelligent reflective surface under backhaul constraints according to the present invention;

[0089] Fig. 9 A schematic diagram of a core network side device of an intelligent reflective surface-assisted network parameter configuration device under backhaul constraints of the present invention;

[0090] Fig.10 A schematic diagram of a network-side device of a smart reflective surface-assisted network parameter configuration device under backhaul constraints according to the present invention;

[0091] Fig.11 A schematic diagram of an intelligent reflecting surface device of an intelligent reflecting surface-assisted network parameter configuration device under backhaul constraints of the present invention;

[0092] Fig.12 A schematic diagram of a terminal device of a smart reflective surface-assisted network parameter configuration device under backhaul constraints according to the present invention; DETAILED DESCRIPTION

[0093] The steps and processes of the present invention are described completely and clearly below in conjunction with the drawings in the present application. It is obvious that the example described in the present application is only an example application scenario of the present invention, and other results based on the content of the present invention without making substantial changes all fall within the protection scope of the present invention.

[0094] Attached Figure 1 As a smart reflector-assisted network system model under backhaul constraints, a backhaul-aware smart reflector-assisted cellular network is considered. The deployed CN provides a backhaul link to the ground BS, and its capacity is much larger than the user access link capacity. c , BS set Φ b 、IRS set Φ I They are subject to density λ c , b , IThe typical user UE0 is located at the origin, and both the base station and the user have a single antenna. Considering that the IRS provides beamforming and random scattering services at limited distances, D1 and D2 respectively; the channel path loss index in the network is α, and the carrier frequency f c ; Consider that the user will connect to the base station with the maximum signal strength considering the impact of the backhaul link and the IRS cascade link.

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

[0096] Step 200: Count the fixed parameters of the intelligent reflector-assisted network under the backhaul constraint, including the 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; the fixed parameters are input parameters 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 the IRS deployment.

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

[0099]

[0100] Where B is the backhaul link capacity,

[0101]

[0102] P c is the CN transmission power, α is the path loss index, λ c is the CN density, λ I is the IRS density, the number of IRS units is N, f c is the carrier frequency, q nst and nst are the distances from a typical base station to the nearest IRS and to the nearest CN, respectively, d j represents the distance from the typical base station to the jth IRS, D1 and D2 are the beamforming service distance and random scattering service distance provided by the IRS, 2F1 is the Gaussian hypergeometric function, σ 2 is the noise power,

[0103] Step 220, considering the impact of the backhaul link capacity factor on the user received signal strength, calculate the theoretical expression of the typical user SINR and conditional coverage probability in the IRS network under the backhaul constraint.

[0104] The SINR at a typical user of the access link can be expressed as:

[0105]

[0106] Where W ac =σ 2 / P0 is the receiver noise normalized by P0, assuming its power is σ 2 AWGN, S ac is the total signal power from BS0 to UE0, I ac is the total interference power received by UE0. Gamma moment matching converts S ac Approximately Gamma distribution Γ(k S ,θ S ), shape parameter k S and the scale parameter θ S It can be expressed as:

[0107]

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

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

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

[0111]

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

[0113]

[0114] Intermediate variables for: in

[0115] Step 230, calculate the coverage probability of typical users in the IRS network under the backhaul constraint, and obtain the intelligent reflector network configuration parameters under the condition of maximizing the coverage probability according to the relationship between the indicator and the network IRS deployment density and the number of IRS units.

[0116] The network coverage probability expression is:

[0117]

[0118] in and Respectively represent floor and ceiling, Represents the interpolation weight coefficient: The probability density function of the backhaul capacity B0 is:

[0119] The relationship between the average network coverage probability index and the network IRS deployment density and the number of IRS units is studied. Taking the coverage probability as the key indicator of network performance, the intelligent reflector network configuration parameter scheme is explored to maximize the coverage probability.

[0120] The simulation results are shown in the attached Figure 3 and 4 shown.

[0121] Attached Figure 3 This is a diagram showing the relationship between network coverage probability and IRS density when the SINR threshold is -3dB. IRS is the station selection criterion based on the maximum biased received signal strength under the IRS reconstruction channel, and N is the number of IRS units. It can be seen from the figure that the network coverage probability increases first and then decreases with the IRS deployment density. There is an optimal IRS deployment density that maximizes the network coverage probability. BRSRP IRS Under the site selection criteria, the number of IRS units increases from 100 to 1000, the maximum network coverage probability increases 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 site selection criterion.

[0122] Attached Figure 4The schematic diagram of the relationship between network coverage probability and the number of IRS units, where the number of IRS reflection units per unit area is given, i.e., Nλ I =1; As can be seen from the figure, under different IRS / BS density ratios, the coverage rate increases first and then decreases with the number of IRS reflection units N. For a fixed base station density and IRS / BS density ratio, there is an optimal number of IRS units to maximize the coverage probability. When the BS density is a fixed parameter of 10 -5 / km 2 , when the IRS / BS density ratio is determined to be 50, the optimal number of IRS units is 100.

[0123] The intelligent reflective surface assisted network parameter configuration device under backhaul constraints, such as Figure 5 As shown, it includes: the core network network side device and the network side device are simultaneously connected to the first determination module, the network side device and the first determination module are connected to the second determination module, the terminal side device is simultaneously connected to the second determination module and the third determination module, the second determination module is connected to the third determination module, and the third determination module sends the configuration information of the deployment density and the number of units to the intelligent reflective surface device.

[0124] The first determination module: Figure 6 As shown, it is used to determine the typical base station backhaul capacity distribution according to the fixed parameters of the smart reflector-assisted network under the backhaul constraints;

[0125] The second determination module: Figure 7 As shown, it is used to determine the strength distribution of the useful signal received by a typical user and the strength distribution of the interference signal received according to the fixed parameters of the intelligent reflector-assisted network under the backhaul constraints;

[0126] The third determination module: Figure 8 As shown, it is used to determine the user coverage probability in the network and the deployment density and number of smart reflective surfaces under the condition of maximizing the coverage probability according to the distribution of typical base station backhaul capacity, the distribution of the useful signal strength and the interference signal strength received by typical users;

[0127] Core network side equipment: such as Fig. 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, the steps of the method for configuring network parameters of the intelligent reflector-assisted network under the backhaul constraint as described above are implemented;

[0128] Network side equipment: such as Fig.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, the steps of the method for configuring network parameters of the intelligent reflector-assisted network under the backhaul constraint as described above are implemented;

[0129] Intelligent reflective surface equipment: such as Fig.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, the steps of the method for configuring network parameters of the intelligent reflector-assisted network under the backhaul constraint as described above are implemented;

[0130] Terminal equipment: such as Fig.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, the steps of the method for configuring network parameters of the intelligent reflector-assisted network under the backhaul constraint as described above are implemented.

Claims

1. A method and device for configuring network parameters of an intelligent reflector-assisted network under backhaul constraints, characterized in that: include: By statistically considering the fixed parameters of the intelligent reflector network with backhaul constraints, the 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 use them as input parameters of the network coverage probability estimation algorithm; consider the interference signals randomly scattered by IRS from other connected nodes, and recalculate the capacity distribution of the backhaul link under the influence of IRS deployment; consider the impact of the backhaul link capacity factor on the user's received signal strength, and calculate the theoretical expressions of SINR and conditional coverage probability of typical users in the IRS network under backhaul constraints; calculate the coverage probability of typical users in the IRS network under backhaul constraints, and according to the relationship between the coverage probability index and the network IRS deployment density and the number of IRS units, obtain the network configuration parameters of the intelligent reflector when the coverage probability is maximized.

2. The method according to claim 1, characterized in that Considering the random scattering of interference signals from other connected nodes by IRS, the capacity distribution of the backhaul link under the influence of IRS deployment is recalculated; the backhaul link capacity distribution C(x) is calculated as follows: Where B is the backhaul link capacity, P c is the CN transmission power, α is the path loss index, λ c is the CN density, λ I is the IRS density, the number of IRS units is N, f c is the carrier frequency, q nst and nst are the distances from a typical base station to the nearest IRS and to the nearest CN, respectively, d j represents the distance from the typical base station to the jth IRS, D1 and D2 are the beamforming service distance and random scattering service distance provided by the IRS, 2F1 is the Gaussian hypergeometric function, σ 2 is the noise power, 3. The method according to claim 1, characterized in that Considering the impact of the backhaul link capacity factor on the user received signal strength, the theoretical expressions of SINR and conditional coverage probability of typical users in the IRS network under backhaul constraints are calculated; the SINR calculation formula at the typical user of the access link is: Where W ac =σ 2 / P0 is the receiver noise normalized by P0, assuming its power is σ 2 AWGN, S ac is the total signal power from BS0 to UE0, I ac is the total interference power received by UE0. Gamma moment matching converts S ac Approximately Gamma distribution Γ(k S ,θ S ), shape parameter k S and the scale parameter θ S It can be expressed as: Where d0 is the distance between the typical user and the serving base station with the maximum offset received signal strength under the IRS reconstructed channel, r0 is the distance from the typical user to the serving IRS, And |h1| 2 The first moment of The second-order moment is expressed as |h2| 2 The first moment of The second moment is When k S When is an integer, the SINR conditional probability distribution for a typical user on the access link can be transformed into: Where Y = T (I ac +W ac ) / θ S , T is the SINR threshold, for integer k S ,satisfy Γ() is the upper incomplete Gamma function, It can be expressed as Total interference power I ac The Laplace transform of Intermediate variables for: in 4. The method according to claim 1, characterized in that: Calculate the coverage probability expression of typical users in the IRS network under the backhaul constraint; the network average coverage probability calculation formula is: in and Respectively represent floor and ceiling, Represents the interpolation weight coefficient: The probability density function of the backhaul capacity B0 is: g i (x) = βx -α , 5. The method according to claim 1, characterized in that: The relationship between the average network coverage probability index and the network IRS deployment density and the number of IRS units is studied. Taking the coverage probability as the key indicator of network performance, the intelligent reflector network configuration parameter scheme is explored to maximize the coverage probability.

6. The device for configuring network parameters assisted by a smart reflective surface under backhaul constraints according to claim 1 is characterized in that: include: A first determining module, a second determining module, a third determining module, a core network side device, a network side device, an intelligent reflecting surface device, and a terminal device.

7. The device for configuring network parameters with intelligent reflective surface assistance under backhaul constraints according to claim 6, characterized in that: The first determination module is used to determine the typical base station backhaul capacity distribution according to the fixed parameters of the smart reflector-assisted network under the backhaul constraints; The second determination module is used to determine the strength distribution of the useful signal received by the typical user and the strength distribution of the interference signal received according to the fixed parameters of the intelligent reflector-assisted network under the backhaul constraint; The third determination module is used to determine the user coverage probability in the network and the deployment density and number of smart reflective surfaces under the condition of maximizing the coverage probability according to the distribution of typical base station backhaul capacity, the distribution of the useful signal strength and the interference signal strength received by typical users; Core network network side equipment: including 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 steps of the method for configuring network parameters of an intelligent reflector-assisted network under backhaul constraints described in any one of claims 1 and 2 are implemented; Network side equipment: comprising 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 steps of the method for configuring network parameters of an intelligent reflector-assisted network under backhaul constraints described in any one of claims 1 to 4 are implemented; Smart reflector device: comprising 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, the steps of the method for configuring network parameters of a smart reflector assisted by a backhaul constraint according to any one of claims 1 to 5 are implemented; Terminal equipment: comprising 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 steps of the method for configuring network parameters of an intelligent reflector-assisted network under a return line constraint described in any one of claims 1 to 5 are implemented.

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