6G Smart Transparent and Reflective Surface-Assisted Street Network Deployment Method and Parameter Setting

Abstract

This invention proposes a 6G intelligent reflective surface-assisted street network deployment method and parameter setting method, relating to the field of wireless communication networks, particularly network planning and network planning tools. The intelligent reflective surface is deployed on streetlights, providing services to vehicle users by reflecting base station signals. The main contents are as follows: Obtaining the locations of users, streetlights, and base stations; calculating the channel gain of the intelligent reflective surface; calculating the user availability rate based on channel information; determining the number of intelligent reflective surface units to be deployed based on distance; solving the problem to obtain the base station beamforming vector and the maximum value of the user's minimum transmission rate; solving the problem to obtain the phase shift of the intelligent reflective surface and the maximum value of the user's minimum transmission rate; solving the problem to obtain the deployment location of the intelligent reflective surface and the maximum value of the user's minimum transmission rate; iteratively solving the problem until the increment of the maximum value of the user's minimum transmission rate is less than a set threshold, thus obtaining the deployment scheme.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to the system deployment and parameter setting method of intelligent omni-surfaces (IOS) assisted vehicle-to-everything networks (V2X). Background Technology

[0002] The communication of common vehicle-to-everything (V2X) wireless communication networks is easily affected by complex road environments, and overcoming adverse environmental effects to improve transmission rates is a goal of continuous progress.

[0003] To overcome the impact of complex road environments on wireless communication, existing Intelligent Reflecting Surfaces (IRS) assist V2X networks. IRSs are deployed on one side of the street, and unified deployment of intelligent surfaces is achieved through joint optimization of spectrum allocation, power allocation, and IRS reflection, addressing issues such as uplink and downlink coexistence and rate maximization, and optimizing the deployment distance from a single IRS to the base station. However, unified or fixed deployment of IRSs cannot fully utilize their advantages, and the performance of V2X communication requires further exploration.

[0004] Emerging intelligent omni-surfaces (IOSs) can simultaneously reflect and transmit signals to bypass road obstacles, representing a significant potential approach to improving transmission rates. However, current research has not considered optimized deployment of intelligent surfaces, and the number of components in each IOS is the same, resulting in limited performance improvements for remote users. We propose a distance-dependent IOS deployment model, based on which V2X communication performance can be further improved. Summary of the Invention

[0005] In this paper, we propose a distance-dependent IOS deployment model and a method for applying IOS in vehicular wireless communication networks. We design a distance weighting factor χ to adjust the number of units contained in each IOS. Based on the proposed IOS deployment model, we solve the minimum rate maximization problem by jointly optimizing the beamforming vector of the base station (BS), the phase of the IOS, and the deployment of the IOS.

[0006] The distance-dependent intelligent transparent and reflective surface-assisted V2X network deployment method proposed in this invention is attached. Figure 1 As shown, it mainly includes steps 200-290.

[0007] Step 200: Obtain the IOS unit location, BS antenna location, and user location; calculate the total channel gain from BS to user to IOS unit based on the Ricean channel model.

[0008] The channel gain between the BS and IOS units is calculated using formula (1).

[0009]

[0010] The parameters are defined as follows:

[0011] n is the IOS unit indicator symbol;

[0012] m is the BS antenna indicator symbol;

[0013] The large-scale path loss between the m-th antenna and the n-th IOS element of the BS;

[0014] This refers to the small-scale Ricean fading of the m-th antenna and the n-th IOS element of the BS.

[0015] Furthermore, based on the channel gains of the m-th antenna and the n-th IOS unit, the channel gain matrices of the BS antenna and IOS units can be obtained, and can be obtained through formula (2).

[0016]

[0017] The parameters are defined as follows:

[0018] N is the number of IOS units;

[0019] M represents the number of BS antennas.

[0020] Similarly, the channel gain between the user and the IOS unit is calculated using formula (3).

[0021]

[0022] The parameters are defined as follows:

[0023] k is the user indicator;

[0024] n is the number of IOS units;

[0025] The large-scale path loss between user k and the nth IOS unit;

[0026] For user k and the nth IOS unit, consider the small-scale Ricean fading.

[0027] The channel gain between the user and the BS antenna is calculated using formula (4).

[0028]

[0029] The parameters are defined as follows:

[0030] m is the BS antenna indicator;

[0031] k is the user indicator;

[0032] For user k and the large-scale path loss of the m-th antenna of BS;

[0033] For user k and the m-th antenna of BS, consider the small-scale Ricean fading.

[0034] Furthermore, obtain all channel gains for both the user and the IOS unit. Channel gain for users and all BS antennas

[0035] Step 210: Calculate the signal response of the IOS unit based on the normalized power radiation modes of the incident and reflected signals. Where C n,m,k For amplitude response, φ n This is the phase response.

[0036] The normalized power radiation mode of the incident signal is obtained according to formula (5).

[0037]

[0038] in,

[0039] s is the direction parameter;

[0040] n is the IOS unit indicator;

[0041] m is the BS antenna indicator;

[0042] θ n,m This is an indicator of the angle between the transmitted signal of the BS antenna and the normal of the IOS element.

[0043] The normalized power radiation diagram of the reflected signal is obtained according to formula (6).

[0044]

[0045] The parameters are defined as follows:

[0046] n is the IOS unit indicator;

[0047] k is the user indicator;

[0048] r is the reflection identifier;

[0049] t is the transmission identifier;

[0050] ∈ represents the power ratio between the reflected and transmitted signals of the IOS;

[0051] Δ n,k This serves as a reflection angle indicator between the iOS unit and the user.

[0052] ε n,k This serves as a transmission angle indicator between the IOS unit and the user.

[0053] This indicates that the user is being served by the reflection transmission of the IOS unit;

[0054] This indicates that the user is being served by the IOS unit's transmission.

[0055] Furthermore, the response expressions for all signals are obtained, and the response matrix is ​​obtained according to formula (7).

[0056]

[0057] Where ° is the identifier for matrix element-wise multiplication. The response matrix between users and iOS. This is the phase vector matrix for all IOS units.

[0058] Step 220: Based on the obtained signal response and the channel gain, the overall channel gain from the base station BS to the user is obtained according to formula (8).

[0059]

[0060] The parameters are defined as follows:

[0061] k is the user identifier;

[0062] This represents the k-th column of the channel gain matrix between the H2 user and the IOS unit;

[0063] This represents the k-th column of the channel gain matrix for H3 users and all BS antennas.

[0064] Step 230: According to the overall channel gain formula, under the additive white Gaussian noise channel model, the received signal of user k is obtained according to formula (9).

[0065]

[0066] Where k is the user indicator;

[0067] For user k-beamforming vector;

[0068] x k Transmit data to users; note that the transmitted data is assumed to be orthogonal.

[0069] ω k It is additive white Gaussian noise;

[0070] Step 240: Calculate the achievable rate for the user according to formula (10).

[0071]

[0072] in,

[0073] k is the user indicator;

[0074] For user k-beamforming vector;

[0075] σ 2 Let Variance be the variance.

[0076] Step 250: Based on the channel gain between the user and the IOS unit, the channel gain between the user and the BS antenna, the channel gain between the BS and the IOS unit, and the overall channel gain from the base station BS to the user, it can be known that the channel gain that the IOS can provide is proportional to a certain power of the number of elements, and the path loss is proportional to a certain power of the distance. Therefore, the number of units at different locations is deployed according to formula (11).

[0077]

[0078] Where N is the total number of IOS units;

[0079] q is the IOS identifier;

[0080] The integer symbol;

[0081] x is the distance weighting factor;

[0082] q represents the distance from the center antenna of the BS to the IOS.

[0083] Setting up an iOS deployment plan Indicates that there are N q IOS q of each IOS unit is deployed on streetlight y; otherwise, it is not deployed. Configure the beamformer of the BS. V k The user's k-beamforming vector.

[0084] Step 260: Given Φ and B, solve the following problem P1 (Equation (12)) to obtain the optimized beamforming vector V and the maximum value η of the user's minimum transmission rate.

[0085]

[0086] Tr(VV H )≤P BS

[0087] P BS The total power of the base station. To reduce interference between users, zero-forcing (ZF) beamforming and optimal transmit power optimization can be adopted. According to formula (13), ZF-based beamforming can be obtained.

[0088]

[0089] in,

[0090] G is the overall channel gain matrix from the base station to user k;

[0091] It is a k x k diagonal matrix, where the diagonal elements represent the user's received power p. k ;

[0092] Furthermore, according to formula (13), problem P1 is simplified to problem P2 (formula 14), and the maximum value η of the received power diagonal array P and the minimum transmission rate of the user is solved.

[0093]

[0094] Where, p k Let P2 be the received power of user k, and K be the total number of users. Note that P2 is a convex problem relative to P, which can be solved using existing convex optimization tools. Then, the obtained P... * Substitute into equation (13) and then solve.

[0095] Step 270: Solve the following problem P3 (Equation (15)) to obtain the maximum value η of IOS phase shift Φ and minimum transmission rate of the user.

[0096]

[0097] Where N is the total number of iOS units; K is the total number of users.

[0098] Set a precision ξ, iteratively solve for each element in Φ until the increment of the maximum value of the minimum user transmission rate η is less than the set precision ξ.

[0099] Step 280: Solve the following problem P4 (Equation (16)) to obtain the maximum value η of the minimum transmission rate of the deployment optimization B and the user.

[0100]

[0101]

[0102] Iterate through all B values ​​in order, calculate the target value of P4, and repeat Y times. Finally, select the largest target value from the iterations and output the corresponding B as the final deployment result.

[0103] Step 290, iterate through steps 260, 270 and 280 until the increment of the maximum value η of the user's minimum transmission rate is less than the set threshold.

[0104] Beneficial effects

[0105] This invention presents a 6G intelligent transparent-reflective surface-assisted street network deployment method, constructing a macroscopic process framework for vehicle-to-everything (V2X) wireless communication networks in complex road environments. It utilizes a distance-dependent IOS deployment method to provide optimized deployment schemes for IOS beamforming, cells, and phase. Specifically, a BS-IOS-UE cascaded channel model under Ricean fading is constructed, the overall channel gain of the IOS is derived, and a low-complexity optimized IOS deployment design algorithm is provided using block coordinate descent and fast search techniques. Furthermore, simulation scenarios are constructed based on real-world scene parameters, and extensive simulations and comparisons are conducted.

[0106] We analyzed in detail the relationship between IOS distance deployment under different power allocations, IOS heights, and the number of IOS units, which verified the effectiveness of the proposed solution. We further compared the proposed IOS distance dependency algorithm with a uniform distribution scheme, and the comparison results demonstrated the superiority of the proposed algorithm. Attached Figure Description

[0107] 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.

[0108] Appendix Figure 1 This is the implementation process of the present invention;

[0109] Appendix Figure 2 This is an example application scenario of the present invention;

[0110] Appendix Figure 3 This is a distance deployment scheme provided by the present invention in an example application;

[0111] Appendix Figure 4 This invention provides a deployment scheme for different numbers and height distances of iOS devices in an example application.

[0112] Appendix Figure 5 This is an example application of the IOS power allocation deployment scheme provided by the present invention;

[0113] Appendix Figure 6 This is an example application of the iOS high-level deployment scheme provided by the present invention. Detailed Implementation

[0114] 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.

[0115] Appendix Figure 1 This is an example scenario of the present invention, demonstrating the basic elements included in the invention and some parameters that will be used subsequently. In this example scenario, all users are equipped with a single antenna, the base station is equipped with multiple antennas, a three-dimensional Cartesian coordinate system is used, multiple IOSs are configured on streetlights, with at most one IOS per streetlight, and the IOS are deployed in the vehicle-to-everything (V2X) wireless communication network to provide data transmission services for users inside the vehicle. The following section will utilize the method proposed in this invention to obtain an IOS deployment scheme.

[0116] This invention operates in a single-base-station, multi-user network scenario, disregarding interference between users. It can employ various multiple access methods, such as frequency division multiple access (FDMA) and time division multiple access (TDMA), to provide services to multiple users. The mechanism of this invention is implemented during the deployment of the communication IOS in the vehicle-to-everything (V2X) wireless communication network.

[0117] According to the appendix Figure 1 For example scenarios, the relevant parameters are:

[0118] In the example scenario, the heights of the base station, user, IOS, and streetlights are 4m, 1.7m, 3m, and 7m, respectively; the street length is 200m; the path propagation loss per unit distance is -69dBm; the Gaussian white noise power density is -174dBm / Hz; the path loss coefficients of the IOS element and BS antenna are 2.2; the path loss coefficients of the IOS element and user are 2.5; the path loss coefficients of the user and BS antenna are 3.6; the number of BS antennas is 8; the number of IOS elements is 1000; the number of users is 10; the number of IOSs is 5; and the number of streetlights is 10.

[0119] The specific steps for deploying intelligent transparent and reflective surface-assisted street network communication are as follows:

[0120] Step 300: Obtain the IOS unit position, BS antenna position, and user position in the three-dimensional Cartesian coordinate system, and calculate the total channel gain from BS to user to IOS unit based on the Ricean channel model.

[0121] For ease of representation, we define some symbols. The number of users is K, and the two-dimensional position of user k is u. k ∈R 2×1 The number of streetlights is Y, and the location of the streetlights is s. y ∈R 2×1 The number of IOSs is Q, the number of IOS components is N, and the q-th IOS has N components. q The number of components, and the heights of the base station, streetlights, and all users are respectively H. BS H SL and H UE The transmission power of the BS base station is P BS .

[0122] Assuming there are L fading blocks in each time slot, the channel gain between the m-th antenna and the n-th IOS element of the BS is calculated using formula (17) based on the NLOS channel state information statistical model.

[0123]

[0124] The channel gain between the nth IOS element and user k is calculated using formula (18).

[0125]

[0126] The channel gain between the m-th antenna and user k is calculated using formula (19).

[0127]

[0128] Furthermore, based on the channel gains of the m-th antenna and the n-th IOS unit, the channel gain matrices of the BS antenna and IOS units can be obtained, and can be obtained through formula (20).

[0129]

[0130] Based on the channel gains of the k-th user and the n-th IOS unit, the channel gain matrices of the BS antenna and IOS units can be obtained, and can be obtained through formula (21).

[0131]

[0132] Based on the channel gains of the m-th antenna and the k-th user, the channel gain matrices of the BS antenna and IOS unit can be obtained, and can be obtained through formula (22).

[0133]

[0134] Where β0 is the unit path loss coefficient. These are complex Gaussian distribution functions with a mean of 0 and a unit variance. This refers to the large-scale path loss coefficients between the antenna and IOS components, between the IOS components and the user, and between the user and the antenna. This refers to small-scale Ricean fading between the antenna and IOS components, between the IOS components and the user, and between the user and the antenna. These represent the distances between the antenna and the IOS component, the distance between the IOS component and the user, and the distance between the user and the antenna, respectively.

[0135] Step 310: Calculate the signal response of the IOS element based on the normalized power radiation modes of the incident and reflected signals. Where C n,m,k For amplitude response, φ n This is the phase response.

[0136] The normalized power radiation diagram of the incident signal is obtained by formula (23).

[0137]

[0138] Where, θ n,m Let θ be the angle between the transmitted signal of the BS antenna and the normal of the IOS element. Since the distance between the BS antennas is much smaller than the distance from the BS to the IOS, therefore, θ n,m After simplification, it becomes θ n It is approximately the angle between the transmitted signal of the BS antenna and the normal of the IOS element.

[0139] The normalized power radiation map of the reflected signal is obtained by formula (24).

[0140]

[0141] Among them, when This indicates that the user is being served by the reflection transmission of the iOS element, and vice versa. This indicates that it is not under reflection transmission, while when This indicates that the user is being served by the transmission of the IOS element, and vice versa. This indicates that the service is not available under transmission.

[0142] Furthermore, based on the obtained channel state information, under the Rice fading channel model, the response expressions of all signals are obtained through formula (25).

[0143]

[0144] in, For matrix multiplication, A frequency response matrix for users and iOSs; This is the phase vector matrix for all IOS components.

[0145] Step 320: Based on the obtained signal response and channel gain, the overall channel gain from the base station (BS) to the user is obtained using formula (26).

[0146]

[0147] in, This represents the transpose of the channel gain matrix of the IOS element and the BS antenna. This represents the k-th column of the channel gain matrix between the H2 user and the IOS element. This represents the k-th column of the channel gain matrix for H3 users and all BS antennas.

[0148] Step 330: Based on the overall channel gain formula, under the additive white Gaussian noise (AWGN) channel model, the received signal of user k is obtained according to formula (27).

[0149]

[0150] Among them, V k For the user's beamforming vector, x k For the data stream transmitted to the user, ω k This is external additive white Gaussian noise.

[0151] Step 340: Calculate the achievable rate for the user according to formula (28).

[0152]

[0153] Among them, V k For the user's beamforming vector, The overall communication channel gain can be obtained by formula (26).

[0154] Step 350: Based on the channel gain between the user and the IOS unit, the channel gain between the user and the BS antenna, the channel gain between the BS and the IOS unit, and the overall channel gain from the base station BS to the user, it can be seen that the channel gain that the IOS can provide is proportional to a certain power of the number of components, and the path loss is proportional to a certain power of the distance. Therefore, the number of units at different locations is deployed according to formula (29).

[0155]

[0156] in, The distance from the BS center antenna to the IOS. This is the integer operator.

[0157] Step 360: Given Φ and B, solve the following problem P5 (Equation (30)) to obtain the optimized beamforming vector V and the maximum value η of the user's minimum transmission rate.

[0158]

[0159] Tr(VV H )≤P BS

[0160] Since V, Φ, and B are not coconvex, it is difficult to solve these three variables simultaneously. The block coordinate descent method is used to iteratively solve these three variables, that is, to correct two of the variables and solve the third variable.

[0161] To reduce interference between users, zero-forcing (ZF) beamforming and optimal transmit power optimization can be adopted. The ZF-based beamforming can be obtained through formula (31).

[0162]

[0163] Where G is the overall channel gain matrix from the base station to user k. It is a k x k diagonal matrix, where the diagonal elements represent the received power of the user, denoted by {p k ,k∈K}.

[0164] Furthermore, according to formula (31), problem P5 is optimized into problem P6 (formula 32) and solved to obtain the maximum value η of the received power diagonal array P and the minimum transmission rate of the user.

[0165]

[0166] Here, P3 is a convex problem relative to P, which can be solved using existing convex optimization tools such as MATLAB's CVX toolbox. Then, the obtained P... * Substitute into equation (31) and then solve.

[0167] Step 370: Solve the following problem P6 (Equation (33)) to obtain the maximum value η of IOS phase shift Φ and minimum transmission rate of the user.

[0168]

[0169] The formula (31) yields Substituting the problem into the equation yields problem P7, although R... k For all elements in Φ, none are coconcave, but R kEach element in Φ is a concave function of the other elements. With a set precision ξ, iteratively solve for each element in Φ until the increment of the maximum value η of the minimum user transmission rate is less than the set precision ξ.

[0170] Step 380: Solve the following problem P8 (Equation (34)) to obtain the maximum value η of the minimum transmission rate of the deployment optimization B and the user.

[0171]

[0172] Iterate through all B values ​​in order, calculate the target value of P8, and repeat Y times. Finally, select the largest target value from the iterations and output the corresponding B as the final deployment result.

[0173] Step 390, iterate through steps 360, 370 and 380 until the increment of the maximum value η of the user's minimum transmission rate is less than the set threshold.

[0174] The following explains some of the resulting images obtained in this example scenario.

[0175] Appendix Figure 2 It is a distance-dependent intelligent transflective surface-assisted street network deployment method model, which clearly shows the IOS transflection and reflection scheme and its deployment on street lights under the vehicle-to-everything (V2X) wireless communication network.

[0176] Appendix Figure 3 This invention provides an example of a relationship between the maximum-minimum rate and the distance weighting factor, plotted in terms of the power ratio of reflected and transmitted signals, along with a distance deployment scheme. As the weighting factor changes, the maximum-minimum rate initially increases and then decreases, indicating that under different power allocations, there exists an optimal distance weighting factor, and the maximum-minimum rate is optimal when the power allocation factor is 0.5. This is because increasing the distance weighting factor increases the IOS gain for remote users with poorer performance, leading to an increase in the maximum-minimum rate. However, further increases in the distance weighting factor result in an increase in edge IOS, leading to a decrease in the maximum-minimum rate.

[0177] Appendix Figure 4 This is a deployment scheme provided in this invention for different heights and numbers of IOS. Generally speaking, as the IOS height increases, the optimal distance weight increases, as shown in the attached diagram. Figure 4 (a) With an IOS height of 1m, the optimal distance weighting factor is between 0 and 0.5. Figure 4 (b) With an IOS height of 3m, the optimal distance weighting factor is between 0.5 and 1. Figure 4(c) With an IOS height of 5m, the optimal distance weighting factor is between 1.5 and 2. This is because the higher the IOS height, the lower the IOS benefits for users further away, necessitating an increase in the number of IOS units to compensate, which is consistent with expectations. As the number of IOSs increases, the maximum-minimum rate shows a trend of first increasing and then decreasing, indicating that there is also an optimal choice for the number of IOSs, and it is particularly good when Q=5.

[0178] Appendix Figure 5 This invention provides a comparison between the IOS deployment scheme and uniform deployment in this example, with appended details. Figure 6 This invention provides a comparison between the height-based deployment scheme and the uniform deployment scheme. Overall, as the number of IOS increases, the relative gain decreases, while the power ratio of reflected and transmitted signals has an optimal value, reaching 9.5% when Q = 5 and ∈ = 0.5. The optimal deployment height increases with increasing base station height, and is superior to the uniform deployment scheme in all cases. This is because uniform deployment cannot provide sufficient gain for edge users, which will reduce the system's lower limit rate. The optimal deployment height needs to be determined based on the base station height, which increases with increasing base station height, as shown in the attached figure. Figure 6 According to formulas (20) and (21), the amplitude gain of IOS is related to the angle. Therefore, the height of IOS needs to be increased synchronously to maintain a relatively high amplitude gain of IOS.

Claims

1. A method for deploying a 6G intelligent transparent and reflective surface-assisted street network and a method for setting parameters, characterized in that, include: A smart reflective surface is deployed on streetlights to provide services to vehicle users on the street by simultaneously transmitting and reflecting base station signals. The locations of users, streetlights, and base stations are obtained, and the channel gain of the smart reflective surface is calculated. The achievable data rate for users is calculated based on the channel gain. The number of smart reflective surface units deployed is determined based on the distance from the base station's center antenna to the smart reflective surface. The following problem P1 is solved to obtain V and η, where... Tr(VV H )≤P BS η is the maximum value of the user's minimum transmission rate, R k Let P be the achievable rate for user k, K be the number of users, v be the base station beamforming vector, and P be the base station beamforming vector. BS Let represent the total power of the base station, and Tr(·) be the trace operation; solve problem P3 below to obtain Φ and η, where, Φ is the phase shift vector matrix of all intelligent transparent and reflective surface elements, and N is the total number of intelligent transparent and reflective surface elements; solve problem P4 to obtain B and η, where, B represents the deployment location of the intelligent transparent and reflective surface. Indicates that there are N q IOS q of each IOS unit is deployed on streetlight y, otherwise it is not deployed; Q is the number of smart transparent and reflective surfaces, and Y is the number of streetlights; iteratively solve problems P1, P3, and P4 until the increment of the maximum value of the user's minimum transmission rate is less than the set threshold, and obtain V, Φ, and B.

2. The method according to claim 1, characterized in that, The normal direction of the intelligent transmissive surface is parallel to the tangential direction of the road, so that the intelligent transmissive surface can simultaneously transmit and reflect base station signals.

3. The method according to claim 1, characterized in that, Calculate the channel gain from the m-th antenna of the base station to the n-th element of the smart transmissive surface, and from the n-th element of the smart transmissive surface to user k, respectively, using the following formulas. Where β0 is the unit path loss coefficient. and These represent the large-scale path losses from the m-th antenna to the n-th element of the smart reflective surface, and from the n-th element of the smart reflective surface to user k, respectively. and These represent the small-scale Ricean fading from the m-th antenna to the n-th element of the smart reflective surface, and from the n-th element of the smart reflective surface to user k, respectively. and Let α1 and α2 be the distances from the m-th antenna to the n-th element of the smart transparent-reflective surface and from the n-th element of the smart transparent-reflective surface to user k, respectively. Let α1 and α2 be the path loss coefficients from the antenna to the smart transparent-reflective surface channel and from the smart transparent-reflective surface to the user channel, respectively. Let K1 and K2 be the Ricean fading factors from the antenna to the smart transparent-reflective surface channel and from the smart transparent-reflective surface to the user channel, respectively.

4. The method according to claim 1, characterized in that, Number of intelligent transflective surface units N q It is proportional to the x-th power of the distance between the smart reflective surface and the base station, as follows: in, Let q be the distance between the smart reflective surface and the base station, and N be the total number of smart reflective surface units.

5. The method according to claim 1, characterized in that, The base station beamforming vector formula is obtained by using the zero-forcing algorithm. Then, the maximum value of the allocated power for each user and the minimum transmission rate for each user is calculated by substituting the result into P1. The result is then substituted back into the base station beamforming vector formula.

6. The method according to claim 1, characterized in that, The phase shifts of N intelligent transparent and reflective surfaces are solved sequentially. When optimizing the nth phase shift, the other phase shifts are fixed to their previous calculated values. The process is iterated until the increment of the maximum value of the user's minimum transmission rate is less than a set threshold.

7. The method according to claim 1, characterized in that, Iterate through all B values ​​in sequence, calculate the target value of P4, and repeat Y times. Then, select the largest target value during the iteration and output the corresponding B as the final deployment result.

8. The method according to claim 1, characterized in that, When solving P1, the values ​​of Φ and B are taken from the previous solutions of P3 and P4, respectively. When solving P3, the values ​​of V and B are taken from the previous solutions of P1 and P4, respectively. When solving P4, the values ​​of V and Φ are taken from the previous solutions of P3 and P4, respectively.

Images