A RIS Location Optimization Method for RIS-Assisted Wireless Communication

Abstract

The present invention relates to a method for optimizing the position of a RIS in RIS-assisted wireless communication, including creating a first base station total transmission power optimization model with the received signal-to-noise ratio of the user as a constraint and the minimum total transmission power of the base station as the optimization target; using the KM algorithm to match the best RIS reflection unit for each user as the reflection point to obtain the best RIS reflection unit for each user; using trigonometric functions to convert the distance and direction between the user's best RIS reflection unit and the base station into the distance and direction based on the central reflection unit of the RIS; and taking the position and direction of the central reflection unit of the RIS as variables, constructing a second base station total transmission power optimization model with the minimum total transmission power of the base station as the optimization target; solving the second base station total transmission power optimization model through an intelligent optimization algorithm to obtain the optimal position and direction of the central reflection unit of the RIS, thereby reducing the transmission power of the base station.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communication, and particularly relates to a method for optimizing the position of a RIS for RIS-assisted wireless communication. Background Art

[0002] When wireless signals propagate in the environment, they usually undergo phenomena such as reflection, scattering, and diffraction before reaching the receiver, and multipath effects are generated. Therefore, the propagation environment is uncontrollable. As one of the key technologies for 6G communication, the reconfigurable intelligent surface (RIS) is a two-dimensional plane composed of a large number of low-cost and almost passive reflecting elements. By adjusting the amplitude and phase of the incident signal, the signal can reach the receiving end in a desired manner, thereby making the wireless propagation environment controllable and programmable.

[0003] The application of RIS in wireless communication can enhance the signal-to-noise ratio, increase the channel capacity; reduce the transmit power of the base station, improve the spectral efficiency and energy efficiency; and even improve the physical layer security performance. However, the placement position and orientation of the RIS are not uniquely determined. In order to fully utilize the maximum benefits of the RIS in wireless communication, the research on the deployment of the RIS is urgent and necessary.

[0004] Many existing studies on the deployment of the RIS only focus on the position of the RIS and ignore the influence of the RIS orientation; at the same time, many studies only numerically analyze the impact of the position of the RIS on the system performance without giving specific insights on how to deploy the RIS; furthermore, some studies regard the RIS as a continuous surface, while the actual RIS is a two-dimensional plane with multiple reflecting units, and the deployed RIS in this way is not in the optimal position, resulting in the inability to fully utilize communication resources and wasting the transmit power of the base station. Summary of the Invention

[0005] In order to solve the problems in the background art, the present invention provides a method for optimizing the position of a RIS for RIS-assisted wireless communication to reduce the transmit power of the base station, including:

[0006] S1: Based on RIS-assisted wireless communication, create a first base station total transmit power optimization model with the received signal-to-noise ratio of the user as a constraint and the minimum total transmit power of the base station as the optimization objective;

[0007] S2: According to the first base station total transmit power optimization model, use the KM algorithm to match the best RIS reflecting unit as the reflection point for each user to obtain the best RIS reflecting unit for each user;

[0008] S3: Use trigonometric functions to convert the distance and direction between the user's optimal RIS reflection unit and the base station into the distance and direction with respect to the central reflection unit of the RIS; and take the position and direction of the central reflection unit of the RIS as variables, and construct a second base station total transmission power optimization model with the minimum total transmission power of the base station as the optimization objective;

[0009] S4: Solve the second base station total transmission power optimization model through an intelligent optimization algorithm to obtain the optimal position and direction of the central reflection unit of the RIS.

[0010] The present invention has at least the following beneficial effects

[0011] Under the constraint of the user receiving signal-to-noise ratio, by constructing a cost matrix based on the base station transmission power with each RIS reflection unit as the reflection point for each user, finding the maximum weight matching, and matching a corresponding RIS reflection unit as the reflection point for each user to obtain the base station transmission power that meets the constraint conditions. Such processing can not only meet the requirements of the user-end receiving signal-to-noise ratio, but also take into account the distance difference between RIS reflection units in the optimization of the RIS position and direction, which is more in line with the actual model of the RIS; relate the distances and directions between the RIS reflection unit matched with the user and the central RIS reflection unit and the base station through trigonometric functions, optimize the position and direction of the central reflection unit of the RIS as variables, and complete the problem of the position and direction deployment of the actual RIS model under the goal of minimizing the total transmission power of the base station, reducing the transmission power of the base station. Description of the Drawings

[0012] Figure 1 is the flowchart of the method of the present invention;

[0013] Figure 2 is the schematic diagram of the RIS-assisted wireless communication system adopted by the present invention;

[0014] Figure 3 is the weighted bipartite graph generated by the present invention using the KM algorithm;

[0015] Figure 4 is the schematic diagram of the conversion of the distance and direction between the RIS reflection unit and the base station of the present invention;

[0016] Figure 5 is the equivalent schematic diagram of the distance and direction between the RIS reflection unit and the base station of the present invention. Detailed Embodiments

[0017] The following describes the implementation manners of the present invention through specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0018] Among them, the attached drawings are only used for exemplary illustration, showing only schematic diagrams rather than physical diagrams, and should not be construed as a limitation to the present invention; in order to better illustrate the embodiments of the present invention, some components in the attached drawings will be omitted, enlarged or reduced, which do not represent the dimensions of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the attached drawings may be omitted.

[0019] In the attached drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "rear", etc. indicating the orientation or positional relationship, they are based on the orientation or positional relationship shown in the attached drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, the terms describing the positional relationship in the attached drawings are only used for exemplary illustration and should not be construed as a limitation to the present invention. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to specific circumstances.

[0020] Please refer to Figure 2 , the present invention adopts a RIS-assisted wireless communication system, and this communication system includes: a base station BS, K users, and a RIS. The RIS has M×N reflection elements and a size of s M ×s N .

[0021] Please refer to Figure 1 , the present invention provides a RIS position optimization method for RIS-assisted wireless communication, including:

[0022] S1: Based on RIS-assisted wireless communication, with the received signal-to-noise ratio of the user as a constraint and the minimum total transmission power of the base station as the optimization objective, create a first base station total transmission power optimization model;

[0023] Preferably, the calculation steps of the received signal-to-noise ratio of the user include:

[0024] S11: Calculate the channel gain between the user and the RIS reflection unit according to the distance from the user to the RIS reflection unit and the distance from the RIS reflection unit to the base station;

[0025] Preferably, the channel gain between the user and the RIS reflection unit includes:

[0026]

[0027] Wherein, represents the channel gain between the user and the RIS reflection unit, λ represents the wavelength, G represents the antenna gain (transmission antenna gain, which can be obtained according to the user equipment attributes), s M represents the width of the reflection unit, s N represents the length of the reflection unit, D k,m,n represents the distance from the base station to the RIS reflection unit in the m-th row and n-th column, d k,m,n represents the distance from the user to the RIS reflection unit in the m-th row and n-th column, α represents the path loss exponent, j represents the complex plane, e represents the natural base, and π represents the pi approximately equal to 3.14.

[0028] Preferably, the model of RIS-assisted wireless communication is divided into near-field characteristics and far-field characteristics, and the reference point is (2s M ×s N ) / λ. When D k,m,n , d k,m,n >(2s M ×s N ) / λ, it is in the far-field characteristic; otherwise, it is in the near-field characteristic. When the model of RIS-assisted wireless communication is in the far-field characteristic;

[0029] D k,m,n =D k,m',n' , d k,m,n =d k,m',n' , (m'≠m or n'≠n)

[0030] Wherein, D k,m,n and d k,m,n respectively represent the distances from the BS (base station) to the RIS reflection unit in the m-th row and n-th column and from the RIS reflection unit in the m-th row and n-th column to the k-th user, D k,m',n' , d k,m',n' are respectively the distances from the BS to the remaining RIS reflection units and from this RIS reflection unit to the k-th user.

[0031] In the far-field characteristic of RIS-assisted wireless communication, the positions and directions of all RIS reflection units are approximately the same; in the near-field characteristic of RIS-assisted wireless communication, D k,m,n ≠D k,m',n' , d k,m,n ≠d k,m',n' ;

[0032] S12: Calculate the direct channel gain between the user and the base station according to the distance from the user to the base station;

[0033] Preferably, the direct channel gain between the user and the base station includes:

[0034]

[0035] where represents the direct channel gain between the user and the base station, λ represents the wavelength, G represents the antenna gain, j represents the complex plane, e represents the natural base, π represents the pi approximately equal to 3.14, d BU,k represents the distance between the k-th user and the base station.

[0036] S13: Calculate the comprehensive channel gain of the user according to the direct channel gain between the user and the base station and the channel gain between the user and the RIS reflection unit;

[0037] Preferably, the comprehensive channel gain of the user includes:

[0038]

[0039] where h k,m,n represents the comprehensive channel gain when the k-th user uses the RIS m-th row and n-th column reflection unit, Γ m,n represents the reflection coefficient of the RIS m-th row and n-th column reflection unit, Γ represents the reflection amplitude of the RIS, represents the phase shift of the RIS, represents the channel gain between the k-th user and the RIS m-th row and n-th column reflection unit, represents the direct channel gain between the k-th user and the base station.

[0040] Preferably, the reflection amplitude Γ can be modeled by cosθ m,n i.e., Γ = cosθ m,n where, θ m,n represents the incident angle from the BS to the RIS m-th row and n-th column reflection unit.

[0041] S14: Calculate the received signal-to-noise ratio of the user according to the comprehensive channel gain of the user and the transmit power allocated by the base station to the user:

[0042] Preferably, the received signal-to-noise ratio of the user includes:

[0043]

[0044] where r k,m,n represents the received signal-to-noise ratio when the k-th user uses the RIS m-th row and n-th column reflection unit as the reflection point, Pk,m,n Denote the transmit power allocated by the base station to the k-th user when the (m, n)-th reflection element of the RIS is used as the reflection point by the k-th user, and σ 2 is the variance of the additive white Gaussian noise received by the user, and h k,m,n represents the combined channel gain when the (m, n)-th reflection element of the RIS is used as the reflection point by the k-th user.

[0045] Preferably, the first base station total transmit power optimization model includes:

[0046]

[0047] s.t. r k,m,n ≥ r0

[0048]

[0049] ω k,m,n ∈ {0, 1}, 1 ≤ k ≤ K, 1 ≤ m ≤ M, 1 ≤ n ≤ N

[0050] where r0 is the set signal-to-noise ratio at the user receiver; ω k,m,n is the weight, taking values of 0 and 1, and when w k,m,n = 0, it means that the k-th user is not matched with the (m, n)-th reflection element of the RIS, and when w k,m,n = 1, it means that the k-th user is matched with the (m, n)-th reflection element of the RIS, and P k,m,n represents the transmit power allocated by the base station to the k-th user when the (m, n)-th reflection element of the RIS is used as the reflection point by the k-th user, and r k,m,n represents the received signal-to-noise ratio when the (m, n)-th reflection element of the RIS is used as the reflection point by the k-th user, and r0 is preset at the user receiver, generally set by the manufacturer when the device leaves the factory.

[0051] S2: According to the first base station total transmit power optimization model, use the KM algorithm to match the best RIS reflection element as the reflection point for each user to obtain the best RIS reflection element for each user;

[0052] Preferably, when solving for the first base station total transmit power, it can be assumed that the position and orientation of the central reflection element of the RIS are known constants, and by substituting and solving, the best RIS reflection element can be allocated to each user when the base station total transmit power is minimized.

[0053] Preferably, the step of using the KM algorithm to match the best RIS reflection element as the reflection point for each user includes:

[0054] Please refer to Figure 3, calculate the transmit power allocated by the base station to each user when each RIS unit serves as a reflection point, so as to construct a cost matrix Φ, where the element in the k-th row and m-th column of the matrix Φ is φ k,m , thus, construct a weighted bipartite graph G=(V, E) according to the minimum transmit power. Let X = [x1, x2,..., x K , Y = [y1, y2,..., y M , x k represents the k-th user, and y m represents the m-th RIS unit. Therefore, we can obtain Let V = X ∪ Y, represents the set of all possible matches. The weight of the edge (x k , y m ) in G is φ k,m .

[0055] First, give the following notations: I(v) represents the label of vertex v, and w(uv) represents the weight of the edge (u, v). Let l(u) represent a feasible vertex label of G, and it needs to satisfy the following condition l(x k ) + l(y m ) ≥ w(x k y m ) = φ k,m . Let Νl(x k ) represent the neighbor set of x k . If x k is connected to y m by an edge, then Nl(x k ) = {y m : l(x k ) + l(y m ) = φ k,m}. Then the neighbor set of S Let Ρ be a matching of G. The maximum matching Ρ satisfies |Ρ′| ≤ |Ρ|, where |Ρ| represents the number of edges in Ρ. In addition, a perfect matching means that all points in the X point set and the Y point set have corresponding matches. If a path alternates between Ρ and E - Ρ and the starting and ending points of the path are not matched, then the path is an augmenting path.

[0056] The steps of the KM algorithm are as follows:

[0057] (1) Add some vertices and edges with weight 0 to G to make it a weighted complete bipartite graph.

[0058] (2) Initialize l(y m ) = 0. For any y m ∈ Y, and

[0059] ⑶ If Ρ is a perfect matching, after deleting the edges with weight 0 and their endpoints in it, the maximum-weight matching of the original graph can be obtained. Otherwise, select an unmatched point x k ∈ X, and let

[0060] ⑷ If N l (S) = Τ, update the feasible vertex labels, and then return to step ⑶.

[0061] The update of the feasible vertex labels includes:

[0062]

[0063]

[0064] ⑸ If N l (S) ≠ Τ, select y m ∈ N l (S) - Τ. If y m is not matched, then x k ~y m is an augmenting path. Update Ρ and go to step ⑶. If y m is matched with x j , then let S = S ∪ x j , Τ = Τ ∪ y m , and then go to step ⑷.

[0065] S3: Use trigonometric functions to convert the distance and direction between the user's best RIS reflection unit and the base station into the distance and direction based on the RIS central reflection unit; and take the position and direction of the RIS central reflection unit as variables, and construct a second base station total transmission power optimization model with the minimum total transmission power of the base station as the optimization objective;

[0066] Please refer to Figure 4 and Figure 5 , preferably, the use of trigonometric functions to convert the distance and direction between the user's best RIS reflection unit and the base station into the distance and direction based on the RIS central reflection unit includes:

[0067] D c = D 0,0 tanθ c

[0068]

[0069] where θ m,n is the incident angle between the base station and the RIS reflection unit in the m-th row and n-th column; D m,n is the distance between the base station and the RIS reflection unit in the m-th row and n-th column; θ c is the incident angle between the base station and the RIS central reflection unit; D0,0 is the distance from the base station to the central reflecting unit of the RIS; dx and dy are the distances between two adjacent RIS units along the x-axis and the y-axis respectively, M×N represents the number of RIS reflecting units, M represents the number of RIS columns, and N represents the number of RIS rows.

[0070] The RIS is a two-dimensional plane. Taking the position and direction of the central reflecting unit of the RIS as variables facilitates the establishment of an optimization model. Considering the position and direction of each RIS reflecting unit is complex and unrealistic because the actual model of the RIS is fixed, the direction of each RIS reflecting unit is the same, and the distance between each RIS reflecting unit is also fixed;

[0071] S31: Calculate the total transmission power of the base station according to the distance and direction between the optimal RIS reflecting unit of the user and the base station with the central reflecting unit of the RIS as a reference;

[0072] Preferably, the total transmission power of the base station includes:

[0073]

[0074]

[0075] where r k,m,n represents the received signal-to-noise ratio when the k-th user uses the reflecting unit in the m-th row and n-th column of the RIS as the reflection point, P k,m,n represents the transmission power allocated by the base station to the k-th user when the k-th user uses the reflecting unit in the m-th row and n-th column of the RIS as the reflection point, σ 2 is the variance of the additive white Gaussian noise received by the user, λ represents the wavelength, G represents the antenna gain, s M represents the width of the reflecting unit, s N represents the length of the reflecting unit, θ m,n is the incident angle between the base station and the reflecting unit in the m-th row and n-th column of the RIS, d BU,k represents the distance between the k-th user and the base station, α represents the path loss exponent, π represents the pi approximately equal to 3.14, M×N represents the number of RIS reflecting units, D m,n is the distance between the base station and the reflecting unit in the m-th row and n-th column of the RIS; M represents the number of RIS columns, N represents the number of RIS rows, P tol is the total transmission power of the base station.

[0076] S32: Take the position and direction of the central reflecting unit of the RIS as variables, and construct a second base station total transmission power optimization model with the minimum total transmission power of the base station as the optimization goal;

[0077] Preferably, the second base station total transmission power optimization model includes:

[0078]

[0079] s.t. 0 ≤ θ c ≤ π / 2,

[0080] D 0,0 > 0

[0081] where θ c is the incident angle between the base station and the RIS central reflection unit, and D 0,0 is the distance from the base station to the RIS central reflection unit; P tol (θ c , D 0,0 ) represents the total transmission power of the base station at θ c and D 0,0 .

[0082] S4: Solve the optimization model of the total transmission power of the second base station through an intelligent optimization algorithm to obtain the optimal position and direction of the RIS central reflection unit. The intelligent optimization algorithm is the optimal algorithm for solving multi-objective problems so far. Although the obtained solution may not be the optimal solution, it will approach the optimal solution infinitely as the number of iterations increases.

[0083] Preferably, the steps of solving the optimization model of the total transmission power of the second base station through the intelligent optimization algorithm are as follows:

[0084] S41: Take the position and direction of the RIS central reflection unit as optimization variables, and randomly generate an initial population P t (t = 0), where t represents the number of iterations, and the initial population contains feasible solutions of the optimization variables;

[0085] S42: Select an evolutionary algorithm to perform an evolutionary operation on the population P t to obtain a new population Q t ; The evolutionary algorithms include: genetic algorithm, differential evolution algorithm, immune algorithm, particle swarm algorithm, whale algorithm, and ant colony algorithm, etc.

[0086] S43: Combine the population P t and Q t to obtain a temporary population P t ∪Q t , and solve the non-dominated solution set R t ∪Q t from P t ∪Q; In multi-objective optimization problems, the dominance relationship is used as a criterion to measure the quality of solutions;

[0087] S44: Increment the current iteration number by 1, and determine whether the current iteration number has reached the preset upper limit. If it has reached, stop running and output R t ; Otherwise, use Rt Copy to P t+1 , and repeat the execution of S42 - S44.

[0088] Under the constraint of the signal - to - noise ratio received by the user, the present invention constructs a cost matrix based on the base - station transmission power with each RIS reflection unit as the reflection point for each user, finds the maximum - weight matching, matches the corresponding RIS reflection unit as the reflection point for each user to obtain the minimum base - station transmission power that meets the constraint conditions, and then obtains the relationship between the distance and direction of each RIS reflection unit from the base station and the central RIS reflection unit through trigonometric relations. By studying the RIS deployment problem, that is, optimizing the two variables of the distance between the central RIS reflection unit and the base station and the RIS direction, the base - station transmission power is reduced.

[0089] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the purpose and scope of the present technical solution, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A method for optimizing the location of RIS in RIS-assisted wireless communication, characterized in that, Including: S1: Based on RIS-assisted wireless communication, with the received signal-to-noise ratio of the user as a constraint and the minimum total transmit power of the base station as the optimization objective, create the first total transmit power optimization model of the base station; The first total transmit power optimization model of the base station includes: s.t.r k,m,n ≥r0 ω k,m,n ∈ {0, 1}, 1 ≤ k ≤ K, 1 ≤ m ≤ M, 1 ≤ n ≤ N where \(r_0\) is the set signal-to-noise ratio of the user receiver; \(\omega\) k,m,n is the weight value, taking values of 0 and 1, \(w\) k,m,n = 0 indicates that the \(k\)-th user is not matched with the reflection unit at the \(m\)-th row and \(n\)-th column of the RIS, \(w\) k,m,n = 1 indicates that the \(k\)-th user is matched with the reflection unit at the \(m\)-th row and \(n\)-th column of the RIS, \(P\) k,m,n represents the transmit power allocated by the base station to the \(k\)-th user when the \(k\)-th user uses the reflection unit at the \(m\)-th row and \(n\)-th column of the RIS as the reflection point, \(r\) k,m,n represents the received signal-to-noise ratio when the \(k\)-th user uses the reflection unit at the \(m\)-th row and \(n\)-th column of the RIS as the reflection point; S2: According to the first total transmit power optimization model of the base station, use the KM algorithm to match the best RIS reflection unit as the reflection point for each user, and obtain the best RIS reflection unit for each user; The process of using the KM algorithm to match the best RIS reflection unit as the reflection point for each user includes: Calculate the transmit power allocated by the base station to each user when each RIS unit is used as a reflection point, so as to construct the cost matrix Φ, where the element in the k-th row and m-th column of the matrix Φ is φ k,m , thus, construct a weighted bipartite graph G=(V,E) according to the minimum transmit power; let X = [x1, x2,..., x K , Y = [y1, y2,..., y M , x k represents the k-th user, y m represents the m-th RIS unit, so we can get Let V = X ∪ Y, represents the set of all possible matches. The weight of the edge (x k , y m ) in G is φ k,m； First, the following symbols are given: I(v) represents the label of vertex v, and w(uv) represents the weight of edge (u, v); l(u) is used to represent the feasible vertex label of G, and the following conditions need to be satisfied lx k ) + l(y m ) ≥ w(x k y m ) = φ k,m , use Ν l (x k ) to represent the neighbor set of x k , and if x k is connected to y m by an edge, then N l (x k ) = {y m : l(x k ) + l(y m ) = φ k,m}, then the neighbor set of S Let Ρ be a matching of G. The maximum matching Ρ satisfies |Ρ′| ≤ |Ρ|, where |Ρ| represents the number of edges in Ρ; in addition, a perfect matching means that all points in the X point set and the Y point set have corresponding matchings; if a path alternates between Ρ and E - Ρ and the start and end points of the path are not matched, then the path is an augmenting path; The steps of the KM algorithm are as follows: ⑴ Add some vertices and edges with weight 0 to G to make it a weighted complete bipartite graph; ⑵ Initialize l(y m ) = 0, for any y m ∈ Y, and ⑶ If Ρ is a perfect matching, after deleting the edges with weight 0 and their endpoints in it, the maximum weight matching of the original graph can be obtained. Otherwise, select an unmatched point x k ∈ X, and let S = x k , ⑷ If N l (S) = Τ, update the feasible vertex label, and then return to step ⑶; The process of updating the feasible vertex labels includes: ⑸ If N l (S)≠Τ, select y m ∈N l (S)-Τ, if y m is not matched, then x k ~y m is an expandable path, update Ρ and go to step ⑶, if y m is matched with x j , then let S = S∪x j , Τ = Τ∪y m , and then go to step ⑷; S3: Use trigonometric functions to transform the distance and direction between the user's best RIS reflection unit and the base station into the distance and direction based on the central reflection unit of the RIS; and use the position and direction of the central reflection unit of the RIS as variables, and construct the second total transmit power optimization model of the base station with the minimum total transmit power of the base station as the optimization objective; The construction process of the second total transmit power optimization model of the base station includes: S31: Calculate the total transmit power of the base station according to the distance and direction between the user's best RIS reflection unit and the base station based on the central reflection unit of the RIS; S32: Use the position and direction of the central reflection unit of the RIS as variables, and construct the second total transmit power optimization model of the base station with the minimum total transmit power of the base station as the optimization objective; S4: Solve the second total transmit power optimization model through an intelligent optimization algorithm to obtain the optimal position and direction of the central reflection unit of the RIS.

2. A method for optimizing the position of RIS in RIS-assisted wireless communication according to claim 1, characterized in that the calculation steps of the received signal-to-noise ratio of the user include: S11: Calculate the channel gain between the user and the RIS reflection unit according to the distance from the user to the RIS reflection unit and the distance from the RIS reflection unit to the base station; S12: Calculate the direct channel gain between the user and the base station according to the distance from the user to the base station; S13: Calculate the comprehensive channel gain of the user according to the direct channel gain between the user and the base station and the channel gain between the user and the RIS reflection unit; S14: Calculate the received signal-to-noise ratio of the user according to the comprehensive channel gain of the user and the transmit power allocated by the base station to the user.

3. A method for optimizing the position of RIS in RIS-assisted wireless communication according to claim 2, characterized in that the received signal-to-noise ratio of the user includes: where, r k,m,n represents the received signal-to-noise ratio when the k-th user uses the reflection unit at the m-th row and n-th column of the RIS as the reflection point, and P k,m,n represents the transmit power allocated by the base station to the k-th user when the k-th user uses the reflection unit at the m-th row and n-th column of the RIS as the reflection point, and σ 2 is the variance of the additive white Gaussian noise received by the user, and h k,m,n represents the combined channel gain when the k-th user uses the reflection unit at the m-th row and n-th column of the RIS as the reflection point.

4. A method for optimizing the position of RIS in RIS-assisted wireless communication according to claim 1, characterized in that the process of using trigonometric functions to transform the distance and direction between the user's best RIS reflection unit and the base station into the distance and direction based on the central reflection unit of the RIS includes: D c = D 0,0 tanθ c where, θ m,n is the incident angle between the base station and the reflection element at the m-th row and n-th column of the RIS; D m,n is the distance between the base station and the reflection element at the m-th row and n-th column of the RIS; θ c is the incident angle between the base station and the central reflection element of the RIS; D 0,0 is the distance from the base station to the central reflection element of the RIS; dx and dy are the distances between two adjacent RIS units along the x-axis and the y-axis respectively, M×N represents the number of RIS reflection elements, M represents the number of RIS columns, and N represents the number of RIS rows.

5. A method for optimizing the position of RIS in RIS-assisted wireless communication according to claim 1, characterized in that the total transmit power of the base station includes: where, r k,m,n represents the received signal-to-noise ratio when the k-th user uses the reflection unit at the m-th row and n-th column of the RIS as the reflection point, P k,m,n represents the transmit power allocated by the base station to the k-th user when the k-th user uses the reflection unit at the m-th row and n-th column of the RIS as the reflection point, σ 2 is the variance of the additive white Gaussian noise received by the user, λ represents the wavelength, G represents the antenna gain, s M represents the width of the reflection unit, s N represents the length of the reflection unit, θ m,n is the incident angle between the base station and the reflection unit at the m-th row and n-th column of the RIS, d BU,k represents the distance between the k-th user and the base station, α represents the path loss exponent, π represents the pi, M×N represents the number of reflection units of the RIS, D m,n is the distance between the base station and the reflection unit at the m-th row and n-th column of the RIS; M represents the number of columns of the RIS, N represents the number of rows of the RIS, P tol is the total transmit power of the base station.

6. The RIS location optimization method for RIS-assisted wireless communication according to claim 1, wherein the second base station total transmission power optimization model includes: s.t. 0 ≤ θ c ≤ π / 2, D 0,0 ＞0 Among them, θ c is the incident angle between the base station and the central reflection unit of the RIS, and D 0,0 is the distance from the base station to the central reflection unit of the RIS; P tol (θ c , D 0,0 ) represents the total transmission power of the base station at θ c and D 0,0 .

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