Multi-RIS collaborative assisted user centralized network rate optimization method

Abstract

The invention belongs to the field of wireless communication, and particularly relates to a multi-RIS cooperative-assisted user centralization network rate optimization method, which comprises the following steps: constructing a user-centered multi-RIS-assisted dense base station deployment wireless communication system; with maximization of the achievable rate of the user as a target, a non-convex and multivariable coupled wireless resource allocation problem is modeled mathematically; the method comprises the following steps of: converting an original complex and difficult-to-solve optimization problem into three sub-problems of respectively solving user multi-association, base station transmitted beam forming design and multi-RIS collaborative phase shift matrix design by utilizing a Lagrange duality theory, a fractional programming method and the like through an alternative iterative optimization idea; according to the invention, under the condition of considering the association of different base stations and RISs, the multi-link reachable rate of the user is maximized, the influence when the line-of-sight link between the base station and the user is blocked is reduced, and the practicability is high.

Description

Technical Field

[0001] The invention belongs to the field of wireless communications, and in particular relates to a user-centric network rate optimization method assisted by multi-RIS collaboration. Background Art

[0002] High-frequency wireless communications, such as millimeter-wave communications and terahertz communications, have significantly improved system capacity with their extensive spectrum resources and effectively alleviated the problem of spectrum resource shortage. However, compared with microwave communications, high-frequency signals face more serious path loss during transmission and are more easily blocked by obstacles (such as buildings, trees, etc.). To solve this problem, a reconfigurable intelligent surface (RIS) can be introduced to reconstruct the wireless signal transmission environment. RIS is a two-dimensional surface composed of programmable electromagnetic metamaterials that can be adjusted in real time according to the environment and communication requirements. By adjusting the amplitude or phase of each reflective unit, the output signal can be adjusted, optimized or enhanced. Using the above characteristics, RIS can build a virtual line-of-sight link, thereby eliminating adverse channel factors such as blocking and fading, and improving the performance of the communication system. In addition, RIS has the advantages of green energy saving, easy deployment and low cost, so it is widely used in wireless communication systems.

[0003] For RIS-assisted wireless communication systems, most of the existing literature focuses on the scenarios of a single RIS or multiple distributed RISs, and a small number of them consider the potential gains brought by the collaboration between multiple RISs. The research results show that compared with the traditional single RIS or distributed RIS assistance, RIS collaborative assistance can further improve the system performance. However, the existing research schemes for RIS collaborative assistance are mainly limited to single base station communication systems, and the user-centric dense base station deployment scenario needs to be further studied. The present invention is committed to breaking this limitation. In the dense base station deployment scenario, the user multi-association problem is the key to ensuring the user service quality, involving power allocation and multi-cell interference issues; for the same service base station, different RIS collaborative assistance effects vary greatly; active-passive joint beamforming design is the basis for RIS-assisted high-frequency wireless communication systems to achieve higher transmission rates.

[0004] In summary, the problem of the existing technology is that the impact of RIS collaboration is rarely considered, the advantages of RIS are not fully utilized, and it is limited to a single base station communication system. Therefore, in order to maximize the user's achievable rate and fully utilize the effective gain of RIS collaboration, a resource scheduling method for a dense high-frequency wireless communication system with multiple RIS collaboration is urgently needed. Summary of the invention

[0005] To solve the above problems, the present invention provides a user-centric network rate optimization method assisted by multi-RIS collaboration, which aims to maximize the user multi-link reachability and rate through wireless resource allocation under strict constraints of parameters such as the maximum number of user associations, the maximum transmission power of the base station and the phase of the RIS reflection unit.

[0006] The specific plan includes the following steps:

[0007] S1. Construct a user-centric multi-RIS collaborative assisted dense base station deployment high-frequency wireless communication network model;

[0008] S2. Taking the user's achievable rate as the goal, a non-convex multi-variable coupled wireless resource allocation optimization problem is constructed; wherein the user's achievable rate is the sum of the rates of all achievable links between the user and multiple base stations associated with it;

[0009] S3. Decouple the wireless resource allocation optimization problem into three sub-problems: user multi-association, base station transmit beamforming design, and multi-RIS coordinated phase shift matrix design;

[0010] S4. The three sub-problems are jointly solved iteratively to obtain the optimal user multi-association parameters, the optimal base station transmit beamforming vector and the optimal multi-RIS cooperative phase shift matrix.

[0011] Furthermore, the wireless resource allocation optimization problem is decoupled into three sub-problems, including:

[0012] S31. In the wireless resource allocation optimization problem, the base station beamforming vector and the multi-RIS coordinated phase shift matrix are fixed, and the constraint C2 is relaxed to 0≤c b ≤1, we get the user multi-association sub-problem;

[0013] S32. In the wireless resource allocation optimization problem, the user association parameters are fixed and auxiliary variables are introduced Construct a new optimization problem; after processing the new optimization problem, obtain the conversion problem, and given the multi-RIS cooperative phase shift matrix, obtain the base station transmit beamforming design sub-problem;

[0014] S33. In the conversion problem, given the base station beamforming vector, the multi-RIS coordinated phase shift matrix sub-problem is obtained.

[0015] Furthermore, step S4 specifically includes:

[0016] S41. Use Lagrangian dual decomposition method to solve the user multi-association sub-problem and update the user association parameters;

[0017] S42. Selecting an auxiliary RIS group for the user and its associated base station based on the shortest distance criterion, solving the base station transmit beamforming matrix design subproblem according to the auxiliary RIS group and the user association parameters updated in step S41, and updating the base station transmit beamforming vector;

[0018] S43. Based on the user association parameters updated in step S41 and the base station transmit beamforming vector updated in step S42, solve the multi-RIS coordinated phase shift matrix design sub-problem and update the multi-RIS coordinated phase shift matrix;

[0019] S44. Determine whether the wireless resource allocation optimization problem converges (i.e., the user achievable rate obtained in the latest iteration does not increase compared to the user achievable rate obtained in the previous iteration, or the maximum number of iterations is reached). If so, obtain the optimal user multi-association parameters, the optimal base station transmit beamforming vector, and the optimal multi-RIS coordinated phase shift matrix. If not, return to step S41.

[0020] Furthermore, step S41 adopts the Lagrangian dual decomposition method to solve the user multi-correlation sub-problem, including:

[0021] S411. Introducing Lagrange multiplier v to construct Lagrange function and the Lagrangian dual function g(v);

[0022] S412. Minimizing the Lagrangian dual function forms a dual problem;

[0023] S413. Update the user association parameter according to the Lagrange multiplier v;

[0024] S414. Use the subgradient algorithm to update the Lagrange multiplier v; then determine whether the Lagrange multiplier v converges. If so, the dual problem reaches the global optimum and the current user association parameters are output. If not, return to step S413.

[0025] Further, step S42 specifically includes:

[0026] S421. Pair all RISs in pairs to obtain multiple RIS pairs. For each base station b=1, 2, .., B. associated with the user, calculate the total path distance between all RIS pairs and the base station b, and select a RIS pair with the smallest total path distance as the auxiliary RIS group of the base station b; wherein the total path distance between any RIS pair (RIS i, RIS j) and the base station b is the sum of the distance from the base station b to RIS i, the distance from RIS i to RIS j, and the distance from RIS j to the user;

[0027] S422. Introducing a complex auxiliary vector ξ, the base station transmit beamforming design sub-problem is transformed into a biconvex optimization problem about the base station transmit beamforming vector and the complex auxiliary vector ξ;

[0028] S423. Fix the base station transmit beamforming vector in the double convex optimization problem and solve to obtain the optimal complex auxiliary vector ξ opt ;

[0029] S424. The optimal complex auxiliary vector ξ opt Substitute the double convex optimization problem into the solution to obtain the optimal base station transmit beamforming vector.

[0030] Further, step S43 specifically includes:

[0031] S431. Introduce auxiliary parameter ε to reconstruct the multi-RIS cooperative phase shift matrix design sub-problem to obtain the reconstruction optimization problem;

[0032] S432. Fixed multi-RIS coordinated phase shift matrix in reconstruction optimization problem, and solved to obtain the optimal auxiliary parameter ε opt ;

[0033] S433. The optimal auxiliary parameter ε opt Substitute it into the reconstruction optimization problem and solve it to obtain the optimal multi-RIS cooperative phase shift matrix.

[0034] Beneficial effects of the present invention:

[0035] Compared with the prior art, the present invention not only considers the optimization of user multi-association, but also particularly proposes the joint optimization design of base station transmit beamforming matrix and multi-RIS cooperative phase shift matrix under multi-association conditions. In addition, the present invention maximizes the reflection characteristics of multi-RIS by dynamically adjusting the cooperative strategy of base station and RIS, and significantly improves the multi-link reachable rate of users. For the determination of cooperative RIS, the present invention uses the minimum distance criterion, that is, by calculating the minimum value of the sum of the distances between the base station, RIS and the user to determine the optimal auxiliary RIS combination.

[0036] Faced with the non-convex optimization problem of multi-variable coupling, the present invention decouples it into three sub-problems through decomposition technology, and adopts the method of alternating iterative optimization to achieve it. It presents closed-form updates during the optimization process, which can reduce the computational complexity. First, when the base station transmit beamforming matrix and RIS phase shift matrix are fixed, the user multi-correlation sub-problem is solved by the Lagrangian dual decomposition method. Subsequently, by applying the fractional programming method, the optimization problem of the base station transmit beamforming matrix design and the RIS phase shift matrix design is simplified, and the closed-form optimal solution is obtained more easily. The algorithm proposed in the present invention can greatly reduce the computational complexity while improving the system performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flow chart of a multi-RIS collaboratively assisted user-centric network rate optimization method of the present invention;

[0038] Figure 2 A flow chart for solving the joint optimization problem in the present invention;

[0039] Figure 3 This is a diagram of a user-centric wireless communication network model assisted by multiple RIS collaboration of the present invention;

[0040] Figure 4 is the sum rate performance for multi-user association and single-user association cases;

[0041] Figure 5 Simulation diagram of users and rates under different RIS selection methods. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] The present invention provides a user-centric network rate optimization method assisted by multiple RIS collaboration, such as Figure 1-Figure 3 As shown, the following steps are included:

[0044] S1. Construct a user-centric multi-RIS collaboratively assisted dense base station deployment high-frequency wireless communication network model.

[0045] Specifically, the present invention considers a user-centric multi-RIS-assisted dense base station deployment high-frequency wireless communication network, the network model is as follows Figure 3 As shown in the figure, it consists of multiple base stations, multiple RIS and one user, where all base stations are millimeter wave base stations, the millimeter wave base stations are equipped with uniform linear arrays, and the RIS are equipped with uniform planar arrays. The RIS are randomly distributed between the base stations and the users, and it is assumed that there is a certain blocking probability in the line-of-sight link between the base stations and the users. By properly adjusting the phase of the RIS, a virtual line-of-sight link can be collaboratively constructed to ensure the communication quality of the users.

[0046] Specifically, in the network model constructed by the present invention, the base station set is recorded as The RIS set is denoted as B represents the number of base stations, and L represents the number of RISs. The user is equipped with M antennas, and each base station is equipped with M tantennas, assuming that each antenna of the user can be regarded as a virtual single-antenna user at the same location. Each antenna, i.e., each virtual single-antenna user, is associated with different base stations and receives independent data streams from these base stations, thus realizing virtual space division multiplexing and improving the user's achievable rate. Each RIS is equipped with N = N x ×N y Reflection units, N x Represents the number of horizontally arranged reflection units in RIS, N y represents the number of vertically arranged reflection units in RIS; define Φ i =diag(φ i )express The phase shift matrix, where φ i =[φ i,1 ,φ i,2 ,...,φ i,N ] T represents the phase shift vector of RIS i. Each element in the phase shift vector can be further described as represents the phase shift of the nth reflector unit of RIS i, θ i,n ∈[0,2π) represents the phase shift angle of the nth reflection unit of RISi; definition Indicates base station Channels with users; represents the channel between base station b and RIS i, represents base station b and The channel between represents the channel between RIS i and RIS j; represents the channel between RIS i and the user, represents the channel between RIS j and the user; All are real matrices.

[0047] Most studies on RIS-assisted millimeter wave communications assume that all line-of-sight links between base stations and users are blocked, but this assumption is too idealistic. To be more realistic, this paper considers introducing an equivalent line-of-sight sphere model to approximate the actual line-of-sight area, where the equivalent line-of-sight sphere is a radius of r. b A spherical shape of fixed size; length L d The probability that the link is a line-of-sight link is Define the blocking coefficient e b , used to indicate whether there is a line-of-sight link between the user and base station b. LOS When it is greater than or equal to the threshold τ, define e b =1, indicating that the line-of-sight link exists, otherwise e b =0, indicating that the line-of-sight link is blocked.

[0048] Therefore, the equivalent cascade channel between base station b and user can be expressed as

[0049]

[0050] The present invention uses the Saleh-Valenzuela channel model to study, taking the channels from base station b to RIS i and RIS i to the user as examples, which are respectively

[0051]

[0052] Where N BI represents the number of paths between base station b and RIS i, N IU represents the number of paths between RIS i and the user; ρ BI,l represents the complex gain of the lth link between base station b and RIS i, ρ IU,l represents the complex gain of the lth link between RIS i and the user; a r represents the array response vector at the receiving end, a t represents the array response vector of the transmitter; and Respectively represent the direction angle and pitch angle of arrival at RIS; θ l is the direction angle away from the base station; and Denote the direction angle and elevation angle from RIS respectively; define the association coefficient c between the user and base station b b , if c b =1, it means the user is associated with base station b. If c b = 0, it means that the user is not associated with base station b; the set of base stations associated with the user (i.e., the base stations providing services to the user) is defined as The signal sent by base station b is defined as:

[0053] x b =w b s b

[0054] where s b represents the signal sent by base station b to the user, represents the transmit beamforming vector of base station b, is a real number matrix; therefore, for base station b, the signal received at the user is the useful signal received by the virtual single-antenna user from the relevant base station b, and the signal sent by other base stations to other virtual users; it can also be understood that the signal received at the user is the sum of the useful signals received by M virtual single-antenna users from their associated base stations, which can be expressed as:

[0055]

[0056] in represents the complex additive white Gaussian noise at the user; the received signal-to-interference-noise ratio at the user can be expressed as:

[0057]

[0058] where σ 2 is the noise power. According to Shannon’s formula, the rate from base station b to the user is

[0059] R b =log(1+γ b ).

[0060] S2. With the goal of maximizing the user's achievable rate, a non-convex multivariable coupled wireless resource allocation optimization problem is constructed.

[0061] Specifically, the user achievable rate in the present invention is defined as the sum of the rates of all achievable links between the user and multiple base stations associated with the user, which is mainly affected by three key variables: user multi-association parameters, base station transmit beamforming matrix and RIS phase shift matrix; by setting the constraints of the key variables, a non-convex multi-variable coupled wireless resource allocation optimization problem with the goal of maximizing the user achievable rate is established, which is expressed as:

[0062]

[0063] Where C1 represents the number of base stations that users can associate with, C2 is the association coefficient constraint, C3 is the maximum base station transmission power constraint, and C4 is the RIS reflection unit phase shift constraint; C={c1,c2,…,c B} represents the user association parameter, W = {w1,w2,…,w B} represents the base station beamforming vector, Φ i represents the phase shift matrix of RIS i, Φ j express The phase shift matrix of B represents the number of base stations; define c b =1, indicating that the user and the base station association, otherwise c b =0, indicating that the user is not associated with base station b; R b represents the rate of the link between the user and base station b; M represents the maximum number of associations, P max Indicates the maximum transmission power of the base station, w b represents the transmit beamforming vector of base station b; L represents the number of RIS, N represents the total number of reflection units of a single RIS; φ l,n represents the phase shift of the nth reflection unit of RIS 1.

[0064] S3. Decouple the wireless resource allocation optimization problem into three sub-problems: user multi-association, base station transmit beamforming design, and multi-RIS cooperative phase shift matrix design.

[0065] Specifically, the wireless resource allocation optimization problem involves a high degree of coupling of multiple optimization variables, which is a non-convex optimization problem and is difficult to solve directly. By using the idea of ​​alternating iterative optimization and the Lagrange duality theory, fractional programming and other methods, the complex and difficult wireless resource allocation optimization problem in step 2 is decoupled into three sub-problems: user multi-association, base station transmit beamforming matrix design, and multi-RIS collaborative phase shift matrix design, including:

[0066] S31. In the wireless resource allocation optimization problem, the base station beamforming vector W and the multi-RIS coordinated phase shift matrix Φ are fixed, and the constraint C2 in equation (1) is relaxed to 0≤c b ≤1, we get the user multi-association sub-problem, which can be expressed as:

[0067]

[0068] S32. In the wireless resource allocation optimization problem, the user association parameter C is fixed according to the optimization result of the user multi-association sub-problem, and an auxiliary variable is introduced Construct a new optimization problem, expressed as:

[0069]

[0070] in When α is fixed, let The signal-to-interference-noise ratio γ b Substituting f2 into the conversion problem, it can be expressed as:

[0071]

[0072] Given the multi-RIS coordinated phase shift matrix Φ, the base station transmit beamforming design sub-problem is obtained:

[0073]

[0074] S33. Similarly, based on the conversion problem of equation (4), given the base station beamforming vector W, the multi-RIS coordinated phase shift matrix sub-problem is obtained:

[0075]

[0076] At this point, the original optimization problem (1) is successfully decoupled into the user multi-association sub-problem (2), the base station transmit beamforming matrix design sub-problem (5) and the multi-RIS cooperative phase shift matrix design sub-problem (6).

[0077] S4. The three sub-problems are jointly solved iteratively to obtain the optimal user multi-association parameters, the optimal base station transmit beamforming vector and the optimal multi-RIS cooperative phase shift matrix.

[0078] Specifically, Figure 2 As shown, step S4 includes:

[0079] S41. Parameter initialization: user association parameter C, base station transmit beamforming vector W, phase shift matrix of each RIS Perform initialization to provide initial conditions for subsequent iterative optimization;

[0080] S42. Using the Lagrange dual decomposition method to solve the user multi-association sub-problem, that is, determining which base stations the user should associate with to provide services for the user, and updating the user association parameter C, specifically including:

[0081] The user multi-correlation subproblem is a convex optimization problem, which can be analyzed using the Lagrangian duality theory. The Lagrangian multiplier v is introduced to construct the Lagrangian function

[0082]

[0083] To relax the coupling constraints, we can further construct the Lagrangian dual function Minimize the Lagrangian dual function on the feasible domain of the dual multiplier, forming the dual problem as

[0084]

[0085] In each iteration, the user association parameter (the association coefficient between the user and each base station) can be updated in the following way

[0086]

[0087] Indicates the relationship between the user and the base station at the t+1th iteration The correlation coefficient of The index value b of base station b is equal to

[0088] The Lagrange multiplier v is updated by the subgradient method as follows,

[0089]

[0090] is the step size of the tth iteration

[0091] According to equations (8) and (9), the parameters of the user end and the base station end are iteratively updated, and finally the optimal user multi-association solution is obtained.

[0092] S43. Based on the shortest distance criterion, multiple RIS are selected for the user and its associated base station for auxiliary communication; then the base station transmit beamforming matrix design sub-problem is solved, and the base station transmit beamforming vector is updated.

[0093] Specifically, after successfully solving the user multi-association sub-problem, it is crucial to select a RIS combination that provides services for the target user and the associated base station. In order to give full play to the advantages of RIS, the present invention proposes a RIS selection scheme based on the shortest distance. It mainly includes: first, numbering all RIS, such as RIS1, RIS 2, ..., RIS L; then, pairing any two RIS, such as (RIS1, RIS2), (RIS2, RIS 1), (RIS1, RIS 3), ..., (RIS i, RIS j); finally, selecting an auxiliary RIS group for each base station associated with the user, specifically including, for each base station associated with the user, calculating the total path distance of each RIS pair between the base station and the user, until all the above RIS combinations are traversed; record d b,i d i,j and d j are the distances from base station b to RIS i, RIS i to RIS j, and RIS j to the user, respectively. According to the formula A RIS pair with the smallest total path distance is selected for cooperatively assisting user-base station communication. This selection method can reduce transmission loss and improve the overall transmission rate and reliability of the system.

[0094] Specifically, the base station transmit beamforming design sub-problem of equation (5) is a multi-factor programming problem. Therefore, the present invention considers further converting it into a more tractable function form by using a quadratic transformation, such as

[0095]

[0096] in is a complex auxiliary vector. Therefore, the optimization problem of the base station transmit beamforming vector W can be equivalent to a biconvex optimization problem about W and ξ, which can be specifically expressed as

[0097]

[0098] The solution to the optimization problem (10) can be divided into two steps. First, the complex auxiliary vector ξ is optimized when the base station transmits the beamforming vector W, and the optimal complex auxiliary vector ξ is obtained. opt ; Then, using the complex auxiliary vector ξ opt Solve for the optimal base station beamforming vector W opt .

[0099] When the fixed base station transmits the beamforming vector W, let f 2,2 Yes b Taking partial derivatives, we can get

[0100]

[0101] By setting Available

[0102]

[0103] Afterwards, the optimization objective function can be reformulated as f 2.2 (W,ξ opt ). The optimization problem of the base station transmit beamforming vector W can be solved using the Lagrange multiplier method. Specifically, the Lagrange multiplier λ = [λ1,λ2,...,λ B ] T Construct the Lagrangian function, which is described as

[0104]

[0105] The optimal base station transmit beamforming vector W opt You can set Get, specifically The optimal Lagrange multiplier λ should satisfy And can be obtained by dichotomy.

[0106] In the whole process, the complex auxiliary vector, the base station transmit beamforming vector and the Lagrange multiplier are iteratively updated until the base station transmit beamforming problem converges.

[0107] S44. Based on the user association parameters updated in step S41 and the base station transmit beamforming vector updated in step S42, solve the multi-RIS coordinated phase shift matrix design sub-problem and update the multi-RIS coordinated phase shift matrix.

[0108] Specifically, after updating the base station transmit beamforming matrix, the wireless resource allocation optimization problem is decomposed according to step S3 to obtain a multi-RIS collaborative phase shift matrix design subproblem (6). The multi-RIS collaborative phase shift matrix design subproblem is a non-convex optimization problem. The present invention transforms this subproblem into a problem of lower complexity through a fractional programming method. First, an auxiliary parameter is introduced to reconstruct the multi-RIS collaborative phase shift matrix design subproblem, and the auxiliary parameter is solved by setting the partial derivative to zero; then, the multi-RIS collaborative phase shift matrix design subproblem is transformed into a reconstruction optimization problem, and a closed-form approximate solution to the RIS phase shift problem can be directly obtained; finally, the phase shift matrices of other auxiliary RIS are optimized according to the same process. Among them, it can be noted that Φ i and Φ jIn the channel Therefore, the proposed Φ i The optimization method is also applicable to Φ j The present invention has a fixed Φ j Time pair Φ i First, the channel is reconstructed as follows:

[0109]

[0110] Among them A i,j =g i +g j Φ j F i,j , B b,j =e b H b +g j Φ j G b,j is the reconstructed channel parameter.

[0111] Due to Φ i =diag(φ i ), φ i =[φ i,1 ,φ i,2 ,...,φ i,N ] T , Channel It can be further expressed as Substituting the reconstructed channel into the objective function of formula (6), we can get the new optimization objective function:

[0112]

[0113] Define auxiliary variable x b,i,j =diag(A i,j )G b,i w b , z b,j =B b,j w b , x b′,i,j =diag(A i,j )G b′,i w b′ , z b′,j =B b′,j w b′ . Through the second transformation, f 3.1 (φ i ) is converted to

[0114]

[0115] in is the auxiliary variable introduced. Therefore, the corresponding reconstruction optimization problem can be described as

[0116]

[0117] The solution of this optimization problem can be divided into two main steps. First, fix the variable φ i To simplify the original problem and solve the optimal auxiliary variable ε opt . Secondly, after obtaining the optimal auxiliary variable ε opt On the basis of i Specifically, let Available Thus, the optimization objective function can be obtained as

[0118]

[0119] in

[0120]

[0121] The second term in formula (17) can be expanded as Since P is a Hermitian matrix, φ i H Pφ i can be rewritten as

[0122]

[0123] Then, substitute equation (18) into equation (17) and ignore the constant term q2, f 3.3 (φ i ,ε opt ) can be restated as

[0124]

[0125] (a) holds because η n and the constant term q3 is as follows

[0126]

[0127] Discarding terms unrelated to phase shift, maximizing f 3.4 (φ i,n ,ε opt ) is equivalent to maximizing Therefore, the optimal Can be Update. In addition, due to Φ i and Φ j In the channel is symmetric, that is, we only need to consider optimizing Φi and Φ j One of them, the other can be solved using a similar method.

[0128] S45. Determine whether the wireless resource allocation optimization problem converges. If so, obtain the optimal user multi-association parameters, the optimal base station transmit beamforming vector and the optimal multi-RIS coordinated phase shift matrix. If not, return to step S42.

[0129] In one embodiment, the present invention compares the sum rate (i.e., the user achievable rate) and the maximum transmission power P of the base station under different numbers of antennas (i.e., different association numbers) of the user. max The relationship between Figure 4 As shown in the figure, when M = 1, that is, the user can only associate with one base station at most, the system total rate is significantly lower than the case of multiple associations. This is because multiple associations can better utilize the resources of multiple base stations. In addition, as P max As the rate increases, the sum of all schemes will increase, and the algorithm proposed in this invention is superior to other schemes, which shows that multi-RIS cooperation can effectively improve the system sum rate.

[0130] Figure 5 Shows the maximum transmission power P of the base station for different schemes max The sum rate changes under P max As the number of RIS pairs increases, the sum of the rates of all algorithms increases. Since the scheme proposed by the present invention can select RIS pairs that provide excellent auxiliary effects, its performance is better than that of the fixed cooperative RIS pair scheme. In addition, it can be found from the figure that the performance of the single RIS scheme is poor compared with other multi-RIS cooperative schemes.

[0131] In the present invention, unless otherwise clearly stipulated and limited, the terms such as "installation", "setting", "connection", "fixation" and "rotation" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral one; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium; it can be the internal connection of two elements or the interaction relationship between two elements. Unless otherwise clearly defined, ordinary technicians in this field can understand the specific meanings of the above terms in the present invention according to the specific circumstances.

[0132] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A user-centric network rate optimization method assisted by multi-RIS collaboration, characterized in that: The following steps are involved: S1. Construct a user-centric multi-RIS collaborative assisted dense base station deployment high-frequency wireless communication network model; S2. Taking the user's achievable rate as the goal, a non-convex multi-variable coupled wireless resource allocation optimization problem is constructed; wherein the user's achievable rate is the sum of the rates of all achievable links between the user and multiple base stations associated with it; S3. Decouple the wireless resource allocation optimization problem into three sub-problems: user multi-association, base station transmit beamforming design, and multi-RIS coordinated phase shift matrix design; S4. The three sub-problems are jointly solved iteratively to obtain the optimal user multi-association parameters, the optimal base station transmit beamforming vector and the optimal multi-RIS cooperative phase shift matrix.

2. According to claim 1, a multi-RIS collaborative assisted user-centric network rate optimization method is characterized in that: The high-frequency wireless communication network model with dense base station deployment includes multiple base stations, multiple RIS and one user. The base station set is denoted as The RIS set is denoted as B represents the number of base stations, and L represents the number of RISs. The user is equipped with M antennas, and each base station is equipped with M t antennas, assuming that the user's M antennas are regarded as M virtual single-antenna users at the same location, each antenna is associated with a different base station and receives independent data streams from these base stations to achieve virtual space division multiplexing; each RIS is equipped with N = N x ×N y Reflection units, N x Represents the number of horizontally arranged reflection units in RIS, N y It represents the number of vertically arranged reflection units in RIS; Definition Φ i =diag(φ i )express The phase shift matrix, where φ i =[φ i,1 ,φ i,2 ,...,φ i,N ] T represents the phase shift vector of RISi, represents the phase shift of the nth reflector unit of RISi, θ i,n ∈[0,2π) represents the phase shift angle of the nth reflection unit of RISi; define H b Indicates base station Channel between users; G b,i represents the channel between base station b and RISi; F i,j Represents RISi and The channel between i represents the channel between RISi and the user; The introduction radius is r b The equivalent sight sphere model with a length of L d The probability that the link is a line-of-sight link is Define the blocking coefficient e b , used to indicate whether there is a line-of-sight link between the user and base station b. LOS When it is greater than or equal to the threshold τ, define e b =1, indicating that the line-of-sight link exists, otherwise e b =0, indicating that the line-of-sight link is blocked; Therefore, the equivalent cascade channel between base station b and the user is expressed as: Φ j express The phase shift matrix, g j represents the channel between RIS j and the user, G b,j represents the channel between base station b and RIS j; where: Where N BI represents the number of paths between base station b and RISi, N IU represents the number of paths between RISi and the user; ρ BI,l represents the complex gain of the lth link between base station b and RISi, ρ IU,l represents the complex gain of the lth link between RISi and the user; a r represents the array response vector at the receiving end, a t represents the array response vector of the transmitter; and Respectively represent the direction angle and pitch angle of arrival at RIS; θ l is the direction angle away from the base station; and Denote the direction angle and elevation angle from RIS respectively; define the association coefficient c between the user and base station b b , if c b =1, it means the user is associated with base station b. If c b = 0, it means that the user is not associated with base station b; the base station set associated with the user is defined as The signal sent by base station b is defined as: x b =w b s b where s b represents the signal sent by base station b to the user, w b represents the transmit beamforming vector of base station b; therefore, for base station b, the signal received at the user is the useful signal received by the virtual user from the relevant base station b, as well as the signal sent by other base stations to other virtual users, expressed as: in represents the complex additive Gaussian white noise at the user; the received signal-to-interference-noise ratio at the user is expressed as: where σ 2 is the noise power.

3. According to claim 1, a multi-RIS collaboratively assisted user-centric network rate optimization method is characterized in that: The non-convex multivariable coupled wireless resource allocation optimization problem is expressed as: C4:φ l,n |=1,l=1,2,...,L,n=1,2,...,N Where C1 represents the number of base stations that users can associate with, C2 is the association coefficient constraint, C3 is the maximum base station transmission power constraint, and C4 is the RIS reflection unit phase shift constraint; C={c1,c2,…,c B } represents user-related parameters, c b ∈C represents the association coefficient between the user and the base station b; W = {w1,w2,…,w B } represents the base station beamforming vector, w b represents the transmit beamforming vector of base station b; Φ i represents the phase shift matrix of RISi, Φ j express The phase shift matrix of B represents the number of base stations; define c b =1, indicating that the user and the base station association, otherwise c b =0, indicating that the user is not associated with base station b; R b represents the rate of the link between the user and base station b; M represents the maximum number of associations, P max Indicates the maximum transmission power of the base station, w b represents the transmit beamforming vector of base station b; L represents the number of RIS; φ l,n represents the phase shift of the nth reflection unit of RIS 1, and N represents the total number of reflection units in a single RIS.

4. According to claim 3, a multi-RIS collaboratively assisted user-centric network rate optimization method is characterized in that: The wireless resource allocation optimization problem is decoupled into three sub-problems, including: S31. In the wireless resource allocation optimization problem, the base station beamforming vector and the multi-RIS coordinated phase shift matrix are fixed, and the constraint C2 is relaxed to 0≤c b ≤1, we get the user multi-association sub-problem; S32. In the wireless resource allocation optimization problem, the user association parameters are fixed and auxiliary variables are introduced Construct a new optimization problem; after processing the new optimization problem, obtain the conversion problem, and given the multi-RIS cooperative phase shift matrix, obtain the base station transmit beamforming design sub-problem; S33. In the conversion problem, given the base station beamforming vector, the multi-RIS coordinated phase shift matrix sub-problem is obtained.

5. According to claim 1, a multi-RIS collaboratively assisted user-centric network rate optimization method is characterized in that: Step S4 specifically includes: S41. Use Lagrangian dual decomposition method to solve the user multi-association sub-problem and update the user association parameters; S42. Selecting an auxiliary RIS group for the user and its associated base station based on the shortest distance criterion, solving the base station transmit beamforming matrix design subproblem according to the auxiliary RIS group and the user association parameters updated in step S41, and updating the base station transmit beamforming vector; S43. Based on the user association parameters updated in step S41 and the base station transmit beamforming vector updated in step S42, solve the multi-RIS coordinated phase shift matrix design sub-problem and update the multi-RIS coordinated phase shift matrix; S44. Determine whether the wireless resource allocation optimization problem converges. If so, obtain the optimal user multi-association parameters, the optimal base station transmit beamforming vector and the optimal multi-RIS coordinated phase shift matrix. If not, return to step S41.

6. The method for user-centric network rate optimization assisted by multi-RIS collaboration according to claim 5 is characterized in that: Step S41 uses the Lagrange dual decomposition method to solve the user multi-association sub-problem, including: S411. Introducing Lagrange multiplier v to construct Lagrange function and the Lagrangian dual function g(v); S412. Minimizing the Lagrangian dual function forms a dual problem; S413. Update the user association parameter according to the Lagrange multiplier v; S414. Use the subgradient algorithm to update the Lagrange multiplier v; then determine whether the Lagrange multiplier v converges. If so, the dual problem reaches the global optimum and the current user association parameters are output. If not, return to step S413.

7. The method for user-centric network rate optimization assisted by multi-RIS collaboration according to claim 5, characterized in that: Step S42 specifically includes: S421. Pair all RISs in pairs to obtain multiple RIS pairs, for each base station associated with the user Calculate the total path distance between all RIS pairs and base station b, and select the RIS pair with the smallest total path distance as the auxiliary RIS group of base station b; the total path distance between any RIS pair (RISi, RIS j) and base station b is the distance from base station b to Distance from RISi to The sum of the distance from RIS j to the user and the distance from RIS j to the user; represents the base station set, Represents a RIS set; S422. Introducing a complex auxiliary vector ξ, the base station transmit beamforming design sub-problem is transformed into a biconvex optimization problem about the base station transmit beamforming vector and the complex auxiliary vector ξ; S423. Fix the base station transmit beamforming vector in the double convex optimization problem and solve to obtain the optimal complex auxiliary vector ξ opt ; S424. The optimal complex auxiliary vector ξ opt Substitute the double convex optimization problem into the solution to obtain the optimal base station transmit beamforming vector.

8. The method for user-centric network rate optimization assisted by multi-RIS collaboration according to claim 5, characterized in that: Step S43 specifically includes: S431. Introduce auxiliary parameter ε to reconstruct the multi-RIS cooperative phase shift matrix design sub-problem to obtain the reconstruction optimization problem; S432. Fixed multi-RIS coordinated phase shift matrix in reconstruction optimization problem, and solved to obtain the optimal auxiliary parameter ε opt ; S433. The optimal auxiliary parameter ε opt Substitute it into the reconstruction optimization problem and solve it to obtain the optimal multi-RIS cooperative phase shift matrix.

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