Parameter optimization method for MIMO communication system assisted by reconfigurable smart surface

Abstract

The present application relates to a parameter optimization method for a reconfigurable intelligent surface-assisted MIMO communication system, including: taking the weighted rate and WSR maximization of all users as the optimization target, maximizing the weighted rate and WSR of all users by jointly optimizing the transmit precoding matrix F at the base station and the reflection phase shift matrix θ at the RIS; decoupling the optimization target into two sub-targets, and achieving the optimization target of maximizing the weighted rate and WSR of all users by alternately optimizing the transmit precoding matrix F at the base station and the reflection phase shift matrix θ at the RIS. The present application can be applied to the case of multiple cells and when hardware loss is considered, a weighted rate and WSR maximization model is constructed in this way, so that the weighted rate and the weighted rate of users in the maximized system are obtained very accurately.

Description

Technical Field

[0001] The present application relates to the field of MIMO communication systems, and in particular to a parameter optimization method for a MIMO communication system assisted by a reconfigurable intelligent surface. Background Art

[0002] With the rapid growth of mobile communication devices, the diversified data streams, applications and communication requirements have brought huge challenges to wireless communication. Complex communication environments and communication requirements require the combination of multiple communication technologies to improve the performance of communication systems. Technologies that are easy to combine with existing communication systems have become the main research direction of the sixth-generation (6G) and future wireless communications. As one of the cutting-edge technologies that may be used in future 6G communications, reconfigurable intelligent surface RIS technology can effectively cope with the challenges arising in 6G wireless communications.

[0003] In the prior art, the related literature first derived a closed-form solution of the BS transmit beamforming vector for RIS-assisted multi-antenna communication systems with transceiver hardware loss in order to maximize the received signal-to-interference-plus-noise ratio (SINR), and then optimized the unit mode constraint of the RIS reflection unit phase shift angle, and obtained the upper bound of the optimal target value by solving a single convex problem. However, this technology only considers the simple case of a single base station, a single cell and a user, and there is currently no research on the weighted sum rate (WSR) of users in a system that maximizes hardware loss in the case of multiple cells. Summary of the invention

[0004] In order to obtain a weighted rate sum of users in a system that is maximized when hardware loss is considered in a multi-cell situation, the present application provides a parameter optimization method for a reconfigurable smart surface-assisted MIMO communication system.

[0005] The present application provides a parameter optimization method for a reconfigurable intelligent surface-assisted MIMO communication system using the following technical solution:

[0006] A parameter optimization method for a MIMO communication system assisted by a reconfigurable intelligent surface, comprising:

[0007] The weighted rate and WSR of all users are maximized as the optimization goal, and the weighted rate and WSR of all users are maximized by jointly optimizing the transmit precoding matrix F at the base station and the reflection phase shift matrix θ at the RIS;

[0008] Decoupling the optimization objective into two sub-objectives, and achieving the optimization objective of maximizing the weighted rate and WSR of all users by alternately optimizing the transmit precoding matrix F at the base station and the reflection phase shift matrix θ at the RIS respectively;

[0009] The said maximizing the weighted rate and WSR of all users as the optimization goal includes:

[0010] For a MIMO communication system consisting of M base stations, each serving K users, the weighted sum rate of all cell users is maximized and subject to constraints; that is, the transmit precoding matrix at the base station should satisfy the sum of the squares of the elements of the precoding matrix accumulated by all users in the kth cell is less than the total transmit power of the kth base station, and the phase shift angle of each reflection unit of the reconfigurable smart surface is distributed in [0, 2π].

[0011] In the present application, for a MIMO communication system comprising M base stations, each base station serving K users, the weighted sum rate of all cell users is maximized and subject to constraints; that is, the transmit precoding matrix at the base station should satisfy that the sum of the squares of the elements of the precoding matrix accumulated by all users in the kth cell is less than the total transmit power of the kth base station, and the phase shift angle of each reflection unit of the reconfigurable smart surface is distributed within [0, 2π], so that it can be applicable to the case of multiple cells and considering hardware loss. In this way, a weighted rate and WSR maximization model is constructed, so that the obtained weighted rate and the sum of the users in the maximized system are very accurate.

[0012] Preferably, the weighted rate and WSR maximization model is:

[0013]

[0014]

[0015] 0≤θ n ≤2π, n=1,...,N;

[0016] The achievable data rate R of the kth user served by the mth base station is m，k (F, θ) is obtained by:

[0017]

[0018]

[0019] Among them, w m，k is a weighting factor, representing the priority of the corresponding user; R m，k (F, θ) is the achievable data rate of the kth user served by the mth base station, μ Bis the hardware loss coefficient of the base station, F m，k is the linear transmit precoding matrix used by the m-th base station to transmit its data vector x to the k-th user, P m，max is the maximum total transmit power of the mth base station, N is the number of IRS reflection units; θ = [θ1, ..., θ N ]; T m，k is the interference plus noise covariance matrix received by the user, where the interference includes interference from different users in the cell, interference from other users outside the cell, interference from the base station and the user's own hardware loss, and noise interference; σ 2 is the covariance of the noise vector; is the channel from the mth base station to the kth user served by the mth base station; μ U is the user's hardware loss coefficient, is the Ar*Ar dimensional identity matrix.

[0020] The weighted rate and WSR maximization model constructed by the method in this application, especially the achievable data rate R of the kth user served by the mth base station constructed in this application m，k The (F, θ) model is applicable to the case of multiple cells and takes into account hardware loss, so that the final weighted rate and WSR are more accurate. In addition, the present application introduces RIS into the MIMO communication system and studies the case of considering transceiver hardware loss. By adjusting the phase shift of RIS in the manner of the present application, the reflected signal can be constructively added to the desired receiver to enhance the power of the received signal, and can also be destructively superimposed on the unintended receiver to reduce co-channel interference.

[0021] Preferably, the method further comprises:

[0022] Introduce an auxiliary matrix A m，k And consider a linear decoding matrix D m，k , decode the received signal vector of the kth user served by the mth base station. Thus, the above optimization problem can be reformulated into a form that is easier to solve.

[0023] Preferably, when the transmit precoding matrix F at the base station and the reflection phase shift matrix θ at the RIS are alternately optimized, the reflection phase shift matrix θ at the RIS is regarded as a constant, and the transmit precoding matrix F at the base station is optimized: the optimization problem of the transmit precoding matrix F of the m-th base station is expressed as:

[0024]

[0025]

[0026] In the formula, A m，k is the optimal auxiliary matrix; D m，k The decoding matrix for the kth user served by the mth base station; is the channel from the mth base station to the kth user served by the mth base station; μ U is the user's hardware loss coefficient;

[0027] When processing, through the inequality:

[0028]

[0029] The Y The solution is transformed into

[0030] The solution of ; among them, Indicates behavior A t The columns are the identity matrix of s;

[0031] The Lagrange multiplier method is used to solve the problem and obtain the optimal transmit precoding matrix F m，k (λ m ), where λm>0 is the Lagrange multiplier associated with the power constraint of the mth base station.

[0032] Generally speaking, the optimal solution of the transmit precoding matrix can be obtained by using the traditional Lagrange multiplier method. However, since the hardware loss of the transceiver is taken into account in this model, the optimization solution becomes more complicated and an accurate solution result cannot be obtained. Therefore, during the optimization, the present application converts the original problem into a more manageable form by using a matrix inequality, and then uses the Lagrange multiplier method to solve it, so as to obtain the optimal transmit precoding matrix, and finally obtain the accurate value of the weighted rate sum of users in the system that is maximized when hardware loss is considered in the case of multiple cells.

[0033] Preferably, the optimal solution of Fm,k is obtained by the following formula:

[0034]

[0035] Among them, λ m is the Lagrange multiplier.

[0036] Preferably, the optimal auxiliary matrix A m，k Determined by:

[0037]

[0038] Among them, E m，k is the mean square error matrix of the user;

[0039]

[0040] Where, T m，k is the covariance matrix of interference and noise received by the user; is the data stream vector after decoding by the kth user; x m，k It is an s×1-dimensional data stream transmitted to the kth user served by m base stations.

[0041] Preferably, when the transmit precoding matrix F at the base station and the reflection phase shift matrix θ at the RIS are alternately optimized, the transmit precoding matrix F at the base station is regarded as a constant, and the reflection phase shift matrix θ at the RIS is optimized, wherein:

[0042] The optimization problem of the phase shift θ at the RIS is expressed as:

[0043]

[0044] st0≤θ n ≤2π, n=1,...,N,

[0045] In the formula, μ U is the user's hardware loss coefficient; the MM algorithm is used to obtain the optimal reflection phase shift matrix solution.

[0046] When optimizing the phase shift angle matrix, the present application adopts the MM algorithm to introduce a series of easier-to-handle sub-problems to replace the original problem to find the optimal solution of the IRS phase shift angle matrix, thereby obtaining the optimal solution more efficiently.

[0047] Preferably, the BCD algorithm is used to decouple the optimization target into two sub-targets. In the optimization problem of maximizing WSR, the TPM of the base station, the phase shift angle matrix of RIS, the introduced linear decoding matrix and the auxiliary matrix variables are highly coupled, and the BCD algorithm is used for decoupling, that is, three of the variables are fixed to optimize another variable, and then the optimized variable is fixed and the next variable to be optimized is optimized, and it is continuously iterated until all variables approach the optimal solution.

[0048] In summary, the present application includes at least one of the following beneficial technical effects:

[0049] 1. In this application, for a MIMO communication system comprising M base stations, each base station serving K users, the weighted sum rate of all cell users is maximized and subject to constraints; that is, the transmit precoding matrix at the base station should satisfy that the sum of the squares of the elements of the precoding matrix accumulated by all users in the kth cell is less than the total transmit power of the kth base station, and the phase shift angle of each reflection unit of the reconfigurable smart surface is distributed in [0,2π], so that it can be applied to multi-cell situations and considering hardware loss. In this way, a weighted rate and WSR maximization model is constructed, so that the obtained weighted rate and the sum of the users in the maximized system are very accurate.

[0050] 2. The weighted rate and WSR maximization model constructed by the method in this application, especially the achievable data rate R of the kth user served by the mth base station constructed in this application m,k The (F,θ) model is applicable to the case of multiple cells and takes into account hardware loss, so that the final weighted rate and WSR are more accurate. In addition, the present application introduces RIS into the MIMO communication system and studies the case of considering transceiver hardware loss. By adjusting the phase shift of RIS in the manner of the present application, the reflected signal can be constructively added to the desired receiver to enhance the power of the received signal, and can also be destructively superimposed on the unintended receiver to reduce co-channel interference.

[0051] 3. This application uses the Lagrange multiplier method to solve the optimization of the TPM of the base station. In order to transform the original problem into a form that is easier to solve, a matrix inequality transformation is first cited, and then the best Lagrange multiplier is found in combination with the bisection method, and finally the upper bound optimal solution of the transmit beamforming matrix is ​​obtained in a closed form. When optimizing the phase shift angle matrix, the MM algorithm is used to introduce a series of easier-to-handle sub-problems to replace the original problem to find the optimal solution of the IRS phase shift angle matrix, so that the optimal solution can be obtained more efficiently.

[0052] The difficulty of this application lies in that the optimization problem of maximizing the weighted rate and WSR is relatively complex due to the consideration of the hardware loss of the transceiver. The optimal solution cannot be obtained by using the general optimal solution method, such as direct derivation. Therefore, this application uses matrix inequalities to transform the optimization problem into an easy-to-handle form, thereby obtaining a suboptimal solution and ultimately achieving the goal of maximizing the weighted rate and WSR. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a schematic diagram of a reconfigurable smart surface assisted MIMO system in an embodiment of the present application.

[0054] Figure 2 It is a method flow chart of an embodiment of the present application.

[0055] Figure 3 It is a process diagram of the MM algorithm.

[0056] Figure 4 It is a schematic diagram of simulation scene setting.

[0057] Figure 5 It is a convergence diagram of the MM algorithm.

[0058] Figure 6 This is a convergence diagram of the BCD algorithm.

[0059] Figure 7 Schematic diagram of the comparison between the achievable WSR and the number of reflection units N.

[0060] Figure 8 FIG. 4 is a schematic diagram showing the comparison between the achievable WSR and the maximum transmit power Pmax.

[0061] Fig. 9 It is a schematic diagram of the comparison between the achievable WSR and the hardware loss coefficient μ. DETAILED DESCRIPTION

[0062] The following is combined with Figure 1-Figure 9 This application is described in further detail.

[0063] The present application embodiment discloses a parameter optimization method for a MIMO communication system assisted by a reconfigurable intelligent surface. Figure 1 , Figure 2 , a parameter optimization method for a MIMO communication system assisted by a reconfigurable intelligent surface, comprising:

[0064] S1, taking the maximization of the weighted rate and WSR of all users as the optimization goal, the weighted rate and WSR of all users are maximized by jointly optimizing the transmit precoding matrix F at the base station and the reflection phase shift matrix θ at the RIS;

[0065] The optimization goal of maximizing the weighted rate and WSR of all users includes:

[0066] like Figure 1 As shown, for a MIMO communication system including M base stations, each base station serves K users, each base station and user is configured with A t ≥1 transmitting antenna and A r ≥1 receiving antenna, maximize the weighted sum rate of all cell users, and obey the constraints; that is, the transmit precoding matrix at the base station should satisfy the sum of the squares of the elements of the precoding matrix accumulated by all users in the kth cell is less than the total transmit power of the kth base station, and the phase shift angle of each reflection unit of the reconfigurable smart surface is distributed in [0, 2π]; that is, the weighted rate and WSR maximization model is:

[0067]

[0068]

[0069] 0≤θ n ≤2π, n=1,...,N;

[0070] Among them, w m，k is a weighting factor, representing the priority of the corresponding user, which can be 1 in specific implementation; R m，k (F, θ) is the achievable data rate of the kth user served by the mth base station, μ B is the hardware loss coefficient of the base station, μ B ∈[0, 1], μ B The size of F can be 0.01, 0.02, 0.05, etc. m，k is the linear transmit precoding matrix used by the m-th base station to transmit its data vector x to the k-th user, ( Among them, A t represents the number of transmitting antennas of the base station, s represents the s-dimensional data stream transmitted to the users served by the base station, and the initialization of the transmit precoding matrix should satisfy the requirement that the sum of all squares of the precoding matrix elements accumulated by all users in the mth cell is less than the total transmit power of the mth base station). m，max is the maximum total transmit power of the mth base station, N is the number of IRS reflection units;

[0071] In order to reformulate the above optimization problem into a form that is easier to solve, an auxiliary matrix A can be introduced m，k And consider a linear decoding matrix D m，k , decode the received signal vector of the kth user served by the mth base station.

[0072] The received signal vector of the kth user served by the mth base station is given by:

[0073]

[0074] In the formula μ U ∈[0, 1] represents the user's hardware loss coefficient.

[0075] It can be expressed as:

[0076]

[0077] where n m，k is satisfied The noise vector is They represent the channel from the lth BS to the kth user served by the mth BS, the channel from the RIS to the kth user served by the mth BS, and the channel from the lth BS to the RIS. is the phase shift angle matrix of RIS, θ n ∈[0, 2π] is the phase shift angle of the nth reflection unit. The above formula is transformed into:

[0078]

[0079] Then the achievable data rate for the kth user served by the mth base station is obtained as follows:

[0080]

[0081] where θ = [θ1, ..., θ N ];

[0082]

[0083] Where, T m，k is the interference plus noise covariance matrix received by the user, where the interference includes interference from different users in the cell (corresponding to the ), interference from other users outside the cell (corresponding to the above formula ), interference from base station and user hardware loss (corresponding to and ) and noise interference (corresponding to );σ 2 is the covariance of the noise vector (a constant, which can be deleted at the end or customized during initialization);

[0084] Furthermore, to reformulate the original problem into a more tractable form, we can consider a linear decoding matrix, and the decoded data stream vector of the kth user can be written as

[0085]

[0086] in is the decoding matrix of the kth user served by the mth BS.

[0087] The mean square error matrix for each user is given by:

[0088]

[0089] By introducing a set of auxiliary matrices A = {A m，k ≥0} and define (P1) can be restated as follows:

[0090]

[0091]

[0092] 0≤θ n ≤2π, n=1,...,N,

[0093] in

[0094] h m，k (F, θ, D, A) = log | A m，k |-Tr(A m，k E m，k )+d.

[0095] For a given F, θ, and A, h m，k (F, θ, D, A) relative to D m，k The first-order derivative of is set to zero. Then the optimal D m，k It can be obtained by the following formula

[0096]

[0097] For a given F, θ, and D, h m，k (F, θ, D, A) relative to A m，k The first-order derivative of is set to zero, and the optimal auxiliary matrix A m，k Can be obtained as

[0098]

[0099] S2, decoupling the optimization target into two sub-targets, and achieving the optimization target of maximizing the weighted rate and WSR of all users by alternately optimizing the transmit precoding matrix F at the base station and the reflection phase shift matrix θ at the RIS; specifically, the BCD algorithm can be used to decouple the optimization target into two sub-targets;

[0100] When the transmit precoding matrix F at the base station and the reflection phase shift matrix θ at the RIS are alternately optimized, the reflection phase shift matrix θ at the RIS is regarded as a constant, and the transmit precoding matrix F at the base station is optimized:

[0101] The optimization problem of the transmit precoding matrix F of the mth base station is expressed as:

[0102]

[0103]

[0104] In the formula, (To simplify the optimization formula, it is represented by Y); Am，k is the optimal auxiliary matrix; D m，k The decoding matrix for the kth user served by the mth base station ( 4 r is the number of receiving antennas of the user); is the channel from the mth base station to the kth user served by the mth base station, μ U is the user's hardware loss coefficient (μ U ∈[0,1]);

[0105] During processing, since the hardware loss of the transceiver is taken into account in this model of the present application, the optimization solution becomes more complicated than that without considering the hardware loss of the transceiver. The general optimal solution solution method, such as direct derivation, cannot obtain the optimal solution. Therefore, the inventor innovatively thinks of using a matrix inequality to transform the original problem into a more tractable form for solution, and then uses the traditional Lagrange multiplier method to obtain the optimal solution of the transmit precoding matrix; that is, through the inequality:

[0106]

[0107] The Y The solution is transformed into

[0108] The solution of ; among them, Indicates behavior A t The columns are the identity matrix of s;

[0109] The Lagrange multiplier method is used to solve the problem and obtain the optimal transmit precoding matrix F m，k (λ m ), where λm>0 is the Lagrange multiplier associated with the power constraint of the mth base station (which can be found by using the bisection method).

[0110] Then the optimization formula P3 of the transmit precoding matrix F of the mth base station is transformed into:

[0111]

[0112]

[0113] Among them, the simplified matrix

[0114] Then the Lagrange multiplier method is used to find the approximate optimal closed-form expression of the transmit precoding matrix TPM. The Lagrange function of the above formula can be expressed as:

[0115]

[0116] where λ m ≥0 is the Lagrange multiplier associated with the power constraint of the mth BS. By substituting Γ(F m，k ,λ m )About F m，k The first-order derivative of is set to zero, F m，k The optimal solution of

[0117]

[0118] in, represents the matrix pseudo-inverse, λ m is the Lagrange multiplier. m The value of should satisfy the complementary relaxation condition of the following power constraint

[0119]

[0120] The following explains how to obtain the optimal λ in two cases: m :

[0121] 1) C m is a full rank positive definite matrix and can be decomposed into in Λ m Yes

[0122] A diagonal matrix with positive diagonal elements. Then,

[0123]

[0124] in [Z m ] i，i , [Λ m ] i，i ,and Represents the matrix Z m , the matrix Λ m and matrix The i-th diagonal element of . From the above formula and Figure 3 It can be seen that f m (λ m ) is a monotonically decreasing function. If Best TPM by In other cases, the optimal λ can be obtained by finding the solution of the following equation by bisection method m

[0125]

[0126] 2) C m is not a full rank matrix. C m can be decomposed into where Λ m Indicates that it contains positive eigenvalues ​​and A diagonal matrix with zero eigenvalues. Representation and The positive eigenvalues ​​correspond to Singular vectors. Contains corresponding to After the zero eigenvalue By applying similar steps as in 1), we can find λ in this case m The optimal value of .

[0127] When the transmit precoding matrix F at the base station and the reflection phase shift matrix θ at the RIS are alternately optimized, the transmit precoding matrix F at the base station is regarded as a constant, and the reflection phase shift matrix θ at the RIS is optimized, wherein the optimization problem of the phase shift θ at the RIS is expressed as:

[0128]

[0129] st0≤θ n ≤2π, n=1,...,N,

[0130] In the formula, μ U is the user's hardware loss coefficient (μ U ∈[0,1]);

[0131] By reintroduction Removing the constant term, (P5) can be transformed into

[0132]

[0133] st0≤θ n ≤2π, n=1,...,N,

[0134] Where B, J and W are:

[0135]

[0136]

[0137]

[0138] The set of diagonal elements of Φ is defined as And define w as the set of diagonal elements of the matrix W, expressed as w = [[W] 1，1 , …, [W] N，N ] T By using the following matrix transformation

[0139] Tr(Φ H BΦJ)=φ H (B⊙J T )φ,

[0140] (P6) can be rewritten as

[0141]

[0142] st0≤θ n ≤2π, n=1,...,N,

[0143] where γ=B⊙C T It is easy to verify that γ is a semidefinite matrix, so (P7) can be equivalently rewritten as

[0144]

[0145] st|φ n |=1,n=1,...,N。

[0146] (P8) is a non-convex optimization problem. The MM algorithm is used to solve (P8). The basic principle is to transform a challenging problem into a series of more tractable sub-problems. Figure 3 As shown, by expressing the objective function of (P8) with f(φ), the upper limit of the target must be found at the (t+1)th iteration, expressed as g(φ|φ t ), it should satisfy the following three conditions g(φ t |φ t )=f(φ t ),

[0147]

[0148] g(φ|φ t )≥f(φ),

[0149] Then, solve the new objective function g(φ|φ t ) is defined as an approximate subproblem. For any φ in the tth iteration t And any feasible φ has

[0150] φ H γφ≤φ H Vφ-2Re{φ H (V-γ)φ t}+(φ t ) H (V-γ)φ t =y(φ|φ t ),

[0151] Where V = λmax I N ,λ max is the maximum eigenvalue of γ. Then construct the following proxy objective function

[0152] g(φ|φ t )=y(φ|φ t )+2Re{φ H w *},

[0153] where y(φ|φ t ) is given in (31). g(φ|φ) given in (32) t ) obviously satisfies the three conditions of the MM algorithm. Then, the subproblem to be solved in the tth iteration is given by the following formula

[0154]

[0155] st|φ n |=1,n=1,...,N。

[0156] By using φ H φ=X, we can get φ H Vφ=Xλ max , removing the constant, (P9) can be expressed as

[0157]

[0158] st|φ n |=1,n=1,...,N,

[0159] where b t =(λ max I N -γ)φ t -w * Specifically, the optimal solution of (P10) can be obtained as follows

[0160]

[0161] Therefore, the optimal phase shift can be obtained as θ ★ =arg(b t ).

[0162] The details of the MM algorithm are shown in Algorithm 1:

[0163]

[0164] Right now:

[0165] 1. Define the initial number of iterations t = 1, accuracy ε, and input the feasible solution φ 0 ;

[0166] 2. Calculate the objective function f(φ) = φ H γφ+2Re{φ H w * The value of} is f(φ 1 );

[0167] 3. Calculate b t =(λ max I N -γ)φ t -w * ;

[0168] 4. Bring in b and update The value in ;

[0169] 5. Calculate the target equation f(φ t+1 ), if |f(φ t+1 )-f(φ t )| / f(φ t+1 )≤ε, otherwise, set t+1 and return to step 2.

[0170] The overall algorithm for solving (P1) in this application is:

[0171] Based on the above content, the detailed steps of the BCD algorithm conceived by this application to solve (P1) are shown in Algorithm 2.

[0172]

[0173]

[0174] Right now:

[0175] 1. Initialize the number of iterations n = 1, and set the maximum number of iterations to n max , define feasible F (1) ,θ (1) Initial value, error tolerance ε, calculation formula P1 mentioned the value of the optimization problem, expressed as Obj(F (1) ,θ (1) );

[0176] 2. Given F (n) and θ (n) ,calculate The optimal decoding matrix D in (n) ;

[0177] 3. Given F (n) ,θ (n) and D (n) ,pass Calculate the optimal auxiliary variable A (n) ;

[0178] 4. Given θ(n) , D (n) and A (n) , the optimal transmit precoding matrix F is obtained by optimizing the transmit precoding matrix (n+1) ;

[0179] 5. Given D (n) , A (n) and F (n+1) , optimize the reflection phase shift matrix to obtain the optimal reflection phase shift matrix θ (n+1) ;

[0180] 6. If n ≥ n max or |Obj(F (n+1) ,θ (n+1) )-Obj(F (n) ,θ (n) )| / Obj(F (n+1) ,θ (n+1) )≤ε, the optimization terminates, otherwise set n+1 and return to step 2.

[0181] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

[0182] Simulation experiment:

[0183] The simulation scene is set up as follows Figure 4 As shown in the figure, the positions of the two BSs and RIS are set to (0, 0, 20), (500, 0, 20) and (250, 0, 6) respectively. The users served by BS1 are distributed in a circle with a center of (230, 0, 1) and a radius of r = 20 m on the xy plane. The users served by BS2 are distributed in a circle with a center of (270, 0, 1) and a radius of r = 20 m on the xy plane. The units are meters. The large-scale path loss in dB can be modeled as

[0184]

[0185] Where PL0 is the path loss at the reference distance d0, α is the path loss coefficient of the link, and d represents the link distance. For the direct channel from the base station to the user, the small-scale fading is assumed to be Rayleigh fading due to the large amount of scattering. For the RIS-related channel, the small-scale fading is assumed to be Rician fading. Specifically, the small-scale channel is given by

[0186]

[0187] Where β is the Rician factor. is the Light of Sight (LoS) component, and It is the Non-Light of Sight (NLoS) component. The component LoS is given by Given, where and They are defined as

[0188]

[0189]

[0190] Where D r and D t Represents the number of antennas / reflection units at the receiving and transmitting ends respectively. AoD is the departure angle, θ AoA is the angle of arrival, assuming θ AoD and θ AoA Randomly distributed in [0, 2π]. d is the antenna spacing, λ is the wavelength, and for simplicity, d / λ=1 / 2 is set. Unless otherwise specified, the simulation parameters are shown in Table 1. For comparison, the following benchmark scheme is considered:

[0191] Table 1 Simulation parameters

[0192]

[0193]

[0194] NoRISnoro: The traditional beamforming design in the literature "Wu QQ, Zhang R. Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming. IEEE Transactions on Wireless Communications, 2019, 18: 5394-5409" does not consider hardware losses in traditional communication systems without RIS.

[0195] RISnoro: The traditional beamforming design in the literature "Wu QQ, Zhang R. Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming. IEEE Transactions on Wireless Communications, 2019, 18: 5394-5409" does not consider hardware loss in RIS-assisted communication systems.

[0196] NoRISro: This application proposes a BCD algorithm design and considers hardware losses in traditional communication systems without RIS.

[0197] RISro: This application proposes a BCD algorithm design and considers hardware losses in RIS-assisted communication systems.

[0198] In each iteration of the BCD algorithm, the MM algorithm is used to find the optimal solution for the RIS phase shift. Figure 5 It shows the convergence performance of the MM algorithm in the first iteration of the BCD algorithm. max =30dBm, μ B =μ U = 0.01. As can be seen from the figure, the number of iterations required for the algorithm to converge increases with the increase in the number of reflection units. This is because as the number of RIS reflection units increases, the dimension of the phase shift angle matrix increases, and the number of convergence times will also increase accordingly.

[0199] Figure 6 The convergence performance of the BCD algorithm is shown when the number of reflection units is N = 10, 30, and 60. max =30dBm, μ B =μ U = 0.01. It is obvious that with the increase of BCD iteration number, WSR increases rapidly, then gradually converges and remains at a stable value.

[0200] Figure 7 The relationship between WSR performance and the number of reflection units N is compared. The maximum transmission power and hardware loss coefficient of the base station are set to P max =30dBm and μ B =μ U=0.01. Since the power of the reflected signal received by the user increases with the increase of N, the WSR of the "RISnoro" scheme and the "RISro" scheme increases with the increase of N. The WSR of the "RISro" scheme and the "NoRISro" scheme is significantly greater than that of the "RISnoro" scheme and the "NoRISnoro" scheme, which can prove the superiority of the algorithm proposed in this application.

[0201] Figure 8 The relationship between WSR performance and the maximum transmission power of BS under various conditions is compared. The number of reflector units and the hardware loss coefficient are set to N = 50, μ B =μ U = 0.01. It can be observed that in all four benchmark schemes, WSR increases with the increase of Pmax. Specifically, the WSR of the “RISro” scheme and the “NoRISro” scheme is significantly higher than that of the “RISnoro” scheme and the “NoRISnoro” scheme as Pmax increases, because the performance improvement of the BCD algorithm becomes more obvious as Pmax increases.

[0202] Fig. 9 The relationship between WSR performance and hardware loss coefficient μ is compared. B =μ U =μ. Set the number of reflection units and the maximum transmission power of the base station to N = 50, P max =30dBm. It is obvious that in all four benchmark schemes, WSR decreases significantly with the increase of μ. The reason is that the interference caused by hardware loss increases with the increase of μ, which proves the necessity of considering hardware loss in beamforming. In addition, the WSR of the "RISro" scheme is still higher than that of the other three benchmark schemes, which proves the effectiveness of the algorithm proposed in this application in the scenario of considering hardware loss.

Claims

1. A parameter optimization method for a MIMO communication system assisted by a reconfigurable intelligent surface, characterized in that: include: The weighted rate and WSR of all users are maximized as the optimization goal, and the weighted rate and WSR of all users are maximized by jointly optimizing the transmit precoding matrix F at the base station and the reflection phase shift matrix θ at the RIS; Decoupling the optimization objective into two sub-objectives, and achieving the optimization objective of maximizing the weighted rate and WSR of all users by alternately optimizing the transmit precoding matrix F at the base station and the reflection phase shift matrix θ at the RIS respectively; The said maximizing the weighted rate and WSR of all users as the optimization goal includes: For a MIMO communication system with M base stations, each of which serves K users, the weighted sum rate of all users in the cell is maximized and subject to the constraints; that is, the transmit precoding matrix at the base station should satisfy that the sum of the squares of the elements of the precoding matrix accumulated by all users in the kth cell is less than the total transmit power of the kth base station, and the phase shift angle of each reflective unit of the reconfigurable smart surface is distributed within [0,2π]; Among them, the weighted rate and WSR maximization model is: 0≤θ n ≤2π,n=1,…,N; The achievable data rate R for the kth user served by the mth base station m,k (F,θ) is obtained as follows: Among them, w m,k is a weighting factor, representing the priority of the corresponding user; R m,k (F,θ) is the achievable data rate of the kth user served by the mth base station, μ B is the hardware loss coefficient of the base station, F m,k is the linear transmit precoding matrix used by the m-th base station to transmit its data vector x to the k-th user, P m,max is the maximum total transmit power of the mth base station, N is the number of IRS reflection units; θ=[θ1,…,θ N ]; T m,k is the interference plus noise covariance matrix received by the user, where the interference includes interference from different users in the cell, interference from other users outside the cell, interference from the base station and the user's own hardware loss, and noise interference; σ 2 is the covariance of the noise vector; is the channel from the mth base station to the kth user served by the mth base station; μ U is the user's hardware loss coefficient, is the Ar*Ar dimensional identity matrix.

2. The parameter optimization method of the reconfigurable intelligent surface-assisted MIMO communication system according to claim 1, characterized in that: The method further comprises: Introduce an auxiliary matrix A m,k And consider a linear decoding matrix D m,k ,decode the received signal vector of the kth user served by the mth base station.

3. The parameter optimization method of the reconfigurable intelligent surface-assisted MIMO communication system according to claim 2, characterized in that: When the transmit precoding matrix F at the base station and the reflection phase shift matrix θ at the RIS are alternately optimized, the reflection phase shift matrix θ at the RIS is regarded as a constant, and the transmit precoding matrix F at the base station is optimized: The optimization problem of the transmit precoding matrix F of the mth base station is expressed as: In the formula, A m,k is the optimal auxiliary matrix; D m,k The decoding matrix for the kth user served by the mth base station; When processing, through the inequality: The Y The solution is transformed into The solution of ; among them, Indicates behavior A t The columns are the identity matrix of s; The Lagrange multiplier method is used to solve the problem and obtain the optimal transmit precoding matrix F m,k (λ m ), where λm>0 is the Lagrange multiplier associated with the power constraint of the mth base station.

4. The parameter optimization method of the reconfigurable intelligent surface-assisted MIMO communication system according to claim 3, characterized in that: The F m,k The optimal solution of is obtained by the following formula: Among them, λ m is the Lagrange multiplier.

5. The parameter optimization method of the reconfigurable intelligent surface-assisted MIMO communication system according to claim 2 or 3, characterized in that: Optimal auxiliary matrix A m,k Determined by: Among them, E m,k is the mean square error matrix of the user; Where, T m,k is the covariance matrix of interference and noise received by the user; is the data stream vector after decoding by the kth user; x m,k It is an s×1-dimensional data stream transmitted to the kth user served by m base stations.

6. The parameter optimization method of the reconfigurable intelligent surface-assisted MIMO communication system according to claim 2, characterized in that: When the transmit precoding matrix F at the base station and the reflection phase shift matrix θ at the RIS are alternately optimized, the transmit precoding matrix F at the base station is regarded as a constant, and the reflection phase shift matrix θ at the RIS is optimized, wherein the optimization problem of the phase shift θ at the RIS is expressed as: s.t.0≤θ n ≤2π,n=1,…,N, In the formula, μ U is the user's hardware loss coefficient; The MM algorithm is used to obtain the optimal reflection phase shift matrix solution.

7. The parameter optimization method of the reconfigurable intelligent surface-assisted MIMO communication system according to claim 1, characterized in that: The BCD algorithm is used to decouple the optimization objective into two sub-objectives.

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