Intelligent reflecting surface assisted communication sensing integrated system beamforming method

Abstract

This invention relates to a beamforming method for an integrated communication and sensing system assisted by an intelligent reflective surface, belonging to the field of wireless communication technology. The method first synthesizes multiple beams for multiple communication users and radar targets using spatial degrees of freedom, based on the transmit beamforming design requirements. Then, by jointly optimizing the transmit beamforming of the base station and the phase shift matrix of the RIS (Radio Response System), the beamforming design method is expressed as a constrained optimization problem for solution. Under the constraints of signal leakage-to-noise ratio and RIS phase shift, the performance of MIMO radar transmit beamforming is maximized. Finally, the solution is obtained through an alternating iterative optimization method. The joint beamforming method of this invention is simple to implement, has a wide range of applications, and can improve the spectral efficiency of integrated communication and sensing systems.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology and relates to a beamforming method for an integrated communication and sensing system assisted by an intelligent reflective surface. Background Technology

[0002] With the ever-increasing demand for wireless communication networks, spectrum congestion is becoming a serious problem, and future communication systems will need to explore the feasibility of coexisting with other electronic devices on the same frequency band. To address this congestion, an Integrated Sensing and Communication (ISAC) system has been proposed, where wireless communication and radar systems share the spectrum. This combines radar sensing and wireless communication through shared spectrum, hardware platforms, and a joint signal processing framework. Meanwhile, Intelligent Reflecting Surfaces (RIS), a revolutionary technology, significantly improves the performance of wireless communication systems by reconfiguring the wireless propagation environment using a large number of low-cost passive reflective elements integrated on a plane. Directional signal enhancement can be achieved by controlling the amplitude or phase of different elements in the RIS to reflect the incident signal.

[0003] In ongoing research, various beamforming schemes have been proposed. Early work considered single-antenna devices, utilizing separate orthogonal radar and communication signals to achieve spectrum sharing. However, single-antenna schemes cannot detect multiple targets while communicating with multiple users simultaneously, resulting in a loss of radar and communication performance. Later research adopted Multiple-Input Multiple-Output (MIMO) systems, and these studies can be divided into two main categories: information embedding and multi-beaming.

[0004] In information embedding systems, radar is often considered the primary function, and communication information is encoded into the radar waveform. For example, embedding data bits into the parameters of the radar waveform can generate communication in the form of phase modulation, spatial modulation, and carrier frequency modulation, or embedding communication bits by controlling the amplitude and phase of the radar sidelobes. In these schemes, the system produces a low information rate because the number of communication symbols carried by each radar pulse is very limited. A second approach is based on multi-beam beamforming, which uses spatial degrees of freedom to synthesize multiple beams for multiple communication users and radar targets. Compared to information embedding strategies, multi-beam beamforming can use individual waveforms, achieving higher data rates while maintaining radar detection performance by using traditional dedicated signals for each function. In this approach, the main design goal is for both radar and communication signals to form correct transmit beams and operate reliably. However, under strict radar beam pattern constraints, the designed transmit beamforming cannot effectively suppress inter-user interference, resulting in unsatisfactory communication performance for integrated communication and sensing. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a beamforming method for an integrated communication and sensing system assisted by an intelligent reflective surface, which solves the problems of severe loss of system communication performance and severe signal attenuation in millimeter waves under strict radar beam pattern constraints in existing multi-beam schemes.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A beamforming method for a smart reflective surface-assisted integrated communication and sensing system specifically includes the following steps:

[0008] S1: Based on the design requirements for transmitting beamforming, deploy an intelligent reflective surface (RIS) assisted communication sensing integration (ISAC) system with N reflective elements to synthesize multiple beams for multiple communication users and radar targets using spatial degrees of freedom;

[0009] S2: By jointly optimizing the base station's transmit beamforming matrix W and RIS phase shift matrix Θ, the beamforming design method is expressed as a constrained optimization problem P1, which maximizes the performance of MIMO radar transmit beamforming under the constraints of signal leakage noise ratio and RIS phase shift.

[0010] S3: Solve the optimization problem P1 using an alternating iterative optimization method.

[0011] Furthermore, in step S1, the deployed system structure is as follows: Assume the millimeter-wave RIS-assisted ISAC system is a monostatic MIMO radar system and a multi-user MIMO communication transmitter sharing an antenna array. The base station is equipped with M antennas, and there are K single-antenna communication users. The RIS is equipped with N reflective elements. Assume both the base station and users are equipped with uniform linear arrays (ULAs) with a normalized array spacing of ρ. Assume K ≤ M. The radar and communication operate simultaneously through joint beamforming. The base station's transmitted signal x(n) is:

[0012] x(n)=w r s(n)+w c c(n), n = 0, ..., N-1

[0013] Where s(n)=[s1(n),...,s M (n)] T ∈C M×1 It is a vector containing M radar waveforms, C x×y Let x×y be the complex-valued matrix space, and the superscript T denotes the transpose of the matrix; c(n) = [c1(n),...,c K (n)] T ∈C K×1 It is a vector containing K parallel communication symbols, w r ∈C M×M and w c ∈C M×K These are the radar precoding matrix and the communication precoding matrix, respectively.

[0014] The received signal for the kth user is:

[0015]

[0016] Where, G∈C N×M , These represent the equivalent channels from the base station to the IRS and from the IRS to the k-th user, respectively. The superscript H denotes the conjugate transpose of the matrix, and k∈{1,...,K} represents the serial number of the communication user; w c,k w represents the k-th column of the communication precoding matrix. r,k c represents the k-th column of the radar precoding matrix. k Let s represent the k-th communication symbol. k This represents the k-th radar waveform; Let θ represent the phase shift matrix of RIS, where θ n ∈[0,2π) and β n ∈[0,1] represent the phase shift and reflection coefficient of the nth reflecting element of RIS, respectively, diag(·) represents a diagonal matrix, each diagonal element is the corresponding element·, and j represents the imaginary unit; For a vector with mean 0 and covariance matrix Let be the zero-mean circularly symmetric complex Gaussian noise, represent the Gaussian white noise at the receiver for the k-th user, and represent the distribution as .

[0017] Furthermore, in step S1, multi-beam synthesis is performed using spatial degrees of freedom to synthesize multiple communication users and radar targets. Specifically, this includes assuming there are Q potential target directions within the airspace, the set of airspace angles formed by these airspace directions can be represented as... The cumulative signal energy E(θ) radiated by the radar through beamforming in these directions is expressed as:

[0018]

[0019] Where R represents the correlation matrix of the transmitted waveform, defined as in, It is an M×M dimensional spatial cumulative energy matrix, a(θ) q ) represents the guide vector;

[0020] The SLNR (signal leakage noise ratio) of the kth user is:

[0021]

[0022] Among them, H k This represents the cascaded channel for the k-th user. This represents the equivalent channel from the base station to the k-th user.

[0023] Furthermore, in step S2, the optimization problem P1 for designing the transmit beamforming matrix W and the RIS phase shift matrix Θ on the base station side specifically includes: expressing the joint design of radar and communication beamforming as maximizing the energy of the radar target in the target direction under the SLNR constraint, RIS phase shift constraint, and transmit power constraint of each downlink user. The optimization problem P1 is expressed as:

[0024] P1:

[0025]

[0026]

[0027] SLNR k ≥Γ k k = 1, ..., K

[0028] 0≤θ n ≤2π

[0029] Where max tr{RX} is used to find the maximum value W and the value of Θ of tr{RX}, st is an abbreviation for subject to, tr{·} represents the trace, and E{·} represents the statistical expectation; J i It is an M×M dimensional column selection matrix, where only the i-th element on the diagonal is non-zero, taking the value 1, where i∈{1,...,M} represents the antenna index; P t This represents the total transmit power of the array antenna. This indicates that R is positive semi-definite, SLNR k Γ represents the signal leakage noise ratio of the k-th user. k The threshold representing the signal leakage noise ratio of the k-th user, where k∈{1,...,K} represents the user's index.

[0030] Furthermore, in step S3, the optimization problem P1 is solved using an alternating iterative optimization method, specifically including the following steps:

[0031] S31: Initialize the RIS phase shift matrix Θ and set the iteration number r;

[0032] S32: Fix the RIS phase shift matrix Θ and solve for the beamforming matrix W;

[0033] S33: With the beamforming matrix W fixed, solve for the RIS phase shift matrix Θ;

[0034] S34: Alternately iteratively optimize the RIS phase shift matrix Θ and beamforming matrix W until the target value decreases below the threshold or the problem becomes infeasible.

[0035] Furthermore, in step S32, the RIS phase shift matrix Θ is fixed, and the beamforming matrix W is solved, so that the optimization problem P1 can be expressed as:

[0036]

[0037]

[0038]

[0039] SLNR k ≥Γ k k = 1, ..., K

[0040] The SDR method is used to handle non-convex optimization problems.

[0041] Furthermore, step 33 specifically includes: after solving for W, for a given beamforming matrix W, the original optimization problem P1 can be reduced to a feasibility verification problem for solution:

[0042] FindΘ

[0043]

[0044] 0≤θ n ≤2π.

[0045] The beneficial effects of this invention are as follows: This invention achieves beamforming through a Smart Reflective Surface (RIS)-assisted Integrated Communication and Sensing (ISAC) system. By optimizing the base station transmit beamforming and RIS phase shift matrix design, multiple beams are synthesized for communication users and detection targets. The problem is expressed as a constrained optimization problem and solved using an alternating optimization method. The system uses independent communication signals and radar waveforms, with separate beamforming designs, supporting higher information rates while ensuring the system's detection capabilities, further enhancing the system's communication and sensing performance. Simultaneously, by designing the RIS phase shift matrix, interference from radar waveforms to multi-user communication can be reduced, improving the spectral efficiency of the integrated communication and sensing system.

[0046] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0047] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0048] Figure 1 This is a schematic diagram of the integrated communication and sensing system model assisted by the intelligent reflective surface of the present invention;

[0049] Figure 2 This is a flowchart illustrating the overall process of the beamforming method provided by the present invention.

[0050] Figure 3 This is a flowchart illustrating the specific implementation of the alternating iterative optimization algorithm in this invention. Detailed Implementation

[0051] The following specific examples illustrate the implementation of the present invention. 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 embodiments, and various details in this specification can 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 illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0052] Please see Figures 1-3 The system model of the intelligent reflective surface-assisted communication and sensing integrated system of the present invention is as follows: Figure 1 As shown, a millimeter-wave RIS-assisted ISAC system is a monostatic MIMO radar system and a multi-user MIMO communication transmitter sharing an antenna array. The base station is equipped with M antennas, and there are K single-antenna communication users. The RIS is equipped with N reflective elements. Assume that both the base station and users are equipped with uniform linear arrays (ULAs) with a normalized array spacing of ρ, and assume that K ≤ M. The radar and communication operate simultaneously through joint beamforming. The base station transmits the following signal:

[0053] x(n)=w r s(n)+w c c(n), n = 0, ..., N-1

[0054] Where s(n)=(s1(n),...,s M (n)) T ∈C M×1 It contains M radar waveforms, C x×y Let T denote the complex-valued matrix space of x×y, and let T denote the transpose of the matrix; c(n) = (c1(n),...,c k (n)) T ∈C K×1 It is a vector containing K parallel communication symbols, w r ∈C M×M and w c ∈C M×K These are the radar and encoder, and the communication pre-encoder, respectively.

[0055] Assume the transmitted communication signal c(n) is a random independent variable with zero mean and variance. The radar signals are mutually orthogonal: E{s(n)s H (n)}=I M There is no correlation between radar and communication signals: E{s(n)c H (n)}=0 M×K 0 M×K Let represent the M×K dimensional zero matrix. Assuming a Rayleigh fading channel model is used between the DRFC BS and the user, it can be represented as:

[0056]

[0057] Among them, κ u,i The random factor explains the proportion of large-scale fading paths to other dispersed paths, h. s,i Therefore, g u,i Let a(θ) be the small-scale Rayleigh fading component of the path loss, assuming these Rayleigh fading components are uncorrelated with each other.j2 πρ(M-1)sinθ ] T It is the guide vector, and the line-of-sight part can be expressed as: θ u,i and Δ u,i Let represent the azimuth angle and propagation distance of the k-th user, respectively.

[0058] To achieve joint transmission beamforming in an integrated communication and sensing system, a joint design of the precoding matrix w is considered. c and w r The received signal for the kth user is:

[0059]

[0060] Where, G∈C N×M , Let $k$ represent the equivalent channels from the base station to the RIS, from the RIS to the k-th user, and from the base station to the k-th user, respectively, where $k \in {1, ..., K}$ represents the sequence number of the communication user. Let θ represent the phase shift matrix of RIS, where θ n ∈[0,2π) and β n ∈[0,1] represent the phase shift and reflection coefficient of the nth reflecting element of the RIS, respectively. Let be zero-mean circularly symmetric complex Gaussian noise, and let represent the Gaussian white noise at the receiver for the k-th user.

[0061] For a MIMO radar array with M transmit antenna elements, the transmit array is a uniform linear array, and the steering vector a(θ) of the transmit array is known for any spatial direction θ. Therefore, based on the transmit signal model, the transmit beam pattern of the radar signal at spatial direction θ can be defined as...

[0062] P(θ=||a H (θ)x(n)|| 2 =a H (θ)Ra(θ)

[0063] Where R represents the correlation matrix of the transmitted waveform, defined as follows:

[0064]

[0065] Where W = [w c ,w r Given an average transmit power P t At that time, R should maintain tr{R} = P t The correlation matrix is ​​Hermitian symmetric and satisfies the positive semi-definite condition, meaning all eigenvalues ​​are non-negative real numbers. As can be seen from the radar's transmit beam pattern, the performance of a MIMO radar largely depends on its transmit covariance.

[0066] The primary purpose of MIMO radar beamforming is to direct the transmitted beam to multiple given directions. To achieve better output processing gain in the target directions, a typical approach is to maximize the transmitted energy of these targets in their potential directions, while ensuring that the radiated signal energy of each transmitting element is as equal as possible. Assuming there are Q potential target directions of interest in the spatial domain, the set of spatial angles formed by these directions can be represented as follows: The cumulative signal energy radiated by the radar in these directions through beamforming is expressed as:

[0067]

[0068] in, It is an M×M dimensional spatial cumulative energy matrix. Considering only radar performance, the optimization problem with the cumulative signal energy in the target's spatial direction as the primary factor can be expressed as follows:

[0069]

[0070]

[0071]

[0072] Among them, J i It is an M×M dimensional column selection matrix, where only the i-th element on the diagonal is non-zero, taking the value 1. t Let P be the total transmit power of the radar antenna. The first condition constrains the radiated power on each transmit element to be P. t / M.

[0073] For downlink multi-user communication, the signal is precoded to increase the signal power of the intended users and reduce interference to unexpected users and radar waveforms. Consider a downlink multi-user MIMO transmission scenario with K≤M single-antenna users in a flat Gaussian noise channel output, let H=[h1,...,h k ]∈C M×K , Then the channel output representation of K users at time n

[0074] r(n) = Hw c c(n)+Hw r s(n)+z(n)

[0075] Because the echo reflected from the target has bidirectional propagation loss, the transmission power of radar signals is much higher than that of typical communication transmitters. High-power radar interference significantly reduces the ability of communication receivers to recover transmitted signals. Therefore, a SLNR constraint is proposed to mitigate radar interference experienced by users. The SLNR of the k-th user is...

[0076]

[0077] in, k∈{1,...,K} represents the user's sequence number.

[0078] The objective of this invention is to optimize radar target detection performance under communication quality constraints. Therefore, this invention expresses radar and communication beamforming design as maximizing the radar target's energy in the target direction under the SLNR constraint, RIS phase shift constraint, and power constraint of each downlink user. The optimization problem is expressed as follows:

[0079] P1:

[0080]

[0081]

[0082] SLNR k ≥Γ k k = 1, ..., K

[0083] 0≤θ n ≤2π

[0084] Because the optimization problem described above involves non-convex constraints related to the transmitted beam and phase shift coupling, the problem itself is non-convex, posing a challenge to its solution. Generally, for such non-convex optimization problems, an alternating optimization approach is considered.

[0085] First, fix the RIS phase shift matrix Θ and solve for the transmit beamforming matrix W. Then, use the SDR method to handle the aforementioned non-convex problem. Because... First, R is written as a quadratic constraint on each column of the beamforming matrix W. W... i Let the i-th column of W be... i = 1, ..., K i = K+1,...,M+K, then the constraints Represented as:

[0086]

[0087] Transform the SLNR constraint into a constraint on the rank-1 matrix {R} i The linear constraint of}, and correspondingly, the SLNR constraint is expressed as

[0088]

[0089] in, Q = HH H The constraints of the SLNR of the k-th user can be simplified to:

[0090]

[0091] The optimization problem P1 is transformed into an equivalent quadratic semidefinite programming (QSDP) problem with rank-1 constraints.

[0092]

[0093]

[0094]

[0095]

[0096]

[0097] Because of the rank-1 constraint, the optimization problem remains non-convex. Therefore, ignoring the rank-1 constraint yields the following relaxation problem:

[0098]

[0099]

[0100]

[0101]

[0102] The optimization problem described above is a quadratic programming convex problem with linear constraints, because the objective function is a positive semi-definite quadratic form, and all constraints are linear or semi-definite. Using the convex optimization toolbox, the global optimum can be obtained in polynomial time.

[0103] If the optimal solution to the optimization problem is R i i = 1, ..., M and R K+1 If the rank of the problem is exactly 1, then the solution to the relaxation problem is also the solution to the non-convex problem. It can be proven that the SDR method used in the above problem is compact. Although the convex relaxation problem can be solved by CVX, it cannot guarantee that a rank-1 solution will be obtained, and the global optimal solution may not be unique. Therefore, a construction method can be used to directly extract the exact rank-1 optimal solution from the solution of the convex relaxation problem.

[0104] The optimal solution to the above optimization problem is expressed as: and It can be used through the following expression

[0105]

[0106] Obtain the optimal solution of rank 1 and And the corresponding precoding matrix. and It is its optimal solution for W. k If k > K, it can be determined by considering the rank-1 matrix. Perform Cholesky decomposition to obtain w r It is a lower triangular matrix.

[0107] After solving for the transmit beamforming matrix W, for a given transmit beamforming W, the original optimization problem can be reduced to a feasibility verification problem:

[0108] FindΘ

[0109]

[0110] 0≤θ n ≤2π

[0111] The above optimization problem shows that it is sufficient to find a Θ that satisfies the constraints, and it can be proven that such alternating optimization is convergent. For the above optimization problem, let... in The problem can be represented as:

[0112] Find v

[0113]

[0114] v n |=1,n=1,...,N

[0115] Therefore, by finding a Θ that satisfies the conditions, it can be proven that such alternating optimization is convergent. For a feasible solution v, the constraints can be transformed into quadratic constraints, and the problem can be efficiently approximated using the SDR method. By introducing an auxiliary variable t, the problem can be equivalently written as:

[0116] Find v

[0117]

[0118] v n | 2 =1,n=1,...,N

[0119] in

[0120]

[0121] because make Need to meet And rank(V) = 1. Since the rank-1 constraint is non-convex, we first relax this constraint and express the problem as follows:

[0122] Find V

[0123]

[0124] V n,n =1, n=1,....,N

[0125]

[0126] It is easy to see that the above optimization problem is an SDP problem, and therefore it can be solved using existing convex optimization solvers such as CVX.

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A beamforming method for a smart reflective surface-assisted integrated communication and sensing system, characterized in that, The method specifically includes the following steps: S1: Based on the transmit beamforming design requirements, deploy with... N The RIS-assisted ISAC system with one reflector unit utilizes spatial degrees of freedom to synthesize multiple beams for multiple communication users and radar targets; where RIS stands for Intelligent Reflector Surface and ISAC stands for Integrated Communication and Sensing. S2: By jointly optimizing the base station's transmit beamforming matrix and RIS phase shift matrix The beamforming design method is expressed as a constrained optimization problem P1, which maximizes the performance of MIMO radar transmit beamforming under the constraints of signal leakage noise ratio and RIS phase shift. S3: Solve the optimization problem P1 using an alternating iterative optimization method; In step S1, the deployed system architecture is as follows: The millimeter-wave RIS-assisted ISAC system is a monostatic MIMO radar system and a multi-user MIMO communication transmitter sharing an antenna array; the base station is equipped with... There are 1 antenna, with a total of 1 antenna. For a single-antenna communication user, the RIS is equipped with There are 10 reflective elements, assuming that both the base station and the user are equipped with uniform linear arrays, and their normalized array spacing is 1. Assuming Radar and communications operate simultaneously via joint beamforming; the base station transmits signals. for: in, It includes A vector of radar waveforms, express The complex-valued matrix space, superscript Represents the transpose of a matrix; It includes A vector of parallel communication symbols, and These are the radar precoding matrix and the communication precoding matrix, respectively. No. The received signal for each user is: in, , These represent the distance from the base station to the IRS, and the distance from the IRS to the [missing information]. Equivalent channel for each user, superscript H This represents the conjugate transpose of a matrix. Indicates the serial number of the communication user; The first term of the communication precoding matrix List, The first term of the radar precoding matrix represents the... List, Indicates the first A communication symbol, Indicates the first One radar waveform; Denotes the phase shift matrix of RIS, where and They represent the RIS-1 The phase shift and reflection coefficient of each reflecting element, This represents a diagonal matrix, where each diagonal element is a corresponding element. , Represents the imaginary unit; For a vector with mean 0 and covariance matrix Zero-mean circularly symmetric complex Gaussian noise, representing the first Gaussian white noise at the receiver for each user The distribution is represented as follows; In step S2, a transmit beamforming matrix is ​​designed on the base station side. and RIS phase shift matrix The optimization problem P1 specifically includes: representing the joint design of radar and communication beamforming as maximizing the energy of the radar target in the target direction under the constraints of SLNR, RIS phase shift, and transmit power for each downlink user. The optimization problem P1 is expressed as: in, The correlation matrix represents the transmitted waveform. It is The spatial cumulative energy matrix of dimension, Is seeking maximum value and The value of , This is an abbreviation for subject to. Indicates trace, Indicates statistical expectation; yes A dimensional column selection matrix, which contains only the first column on the diagonal. i Each element is non-zero and takes the value 1. Indicates the antenna's serial number; This represents the total transmit power of the array antenna. express It is positive semidefinite. Indicates the first Signal leakage noise ratio per user Indicates the first The threshold for the signal leakage noise ratio of an individual user; In step S3, the optimization problem P1 is solved using an alternating iterative optimization method, specifically including the following steps: S31: Initialize the RIS phase shift matrix Set the number of iterations ; S32: Fixed RIS phase shift matrix Solve the beamforming matrix The optimization problem P1 can be expressed as: Use the SDR method to handle non-convex optimization problems; S33: Fixed Beamforming Matrix Solve the RIS phase shift matrix ; Solve Then, for a given beamforming matrix The original optimization problem P1 is reduced to a feasibility verification problem for solution: S34: Alternating Iterative Optimization of the RIS Phase Shift Matrix and beamforming matrix This continues until the target value drops below the threshold or the problem becomes infeasible.

2. The beamforming method for an integrated communication and sensing system according to claim 1, characterized in that, In step S1, multi-beam synthesis is performed using spatial degrees of freedom on multiple communication users and radar targets. Specifically, this includes assuming that there are multiple beams within the airspace. There are 10 potential target directions, and the set of spatial angles formed by these directions is represented as follows: The radar accumulates signal energy radiated in these directions by transmitting beamforming. Represented as: in, The correlation matrix representing the transmitted waveform is defined as follows: ,in, , It is The spatial cumulative energy matrix of dimension, Indicates the guide vector; No. The SLNR of each user is: in, Indicates the first cascaded channels for individual users , , Indicates the base station to the Equivalent channel for each user.

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