Hybrid intelligent reflecting surface assistance-based sensing integration method and system

Abstract

The invention discloses a hybrid intelligent reflecting surface assistance-based communication and sensing integration method, and relates to the technical field of wireless communication, and the method comprises the steps: building a communication and sensing integration system which comprises a dual-function base station, a hybrid intelligent reflecting surface, K communication users and L sensing targets; the method comprises the following steps of: constructing a communication model and a perception model; establishing an optimization problem of maximizing the minimum sensing beam gain in the L sensing targets under the condition of satisfying the maximum transmission total power constraint of the dual-function base station, the communication user communication quality constraint, the maximum transmission total power constraint of the hybrid intelligent reflecting surface and the noise power constraint; and carrying out conversion solution on the optimization problem based on an alternating variable iterative optimization algorithm to obtain a final optimal solution of the optimization problem. According to the invention, the minimum sensing beam gain of the sensing target can be maximized, and the sensing capability of the system is improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and more specifically, to a synaesthesia integration method based on the assistance of a hybrid intelligent reflective surface. Background Art

[0002] The intelligent reflecting surface (RIS) is composed of a large number of passive components, each of which can independently cause phase changes. By adjusting the phase of the components and controlling the reflection of the signal, the coverage of the sensing area can be expanded under the non-line-of-sight path and the sensing accuracy can be improved. Purely passive RIS has the advantages of low power consumption, low cost, and easy deployment. However, pure passive RIS can only reflect the incident signal and cannot amplify the signal, resulting in significant attenuation of the signal after reflection, especially in long-distance and non-line-of-sight environments. Active RIS can effectively alleviate the above problems. Active RIS can not only reflect the incident signal according to the phase shift required by the system, but also amplify the reflected signal, thereby effectively compensating for the path loss, and can bring significant performance improvement even when the line-of-sight link is blocked. It can also expand the coverage, effectively enhance signal coverage, reduce blind spots, and improve the coverage of communication and perception systems. However, these advantages are accompanied by higher power consumption and cost, and the active amplifier components will introduce a certain amount of thermal noise, which will have a negative impact on system performance.

[0003] Hybrid active-passive RIS combines the advantages of passive and active RIS. By switching the switch, the hybrid RIS element can transition between active and passive modes according to the real-time environment and needs. In the case of weak signal strength or non-line-of-sight environment, active RIS can amplify the signal and enhance the coverage; in the case of strong signal or line-of-sight environment, the passive mode can maintain low power consumption and reduce unnecessary energy consumption to meet the actual needs of specific applications. This hybrid mode not only combines the advantages of active RIS, but also can avoid the disadvantages of active RIS to a certain extent. Therefore, hybrid active-passive RIS is a very attractive technology in future networks.

[0004] Studies have shown that the introduction of hybrid active-passive RIS can bring about a very considerable performance improvement to the system, but the current research direction is mainly focused on the joint optimization of the beamforming of the base station and the reflection coefficient of RIS, without considering the position optimization of the active components of the hybrid RIS. In addition, the current position setting of the active components of the hybrid RIS is usually randomly selected and pre-set without algorithm optimization, and there is still a large gap with the performance upper limit of RIS. Summary of the invention

[0005] The present invention provides a synaesthesia integration method based on the assistance of a hybrid intelligent reflective surface in order to improve the perception ability of the system.

[0006] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0007] The present invention proposes a synaesthesia integration method based on the assistance of a hybrid intelligent reflective surface, comprising:

[0008] Establishing a synaesthesia integrated system, the system comprising a dual-function base station, a hybrid intelligent reflective surface, K communication users and L sensing targets;

[0009] Establish a communication model according to the signal transmission tasks between the dual-function base station, the hybrid intelligent reflection surface and K communication users, and establish a perception model according to the perception tasks of the dual-function base station and the hybrid intelligent reflection surface for L perception targets;

[0010] According to the communication model and the perception model, an optimization problem of maximizing the minimum perception beam gain among L perception targets under the constraints of the maximum total transmission power of the dual-function base station, the communication quality of the communication user, the maximum total transmission power of the hybrid intelligent reflection surface and the noise power is established;

[0011] The optimization problem is transformed and solved based on an alternating variable iterative optimization algorithm to obtain a final optimal solution to the optimization problem.

[0012] Preferably, the hybrid intelligent reflective surface comprises N a active reflective elements and NN a Passive reflective elements, 1≤N a ≤N, where N represents the number of reflective elements of the hybrid intelligent reflective surface; and the mode scheduling matrix and reflection coefficient matrix of the hybrid intelligent reflective surface are set.

[0013] Preferably, the establishing of a communication model according to the signal transmission tasks among the dual-function base station, the hybrid intelligent reflective surface and the K communication users comprises:

[0014] There are no obstacles between the dual-function base station and the K communication users. The dual-function base station generates a transmission signal. The transmission signal is transmitted to each user through a first channel and is also reflected by a hybrid intelligent reflection surface before being transmitted to each user through a second channel.

[0015] The received signal of the kth communication user is determined by the following formula:

[0016]

[0017] Among them, y k represents the received signal of the kth communication user, represents the communication user set, represents the equivalent channel vector from the dual-function base station to the kth communication user, It represents the channel matrix of the second channel from the dual-function base station to the hybrid intelligent reflection surface. Represents the reflection coefficient matrix of the hybrid smart reflective surface, where β n and φ n ∈(0,2π] are the reflection amplitude and phase of the nth reflection element of the hybrid intelligent reflection surface, β n ≥0, Represents the set of reflective elements of the hybrid smart reflective surface, N a The reflection amplitude of an active reflection element is 0≤β n ≤β max , β max Represents the maximum threshold of reflection amplitude, NN a The reflection amplitude of the passive reflective element is 1, Q = diag (q1, q2, ..., q n ,…,q N ) represents the mode scheduling matrix of the hybrid intelligent reflective surface, For passive reflective elements, q n = 0, for active reflective elements, q n =1; Additive white Gaussian noise caused by active reflective elements of hybrid smart reflective surfaces, represents the additive white noise of the kth communication user at the receiving end, represents the noise power of the kth communication user; x represents the transmission signal of the dual-function base station, and They respectively represent the fading channel vector of the first channel from the dual-function base station to the k-th communication user and the fading channel vector of the second channel from the hybrid intelligent reflection surface to the k-th communication user, where M represents the number of antennas configured in the dual-function base station, and M>1.

[0018] Preferably, the method for determining the transmission signal of the dual-function base station includes:

[0019]

[0020] Wherein, x represents the transmission signal of the dual-function base station, x0 represents the zero-mean dedicated sensing signal of the dual-function base station; w k represents the transmit beamforming vector; s k Represents the information symbol sent by the dual-function base station to the kth communication user.

[0021] Preferably, the establishing of a perception model according to the perception tasks of the dual-function base station and the hybrid intelligent reflector for L perception targets includes:

[0022] There are obstacles between the dual-function base station and L sensing targets, and the dual-function base station generates a transmission signal, which is reflected by the hybrid intelligent reflection surface and senses each sensing target through a third channel;

[0023] The perception model of the perception target is determined by the following formula:

[0024]

[0025] in, represents the cascade channel vector from the dual-function base station to the lth sensing target via the hybrid intelligent reflection surface, represents the array response matrix of the hybrid smart reflector, Determined by the following formula:

[0026]

[0027] in, represents the azimuth of the lth perceived target relative to the hybrid intelligent reflective surface, represents the pitch angle of the lth perceived target relative to the hybrid intelligent reflective surface, d x and d y They represent the distances between two continuous reflective elements of the intelligent reflective surface on the x-axis and y-axis respectively, λ represents the wavelength of the RF transmission signal of the dual-function base station, represents the Kronecker product, N x and N y Respectively represent the number of elements in the x-axis and y-axis directions of the hybrid intelligent reflector, N = N x ×N y .

[0028] Preferably, the establishing of an optimization problem of maximizing the minimum perception beam gain among L perception targets under the constraints of the maximum total transmission power of the dual-function base station, the communication quality of the communication user, the maximum total transmission power of the hybrid intelligent reflection surface, and the noise power according to the communication model and the perception model includes:

[0029]

[0030] Among them, P0 represents the maximum total transmission power of the dual-function base station, represents the covariance matrix of the sensing signal of the dual-function base station, Indicates the maximum amplified power of the hybrid smart reflector, SINR k represents the signal-to-noise ratio of the kth communication user, represents the noise power of the hybrid intelligent reflector generated at the lth sensing target, represents the noise power threshold of the hybrid intelligent reflector, Γ k Indicates the preset user signal-to-noise ratio threshold. represents the set of sensing targets; formula (a) represents the maximum minimization sensing beam gain optimization objective function, formula (b) represents the maximum total transmission power limit of the inter-sensory integrated dual-function base station; formula (c) represents the semi-positive definite requirement of the sensing signal covariance of the base station; formula (d) represents that the signal-to-noise ratio of the kth communication user must be greater than the preset user signal-to-noise ratio threshold; formula (e) represents that the noise power of the hybrid intelligent reflection surface generated at the lth sensing target cannot exceed the preset threshold; formula (f) represents the amplification power constraint of the hybrid intelligent reflection surface; formula (g) represents the reflection phase shift coefficient limit of the hybrid intelligent reflection surface; formulas (h) and (i) represent the reflection amplitude limits of the passive reflection element and the active reflection element respectively: for the passive reflection element, q n =0, 1≤β n ≤1; for active reflective elements, q n =1,0≤β n ≤β max .

[0031] Preferably, the transforming and solving the optimization target based on the alternating variable iterative optimization algorithm to obtain the final optimal solution of the optimization problem includes:

[0032] S4.1: The mode scheduling matrix and the reflection coefficient matrix of the hybrid intelligent reflector are fixed, the transmit beamforming vector and the sensing signal covariance matrix are optimized, and the optimization problem is converted into the first sub-optimization problem;

[0033] The first sub-optimization problem is solved by using the interior point method to obtain the current optimal solution of the transmit beamforming vector and the perception signal covariance matrix;

[0034] S4.2: Fix the current optimal solution of the beamforming vector and the sensing signal covariance matrix, optimize the mode scheduling matrix and the reflection coefficient matrix of the hybrid intelligent reflection surface, and transform the optimization problem into a second sub-optimization problem;

[0035] The second sub-optimization problem is solved by using a convex solver to obtain the current optimal solution of the mode scheduling matrix and the reflection coefficient matrix of the hybrid intelligent reflection surface;

[0036] S4.3: Calculate the increase of the beam gain of the current optimization target and the beam gain of the last optimization target, and compare it with the preset minimum tolerance limit. When the increase is greater than the minimum tolerance limit, execute step S4.4; otherwise, execute step S4.5;

[0037] S4.4: fix the current optimal solution of the mode scheduling matrix and the reflection coefficient matrix of the hybrid intelligent reflective surface, and repeat the above steps S4.1-S4.3;

[0038] S4.5: Take the current optimal solution of the beamforming vector corresponding to the current optimization target, the current optimal solution of the perception signal covariance matrix, the current optimal solution of the mode scheduling matrix of the hybrid intelligent reflection surface, and the current optimal solution of the reflection coefficient matrix of the hybrid intelligent reflection surface as the final optimal solution of the optimization problem, and calculate the corresponding minimum perception beam gain.

[0039] Preferably, in S4.1, solving the first sub-optimization problem by using an interior point method to obtain a current optimal solution of a transmit beamforming vector and a perception signal covariance matrix includes:

[0040] Introduce the beamforming matrix for the kth communication user The rank-one constraint is removed by using the semidefinite relaxation technique, and the first sub-optimization problem is relaxed into a convex semidefinite programming problem for solving the transmit beamforming vector and the perception signal covariance matrix. Then, the interior point method is used to solve the problem to obtain the current optimal transmit beamforming vector and the perception signal covariance matrix.

[0041] Preferably, in S4.2, solving the second sub-optimization problem using a convex solver to obtain the current optimal solution of the mode scheduling matrix and the reflection coefficient matrix of the hybrid smart reflective surface includes:

[0042] definition Introducing the Matrix and the relaxed optimization variables And restrict the Z optimization variable according to the following conditions:

[0043]

[0044] In the above conditions, Z=QUQ H =QUQ, where u i,j and z i,j Represents the elements of the i-th row and j-th column of matrices U and Z, respectively. i,i represents the element in the i-th row and i-th column of the matrix Q, that is, the i-th diagonal element, is an arbitrary constant; when the i-th diagonal element of Q is 0, equations (j), (k), (l), and (m) ensure that all elements in the i-th row and i-th column of Z are zero; equations (n) and (o) ensure that the remaining elements z of Z i,j =u i,j ;

[0045] The QUQ in equations (d), (e), and (f) in the optimization problem is H Substitute Z and rewrite equation (i) as the following equality constraint:

[0046]

[0047] A penalty method is used to solve the non-convex binary constraint (i1), the equality constraint is converted into a penalty term added to the optimization objective function, the upper bound of the penalty term in the objective function is constructed using continuous convex approximation technology, the second optimization sub-problem is transformed into a convex function, the second sub-optimization problem is converted into a convex optimization problem, and a standard convex solver is used to obtain the mode scheduling matrix and reflection coefficient matrix of the current optimal hybrid intelligent reflective surface.

[0048] The present invention also provides a synaesthesia integrated system based on hybrid intelligent reflective surface assistance, which is used in the above method, and the system includes the following modules:

[0049] A system building module for establishing a synaesthesia integrated system, the system comprising a dual-function base station, a hybrid intelligent reflective surface, K communication users and L sensing targets;

[0050] A model building module is used to establish a communication model according to the signal transmission tasks between the dual-function base station, the hybrid intelligent reflection surface and K communication users, and to establish a perception model according to the perception tasks of the dual-function base station and the hybrid intelligent reflection surface for L perception targets;

[0051] An optimization problem building module is used to establish an optimization problem of maximizing the minimum perception beam gain among L perception targets under the constraints of the maximum total transmission power of the dual-function base station, the communication quality of the communication user, the maximum total transmission power of the hybrid intelligent reflection surface, and the noise power according to the communication model and the perception model;

[0052] The optimization problem solving module is used to transform and solve the optimization problem based on an alternating variable iterative optimization algorithm to obtain a final optimal solution to the optimization problem.

[0053] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0054] The present invention proposes a synaesthesia integration method based on the assistance of a hybrid intelligent reflective surface. First, a synaesthesia integration system is established, and the system includes a dual-function base station, a hybrid intelligent reflective surface, K communication users and L perception targets; then a communication model and a perception model are established, and according to the communication model and the perception model, an optimization problem of maximizing the minimum perception beam gain among the L perception targets that meets specific constraints is established; based on an alternating variable iterative optimization algorithm, the optimization problem is transformed and solved to obtain the final optimal solution of the optimization problem. The present invention can maximize the minimum perception beam gain of the perception target and improve the perception ability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a flow chart of the synaesthesia integration method based on the assistance of a hybrid intelligent reflective surface as described in Example 1;

[0056] Figure 2 This is a system block diagram of the synaesthesia integration method based on the assistance of a hybrid intelligent reflective surface as described in Example 2;

[0057] Figure 3 This is a structural schematic diagram of the synaesthesia integration system based on the assistance of a hybrid intelligent reflective surface as described in Example 3. DETAILED DESCRIPTION

[0058] The drawings are for illustrative purposes only and should not be construed as limiting the present patent;

[0059] In order to better illustrate the present embodiment, some parts in the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product;

[0060] It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0061] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0062] Example 1

[0063] This embodiment provides a synaesthesia integration method based on the assistance of a hybrid intelligent reflective surface, such as Figure 1 As shown, including:

[0064] S1: Establishing a synaesthesia integrated system, the system comprising a dual-function base station, a hybrid intelligent reflective surface, K communication users and L sensing targets;

[0065] S2: Establishing a communication model according to the signal transmission tasks between the dual-function base station, the hybrid intelligent reflection surface and K communication users, and establishing a perception model according to the perception tasks of the dual-function base station and the hybrid intelligent reflection surface for L perception targets;

[0066] S3: According to the communication model and the perception model, an optimization problem of maximizing the minimum perception beam gain among L perception targets under the constraints of the maximum total transmission power of the dual-function base station, the communication quality of the communication user, the maximum total transmission power of the hybrid intelligent reflection surface, and the noise power is established;

[0067] S4: transforming and solving the optimization problem based on an alternating variable iterative optimization algorithm to obtain a final optimal solution to the optimization problem.

[0068] During the specific implementation process, this embodiment establishes a communication model of the communication user and a perception model of the perception user, and under the constraints of the maximum total transmission power of the dual-function base station, the communication quality constraint of the communication user, the maximum total transmission power constraint of the hybrid intelligent reflection surface, and the noise power constraint, establishes and solves the optimization problem of maximizing the minimum perception beam gain among L perception targets, which can maximize the minimum perception beam gain of the perception target of the integrated synaesthesia system.

[0069] Example 2

[0070] This embodiment provides a synaesthesia integration method based on the assistance of a hybrid intelligent reflective surface, including:

[0071] S1: Establishing a synaesthesia integrated system, the system comprising a dual-function base station, a hybrid intelligent reflective surface, K communication users and L sensing targets;

[0072] like Figure 2 As shown, it is a system block diagram of the synaesthesia integration method described in this embodiment, which is composed of a dual-function base station, a hybrid intelligent reflector, a communication user and a sensing target. There are L sensing targets and K single-antenna communication users (CUs), and the base station is equipped with M antennas, M>1.

[0073] With the help of the hybrid intelligent reflector, the base station is responsible for sending communication signals to K single-antenna communication users and sending dedicated sensing signals to L targets. x ×N y A uniform planar array (UPA) consisting of reflective elements, where N x and N y Respectively represent the number of reflective elements of the hybrid smart reflective surface along the x-axis and y-axis. The hybrid smart reflective surface includes N a active reflective elements and NN a Passive reflective elements, 1≤N a ≤N, where N represents the number of reflective elements of the hybrid intelligent reflective surface; and the mode scheduling matrix and reflection coefficient matrix of the hybrid intelligent reflective surface are set.

[0074] S2: Establishing a communication model according to the signal transmission tasks between the dual-function base station, the hybrid intelligent reflection surface and K communication users, and establishing a perception model according to the perception tasks of the dual-function base station and the hybrid intelligent reflection surface for L perception targets;

[0075] There are no obstacles between the dual-function base station and the K communication users. The dual-function base station generates a transmission signal. The transmission signal is transmitted to each user through a first channel and is also reflected by a hybrid intelligent reflection surface before being transmitted to each user through a second channel.

[0076] The received signal of the kth communication user is determined by the following formula:

[0077]

[0078] Among them, y k represents the received signal of the kth communication user, represents the communication user set, represents the equivalent channel vector from the dual-function base station to the kth communication user, It represents the channel matrix of the second channel from the dual-function base station to the hybrid intelligent reflection surface. Represents the reflection coefficient matrix of the hybrid smart reflective surface, where β n and φ n ∈(0,2π] are the reflection amplitude and phase of the nth reflection element of the hybrid intelligent reflection surface, β n ≥0, Represents the set of reflective elements of the hybrid smart reflective surface, N a The reflection amplitude of an active reflection element is 0≤β n ≤β max , β max Represents the maximum threshold of reflection amplitude, NN a The reflection amplitude of the passive reflective element is 1, Q = diag (q1, q2, ..., q n ,…,q N ) represents the mode scheduling matrix of the hybrid intelligent reflective surface, For passive reflective elements, q n = 0, for active reflective elements, q n =1; Additive white Gaussian noise caused by active reflective elements of hybrid smart reflective surfaces, represents the additive white noise of the kth communication user at the receiving end, represents the noise power of the kth communication user; x represents the transmission signal of the dual-function base station, and They respectively represent the fading channel vector of the first channel from the dual-function base station to the k-th communication user and the fading channel vector of the second channel from the hybrid intelligent reflection surface to the k-th communication user, where M represents the number of antennas configured in the dual-function base station, and M>1.

[0079] The method for determining the transmission signal of the dual-function base station includes:

[0080]

[0081] Wherein, x represents the transmission signal of the dual-function base station, x0 represents the zero-mean dedicated sensing signal of the dual-function base station; wk represents the transmit beamforming vector; s k Represents the information symbol sent by the dual-function base station to the kth communication user.

[0082] Considering that the sensing signal of the base station is pre-set and known at the communication user, eliminating the interference caused by x0, the signal-to-noise ratio of the kth communication user is:

[0083]

[0084] In this case, the total transmit power of the dual-function base station is:

[0085]

[0086] Among them, P BS represents the transmission power of the dual-function base station, represents the covariance matrix of the sensing signal of the dual-function base station, [·] H represents the conjugate transpose.

[0087] The total output power of the active reflective element in the hybrid intelligent reflective surface is:

[0088]

[0089] Among them, P ris represents the total output power of the active reflective element in the hybrid intelligent reflective surface, Q = diag (q1, q2, ..., q n ,…,q N ) represents the mode scheduling matrix of the hybrid intelligent reflective surface, q n =1 indicates that the nth element is an active reflective element of the hybrid smart reflective surface, q n = 0 means that the nth element is a passive reflective element of the hybrid intelligent reflective surface; is the reflection coefficient matrix of the hybrid smart reflector, where β n ≥0,n∈{1,…,N} and φ n ∈(0,2π] are the reflection amplitude and phase of the nth reflection element of the hybrid smart reflection surface, N is the number of reflection elements of the hybrid smart reflection surface, N a The reflection amplitude of an active reflection element is 0≤β n ≤β max , β max Represents the maximum threshold of reflection amplitude, NN a The reflection amplitude of a passive reflective element is 1.

[0090] This embodiment senses potential L targets. The line-of-sight link between the base station and the sensed target is blocked by obstacles, so a virtual link created by a smart reflective surface is used to sense the target. The present invention uses the beam pattern gain at the potential target at the location of interest as a performance metric for sensing.

[0091] There are obstacles between the dual-function base station and L sensing targets, and the dual-function base station generates a transmission signal, which is reflected by the hybrid intelligent reflection surface and senses each sensing target through a third channel;

[0092] The perception model of the perception target is determined by the following formula:

[0093]

[0094] in, represents the cascade channel vector from the dual-function base station to the lth sensing target via the hybrid intelligent reflection surface, represents the array response matrix of the hybrid smart reflector, Determined by the following formula:

[0095]

[0096] in, represents the azimuth of the lth perceived target relative to the hybrid intelligent reflective surface, represents the pitch angle of the lth perceived target relative to the hybrid intelligent reflective surface, d x and d y They represent the distances between two continuous reflective elements of the intelligent reflective surface on the x-axis and y-axis respectively, λ represents the wavelength of the RF transmission signal of the dual-function base station, represents the Kronecker product.

[0097] The noise power of the hybrid intelligent reflector generated at the lth sensing target for:

[0098]

[0099] S3: According to the communication model and the perception model, an optimization problem of maximizing the minimum perception beam gain among L perception targets under the constraints of the maximum total transmission power of the dual-function base station, the communication quality of the communication user, the maximum total transmission power of the hybrid intelligent reflection surface, and the noise power is established;

[0100]

[0101] Among them, P0 represents the maximum total transmission power of the dual-function base station, represents the covariance matrix of the sensing signal of the dual-function base station, Indicates the maximum amplified power of the hybrid smart reflector, SINR k represents the signal-to-noise ratio of the kth communication user, represents the noise power of the hybrid intelligent reflector generated at the lth sensing target, represents the noise power threshold of the hybrid intelligent reflector, Γ k Indicates the preset user signal-to-noise ratio threshold. represents the set of sensing targets; formula (a) represents the maximum minimization sensing beam gain optimization objective function, formula (b) represents the maximum total transmission power limit of the inter-sensory integrated dual-function base station; formula (c) represents the semi-positive definite requirement of the sensing signal covariance of the base station; formula (d) represents that the signal-to-noise ratio of the kth communication user must be greater than the preset user signal-to-noise ratio threshold; formula (e) represents that the noise power of the hybrid intelligent reflection surface generated at the lth sensing target cannot exceed the preset threshold; formula (f) represents the amplification power constraint of the hybrid intelligent reflection surface; formula (g) represents the reflection phase shift coefficient limit of the hybrid intelligent reflection surface; formulas (h) and (i) represent the reflection amplitude limits of the passive reflection element and the active reflection element respectively: for the passive reflection element, q n =0, 1≤β n ≤1; for active reflective elements, q n =1,0≤β n ≤β max .

[0102] S4: transforming and solving the optimization problem based on an alternating variable iterative optimization algorithm to obtain a final optimal solution to the optimization problem.

[0103] In the optimization problem, the transmit beamforming vector, the perception signal covariance, the reflection coefficient matrix of the hybrid intelligent reflector and the mode scheduling matrix are coupled with each other. The non-convex constraint that each component of the mode scheduling matrix can only be 0 or 1 makes it difficult to optimally solve. This optimization problem is a non-convex optimization problem. In order to solve this non-convex problem, the problem is first decomposed into two sub-problems, and then the alternating variable iterative optimization technology is used to solve it:

[0104] S4.1: The mode scheduling matrix and the reflection coefficient matrix of the hybrid intelligent reflector are fixed, the transmit beamforming vector and the sensing signal covariance matrix are optimized, and the optimization problem is converted into the first sub-optimization problem;

[0105] The first sub-optimization problem is solved by using the interior point method to obtain the current optimal solution of the transmit beamforming vector and the perception signal covariance matrix;

[0106] Introduce the beamforming matrix for the kth communication user The rank-one constraint is removed by using the semidefinite relaxation technique, and the first sub-optimization problem is relaxed into a convex semidefinite programming problem for solving the transmit beamforming vector and the perception signal covariance matrix. Then, the interior point method is used to solve the problem to obtain the current optimal transmit beamforming vector and the perception signal covariance matrix.

[0107] S4.2: Fix the current optimal solution of the beamforming vector and the sensing signal covariance matrix, optimize the mode scheduling matrix and the reflection coefficient matrix of the hybrid intelligent reflection surface, and transform the optimization problem into a second sub-optimization problem;

[0108] The second sub-optimization problem is solved by using a convex solver to obtain the current optimal solution of the mode scheduling matrix and the reflection coefficient matrix of the hybrid intelligent reflection surface;

[0109] Based on the transmission beam of the base station's transmitted communication signal and the covariance matrix of the sensing signal, we define Introducing the Matrix and the relaxed optimization variables And restrict the Z optimization variable according to the following conditions:

[0110]

[0111] In the above conditions, Z=QUQ H =QUQ, where u i,j and z i,j Represents the elements of the i-th row and j-th column of matrices U and Z, respectively. i,i represents the element in the i-th row and i-th column of the matrix Q, that is, the i-th diagonal element, is an arbitrary constant; when the i-th diagonal element of Q is 0, equations (j), (k), (l), and (n) ensure that all elements in the i-th row and i-th column of Z are zero; equations (n) and (o) ensure that the remaining elements z of Z i,j =u i,j ;

[0112] The QUQ in equations (d), (e), and (f) in the optimization problem is H Substitute Z and rewrite equation (i) as the following equality constraint:

[0113]

[0114] Infer q that satisfies the equality constraint n The value of is either 1 or 0, otherwise q n (q n-1)>0 always holds true. According to this characteristic, the penalty method is used to solve the non-convex binary constraint (i1), the equality constraint is converted into a penalty term added to the optimization objective function, the upper bound of the penalty term in the objective function is constructed using the successive convex approximation (SCA) technology, the second optimization subproblem is transformed into a convex function, and a standard convex solver (such as CVX) is used to solve it. If the rank of the current optimal solution of the second optimization subproblem is greater than 1, a series of Gaussian randomizations are implemented to obtain the best rank-one solution, and then the mode scheduling matrix and reflection coefficient matrix of the current optimal hybrid smart reflective surface are obtained.

[0115] S4.3: Calculate the increase of the beam gain of the current optimization target and the beam gain of the last optimization target, and compare it with the preset minimum tolerance limit. When the increase is greater than the minimum tolerance limit, execute step S4.4; otherwise, execute step S4.5;

[0116] S4.4: fix the current optimal solution of the mode scheduling matrix and the reflection coefficient matrix of the hybrid intelligent reflective surface, and repeat the above steps S4.1-S4.3;

[0117] S4.5: Take the current optimal solution of the beamforming vector corresponding to the current optimization target, the current optimal solution of the perception signal covariance matrix, the current optimal solution of the mode scheduling matrix of the hybrid intelligent reflection surface, and the current optimal solution of the reflection coefficient matrix of the hybrid intelligent reflection surface as the final optimal solution of the optimization problem, and calculate the corresponding minimum perception beam gain.

[0118] On the basis of two optimization sub-problems, an alternating optimization algorithm is used to achieve system performance optimization. The solution to the problem obtained by each alternating optimization needs to meet the maximum total transmission power limit of the base station; the semi-positive definiteness of the perception signal covariance matrix; the minimum signal-to-noise ratio at the communication user to ensure communication quality; the noise power limit of the hybrid intelligent reflector to achieve good radar perception effect; the amplification power constraint of the hybrid intelligent reflector; the amplification amplitude and reflection phase constraints of the intelligent reflector.

[0119] Example 3

[0120] This embodiment provides a synaesthesia integrated system based on the assistance of a hybrid intelligent reflective surface, such as Figure 3 As shown, it includes the following modules:

[0121] A system building module for establishing a synaesthesia integrated system, the system comprising a dual-function base station, a hybrid intelligent reflective surface, K communication users and L sensing targets;

[0122] A model building module is used to establish a communication model according to the signal transmission tasks between the dual-function base station, the hybrid intelligent reflection surface and K communication users, and to establish a perception model according to the perception tasks of the dual-function base station and the hybrid intelligent reflection surface for L perception targets;

[0123] An optimization problem building module is used to establish an optimization problem of maximizing the minimum perception beam gain among L perception targets under the constraints of the maximum total transmission power of the dual-function base station, the communication quality of the communication user, the maximum total transmission power of the hybrid intelligent reflection surface, and the noise power according to the communication model and the perception model;

[0124] The optimization problem solving module is used to transform and solve the optimization problem based on an alternating variable iterative optimization algorithm to obtain a final optimal solution to the optimization problem.

[0125] The same or similar reference numerals correspond to the same or similar components;

[0126] The terms used in the drawings to describe positional relationships are only used for illustrative purposes and should not be construed as limiting this patent;

[0127] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A synaesthesia integration method based on hybrid intelligent reflective surface assistance, characterized in that: include: Establishing a synaesthesia integrated system, the system comprising a dual-function base station, a hybrid intelligent reflective surface, K communication users and L sensing targets; Establish a communication model according to the signal transmission tasks between the dual-function base station, the hybrid intelligent reflection surface and K communication users, and establish a perception model according to the perception tasks of the dual-function base station and the hybrid intelligent reflection surface for L perception targets; According to the communication model and the perception model, an optimization problem of maximizing the minimum perception beam gain among L perception targets under the constraints of the maximum total transmission power of the dual-function base station, the communication quality of the communication user, the maximum total transmission power of the hybrid intelligent reflection surface and the noise power is established; The optimization problem is transformed and solved based on an alternating variable iterative optimization algorithm to obtain a final optimal solution to the optimization problem.

2. The synaesthesia integration method based on hybrid intelligent reflective surface assistance according to claim 1 is characterized in that: The hybrid intelligent reflective surface includes N a active reflective elements and NN a Passive reflective elements, 1≤N a ≤N, where N represents the number of reflective elements of the hybrid intelligent reflective surface; and the mode scheduling matrix and reflection coefficient matrix of the hybrid intelligent reflective surface are set.

3. The synaesthesia integration method based on hybrid intelligent reflective surface assistance according to claim 2 is characterized in that: The establishing of a communication model according to the signal transmission tasks between the dual-function base station, the hybrid intelligent reflection surface and the K communication users comprises: There are no obstacles between the dual-function base station and the K communication users. The dual-function base station generates a transmission signal. The transmission signal is transmitted to each user through a first channel and is also reflected by a hybrid intelligent reflection surface before being transmitted to each user through a second channel. The received signal of the kth communication user is determined by the following formula: Where yk represents the received signal of the kth communication user, represents the communication user set, represents the equivalent channel vector from the dual-function base station to the kth communication user, It represents the channel matrix of the second channel from the dual-function base station to the hybrid intelligent reflection surface. Represents the reflection coefficient matrix of the hybrid smart reflective surface, where β n and φ n ∈(0, 2π] are the reflection amplitude and phase of the nth reflection element of the hybrid intelligent reflection surface, β n ≥0, Represents the set of reflective elements of the hybrid smart reflective surface, N a The reflection amplitude of an active reflection element is 0≤β n ≤β max , β max Represents the maximum threshold of reflection amplitude, NN a The reflection amplitude of the passive reflection element is 1, Q = diag (q1, q2, ..., q n , …, q N ) represents the mode scheduling matrix of the hybrid smart reflector, q n ∈{0, 1}, For passive reflective elements, q n = 0, for active reflective elements, q n =1; Additive white Gaussian noise caused by active reflective elements of hybrid smart reflective surfaces, represents the additive white noise of the kth communication user at the receiving end, represents the noise power of the kth communication user; x represents the transmission signal of the dual-function base station, and They respectively represent the fading channel vector of the first channel from the dual-function base station to the k-th communication user and the fading channel vector of the second channel from the hybrid intelligent reflection surface to the k-th communication user, where M represents the number of antennas configured in the dual-function base station, and M>

1.

4. The synaesthesia integration method based on hybrid intelligent reflective surface assistance according to claim 3 is characterized in that: The method for determining the transmission signal of the dual-function base station includes: Wherein, x represents the transmission signal of the dual-function base station, x0 represents the zero-mean dedicated sensing signal of the dual-function base station; w k represents the transmit beamforming vector; s k Represents the information symbol sent by the dual-function base station to the kth communication user.

5. The synaesthesia integration method based on hybrid intelligent reflective surface assistance according to claim 1, characterized in that: The establishing of a perception model according to the perception tasks of the dual-function base station and the hybrid intelligent reflector for the L perception targets includes: There are obstacles between the dual-function base station and L sensing targets, and the dual-function base station generates a transmission signal, which is reflected by the hybrid intelligent reflection surface and senses each sensing target through a third channel; The perception model of the perception target is determined by the following formula: in, represents the cascade channel vector i from the dual-function base station to the lth sensing target via the hybrid intelligent reflection surface represents the array response matrix of the hybrid smart reflector, Determined by the following formula: in, represents the azimuth of the lth perceived target relative to the hybrid intelligent reflective surface, represents the pitch angle of the lth perceived target relative to the hybrid intelligent reflective surface, d x and d y They represent the distances between two continuous reflective elements of the intelligent reflective surface on the x-axis and y-axis respectively, λ represents the wavelength of the RF transmission signal of the dual-function base station, represents the Kronecker product, N x and N y Respectively represent the number of elements in the x-axis and y-axis directions of the hybrid intelligent reflector, N = N x ×N y .

6. The synaesthesia integration method based on hybrid intelligent reflective surface assistance according to claim 1, characterized in that: The optimization problem of maximizing the minimum perception beam gain among L perception targets under the constraints of the maximum total transmission power of the dual-function base station, the communication quality of the communication user, the maximum total transmission power of the hybrid intelligent reflection surface and the noise power according to the communication model and the perception model includes: Among them, P0 represents the maximum total transmission power of the dual-function base station, represents the covariance matrix of the sensing signal of the dual-function base station, Indicates the maximum amplified power of the hybrid smart reflector, SINR k represents the signal-to-noise ratio of the kth communication user, represents the noise power of the hybrid intelligent reflector generated at the lth sensing target, represents the noise power threshold of the hybrid intelligent reflector, Γ k Indicates the preset user signal-to-noise ratio threshold. represents the set of sensing targets; formula (a) represents the maximum minimization sensing beam gain optimization objective function, formula (b) represents the maximum total transmission power limit of the inter-sensory integrated dual-function base station; formula (c) represents the semi-positive definite requirement of the sensing signal covariance of the base station; formula (d) represents that the signal-to-noise ratio of the kth communication user must be greater than the preset user signal-to-noise ratio threshold; formula (e) represents that the noise power of the hybrid intelligent reflection surface generated at the lth sensing target cannot exceed the preset threshold; formula (f) represents the amplification power constraint of the hybrid intelligent reflection surface; formula (g) represents the reflection phase shift coefficient limit of the hybrid intelligent reflection surface; formulas (h) and (i) represent the reflection amplitude limits of the passive reflection element and the active reflection element respectively: for the passive reflection element, q n =0, 1≤β n ≤1; for active reflective elements, q n =1,0≤β n ≤β max .

7. The synaesthesia integration method based on hybrid intelligent reflective surface assistance according to claim 1, characterized in that: The transformation and solution of the optimization target based on the alternating variable iterative optimization algorithm to obtain the final optimal solution of the optimization problem includes: S4.1: The mode scheduling matrix and the reflection coefficient matrix of the hybrid intelligent reflector are fixed, the transmit beamforming vector and the sensing signal covariance matrix are optimized, and the optimization problem is converted into the first sub-optimization problem; The first sub-optimization problem is solved by using the interior point method to obtain the current optimal solution of the transmit beamforming vector and the perception signal covariance matrix; S4.2: Fix the current optimal solution of the beamforming vector and the sensing signal covariance matrix, optimize the mode scheduling matrix and the reflection coefficient matrix of the hybrid intelligent reflection surface, and transform the optimization problem into a second sub-optimization problem; The second sub-optimization problem is solved by using a convex solver to obtain the current optimal solution of the mode scheduling matrix and the reflection coefficient matrix of the hybrid intelligent reflection surface; S4.3: Calculate the increase of the beam gain of the current optimization target and the beam gain of the last optimization target, and compare it with the preset minimum tolerance limit. When the increase is greater than the minimum tolerance limit, execute step S4.4; otherwise, execute step S4.5; S4.4: fix the current optimal solution of the mode scheduling matrix and the reflection coefficient matrix of the hybrid intelligent reflective surface, and repeat the above steps S4.1-S4.3; S4.5: Take the current optimal solution of the beamforming vector corresponding to the current optimization target, the current optimal solution of the perception signal covariance matrix, the current optimal solution of the mode scheduling matrix of the hybrid intelligent reflection surface, and the current optimal solution of the reflection coefficient matrix of the hybrid intelligent reflection surface as the final optimal solution of the optimization problem, and calculate the corresponding minimum perception beam gain.

8. The synaesthesia integration method based on hybrid intelligent reflective surface assistance according to claim 7, characterized in that: In S4.1, the using of the interior point method to solve the first sub-optimization problem to obtain the current optimal solution of the transmit beamforming vector and the perception signal covariance matrix includes: Introduce the beamforming matrix for the kth communication user rank(W k )≤1, the rank-one constraint is removed by using the semidefinite relaxation technique, and the first sub-optimization problem is relaxed to a convex semidefinite programming problem for solving the transmit beamforming vector and the perception signal covariance matrix, and then the interior point method is used to solve the current optimal transmit beamforming vector and the perception signal covariance matrix.

9. The synaesthesia integration method based on hybrid intelligent reflective surface assistance according to claim 7, characterized in that: In S4.2, the use of a convex solver to solve the second sub-optimization problem to obtain the current optimal solution of the mode scheduling matrix and the reflection coefficient matrix of the hybrid intelligent reflection surface includes: definition Introducing the Matrix and the relaxed optimization variables And restrict the Z optimization variable according to the following conditions: In the above conditions, Z=QUQ H =QUQ, where u i，j and z i，j Represents the elements of the i-th row and j-th column of matrices U and Z, respectively. i , i represents the element in the i-th row and i-th column of matrix Q, that is, the i-th diagonal element, is an arbitrary constant; when the i-th diagonal element of Q is 0, equations (j), (k), (l), and (m) ensure that all elements in the i-th row and i-th column of Z are zero; equations (n) and (o) ensure that the remaining elements z of Z i，j =u i，j ; The QUQ in equations (d), (e), and (f) in the optimization problem is H Substitute Z and rewrite equation (i) as the following equality constraint: A penalty method is used to solve the non-convex binary constraint (i1), the equality constraint is converted into a penalty term added to the optimization objective function, the upper bound of the penalty term in the objective function is constructed using continuous convex approximation technology, the second optimization sub-problem is transformed into a convex function, the second sub-optimization problem is converted into a convex optimization problem, and a standard convex solver is used to obtain the mode scheduling matrix and reflection coefficient matrix of the current optimal hybrid intelligent reflective surface.

10. A synaesthesia integrated system based on hybrid intelligent reflective surface assistance, used to implement the method described in any one of claims 1 to 9, characterized in that: include: A system building module for establishing a synaesthesia integrated system, the system comprising a dual-function base station, a hybrid intelligent reflective surface, K communication users and L sensing targets; A model building module is used to establish a communication model according to the signal transmission tasks between the dual-function base station, the hybrid intelligent reflection surface and K communication users, and to establish a perception model according to the perception tasks of the dual-function base station and the hybrid intelligent reflection surface for L perception targets; An optimization problem building module is used to establish an optimization problem of maximizing the minimum perception beam gain among L perception targets under the constraints of the maximum total transmission power of the dual-function base station, the communication quality of the communication user, the maximum total transmission power of the hybrid intelligent reflection surface, and the noise power according to the communication model and the perception model; The optimization problem solving module is used to transform and solve the optimization problem based on an alternating variable iterative optimization algorithm to obtain a final optimal solution to the optimization problem.

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