A STAR-RIS-assisted synaesthesia integrated optimization method and device

Abstract

The present invention provides a STAR-RIS-assisted synaesthesia integration optimization method and device, relating to the technical field of the Internet of Things. The method comprises: establishing a STAR-RIS-assisted synaesthesia integration system model; taking minimizing the Cramer-Rao bounds of the azimuth and elevation angle estimation of the perception target as the goal, constructing an objective function to be optimized of the STAR-RIS-assisted synaesthesia integration system model under the condition of satisfying the minimum weighted sum rate of communication users; optimizing the objective function by using an optimization algorithm based on a penalized dual decomposition framework; and achieving the joint improvement of the communication performance and perception performance of the STAR-RIS-assisted ISAC system through the optimization of the objective function.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a STAR-RIS-assisted synaesthesia integration optimization method and device. Background Art

[0002] Integrated Sensing and Communication (ISAC) is a technology that closely integrates communication and perception functions and has become one of the key technologies in the 6G communication era. ISAC technology not only reduces equipment cost, size and power consumption, but also further promotes the advancement of perception technology through the development of ultra-large-scale antennas, large bandwidth, intelligent metasurfaces, artificial intelligence and other technologies. However, the perception and communication performance of most ISAC systems are currently limited by the capabilities of base stations. Reconfigurable Intelligence Surface (RIS) technology controls electromagnetic waves and reshapes wireless channels, which is expected to solve problems such as communication coverage, security, and multi-stream transmission. How to use RIS technology to assist ISAC systems in communication and perception has become a hot topic.

[0003] Despite still facing challenges in cost, energy consumption, and hardware design, RIS has demonstrated significant performance gains, heralding its widespread application in the 6G communications era. Traditional RIS, when deployed in an ISAC system, requires the sensing target or communication user to be located on the same side of the RIS as the base station, achieving only half-space coverage. To address this challenge, researchers have proposed using a novel simultaneously transmitting and reflecting reconfigurable smart surface (STAR-RIS) to simultaneously transmit and reflect signals, improving the limited reach of traditional ISAC systems and significantly improving system performance. STAR-RIS splits the input signal into two distinct parts: one portion is reflected in the reflection zone, while the remaining portion is transmitted, achieving 360° full-space coverage.

[0004] The current problem with the STAR-RIS-assisted ISAC system is that it is difficult to achieve a simultaneous improvement in both communication performance and perception performance. Summary of the Invention

[0005] To address the technical problem in the prior art of difficulty in achieving a simultaneous improvement in both the communication and perception performance of a STAR-RIS-assisted ISAC system, the present invention provides a STAR-RIS-assisted synaesthesia integrated optimization method and device. The technical solution is as follows:

[0006] In one aspect, a STAR-RIS-assisted synaesthesia integration optimization method is provided, the method comprising:

[0007] A STAR-RIS-assisted synaesthesia integration system model is established. The STAR-RIS-assisted synaesthesia integration network model includes: a base station, M communication users, a sensing target, and a STAR-RIS. The base station is equipped with N antennas, each communication user is equipped with a single antenna, and the STAR-RIS is equipped with S passive transmission-reflection array elements and N S A unit carrying a sensor, wherein the M, N, S, N S is a positive integer;

[0008] With the goal of minimizing the Cramer-Rao bounds of the azimuth and elevation angle estimates of the perceived target, and under the condition of satisfying the minimum weighted sum rate of the communication users, the objective function to be optimized of the STAR-RIS-assisted synaesthesia integration system model is constructed;

[0009] Optimizing the objective function by an optimization algorithm based on a penalized dual decomposition framework;

[0010] The optimization algorithm based on the penalized dual decomposition framework optimizes the augmented Lagrangian problem of the objective function in an inner loop using the block coordinate descent technique, while the Lagrangian dual variables and the penalty factor are updated in the outer loop, and the optimization variables of the augmented Lagrangian problem of the objective function are divided into two sub-blocks to generate two sub-optimization problems. The two sub-optimization problems are a sub-problem containing the covariance matrix of the perception signal and a sub-problem containing the STAR-RIS phase. The projected gradient method and the semi-positive relaxation method are used to solve the sub-problem containing the covariance matrix of the perception signal and the sub-problem containing the STAR-RIS phase, respectively. The block coordinate descent technique is used to iteratively solve the other sub-block under the condition of fixing one sub-block, and the optimized solutions of the sub-problem containing the covariance matrix of the perception signal and the sub-problem containing the STAR-RIS phase are obtained.

[0011] Optionally, the objective function to be optimized includes:

[0012] P1:

[0013] C1:

[0014] C2:

[0015] C3:Tr(PP H +R s )≤P t

[0016] C4:

[0017] C5:R s ≥0,U≥0,

[0018] in, is Θ i , A vector consisting of elements on the diagonal; constrain C2 C2 represents the minimum weighted sum rate threshold of system communication users; P in constraint C3 t ≥0, C3 represents the maximum average transmission power at the base station; constraint C4 represents the amplitude relationship between the new STAR-RIS transmission coefficient and reflection coefficient, and constraint C5 ensures that the matrix R s and U is positive semidefinite; in the STAR-RIS-assisted synaesthesia integration system model, is the transmission coefficient matrix of STAR-RIS, is the reflection coefficient matrix of STAR-RIS, is the expression of the transmission coefficient matrix or reflection coefficient matrix of STAR-RIS. When i is t, it represents the transmission coefficient matrix; when i is r, it represents the reflection coefficient matrix. i,n ∈[0,1] represents the amplitude of the nth unit, represents the phase shift response of the nth unit, and the amplitude needs to satisfy: Where t is the transmission coefficient, r is the reflection coefficient, and A represents the set of reflection units on STAR-RIS. The base station sends a joint signal x(t) at time index t. in, Representing information flow The transmit beamforming matrix for transmitting signals to M communication users in the system, the covariance matrix of the joint signal x(t) is: In the communication space of the STAR-RIS-assisted synaesthesia integration system model, represents the channel matrix of the direct link between the transmitter and receiver, represents the baseband channel matrix between the base station and STAR-RIS, represents the baseband channel vector between STAR-RIS and user m. The equivalent channel from the base station to the mth user is expressed as: When the transmitted signal at the base station is given, the received signal at the mth communication user in the communication space can be modeled as: In the perception space of the STAR-RIS-assisted synaesthesia integration system model, the target echo signal received by the STAR-RIS sensor in a coherent time block of length L is: GS =αb(φ h ,φ v )a T (φ h ,φ v )Θ r H b X+N S , where φ h is the estimated azimuth of the target relative to STAR-RIS, and φ v is the target's pitch angle estimate relative to STAR-RIS, represents the steering vector used by STAR-RIS for communication; N represents the guidance vector used by the sensor on STAR-RIS for perception; S Indicates that each item obeys The sensor is deployed along the X-axis, and STAR-RIS is deployed in the (X, Z) plane. The steering vector is expressed as:

[0019]

[0020] Optionally, the optimization algorithm based on the penalty dual decomposition framework includes:

[0021] Step 1: Initialize χ [0] ,Y [0] ,ρ [0] >0 and set to 0 <c<1,n=1;

[0022] Step 2: P AL (ρ [n] ,Y [n] ) is assigned to χ [n+1] ;

[0023] Step 3: If h(χ [n+1] )≤η [n] ,but:

[0024]

[0025] Otherwise, proceed to step 4;

[0026] Step 4, Y [n+1] =Y [n] , ρ [n+1] =cρ [n] ;

[0027] Step 5: Set n=n+1 and repeat steps 2 to 4 until h(x) reaches the threshold;

[0028] Among them, P AL (ρ,Y) is the augmented Lagrangian problem of the objective function; and is an auxiliary variable.

[0029] Optionally, the two sub-optimization problems include: a sub-problem involving a covariance matrix of a perception signal:

[0030]

[0031] C1:

[0032] C2:

[0033] C3:Tr(PP H +R s )≤P t

[0034] C4:R S ≥0,U≥0,

[0035] And the subproblem involving STAR-RIS phase:

[0036]

[0037] C1:

[0038] C2:

[0039] Optionally, the projected gradient method is used to solve the subproblem of the covariance matrix containing the perception signal.

[0040] include:

[0041] Let the number of iterations n = 0, initialize R 0 , μ>0 and the maximum number of iterations n max ;

[0042] pass Update R, and the gradient of f with respect to R is: Repeat until the maximum number of iterations is reached.

[0043] Optionally, the using of a semi-definite relaxation method to solve the sub-problem containing the STAR-RIS phase includes:

[0044] The subproblem containing the STAR-RIS phase is converted into a semi-positive definite relaxation form and solved by the existing optimizer:

[0045]

[0046] C1:

[0047] C2:

[0048] C3:Q t ≥0,Q r ≥0.

[0049] Optionally, the using of a block coordinate descent technique to iteratively solve another sub-block while fixing one sub-block to obtain an optimized solution to the sub-problem of the covariance matrix containing the perception signal and the sub-problem of the STAR-RIS phase includes:

[0050] Step a, initialize the variables in the two sub-problems, for example, initialize the variable χ;

[0051] Step b, by solving {U,F,P,R s} subproblem, update {U,F,P,R s}, wherein the {U,F,P,R s The subproblem is the covariance matrix subproblem containing the perception signal;

[0052] Step c, by solving {θ t ,θ r} subproblem, update {θ t ,θ r}, wherein the {θ t ,θ r}Subproblems are subproblems of the STAR-RIS phase;

[0053] Repeat steps b and c until the target value drops below a preset threshold.

[0054] On the other hand, a STAR-RIS-assisted synaesthesia integrated optimization device is provided. The STAR-RIS-assisted synaesthesia integrated optimization device is used to implement the STAR-RIS-assisted synaesthesia integrated optimization method provided in an embodiment of the present invention. The device includes:

[0055] Establish a module for establishing a STAR-RIS-assisted synaesthesia integration system model, the STAR-RIS-assisted synaesthesia integration network model includes: a base station, M communication users, a perception target and a STAR-RIS, the base station is equipped with N antennas, each communication user is equipped with a single antenna, the STAR-RIS is equipped with S passive transmission-reflection array elements and N S A unit carrying a sensor, wherein the M, N, S, N S is a positive integer;

[0056] A construction module is used to construct an objective function to be optimized of the STAR-RIS-assisted synaesthesia integration system model with the goal of minimizing the Cramer-Rao bound of the azimuth and elevation angle estimation of the perceived target and under the condition of satisfying the minimum weighted sum rate of the communication users;

[0057] An optimization module, configured to optimize the objective function using an optimization algorithm based on a penalty dual decomposition framework;

[0058] The optimization algorithm based on the penalized dual decomposition framework optimizes the augmented Lagrangian problem of the objective function in an inner loop using the block coordinate descent technique, while the Lagrangian dual variables and the penalty factor are updated in the outer loop, and the optimization variables of the augmented Lagrangian problem of the objective function are divided into two sub-blocks to generate two sub-optimization problems. The two sub-optimization problems are a sub-problem containing the covariance matrix of the perception signal and a sub-problem containing the STAR-RIS phase. The projected gradient method and the semi-positive relaxation method are used to solve the sub-problem containing the covariance matrix of the perception signal and the sub-problem containing the STAR-RIS phase, respectively. The block coordinate descent technique is used to iteratively solve the other sub-block under the condition of fixing one sub-block, and the optimized solutions of the sub-problem containing the covariance matrix of the perception signal and the sub-problem containing the STAR-RIS phase are obtained.

[0059] In another aspect, a STAR-RIS-assisted synaesthesia integrated optimization device is provided, wherein the STAR-RIS-assisted synaesthesia integrated optimization device comprises:

[0060] processor;

[0061] A memory having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by the processor, implement the method provided by the embodiment of the present invention.

[0062] On the other hand, a computer-readable storage medium is provided, in which a program code is stored. The program code can be called by a processor to execute the method provided by an embodiment of the present invention.

[0063] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0064] The embodiment of the present invention proposes a STAR-RIS-assisted synaesthesia integration system model, takes minimizing the Cramer-Rao bounds for the estimation of the azimuth and elevation angles of the perceived target as the goal, and constructs an objective function to be optimized for the STAR-RIS-assisted synaesthesia integration system model while satisfying the minimum weighted sum rate of communication users. By optimizing the objective function, both the communication performance and the perception performance of the STAR-RIS-assisted ISAC system are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0066] Figure 1 This is a flow chart of a STAR-RIS-assisted synaesthesia integration optimization method provided by an embodiment of the present invention;

[0067] Figure 2 This is a schematic diagram of the structure of a STAR-RIS-assisted synaesthesia integration network model provided by an embodiment of the present invention;

[0068] Figure 3 1 is a schematic structural diagram of a STAR-RIS-assisted synaesthesia integrated optimization device provided in an embodiment of the present invention;

[0069] Figure 4 It is a structural schematic diagram of a STAR-RIS-assisted synaesthesia integrated optimization device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0070] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0071] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0072] In the embodiments of the present invention, the terms "image" and "picture" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same. The terms "of," "corresponding," and "corresponding" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same.

[0073] In the embodiments of the present invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript form such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0074] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0075] To address the technical problem in the prior art of difficulty in achieving a simultaneous improvement in both the communication and perception performance of a STAR-RIS-assisted ISAC system, the present invention provides a STAR-RIS-assisted synaesthesia integrated optimization method and device. The technical solution is as follows:

[0076] On the one hand, a STAR-RIS-assisted synaesthesia integration optimization method is provided, such as Figure 1 As shown, the method includes:

[0077] S1. Establish a STAR-RIS-assisted synaesthesia integrated system model.

[0078] The STAR-RIS-assisted synaesthesia integration network model, such as Figure 2 As shown, it includes: a base station, M communication users, a sensing target and a STAR-RIS, wherein the base station is equipped with N antennas, each communication user is equipped with a single antenna, and the STAR-RIS is equipped with S passive transmission-reflection array elements and N S A unit carrying a sensor, wherein the M, N, S, N S Is a positive integer.

[0079] S2. With the goal of minimizing the Cramer-Rao bounds of the azimuth and elevation angle estimation of the perceived target, and under the condition of satisfying the minimum weighted sum rate of the communication users, construct an objective function to be optimized for the STAR-RIS-assisted synaesthesia integration system model.

[0080] S3. Optimizing the objective function through an optimization algorithm based on a penalty dual decomposition framework.

[0081] The optimization algorithm based on the penalized dual decomposition framework optimizes the augmented Lagrangian problem of the objective function in an inner loop using the block coordinate descent technique, while the Lagrangian dual variables and the penalty factor are updated in the outer loop, and the optimization variables of the augmented Lagrangian problem of the objective function are divided into two sub-blocks to generate two sub-optimization problems. The two sub-optimization problems are a sub-problem containing the covariance matrix of the perception signal and a sub-problem containing the STAR-RIS phase. The projected gradient method and the semi-positive relaxation method are used to solve the sub-problem containing the covariance matrix of the perception signal and the sub-problem containing the STAR-RIS phase, respectively. The block coordinate descent technique is used to iteratively solve the other sub-block under the condition of fixing one sub-block, and the optimized solutions of the sub-problem containing the covariance matrix of the perception signal and the sub-problem containing the STAR-RIS phase are obtained.

[0082] Optionally, the objective function to be optimized includes:

[0083]

[0084] C1:

[0085] C2:

[0086] C3:Tr(PP H +R s )≤P t

[0087] C4:

[0088] C5:R s ≥0,U≥0,

[0089] in, is Θ i , A vector consisting of elements on the diagonal; constrain C2 C2 represents the minimum weighted sum rate threshold of system communication users; P in constraint C3 t ≥0, C3 represents the maximum average transmission power at the base station; constraint C4 represents the amplitude relationship between the new STAR-RIS transmission coefficient and reflection coefficient, and constraint C5 ensures that the matrix R s and U is positive semidefinite; in the STAR-RIS-assisted synaesthesia integration system model, is the transmission coefficient matrix of STAR-RIS, is the reflection coefficient matrix of STAR-RIS, is the expression of the transmission coefficient matrix or reflection coefficient matrix of STAR-RIS. When i is t, it represents the transmission coefficient matrix; when i is r, it represents the reflection coefficient matrix. i,n ∈[0,1] represents the amplitude of the nth unit, represents the phase shift response of the nth unit, and the amplitude needs to satisfy: Where t is the transmission coefficient, r is the reflection coefficient, and A represents the set of reflection units on STAR-RIS. The base station sends a joint signal x(t) at time index t. in, Representing information flow The transmit beamforming matrix for transmitting signals to M communication users in the system, the covariance matrix of the joint signal x(t) is: In the communication space of the STAR-RIS-assisted synaesthesia integration system model, represents the channel matrix of the direct link between the transmitter and receiver, represents the baseband channel matrix between the base station and STAR-RIS, represents the baseband channel vector between STAR-RIS and user m. The equivalent channel from the base station to the mth user is expressed as: When the transmitted signal at the base station is given, the received signal at the mth communication user in the communication space can be modeled as: In the perception space of the STAR-RIS-assisted synaesthesia integration system model, the target echo signal received by the STAR-RIS sensor in a coherent time block of length L is: G S =αb(φ h ,φ v )a T (φ h ,φ v )Θ r H b X+N S , where φ h is the estimated azimuth of the target relative to STAR-RIS, and φ v is the target's pitch angle estimate relative to STAR-RIS, represents the steering vector used by STAR-RIS for communication; N represents the guidance vector used by the sensor on STAR-RIS for perception; S Indicates that each item obeys The sensor is deployed along the X-axis, and STAR-RIS is deployed in the (X, Z) plane. The steering vector is expressed as:

[0090]

[0091] Optionally, the optimization algorithm based on the penalty dual decomposition framework includes:

[0092] Step 1: Initialize χ [0] ,Υ [0] ,ρ [0] >0 and set to 0 <c<1,n=1;

[0093] Step 2: P AL (ρ [n] ,Y [n] ) is assigned to χ [n+1] ;

[0094] Step 3: If h(χ [n+1] )≤η [n] ,but:

[0095]

[0096] Otherwise, proceed to step 4;

[0097] Step 4, Y [n+1] =Y [n] , ρ [n+1] =cρ [n] ;

[0098] Step 5: Set n=n+1 and repeat steps 2 to 4 until h(χ) reaches the threshold;

[0099] Among them, P AL (ρ,Y) is the augmented Lagrangian problem of the objective function; and is an auxiliary variable.

[0100] Optionally, the two sub-optimization problems include: a sub-problem involving a covariance matrix of a perception signal:

[0101]

[0102] C1:

[0103] C2:

[0104] C3:Tr(PP H +R s )≤P t

[0105] C4:R s ≥0,U≥0,

[0106] And the subproblem involving STAR-RIS phase:

[0107]

[0108] C1:

[0109] C2:

[0110] Optionally, the using the projected gradient method to solve the subproblem of the covariance matrix containing the perception signal includes:

[0111] Let the number of iterations n = 0, initialize R 0 , μ>0 and the maximum number of iterations n max ;

[0112] pass Update R, and the gradient of f with respect to R is: Repeat until the maximum number of iterations is reached.

[0113] Optionally, the using of a semi-definite relaxation method to solve the sub-problem containing the STAR-RIS phase includes:

[0114] The subproblem containing the STAR-RIS phase is converted into a semi-positive definite relaxation form and solved by the existing optimizer:

[0115]

[0116] C1:

[0117] C2:

[0118] C3:Q t ≥0,Q r ≥0.

[0119] Optionally, the using of a block coordinate descent technique to iteratively solve another sub-block while fixing one sub-block to obtain an optimized solution to the sub-problem of the covariance matrix containing the perception signal and the sub-problem of the STAR-RIS phase includes:

[0120] Step a, initialize the variable χ.

[0121] Step b, by solving {U,F,P,R s} subproblem, update {U,F,P,R s}, wherein the {U,F,P,R s The subproblem is the covariance matrix subproblem containing the perception signal;

[0122] Step c, by solving {θ t ,θ r} subproblem, update {θ t ,θ r}, wherein the {θ t ,θ r}Subproblems are subproblems of the STAR-RIS phase;

[0123] Repeat steps b and c until the target value drops below a preset threshold.

[0124] The main idea of ​​the present invention is to establish a STAR-RIS-assisted synaesthesia integrated system model and propose an optimization algorithm based on the penalized dual decomposition framework. The penalized dual decomposition and semi-positive relaxation methods are used to solve the covariance matrix of the system's perception signal and the phase of STAR-RIS, respectively, to achieve optimization of the system's communication performance and perception performance.

[0125] The present invention provides a STAR-RIS-assisted synaesthesia integration optimization method, comprising the following steps:

[0126] S101. Establish a STAR-RIS-assisted synaesthesia integrated system model.

[0127] The STAR-RIS-assisted synaesthesia integration system model includes a base station, M communication users, a sensing target and a STAR-RIS; the base station is equipped with N antennas, and each communication user is equipped with a single antenna; the STAR-RIS is equipped with S passive transmission-reflection array elements and a sensor consisting of N S A sensor composed of units.

[0128] S102: With the goal of minimizing the Cramer-Rao bounds of the azimuth and elevation angle estimates of the perceived target, and under the condition of satisfying the minimum weighted sum rate of the communication users, construct an objective function to be optimized.

[0129] Select G S The azimuth and pitch of the perceived target are estimated and vectorized. is the unknown parameter to be estimated, where φ=[φ h ,φ v ] T , The Fisher information matrix of the estimated vector ξ can be decomposed into: Then the Cramer-Rao bound matrix used to estimate φ is:

[0130] Establish the optimization objective function: minimize the trace of the Cramer-Rao bound matrix while ensuring the minimum weighted sum rate of system users. is the auxiliary matrix, Ω is the original optimization variable, and the objective function is expressed as:

[0131]

[0132] C1:

[0133] C2:

[0134] C3:Tr(PP H +R s )≤P t

[0135] C4:

[0136] C5:R s ≥0,U≥0,

[0137] in, is Θ i , A vector consisting of elements on the diagonal; constrain C2 C2 represents the minimum weighted sum rate threshold of system communication users; P in constraint C3 t ≥0, C3 represents the maximum average transmission power at the base station; constraint C4 represents the amplitude relationship between the new STAR-RIS transmission coefficient and reflection coefficient, and constraint C5 ensures that the matrix R s and U is positive semidefinite; in the STAR-RIS-assisted synaesthesia integration system model, is the transmission coefficient matrix of STAR-RIS, is the reflection coefficient matrix of STAR-RIS, is the expression of the transmission coefficient matrix divided by the reflection coefficient matrix of STAR-RIS. When i is t, it represents the transmission coefficient matrix, and when i is r, it represents the reflection coefficient matrix. i,n ∈[0,1] represents the amplitude of the nth unit, represents the phase shift response of the nth unit, and the amplitude needs to satisfy: Where t is the transmission coefficient, r is the reflection coefficient, S represents the set of reflection units on STAR-RIS; the base station sends the joint signal x(t) at time index t, in, Representing information flow The transmit beamforming matrix for transmitting signals to M communication users in the system, the covariance matrix of the joint signal x(t) is: In the communication space of the STAR-RIS-assisted synaesthesia integration system model, represents the channel matrix of the direct link between the transmitter and receiver, represents the baseband channel matrix between the base station and STAR-RIS, represents the baseband channel vector between STAR-RIS and user m. The equivalent channel from the base station to the mth user is expressed as: When the transmitted signal at the base station is given, the received signal at the mth communication user in the communication space can be modeled as: In the perception space of the STAR-RIS-assisted synaesthesia integration system model, the target echo signal received by the STAR-RIS sensor in a coherent time block of length L is: G S =αb(φ h ,φ v )a T (φ h ,φ v )Θ rH b X+N S , where φ h is the estimated azimuth of the target relative to STAR-RIS, and φ v is the target's pitch angle estimate relative to STAR-RIS, represents the steering vector used by STAR-RIS for communication; N represents the guidance vector used by the sensor on STAR-RIS for perception; S Indicates that each item obeys The sensor is deployed along the X-axis, and STAR-RIS is deployed in the (X, Z) plane. The steering vector is expressed as:

[0138]

[0139] S103. An optimization algorithm based on the penalty dual decomposition framework is proposed, in which the augmented Lagrangian problem of the objective function is optimized in the inner loop using the block coordinate descent technique, while the Lagrangian dual variables and penalty factors are updated in the outer loop.

[0140] The core idea of ​​the proposed penalized dual decomposition algorithm is to construct a problem that has a simple or closed solution at each step of block coordinate descent. and The optimization function is reformulated as:

[0141] P2:

[0142] C1:

[0143] C2:

[0144] C3:

[0145] C4:Tr(PP H +R s )≤P t

[0146] C5:

[0147] C6:R s ≥0,U≥0,

[0148] By introducing the Lagrange dual variable and penalty factor ρ>0, we can get the augmented Lagrangian problem of the objective function:

[0149]

[0150] C1:

[0151] C2:

[0152] C3:Tr(PP H +R s )≤P t

[0153] C4:

[0154] C5:R s ≥0,U≥0,

[0155] in,

[0156] In summary, an optimization algorithm based on the penalty dual decomposition framework is designed, including:

[0157] Step 1: Initialize χ [0] ,Υ [0] ,ρ [0] >0 and set to 0 <c<1,n=1。

[0158] Step 2: P AL (ρ [n] ,Y [n] ) is assigned to χ [n+1] .

[0159] Step 3: If h(χ [n+1] )≤η [n] ,but:

[0160]

[0161] Otherwise, proceed to step 4.

[0162] Step 4, Y [n+1] =Y [n] , ρ [n+1] =cρ [n] .

[0163] Step 5: n=n+1, repeat steps 2 to 4 until h(χ) reaches the threshold.

[0164] S104. Divide the optimization variables of the augmented Lagrangian problem of the objective function into two sub-blocks, thereby generating two sub-optimization problems.

[0165] The key to solving the optimization algorithm based on the penalty dual decomposition framework is to solve the augmented Lagrangian problem of the objective function, dividing χ into two blocks, namely {U,F,P,R s} and {θ t ,θ r}, we get the following two sub-problems.

[0166] (1){U,F,P,R s}Subproblem (subproblem involving the covariance matrix of the perception signal):

[0167]

[0168] C1:

[0169] C2:

[0170] C3:Tr(PP H +R s )≤P t

[0171] C4:R s ≥0,U≥0,

[0172] (2){θ t ,θ r Subproblem (subproblem of STAR-RIS phase):

[0173]

[0174] C1:

[0175] C2:

[0176] S105. Use the projected gradient method and the semi-definite relaxation method to solve the sub-problem containing the covariance matrix of the perception signal and the sub-problem containing the phase of STAR-RIS respectively.

[0177] Define R = {R x ∈C N×M :Tr(R x )≤P t ,R x ≥0}, use Represents point u to set The Euclidean projection of The projected gradient method is used to solve the covariance matrix subproblem of the perception signal, including:

[0178] Step a: Set the number of iterations n = 0 and initialize R 0 , μ>0 and the maximum number of iterations n max .

[0179] Step b, by Update R, and the gradient of f with respect to R is:

[0180] Step c: repeat step b until the maximum number of iterations is reached.

[0181] If a Y ≥ 0 is given, the projection of Y on R is the solution to the following problem:

[0182]

[0183] C1:Tr(R)≤P t ;R±0

[0184] Let Y = U∑U H is the eigenvalue decomposition of Y, where ∑=diag(d1,...,d N ), then the above formula is equivalent to:

[0185]

[0186] C1:

[0187] The solution to the above problem is given according to the water injection algorithm: d i =(σ i -γ) + ,i=1,...,N。

[0188] The semi-positive relaxation method is used to solve the sub-problem containing the STAR-RIS phase, and {θ t ,θ r The subproblem is transformed into a semidefinite relaxation form and solved by existing optimizers, including:

[0189]

[0190] C1:

[0191] C2:

[0192] C3:Q t ≥0,Q r ≥0.

[0193] S106. Using the block coordinate descent technique, under the condition of fixing one sub-block, iteratively solve the other sub-block to obtain the optimal solution of the covariance matrix of the perception signal and the phase of STAR-RIS.

[0194] Step a1, initialize variable χ.

[0195] Step b1, by solving {U,F,P,R s}Subproblem update {U,F,P,R s}.

[0196] Step c1, by solving {θt ,θ r}Subproblem update {θ t ,θ r}.

[0197] Repeat steps b1 and c1 until the target value drops below the threshold.

[0198] On the other hand, Figure 3 As shown, a STAR-RIS-assisted synaesthesia integrated optimization device is provided. The STAR-RIS-assisted synaesthesia integrated optimization device is used to implement the STAR-RIS-assisted synaesthesia integrated optimization method provided in an embodiment of the present invention. The method includes:

[0199] Establishing module 301, for establishing a STAR-RIS-assisted synaesthesia integration system model.

[0200] The STAR-RIS-assisted synaesthesia integrated network model includes: a base station, M communication users, a sensing target and a STAR-RIS, wherein the base station is equipped with N antennas, each communication user is equipped with a single antenna, and the STAR-RIS is equipped with S passive transmission-reflection array elements and N S A unit carrying a sensor, wherein the M, N, S, N S Is a positive integer.

[0201] The construction module 302 is used to construct the objective function to be optimized of the STAR-RIS-assisted synaesthesia integration system model with the goal of minimizing the Cramer-Rao bound of the perception target azimuth and pitch angle estimation and under the condition of meeting the minimum weighted sum rate of communication users.

[0202] The optimization module 304 is configured to optimize the objective function using an optimization algorithm based on a penalty dual decomposition framework.

[0203] The optimization algorithm based on the penalized dual decomposition framework optimizes the augmented Lagrangian problem of the objective function in an inner loop using the block coordinate descent technique, while the Lagrangian dual variables and the penalty factor are updated in the outer loop, and the optimization variables of the augmented Lagrangian problem of the objective function are divided into two sub-blocks to generate two sub-optimization problems. The two sub-optimization problems are a sub-problem containing the covariance matrix of the perception signal and a sub-problem containing the STAR-RIS phase. The projected gradient method and the semi-positive relaxation method are used to solve the sub-problem containing the covariance matrix of the perception signal and the sub-problem containing the STAR-RIS phase, respectively. The block coordinate descent technique is used to iteratively solve the other sub-block under the condition of fixing one sub-block, and the optimized solutions of the sub-problem containing the covariance matrix of the perception signal and the sub-problem containing the STAR-RIS phase are obtained.

[0204] In another aspect, a STAR-RIS-assisted synaesthesia integrated optimization device is provided, wherein the STAR-RIS-assisted synaesthesia integrated optimization device comprises:

[0205] processor;

[0206] A memory having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by the processor, implement the method provided by the embodiment of the present invention.

[0207] On the other hand, a computer-readable storage medium is provided, in which a program code is stored. The program code can be called by a processor to execute the method provided by an embodiment of the present invention.

[0208] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0209] The embodiment of the present invention proposes a STAR-RIS-assisted synaesthesia integration system model, takes minimizing the Cramer-Rao bounds for the estimation of the azimuth and elevation angles of the perceived target as the goal, and constructs an objective function to be optimized for the STAR-RIS-assisted synaesthesia integration system model while satisfying the minimum weighted sum rate of communication users. By optimizing the objective function, both the communication performance and the perception performance of the STAR-RIS-assisted ISAC system are improved.

[0210] Figure 4 : is a structural diagram of a STAR-RIS-assisted synaesthesia integration optimization device provided by an embodiment of the present invention, such as Figure 4 As shown, optionally, the STAR-RIS-assisted synaesthesia integration optimization device 410 may include a first processor 2001 .

[0211] Optionally, the STAR-RIS-assisted synaesthesia integration optimization device 410 may further include a memory 2002 and a transceiver 2003 .

[0212] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0213] The following combination Figure 4 The following is a detailed introduction to the various components of the STAR-RIS-assisted synaesthesia integrated optimization device 410:

[0214] The first processor 2001 is the control center of the STAR-RIS-assisted synaesthesia integration optimization device 410 and can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), or application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs) or one or more field programmable gate arrays (FPGAs).

[0215] Optionally, the first processor 2001 may execute various functions of the STAR-RIS-assisted synaesthesia integration optimization device 410 by running or executing a software program stored in the memory 2002 and calling data stored in the memory 2002 .

[0216] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 4 CPU0 and CPU1 are shown in FIG.

[0217] In a specific implementation, as an embodiment, the STAR-RIS-assisted synaesthesia integration optimization device 410 may also include multiple processors, such as Figure 4 1 and 2. The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). A processor herein can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0218] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled by the first processor 2001 for execution. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0219] Alternatively, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, a random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and capable of being accessed by a computer, but not limited thereto. The memory 2002 may be integrated with the first processor 2001 or may exist independently and accessed through the interface circuit ( Figure 4 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0220] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0221] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 4 (not shown separately in the figure). The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0222] Optionally, the transceiver 2003 may be integrated with the first processor 2001, or may exist independently and be connected to the interface circuit ( Figure 4 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0223] It should be noted that Figure 4 The structure of the STAR-RIS-assisted synaesthesia integration optimization device 410 shown in the figure does not constitute a limitation on the router. The actual knowledge structure recognition device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0224] In addition, the technical effects of the STAR-RIS-assisted synaesthesia integrated optimization device 410 can refer to the technical effects of the multimodal emotion recognition method described in the above method embodiment, and will not be repeated here.

[0225] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0226] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0227] The above embodiments can be implemented in whole or in part through software, hardware (such as circuits), firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via infrared, microwave, or other means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, or a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0228] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0229] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0230] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0231] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0232] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0233] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0234] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0235] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0236] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0237] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A STAR-RIS-assisted synaesthesia integration optimization method, characterized in that: The method comprises: A STAR-RIS-assisted synaesthesia integration system model is established. The STAR-RIS-assisted synaesthesia integration network model includes: a base station, M communication users, a sensing target, and a STAR-RIS. The base station is equipped with N antennas, each communication user is equipped with a single antenna, and the STAR-RIS is equipped with S passive transmission-reflection array elements and N S units carrying sensors, wherein the M, N, S, N S is a positive integer; With the goal of minimizing the Cramer-Rao bounds of the azimuth and elevation angle estimates of the perceived target, and under the condition of satisfying the minimum weighted sum rate of the communication users, the objective function to be optimized of the STAR-RIS-assisted synaesthesia integration system model is constructed; Optimizing the objective function by an optimization algorithm based on a penalized dual decomposition framework; The optimization algorithm based on the penalized dual decomposition framework optimizes the augmented Lagrangian problem of the objective function in an inner loop using the block coordinate descent technique, while the Lagrangian dual variables and the penalty factor are updated in the outer loop, and the optimization variables of the augmented Lagrangian problem of the objective function are divided into two sub-blocks to generate two sub-optimization problems. The two sub-optimization problems are a sub-problem containing the covariance matrix of the perception signal and a sub-problem containing the STAR-RIS phase. The projected gradient method and the semi-positive relaxation method are used to solve the sub-problem containing the covariance matrix of the perception signal and the sub-problem containing the STAR-RIS phase, respectively. The block coordinate descent technique is used to iteratively solve the other sub-block under the condition of fixing one sub-block, and the optimized solutions of the sub-problem containing the covariance matrix of the perception signal and the sub-problem containing the STAR-RIS phase are obtained.

2. The method according to claim 1, characterized in that The objective function to be optimized includes: C3:Tr(PP H +R s )≤P t C5:R s ≥0, U≥0, in, yes A vector consisting of elements on the diagonal; constrain C2 C2 represents the minimum weighted sum rate threshold of system communication users; P in constraint C3 t ≥0, C3 represents the maximum average transmission power at the base station; constraint C4 represents the amplitude relationship between the new STAR-RIS transmission coefficient and reflection coefficient, and constraint C5 ensures that the matrix R s and U is positive semidefinite; in the STAR-RIS-assisted synaesthesia integration system model, is the transmission coefficient matrix of STAR-RIS, is the reflection coefficient matrix of STAR-RIS, is the expression of the transmission coefficient matrix or reflection coefficient matrix of STAR-RIS. When i is t, it represents the transmission coefficient matrix; when i is r, it represents the reflection coefficient matrix. i,n ∈[0,1] represents the amplitude of the nth unit, represents the phase shift response of the nth unit, and the amplitude needs to satisfy: Where t is the transmission coefficient, r is the reflection coefficient, and A represents the set of reflection units on STAR-RIS. The base station sends a joint signal x(t) at time index t. in, Representing information flow The transmit beamforming matrix for transmitting signals to M communication users in the system, the covariance matrix of the joint signal x(t) is: In the communication space of the STAR-RIS-assisted synaesthesia integration system model, represents the channel matrix of the direct link between the transmitter and receiver, represents the baseband channel matrix between the base station and STAR-RIS, represents the baseband channel vector between STAR-RIS and user m. The equivalent channel from the base station to the mth user is expressed as: When the transmitted signal at the base station is given, the received signal at the mth communication user in the communication space can be modeled as: In the perception space of the STAR-RIS-assisted synaesthesia integration system model, the target echo signal received by the STAR-RIS sensor in a coherent time block of length L is: G S =αb(φ h ,φ v )a T (φ h ,φ v )Θ r H b X+N S , where φ h is the estimated azimuth of the target relative to STAR-RIS, and φ v is the target's pitch angle estimate relative to STAR-RIS, represents the steering vector used by STAR-RIS for communication; N represents the guidance vector used by the sensor on STAR-RIS for perception; S Indicates that each item obeys The sensor is deployed along the X-axis, and STAR-RIS is deployed in the (X, Z) plane. The steering vector is expressed as:

3. The method according to claim 2, characterized in that The optimization algorithm based on the penalty dual decomposition framework includes: Step 1: Initialize χ [0] ,γ [0] ,ρ [0] >0 and set to 0 <c<1,n=1; Step 2: P AL (ρ [n] ,Y [n] ) is assigned to χ [n+1] ; Step 3: If h(χ [n+1] )≤η [n] ,but: Otherwise, proceed to step 4; Step 4, Y [n+1] =Y [n] ,ρ [n+1] =cρ [n] ; Step 5: Set n=n+1 and repeat steps 2 to 4 until h(χ) reaches the threshold; Among them, P AL (ρ,Y) is the augmented Lagrangian problem of the objective function; and is an auxiliary variable.

4. The method according to claim 2, characterized in that The two sub-optimization problems include: Subproblem involving the covariance matrix of the sensory signal: C3:Tr(PP H +R s )≤P t C4:R s ≥0,U≥0 And the subproblem involving STAR-RIS phase:

5. The method according to claim 2, characterized in that The projected gradient method is used to solve the sub-problem of the covariance matrix containing the perception signal, including: Let the number of iterations n = 0, initialize R 0 , μ>0 and the maximum number of iterations n max ; pass Update R, and the gradient of f with respect to R is: Repeat until the maximum number of iterations is reached.

6. The method according to claim 2, characterized in that The semi-definite relaxation method is used to solve the sub-problem containing the STAR-RIS phase, including: The subproblem containing the STAR-RIS phase is converted into a semi-definite relaxation form and solved by the existing optimizer: C3:Q t ≥0,Q r ≥0。 7. The method according to claim 2, characterized in that The block coordinate descent technique is used to iteratively solve another sub-block under the condition of fixing one sub-block to obtain an optimized solution to the covariance matrix sub-problem containing the perception signal and the STAR-RIS phase sub-problem, including: Step a, initializing variable χ; Step b, by solving {u,F,P,R s } subproblem, update {U,F,P,R s }, wherein the {U,F,P,R s The subproblem is the covariance matrix subproblem containing the perception signal; Step c, by solving {θ t ,θ r } subproblem, update {θ t ,θ r }, wherein the {θ t ,θ r }Subproblems are subproblems of the STAR-RIS phase; Repeat steps b and c until the target value drops below a preset threshold.

8. A STAR-RIS-assisted synaesthesia integrated optimization device, wherein the STAR-RIS-assisted synaesthesia integrated optimization device is used to implement the STAR-RIS-assisted synaesthesia integrated optimization method according to any one of claims 1 to 7, characterized in that: The device comprises: Establish a module for establishing a STAR-RIS-assisted synaesthesia integration system model, the STAR-RIS-assisted synaesthesia integration network model includes: a base station, M communication users, a perception target and a STAR-RIS, the base station is equipped with N antennas, each communication user is equipped with a single antenna, the STAR-RIS is equipped with S passive transmission-reflection array elements and N S units carrying sensors, wherein the M, N, S, N S is a positive integer; A construction module is used to construct an objective function to be optimized of the STAR-RIS-assisted synaesthesia integration system model with the goal of minimizing the Cramer-Rao bound of the azimuth and elevation angle estimation of the perceived target and under the condition of satisfying the minimum weighted sum rate of the communication users; An optimization module, configured to optimize the objective function using an optimization algorithm based on a penalty dual decomposition framework; The optimization algorithm based on the penalized dual decomposition framework optimizes the augmented Lagrangian problem of the objective function in an inner loop using the block coordinate descent technique, while the Lagrangian dual variables and the penalty factor are updated in the outer loop, and the optimization variables of the augmented Lagrangian problem of the objective function are divided into two sub-blocks to generate two sub-optimization problems. The two sub-optimization problems are a sub-problem containing the covariance matrix of the perception signal and a sub-problem containing the STAR-RIS phase. The projected gradient method and the semi-positive relaxation method are used to solve the sub-problem containing the covariance matrix of the perception signal and the sub-problem containing the STAR-RIS phase, respectively. The block coordinate descent technique is used to iteratively solve the other sub-block under the condition of fixing one sub-block, and the optimized solutions of the sub-problem containing the covariance matrix of the perception signal and the sub-problem containing the STAR-RIS phase are obtained.

9. A STAR-RIS-assisted synaesthesia integrated optimization device, characterized in that: The STAR-RIS-assisted synaesthesia integration optimization equipment includes: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program code, which can be called by a processor to execute the method according to any one of claims 1 to 7.

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