STAR-RIS-assisted communication and inductance integrated optimization method and device

Abstract

The invention provides an STAR-RIS-assisted communication and inductance integration optimization method and device, and relates to the technical field of the Internet of Things, and the method comprises the steps: building an STAR-RIS-assisted communication and inductance integration system model; taking the Cramer-Rao bound for minimizing the estimation of the azimuth angle and the pitch angle of a sensing target as a target, constructing a to-be-optimized target function of the STAR-RIS-assisted sensing integrated system model under the condition that the minimum weight and rate of a communication user are met, and optimizing the target function through an optimization algorithm based on a penalty dual decomposition framework to obtain an optimized target function of the STAR-RIS-assisted sensing integrated system model. Through the optimization of the objective function, the communication performance and the sensing performance of the STAR-RIS assisted ISAC system are improved together.

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 combines communication and perception functions, and has become one of the key technologies in the 6G communication era. ISAC technology not only reduces equipment cost, volume, 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 is expected to solve problems such as communication coverage, security, and multi-stream transmission by controlling electromagnetic waves and reshaping wireless channels. How to use RIS technology to assist ISAC systems in communication and perception has become a hot issue.

[0003] Although it still faces challenges in cost, energy consumption and hardware design, RIS has shown significant performance gains, indicating its wide application prospects in the 6G communication era. When traditional RIS is deployed in an ISAC system, it requires that the sensing target or communication user and the base station are located on the same side of the RIS, and only half-space coverage can be achieved. In order to meet this challenge, some researchers have proposed using a new type of simultaneously transmitting and reflecting reconfigurable smart surface (Simultaneously Transmitting and ReflectingRIS, STAR-RIS) technology to simultaneously transmit and reflect signals, improve the limited reach of traditional ISAC systems, and significantly improve system performance. STAR-RIS divides the input signal into two different parts, one part of the signal is reflected in the reflection area, and the rest is transmitted to achieve 360° full space coverage.

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

[0005] In order to solve the technical problem that it is difficult to improve both the communication performance and the perception performance of the ISAC system assisted by STAR-RIS in the prior art, an embodiment of the present invention provides a STAR-RIS assisted synaesthesia integrated optimization method and device. The technical solution is as follows:

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

[0007] A STAR-RIS-assisted synaesthesia integration system model is established, wherein the STAR-RIS-assisted synaesthesia integration 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 sensor-carrying unit, wherein the M, N, S, N S is a positive integer;

[0008] Taking minimizing the Cramer-Rao bound of the azimuth and elevation angle estimation of the perceived target as the goal, under the condition of satisfying the minimum weighted sum rate of the communication users, constructing the objective function to be optimized of the STAR-RIS-assisted synaesthesia integration system model;

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

[0010] The optimization algorithm based on the penalty 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 a covariance matrix of a perceptual signal and a sub-problem containing a STAR-RIS phase, and the sub-problem containing the covariance matrix of the perceptual signal and the sub-problem containing the STAR-RIS phase are solved respectively by using the projected gradient method and the semi-positive definite relaxation method, and the block coordinate descent technique is used to iteratively solve another sub-block under the condition of fixing one sub-block, so as to obtain the optimized solutions of the sub-problem containing the covariance matrix of the perceptual signal and the sub-problem of the STAR-RIS phase.

[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 value of the 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 semi-positive definite; 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; where β i,n ∈[0,1] represents the amplitude of the nth unit, represents the phase shift response of the nth unit, 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, Representation of 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 the receiver, represents the baseband channel matrix between the base station and STAR-RIS, represents the baseband channel vector between STAR-RIS and user m, and 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 ) 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 Gaussian white noise; the sensor is deployed along the X axis, STAR-RIS is deployed in the (X, Z) plane, and 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 perceptual 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 with STAR-RIS phase:

[0036]

[0037] C1:

[0038] C2:

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

[0040] include:

[0041] Let the number of iterations n = 0 and 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 adopting of a semi-definite relaxation method to solve the sub-problem containing the phase of STAR-RIS comprises:

[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 the block coordinate descent technique to iteratively solve another sub-block under the condition of fixing one sub-block to obtain an optimized solution of the covariance matrix sub-problem containing the perception signal and the STAR-RIS phase sub-problem includes:

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

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

[0052] Step c, by solving {θ t ,θ r} subproblem, update {θ t ,θ r}, where {θ t ,θ r}Subproblem is the subproblem of the STAR-RIS phase;

[0053] Repeat step b and step 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, and 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, and the device includes:

[0055] Establishing a module for establishing a STAR-RIS-assisted synaesthesia integrated system model, the STAR-RIS-assisted synaesthesia integrated network model comprising: 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, the STAR-RIS is equipped with S passive transmission-reflection array elements and N S A sensor-carrying unit, 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, used for optimizing the objective function by an optimization algorithm based on a penalty dual decomposition framework;

[0058] The optimization algorithm based on the penalty 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 a covariance matrix of a perceptual signal and a sub-problem containing a STAR-RIS phase, and the sub-problem containing the covariance matrix of the perceptual signal and the sub-problem containing the STAR-RIS phase are solved respectively by using the projected gradient method and the semi-positive definite relaxation method, and the block coordinate descent technique is used to iteratively solve another sub-block under the condition of fixing one sub-block, so as to obtain the optimized solutions of the sub-problem containing the covariance matrix of the perceptual signal and the sub-problem of the STAR-RIS phase.

[0059] On the other hand, 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 bound of the perception target azimuth and pitch angle estimation as the goal, and constructs the 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. By optimizing the objective function, the communication performance and perception performance of the STAR-RIS-assisted ISAC system are improved together. 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 is a flow chart of a STAR-RIS-assisted synaesthesia integration optimization method provided by an embodiment of the present invention;

[0067] Figure 2 It 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 It is a structural schematic 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 "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0072] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express 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 such as W1. When the difference is not emphasized, the meanings to be expressed are consistent.

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

[0075] In order to solve the technical problem that it is difficult to improve both the communication performance and the perception performance of the ISAC system assisted by STAR-RIS in the prior art, an embodiment of 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, 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 sensor-carrying unit, wherein the M, N, S, N S Is a positive integer.

[0079] S2. 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, construct the objective function to be optimized of the STAR-RIS-assisted synaesthesia integration system model.

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

[0081] The optimization algorithm based on the penalty 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 a covariance matrix of a perceptual signal and a sub-problem containing a STAR-RIS phase, and the sub-problem containing the covariance matrix of the perceptual signal and the sub-problem containing the STAR-RIS phase are solved respectively by using the projected gradient method and the semi-positive definite relaxation method, and the block coordinate descent technique is used to iteratively solve another sub-block under the condition of fixing one sub-block, so as to obtain the optimized solutions of the sub-problem containing the covariance matrix of the perceptual signal and the sub-problem of the STAR-RIS phase.

[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 value of the 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 semi-positive definite; 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; where β i,n ∈[0,1] represents the amplitude of the nth unit, represents the phase shift response of the nth unit, 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, Representation of 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 the receiver, represents the baseband channel matrix between the base station and STAR-RIS, represents the baseband channel vector between STAR-RIS and user m, and 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 ) 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 Gaussian white noise; the sensor is deployed along the X axis, STAR-RIS is deployed in the (X, Z) plane, and 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 perceptual 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 with STAR-RIS phase:

[0107]

[0108] C1:

[0109] C2:

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

[0111] Let the number of iterations n = 0 and 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 adopting of a semi-definite relaxation method to solve the sub-problem containing the phase of STAR-RIS comprises:

[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 the block coordinate descent technique to iteratively solve another sub-block under the condition of fixing one sub-block to obtain an optimized solution of the covariance matrix sub-problem containing the perception signal and the STAR-RIS phase sub-problem includes:

[0120] Step a, initialize the variable x.

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

[0122] Step c, by solving {θ t ,θ r} subproblem, update {θ t ,θ r}, where {θ t ,θ r}Subproblem is the subproblem of the STAR-RIS phase;

[0123] Repeat step b and step 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, propose an optimization algorithm based on the penalized dual decomposition framework, and use penalized dual decomposition and semi-positive relaxation methods to solve the covariance matrix of the system's perception signal and the phase of STAR-RIS respectively, so as to optimize 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 integrated 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; STAR-RIS is equipped with S passive transmission-reflection array elements and a sensor composed of N S The sensor consists of a unit.

[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, constructing 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 value of the 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 semi-positive definite; 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 of STAR-RIS divided by the reflection coefficient matrix. 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, 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 a joint signal x(t) at time index t, in, Representation of 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 the receiver, represents the baseband channel matrix between the base station and STAR-RIS, represents the baseband channel vector between STAR-RIS and user m, and 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 ) 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 Gaussian white noise; the sensor is deployed along the X axis, STAR-RIS is deployed in the (X, Z) plane, and 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, go 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, dividing 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 and divide 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 perceptual 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 Sub-problems (sub-problems of the STAR-RIS phase):

[0173]

[0174] C1:

[0175] C2:

[0176] S105. Use the projected gradient method and the semi-positive 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, 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.

[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: 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 semi-positive definite 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, initializing variable x.

[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, and 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, and the method includes:

[0199] The module 301 is established to establish 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 sensor-carrying unit, 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 elevation angle estimation and under the condition of satisfying the minimum weighted sum rate of the communication users.

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

[0203] The optimization algorithm based on the penalty 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 a covariance matrix of a perceptual signal and a sub-problem containing a STAR-RIS phase, and the sub-problem containing the covariance matrix of the perceptual signal and the sub-problem containing the STAR-RIS phase are solved respectively by using the projected gradient method and the semi-positive definite relaxation method, and the block coordinate descent technique is used to iteratively solve another sub-block under the condition of fixing one sub-block, so as to obtain the optimized solutions of the sub-problem containing the covariance matrix of the perceptual signal and the sub-problem of the STAR-RIS phase.

[0204] On the other hand, 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 bound of the perception target azimuth and pitch angle estimation as the goal, and constructs the 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. By optimizing the objective function, the communication performance and perception performance of the STAR-RIS-assisted ISAC system are improved together.

[0210] Figure 4 is a schematic diagram of the structure 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] Combine the following Figure 4 The following is a detailed introduction to the 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 integrated optimization device 410, which can be a processor or a general term for multiple processing elements. For example, the first processor 2001 is one or more central processing units (CPUs), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (field programmable gate arrays, FPGAs).

[0215] Optionally, the first processor 2001 may perform 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 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). The processor here 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 to be executed by the first processor 2001. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

[0219] Optionally, the memory 2002 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store 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, optical disc storage (including compressed optical 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 that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001, or may exist independently, and access the memory 2002 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 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 optimized 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.

[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, and 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 the processor may also be 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 by software, hardware (such as circuits), firmware or any other combination. When implemented by 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 programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is 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 computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by infrared, microwave or the like. 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 one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0228] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0229] In the present invention, "at least one" means one or more, and "more than one" 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 be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[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. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0232] Those skilled in the art can 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 only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, 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 interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

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

[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 can be essentially or partly embodied in the form of a software product that contributes to the prior art. 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, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments 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 is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope 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, wherein the STAR-RIS-assisted synaesthesia integration 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 sensor-carrying unit, wherein the M, N, S, N S is a positive integer; Taking minimizing the Cramer-Rao bound of the azimuth and elevation angle estimation of the perceived target as the goal, under the condition of satisfying the minimum weighted sum rate of the communication users, constructing the objective function to be optimized of the STAR-RIS-assisted synaesthesia integration system model; Optimizing the objective function by an optimization algorithm based on a penalty dual decomposition framework; The optimization algorithm based on the penalty 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 a covariance matrix of a perceptual signal and a sub-problem containing a STAR-RIS phase, and the sub-problem containing the covariance matrix of the perceptual signal and the sub-problem containing the STAR-RIS phase are solved respectively by using the projected gradient method and the semi-positive definite relaxation method, and the block coordinate descent technique is used to iteratively solve another sub-block under the condition of fixing one sub-block, so as to obtain the optimized solutions of the sub-problem containing the covariance matrix of the perceptual signal and the sub-problem of the STAR-RIS phase.

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 value of the 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 semi-positive definite; 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; where β i,n ∈[0,1] represents the amplitude of the nth unit, represents the phase shift response of the nth unit, 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, Representation of 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 the receiver, represents the baseband channel matrix between the base station and STAR-RIS, represents the baseband channel vector between STAR-RIS and user m, and 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 ) 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 Gaussian white noise; the sensor is deployed along the X axis, STAR-RIS is deployed in the (X, Z) plane, and 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 with STAR-RIS phase:

5. The method according to claim 2, characterized in that: The method of using the projected gradient method to solve the sub-problem of the covariance matrix containing the perception signal includes: Let the number of iterations n = 0 and 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 of the phase containing STAR-RIS, including: The subproblem containing the STAR-RIS phase is converted into a semi-positive 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 the optimal solution of 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} sub-problem is the covariance matrix sub-problem containing the perception signal; Step c, by solving {θ t ,θ r } subproblem, update {θ t ,θ r }, where {θ t ,θ r }Subproblem is the subproblem of the STAR-RIS phase; Repeat step b and step c until the target value drops below a preset threshold.

8. A STAR-RIS-assisted synaesthesia integrated optimization device, 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: Establishing a module for establishing a STAR-RIS-assisted synaesthesia integrated system model, the STAR-RIS-assisted synaesthesia integrated network model comprising: 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, the STAR-RIS is equipped with S passive transmission-reflection array elements and N S A sensor-carrying unit, 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, used for optimizing the objective function by an optimization algorithm based on a penalty dual decomposition framework; The optimization algorithm based on the penalty 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 a covariance matrix of a perceptual signal and a sub-problem containing a STAR-RIS phase, and the sub-problem containing the covariance matrix of the perceptual signal and the sub-problem containing the STAR-RIS phase are solved respectively by using the projected gradient method and the semi-positive definite relaxation method, and the block coordinate descent technique is used to iteratively solve another sub-block under the condition of fixing one sub-block, so as to obtain the optimized solutions of the sub-problem containing the covariance matrix of the perceptual signal and the sub-problem of the STAR-RIS phase.

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 codes, which can be called by a processor to execute the method according to any one of claims 1 to 7.

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