Intelligent metasurface-assisted near field communication sensing integrated beam forming method

Abstract

The invention relates to an intelligent metasurface-assisted near field communication sensing integrated beam forming method, and belongs to the technical field of communication. The method comprises the following steps: firstly, establishing a communication and perception integrated system framework, then establishing a communication model according to the reachable rate of a system under the factors of multi-user interference and perception signal interference in a near-field communication system, then establishing a near-field perception channel containing distance information and angle information, deriving a Cramer-Rao bound of a perception target, and establishing a perception model; establishing an optimization problem which takes the minimum communication sum rate and the maximum transmitting power as constraints and takes the minimum Cramer-Rao lower bound as a target; the method comprises the steps of converting an objective function into an augmented Lagrange problem through a PDD framework, dividing an optimization variable into two blocks to obtain two sub-problems, calling a BCD algorithm to iteratively update the two sub-problems, and finally solving to obtain a beam forming scheme. According to the method, the sensing performance is remarkably improved, and the stability and high efficiency of the communication performance are ensured.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology and relates to an intelligent metasurface-assisted near-field communication perception integrated beamforming method. Background Art

[0002] Amidst the rapid development of global communications technology, 6G has become a hot topic in the technology world. Currently, research on 6G communication and perception integration is gaining increasing attention from both domestic and international industry and academia, and the vision, requirements, and key technologies for integrated interawareness (ISAC) are gradually becoming clearer. However, traditional ISAC technology faces numerous challenges in practical application, particularly in complex propagation environments, where signal transmission efficiency and perception accuracy often struggle to achieve ideal levels.

[0003] It is against this backdrop that intelligent metasurface (RIS)-assisted near-field interawareness integration technology has emerged. ISAC technology achieves both sensing and communication functions by sharing spectrum resources and hardware infrastructure, significantly improving resource utilization efficiency. However, due to the complex and changing propagation environment, its performance still needs further improvement. RIS technology, with its low cost and ability to dynamically manipulate electromagnetic waves, provides strong support for improving ISAC performance. By intelligently adjusting the reflected phase and amplitude of electromagnetic waves, RIS can effectively optimize signal propagation paths and enhance signal strength, significantly improving communication and perception performance. In recent years, with the increase in electromagnetic wave frequency and the growth of antenna arrays, the near-field region has gradually expanded. Unlike far-field channels, which only contain angular information, the additional distance information contained in near-field channels provides a new dimension for sensing target distance, further enhancing the perception performance of ISAC systems.

[0004] The existing intelligent metasurface-assisted near-field synaesthesia integration technology still has the following problems:

[0005] 1) Traditional RIS has certain limitations. The sensing target or communication user must be located on the same side of the RIS and the base station, which can only achieve half-space coverage but not full coverage.

[0006] 2) The near-field channel has not been effectively modeled, and the distance information implicit in the near-field channel has not been fully utilized;

[0007] 3) The problem of jointly optimizing the base station transmit beamforming matrix, the transmit signal covariance matrix, and the STAR-RIS transmission and reflection coefficient is essentially a non-convex optimization problem and is difficult to solve. Summary of the Invention

[0008] In view of this, the purpose of the present invention is to provide a near-field communication perception integrated beamforming method assisted by an intelligent metasurface, which divides the near-field space into a communication space and a perception space by deploying STAR-RIS in a dual-function radar communication system (DFRC). Communication modeling is performed based on the reachability and rate in the communication process, and perception modeling is performed based on the Cramer-Rao lower bound of the target position information in the perception process; the perception performance is enhanced by minimizing the Cramer-Rao lower bound of the perception information under the minimum communication and rate constraints; an algorithm combining PDD and BCD is used to process complex coupling variables, thereby obtaining the optimal beamforming matrix, the optimal perception information matrix, and the optimal phase shift matrix of the intelligent metasurface that can simultaneously reflect and transmit. After the beamforming of the present invention, the perception performance is significantly enhanced and the communication performance is also guaranteed.

[0009] In order to achieve the above object, the present invention provides the following technical solutions:

[0010] A near-field communication sensing integrated beamforming method assisted by an intelligent metasurface, the method comprising:

[0011] Establish a communication and perception integrated system framework for a network scenario involving multiple users, an antenna base station, a smart metasurface STAR-RIS capable of simultaneous reflection and transmission, and a target.

[0012] Under the framework of the established communication-awareness integrated system, a communication model is established based on the system reachability and rate under multi-user interference and perception signal interference factors in the near-field communication system.

[0013] Within the framework of the established communication and perception integrated system, a near-field perception channel containing distance and angle information is established, and the Cramer-Rao bound of the perception target is derived to establish a perception model.

[0014] According to the communication model and the perception model, an optimization problem is established with the constraints of minimum communication sum rate and maximum transmission power and the goal of minimizing the Cramer-Rao lower bound.

[0015] The phase shift constraint of STAR-RIS is penalized by the PDD framework and incorporated into the objective function, which transforms the objective function into an augmented Lagrangian problem.

[0016] In the augmented Lagrangian problem, the optimization variables are divided into two blocks to obtain two sub-problems. The BCD algorithm is then called to iteratively update the two sub-problems, and the beamforming solution is finally solved.

[0017] Furthermore, the intelligent metasurface-assisted communication and perception integrated system architecture includes a base station with M antennas, A single-antenna user and a reflective and transmissive smart metasurface with N reflective units; the sensor for receiving the target echo signal is deployed on the reflective and transmissive smart metasurface; the reflective and transmissive smart metasurface divides the entire near-field space into two half-spaces, namely the sensing space and the communication space; the communication space is on the transmission side, and the system is in the communication space. The communication service is provided to a single-antenna user; the sensing space is on the reflection side, and there is a sensing target in the sensing space.

[0018] Furthermore, the process of establishing a communication model within the framework of the communication-awareness integrated system includes:

[0019] Based on the transmission link between the base station and STAR-RIS, the transmission link between STAR-RIS and the user, and the corresponding transmission matrix, the equivalent channel between the base station and the user is constructed;

[0020] According to the mapping relationship between the signal sent from the reflection segment and the signal matrix received at the receiving end, the equivalent channel, multi-user interference, perceived signal interference and user noise are calculated;

[0021] According to the mapping relationship, the reachable sum rate of the communication system is calculated;

[0022] According to the reachability and rate of the communication system, a communication model is constructed:

[0023]

[0024]

[0025] Where x[t] represents the joint communication sensing signal sent by the base station at time t; Indicates the total number of users; w k represents the beamforming matrix used to send to user k; c k [t] represents the communication signal sent to user k at time t; s[t] represents the perception signal sent at time t; R x represents the covariance matrix of x[t], represents statistical expectation, (×) H Represents the transpose operation; R s represents the covariance matrix of s[t]; h sk represents the near-field communication channel from STAR-RIS to the user; β k represents the channel attenuation caused by large-scale path loss; a(θ k ,r k ) represents the steering vector, r k represents the distance between user k and STAR-RIS, θ k represents the angle of user k relative to STAR-RIS; y k[t] represents the signal received by user k at time t; Θ t represents the transmission matrix of STAR-RIS; h Bs represents the communication channel between the base station and the base station to STAR-RIS; w i represents the beamforming matrix used to send to user i, c i [t] represents the communication signal sent to user i at time t; (×) * indicates conjugation; represents the additive white Gaussian noise of user k.

[0026] Furthermore, the process of establishing a communication model within the framework of the communication-awareness integrated system includes:

[0027] Based on the transmission link between the base station and STAR-RIS, the transmission link between STAR-RIS and the target, and the corresponding reflection matrix, the echo signal received at the sensor is constructed:

[0028] Y s =β s b(θ s ,r s )b T (θ s ,r s )Θ r h Bs X+N s

[0029] Among them, β s represents the complex channel gain; θ s represents the angle of the sensing target, r s represents the distance between the sensor and the sensing target; b(θ s ,r s ) represents the steering vector; β s b(θ s ,r s )b T (θ s ,r s ) represents the near-field round-trip channel matrix of target perception; Θ r represents the reflection matrix of STAR-RIS; h Bs Indicates the communication channel between the base station and the base station to STAR-RIS; X indicates that the base station sends a signal; N s represents the noise matrix;

[0030] According to the echo signal received at the sensor, the Cramer-Rao lower bound of the perceived target is derived to obtain the perception model; in the perception model, the unknown parameter vector to be perceived is in, Re{·} represents the real part, Im{·} represents the imaginary part; define u=vec(Y s ), vec(×) represents a vectorized operation.

[0031] First, the Fisher information matrix is ​​used to estimate the unknown parameter ξ. The Fisher information matrix is ​​expressed as:

[0032]

[0033] Among them, the Fisher information matrix J ξ is divided into four sub-matrices, Contains Fisher information between distance and angle, Contains Fisher information between distance, angle and channel gain, Contains Fisher information between channel gains;

[0034] Redefine B=b(θ s ,r s )b T (θ s ,r s ), and For any l,p∈{r s ,θ s},have:

[0035]

[0036] Finally, we get the value used to estimate θ s and r s The Cramer-Rao matrix of :

[0037]

[0038] In the future, the Cramer-Rao lower bound of perception information will be used as the perception performance indicator.

[0039] Furthermore, with the constraints of satisfying the minimum communication sum rate and the maximum transmit power, the optimization problem with the goal of minimizing the Cramer-Rao lower bound is expressed as:

[0040]

[0041] Where W represents the base station transmit beamforming matrix; R x represents the covariance matrix of the transmitted signal; θ r represents the reflection matrix of STAR-RIS; θ t represents the transmission matrix of STAR-RIS; θ s Represents the angle information of the perceived target; r srepresents the distance information of the perceived target; CRB(·) represents the Cramer-Rao lower bound; R min,k represents the lower bound of the communication sum rate of user k; R(w k ,R x ) represents the communication sum rate of user k; P max represents the maximum transmission power; β t,n and β r,n represent the transmission and reflection amplitudes of STAR-RIS, respectively.

[0042] Furthermore, the phase shift constraint of STAR-RIS is penalized through the PDD framework and incorporated into the objective function, which is then transformed into an augmented Lagrangian problem, which includes:

[0043] Define an auxiliary matrix U and the constraints Minimize tr(CRB(θ s ,r s )) is equivalent to minimizing tr(U -1 );

[0044] According to the Schur complement condition, a modified FIM constraint condition is obtained from the original Fisher information matrix FIM, and the non-convex objective function of the original problem is converted into a new convex constraint

[0045] The optimization problem after transformation is obtained:

[0046]

[0047] Then, define the auxiliary variables: By introducing the Lagrangian dual variable and penalty factor of the constraint, the augmented Lagrangian problem of the original optimization problem is obtained:

[0048]

[0049] in, Define the violation function Used to measure the degree of violation.

[0050] Furthermore, in the augmented Lagrangian problem, the optimization variables are divided into two blocks, and the two sub-problems obtained are:

[0051] About {W,R x ,F,U}’s sub-problems:

[0052]

[0053] This subproblem is a non-convex optimization problem and cannot be directly solved by existing convex optimization schemes.

[0054] About {θ t ,θ r}Sub-problems:

[0055] This subproblem is a non-convex optimization problem and cannot be directly solved by existing convex optimization schemes.

[0056]

[0057] This subproblem is also a non-convex optimization problem and cannot be directly solved by existing convex optimization schemes.

[0058] Furthermore, the BCD algorithm is called to iteratively update the two sub-problems. The iterative solution process includes:

[0059] The first subproblem is transformed into a semidefinite programming problem through the semidefinite relaxation method, and then solved by the existing convex optimization solver;

[0060] The second sub-problem is transformed into a semi-definite programming problem through eigenvalue decomposition and semi-definite relaxation method, and then solved by the existing convex optimization solver;

[0061] First, the parameters {θ t ,θ r}, according to the first sub-problem, solve the parameters {W,R x ,F,U}, and then bring the solved parameters into the second sub-problem to further optimize the parameters {θ t ,θ r}, and repeat until convergence.

[0062] Furthermore, in the process of solving the first sub-problem, an auxiliary variable is defined Meet W k ≥0 and rank(W k )=1, and then the constraints of the first subproblem are transformed into convex form by semidefinite relaxation method:

[0063]

[0064] in, constraint Convert to Because rank(W k )=1 is non-convex. Relax it and allow it to be a semi-positive definite matrix. Then the first subproblem can be reformulated as follows:

[0065]

[0066] The first subproblem after reformulation is a semidefinite programming problem, and its global optimal solution is obtained in MATLAB using existing convex optimization tools.

[0067] Furthermore, in the process of solving the second sub-problem, the objective function and communication constraints of the second sub-problem are first converted into a form that is easier to track, and the definition is The matrix Perform eigenvalue decomposition as:

[0068]

[0069] in, and v j Represents the corresponding eigenvalues ​​and eigenvectors, and R represents the matrix The rank of , then the objective function is reformulated as follows:

[0070]

[0071] Define the following variables:

[0072]

[0073] The second sub-problem is transformed into:

[0074]

[0075] The solution is approximated by the semi-definite relaxation method, and the definition is satisfy and rank(Q i )=1, and further transform the optimization problem into:

[0076]

[0077] The second subproblem is finally transformed into a semidefinite programming problem, and its global optimal solution is obtained in MATLAB using the existing convex optimization solver.

[0078] The beneficial effects of the present invention are:

[0079] The present invention constructs a communication model based on the reachability and rate during the communication process; establishes a near-field perception channel containing target distance and angle information, and derives the Cramer-Rao lower bound of the target position information to construct a perception model; enhances perception performance by minimizing the Cramer-Rao lower bound of the perception information while satisfying communication performance constraints; and adopts a method combining a PDD algorithm with a BCD algorithm to effectively process complex coupling variables, thereby solving the optimal beamforming matrix, the optimal perception information matrix, and the optimal phase shift matrix of STAR-RIS.

[0080] The present invention adopts the achievable sum rate in the communication process as the communication performance indicator, and adopts the Cramer-Rao lower bound of the perception information as the perception performance indicator. The Cramer-Rao lower bound of the perception information is minimized under the minimum communication sum rate constraint to design an optimization problem. The PDD algorithm is used to transform the original optimization problem into an augmented Lagrangian problem, and the BCD framework is used to transform the original non-convex optimization problem into several sub-problems for solution. The perception performance is significantly enhanced while ensuring the communication performance.

[0081] The beamforming method of the present invention not only significantly improves the perception performance, but also ensures the stability and efficiency of communication performance, providing an innovative technical solution for the near-field communication perception integrated system.

[0082] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0084] Figure 1 This is a flow chart of a near-field communication perception integrated beamforming method assisted by an intelligent metasurface according to an embodiment of the present invention;

[0085] Figure 2 Schematic diagram of the framework of an intelligent metasurface-assisted near-field communication and perception integrated system according to an embodiment of the present invention;

[0086] Figure 3 Flowchart of the BCD algorithm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0087] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0088] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0089] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "back", etc. indicating directions or positional relationships, they are based on the directions or positional relationships shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0090] See also Figures 1 to 3 , which is an intelligent metasurface-assisted near-field communication perception integrated beamforming method.

[0091] like Figure 1 As shown, this embodiment provides a detailed implementation process of a near-field communication perception integrated beamforming method assisted by an intelligent metasurface, which includes:

[0092] S1: Establish a communication and perception integrated system framework for a network scenario consisting of multiple users, an antenna base station, a simultaneously reflective and transmissive smart metasurface STAR-RIS, and a target.

[0093] In the embodiment of the present invention, Figure 2 As shown in Figure 2, the intelligent metasurface-assisted communication and perception integrated system architecture includes a base station with M antennas, The system adopts a single-antenna user and a simultaneously reflective and transmissive smart metasurface with N reflecting units; the entire near-field space is divided into two half-spaces by the simultaneously reflective and transmissive smart metasurface, namely the sensing space and the communication space; the communication space is on the transmission side, and the system provides communication services for K single-antenna users in the space; the sensing space is on the reflection side, and there is a sensing target in the space; the sensor for receiving the target echo signal is deployed on the simultaneously reflective and transmissive smart metasurface.

[0094] S2: Under the system framework of step S1, a communication model is established based on the system reachability and rate under multi-user interference and perceived signal interference factors in the near field communication system;

[0095] In an example of the present invention, an equivalent channel between the base station and the user is constructed based on the transmission link between the base station and STAR-RIS, the transmission link between STAR-RIS and the user, and the corresponding transmission matrix. Based on the mapping relationship between the signal transmitted by the equivalent channel, multi-user interference, perceived signal interference, and the user noise profile reflection segment and the received signal matrix at the receiving end, the achievable sum rate of the communication system is calculated based on the mapping relationship. Based on the achievable sum rate of the system, a communication model is constructed. Specifically, the communication model can be expressed as follows:

[0096]

[0097] Where x[t] represents the joint communication sensing signal sent by the base station at time t; Indicates the total number of users; w k represents the beamforming matrix used to send to user k; c k [t] represents the communication signal sent to user k at time t; s[t] represents the perception signal sent at time t; R x represents the covariance matrix of x[t], represents statistical expectation, (×) H Represents the transpose operation; R s represents the covariance matrix of s[t]; h sk represents the near-field communication channel from STAR-RIS to the user; β k represents the channel attenuation caused by large-scale path loss; a(θ k ,r k ) represents the steering vector, r k represents the distance between user k and STAR-RIS, θ k represents the angle of user k relative to STAR-RIS; y k [t] represents the signal received by user k at time t; Θ t represents the transmission matrix of STAR-RIS; h Bs represents the communication channel between the base station and the base station to STAR-RIS; w i represents the beamforming matrix used to send to user i, c i [t] represents the communication signal sent to user i at time t; (×) * indicates conjugation; represents the additive white Gaussian noise of user k.

[0098] S3: Under the system framework of step S1, a near-field perception channel including distance information and angle information is established, and the Cramer-Rao bound of the perception target is derived to establish a perception model;

[0099] In this embodiment, the echo signal received at the sensor is constructed based on the transmission link between the base station and STAR-RIS, the transmission link between STAR-RIS and the target, and the corresponding reflection matrix. Based on the echo signal received at the sensor, the Cramer-Rao lower bound of the perceived target is derived, and a perception model is constructed. The details are as follows:

[0100] The echo signal received at the sensor can be expressed as

[0101] Y s =β s b(θ s ,r s )b T (θ s ,r s )Θ r h Bs X+N s

[0102] Among them, θ s represents the angle of the sensing target; r s Indicates the distance between the sensor and the sensing target; β s represents the complex channel gain; b(θ s ,r s ) represents the steering vector; β s b(θ s ,r s )b T (θ s ,r s ) represents the near-field round-trip channel matrix of target perception; Θ r represents the reflection matrix of STAR-RIS; h Bs Indicates the communication channel between the base station and the base station to STAR-RIS; X indicates that the base station sends a signal; N s represents the noise matrix.

[0103] In this embodiment, the unknown parameter vector is in, Re{·} represents the real part, Im{·} represents the imaginary part; define u=vec(Y s ), vec(×) represents a vectorized operation.

[0104]

[0105] J ξ is divided into four sub-matrices, where Contains Fisher information between distance and angle, Contains Fisher information between distance, angle and channel gain, Contains Fisher information between channel gains.

[0106] Define B = b(θ s ,r s )b T (θ s ,r s ), and For any l,p∈{r s ,θ s}, we can get:

[0107]

[0108] Therefore, for estimating θ s and r s The CRB matrix can be expressed as:

[0109]

[0110] In the future, the Cramer-Rao lower bound of perception information will be used as the perception performance indicator.

[0111] S4: Based on the communication model and the perception model, an optimization problem is established with the minimum communication sum rate and the maximum transmit power as constraints and the goal of minimizing the Cramer-Rao lower bound;

[0112] The optimization problem established in the embodiment of the present invention is as follows:

[0113]

[0114] Where W represents the base station transmit beamforming matrix; R x represents the covariance matrix of the transmitted signal; θ r represents the reflection matrix of STAR-RIS; θ t represents the transmission matrix of STAR-RIS; θ s Represents the angle information of the perceived target; r s Represents the distance information of the perceived target; CRB represents the Cramer-Rao lower bound; R min,k represents the lower bound of the communication sum rate of user k; R(w k ,R x ) represents the communication sum rate of user k; P max represents the maximum transmission power; β t,n and β r,n represent the transmission and reflection amplitudes of STAR-RIS, respectively;

[0115] S5: Based on the optimization problem proposed in step S4, the phase shift constraint of STAR-RIS is penalized by the PDD framework and incorporated into the objective function, and the objective function is converted into an augmented Lagrangian problem;

[0116] According to the step S4 derived Fisher matrix is a positive semidefinite matrix, that is, all eigenvalues ​​are non-negative, and tr((·) -1 ) is a decreasing matrix in the space of positive semidefinite matrices, so we define an auxiliary matrix U and the constraint Minimize tr(CRB(θ s ,r s )) is equivalent to minimizing tr(U -1 )

[0117] According to the Schur complement condition, a modified FIM constraint can be obtained from the original FIM. Therefore, the non-convex objective function of the original problem can be converted into a new convex constraint

[0118] Therefore, the original optimization problem can be transformed into:

[0119]

[0120] Since STARS introduces a non-convex phase shift constraint, the phase shift constraint is incorporated into the objective function through the PDD framework to penalize it, and the objective function is converted into an augmented Lagrangian (AL) problem.

[0121] First, define the auxiliary variables: By introducing the Lagrangian dual variable and penalty factor of the constraint, the augmented Lagrangian problem of the original optimization problem can be obtained:

[0122]

[0123] in, Define the violation function Used to measure the degree of violation.

[0124] In the example of the present invention, when the penalty factor is small enough, the penalty term and the constraint violation function will drop to zero, indicating that all equality constraints are satisfied.

[0125] S6: Divide the optimization variables into two blocks to obtain two sub-problems, and then call the BCD algorithm to iteratively update the two sub-problems and perform iterative solutions; Figure 3 As shown, it specifically includes:

[0126] S61. First, transform the augmented Lagrangian problem into two sub-problems:

[0127] About {W,R x ,F,U}’s sub-problems:

[0128]

[0129] This subproblem is a non-convex optimization problem and cannot be directly solved by existing convex optimization schemes.

[0130] About {θ t ,θ r}Sub-problems:

[0131]

[0132] This subproblem is also a non-convex optimization problem and cannot be directly solved by existing convex optimization schemes.

[0133] Call the BCD algorithm to iteratively update the two sub-problems. The iterative solution process includes:

[0134] S62. The first subproblem is transformed into a semidefinite programming problem through the semidefinite relaxation method, and then solved by the existing convex optimization solver.

[0135] In this example, an auxiliary variable is defined Meet W k ≥0 and rank(W k )=1, and then the constraints are transformed into the following convex form by semi-definite relaxation method:

[0136]

[0137] in constraint Can be converted into Because rank(W k )=1 is non-convex, so it is relaxed to allow it to be a semi-positive definite matrix. Therefore, the first sub-problem of the example of the present invention can be reformulated as follows:

[0138]

[0139] This is a semidefinite programming problem, and its global optimal solution can be effectively obtained in MATLAB using existing convex optimization tools.

[0140] S63. The second subproblem is transformed into a semi-definite programming problem through eigenvalue decomposition and semi-definite relaxation method, and then solved by the existing convex optimization solver.

[0141] In order to optimize the phase shift array, we first transform the objective function and communication constraints of the second sub-problem into a form that is easier to track. The matrix Perform eigenvalue decomposition as:

[0142]

[0143] in, and v j Represents the corresponding eigenvalues ​​and eigenvectors, and R represents the matrix The rank of , so the objective function can be reformulated as follows:

[0144]

[0145] Define the following variables:

[0146]

[0147] The optimization problem can be transformed into:

[0148]

[0149] The solution is approximated by the semi-definite relaxation method, and the definition is satisfy and rank(Q i )=1, the optimization problem can be further transformed into:

[0150]

[0151] This problem is still a semidefinite programming problem, and its global optimal solution can be effectively obtained in MATLAB using the existing convex optimization solver.

[0152] First, the parameters {θ t ,θ r}, according to the first sub-problem, solve the parameters {W,R x ,F,U}, and then bring the solved parameters into the second sub-problem to further optimize the parameters {θ t ,θ r}, and repeat until convergence.

[0153] Based on the solution calculation of the embodiment of the present invention, an optimal beamforming solution can be obtained, which significantly improves the perception performance of the system while ensuring communication performance.

[0154] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium, which may include: ROM, RAM, disk or CD, etc.

[0155] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A near-field communication sensing integrated beamforming method assisted by an intelligent metasurface, characterized by: The method comprises: Establish a communication and perception integrated system framework for a network scenario involving multiple users, an antenna base station, a smart metasurface STAR-RIS capable of simultaneous reflection and transmission, and a target. Under the framework of the established communication-awareness integrated system, a communication model is established based on the system reachability and rate under multi-user interference and perception signal interference factors in the near-field communication system. Within the framework of the established communication and perception integrated system, a near-field perception channel containing distance and angle information is established, and the Cramer-Rao bound of the perception target is derived to establish a perception model. According to the communication model and the perception model, an optimization problem is established with the constraints of minimum communication sum rate and maximum transmission power and the goal of minimizing the Cramer-Rao lower bound. The phase shift constraint of STAR-RIS is penalized by the PDD framework and incorporated into the objective function, which transforms the objective function into an augmented Lagrangian problem. In the augmented Lagrangian problem, the optimization variables are divided into two blocks to obtain two sub-problems. The BCD algorithm is then called to iteratively update the two sub-problems, and the beamforming solution is finally solved.

2. The intelligent metasurface-assisted near-field communication sensing integrated beamforming method according to claim 1, characterized in that: The intelligent metasurface-assisted communication and perception integrated system architecture includes a base station with M antennas, A single-antenna user and a reflective and transmissive smart metasurface with N reflective units; The sensor for receiving the target echo signal is deployed on the reflective and transmissive smart metasurface; the reflective and transmissive smart metasurface divides the entire near-field space into two half-spaces, namely the sensing space and the communication space; the communication space is on the transmissive side, and the system is in the communication space. The communication service is provided to a single-antenna user; the sensing space is on the reflection side, and there is a sensing target in the sensing space.

3. The intelligent metasurface-assisted near-field communication sensing integrated beamforming method according to claim 1, characterized in that: The process of establishing a communication model within the framework of the communication perception integrated system includes: Based on the transmission link between the base station and STAR-RIS, the transmission link between STAR-RIS and the user, and the corresponding transmission matrix, the equivalent channel between the base station and the user is constructed; According to the mapping relationship between the signal sent from the reflection segment and the signal matrix received at the receiving end, the equivalent channel, multi-user interference, perceived signal interference and user noise are calculated; According to the mapping relationship, the reachable sum rate of the communication system is calculated; According to the reachability and rate of the communication system, a communication model is constructed: h sk =b k a(θ k ,r k ) Where x[t] represents the joint communication sensing signal sent by the base station at time t; Indicates the total number of users; w k represents the beamforming matrix used to send to user k; c k [t] represents the communication signal sent to user k at time t; s[t] represents the perception signal sent at time t; R x represents the covariance matrix of x[t], represents statistical expectation, (×) H Represents the transpose operation; R s represents the covariance matrix of s[t]; h sk represents the near-field communication channel from STAR-RIS to the user; β k represents the channel attenuation caused by large-scale path loss; a(θ k ,r k ) represents the steering vector, r k represents the distance between user k and STAR-RIS, θ k represents the angle of user k relative to STAR-RIS; y k [t] represents the signal received by user k at time t; Θ t represents the transmission matrix of STAR-RIS; h Bs represents the communication channel between the base station and the base station to STAR-RIS; w i represents the beamforming matrix used to send to user i, c i [t] represents the communication signal sent to user i at time t; (×) * indicates conjugation; represents the additive white Gaussian noise of user k.

4. The intelligent metasurface-assisted near-field communication sensing integrated beamforming method according to claim 1, characterized in that: The process of establishing a communication model within the framework of the communication perception integrated system includes: Based on the transmission link between the base station and STAR-RIS, the transmission link between STAR-RIS and the target, and the corresponding reflection matrix, the echo signal received at the sensor is constructed: Y s =b s b(θ s ,r s )b T (i s ,r s )I r h Bs X+N s Among them, β s represents the complex channel gain; θ s represents the angle of the sensing target, r s represents the distance between the sensor and the sensing target; b(θ s ,r s ) represents the steering vector; β s b(θ s ,r s )b T (θ s ,r s ) represents the near-field round-trip channel matrix of target perception; Θ r represents the reflection matrix of STAR-RIS; h Bs Indicates the communication channel between the base station and the base station to STAR-RIS; X indicates that the base station sends a signal; N s represents the noise matrix; According to the echo signal received at the sensor, the Cramer-Rao lower bound of the perceived target is derived to obtain the perception model; in the perception model, the unknown parameter vector to be perceived is in, Re{·} represents the real part, Im{·} represents the imaginary part; define u=vec(Y s ), vec(×) represents vectorized operation; First, the Fisher information matrix is ​​used to estimate the unknown parameter ξ. The Fisher information matrix is ​​expressed as: Among them, the Fisher information matrix J ξ is divided into four sub-matrices, Contains Fisher information between distance and angle, Contains Fisher information between distance, angle and channel gain, Contains Fisher information between channel gains; Redefine B=b(θ s ,r s )b T (θ s ,r s ), and For any p∈{r s ,θ s },have: Finally, we get the value used to estimate θ s and r s The Cramer-Rao matrix of : In the future, the Cramer-Rao lower bound of perception information will be used as the perception performance indicator.

5. The intelligent metasurface-assisted near-field communication sensing integrated beamforming method according to claim 1, characterized in that: The optimization problem with the minimum communication sum rate and maximum transmission power as constraints and the goal of minimizing the Cramer-Rao lower bound is expressed as: Where W represents the base station transmit beamforming matrix; R x represents the covariance matrix of the transmitted signal; θ r represents the reflection matrix of STAR-RIS; θ t represents the transmission matrix of STAR-RIS; θ s Represents the angle information of the perceived target; r s represents the distance information of the perceived target; CRB(·) represents the Cramer-Rao lower bound; R min,k represents the lower bound of the communication sum rate of user k; R(w k ,R x ) represents the communication sum rate of user k; P max represents the maximum transmission power; β t,n and β r,n represent the transmission and reflection amplitudes of STAR-RIS, respectively.

6. The intelligent metasurface-assisted near-field communication sensing integrated beamforming method according to claim 5, characterized in that: In the process of penalizing the phase shift constraint of STAR-RIS through the PDD framework and incorporating it into the objective function, the objective function is converted into an augmented Lagrangian problem, which includes: Define an auxiliary matrix U and the constraints Minimize tr(CRB(θ s ,r s )) is equivalent to minimizing tr(U -1 ); According to the Schur complement condition, a modified FIM constraint condition is obtained from the original Fisher information matrix FIM, and the non-convex objective function of the original problem is converted into a new convex constraint The optimization problem after transformation is obtained: Then, define the auxiliary variables: By introducing the Lagrangian dual variable and penalty factor of the constraint, the augmented Lagrangian problem of the original optimization problem is obtained: in, Define the violation function Used to measure the degree of violation.

7. The intelligent metasurface-assisted near-field communication sensing integrated beamforming method according to claim 6, characterized in that: In the augmented Lagrangian problem, the optimization variables are divided into two blocks, and the two sub-problems obtained are: About {W,R x ,F,U}’s sub-problems: This subproblem is a non-convex optimization problem and cannot be directly solved by existing convex optimization solutions; About {θ t ,θ r }Sub-problems: This subproblem is also a non-convex optimization problem and cannot be directly solved by existing convex optimization schemes.

8. The intelligent metasurface-assisted near-field communication sensing integrated beamforming method according to claim 7, characterized in that: Call the BCD algorithm to iteratively update the two sub-problems. The iterative solution process includes: The first subproblem is transformed into a semidefinite programming problem through the semidefinite relaxation method, and then solved by the existing convex optimization solver; The second sub-problem is transformed into a semi-definite programming problem through eigenvalue decomposition and semi-definite relaxation method, and then solved by the existing convex optimization solver; First, the parameters {θ t ,θ r }, according to the first sub-problem, solve the parameters {W,R x ,F,U}, and then bring the solved parameters into the second sub-problem to further optimize the parameters {θ t ,θ r }, and repeat until convergence.

9. The intelligent metasurface-assisted near-field communication sensing integrated beamforming method according to claim 8, characterized in that: In the process of solving the first subproblem, define an auxiliary variable Satisfy W k ≥0 and rank(W k )=1, and then the constraints of the first subproblem are transformed into convex form by semidefinite relaxation method: in, constraint Convert to Because rank(W k )=1 is non-convex. Relax it and allow it to be a semi-positive definite matrix. Then the first subproblem can be reformulated as follows: The first subproblem is reformulated as a semidefinite programming problem, and its global optimal solution is obtained in MATLAB using existing convex optimization tools.

10. The intelligent metasurface-assisted near-field communication sensing integrated beamforming method according to claim 8, characterized in that: In the process of solving the second sub-problem, we first transform the objective function and communication constraints of the second sub-problem into a form that is easier to track, and define The matrix Perform eigenvalue decomposition as: in, and v j Represents the corresponding eigenvalues ​​and eigenvectors, and R represents the matrix The rank of , then the objective function is reformulated as follows: Define the following variables: The second sub-problem is transformed into: The solution is approximated by the semi-definite relaxation method, and the definition is satisfy and rank(Q i )=1, and further transform the optimization problem into: The second subproblem is finally transformed into a semidefinite programming problem, and its global optimal solution is obtained in MATLAB using the existing convex optimization solver.

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